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Archive for September 2018

 

FIGURE 1: PALEO TEMPERATURE DATA FOR THE ARCTIC FROM 20 SOURCES

01campcentury02GISP203AUSTFONNA04NGTB1605NGTB11806NGTB2107NGRIP109CRETE10DYE11GRIP12DEVON13PENNY14RENLAND15NAUK16LOMONOSOV17PRINCEOFWALES18WINDYDOME19LAKEPIENI20LAKE408AGASSIZ

 

FIGURE 2: SUMMARY TABLE

SUMMARY-TABLE

 

FIGURE 3: GIF IMAGE CYCLES THROUGH ALL DATA STATIONS

paleo-gif

 

FIGURE 4: ARCTIC OCEAN GEOTHERMAL ACTIVITY   [JAMES KAMIS]

ArcticGeothermals

 

 

[LIST OF POSTS ON THIS SITE]

RELATEDDoes Global Warming Drive Changes in Arctic Sea Ice?

 

  1. In late 2017 and early 2018 a number of high profile news media sources cited a US Federal Government report to claim that “warming in the Arctic is unprecedented in 1,500 years”. Here are some examples:[DIGITAL JOURNAL, Scientists alarmed by unprecedented warming in Arctic: By Karen Graham, Feb 28, 2018 in Environment], [USA TODAY, The Arctic is warming faster than it has in 1,500 years: Doyle Rice, Dec. 12, 2017], [MASHABLE ASIA, Recent Arctic warming ‘unprecedented’ in human history, Andrew Freedman, DEC 13, 2017], [DISCOVER MAGAZINE, A major federal report about the Arctic released yesterday finds that the current rate of Arctic warming is unprecedented in at least the past 2,000 years. And the pace of Arctic sea ice loss experienced in the past few decades has not been seen in at least the past 1,450 years. Tom Yulsman, December 13, 2017], [WASHINGTON POST, Warming of the Arctic is ‘unprecedented over the last 1,500 years, Chris Mooney December 12, 2017], [THE GUARDIAN, Record warmth in the Arctic this month could yet prove to be a freak occurrence, but experts warn the warming event is unprecedented. Jonathan Watts, environment editor, Tue 27 Feb 2018]. Their source was the 2017 NOAA Arctic Report Card a summary of which appears in the next paragraph.
  2. NOAA: “The Arctic shows no sign of returning to reliably frozen region of recent past decades. Despite relatively cool summer temperatures, observations in 2017 continue to indicate that the Arctic environmental system has reached a ‘new normal’, characterized by long-term losses in the extent and thickness of the sea ice cover, the extent and duration of the winter snow cover and the mass of ice in the Greenland Ice Sheet and Arctic glaciers, and warming sea surface and permafrost temperatures. The average surface air temperature for the year ending September 2017 is the 2nd warmest since 1900; however, cooler spring and summer temperatures contributed to a rebound in snow cover in the Eurasian Arctic, slower summer sea ice loss, and below-average melt extent for the Greenland ice sheet. The sea ice cover continues to be relatively young and thin with older, thicker ice comprising only 21% of the ice cover in 2017 compared to 45% in 1985. In August 2017, sea surface temperatures in the Barents and Chukchi seas were up to 4° C warmer than average, contributing to a delay in the autumn freeze-up in these regions. Pronounced increases in ocean primary productivity, at the base of the marine food web, were observed in the Barents and Eurasian Arctic seas from 2003 to 2017. Arctic tundra is experiencing increased greenness and record permafrost warming. Pervasive changes in the environment are influencing resource management protocols, including those established for fisheries and wildfires. The unprecedented rate and global reach of Arctic change disproportionately affect the people of northern communities, further pressing the need to prepare for and adapt to the new Arctic.
  3. The claim that “warming in the Arctic is unprecedented” can be interpreted as a reference to temperature or to the rate of warming, or perhaps to both. The importance of the rate of warming in climate science is underscored by this statement from the NASA websiteIf Earth has warmed and cooled throughout history, what makes scientists think that humans are causing global warming now?The first piece of evidence that the warming over the past few decades isn’t part of a natural cycle is how fast the change is happening. The biggest temperature swings our planet has experienced in the past million years are the ice ages. Based on a combination of paleoclimate data and models, scientists estimate that when ice ages have ended in the past, it has taken about 5,000 years for the planet to warm between 4 and 7 degrees Celsius. The warming of the past century of 0.7C is roughly eight times faster than the ice-age-recovery warming on average.” [SOURCE]We therefore interpret the Arctic warming claim broadly to include both temperature and the rate of warming.
  4. This work is a test of the claim of unprecedented warming in the Arctic. Here we use paleo temperature data provided by the NOAA Pages2k database [LINK] (caution: the link automatically initiates the download of a large Excel file). Twenty sources for Arctic air temperature were found in this database and the data from all twenty sources are shown in the charts in Figure 1. Eighteen of these sources provide d180 data. The term d180 is a reference to delta-O-18, a reference to the ratio of oxygen isotopes O(18) to O(16) compared against a standardized ratio. There is a linear relationship between this ratio and temperature and because of that d180 is often used as a proxy for temperature. The magnitudes are different but in a comparative study such as this, the d180 values can be used directly instead of temperature. The other two are lake bed sediment data and are included for completeness but the greater weight will be given to the cleaner d180 air temperature proxies.
  5. Each data set is presented graphically in Figure 1 as two side by side frames. The left frame traces the maximum d180 value (in black) in a moving 100-year window as a test of whether the maximum temperature occurs at the end of the series. The right frame traces the d180 OLS trend (in red) in a moving 100-year window as a test of whether the greatest rate of warming occurs at the end of the series. If the highest value in the curve is found at the end of the series, the data are taken to be consistent with the “unprecedented warming” hypothesis when stated in terms of the variable being tested. Otherwise, the data are taken to be inconsistent with the “unprecedented warming” hypothesis (when stated in terms of the variable being tested). The chart titles are color coded. Green indicates that the data are consistent with the hypothesis and red indicates that they are not.
  6. These interpretations of the data displayed in Figure 1 are summarized in the table in Figure 2. The lake sediment are marked in red as they may not be directly comparable with the surface temperature proxies in the other 18 datasets. The summary statistics at the bottom of the table in Figure 2 are in two parts – the part in black does not include the lake sediment data and the part in red does. Here we find as follows: (a) If we remove the lake sediment data, support for unprecedented high temperature in the Arctic at the end of the datasets is  an impressive 50% but that for highest rate of warming is rather low at 22% if the end of the dataset is strictly defined as the trend in the last of the 100-year moving windows. If this definition is broadened to the “post industrial era” as any date after 1900, then support for the “unprecedented” hypothesis grows to 61%. The last column of Figure 2 shows that support for “unprecedented warming” defined as both highest temperature and highest rate of warming is a low 39% even when including the broad definition of the current era.
  7. An issue raised by James Kamis in this regard  [LINK] is that extensive natural geothermal sources of heat in the Arctic Ocean are not included in the analysis. These volcanic and mantle plume sources are shown in Figure 4.
  8. We conclude from this analysis that the paleo data presented in Figure 1 (not including the Lake data) do not provide convincing evidence of “unprecedented” warming in the Arctic in the current era either in terms of temperature or in terms of the rate of warming. However, the evidence for unprecedented warming trend is strengthened significantly if the current era is defined broadly to include the period from 1900 to the present.
  9. A further conclusion from these data relate to the claim by NASA and by climate science in general (paragraph#3) that the usual argument by skeptics that “the climate has always changed” ignores the speed issue. The response by climate science is that yes, the climate has always changed but what makes the current warming different, and therefore human caused, is the high rate of warming never before seen in natural climate change. The paleo data presented above shows that in 12 out of 20 temperature datasets (including lake sediment data) and in 10 out of 18 temperature datasets with only d180 data, we find evidence that climate has changed faster than it is changing now. These changes occurred within the last 2,000 years in the pre-industrial era.

 

MAP SHOWING SOME OF THE DATA STATIONS

RELATED POSTDoes Global Warming Drive Changes in Arctic Sea Ice?

 

 

ARCTIC WARMING BIBLIOGRAPHY

  1. 2002: Polyakov, Igor V., et al. “Observationally based assessment of polar amplification of global warming.” Geophysical research letters 29.18 (2002): 25-1. Arctic variability is dominated by multi‐decadal fluctuations. Incomplete sampling of these fluctuations results in highly variable arctic surface‐air temperature (SAT) trends. Modulated by multi‐decadal variability, SAT trends are often amplified relative to northern‐hemispheric trends, but over the 125‐year record we identify periods when arctic SAT trends were smaller or of opposite sign than northern‐hemispheric trends. Arctic and northern‐hemispheric air‐temperature trends during the 20th century (when multi‐decadal variablity had little net effect on computed trends) are similar, and do not support the predicted polar amplification of global warming. The possible moderating role of sea ice cannot be conclusively identified with existing data. If long‐term trends are accepted as a valid measure of climate change, then the SAT and ice data do not support the proposed polar amplification of global warming. Intrinsic arctic variability obscures long‐term changes, limiting our ability to identify complex feedbacks in the arctic climate system.
  2. 2002: Rigor, Ignatius G., John M. Wallace, and Roger L. Colony. “Response of sea ice to the Arctic Oscillation.” Journal of Climate 15.18 (2002): 2648-2663.Data collected by the International Arctic Buoy Programme from 1979 to 1998 are analyzed to obtain statistics of sea level pressure (SLP) and sea ice motion (SIM). The annual and seasonal mean fields agree with those obtained in previous studies of Arctic climatology. The data show a 3-hPa decrease in decadal mean SLP over the central Arctic Ocean between 1979–88 and 1989–98. This decrease in SLP drives a cyclonic trend in SIM, which resembles the structure of the Arctic Oscillation (AO). Regression maps of SIM during the wintertime (January–March) AO index show 1) an increase in ice advection away from the coast of the East Siberian and Laptev Seas, which should have the effect of producing more new thin ice in the coastal flaw leads; 2) a decrease in ice advection from the western Arctic into the eastern Arctic; and 3) a slight increase in ice advection out of the Arctic through Fram Strait. Taken together, these changes suggest that at least part of the thinning of sea ice recently observed over the Arctic Ocean can be attributed to the trend in the AO toward the high-index polarity. Rigor et al. showed that year-to-year variations in the wintertime AO imprint a distinctive signature on surface air temperature (SAT) anomalies over the Arctic, which is reflected in the spatial pattern of temperature change from the 1980s to the 1990s. Here it is shown that the memory of the wintertime AO persists through most of the subsequent year: spring and autumn SAT and summertime sea ice concentration are all strongly correlated with the AO index for the previous winter. It is hypothesized that these delayed responses reflect the dynamical influence of the AO on the thickness of the wintertime sea ice, whose persistent “footprint” is reflected in the heat fluxes during the subsequent spring, in the extent of open water during the subsequent summer, and the heat liberated in the freezing of the open water during the subsequent autumn.
  3. 2003: Semenov, Vladimir A., and Lennart Bengtsson. “Modes of the wintertime Arctic temperature variability.” Geophysical Research Letters 30.15 (2003).  It is shown that the Arctic averaged wintertime temperature variability during the 20th century can be essentially described by two orthogonal modes. These modes were identified by an Empirical Orthogonal Function (EOF) decomposition of the 1892–1999 surface wintertime air temperature anomalies (40°N–80°N) using a gridded dataset covering high Arctic. The first mode (1st leading EOF) is related to the NAO and has a major contribution to Arctic warming during the last 30 years. The second one (3rd leading EOF) dominates the SAT variability prior to 1970. A correlation between the corresponding principal component PC3 and the Arctic SAT anomalies is 0.79. This mode has the largest amplitudes in the Kara‐Barents Seas and Baffin Bay and exhibits no direct link to the large‐scale atmospheric circulation variability, in contrast to the other leading EOFs. We suggest that the existence of this mode is caused by long‐term sea ice variations presumably due to Atlantic inflow variability.
  4. 2003: Polyakov, Igor V., et al. “Variability and trends of air temperature and pressure in the maritime Arctic, 1875–2000.” Journal of Climate 16.12 (2003): 2067-2077. Arctic atmospheric variability during the industrial era (1875–2000) is assessed using spatially averaged surface air temperature (SAT) and sea level pressure (SLP) records. Air temperature and pressure display strong multidecadal variability on timescales of 50–80 yr [termed low-frequency oscillation (LFO)]. Associated with this variability, the Arctic SAT record shows two maxima: in the 1930s–40s and in recent decades, with two colder periods in between. In contrast to the global and hemispheric temperature, the maritime Arctic temperature was higher in the late 1930s through the early 1940s than in the 1990s. Incomplete sampling of large-amplitude multidecadal fluctuations results in oscillatory Arctic SAT trends. For example, the Arctic SAT trend since 1875 is 0.09 ± 0.03°C decade−1, with stronger spring- and wintertime warming; during the twentieth century (when positive and negative phases of the LFO nearly offset each other) the Arctic temperature increase is 0.05 ± 0.04°C decade−1, similar to the Northern Hemispheric trend (0.06°C decade−1). Thus, the large-amplitude multidecadal climate variability impacting the maritime Arctic may confound the detection of the true underlying climate trend over the past century. LFO-modulated trends for short records are not indicative of the long-term behavior of the Arctic climate system. The accelerated warming and a shift of the atmospheric pressure pattern from anticyclonic to cyclonic in recent decades can be attributed to a positive LFO phase. It is speculated that this LFO-driven shift was crucial to the recent reduction in Arctic ice cover. Joint examination of air temperature and pressure records suggests that peaks in temperature associated with the LFO follow pressure minima after 5–15 yr. Elucidating the mechanisms behind this relationship will be critical to understanding the complex nature of low-frequency variability.
  5. 2003: Johannessen, Ola M., et al. “Arctic climate change—will the ice disappear this century?.” Elsevier Oceanography Series. Vol. 69. Elsevier, 2003. 490-496. A new set of multi-decadal and century-scale sea-ice data is compared with coupled atmosphere-ocean model simulations in order to understand Arctic sea ice and climate variability. It is evident that the two pronounced 20th-century warming events—both amplified in the Arctic—were linked to sea-ice variability. The area of sea ice is observed to have decreased by 8× 105km2 (7.4%) since 1978, with record-low summer ice coverage in 2002. Model predictions are used to quantify changes in the ice cover through the 21st century. A predominantly ice-free Arctic in summer is predicted for the end of this century.
  6. 2003: Polyakov, Igor V., et al. “Long-term ice variability in Arctic marginal seas.” Journal of Climate 16.12 (2003): 2078-2085. Examination of records of fast ice thickness (1936–2000) and ice extent (1900–2000) in the Kara, Laptev, East Siberian, and Chukchi Seas provide evidence that long-term ice thickness and extent trends are small and generally not statistically significant, while trends for shorter records are not indicative of the long-term tendencies due to large-amplitude low-frequency variability. The ice variability in these seas is dominated by a multidecadal, low-frequency oscillation (LFO) and (to a lesser degree) by higher-frequency decadal fluctuations. The LFO signal decays eastward from the Kara Sea where it is strongest. In the Chukchi Sea ice variability is dominated by decadal fluctuations, and there is no evidence of the LFO. This spatial pattern is consistent with the air temperature–North Atlantic Oscillation (NAO) index correlation pattern, with maximum correlation in the near-Atlantic region, which decays toward the North Pacific. Sensitivity analysis shows that dynamical forcing (wind or surface currents) dominates ice-extent variations in the Laptev, East Siberian, and Chukchi Seas. Variability of Kara Sea ice extent is governed primarily by thermodynamic factors.
  7. 2004: Overland, James E., et al. “Seasonal and regional variation of pan-Arctic surface air temperature over the instrumental record.” Journal of Climate 17.17 (2004): 3263-3282. Instrumental surface air temperature (SAT) records beginning in the late 1800s from 59 Arctic stations north of 64°N show monthly mean anomalies of several degrees and large spatial teleconnectivity, yet there are systematic seasonal and regional differences. Analyses are based on time–longitude plots of SAT anomalies and principal component analysis (PCA). Using monthly station data rather than gridded fields for this analysis highlights the importance of considering record length in calculating reliable Arctic change estimates; for example, the contrast of PCA performed on 11 stations beginning in 1886, 20 stations beginning in 1912, and 45 stations beginning in 1936 is illustrated. While often there is a well-known interdecadal negative covariability in winter between northern Europe and Baffin Bay, long-term changes in the remainder of the Arctic are most evident in spring, with cool temperature anomalies before 1920 and Arctic-wide warm temperatures in the 1990s. Summer anomalies are generally weaker than spring or winter but tend to mirror spring conditions before 1920 and in recent decades. Temperature advection in the trough–ridge structure in the positive phase of the Arctic Oscillation (AO) in the North Atlantic establishes wintertime temperature anomalies in adjacent regions, while the zonal/annular nature of the AO in the remainder of the Arctic must break down in spring to promote meridional temperature advection. There were regional/decadal warm events during winter and spring in the 1930s to 1950s, but meteorological analysis suggests that these SAT anomalies are the result of intrinsic variability in regional flow patterns. These midcentury events contrast with the recent Arctic-wide AO influence in the 1990s. The preponderance of evidence supports the conclusion that warm SAT anomalies in spring for the recent decade are unique in the instrumental record, both in having the greatest longitudinal extent and in their associated patterns of warm air advection.
  8. 2004: Johannessen, Ola M., et al. “Arctic climate change: observed and modelled temperature and sea-ice variability.” Tellus A: Dynamic Meteorology and Oceanography 56.4 (2004): 328-341. Changes apparent in the arctic climate system in recent years require evaluation in a century-scale perspective in order to assess the Arctic’s response to increasing anthropogenic greenhouse-gas forcing. Here, a new set of centuryand multidecadal-scale observational data of surface air temperature (SAT) and sea ice is used in combination with ECHAM4 and HadCM3 coupled atmosphere’ice’ocean global model simulations in order to better determine and understand arctic climate variability. We show that two pronounced twentieth-century warming events, both amplified in the Arctic, were linked to sea-ice variability. SAT observations and model simulations indicate that the nature of the arctic warming in the last two decades is distinct from the early twentieth-century warm period. It is suggested strongly that the earlier warming was natural internal climate-system variability, whereas the recent SAT changes are a response to anthropogenic forcing. The area of arctic sea ice is furthermore observed to have decreased~8 · 105 km2 (7.4%) in the past quarter century, with record-low summer ice coverage in September 2002. A set of model predictions is used to quantify changes in the ice cover through the twenty-first century, with greater reductions expected in summer than winter. In summer, a predominantly sea-ice-free Arctic is predicted for the end of this century.
  9. 2004: Bengtsson, Lennart, Vladimir A. Semenov, and Ola M. Johannessen. “The early twentieth-century warming in the Arctic—A possible mechanism.” Journal of Climate 17.20 (2004): 4045-4057. The huge warming of the Arctic that started in the early 1920s and lasted for almost two decades is one of the most spectacular climate events of the twentieth century. During the peak period 1930–40, the annually averaged temperature anomaly for the area 60°–90°N amounted to some 1.7°C. Whether this event is an example of an internal climate mode or is externally forced, such as by enhanced solar effects, is presently under debate. This study suggests that natural variability is a likely cause, with reduced sea ice cover being crucial for the warming. A robust sea ice–air temperature relationship was demonstrated by a set of four simulations with the atmospheric ECHAM model forced with observed SST and sea ice concentrations. An analysis of the spatial characteristics of the observed early twentieth-century surface air temperature anomaly revealed that it was associated with similar sea ice variations. Further investigation of the variability of Arctic surface temperature and sea ice cover was performed by analyzing data from a coupled ocean–atmosphere model. By analyzing climate anomalies in the model that are similar to those that occurred in the early twentieth century, it was found that the simulated temperature increase in the Arctic was related to enhanced wind-driven oceanic inflow into the Barents Sea with an associated sea ice retreat. The magnitude of the inflow is linked to the strength of westerlies into the Barents Sea. This study proposes a mechanism sustaining the enhanced westerly winds by a cyclonic atmospheric circulation in the Barents Sea region created by a strong surface heat flux over the ice-free areas. Observational data suggest a similar series of events during the early twentieth-century Arctic warming, including increasing westerly winds between Spitsbergen and Norway, reduced sea ice, and enhanced cyclonic circulation over the Barents Sea. At the same time, the North Atlantic Oscillation was weakening.
  10. 2005: Stroeve, J. C., et al. “Tracking the Arctic’s shrinking ice cover: Another extreme September minimum in 2004.” Geophysical Research Letters 32.4 (2005). Satellite passive microwave observations document an overall downward trend in Arctic sea ice extent and area since 1978. While the record minimum observed in September 2002 strongly reinforced this downward trend, extreme ice minima were again observed in 2003 and 2004. Although having three extreme minimum years in a row is unprecedented in the satellite record, attributing these recent trends and extremes to greenhouse gas loading must be tempered by recognition that the sea ice cover is variable from year to year in response to wind, temperature and oceanic forcings.
  11. 2014: Woollings, Tim, Ben Harvey, and Giacomo Masato. “Arctic warming, atmospheric blocking and cold European winters in CMIP5 models.” Environmental Research Letters 9.1 (2014): 014002. Amplified Arctic warming is expected to have a significant long-term influence on the midlatitude atmospheric circulation by the latter half of the 21st century. Potential influences of recent and near future Arctic changes on shorter timescales are much less clear, despite having received much recent attention in the literature. In this letter, climate models from the recent CMIP5 experiment are analysed for evidence of an influence of Arctic temperatures on midlatitude blocking and cold European winters in particular. The focus is on the variability of these features in detrended data and, in contrast to other studies, limited evidence of an influence is found. The occurrence of cold European winters is found to be largely independent of the temperature variability in the key Barents–Kara Sea region. Positive correlations of the Barents–Kara temperatures with Eurasian blocking are found in some models, but significant correlations are limited.
  12. 2015: Francis, Jennifer, and Natasa Skific. “Evidence linking rapid Arctic warming to mid-latitude weather patterns.” Phil. Trans. R. Soc. A 373.2045 (2015): 20140170. The effects of rapid Arctic warming and ice loss on weather patterns in the Northern Hemisphere is a topic of active research, lively scientific debate and high societal impact. The emergence of Arctic amplification—the enhanced sensitivity of high-latitude temperature to global warming—in only the last 10–20 years presents a challenge to identifying statistically robust atmospheric responses using observations. Several recent studies have proposed and demonstrated new mechanisms by which the changing Arctic may be affecting weather patterns in mid-latitudes, and these linkages differ fundamentally from tropics/jet-stream interactions through the transfer of wave energy. In this study, new metrics and evidence are presented that suggest disproportionate Arctic warming—and resulting weakening of the poleward temperature gradient—is causing the Northern Hemisphere circulation to assume a more meridional character (i.e. wavier), although not uniformly in space or by season, and that highly amplified jet-stream patterns are occurring more frequently. Further analysis based on self-organizing maps supports this finding. These changes in circulation are expected to lead to persistent weather patterns that are known to cause extreme weather events. As emissions of greenhouse gases continue unabated, therefore, the continued amplification of Arctic warming should favour an increased occurrence of extreme events caused by prolonged weather conditions.
  13. 2015: Kug, Jong-Seong, et al. “Two distinct influences of Arctic warming on cold winters over North America and East Asia.” Nature Geoscience 8.10 (2015): 759. Arctic warming has sparked a growing interest because of its possible impacts on mid-latitude climate1,2,3,4,5. A number of unusually harsh cold winters have occurred in many parts of East Asia and North America in the past few years2,6,7, and observational and modelling studies have suggested that atmospheric variability linked to Arctic warming might have played a central role1,3,4,8,9,10,11. Here we identify two distinct influences of Arctic warming which may lead to cold winters over East Asia or North America, based on observational analyses and extensive climate model results. We find that severe winters across East Asia are associated with anomalous warmth in the Barents–Kara Sea region, whereas severe winters over North America are related to anomalous warmth in the East Siberian–Chukchi Sea region. Each regional warming over the Arctic Ocean is accompanied by the local development of an anomalous anticyclone and the downstream development of a mid-latitude trough. The resulting northerly flow of cold air provides favourable conditions for severe winters in East Asia or North America. These links between Arctic and mid-latitude weather are also robustly found in idealized climate model experiments and CMIP5 multi-model simulations. We suggest that our results may help improve seasonal prediction of winter weather and extreme events in these regions.
  14. 2015: Park, Jong-Yeon, et al. “Amplified Arctic warming by phytoplankton under greenhouse warming.” Proceedings of the National Academy of Sciences (2015): 201416884. One of the important impacts of marine phytoplankton on climate systems is the geophysical feedback by which chlorophyll and the related pigments in phytoplankton absorb solar radiation and then change sea surface temperature. Yet such biogeophysical impact is still not considered in many climate projections by state-of-the-art climate models, nor is its impact on the future climate quantified. This study shows that, by conducting global warming simulations with and without an active marine ecosystem model, the biogeophysical effect of future phytoplankton changes amplifies Arctic warming by 20%. Given the close linkage between the Arctic and global climate, the biologically enhanced Arctic warming can significantly modify future estimates of global climate change, and therefore it needs to be considered as a possible future scenario.
  15. 2015: Francis, Jennifer A., and Stephen J. Vavrus. “Evidence for a wavier jet stream in response to rapid Arctic warming.” Environmental Research Letters 10.1 (2015): 014005. New metrics and evidence are presented that support a linkage between rapid Arctic warming, relative to Northern hemisphere mid-latitudes, and more frequent high-amplitude (wavy) jet-stream configurations that favor persistent weather patterns. We find robust relationships among seasonal and regional patterns of weaker poleward thickness gradients, weaker zonal upper-level winds, and a more meridional flow direction. These results suggest that as the Arctic continues to warm faster than elsewhere in response to rising greenhouse-gas concentrations, the frequency of extreme weather events caused by persistent jet-stream patterns will increase.
  16. 2016: Goss, Michael, Steven B. Feldstein, and Sukyoung Lee. “Stationary wave interference and its relation to tropical convection and Arctic warming.” Journal of Climate 29.4 (2016): 1369-1389.The interference between transient eddies and climatological stationary eddies in the Northern Hemisphere is investigated. The amplitude and sign of the interference is represented by the stationary wave index (SWI), which is calculated by projecting the daily 300-hPa streamfunction anomaly field onto the 300-hPa climatological stationary wave. ERA-Interim data for the years 1979 to 2013 are used. The amplitude of the interference peaks during boreal winter. The evolution of outgoing longwave radiation, Arctic temperature, 300-hPa streamfunction, 10-hPa zonal wind, Arctic sea ice concentration, and the Arctic Oscillation (AO) index are examined for days of large SWI values during the winter. Constructive interference during winter tends to occur about one week after enhanced warm pool convection and is followed by an increase in Arctic surface air temperature along with a reduction of sea ice in the Barents and Kara Seas. The warming of the Arctic does occur without prior warm pool convection, but it is enhanced and prolonged when constructive interference occurs in concert with enhanced warm pool convection. This is followed two weeks later by a weakening of the stratospheric polar vortex and a decline of the AO. All of these associations are reversed in the case of destructive interference. Potential climate change implications are briefly discussed.
  17. 2016: Baggett, Cory, Sukyoung Lee, and Steven Feldstein. “An investigation of the presence of atmospheric rivers over the North Pacific during planetary-scale wave life cycles and their role in Arctic warming.” Journal of the Atmospheric Sciences73.11 (2016): 4329-4347. Heretofore, the tropically excited Arctic warming (TEAM) mechanism put forward that localized tropical convection amplifies planetary-scale waves, which transport sensible and latent heat into the Arctic, leading to an enhancement of downward infrared radiation and Arctic surface warming. In this study, an investigation is made into the previously unexplored contribution of the synoptic-scale waves and their attendant atmospheric rivers to the TEAM mechanism. Reanalysis data are used to conduct a suite of observational analyses, trajectory calculations, and idealized model simulations. It is shown that localized tropical convection over the Maritime Continent precedes the peak of the planetary-scale wave life cycle by ~10–14 days. The Rossby wave source induced by the tropical convection excites a Rossby wave train over the North Pacific that amplifies the climatological December–March stationary waves. These amplified planetary-scale waves are baroclinic and transport sensible and latent heat poleward. During the planetary-scale wave life cycle, synoptic-scale waves are diverted northward over the central North Pacific. The warm conveyor belts associated with the synoptic-scale waves channel moisture from the subtropics into atmospheric rivers that ascend as they move poleward and penetrate into the Arctic near the Bering Strait. At this time, the synoptic-scale waves undergo cyclonic Rossby wave breaking, which further amplifies the planetary-scale waves. The planetary-scale wave life cycle ceases as ridging over Alaska retrogrades westward. The ridging blocks additional moisture transport into the Arctic. However, sensible and latent heat amounts remain elevated over the Arctic, which enhances downward infrared radiation and maintains warm surface temperatures.
  18. 2016: Woods, Cian, and Rodrigo Caballero. “The role of moist intrusions in winter Arctic warming and sea ice decline.” Journal of Climate 29.12 (2016): 4473-4485. This paper examines the trajectories followed by intense intrusions of moist air into the Arctic polar region during autumn and winter and their impact on local temperature and sea ice concentration. It is found that the vertical structure of the warming associated with moist intrusions is bottom amplified, corresponding to a transition of local conditions from a “cold clear” state with a strong inversion to a “warm opaque” state with a weaker inversion. In the marginal sea ice zone of the Barents Sea, the passage of an intrusion also causes a retreat of the ice margin, which persists for many days after the intrusion has passed. The authors find that there is a positive trend in the number of intrusion events crossing 70°N during December and January that can explain roughly 45% of the surface air temperature and 30% of the sea ice concentration trends observed in the Barents Sea during the past two decades.
  19. 2017: Tokinaga, Hiroki, Shang-Ping Xie, and Hitoshi Mukougawa. “Early 20th-century Arctic warming intensified by Pacific and Atlantic multidecadal variability.” Proceedings of the National Academy of Sciences 114.24 (2017): 6227-6232. Arctic amplification is a robust feature of climate response to global warming, with large impacts on ecosystems and societies. A long-standing mystery is that a pronounced Arctic warming occurred during the early 20th century when the rate of interdecadal change in radiative forcing was much weaker than at present. Here, using observations and model experiments, we show that the combined effect of internally generated Pacific and Atlantic interdecadal variabilities intensified the Arctic land warming in the early 20th century. The synchronized Pacific–Atlantic warming drastically alters planetary-scale atmospheric circulations over the Northern Hemisphere that transport warm air into the Arctic. Our results highlight the importance of regional sea surface temperature changes for Arctic climate and constrain model projections in this important region.
  20. 2017: Breider, Thomas J., et al. “Multidecadal trends in aerosol radiative forcing over the Arctic: Contribution of changes in anthropogenic aerosol to Arctic warming since 1980.” Journal of Geophysical Research: Atmospheres 122.6 (2017): 3573-3594. Arctic observations show large decreases in the concentrations of sulfate and black carbon (BC) aerosols since the early 1980s. These near‐term climate‐forcing pollutants perturb the radiative balance of the atmosphere and may have played an important role in recent Arctic warming. We use the GEOS‐Chem global chemical transport model to construct a 3‐D representation of Arctic aerosols that is generally consistent with observations and their trends from 1980 to 2010. Observations at Arctic surface sites show significant decreases in sulfate and BC mass concentrations of 2–3% per year. We find that anthropogenic aerosols yield a negative forcing over the Arctic, with an average 2005–2010 Arctic shortwave radiative forcing (RF) of −0.19 ± 0.05 W m−2 at the top of atmosphere (TOA). Anthropogenic sulfate in our study yields more strongly negative forcings over the Arctic troposphere in spring (−1.17 ± 0.10 W m−2) than previously reported. From 1980 to 2010, TOA negative RF by Arctic aerosol declined, from −0.67 ± 0.06 W m−2 to −0.19 ± 0.05 W m−2, yielding a net TOA RF of +0.48 ± 0.06 W m−2. The net positive RF is due almost entirely to decreases in anthropogenic sulfate loading over the Arctic. We estimate that 1980–2010 trends in aerosol‐radiation interactions over the Arctic and Northern Hemisphere midlatitudes have contributed a net warming at the Arctic surface of +0.27 ± 0.04 K, roughly one quarter of the observed warming. Our study does not consider BC emissions from gas flaring nor the regional climate response to aerosol‐cloud interactions or BC deposition on snow.

 

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[LINK TO 2019 UPDATE]

 

FIGURE 1: ANNUAL MEAN CO2 CONCENTRATION: FROM 124,813 OBSERVATIONSCO2-TREND

TEMP

TREND-TEMP-ADJUSTED-CO2

 

 

FIGURE 2: CORRELATION OF CHANGES IN CO2 CONCENTRATION WITH EMISSIONSEMISSIONSANNUAL-CHANGES-TEMP-ADJUSTEDDETCORR-TEMP-ADJUSTED

 

FIGURE 3: CO2 CONCENTRATION AGAINST DEPTHCO2-DEPTH

 

FIGURE 4: MASS BALANCEMASS-BALANCE

 

[LIST OF POSTS ON THIS SITE]

 

 

  1. The dramatic ocean acidification event in the Paleocene-Eocene Thermal Maximum (PETM) [LINK] , may have inspired climate science to paint a similar scenario for the current episode of climate change and to claim that “combustion of fossil fuels has enriched levels of CO2 in the world’s oceans and decreased ocean pH” and that such acidification is causing dangerous detrimental effects on coral, shellfish, and on the oceanic ecosystem in general. These effects of the use of fossil fuels are cited as the rationale for climate action in the form of reducing or eliminating the use of fossil fuels. Citations below.
  2. Here we present empirical evidence from 124,813 measurements of ocean CO2 concentration expressed in millimoles per liter (MM/L) 1958 TO 2014 provided by the Scripps Institution of Oceanography. The data are presented in Figure 1. They show a rising trend in the CO2 concentration of oceans at depths of 50 to 5000 feet. The temperatures at which these measurements were made vary from 5C to 25C. The CO2 concentrations are adjusted for these temperature differences and the temperature adjusted concentrations are shown in the third panel of Figure 1.
  3. If fossil fuel emissions are responsible for these changes, we expect to find a correlation between the rate of emissions and changes in oceanic CO2 at an annual time scale. Figure 2 displays the rate of annual emissions and the corresponding annual changes in oceanic CO2. The correlation corrected for trend effects is shown in the third panel of Figure 2. No evidence is found that changes in oceanic CO2 are related to fossil fuel emissions at an annual time scale.
  4. A further test of human cause is presented in Figure 3 and Figure 4. If the source of the ocean’s CO2 enrichment is the atmosphere, we should find a concentration gradient with higher concentrations closer to the surface. No such gradient is found in the depth chart shown in Figure 3.
  5. A mass balance is presented in Figure 4. It hows that cumulative emissions in the period 1958 to 2014 was 328 gigatons of carbon. In the very unlikely event that all of this carbon had ended up in the ocean, it could cause an ocean acidification in the amount of 0.021 millimoles/liter. Therefore, it is not possible to explain an observed change of 0.3 millimoles/liter in terms of fossil fuel emissions.
  6. The mass balance assumes that the dissolved CO2 is evenly distributed throughout the full depth of the ocean to 12,000 feet. However the oceanic CO2 data presented above go down to a depth of 5,000 feet that contains not 100% but 80% of the ocean’s waters. The mass balance presented above can be adjusted for the depth of 5,000 feet by assuming that not 100% but only 80% of the fossil fuel emissions end up in the ocean with the other 20% distributed to the atmospheric and to land surface sinks.
  7. We conclude from the correlation and mass balance analysis presented above, that no evidence is found in oceanic CO2 measurements that the observed increase in the inorganic CO2 concentration of the oceans is related to fossil fuel emissions.
  8. It is likely that the ocean acidification hypothesis entered the climate change narrative by way of the PETM climate change event when extensive and devastating ocean acidification had occurred as described in a related post [LINK] . However, there is no parallel between PETM and AGW that can be used to relate the characteristics of one to those of the other. In the case of ocean acidification in the PETM event, the source of carbon was a monstrous release of either methane hydrates from the sea floor along with geothermal heat of some form or perhaps both heat and carbon directly injected into the ocean from the mantle. The event caused the ocean to lose all its elemental oxygen by way of carbon oxidation and undergo significant decline in pH. Much of the carbon dioxide was also vented to the atmosphere and that caused atmospheric CO2 to rise precipitously.  But this correspondence of ocean acidification in the presence of rising atmospheric CO2 does not apply to AGW. Such parallels usually drawn to relate rising atmospheric CO2 to ocean acidification overlooks the reversal in direction. Whereas PETM started in the ocean and spread to the atmosphere, the AGW event started in the atmosphere and is thought to have spread to the oceans. The evidence presented here does not support this hypothesis. For details about the PETM, please see the related post on the Paleocene-Eocene Thermal Maximum event [LINK] .
  9. What the PETM shows us is that the ocean is able to acidify itself to a much greater extent with natural causes by using carbon and heat from the bottom of the ocean or from the mantle. This reference event, rather than supporting human caused ocean acidification, serves instead as a caution against the simplistic assignment of human cause to all observed changes in the ocean. Natural geological sources of carbon dioxide must be studied to understand changes in ocean pH. The simplistic assignment of all observed changes in the ocdan to fossil fuel emissions is a form of circular reasoning that likely derives from an atmosphere bias in climate science and perhaps a perceived need to insert AGW climate change causation in all observed changes that can be shown to be undesirable. 

 

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OCEAN ACIDIFICATION BIBLIOGRAPHY

  1. 2005: Orr, James C., et al. “Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms.” Nature 437.7059 (2005): 681. Today’s surface ocean is saturated with respect to calcium carbonate, but increasing atmospheric carbon dioxide concentrations are reducing ocean pH and carbonate ion concentrations, and thus the level of calcium carbonate saturation. Experimental evidence suggests that if these trends continue, key marine organisms—such as corals and some plankton—will have difficulty maintaining their external calcium carbonate skeletons. Here we use 13 models of the ocean–carbon cycle to assess calcium carbonate saturation under the IS92a ‘business-as-usual’ scenario for future emissions of anthropogenic carbon dioxide. In our projections, Southern Ocean surface waters will begin to become undersaturated with respect to aragonite, a metastable form of calcium carbonate, by the year 2050. By 2100, this undersaturation could extend throughout the entire Southern Ocean and into the subarctic Pacific Ocean. When live pteropods were exposed to our predicted level of undersaturation during a two-day shipboard experiment, their aragonite shells showed notable dissolution. Our findings indicate that conditions detrimental to high-latitude ecosystems could develop within decades, not centuries as suggested previously.
  2. 2007: Hoegh-Guldberg, Ove, et al. “Coral reefs under rapid climate change and ocean acidification.” science 318.5857 (2007): 1737-1742. Atmospheric carbon dioxide concentration is expected to exceed 500 parts per million and global temperatures to rise by at least 2°C by 2050 to 2100, values that significantly exceed those of at least the past 420,000 years during which most extant marine organisms evolved. Under conditions expected in the 21st century, global warming and ocean acidification will compromise carbonate accretion, with corals becoming increasingly rare on reef systems. The result will be less diverse reef communities and carbonate reef structures that fail to be maintained. Climate change also exacerbates local stresses from declining water quality and overexploitation of key species, driving reefs increasingly toward the tipping point for functional collapse. This review presents future scenarios for coral reefs that predict increasingly serious consequences for reef-associated fisheries, tourism, coastal protection, and people. As the International Year of the Reef 2008 begins, scaled-up management intervention and decisive action on global emissions are required if the loss of coral-dominated ecosystems is to be avoided.
  3. 2008: Anthony, Kenneth RN, et al. “Ocean acidification causes bleaching and productivity loss in coral reef builders.” Proceedings of the National Academy of Sciences (2008). Ocean acidification represents a key threat to coral reefs by reducing the calcification rate of framework builders. In addition, acidification is likely to affect the relationship between corals and their symbiotic dinoflagellates and the productivity of this association. However, little is known about how acidification impacts on the physiology of reef builders and how acidification interacts with warming. Here, we report on an 8-week study that compared bleaching, productivity, and calcification responses of crustose coralline algae (CCA) and branching (Acropora) and massive (Porites) coral species in response to acidification and warming. Using a 30-tank experimental system, we manipulated CO2 levels to simulate doubling and three- to fourfold increases [Intergovernmental Panel on Climate Change (IPCC) projection categories IV and VI] relative to present-day levels under cool and warm scenarios. Results indicated that high CO2 is a bleaching agent for corals and CCA under high irradiance, acting synergistically with warming to lower thermal bleaching thresholds. We propose that CO2 induces bleaching via its impact on photoprotective mechanisms of the photosystems. Overall, acidification impacted more strongly on bleaching and productivity than on calcification. Interestingly, the intermediate, warm CO2 scenario led to a 30% increase in productivity in Acropora, whereas high CO2 lead to zero productivity in both corals. CCA were most sensitive to acidification, with high CO2 leading to negative productivity and high rates of net dissolution. Our findings suggest that sensitive reef-building species such as CCA may be pushed beyond their thresholds for growth and survival within the next few decades whereas corals will show delayed and mixed responses.
  4. 2008: Fabry, Victoria J., et al. “Impacts of ocean acidification on marine fauna and ecosystem processes.” ICES Journal of Marine Science 65.3 (2008): 414-432. Oceanic uptake of anthropogenic carbon dioxide (CO2) is altering the seawater chemistry of the world’s oceans with consequences for marine biota. Elevated partial pressure of CO2 (pCO2) is causing the calcium carbonate saturation horizon to shoal in many regions, particularly in high latitudes and regions that intersect with pronounced hypoxic zones. The ability of marine animals, most importantly pteropod molluscs, foraminifera, and some benthic invertebrates, to produce calcareous skeletal structures is directly affected by seawater CO2 chemistry. CO2influences the physiology of marine organisms as well through acid-base imbalance and reduced oxygen transport capacity. The few studies at relevant pCO2 levels impede our ability to predict future impacts on foodweb dynamics and other ecosystem processes. Here we present new observations, review available data, and identify priorities for future research, based on regions, ecosystems, taxa, and physiological processes believed to be most vulnerable to ocean acidification. We conclude that ocean acidification and the synergistic impacts of other anthropogenic stressors provide great potential for widespread changes to marine ecosystems.
  5. 2009: Miller, A. Whitman, et al. “Shellfish face uncertain future in high CO2 world: influence of acidification on oyster larvae calcification and growth in estuaries.” Plos one 4.5 (2009): e5661. Human activities have increased atmospheric concentrations of carbon dioxide by 36% during the past 200 years. One third of all anthropogenic CO2 has been absorbed by the oceans, reducing pH by about 0.1 of a unit and significantly altering their carbonate chemistry. There is widespread concern that these changes are altering marine habitats severely, but little or no attention has been given to the biota of estuarine and coastal settings, ecosystems that are less pH buffered because of naturally reduced alkalinity.
  6. 2009: Doney, Scott C., et al. “Ocean acidification: the other CO2 problem.” Annual Review of Marine Science (2009). Rising atmospheric carbon dioxide (CO2), primarily from human fossil fuel combustion, reduces ocean pH and causes wholesale shifts in seawater carbonate chemistry. The process of ocean acidification is well documented in field data, and the rate will accelerate over this century unless future CO2 emissions are curbed dramatically. Acidification alters seawater chemical speciation and biogeochemical cycles of many elements and compounds. One well-known effect is the lowering of calcium carbonate saturation states, which impacts shell-forming marine organisms from plankton to benthic molluscs, echinoderms, and corals. Many calcifying species exhibit reduced calcification and growth rates in laboratory experiments under high-CO2 conditions. Ocean acidification also causes an increase in carbon fixation rates in some photosynthetic organisms (both calcifying and noncalcifying). The potential for marine organisms to adapt to increasing CO2 and broader implications for ocean ecosystems are not well known; both are high priorities for future research. Although ocean pH has varied in the geological past, paleo-events may be only imperfect analogs to current conditions.
  7. 2010: Talmage, Stephanie C., and Christopher J. Gobler. “Effects of past, present, and future ocean carbon dioxide concentrations on the growth and survival of larval shellfish.” Proceedings of the National Academy of Sciences 107.40 (2010): 17246-17251. The combustion of fossil fuels has enriched levels of CO2 in the world’s oceans and decreased ocean pH. Although the continuation of these processes may alter the growth, survival, and diversity of marine organisms that synthesize CaCO3 shells, the effects of ocean acidification since the dawn of the industrial revolution are not clear. Here we present experiments that examined the effects of the ocean’s past, present, and future (21st and 22nd centuries) CO2 concentrations on the growth, survival, and condition of larvae of two species of commercially and ecologically valuable bivalve shellfish (Mercenaria mercenaria and Argopecten irradians). Larvae grown under near preindustrial CO2 concentrations (250 ppm) displayed significantly faster growth and metamorphosis as well as higher survival and lipid accumulation rates compared with individuals reared under modern day CO2 levels. Bivalves grown under near preindustrial CO2 levels displayed thicker, more robust shells than individuals grown at present CO2 concentrations, whereas bivalves exposed to CO2 levels expected later this century had shells that were malformed and eroded. These results suggest that the ocean acidification that has occurred during the past two centuries may be inhibiting the development and survival of larval shellfish and contributing to global declines of some bivalve populations.
  8. 2010: Kroeker, Kristy J., et al. “Meta‐analysis reveals negative yet variable effects of ocean acidification on marine organisms.” Ecology letters 13.11 (2010): 1419-1434. Ocean acidification is a pervasive stressor that could affect many marine organisms and cause profound ecological shifts. A variety of biological responses to ocean acidification have been measured across a range of taxa, but this information exists as case studies and has not been synthesized into meaningful comparisons amongst response variables and functional groups. We used meta‐analytic techniques to explore the biological responses to ocean acidification, and found negative effects on survival, calcification, growth and reproduction. However, there was significant variation in the sensitivity of marine organisms. Calcifying organisms generally exhibited larger negative responses than non‐calcifying organisms across numerous response variables, with the exception of crustaceans, which calcify but were not negatively affected. Calcification responses varied significantly amongst organisms using different mineral forms of calcium carbonate. Organisms using one of the more soluble forms of calcium carbonate (high‐magnesium calcite) can be more resilient to ocean acidification than less soluble forms (calcite and aragonite). Additionally, there was variation in the sensitivities of different developmental stages, but this variation was dependent on the taxonomic group. Our analyses suggest that the biological effects of ocean acidification are generally large and negative, but the variation in sensitivity amongst organisms has important implications for ecosystem responses.
  9. 2012: Narita, Daiju, Katrin Rehdanz, and Richard SJ Tol. “Economic costs of ocean acidification: a look into the impacts on global shellfish production.” Climatic Change 113.3-4 (2012): 1049-1063. Ocean acidification is increasingly recognized as a major global problem. Yet economic assessments of its effects are currently almost absent. Unlike most other marine organisms, mollusks, which have significant commercial value worldwide, have relatively solid scientific evidence of biological impact of acidification and allow us to make such an economic evaluation. By performing a partial-equilibrium analysis, we estimate global and regional economic costs of production loss of mollusks due to ocean acidification. Our results show that the costs for the world as a whole could be over 100 billion USD with an assumption of increasing demand of mollusks with expected income growths combined with a business-as-usual emission trend towards the year 2100. The major determinants of cost levels are the impacts on the Chinese production, which is dominant in the world, and the expected demand increase of mollusks in today’s developing countries, which include China, in accordance with their future income rise. Our results have direct implications for climate policy. Because the ocean acidifies faster than the atmosphere warms, the acidification effects on mollusks would raise the social cost of carbon more strongly than the estimated damage adds to the damage costs of climate change.
  10. 2013: Andersson, Andreas J., and Dwight Gledhill. “Ocean acidification and coral reefs: effects on breakdown, dissolution, and net ecosystem calcification.” Annual Review of Marine Science 5 (2013): 321-348. The persistence of carbonate structures on coral reefs is essential in providing habitats for a large number of species and maintaining the extraordinary biodiversity associated with these ecosystems. As a consequence of ocean acidification (OA), the ability of marine calcifiers to produce calcium carbonate (CaCO3) and their rate of CaCO3production could decrease while rates of bioerosion and CaCO3 dissolution could increase, resulting in a transition from a condition of net accretion to one of net erosion. This would have negative consequences for the role and function of coral reefs and the eco-services they provide to dependent human communities. In this article, we review estimates of bioerosion, CaCO3 dissolution, and net ecosystem calcification (NEC) and how these processes will change in response to OA. Furthermore, we critically evaluate the observed relationships between NEC and seawater aragonite saturation state (Ωa). Finally, we propose that standardized NEC rates combined with observed changes in the ratios of dissolved inorganic carbon to total alkalinity owing to net reef metabolism may provide a biogeochemical tool to monitor the effects of OA in coral reef environments.

 

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FIGURE 1: COMPARISON OF OLS TREND WITH MEAN 15YR TREND

 

FIGURE 2: DATA AND GAUSSIAN SIMULATION COMPARED

HURSTGIFDONE

 

 

FIGURE 3: HURST EXPONENTS OF DATA AND GAUSSIAN SIMULATION COMPARED

HURST-TABLE

 

IS THERE HURST PERSISTENCE IN UAH ZONAL MEAN TEMPERATURE ANOMALIES?

REFERENCE PAPER FULL TEXT DOWNLOAD:  [SSRN.COM]  [ACADEMIA.EDU]

 

  1. It was shown in a prior work (see links above) that when the UAH lower troposphere temperatures Dec 1978 to Dec 2017 are studied on a monthly time scale (as a sequence of 481 months), evidence of Hurst persistence is found in the data. Here we present a further investigation into these data using an annual time scale and studying each calendar month separately for the seven calendar months January to July in study period 1979 to 2018. As of this writing data for the full time span are available for only these seven months.
  2. Almost universally, the study of temperature trends are presented in terms of OLS (ordinary least squares) linear regression. The procedure contains some unforgiving assumptions that may not apply in time series field data such as the temperature data in climate studies but the use of OLS has gained such widespread acceptance that the limitations of the procedure imposed by assumptions are generally overlooked. The important assumption relevant here is the so called “iid” constraint. The procedure assumes that all occurrences of the time series are taken from an identical Gaussian distribution that differ only in magnitude and that each occurrence is independent of prior occurrences. Violations of these assumptions in time series of field data are common.
  3. A serious violation is that of persistence first discovered in the Nile River flow data by Edwin Hurst who was designing the Aswan Dam in Egypt (pictured above). He found that changes in the flow rate were not random but contained a persistence so that an increase was more likely to be followed by an increase than a decrease and a decrease was more likely to be followed by a decrease than an increase. This kind of behavior violates “iid” Gaussian randomness and invalidates OLS regression because OLS assumes Gaussian randomness. In 1950, Hurst published his first and only journal publication [Hurst, E. (1951). Long-term storage capacity of reservoirs. Trans. Amer. Soc. Civil Eng. , 116 (1951): 770-808]. In it he detailed a procedure by which such persistence in time series can be detected by tracking how distant (the range) the cumulative deviations from the mean can be measured as number of standard deviations. He proposed the ratio H = ln(range)/ln(sample size) as a measure of persistence where H = Hurst exponent. In a pure Gaussian iid series H=0.5 and in the case of persistence, H>0.5. As a practical matter, because the empirical setup can also affect the value of H (Granero, S. (2008). Some comments on Hurst exponent and the long memory processes on capital markets. Physica A: Statistical Mechanics and its applications , 387.22 (2008): 5543-5551), the best way to test for persistence is to compare the H-value of the test series with that of its Gaussian twin. If the test series H-value is statistically significantly greater than the H-value of its Gaussian twin, there is evidence of persistence, and otherwise not.
  4. This comparison is made in Figure 3 for all ten unique zonal regions for which satellite temperature anomaly data are published by the UAH. For each zonal region, we compute the value of H in the data and again in a Gaussian simulation of the data. The Gaussian simulation retains the standard deviation and OLS trend value and generates the actual values in a Monte Carlo simulation (labeled as SIMUL). Figure 3 shows that no significant difference is found between data and the iid Gaussian simulation. We conclude from this comparison that the Gaussian iid assumption is not violated in the UAH temperature data at an annual time scale.
  5. This conclusion finds further support in Figure 1 where the OLS trend (the yellow line) is compared with the average of trends computed in a moving 15-year window that moves one year at a time from an end-year of 1993 to an end-year of 2018. If OLS assumptions are violated we should find significant differences between these two measures of overall trend. But no difference is evident in the graphic display. We therefore find no evidence of persistence or that the UAH data violate OLS assumptions.
  6. Additional support for this conclusion is found in Figure 2. Here the data (blue dots) and the corresponding Gaussian simulation (red dots) are compared directly in search for a visual incongruence that might identify non-Gaussian behavior. No such incongruence is found.
  7. We conclude that the evidence of Hurst persistence at a monthly time scale reported in previous works (  [SSRN.COM]  [ACADEMIA.EDU]does not apply to the annual time scale when the calendar months are studied in isolation, one at a time.

 

1956: Hurst, Harold Edwin. “Methods of using long-term storage in reservoirs.” Proceedings of the Institution of Civil Engineers5.5 (1956): 519-543.

[FULL TEXT DOWNLOAD]

bandicam 2018-09-28 09-09-46-070

 

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SUMMARY: The testable implication of the GHG theory is that surface temperature should be responsive to atmospheric CO2 concentration such that a detrended correlation exists between the logarithm of atmospheric CO2 and surface temperature at the time scale of interest. This test is carried out for five temperature series in eight calendar months for the sample period 1979-2018 using comparative analysis to test observational data against the theoretical series. The comparison does not show evidence of the existence of GHG forcing by atmospheric CO2 in the observational data. 

 

[HOME PAGE OF THIS SITE]

 

FIGURE 1: OBSERVED CLIMATE SENSITIVITY VALUES FOR EACH CALENDAR MONTH

ECSTABLE

ECSCHART

 

FIGURE 2: CORRELATION BETWEEN TEMPERATURE AND LN(CO2)

CORRTABLE

CORRCHART

 

 

FIGURE 3: SPURIOUS CORRELATIONS IN TIME SERIES DATA

spuriouscorrelation2

 

 

FIGURE 4: DETRENDED CORRELATION BETWEEN TEMPERATURE AND LN(CO2)

DETCORTABLE

PVALTABLE

DETCORCHART

 

 

 

 

[LIST OF POSTS ON THIS SITE]

 

  1. ABSTRACT: The testable implication of the the theory of GHG forcing of surface temperature by atmospheric CO2 is that surface temperature should be responsive to changes in atmospheric CO2 concentration at the time scale of interest. This test is carried out by comparing four observational temperature series against a theoretical series constructed with GHG forcing of atmospheric CO2. The comparison does not show evidence for the existence of GHG forcing of atmospheric CO2 in the observational data. Conventional climate sensitivity estimations in observational data depend on a spurious correlation and this spuriousness likely explains the great instability of sensitivity and the large range of values found in observational data. When that spuriousness is corrected with detrended correlation analysis, no correlation remains to support the existence of climate sensitivity.
  2. DATA AND METHOD: This is a comparative analysis of the responsiveness of surface temperature to changes in atmospheric CO2 concentration in accordance with the GHG theory of atmospheric CO2 concentration described by the climate sensitivity function where surface temperature is a linear function of (LN(CO2)). Three types of temperature data are compared. They are (1) direct observations of global mean temperature with satellite mounted microwave sounding units (UAH, RSS), (2) global mean temperature reconstructions from the instrumental record (HAD, GIS), and (3) a theoretical time series of temperatures expected according to the theory of the GHG effect of atmospheric CO2 concentration where CMIP5 forcings ensure that Temp=f(LN(CO2)). The comparison is used to test the hypothesis that observational data contain the GHG effect of atmospheric CO2 concentration in the form of the climate sensitivity function.
  3. The time span for the study is 40 year satellite era from 1979 to 2018 when direct measurements of mean global lower troposphere temperatures are available. The other three temperature time series constrained to this sample period so that a direct comparison can be made. Eight calendar months (January to August) of data are available for the full sample period 1979-2018 at the time of this study. The calendar months are studied separately as their trend behaviors have been shown to differ in a related post at this site [LINK] and also because the existence of a seasonal cycle in GHG forcing has been shown to exist [LINK] .
  4. DATA ANALYSIS: FIGURE 1 is a tabulation of the observed climate sensitivity values in each of eight calendar months (January to August) and for each of the five temperature series studied (HAD, GIS, UAH, RSS, RCP8.5). It shows relatively low values of climate sensitivity ranging from 1.5<ECS<2.1 for the direct observations in the satellite data from UAH and RSS; somewhat higher values of 2.1<ECS<2.7 for temperature reconstructions from HAD and GIS; and values of ECS≈3 in theRCP8.5 series derived from a theory that holds that ECS=3±1.5. It is noted that although all observed values are within this large range, the theoretical RCP8.5 sensitivity values are higher than the observational empirical values.
  5. FIGURE 2 displays the correlations between temperature and LN(CO2) required to support the reliability of the corresponding regression coefficients that are translated into climate sensitivities. Strong and statistically significant correlations are seen in all five time series. As expected, the highest correlations, all eight of them close to the near perfect  correlation of ρ≈1.0, are found in the theoretical RCP8.5 series that was constructed with the GHG forcing of atmospheric CO2. The lowest correlations, 0.55<ρ<0.75 are found in the directly observed satellite data. Intermediate values of 0.7<ρ<0.9 found in the global temperature reconstructions. All observed correlations are statistically significant and they are generally taken as evidence in support of the validity and reliability of the climate sensitivity parameters implied by the corresponding regression coefficients. The implied climate sensitivities are tabulated in Figure 1.
  6. FIGURE 3 is a presentation of the tendency in time series field data to translate long term trends into faux correlations even in the absence of responsiveness at a time scale of interest. The first frame presents an example of such a spurious correlation taken from the Tyler Vigen collection of spurious correlations in time series data [LINK]. The bottom frame is a segment of a lecture on spurious correlations in time series data and their examination with detrended correlation analysis provided on Youtube by Alex Tolley [LINK] These considerations imply that the correlation seen in the source data (Figure 1) may not be reliable and that they must be decomposed so that the part that derives from long term trends can be removed and only the part that derives from responsiveness at the time scale of interest is interpreted into the theory of causation.
  7. FIGURE 4 presents the results of detrended correlation analysis that removes the effect of long term trends such that the correlation that survives into the detrended series more faithfully reflects the responsiveness of temperature to LN(CO2) at an annual time scale. In other words, detrended correlation is independent of the bias imposed by trends  in the correlation between source time series. Therefore they can be interpreted in terms of causal relationships. It is true that “correlation does not imply causation” but it is also true that no causation interpretation of the data can be made without correlation at the causation time scale. That is, detrended correlation at the appropriate time scale is a necessary but not sufficient condition for causation.
  8. FIGURE 4: The table of detrended correlations shows that the strong and statistically significant correlations seen in the source time series (Figure 2) do not survive into the detrended series in the observational data (HAD, GIS, UAH, RSS) implying that the source correlations were creations of long term trends and not the responsiveness of temperature to changes in LN(CO2) at an annual time scale. A very different result is seen in the theoretical temperature series constructed with the GHG effect of atmospheric CO2 concentration (RCP8.5). Here, strong and statistically significant correlations survive into the detrended series in all eight calendar months and serve as evidence that temperature is responsive to LN(CO2) at an annual time scale.
  9. FIGURE 4: COMPARATIVE ANALYSIS: In this comparative analysis, detrended correlations serve as the measure of the responsiveness of temperature to changes in atmospheric CO2. This measure is used to compare direct observations of global mean temperature and reconstructions of global mean temperature (where the presence of a GHG effect of atmospheric CO2 is being tested), with the theoretical series which serves as the benchmark of what we expect to see in the presence of the GHG effect of atmospheric CO2 concentration. The comparison under identical conditions shows no evidence in any of the eight calendar months studied of the responsiveness of direct temperature observations and the HadCRUT4 temperature reconstructions  (UAH, RSS, HAD) to changes in atmospheric CO2 concentration – a necessary condition for the existence of climate sensitivity and for the theory of anthropogenic global warming. A similar result for the GISTEMP global mean temperature reconstruction is also found but with the caveat that the detrended correlations are higher than in the other three observational data series with statistical significance observed in two of the eight calendar months studied (April and May). This unique feature of the NASA GISTEMP series may have implications for how these reconstructions were constructed and whether an assumption of GHG forcing played a role in their reconstruction.
  10. CONCLUSION: The testable implication of the GHG theory is that surface temperature should be responsive to atmospheric CO2 concentration such that a detrended correlation exists between the logarithm of atmospheric CO2 and surface temperature at the time scale of interest. This test is carried out for five temperature series in eight calendar months for the sample period 1979-2018 using comparative analysis to test observational data against the theoretical series. The comparison does not show evidence of the existence of GHG forcing by atmospheric CO2 in the observational data. The assistance and encouragement provided by Mr. Ashley Francis of Salisbury, England in carrying out this work is gratefully acknowledged.

 

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SUMMARY

  1. The results show that statistically significant and stable values of ECS are found for long time spans of 100 years or more at any location within the full span of the HadCRUT4 data 1850-2017. No improvement in stability is found for time spans longer than 100 years.
  2. The stability and reliability of ECS values for time spans of 90 years or less depend on location. Early time spans that end in 1905, 1933, and 1939 are found to be unstable and without a statistically significant value for ECS in the three short time spans tested (56 years, 84 years, and 90 years).
  3. The shorter time spans that end later than 1939 are stable and with statistically significant ECS values. However, an anomalous result of statistically significant ECS>6 is found for 56-year time spans ending in 1961. The location sensitivity of ECS in this temperature time series may imply inconsistencies in the data or that longer time spans are needed for ECS estimation claimed by some authors in the bibliography below. 
  4. The implication of this work is that climate sensitivity to atmospheric carbon dioxide concentration must be measured at sufficiently long time spans of 90 years or more and that at a time span of 100 years climate sensitivity lies somewhere between 1<ECS<3 with an average of ECS=2 as predicted by Manabe and Wetherald in 1964. 

 

 

FIGURE 1: ECS CLIMATE SENSITIVITY IN A 100-YEAR MOVING WINDOWECSGIFDONEMINMAX

 

FIGURE 2: ECS STATISTICS JANUARY-JUNEjan-jun

 

FIGURE 3: ECS STATISTICS: JULY-DECEMBERJUL-DEC

 

RELATED POST #1:  [ECS: Equilibrium Climate Sensitivity]

RELATED POST #2:  [The Greenhouse Effect of Atmospheric CO2]

RELATED POST #3:  [Climate Sensitivity Research: 2014-2018]

RELATED POST #4:  [Transient Climate Response to Cumulative Emissions]

 

  1. Equilibrium Climate Sensitivity (ECS) is the responsiveness of global mean surface temperature to the logarithm of atmospheric CO2 concentration stated as the increase in temperature for a doubling of atmospheric CO2 concentration. The measure at once verifies and quantifies the core principle of anthropogenic global warming (AGW) – the so called “greenhouse effect” of atmospheric CO2.
  2. However, this logical and empirical support for AGW is known to suffer from an uncertainty issue described in a related post. In brief, ECS research with models and with observational data and also with models constrained by observational data, a large range of values for the ECS has been reported from ECS<1 up to ECS>10. The IPCC describes the value of ECS as a mean(ECS)=3 and its 90%CI = [1.5, 4.5] Celsius degrees for each doubling of atmospheric carbon dioxide concentration based on the Jule Charney’s presentation of model results in 1979. The IPCC does not take note of earlier findings by Syukuro Manabe in 1967 that the the sensitivity value should be ECS=2 (See bibliography below).
  3. Yet, the legitimization of certain values of the ECS by selective citation is somewhat arbitrary and prone to bias because a large range of values significantly different from each other can thus be legitimized. In a prior work, it was shown that the ECS values seen in a moving 60-year window in the HadCRUT4 temperature data show negative ECS to values greater than ECS=6C/doubling. The full text of the source document for this work may be downloaded from [SSRN.COM]  or  [ACADEMIA.EDU] . In such a state of empirical confusion, it is possible to claim evidential support for a selected value from a large range of values by judicial choice of the time span and the corresponding citation as described by Wigley (1985).
  4. A similar analysis is presented in this work. Here, a range of spans is tested on the series of 168 years of monthly mean global temperature reconstructions in the HadCRUT4 data set provided by the Hadley Climate Research Unit. They are the full span (168 years), the half span (84 years), and one third of the span (56 years). In addition, spans of 100 and 110 years are tested. A relationship between the time span of the data and the observational ECS estimate is thus derived to determine the appropriate span for the test in view of the latency of the oceans in reaching climate equilibrium as described in (Danabasoglu 2009) below in the bibliography section.
  5. Here it is found by trial and error that in the HadCRUT4 temperature anomaly series, that a time span of at least 100 years is required to stabilize the empirical ECS estimate from observational data. It is shown that 100, 110, and 168-year time spans yield stable ECS values and that time spans of 90 years or less generate unstable and possibly unreliable ECS estimates in the early portion of the full span but not so in more recent data. This difference between early and recent data is difficult to interpret.
  6. Figure 1 displays the empirical ECS estimates in a moving 100-year window as the end of the moving window moves one year at a time from 1949 to 2017. The GIF animation presents these results one calendar month at a time. The blue line contains all 68 ECS values observed and the red line shows their mean. The observed values lie in a wide range of ECS<1 to ECS>3. However, their means are in the tighter range of ECS = [1.8, 2.4].
  7. These averages are summarized in the chart below the GIF animation. In comparison with the very unstable behavior in 60-year and 84-year spans reported in the earlier work posted on  [SSRN.COM] , the stability of ECS estimates from the HadCRUT4 temperature anomalies is improved when a 100-year moving window is used. Figure 1 also highlights the great differences among the calendar months in terms of ECS estimates. This information is lost when data for calendar months are combined into annual means.
  8. A further investigation of the effect of the time span on the stability of empirical ECS estimates is carried out and the results are summarized in Figure 2 &Figure 3. Time spans of 168 years (the full span), 56 years (one third of the full span), 84 years (one half of the full span), 90 years, 100 years, and 110 years are tried. The ECS estimates are tested for statistical significance. Statistically significant results are shown in black and values that failed the significance test are shown in red. The analysis is carried out for each of the twelve calendar months separately. The results for January to June appear in Figure 2 and those for July to December are in Figure 3.
  9. Figure 2 & Figure 3: The results show that statistically significant and stable values of ECS are found for long time spans of 100 years or more at any location within the full span of the data. No improvement in stability is found for time spans longer than 100 years. The stability and reliability of ECS values for time spans of 90 years or less depend on location. Early time spans that end in 1905, 1933, and 1939 are found to be unstable and without a statistically significant value for ECS in the three short time spans tested (56 years, 84 years, and 90 years). These same time spans that end later than 1939 are stable and with statistically significant ECS values. However, an anomalous result of statistically significant ECS>6 is found for 56-year time spans ending in 1961. The location sensitivity of ECS in this temperature time series may imply inconsistencies in the data.
  10. It is noted that in most cases, the empirical estimates for the mean value of the ECS in Figures 2&3 appear to be somewhat stable at or around ECS=2C per doubling of atmospheric CO2. The significance of the value of ECS=2 is that it is consistent with the theoretical prediction of Syukuro Manabe in his earliest published works in the various Manabe and Wetherald papers listed below.

 

ECS Bibliography

  1. 1963: Möller, Fritz. “On the influence of changes in the CO2 concentration in air on the radiation balance of the earth’s surface and on the climate.” Journal of Geophysical Research68.13 (1963): 3877-3886. The numerical value of a temperature change under the influence of a CO2 change as calculated by Plass is valid only for a dry atmosphere. Overlapping of the absorption bands of CO2 and H2O in the range around 15 μ essentially diminishes the temperature changes. New calculations give ΔT = + 1.5° when the CO2 content increases from 300 to 600 ppm. Cloudiness diminishes the radiation effects but not the temperature changes because under cloudy skies larger temperature changes are needed in order to compensate for an equal change in the downward long‐wave radiation. The increase in the water vapor content of the atmosphere with rising temperature causes a self‐amplification effect which results in almost arbitrary temperature changes, e.g. for constant relative humidity ΔT = +10° in the above mentioned case. It is shown, however, that the changed radiation conditions are not necessarily compensated for by a temperature change. The effect of an increase in CO2 from 300 to 330 ppm can be compensated for completely by a change in the water vapor content of 3 per cent or by a change in the cloudiness of 1 per cent of its value without the occurrence of temperature changes at all. Thus the theory that climatic variations are effected by variations in the CO2 content becomes very questionable.
  2. 1964: Manabe, Syukuro, and Robert F. Strickler. “Thermal equilibrium of the atmosphere with a convective adjustment.” Journal of the Atmospheric Sciences 21.4 (1964): 361-385. The states of thermal equilibrium (incorporating an adjustment of super-adiabatic stratification) as well as that of pure radiative equilibrium of the atmosphere are computed as the asymptotic steady state approached in an initial value problem. Recent measurements of absorptivities obtained for a wide range of pressure are used, and the scheme of computation is sufficiently general to include the effect of several layers of clouds. The atmosphere in thermal equilibrium has an isothermal lower stratosphere and an inversion in the upper stratosphere which are features observed in middle latitudes. The role of various gaseous absorbers (i.e., water vapor, carbon dioxide, and ozone), as well as the role of the clouds, is investigated by computing thermal equilibrium with and without one or two of these elements. The existence of ozone has very little effect on the equilibrium temperature of the earth’s surface but a very important effect on the temperature throughout the stratosphere; the absorption of solar radiation by ozone in the upper and middle stratosphere, in addition to maintaining the warm temperature in that region, appears also to be necessary for the maintenance of the isothermal layer or slight inversion just above the tropopause. The thermal equilibrium state in the absence of solar insulation is computed by setting the temperature of the earth’s surface at the observed polar value. In this case, the stratospheric temperature decreases monotonically with increasing altitude, whereas the corresponding state of pure radiative equilibrium has an inversion just above the level of the tropopause. A series of thermal equilibriums is computed for the distributions of absorbers typical of different latitudes. According to these results, the latitudinal variation of the distributions of ozone and water vapor may be partly responsible for the latitudinal variation of the thickness of the isothermal part of the stratosphere. Finally, the state of local radiative equilibrium of the stratosphere overlying a troposphere with the observed distribution of temperature is computed for each season and latitude. In the upper stratosphere of the winter hemisphere, a large latitudinal temperature gradient appears at the latitude of the polar-night jet stream, while in the upper statosphere of the summer hemisphere, the equilibrium temperature varies little with latitude. These features are consistent with the observed atmosphere. However, the computations predict an extremely cold polar night temperature in the upper stratosphere and a latitudinal decrease (toward the cold pole) of equilibrium temperature in the middle or lower stratosphere for winter and fall. This disagrees with observation, and suggests that explicit introduction of the dynamics of large scale motion is necessary.
  3. 1967: Manabe, Syukuro, and Richard T. Wetherald. “Thermal equilibrium of the atmosphere with a given distribution of relative humidity.” Journal of the Atmospheric Sciences 24.3 (1967): 241-259. [ECS=2]bandicam 2018-09-21 13-24-28-297
  4. 1969: Budyko, Mikhail I. “The effect of solar radiation variations on the climate of the earth.” tellus 21.5 (1969): 611-619. It follows from the analysis of observation data that the secular variation of the mean temperature of the Earth can be explained by the variation of short-wave radiation, arriving at the surface of the Earth. In connection with this, the influence of long-term changes of radiation, caused by variations of atmospheric transparency on the thermal regime is being studied. Taking into account the influence of changes of planetary albedo of the Earth under the development of glaciations on the thermal regime, it is found that comparatively small variations of atmospheric transparency could be sufficient for the development of quaternary glaciations.
  5. 1969: Sellers, William D. “A global climatic model based on the energy balance of the earth-atmosphere system.” Journal of Applied Meteorology 8.3 (1969): 392-400. A relatively simple numerical model of the energy balance of the earth-atmosphere is set up and applied. The dependent variable is the average annual sea level temperature in 10° latitude belts. This is expressed basically as a function of the solar constant, the planetary albedo, the transparency of the atmosphere to infrared radiation, and the turbulent exchange coefficients for the atmosphere and the oceans. The major conclusions of the analysis are that removing the arctic ice cap would increase annual average polar temperatures by no more than 7C, that a decrease of the solar constant by 2–5% might be sufficient to initiate another ice age, and that man’s increasing industrial activities may eventually lead to a global climate much warmer than today.
  6. 1971: Rasool, S. Ichtiaque, and Stephen H. Schneider. “Atmospheric carbon dioxide and aerosols: Effects of large increases on global climate.” Science 173.3992 (1971): 138-141. Effects on the global temperature of large increases in carbon dioxide and aerosol densities in the atmosphere of Earth have been computed. It is found that, although the addition of carbon dioxide in the atmosphere does increase the surface temperature, the rate of temperature increase diminishes with increasing carbon dioxide in the atmosphere. For aerosols, however, the net effect of increase in density is to reduce the surface temperature of Earth. Because of the exponential dependence of the backscattering, the rate of temperature decrease is augmented with increasing aerosol content. An increase by only a factor of 4 in global aerosol background concentration may be sufficient to reduce the surface temperature by as much as 3.5 ° K. If sustained over a period of several years, such a temperature decrease over the whole globe is believed to be sufficient to trigger an ice age.
  7. 1975: Manabe, Syukuro, and Richard T. Wetherald. “The effects of doubling the CO2 concentration on the climate of a general circulation model.” Journal of the Atmospheric Sciences 32.1 (1975): 3-15. An attempt is made to estimate the temperature changes resulting from doubling the present CO2 concentration by the use of a simplified three-dimensional general circulation model. This model contains the following simplifications: a limited computational domain, an idealized topography, no beat transport by ocean currents, and fixed cloudiness. Despite these limitations, the results from this computation yield some indication of how the increase of CO2 concentration may affect the distribution of temperature in the atmosphere. It is shown that the CO2 increase raises the temperature of the model troposphere, whereas it lowers that of the model stratosphere. The tropospheric warming is somewhat larger than that expected from a radiative-convective equilibrium model. In particular, the increase of surface temperature in higher latitudes is magnified due to the recession of the snow boundary and the thermal stability of the lower troposphere which limits convective beating to the lowest layer. It is also shown that the doubling of carbon dioxide significantly increases the intensity of the hydrologic cycle of the model. bandicam 2018-09-21 15-17-14-922
  8. 1976: Cess, Robert D. “Climate change: An appraisal of atmospheric feedback mechanisms employing zonal climatology.” Journal of the Atmospheric Sciences 33.10 (1976): 1831-1843. The sensitivity of the earth’s surface temperature to factors which can induce long-term climate change, such as a variation in solar constant, is estimated by employing two readily observable climate changes. One is the latitudinal change in annual mean climate, for which an interpretation of climatological data suggests that cloud amount is not a significant climate feedback mechanism, irrespective of how cloud amount might depend upon surface temperature, since there are compensating changes in both the solar and infrared optical properties of the atmosphere. It is further indicated that all other atmospheric feedback mechanisms, resulting, for example, from temperature-induced changes in water vapor amount, cloud altitude and lapse rate, collectively double the sensitivity of global surface temperature to a change in solar constant. The same conclusion is reached by considering a second type of climate change, that associated with seasonal variations for a given latitude zone. The seasonal interpretation further suggests that cloud amount feedback is unimportant zonally as well as globally. Application of the seasonal data required a correction for what appears to be an important seasonal feedback mechanism. This is attributed to a variability in cloud albedo due to seasonal changes in solar zenith angle. No attempt was made to individually interpret the collective feedback mechanisms which contribute to the doubling in surface temperature sensitivity. It is suggested, however, that the conventional assumption of fixed relative humidity for describing feedback due to water vapor amount might not be as applicable as is generally believed. Climate models which additionally include ice-albedo feedback are discussed within the framework of the present results.
  9. 1978: Ramanathan, V., and J. A. Coakley. “Climate modeling through radiative‐convective models.” Reviews of geophysics16.4 (1978): 465-489. We present a review of the radiative‐convective models that have been used in studies pertaining to the earth’s climate. After familiarizing the reader with the theoretical background, modeling methodology, and techniques for solving the radiative transfer equation the review focuses on the published model studies concerning global climate and global climate change. Radiative‐convective models compute the globally and seasonally averaged surface and atmospheric temperatures. The computed temperatures are in good agreement with the observed temperatures. The models include the important climatic feedback mechanism between surface temperature and H2O amount in the atmosphere. The principal weakness of the current models is their inability to simulate the feedback mechanism between surface temperature and cloud cover. It is shown that the value of the critical lapse rate adopted in radiative‐convective models for convective adjustment is significantly larger than the observed globally averaged tropospheric lapse rate. The review also summarizes radiative‐convective model results for the sensitivity of surface temperature to perturbations in (1) the concentrations of the major and minor optically active trace constituents, (2) aerosols, and (3) cloud amount. A simple analytical model is presented to demonstrate how the surface temperature in a radiative‐convective model responds to perturbations.
  10. 1985: Wigley, Thomas ML, and Michael E. Schlesinger. “Analytical solution for the effect of increasing CO2 on global mean temperature.” Nature 315.6021 (1985): 649. Increasing atmospheric carbon dioxide concentration is expected to cause substantial changes in climate. Recent model studies suggest that the equilibrium warming for a CO2 doubling (Δ T2×) is about 3–4°C. Observational data show that the globe has warmed by about 0.5°C over the past 100 years. Are these two results compatible? To answer this question due account must be taken of oceanic thermal inertia effects, which can significantly slow the response of the climate system to external forcing. The main controlling parameters are the effective diffusivity of the ocean below the upper mixed layer (κ) and the climate sensitivity (defined by Δ T2×). Previous analyses of this problem have considered only limited ranges of these parameters. Here we present a more general analysis of two cases, forcing by a step function change in CO2 concentration and by a steady CO2 increase. The former case may be characterized by a response time which we show is strongly dependent on both κ and Δ T2×. In the latter case the damped response means that, at any given time, the climate system may be quite far removed from its equilibrium with the prevailing CO2 level. In earlier work this equilibrium has been expressed as a lag time, but we show this to be misleading because of the sensitivity of the lag to the history of past CO2 variations. Since both the lag and the degree of disequilibrium are strongly dependent on κ and Δ T2×, and because of uncertainties in the pre-industrial CO2 level, the observed global warming over the past 100 years can be shown to be compatible with a wide range of CO2-doubling temperature changes.
  11. 1991: Lawlor, D. W., and R. A. C. Mitchell. “The effects of increasing CO2 on crop photosynthesis and productivity: a review of field studies.” Plant, Cell & Environment 14.8 (1991): 807-818. Only a small proportion of elevated CO2 studies on crops have taken place in the field. They generally confirm results obtained in controlled environments: CO2increases photosynthesis, dry matter production and yield, substantially in C3 species, but less in C4, it decreases stomatal conductance and transpiration in C3 and C4 species and greatly improves water‐use efficiency in all plants. The increased productivity of crops with CO2 enrichment is also related to the greater leaf area produced. Stimulation of yield is due more to an increase in the number of yield‐forming structures than in their size. There is little evidence of a consistent effect of CO2 on partitioning of dry matter between organs or on their chemical composition, except for tubers. Work has concentrated on a few crops (largely soybean) and more is needed on crops for which there are few data (e.g. rice). Field studies on the effects of elevated CO2 in combination with temperature, water and nutrition are essential; they should be related to the development and improvement of mechanistic crop models, and designed to test their predictions.
  12. 2009: Danabasoglu, Gokhan, and Peter R. Gent. “Equilibrium climate sensitivity: Is it accurate to use a slab ocean model?.” Journal of Climate 22.9 (2009): 2494-2499. The equilibrium climate sensitivity of a climate model is usually defined as the globally averaged equilibrium surface temperature response to a doubling of carbon dioxide. This is virtually always estimated in a version with a slab model for the upper ocean. The question is whether this estimate is accurate for the full climate model version, which includes a full-depth ocean component. This question has been answered for the low-resolution version of the Community Climate System Model, version 3 (CCSM3). The answer is that the equilibrium climate sensitivity using the full-depth ocean model is 0.14°C higher than that using the slab ocean model, which is a small increase. In addition, these sensitivity estimates have a standard deviation of nearly 0.1°C because of interannual variability. These results indicate that the standard practice of using a slab ocean model does give a good estimate of the equilibrium climate sensitivity of the full CCSM3. Another question addressed is whether the effective climate sensitivity is an accurate estimate of the equilibrium climate sensitivity. Again the answer is yes, provided that at least 150 yr of data from the doubled carbon dioxide run are used.
  13. 2010: Connell, Sean D., and Bayden D. Russell. “The direct effects of increasing CO2 and temperature on non-calcifying organisms: increasing the potential for phase shifts in kelp forests.” Proceedings of the Royal Society of London B: Biological Sciences (2010): rspb20092069. Predictions about the ecological consequences of oceanic uptake of CO2 have been preoccupied with the effects of ocean acidification on calcifying organisms, particularly those critical to the formation of habitats (e.g. coral reefs) or their maintenance (e.g. grazing echinoderms). This focus overlooks the direct effects of CO2 on non-calcareous taxa, particularly those that play critical roles in ecosystem shifts. We used two experiments to investigate whether increased CO2 could exacerbate kelp loss by facilitating non-calcareous algae that, we hypothesized, (i) inhibit the recovery of kelp forests on an urbanized coast, and (ii) form more extensive covers and greater biomass under moderate future CO2 and associated temperature increases. Our experimental removal of turfs from a phase-shifted system (i.e. kelp- to turf-dominated) revealed that the number of kelp recruits increased, thereby indicating that turfs can inhibit kelp recruitment. Future CO2 and temperature interacted synergistically to have a positive effect on the abundance of algal turfs, whereby they had twice the biomass and occupied over four times more available space than under current conditions. We suggest that the current preoccupation with the negative effects of ocean acidification on marine calcifiers overlooks potentially profound effects of increasing CO2and temperature on non-calcifying organisms.
  14. 2011: Schmittner, Andreas, et al. “Climate sensitivity estimated from temperature reconstructions of the Last Glacial Maximum.” Science 334.6061 (2011): 1385-1388. Assessing the impact of future anthropogenic carbon emissions is currently impeded by uncertainties in our knowledge of equilibrium climate sensitivity to atmospheric carbon dioxide doubling. Previous studies suggest 3 kelvin (K) as the best estimate, 2 to 4.5 K as the 66% probability range, and nonzero probabilities for much higher values, the latter implying a small chance of high-impact climate changes that would be difficult to avoid. Here, combining extensive sea and land surface temperature reconstructions from the Last Glacial Maximum with climate model simulations, we estimate a lower median (2.3 K) and reduced uncertainty (1.7 to 2.6 K as the 66% probability range, which can be widened using alternate assumptions or data subsets). Assuming that paleoclimatic constraints apply to the future, as predicted by our model, these results imply a lower probability of imminent extreme climatic change than previously thought.
  15. 2012: Fasullo, John T., and Kevin E. Trenberth. “A less cloudy future: The role of subtropical subsidence in climate sensitivity.” science 338.6108 (2012): 792-794. An observable constraint on climate sensitivity, based on variations in mid-tropospheric relative humidity (RH) and their impact on clouds, is proposed. We show that the tropics and subtropics are linked by teleconnections that induce seasonal RH variations that relate strongly to albedo (via clouds), and that this covariability is mimicked in a warming climate. A present-day analog for future trends is thus identified whereby the intensity of subtropical dry zones in models associated with the boreal monsoon is strongly linked to projected cloud trends, reflected solar radiation, and model sensitivity. Many models, particularly those with low climate sensitivity, fail to adequately resolve these teleconnections and hence are identifiably biased. Improving model fidelity in matching observed variations provides a viable path forward for better predicting future climate.
  16. 2012: Andrews, Timothy, et al. “Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere‐ocean climate models.” Geophysical Research Letters 39.9 (2012). We quantify forcing and feedbacks across available CMIP5 coupled atmosphere‐ocean general circulation models (AOGCMs) by analysing simulations forced by an abrupt quadrupling of atmospheric carbon dioxide concentration. This is the first application of the linear forcing‐feedback regression analysis of Gregory et al. (2004) to an ensemble of AOGCMs. The range of equilibrium climate sensitivity is 2.1–4.7 K. Differences in cloud feedbacks continue to be important contributors to this range. Some models show small deviations from a linear dependence of top‐of‐atmosphere radiative fluxes on global surface temperature change. We show that this phenomenon largely arises from shortwave cloud radiative effects over the ocean and is consistent with independent estimates of forcing using fixed sea‐surface temperature methods. We suggest that future research should focus more on understanding transient climate change, including any time‐scale dependence of the forcing and/or feedback, rather than on the equilibrium response to large instantaneous forcing.
  17. 2012: Bitz, Cecilia M., et al. “Climate sensitivity of the community climate system model, version 4.” Journal of Climate 25.9 (2012): 3053-3070.Equilibrium climate sensitivity of the Community Climate System Model, version 4 (CCSM4) is 3.20°C for 1° horizontal resolution in each component. This is about a half degree Celsius higher than in the previous version (CCSM3). The transient climate sensitivity of CCSM4 at 1° resolution is 1.72°C, which is about 0.2°C higher than in CCSM3. These higher climate sensitivities in CCSM4 cannot be explained by the change to a preindustrial baseline climate. This study uses the radiative kernel technique to show that, from CCSM3 to CCSM4, the global mean lapse-rate feedback declines in magnitude and the shortwave cloud feedback increases. These two warming effects are partially canceled by cooling because of slight decreases in the global mean water vapor feedback and longwave cloud feedback from CCSM3 to CCSM4. A new formulation of the mixed layer, slab-ocean model in CCSM4 attempts to reproduce the SST and sea ice climatology from an integration with a full-depth ocean, and it is integrated with a dynamic sea ice model. These new features allow an isolation of the influence of ocean dynamical changes on the climate response when comparing integrations with the slab ocean and full-depth ocean. The transient climate response of the full-depth ocean version is 0.54 of the equilibrium climate sensitivity when estimated with the new slab-ocean model version for both CCSM3 and CCSM4. The authors argue the ratio is the same in both versions because they have about the same zonal mean pattern of change in ocean surface heat flux, which broadly resembles the zonal mean pattern of net feedback strength.
  18. 2012: Rogelj, Joeri, Malte Meinshausen, and Reto Knutti. “Global warming under old and new scenarios using IPCC climate sensitivity range estimates.” Nature climate change 2.4 (2012): 248. Climate projections for the fourth assessment report1 (AR4) of the Intergovernmental Panel on Climate Change (IPCC) were based on scenarios from the Special Report on Emissions Scenarios2 (SRES) and simulations of the third phase of the Coupled Model Intercomparison Project3 (CMIP3). Since then, a new set of four scenarios (the representative concentration pathways or RCPs) was designed4. Climate projections in the IPCC fifth assessment report (AR5) will be based on the fifth phase of the Coupled Model Intercomparison Project5 (CMIP5), which incorporates the latest versions of climate models and focuses on RCPs. This implies that by AR5 both models and scenarios will have changed, making a comparison with earlier literature challenging. To facilitate this comparison, we provide probabilistic climate projections of both SRES scenarios and RCPs in a single consistent framework. These estimates are based on a model set-up that probabilistically takes into account the overall consensus understanding of climate sensitivity uncertainty, synthesizes the understanding of climate system and carbon-cycle behaviour, and is at the same time constrained by the observed historical warming.
  19. 2014: Sherwood, Steven C., Sandrine Bony, and Jean-Louis Dufresne. “Spread in model climate sensitivity traced to atmospheric convective mixing.” Nature 505.7481 (2014): 37. Equilibrium climate sensitivity refers to the ultimate change in global mean temperature in response to a change in external forcing. Despite decades of research attempting to narrow uncertainties, equilibrium climate sensitivity estimates from climate models still span roughly 1.5 to 5 degrees Celsius for a doubling of atmospheric carbon dioxide concentration, precluding accurate projections of future climate. The spread arises largely from differences in the feedback from low clouds, for reasons not yet understood. Here we show that differences in the simulated strength of convective mixing between the lower and middle tropical troposphere explain about half of the variance in climate sensitivity estimated by 43 climate models. The apparent mechanism is that such mixing dehydrates the low-cloud layer at a rate that increases as the climate warms, and this rate of increase depends on the initial mixing strength, linking the mixing to cloud feedback. The mixing inferred from observations appears to be sufficiently strong to imply a climate sensitivity of more than 3 degrees for a doubling of carbon dioxide. This is significantly higher than the currently accepted lower bound of 1.5 degrees, thereby constraining model projections towards relatively severe future warming.
  20. 2015: Mauritsen, Thorsten, and Bjorn Stevens. “Missing iris effect as a possible cause of muted hydrological change and high climate sensitivity in models.” Nature Geoscience 8.5 (2015): 346. Equilibrium climate sensitivity to a doubling of CO2 falls between 2.0 and 4.6 K in current climate models, and they suggest a weak increase in global mean precipitation. Inferences from the observational record, however, place climate sensitivity near the lower end of this range and indicate that models underestimate some of the changes in the hydrological cycle. These discrepancies raise the possibility that important feedbacks are missing from the models. A controversial hypothesis suggests that the dry and clear regions of the tropical atmosphere expand in a warming climate and thereby allow more infrared radiation to escape to space. This so-called iris effect could constitute a negative feedback that is not included in climate models. We find that inclusion of such an effect in a climate model moves the simulated responses of both temperature and the hydrological cycle to rising atmospheric greenhouse gas concentrations closer to observations. Alternative suggestions for shortcomings of models — such as aerosol cooling, volcanic eruptions or insufficient ocean heat uptake — may explain a slow observed transient warming relative to models, but not the observed enhancement of the hydrological cycle. We propose that, if precipitating convective clouds are more likely to cluster into larger clouds as temperatures rise, this process could constitute a plausible physical mechanism for an iris effect.
  21. 2015: Schimel, David, Britton B. Stephens, and Joshua B. Fisher. “Effect of increasing CO2 on the terrestrial carbon cycle.” Proceedings of the National Academy of Sciences 112.2 (2015): 436-441. Feedbacks from terrestrial ecosystems to atmospheric CO2 concentrations contribute the second-largest uncertainty to projections of future climate. These feedbacks, acting over huge regions and long periods of time, are extraordinarily difficult to observe and quantify directly. We evaluated in situ, atmospheric, and simulation estimates of the effect of CO2 on carbon storage, subject to mass balance constraints. Multiple lines of evidence suggest significant tropical uptake for CO2, approximately balancing net deforestation and confirming a substantial negative global feedback to atmospheric CO2 and climate. This reconciles two approaches that have previously produced contradictory results. We provide a consistent explanation of the impacts of CO2 on terrestrial carbon across the 12 orders of magnitude between plant stomata and the global carbon cycle.
  22. 2016: Tan, Ivy, Trude Storelvmo, and Mark D. Zelinka. “Observational constraints on mixed-phase clouds imply higher climate sensitivity.” Science 352.6282 (2016): 224-227. How much global average temperature eventually will rise depends on the Equilibrium Climate Sensitivity (ECS), which relates atmospheric CO2 concentration to atmospheric temperature. For decades, ECS has been estimated to be between 2.0° and 4.6°C, with much of that uncertainty owing to the difficulty of establishing the effects of clouds on Earth’s energy budget. Tan et al. used satellite observations to constrain the radiative impact of mixed phase clouds. They conclude that ECS could be between 5.0° and 5.3°C—higher than suggested by most global climate models.
  23. 2018: Watanabe, Masahiro, et al. “Low clouds link equilibrium climate sensitivity to hydrological sensitivity.” Nature Climate Change (2018): 1. Equilibrium climate sensitivity (ECS) and hydrological sensitivity describe the global mean surface temperature and precipitation responses to a doubling of atmospheric CO2. Despite their connection via the Earth’s energy budget, the physical linkage between these two metrics remains controversial. Here, using a global climate model with a perturbed mean hydrological cycle, we show that ECS and hydrological sensitivity per unit warming are anti-correlated owing to the low-cloud response to surface warming. When the amount of low clouds decreases, ECS is enhanced through reductions in the reflection of shortwave radiation. In contrast, hydrological sensitivity is suppressed through weakening of atmospheric longwave cooling, necessitating weakened condensational heating by precipitation. These compensating cloud effects are also robustly found in a multi-model ensemble, and further constrained using satellite observations. Our estimates, combined with an existing constraint to clear-sky shortwave absorption, suggest that hydrological sensitivity could be lower by 30% than raw estimates from global climate models.

 

 

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Total Hurricane Energy & Fossil Fuel Emissions

Correlation Between Cumulative Emissions and Cumulative Sea Level Rise

TCRE: Transient Climate Response to Cumulative Emissions

A CO2 Radiative Forcing Seasonal Cycle?

Climate Change: Theory vs Data

Correlation of CMIP5 Forcings with Temperature

Stratospheric Cooling

The Anomalies in Temperature Anomalies

The Greenhouse Effect of Atmospheric CO2

ECS: Equilibrium Climate Sensitivity

Climate Sensitivity Research: 2014-2018

TCR: Transient Climate Response

Peer Review of Climate Research: A Case Study

Spurious Correlations in Climate Science

Antarctic Sea Ice: 1979-2018

Arctic Sea Ice 1979-2018

Global Warming and Arctic Sea Ice: A Bibliography

Global Warming and Arctic Sea Ice: A Bibliography

Carbon Cycle Measurement Problems Solved with Circular Reasoning

NASA Evidence of Human Caused Climate Change

Event Attribution Science: A Case Study

Event Attribution Case Study Citations

Global Warming Trends in Daily Station Data

History of the Global Warming Scare

The dearth of scientific knowledge only adds to the alarm

Nonlinear Dynamics: Is Climate Chaotic?

The Anthropocene

Eco-Fearology in the Anthropocene

Carl Wunsch Assessment of Climate Science: 2010

Gerald Marsh, A Theory of Ice Ages

History of the Ozone Depletion Scare

Empirical Test of Ozone Depletion

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Brewer-Dobson Circulation Bibliography

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Climate Change Denial Research: 2001-2018

Climate Change Impacts Research

Tidal Cycles: A Bibliography

 

REFERENCE POST: [Climate Change and Hurricanes]

 

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hurricane

 

RELATED POSTS  

 

TROPICAL CYCLONES AND CLIMATE CHANGE

PRE-INDUSTRIAL TROPICAL CYCLONES

TROPICAL CYCLONES AND SST

TRENDS IN TROPICAL CYCLONE ACTIVITY

HURRICANE FEAROLOGY

 

FIGURE 1: EMANUEL 2005 FINDING

emanuel-figure1

 

FIGURE 2: TOTAL GLOBAL ACE FOR ALL SIX BASINS

 

  1. Sea surface temperature (SST) is the link that connects climate change research with tropical cyclone research. Rising SST is observed (Hadley Centre, 2017) and thought to be an effect of Anthropogenic global warming or AGW (Hansen, 2005) . At the same time, the theory of tropical cyclones holds that cyclone formation, and particularly cyclone intensification are related to SST (Vecchi, 2007) (Knutson, 2010). Testable implications of the theory for empirical research are derived from climate model simulations (Knutson, 2010) and also from sedimentary evidence of land-falling hurricanes over a 1500-year period (Mann, 2009). These studies suggest some guidelines and testable implications for empirical tests of the theory that AGW affects tropical cyclone activity (Knutson, 2010). (Details in footnote below)
  2. A high level of interest in tropical cyclones derives from an unusually active hurricane season in 2004 when more than 14 tropical cyclones formed in the North Atlantic basin . Four of these storms intensified to Category 4 or greater and made landfall in the USA causing considerable damage. The even more dramatic 2005 season followed in its heels with more than thirty depressions. Four of them intensified to Category 5 and three made landfall. The most intense was Hurricane Wilma but the most spectacular was Hurricane Katrina which made landfall in Florida and again in Louisiana. Its devastation was facilitated by a breach in the levee system that was unrelated to AGW but its dramatic consequences made it an icon of the possible extreme weather impacts of AGW.
  3. The Emanuel paper (Emanuel, 2005) came in the heels of these events and is possibly best understood in this context. The assumed attribution by the media of the epic devastation to AGW set the stage for climate science to claim the destructiveness of hurricanes as extreme weather effects of AGW. The Emanuel 2005 paper was one of several published in the heels of these hurricane seasons. The paper presents a new measure of tropical cyclone intensity which the author calls “Power Dissipation Index” and to which he assigns the acronym PDI. The paper finds a statistically significant rising trend in the aggregate annual PDI of North Atlantic Hurricanes in the study period 1949-2004 in tandem with rising sea surface temperature (SST) for the appropriate zone where hurricanes form. The graphical depiction of this result is reproduced in Figure 1.
  4. The usual measure of tropical cyclone activity is the ACE or Accumulated Cyclone Energy. It is computed as the sum of squares of the maximum sustained wind speed in each 6-hour window during the life of the cyclone. It represents the total amount of kinetic energy generated by a tropical cyclone and this energy has been related to the energy in the ocean surface as measured by surface temperatures and temperature differentials such that the cyclone can be described as a heat engine (Rotunno, 1987) (Emanuel, 1987) (Goni, 2003) (Latif, 2007) (Klotzbach, 2006) (Emanuel, 2003).
  5. When the ACE measure did not show the trend that the author was looking for he decided to cube the velocities instead of squaring them as a way of increasing the differences among annual values. This innovation produced the desired result and a trend became evident over the last 30 years of the 55-year study period as seen in Figure 1.
  6. Since the sum of cubes could not be called ACE, the author gave it a new name and called it the Power Dissipation Index or PDI. The object variable in the hypothesis was thus changed from ACE to PDI. The PDI hypothesis was then modified to exclude the first 22 years of the study period where no trend and very little correspondence between PDI and SST are seen (Figure 1). Thus, the tailor made hypothesis to be tested was whether there is a rising trend in the PDI in the most recent 30 years (1975-2004) of the study period. This hypothesis was then tested with the same data over the same time span that was used to construct it. The procedure of testing a hypothesis with the data used to construct the hypothesis constitutes circular reasoning because the methodology subsumes and ensures the desired result.
  7. At this point, a rising trend is seen in the PDI time series 1975-2004 at an annual time scale but the trend is not statistically significant because of extreme year to year variability in cyclone formation and intensification (Knutson, 2010). To smooth out the variance, the author took a 5-year moving average of the PDI data; and when that also failed to show a statistically significant trend, he took 5-year moving averages of the 5-year moving averages (in effect a 10-year moving average) and was finally able to find statistical significance for rising PDI over the last 30 years of the study period (Watkins, 2007). The findings presented by the paper are based on this rising trend and the visual correspondence between PDI and SST seen in Figure 1.
  8. However, in his hypothesis test computations the author failed to correct for degrees of freedom lost in the computation of moving averages. When moving averages are computed some data values are used more than once. It can be shown that the average multiplicity of use is given by the relationship M = (λ/N)*(N-λ+1) where M is the average multiplicity, N is the sample size, and λ is the width of the moving window (Munshi, 2016) (VonStorch, 1995). In the case of a window with λ=10 years moving through a time series of N=30 years, the average multiplicity is M=7. The effective sample size is computed as EFFN=N/M or EFFN=30/7 = 4.285 and the degrees of freedom for the t-test for trend is DF=EFFN-2 or DF=2.285. The statistical significance reported by the author at N=30 and DF=28 is not found when the sample size is corrected for multiplicity. A false sense of statistical power was created by the methodology used when decadal moving averages were taken (Watkins, 2007) (Munshi, 2016). Full text download links for the paper on moving averages [SSRN.COM]  [ACADEMIA.EDU] .
  9. The North Atlantic basin is just one of six major cyclone basins around the world. The other five are The West Pacific, the East Pacific, the South Pacific, the North Indian, and the South Indian. The most active basin is the West Pacific. The theory of anthropogenic global warming as expressed in terms of climate models indicates that only long term changes in global averages of all six cyclone basins may be interpreted in terms of the impacts of climate change (Knutson, 2010). The study of a single basin over a brief 30-year period is unlikely to contain useful information relevant to AGW. Data for all six basins over a 70-year study period 1945-2014 does not show trends in total aggregate annual ACE that can be interpreted as an impact of warming as shown in three related posts on this site [LINK] [LINK] [LINK] . 
  10. The left frame of Figure 2 shows the relative activity of the six basins over the 70-year study period. From left to right the basins are East Pacific, North Atlantic, North Indian, South Indian, South Pacific, and West Pacific. The North Atlantic, although close to home for many climate researchers, is not a significant source of cyclone energy in a global context. The right frame of Figure 2 does not show a sustained trend in tropical cyclone activity on a decadal time scale particularly when the decade 2005-2014 is included.
  11. It is likely that (Emanuel, 2005) was a product of climate activism that had reached a high level of intensity in the years leading up to 2005 by way of the push for the ratification of the Kyoto Protocol for CO2 emission reduction as well as the European heat wave of 2003 that was claimed and widely accepted to be caused by AGW. It was a time when the extreme weather effect of AGW was given credence by the IPCC and generally taken for granted. Given the theoretical basis that connected SST to tropical cyclones, the truth of AGW driven hurricane intensity was thus taken to be a given (Emanuel, 1987) and then apparently proven by the 2004/2005 hurricane seasons. It remained for climate science only to tend to the details of presenting the data in the appropriate format.
  12. Thus the ultimate form of circular reasoning is found in (Emanuel, 2005) in which a high level of confidence ex-ante in the truth of the proposition that AGW causes extreme tropical cyclone activity left the presentation of empirical evidence of that relationship as mere detail. The role of confirmation bias in research of this nature is discussed in a related post [CONFIRMATION BIAS] .
  13. CITATIONS FOR THIS POST:  [CITATIONS]
  14. FOOTNOTE: Guidelines for attribution of tropical cyclone properties to climate change: 1. Globally averaged intensity of tropical cyclones will rise as AGW increases SST. Models predict globally averaged intensity increase of 2% to 11% by 2100. 2. Models predict falling globally averaged frequency of tropical cyclones with frequency decreasing 6%-34% by 2100. 3. The globally averaged frequency of “most intense tropical cyclones” should increase as a result of AGW. The intensity of tropical cyclones is measured as the ACE (Accumulated Cyclone Energy). 4. Models predict increase in precipitation within a 100 km radius of the storm center. A precipitation rise of 20% is projected for the year 2100.
  15. Complications of empirical tests in this line of research (Knutson, 2010) that that introduce excessive uncertainty in research results: 1. Extremely high variance in tropical cyclone data at an annual time scale suggests longer, perhaps a decadal time scale which in turn greatly reduces statistical power. 2. Limited data availability and poor data quality present barriers to research. 3. Limited theoretical understanding of natural variability makes it difficult to ascertain whether the variability observed in the data is in excess of natural variability. 4. Model projections for individual cyclone basins show large differences and conflicting results. Thus, no testable implication can be derived for studies of individual basins. It is necessary that empirical studies have a global geographical span. 5. Advances in data collection activity, methods, and technology create trends in the data that must be separated from climate change effects (Landsea, 2007) (Landsea, 2010).

 

 

 

FIGURE 1: THE 95% CONFIDENCE INTERVAL OF UNCERTAINTY

95CI-JAN-MAR

95CI-APR-JUN

95CI-JUL-SEP

95CI-OCT-DEC

 

 

FIGURE 2: TEN RANDOM OLS LINEAR TRENDS FOR EACH CALENDAR MONTH: 1850-1905

18501905TABLE

18501905CHART

 

 

FIGURE 3: TEN RANDOM OLS LINEAR TRENDS FOR EACH CALENDAR MONTH: 1906-1961

19061961table

19061961chart

 

 

FIGURE 4: TEN RANDOM OLS LINEAR TRENDS FOR EACH CALENDAR MONTH: 1962-2017

19622017table

19622017chart

 

 

FIGURE 5: TEN RANDOM OLS LINEAR TRENDS FOR EACH CALENDAR MONTH: 1850-2017

18502017table

18502017chart

 

 

FIGURE 6: RANGE OF TREND VALUES RANGE

 

FIGURE 7: TEMPERATURE ANOMALIES IN RANDOM 30-YEAR WINDOW30YRGIFDONE

 

FIGURE 8: 30YR WARMING TRENDS ACROSS THE FULL SPAN IN RANDOM MONTHS30YRTPDONE

 

 

 

 

  1. In 2012 the Hadley Centre of the Climate Research Unit (CRU) of the Met Office of the Government of the UK, who publish and maintain the global mean temperature data reconstruction from 1850, completed their work on estimating the uncertainty in temperature in the reconstruction and published them online [LINK] with an online data dictionary posted here [LINK] A detailed description of this work by Colin Morice et al of the CRU is published into the public domain [Morice, Colin P., et al. “Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set.” Journal of Geophysical Research: Atmospheres 117.D8 (2012)]. The full text of Colin’s paper is available online [LINK ]. In brief, for each of 168 temperature anomaly values for each calendar month, a range is provided for the 95% confidence interval of the temperature estimate for different sources of error. Here we use columns 11 and 12 that contain the lower and upper bounds of the 95% confidence interval of the combined effects of all the uncertainties described in the HadCRUT4 error model. As an example, the best mean global temperature anomaly estimate for January 1850 is -0.7C with an uncertainty range of -1.1 to -0.299.
  2. This work is an exploration of the implications of these uncertainties for global warming research in terms of the impact on warming trend estimations. Specifically, we use Monte Carlo simulation to repeatedly select random numbers within the uncertainty range and construct a sample of temperature and warming trend possibilities that can serve as an indication of the impact of these uncertainty values on the study of warming trends.
  3. Figure 1 is a graphical presentation of the width of the 95% confidence intervals for each calendar month across the full span of the data from 1850 to 2017. The data are presented in four charts with each chart containing data for three calendar months. A clear pattern of changes in uncertainty with time and among calendar months is seen in these charts. A high uncertainty band is found in the earliest 40-year period which is particularly high for the months January, February, and March and less than half that size in the other calendar months. In all calendar months a gradual reduction in uncertainty is seen after that high uncertainty period in all calendar months with exceptionally low values after 1960.
  4. These patterns are used to divide the full span of the data into three 55-year segments according to the level of uncertainty. These are a period of high uncertainty in 1850-1905, a period of low uncertainty in 1906-1961, and a period of very low to no uncertainty in 1962-2017. OLS linear trend analysis is carried out for these three sections separately using Monte Carlo simulation to randomly select ten OLS trend values to study as indicators of the impact of uncertainty on OLS warming trends. The monthly mean temperature anomalies are not combined but rather, the twelve calendar months are studied separately so that the impact of the reported uncertainty may be evaluated directly. The results are presented in Figures 2, 3, and 4 respectively for the three uncertainty levels in the three 55-year sub-spans described in paragraph 3. All trend values are in units of DegC/century.
  5.  Figure 2 presents the OLS trends for the twelve calendar months with uncertainty included in ten Monte Carlo trials for the earliest and highest uncertainty period 1850-1905. Here, the trend values vary from 0.5C/century of cooling to  0.25C/century of warming. Most of the trend values show a cooling trend. The standard error of estimate is very high ranging from 0.03C to 0.05C. Only two statistically significant mean trend is found and they are both cooling trends in the summer months of June and July. The sub-span 1850-1905 is found to be a high uncertainty period with little or not trend information available in the data in terms of the new uncertainty data provided. A significant impact of the uncertainty values is seen in this sub-period.
  6. Figure 3 presents the OLS trends for the twelve calendar months with uncertainty included in ten Monte Carlo trials for the mid-uncertainty period 1906-1961. Strong warming trends are seen for all calendar months from 0.7C/century to greater than 0.9C/century. Very little uncertainty is seen in these results. The standard error for the mean trend for each calendar month is very low and all trends are statistically significant warming trends. There is no evidence of an impact of the new uncertainty values on trends in the sub-span 1906-1961.
  7. Figure 4 presents the OLS trends for the twelve calendar months with uncertainty included in ten Monte Carlo trials for the low-uncertainty period 1962-2017. Very strong warming trends are seen for all calendar months. They range from 01.4C/century to greater than 1.6C/century, approximately two times the warming trends seen in Figure 3 for 1906-1961. There is no sign of uncertainty in these results. The standard error for the mean trend for each calendar month is very low and all trends are statistically significant warming trends. There is no impact of the new uncertainty values on trends in the sub-span 1962-2017.
  8. Full span Monte Carlo simulation trends are presented in Figure 5 for the full span of the data 1850-2017. All calendar months show statistically significant warming trends ranging from 0.43C/century to 0.57C/century close to the usually cited figure of 0.5C/century in the HadCRUT4 global temperature anomaly reconstruction. An impact of uncertainties on the full span trends is not apparent.
  9. A graphical visualization of the impact of the uncertainty assessment on temperature trends is provided inn Figure 6. It is a plot of the range of values seen in the ten Monte Carlo simulations computed as [range=maximum-minimum] of the ten randomly selected values. There are four lines shown in different colors, one for each of the four spans for which trends were computed (Full=1850-2017, First=1850-1905, Middle=1906-1961, and Last=1962-2017). The first twelve values in each of the four lines are for the twelve calendar months January to December. The thirteenth value is the average of the twelve values for the calendar months. These curves show that the largest range is seen in the first sub-period 1850-1905 with 0.25C/century to 0.65C/century difference among the ten Monte Carlo trend values. The larges values are seen in August and September and the least in February, March, and December. The lowest ranges are seen in the full span 1850-2017 with values close to 0.05C/century. The very low uncertainty ranges of Middle and Last show ranges as high as 0,4C/century (in September). The average range across the twelve calendar months are 0.45C/century for 1850-1905, 0.3C/century for 1906-1961, and 0.2C/century for 1962-2017.
  10. We conclude from the analysis that the effect of the uncertainty values on temperature trends is evident only in the earliest sub-spans of the data that that begin prior to 1906. No effect of the uncertainty values is found in the full span of the data 1850-2017. The effect of these uncertainties on trends is less severe and not of much consequence in sub-spans of the data that begin after 1905. The published uncertainties appear to be window dressing. They create the appearance that due consideration has been given to uncertainty but no evidence is found that these uncertainties have a real implication for trend analysis. Based on this distribution of uncertainty ranges, it is recommended that trend analysis of the HadCRUT4 data be limited to the period after 1905 and that such analysis should be carried out for each calendar month separately because the uncertainties and trend behaviors of the calendar months are different and a significant loss of information occurs when the months are combined into an annual mean.
  11. Further evidence of unreliability of this dataset is reported by Joanne Nova [LINK] where she reports that Australian researcher John McLean has found serious flaws in the HadCRUT4 temperature anomaly reconstruction. In summary, the flaws reported are “Large gaps where there is no data and where instead averages were calculated from next to no information. For two years, the temperatures over land in the Southern Hemisphere were estimated from just one site in Indonesia. Almost no quality control, with misspelled country names (‘Venezuala” “Hawaai” “Republic of K” (aka South Korea) and sloppy, obviously inaccurate entries. Adjustments – “I wouldn’t be surprised to find that more than 50 percent of adjustments were incorrect,” says McLean – which artificially cool earlier temperatures and warm later ones,  giving an exaggerated impression of the rate of global warming. Methodology so inconsistent that measurements didn’t have a reliable policy on variables like Daylight Saving Time. Sea measurements, supposedly from ships, but mistakenly logged up to 50 miles inland. A Caribbean island – St Kitts – where the temperature was recorded at 0 degrees C for a whole month, on two occasions (somewhat implausibly for the tropics). A town in Romania which in September 1953, allegedly experienced a month where the average temperature dropped to minus 46 degrees C (when the typical average for that month is 10 degrees C).

 

 

 

 

 

BIBLIOGRAPHY 

  1. 2012: Morice, Colin P., et al. “Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set.” Journal of Geophysical Research: Atmospheres 117.D8 (2012). Recent developments in observational near‐surface air temperature and sea‐surface temperature analyses are combined to produce HadCRUT4, a new data set of global and regional temperature evolution from 1850 to the present. This includes the addition of newly digitized measurement data, both over land and sea, new sea‐surface temperature bias adjustments and a more comprehensive error model for describing uncertainties in sea‐surface temperature measurements. An ensemble approach has been adopted to better describe complex temporal and spatial interdependencies of measurement and bias uncertainties and to allow these correlated uncertainties to be taken into account in studies that are based upon HadCRUT4. Climate diagnostics computed from the gridded data set broadly agree with those of other global near‐surface temperature analyses. Fitted linear trends in temperature anomalies are approximately 0.07°C/decade from 1901 to 2010 and 0.17°C/decade from 1979 to 2010 globally. Northern/southern hemispheric trends are 0.08/0.07°C/decade over 1901 to 2010 and 0.24/0.10°C/decade over 1979 to 2010. Linear trends in other prominent near‐surface temperature analyses agree well with the range of trends computed from the HadCRUT4 ensemble members.
  2. 2014: Robeson, Scott M., Cort J. Willmott, and Phil D. Jones. “Trends in hemispheric warm and cold anomalies.” Geophysical Research Letters 41.24 (2014): 9065-9071. Using a spatial percentile approach, we explore the magnitude of temperature anomalies across the Northern and Southern Hemispheres. Linear trends in spatial percentile series are estimated for 1881–2013, the most recent 30 year period (1984–2013), and 1998–2013. All spatial percentiles in both hemispheres show increases from 1881 to 2013, but warming occurred unevenly via modification of cold anomalies, producing a reduction in spatial dispersion. In the most recent 30 year period, trends also were consistently positive, with warm anomalies having much larger warming rates than those of cold anomalies in both hemispheres. This recent trend has largely reversed the decrease in spatial dispersion that occurred during the twentieth century. While the period associated with the recent slowdown of global warming, 1998–2013, is too brief to estimate trends reliably, cooling was evident in NH warm and cold anomalies during January and February while other months in the NH continued to warm.
  3. 2014: Curry, Judith. “Climate science: uncertain temperature trend.” Nature Geoscience 7.2 (2014): 83. Global mean surface temperatures have not risen much over the past 15 years, despite continuing greenhouse gas emissions. An attempt to explain the warming slow-down with Arctic data gaps is only a small step towards reconciling observed and expected warming.
  4. 2014: Cowtan, Kevin, and Robert G. Way. “Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends.” Quarterly Journal of the Royal Meteorological Society 140.683 (2014): 1935-1944. Incomplete global coverage is a potential source of bias in global temperature reconstructions if the unsampled regions are not uniformly distributed over the planet’s surface. The widely used Hadley Centre–Climatic Reseach Unit Version 4 (HadCRUT4) dataset covers on average about 84% of the globe over recent decades, with the unsampled regions being concentrated at the poles and over Africa. Three existing reconstructions with near‐global coverage are examined, each suggesting that HadCRUT4 is subject to bias due to its treatment of unobserved regions.Two alternative approaches for reconstructing global temperatures are explored, one based on an optimal interpolation algorithm and the other a hybrid method incorporating additional information from the satellite temperature record. The methods are validated on the basis of their skill at reconstructing omitted sets of observations. Both methods provide results superior to excluding the unsampled regions, with the hybrid method showing particular skill around the regions where no observations are available. Temperature trends are compared for the hybrid global temperature reconstruction and the raw HadCRUT4 data. The widely quoted trend since 1997 in the hybrid global reconstruction is two and a half times greater than the corresponding trend in the coverage‐biased HadCRUT4 data. Coverage bias causes a cool bias in recent temperatures relative to the late 1990s, which increases from around 1998 to the present. Trends starting in 1997 or 1998 are particularly biased with respect to the global trend. The issue is exacerbated by the strong El Niño event of 1997–1998, which also tends to suppress trends starting during those years.
  5. 2016: Jones, Phil, and Jean Palutikof. “Global temperature record.” Climate research unit, University of East Anglia. http://www. cru. uea. ac. uk/cru/info/warming (2016). The time series shows the combined global land and marine surface temperature record from 1850 to 2014. This year was the equal warmest on record. This record uses the latest analysis, referred to as HadCRUT4 (Morice et al., 2012). The period 2001-2010 (0.488°C above the 1961-90 average) was 0.214°C warmer than the 1991-2000 decade (0.274°C above the 1961-90 average). The equal warmest years of the series are 2010 and 2014. The value for 2014, given uncertainties discussed in Morice et al. (2012), is not distinguishable from the years 2010 (0.555°C), 2005 (0.543°C) and 1998 (0.535°C). The coldest year of the 21st century (2008 with a value of 0.394°C) was warmer than all years in the 20th century with the exception of 1998. The average of the first four years of the present decade (2011-2014) is 0.002°C cooler than the average for 2001-2010, but warmer than all years before 2001 except for 1998. This time series is compiled jointly by the Climatic Research Unit and the UK Met Office Hadley Centre. Increased concentrations of greenhouse gases in the atmosphere due to human activities are most likely the underlying cause of warming in the 20th century. The warmth or coldness of individual years is strongly influenced by whether there was an El Niño or a La Niña event occurring in the equatorial Pacific Ocean
  6. 2017: Haustein, K., et al. “A real-time global warming index.” Scientific Reports 7.1 (2017): 15417. We propose a simple real-time index of global human-induced warming and assess its robustness to uncertainties in climate forcing and short-term climate fluctuations. This index provides improved scientific context for temperature stabilisation targets and has the potential to decrease the volatility of climate policy. We quantify uncertainties arising from temperature observations, climate radiative forcings, internal variability and the model response. Our index and the associated rate of human-induced warming is compatible with a range of other more sophisticated methods to estimate the human contribution to observed global temperature change.
  7. 2017: Cowtan, Kevin. “Coverage bias in the HadCRUT4 temperature series and its impact on recent temperature trends. COBE-SST2 based land-ocean dataset.” (2017). This update document describes an new dataset created using the Cowtan and Way (2014) methodology version 2 (Cowtan 2015, supplement), but using the COBE-SST2 sea surface temperature data in place of HadSST3. The HadSST3 dataset (Kennedy et al., 2011) provides gridded temperature fields based on the ICOADS observational archive, using probably the most extensive analysis of the observation metadata to address inhomogenities in the data. However a recent unexplained drift in ship observations compared to free floating buoys and other sources (Hausfather et al., 2017) has led to an apparent underestimation of 21st century temperature trends. As a result, HadSST3 shows slower warming over the past two decades than ERSSTv4/v5, which upweight the buoy data relative to the ship observations. However the RSST records show hard-to-explain features in the earlier record, including a large warm spike during the second world war and unusual warmth in the 19th century inconsistent with the recorded use of wooden buckets (Folland and Parker, 1995). The new COBE-SST2 dataset (Hirahara et al., 2014) uses a similar if less complete metadata analysis to HadSST3 with very similar results (Kent et al.,2016), except that it does not show the effect of the drift in ship observations since 2005 (Hausfather et al., 2017). A reconstruction based on COBE-SST2 may therefore be useful for the evaluation of temperature trends over the purported “hiatus” period.
  8. 2018: Blesic, Suzana, Davide Zanchettin, and Angelo Rubino. “Effects of the data loss and data homogenization on the long-term properties of the observed temperature data.” EGU General Assembly Conference Abstracts. Vol. 20. 2018. We use scaling analysis in the form of a combination of the detrended fluctuation analysis of the second order (DFA2) and the wavelet transform spectral estimation (WTS) to assess how the calculations of long-term properties of time series of historical temperature records are affected by data loss, or by considerable adjustments due to data inhomogeneities. We have analysed instrumental records and publicly available derived regional temperature data of the HadCRUT4 dataset. We have calculated DFA2-WTS scaling exponents for both the adjusted and unadjusted records retrieved from the NCDC Global Historical Climatology Network land stations monthly dataset. In this contribution, we will illustrate results that demonstrate that in both cases of substantial amount of missing data and of considerable homogenization the DFA2 exponents for the adjusted temperature data used in the gridded HadCRUT4 dataset can differ even substantially from that of the raw unadjusted data. We will discuss how the corresponding WTS can help reveal the possible sources of such discrepancies. In order to further illustrate this artificial alteration of the scaling properties we will present and discuss temporal changes in the global pattern of the long-term persistence (LTP) of the HadCRUT4 during the period from 1850 to 2000. Our findings indicate that for a largely predominant part of the HadCRUT4 grid where there is a large percentage of missing values the true LTP is likely higher than the one estimated from the available data.
  9. 2018: Richardson, Mark, Kevin Cowtan, and Richard J. Millar. “Global temperature definition affects achievement of long-term climate goals.” Environmental Research Letters 13.5 (2018): 054004. The Paris Agreement on climate change aims to limit ‘global average temperature’ rise to ‘well below 2 °C’ but reported temperature depends on choices about how to blend air and water temperature data, handle changes in sea ice and account for regions with missing data. Here we use CMIP5 climate model simulations to estimate how these choices affect reported warming and carbon budgets consistent with the Paris Agreement. By the 2090s, under a low-emissions scenario, modelled global near-surface air temperature rise is 15% higher (5%–95% range 6%–21%) than that estimated by an approach similar to the HadCRUT4 observational record. The difference reduces to 8% with global data coverage, or 4% with additional removal of a bias associated with changing sea-ice cover. Comparison of observational datasets with different data sources or infilling techniques supports our model results regarding incomplete coverage. From high-emission simulations, we find that a HadCRUT4 like definition means higher carbon budgets and later exceedance of temperature thresholds, relative to global near-surface air temperature. 2 °C warming is delayed by seven years on average, to 2048 (2035–2060), and CO2 emissions budget for a >50% chance of <2 °C warming increases by 67 GtC (246 GtCO2).
  10. 2018: Widmann, Martin, et al. “The DAPS data assimilation intercomparison experiment.” EGU General Assembly Conference Abstracts. Vol. 20. 2018. Various approaches for data assimilation for paleoclimatic state estimation have been implemented over the past years. They differ with respect to the assimilation setup and method, the dynamical models, and the type of assimilated information. The setups comprise online approaches, where the background states depend on the outcome of the previous assimilation step, transient offline approaches, where the background states are independent of the previous assimilation step but vary in time due to the influence of climatic forcings, as well as stationary offline approaches, which use the same background states in each assimilation timestep. The main data assimilation methods used in paleoclimate modelling are Particle Filters, where the analysis is given by a weighted version of the background ensemble, and Kalman Filters, where the background ensemble states are changed through the Kalman Gain; variational methods have also been explored. Dynamical models that are used include General Circulation Models, Earth System Models of Intermediate Complexity, and linear models. The empirical information is incorporated either in the form of local or regionally averaged climate variables derived by inverse models from proxy data, or directly as proxy information (currently only used for oxygen isotopes) using forward models. The potential advantages and disadvantages of the different approaches are not well understood. For instance online approaches allow for information propagation in time, but it is not clear whether there is actually any substantial information propagation for the annual or longer time steps used in paleo data assimilation. If information propagation is not relevant, offline approaches would be sufficient and easier to implement, and in particular the stationary offline approach allows for using very large background ensembles. The relative performance of Particle and Kalman Filters depends on the ensemble size, and there may also be differences with respect to physical consistency. Assimilating local climate reconstructions allows to constrain small-scale structures in the climatic states, whereas using regional reconstructions can be expected to be influenced less by non-climatic noise in the proxies. Directly assimilating proxies avoids problems related to proxy-based, statistical climate reconstructions, but requires good forward models and small climate model biases. In the DAPS (PAGES working group on paleoclimate reanalyses, Data Assimilation and Proxy System modeling) data assimilation intercomparison experiment we will apply the different approaches in a pseudo-proxy set-up for the Northern Hemisphere for the period 1900 – 2017 CE to systematically validate the analyses against a reasonably well-known estimate for the true climatic state. We will assimilate local temperature pseudo-proxies over land with annual resolution, constructed by adding white noise to the HadCRUT4 gridded temperature observations. They will be given at the locations of the PAGES2k proxy network at 1500 CE. The analyses will be comprehensively validated against the HadCRUT4 gridded temperature observations and the HadSLP gridded sea level pressure data sets. The poster will present the details of the assimilation and validation set-up, and some preliminary results. The intercomparison has just started and we are inviting contributions from any groups working on paleoclimate data assimilation.

 

 

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FIGURE 1: NORTH ATLANTIC HURRICANE ACE AND EMISSIONS

NA-FIG1

NA-FIG2

NA-FIG3

 

FIGURE 2: GLOBAL TROPICAL CYCLONE ACE AND EMISSIONS

GL-FIG2

GL-FIG3

 

RELATED POST: Climate Change and Hurricanes

 

  1. This post is a parody of the spuriousness of correlations between cumulative values presented in three prior posts Correlation Between Cumulative Emissions and Cumulative Sea Level Rise  TCRE: Transient Climate Response to Cumulative Emissions  Spurious Correlations in Climate Science . It is derived from a source document that presents a study of total globally averaged accumulated cyclone energy of tropical cyclones in all six basins. The full text of this paper may be downloaded from SSRN.COM  or  ACADEMIA.EDU . ABSTRACT: The Accumulated Cyclone Energy (ACE) index is used to compare tropical cyclone activity worldwide among seven decades from 1945 to 2014. Some increase in tropical cyclone activity is found relative to the earliest decades. No trend is found after the decade 1965-1974. A comparison of the six cyclone basins in the study shows that the Western Pacific Basin is the most active basin and the North Indian Basin the least. The advantages of using a general linear model for trend analysis are described
  2. Here we show that the use of spurious correlations between cumulative values can be used to present faux correlations between cumulative emissions and cumulative ACE of tropical cyclones both worldwide (as one would expect in accordance with the climate model works of Knutson and co-authors in his well known 2010 paper) but also a strong faux correlation of the cumulative ACE of North Atlantic Hurricanes with cumulative global fossil fuel emissions.
  3. Figure 1 contains three panels each with two frames. The top panel presents the emissions data at an annual time scale in the left frame and their cumulative values in the right frame. The middle panel, likewise, displays the ACE data for tropical cyclones in the North Atlantic Basin where cyclones are called “hurricanes”. The correlation between emissions and total hurricane ACE is displayed in the bottom panel where a weak or non-existent correlation is seen at an annual time scale in the left panel but a strong faux correlation is found when cumulative values are taken as shown in the right frame.
  4. The corresponding data for all tropical cyclones globally for all six basins are presented in the two panels of Figure 2 where we also find little if any correlation in the source data but a strong proportionality between cumulative values.
  5. The proportionality between cumulative values are specious. They have no interpretation except that when the x and y values tend to have similar signs – either positive or negative – strong correlations will be seen; and when they randomly differ in signs, no correlation will be found. The only information contained in the proportionality between cumulative values is that the two variables being compared tend to have the same sign. Emissions are always positive and ACE values too are also positive. This is the only information contained in the strong correlations between cumulative values shown in Figures 1&2.
  6. Correlation between cumulative values are spurious because the computation of cumulative values involves repeated use of the same data to the point where the effective sample size is reduced to N=2. This means that time series of cumulative values contain neither time scale not degrees of freedom and therefore they do not contain useful information.
  7. The conclusions presented above are supported by prior studies of correlations between cumulative values with full text download available at both SSRN.COM & ACADEMIA.EDU:
  8.  SSRN1 SSRN2  SSRN3  SSRN4   SSRN5  
  9. ACADEMIA1   ACADEMIA2  ACADEMIA3   ACADEMIA4  ACADEMIA5 
  10. A related work on the proportionality between cumulative emissions and cumulative warming may be found here: TCRE: Transient Climate Response to Cumulative Emissions.

 

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Correlation Between Cumulative Emissions and Cumulative Sea Level Rise

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The Anomalies in Temperature Anomalies

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Carbon Cycle Measurement Problems Solved with Circular Reasoning

NASA Evidence of Human Caused Climate Change

Event Attribution Science: A Case Study

Event Attribution Case Study Citations

Global Warming Trends in Daily Station Data

History of the Global Warming Scare

The dearth of scientific knowledge only adds to the alarm

Nonlinear Dynamics: Is Climate Chaotic?

The Anthropocene

Eco-Fearology in the Anthropocene

Carl Wunsch Assessment of Climate Science: 2010

Gerald Marsh, A Theory of Ice Ages

History of the Ozone Depletion Scare

Empirical Test of Ozone Depletion

Ozone Depletion Chemistry

Brewer-Dobson Circulation Bibliography

Elevated CO2 and Crop Chemistry

Little Ice Age Climatology: A Bibliography

Sorcery Killings, Witch Hunts, & Climate Action

Climate Impact of the Kuwait Oil Fires: A Bibliography

Noctilucent Clouds: A Bibliography

Climate Change Denial Research: 2001-2018

Climate Change Impacts Research

Tidal Cycles: A Bibliography

FIGURE 1: EMISSIONS & SEA LEVEL TIME SERIES SLR-DATA

 

FIGURE 2: CORRELATIONS BETWEEN EMISSIONS AND SEA LEVEL RISE

DATA-CORR

 

FIGURE 3: RANDOM EMISSIONS AND RANDOM SEA LEVEL RISE: WITH SIGN CONSTRAINTS

 

 

FIGURE 4: RANDOM EMISSIONS AND RANDOM SEA LEVEL RISE: WITHOUT SIGN CONSTRAINTS

 

 

RELATED POST:  https://tambonthongchai.com/2019/02/20/csiroslr/

 

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  1. 2018: Clark, Peter U., et al. “Sea-level commitment as a gauge for climate policy.” Nature Climate Change 8.8 (2018): 653. ABSTRACT: A well-defined relationship between global mean sea-level rise and cumulative carbon emissions can be used to inform policy about emission limits to prevent dangerous and essentially permanent anthropogenic interference with the climate system. AUTHOR STATEMENT: When we pump more carbon into the atmosphere, the effect on temperature is almost immediate but sea level rise takes a lot longer to respond to that warming. If you take an ice cube out of the freezer and put it on the sidewalk, it doesn’t melt immediately. The same is true for ice sheets. It takes time for them to melt so that the resulting sea level rise will continue for hundreds to thousands of years after we’re done emitting carbon. Since the beginning of the Industrial Revolution – about 1750 – people have emitted roughly 600 billion tons of carbon into the atmosphere, resulting in an increase of roughly one degree (Celsius) in overall global temperature. The global pace today is 10 billion tons of carbon annually, which means we’re on track to reach the 2-degree threshold in about 60 years. We now know how much more carbon we can emit to keep below a certain temperature. One way to begin looking at it from a policy standpoint is to ask the question, ‘how much sea level rise can we tolerate?’. It becomes a fairly simple exercise from there. The more carbon we emit, the more sea level rise we are committed to. We need to ask if there is a target for sea level rise – much like the 2-degree threshold that was established for global warming. Keeping sea level rise to 3-9 meters – or roughly 10 to 30 feet – over several thousand years is likely too optimistic unless society finds ways to quickly reach zero emissions and lower the CO2 in the atmosphere. If cumulative CO2 emissions rise to 3,000 billion tons, it likely will result in sea level rise of between 30 and 40 meters. The sea level rise we’ve seen thus far is just the tip of a very large iceberg. The big question is whether we can stabilize the system and find new energy sources. If not, we’re on the way to a slow-motion catastrophe. The question becomes: What do we owe our grandchildren, and their grandchildren? Economic losses in the world’s largest coastal cities due to coastal flooding in 2005 – the year of Hurricane Katrina – reached $6 billion – a figure that is estimated to grow to $1 trillion by 2050. Losses could be reduced to $60 billion through construction of coastal defenses, but “such well-intended short-term efforts neglect the long-term horizon of sea level rise. You can build a one-meter seawall, but what do you do when sea levels rise by two, or five, or 10 meters? Rising sea levels haven’t really alarmed people yet because their response time is much longer than temperature. Smart countries will use that to their advantage and begin adaptation strategies over time. Many of those people depend directly or indirectly on the oceans for their livelihood – and we don’t know all the ways they will be affected. “But you don’t have to look far away to see the devastating impact of extreme events like the hurricanes in Puerto Rico and Texas that will take decades to recover from.
  2. The “author statement” quoted above was provided by the Oregon State University Newsroom [LINK]. 
  3. This work is a critical review of Clark et al 2018 described above. In the paper the authors use the proportionality between cumulative emissions and cumulative sea level rise to develop a functional and causal relationship that they then use to relate sea level rise to emissions and then to forecast future sea level rise scenarios and their consequences. The relationship is then used to evaluate the Paris Agreement in terms of sea level rise.
  4. We show here that the paper’s conclusions are specious because they are derived from a spurious correlation. A critical evaluation of the TCRE (Transient Climate Response to Cumulative Emissions) was presented in a previous post: [TCRE: Transient Climate Response to Cumulative Emissions ]. There it was shown that the near perfect “proportionality between cumulative warming and cumulative emissions seen in the data is an artifact of a fortuitous sign constraint contained in the data in which emissions are always positive and warming is mostly positive. When this fortuitous constraint in the data is removed the stable proportionality of the TCRE coefficient vanishes. It was concluded in that study that correlations between cumulative values are spurious and that the only information contained in such a stable proportionality is that it happens to contain the necessary sign constraints.
  5. The TCRE analysis applies exactly to the use of correlations between cumulative emissions and cumulative sea level rise. Figure 2 shows that although no correlation is found between emissions and sea level rise at an annual time scale (left frame of Figure 2), a strong proportionality is seen between their cumulative values (right frame of Figure 2). In Figures 3 and Figure 4 we show that the only interpretation of this correlation is that the data contain a fortuitous sign constraint in which emissions are always positive and sea level rise values are mostly positive.
  6. Figure 3 shows correlations between random emissions and random sea level rise values that follow the sign constraint described above where emissions are always positive and sea level rise values are mostly positive. Here as we take samples from these random numbers, we find strong proportionality between cumulative sea level rise and cumulative emissions just as we saw in the data in Figure 2. And yet, these are random numbers that are generating these correlations and statistically significant regression coefficients.
  7. What happens when the sign constraint is relaxed so that a random fraction of both emissions and sea level rise are allowed to be negative? The answer to that question is demonstrated in Figure 4 where we find that the strong and stable proportionality seen in Figure 3 is not seen when the sign constraint is removed. We conclude from Figure 3 and Figure 4 taken together that correlations between cumulative values of time series data that do not exist in the source data at any finite time scale, are spurious and artifacts of sign constraints. Their only information content is that the needed sign constraint is found in the data. Such correlations have no interpretation in terms of causal relationships between the two variables at the time scale of interest.
  8. The theoretical argument for the spuriousness of correlations between cumulative values is that the multiplicity in the use of the data for constructing cumulative values removes the available degrees of freedom in the data. The time series of cumulative values of another time series contains neither time scale nor degrees of freedom. No statistic computed with such a time series contains any causation interpretation because they contain no information except for the existence of a fortuitous sign constraint.
  9. It is of course not feasible that sea level should respond to emissions at an annual time scale but a time scale must be specified from theory so that a testable implication and its empirical test can be carried out. In a related post, time scales of 30 to 50 years were tried but no correlation between emissions and sea level rise was found [LINK] .

 

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