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“The Kuwaiti oil fires were caused by Iraqi military forces setting fire to a reported 605 to 732 oil wells along with an unspecified number of oil filled low-lying areas, such as oil lakes and fire trenches, as part of a scorched earth policy while retreating from Kuwait in 1991 due to the advances of Coalition military forces in the Persian Gulf War. The fires were started in January and February 1991, and the first well fires were extinguished in early April 1991, with the last well capped on November 6, 1991” (Wikipedia). An important researcher in this field was the late great Peter V. Hobbs, Professor of Atmospheric Sciences at the University of Washington. He specialized in cloud and aerosol effects and left us his book on Atmospheric Science as a free pdf online: ATMOSPHERIC SCIENCE BY PETER V. HOBBS

  1. 1991: Browning, K. A., et al. “Environmental effects from burning oil wells in Kuwait.” Nature 351.6325 (1991): 363. Model calculations, constrained by satellite observations, indicate that most of the smoke from the oil fires in Kuwait will remain in the lowest few kilometres of the troposphere. Beneath the plume there is a severe reduction in daylight, and a day-time temperature drop of ~10 °C within ~200 km of the source. Episodic events of acid rain and photochemical smog will occur within ~1,000-2,000km of Kuwait. But changes in the Asian summer monsoon are unlikely to exceed the natural interannual variability and stratospheric ozone concentrations are unlikely to be affected.
  2. 1992: Laursen, Krista K., et al. “Emission factors for particles, elemental carbon, and trace gases from the Kuwait oil fires.” Journal of Geophysical Research: Atmospheres 97.D13 (1992): 14491-14497. Emission factors are presented for particles, elemental carbon (i.e., soot), total organic carbon in particles and vapor, and for various trace gases from the 1991 Kuwait oil fires. Particle emissions accounted for ∼2% of the fuel burned. In general, soot emission factors were substantially lower than those used in recent “nuclear winter” calculations. Differences in the emissions and appearances of some of the individual fires are discussed. Carbon budget data for the composite plumes from the Kuwait fires are summarized; most of the burned carbon in the plumes was in the form of CO2. Fluxes are presented for several combustion products.
  3. 1992: Pilewskie, Peter, and Francisco PJ Valero. “Radiative effects of the smoke clouds from the Kuwait oil fires.” Journal of Geophysical Research: Atmospheres 97.D13 (1992): 14541-14544. The radiative effects of the smoke from the Kuwait oil fires were assessed by measuring downwelling and upwelling solar flux, as well as spectral solar extinction beneath, above, and within the smoke plume. Radiative flux divergence measurements were made to determine smoke‐induced heating and cooling rates. Seven radiation flight missions were undertaken between May 16 and June 2, 1991, to characterize the plume between the source region in Kuwait and approximately 200 km south, near Manama, Bahrain. We present results from one flight representative of conditions of the composite plume. On May 18, 1991, in a homogeneous, well‐mixed region of smoke approximately 100 km downstream of the fires, visible optical depths as high as 2 were measured, at which time transmission to the surface was 8%, while 78% of the solar radiation was absorbed by the smoke. The calculated instantaneous heating rate inside the plume reached 24 K/d. While these effects are probably typical of those regions in the Persian Gulf area directly covered by the smoke, there is no evidence to suggest significant climatic effects in other regions.
  4. 1992: Ferek, Ronald J., et al. “Chemical composition of emissions from the Kuwait oil fires.” Journal of Geophysical Research: Atmospheres 97.D13 (1992): 14483-14489. Airborne measurements in the srnoke from the Kuwait oil fires in May and June 1991 indicate that the combined oil and gas emissions were equivalent to the consumption of about 4.6 million barrels of oil per day. The combustion was relatively efficient, with about 96% of the fuel carbon burned emitted as CO2. Particulate smoke emissions averaged 2% of the fuel burned, of which about 20% was soot. About two‐thirds of the mass of the smoke was accounted for by salt, soot, and sulfate. The salt most likely originated from oil field brines, which were ejected from the wells along with the oil. The salt accounts for the fact that many of the plumes were white. SO2 and NOx were removed from the smoke at rates of about 6 and 22% per hour, respectively. The high salt and sulfate contents explain why a large fraction of the particles in the smoke were efficient cloud condensation nuclei.
    Citing Literature
  5. 1992: Hobbs, Peter V., and Lawrence F. Radke. “Airborne studies of the smoke from the Kuwait oil fires.” Science 256.5059 (1992): 987-991. Airborne studies of smoke from the Kuwait oil fires were carried out in the spring of 1991 when ∼4.6 million barrels of oil were burning per day. Emissions of sulfur dioxide were ∼57% of that from electric utilities in the United States; emissions of carbon dioxide were ∼2% of global emissions; emissions of soot were ∼3400 metric tons per day. The smoke absorbed ∼75 to 80% of the sun’s radiation in regions of the Persian Gulf. However, the smoke probably had insignificant global effects because (i) particle emissions were less than expected, (ii) the smoke was not as black as expected, (iii) the smoke was not carried high in the atmosphere, and (iv) the smoke had a short atmospheric residence time.
  6. 1992: Parungo, F., et al. “Aerosol particles in the Kuwait oil fire plumes: Their morphology, size distribution, chemical composition, transport, and potential effect on climate.” Journal of Geophysical Research: Atmospheres 97.D14 (1992): 15867-15882. Airborne aerosol samples were collected with an impactor in the Kuwait oil fire plumes in late May 1991. A transmission electron microscope was used to examine the morphology and size distribution of the particles, and an X ray energy spectrometer was used to determine the elemental composition of individual particles. A chemical spot test was used to identify particles containing sulfate. The results show that the dominant particles were (1) agglomerates of spherical soot particles coated with sulfate, (2) cubic crystals containing NaCl and S04=, (3) irregular‐shaped dust containing Si, Al, Fe, Ca, K, and/or S, and (4) very small ammonium sulfate spherules. The concentrations of small sulfate particles increased at higher levels or greater distances from the fire, suggesting the transformation of SO2 gas to sulfate particles by photooxidation followed by homogeneous nucleation. The number of soot, salt, and dust particles that were coated with sulfate increased farther from the fire, and the thickness of the coating increased with altitude. This suggested that gas‐to‐particle conversion had occurred by means of catalytic oxidation combined with heterogeneous nucleation during the plume dispersion. Because the sulfate coating can modify the hydrophobic surfaces of soot and dust particles to make them hydrophilic, most of the particles in the plume apparently were active cloud condensation nuclei that could initiate clouds, fog, and smog, which in turn could affect regional surface temperature, air quality, and visibility. Long‐range air trajectories suggested that some aerosols from the fires could have transported to eastern Asia. It seems possible (but is presently unproven) that a severe flood in China in June was influenced by aerosols from the plumes.
  7. 1994: McQueen, Jeffery T., and Roland R. Draxler. “Evaluation of model back trajectories of the Kuwait oil fires smoke plume using digital satellite data.” Atmospheric Environment 28.13 (1994): 2159-2174. This study evaluates the accuracy of the National Weather Service Medium Range Forecast (MRF) global model outputs in simulating the transport and dispersion of the Kuwait oil fire smoke plume. A technique was developed to analyze NOAA polar orbiting satellite imagery to obtain horizontal smoke plume positions. The plume heights were obtained by combining the satellite analysis with back trajectory results. Backward trajectories were computed using both coarse and fine resolution MRF wind fields. The average of the absolute value of relative trajectory error ([R.T.E.]) for the late summer period (24 July–15 September 1991) was about 10°o of the travel distance when using the fine grid trajectories with the optimum plume centroid height and 14°o when using the coarse grid model output. The absolute R.T.E. for the optimum plume height runs was half of the R.T.E. for the constant starting height run ([R.T.E.] = 0.21). This difference indicates the importance of proper specification of plume centroid height when using high resolution meteorological data for transport studies. Use of the standard coarse grid MRF wind fields to drive the transport model was shown to lead to large errors near the source due to the poor horizontal and vertical resolution.
  8. 1994: Herring, John A., and Peter V. Hobbs. “Radiatively driven dynamics of the plume from 1991 Kuwait oil fires.” Journal of Geophysical Research: Atmospheres 99.D9 (1994): 18809-18826. Optical properties of the aerosol from the 1991 Kuwait oil fires are calculated using measured aerosol size distributions and a spectral refractive index based on the measured chemical composition of the particulate matter. At a wavelength of 538 nm the calculated light‐scattering coefficient agrees well with measurements, but the calculated single‐scattering albedo is systematically higher by about 18% than the measured value. Radiative transfer calculations indicate maximum net daytime heating rates of 94 and 56 K d−1 for smoke 1 and 3 hours downwind of the fires, respectively. In the upper regions of the plume, where the calculated heating rates decrease with height, a radiauve‐convective mixed layer developed. There was no significant temperature inversion at the top of this layer, which allowed rapid entrainment of air into the top of the plume, causing it to thicken at an observed rate of ∼0.1 m s−1. In addition, radiative heating of the plume as a whole caused it to lift as a unit at a measured rate of ∼0.1 m s−1 during the first few hours of plume evolution. A theory, based on mixed layer modeling and a scale analysis of the equations of motion, is presented that successfully reproduces the two rates of vertical transport. This model of the dynamics of a radiatively heated plume can be used to predict the evolution and lofting of large composite smoke plumes, such as those from forest fires; it also has implications for the transport, lifetime, and climatic importance of smoke generated on continental scales.
  9. 1997: Nichol, Janet. “Bioclimatic impacts of the 1994 smoke haze event in Southeast Asia.” Atmospheric Environment 31.8 (1997): 1209-1219.A smoke haze event of unprecedented magnitude which occurred in southeast Asia 1994 is statistically evaluated for its impact on regional and global climate using climatic and air quality data from Singapore, and by comparison with the better-known smoke pollution episode resulting from the Kuwait oil fires of 1991. Several local climatic parameters were found to be closely related to air quality on a daily basis. Mean data for the haze period in 1994 appeared to differ significantly from the long-term means for the same period in previous years, with the exception of daily mean air temperature and mean Global Solar Radiation (GSR). The latter is in spite of the inverse relationship between daily GSR and pollution levels. An ENSO-related influence on regional climate (masking some of the perceived regional impacts of the haze) is invoked to explain the apparent contradiction. The significance of the smoke haze at global scale is considered for its impact on the global carbon budget, especially due to the combustion of peat in the coastal lowlands of Sumatra and Kalimantan. The scarcity of available ecological data is regretted and recommendations are made for future cooperation over monitoring and research between scientists and government bodies from the countries in the southeast Asian region.
  10. 2000: Anthes, Richard A., Christian Rocken, and Ying-Hwa Kuo. “Applications of COSMIC to meteorology and climate.” Terrestrial, Atmospheric and Oceanic Sciences 11.1 (2000): 115-156. The GPSIMET (Global Positioning System/Meteorology, Ware et a1. 19996) project demonstrated atmospheric limb sounding from low-earth-orbit (LEO) with high vertical resolution, high accuracy, and global coverage in all weather. Based on the success and scientific results of GPS/MET, Taiwan’s National Space Program Office (NSPO), the University Corporation for Atmospheric Research (UCAR), the Jet Propulsion Laboratory (JPL), the Naval Research Laboratory (NRL), the University of Texas at Austin, the University of Arizona, Florida State University and other partners in the university community are developing COSMIC (Constellation observing System for Meteorology, Ionosphere and Climate), a follow-on project for weather and climate research, climate monitoring, space weather, and geodetic science. COSMIC plans to launch eight LEO satellites in 2004. Each COSMIC satellite will retrieve about 500 daily profiles of key ionospheric and atmospheric properties from the tracked GPS radio-signals as they are occulted behind the Earth limb. The constellation will provide frequent global snapshots of the atmosphere and ionosphere with about 40000 daily soundings.
    This paper discusses some of the applications of COSMIC data for meteorology, including polar meteorology, numerical weather prediction (NWP), and climate. Applications to ionospheric research including space weather and geodesy are described elsewhere in this issue of TAO. In meteorology COSMIC will provide high vertical resolution temperature, pressure and water vapor information for a variety of atmospheric process studies and improve the forecast accuracy of numerical weather prediction models. The COSMIC data set will allow investigation of the global water vapor distribution and map the atmospheric flow of water vapor that is so crucial for understanding and predicting weather and climate. The data set will provide accurate geopotential heights, enable the detection of gravity waves from the upper troposphere to the stratosphere, reveal the height and shape of the tropopause globally with unprecedented accuracy, support the investigation of fronts and other baroclinic structures, and improve our understanding of tropopause-stratosphere exchange processes. COSMIC data will complement other observing systems and improve global weather analyses, particularly over the oceans and polar regions, and NWP forecasts made from these analyses. Through assimilation in numerical models, COSMIC data will improve the resolution and accuracy of the global temperature, pressure and water vapor fields, and through the model’s dynamical and physical adjustment mechanisms, the wind fields as well. These improved analyses and forecasts will provide significant benefits to aviation and other industries. For climate research and monitoring COSMIC will provide an accurate global thermometer that will monitor Earth’s atmosphere in all weather with unprecedented long-term stability, resolution, coverage, and accuracy. COSMIC will provide a data set for the detection of climate variability and change, the separation of natural and anthropogenic causes, the calibration of other satellite observing systems and the verification and improve events especially in remote oceanic regions, and it will enable scientists to monitor the response of the global atmosphere to regional events such as Volcanic eruptions, the Kuwait oil fires, or the recent Indonesian and Mexican forest fires. Upper-tropospheric refractivity data from COSMIC may shed new light on the controversy over the role that tropical convection of COSMIC data will provide new insights into the global hydrologic cycle.
  11. 2003: Rudich, Yinon, Ayelet Sagi, and Daniel Rosenfeld. “Influence of the Kuwait oil fires plume (1991) on the microphysical development of clouds.” Journal of Geophysical Research: Atmospheres 108.D15 (2003). Applications of new retrieval methods to old satellite data allowed us to study the effects of smoke from the Kuwait oil fires in 1991 on clouds and precipitation. The properties of smoke‐affected and smoke‐free clouds were compared on the background of the dust‐laden desert atmosphere. Several effects were observed: (1) clouds typically developed at the top of the smoke plume, probably because of solar heating and induced convection by the strongly absorbing aerosols; (2) large salt particles from the burning mix of oil and brines formed giant cloud condensation nuclei (CCN) close to the source, which initiated coalescence in the highly polluted clouds; (3) farther away from the smoke source, the giant CCN were deposited, and the extremely high concentrations of medium and small CCN dominated cloud development by strongly suppressing drop coalescence and growth with altitude; and (4) the smaller cloud droplets in the smoke‐affected clouds froze at colder temperatures and suppressed both the water and ice precipitation forming processes. These observations imply that over land the smoke particles are not washed out efficiently and can be transported to long distances, extending the observed effects to large areas. The absorption of solar radiation by the smoke induces convection above the smoke plumes and consequently leads to formation of clouds with roots at the top of the smoke layer. This process dominates over the semidirect effect of cloud evaporation due to the smoke‐induced enhanced solar heating, at least in the case of the Kuwait fires.
  12. 2008: Ramanathan, Veerabhadran, and Gregory Carmichael. “Global and regional climate changes due to black carbon.” Nature geoscience 1.4 (2008): 221. Black carbon in soot is the dominant absorber of visible solar radiation in the atmosphere. Anthropogenic sources of black carbon, although distributed globally, are most concentrated in the tropics where solar irradiance is highest. Black carbon is often transported over long distances, mixing with other aerosols along the way. The aerosol mix can form transcontinental plumes of atmospheric brown clouds, with vertical extents of 3 to 5 km. Because of the combination of high absorption, a regional distribution roughly aligned with solar irradiance, and the capacity to form widespread atmospheric brown clouds in a mixture with other aerosols, emissions of black carbon are the second strongest contribution to current global warming, after carbon dioxide emissions. In the Himalayan region, solar heating from black carbon at high elevations may be just as important as carbon dioxide in the melting of snowpacks and glaciers. The interception of solar radiation by atmospheric brown clouds leads to dimming at the Earth’s surface with important implications for the hydrological cycle, and the deposition of black carbon darkens snow and ice surfaces, which can contribute to melting, in particular of Arctic sea ice.

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LINKS TO ALL FOUR SECTIONS OF THIS REVIEW 

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #1:

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #2:

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #3:

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #4:

Source Document: Global Climate Change  

EXTREME WEATHER EVENTS: The number of record high temperature events in the United States has been increasing, while the number of record low temperature events has been decreasing, since 1950. The U.S. has also witnessed increasing numbers of intense rainfall events. OCEAN ACIDIFICATION: Since the beginning of the Industrial Revolution, the acidity of surface ocean waters has increased by about 30 percent
Since the beginning of the Industrial Revolution, the acidity of surface ocean waters has increased by about 30 percent.11,12 This increase is the result of humans emitting more carbon dioxide into the atmosphere and hence more being absorbed into the oceans. The amount of carbon dioxide absorbed by the upper layer of the oceans is increasing by about 2 billion tons per year. SCIENTIFIC CONSENSUS: Ninety-seven percent of climate scientists agree that climate-warming trends over the past century are very likely due to human activities, and most of the leading scientific organizations worldwide have issued public statements endorsing this position. SCARY TRENDS AND EVENTS: The oceans are warming, ice sheets are shrinking, glaciers retreating, snow cover decreasing, the sea level is rising, Arctic sea ice is declining, and extreme weather is happening.

RESPONSE

  1. EXTREME WEATHER EVENTS: The “Event Attribution Science” methodology is derived from the Probabilistic Event Attribution or PEA that was formalized as the Warsaw International Mechanism for Loss and Damage Associated with Climate Change Impacts or WIM by the UNFCCC as a fund allocation tool for the determination of whether a given weather event in a poor non-Annex country qualifies for financial compensation from rich Annex-I countries. It’s elevation to “science” and it’s use to identify extreme weather events as evidence of the harmful nature of climate change is an extreme form of confirmation bias and circular reasoning. A detailed case study of PEA elevated to Event Attribution Science is provided in a related post here: EVENT ATTRIBUTION CASE STUDY.
  2. OCEAN ACIDIFICATION:  The evidence for human caused ocean acidification, presented as “Since the beginning of the Industrial Revolution, the acidity of surface ocean waters has increased by about 30 percent and since the beginning of the Industrial Revolution, the acidity of surface ocean waters has increased by about 30 percent.” contains a common statistical flaw that is known to create spurious correlations. This argument does not serve as evidence that fossil fuel emissions cause ocean acidification. The related post on spurious correlations is here SPURIOUS CORRELATIONS IN CLIMATE SCIENCE and the ocean acidification issue presented here HUMAN CAUSED CLIMATE CHANGE shows that detrended correlation analysis does not show the close association of ocean acidification with emissions implied by the NASA statement above.
  3. SCIENTIFIC CONSENSUS: A strong agreement among climate scientists is often presented as evidence that therefore climate science must be correct. This logic is flawed. Correctness of a scientific finding must rely on data and the scientific method and not on opinion polls. If anything, the 97% consensus statistic works against climate science because it is indicative of a field  of study that harbors a cult-like belief system and is therefore incapable of objective scientific inquiry. A climate science peer review case study seems to indicate a cultist group-think culture in climate science. The case study is presented in a related post here: CLIMATE SCIENCE PEER REVIEW CASE STUDY.
  4. The oceans are warming, ice sheets are shrinking, glaciers retreating, snow cover decreasing, the sea level is rising, Arctic sea ice is declining. These trends are normal in a warming earth still recovering from the last ice age and the little ice age that ended in 1850. They are not evidence of human cause. Still, it should be noted that Arctic sea ice is mentioned but Antarctic sea ice is overlooked. This kind of confirmation bias in climate science is a serious issue because it implies that climate scientists are not objective seekers of truth but biased activists and advocates out to prove a thesis to which they have already wholeheartedly subscribed.
  5. Scary trends and events. The only evidence that extreme weather is caused by fossil fuel emissions is found in the so called Event Attribution studies that are carried out with climate models. The methodology imposes a built-in confirmation bias as well as circular reasoning. Here is a case study of event attribution analysis EVENT ATTRIBUTION CASE STUDY.

 

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LINKS TO ALL FOUR SECTIONS OF THIS REVIEW 

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #1:

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #2:

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #3:

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #4:

Chart1: Hadcrut4 temperature trends for the Northern Hemisphere for each calendar month: 1850-2016

hadcrut4nh2016

Chart2: Hadcrut4 temperature trends for the Southern Hemisphere for each calendar month: 1850-2016

hadcrut4sh2016

Source Document: Global Climate Change  

CLAIM: The planet’s average surface temperature has risen about 1.62 degrees Fahrenheit (0.9 degrees Celsius) since the late 19th century, a change driven largely by increased carbon dioxide and other human-made emissions into the atmosphere. [Source:  Most of the warming occurred in the past 35 years, with the five warmest years on record taking place since 2010. Not only was 2016 the warmest year on record, but eight of the 12 months that make up the year — from January through September, with the exception of June — were the warmest on record for those respective months.

RESPONSE

  1. “The planet’s average surface temperature has risen about 0.9C driven largely by increased carbon dioxide”. This claim assumes that the observed increase in atmospheric CO2 is driven by emissions and that the observed increase in surface temperature is driven by atmospheric CO2 concentration. These relationships exist in climate models because they have been programmed into them but they are not found in the observational data as shown in these two related posts: HUMAN CAUSED CLIMATE CHANGETHE GREENHOUSE EFFECT OF ATMOSPHERIC CO2. No evidence exists outside of climate models that relate warming to emissions outside of climate models and without the use of spurious correlations as discussed in this related post: SPURIOUS CORRELATIONS IN CLIMATE SCIENCE.
  2. It is also noted that the overall warming rate of 0.6C per century is not uniform seasonally or regionally as seen in the two charts above marked as Chart#1 and Chart#2. Seasonally we find that the Southern Hemisphere does show a close agreement in the warming rate with the observed warming rate contained in a narrow range of 0.4C to 0.5C per century but the Northern Hemisphere shows a large seasonal variation in the warming rate. The warming rate is as high as 1.1C per century in the late fall and winter and as low as 0.3C in the summer months of June, July, and August. In terms of regional variation the charts show that the average warming rate across seasons is much higher in the Northern Hemisphere at 0.74C per century than in the Southern Hemisphere at 0.46C per century.
  3. Most of the warming occurred in the past 35 years“. The reference is to the warming trend since the end of the cooling period from the mid 1940s to the late 1970s when climate science was warning us of a return to Little Ice Age conditions. It was only after that cooling period ended and a warming trend began in the late 1970s that the Hansen and Lacis papers of the 1980s and in particular the Hansen 1988 Congressional testimony launched the current interest in and fear of warming. This kind of data analysis where selected sub-spans of a time series are used to buttress theory is a form of circular reasoning and confirmation bias. Such arguments do not show that climate science is correct but instead imply that climate science is weak in scientific and statistical rigor.
  4. The five warmest years on record taking place since 2010. Not only was 2016 the warmest year on record, but eight of the 12 months that make up the year from January through September, with the exception of June were the warmest on record for those respective months.” These “warmest on record” claims may have some value in getting people’s attention but they are irrelevant in the study of global warming. Climate science insists, and correctly so, that only long term trends in temperature and NOT temperature events, however dramatic they may be, are relevant in terms of the theory of anthropogenic global warming. The continued use of temperature events as evidence of anthropogenic global warming by climate scientists is puzzling and disturbing. Clearly the temperature events are being used to create a sense of alarm. It may be that climate scientists are also climate activists and that they are having a difficult time wearing both hats.

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LINKS TO ALL FOUR SECTIONS OF THIS REVIEW 

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #1:

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #2:

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #3:

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #4:

Source Document: Global Climate Change  

CLAIM: Earth-orbiting satellites and other technological advances have enabled scientists to see the big picture, collecting many different types of information about our planet and its climate on a global scale. This body of data, collected over many years, reveals the signals of a changing climateThe heat-trapping nature of carbon dioxide and other gases was demonstrated in the mid-19th century. Their ability to affect the transfer of infrared energy through the atmosphere is the scientific basis of many instruments flown by NASA. There is no question that increased levels of greenhouse gases must cause the Earth to warm in responseIce cores drawn from Greenland, Antarctica, and tropical mountain glaciers show that the Earth’s climate responds to changes in greenhouse gas levels. Ancient evidence can also be found in tree rings, ocean sediments, coral reefs, and layers of sedimentary rocks. This ancient, or paleoclimate, evidence reveals that current warming is occurring roughly ten times faster than the average rate of ice-age-recovery warming.

 

RESPONSE

  1. This body of data, collected over many years, reveals the signals of a changing climate“. There is no dispute that the evidence shows that the world has been on an overall warming trend since the end of the LITTLE ICE AGE. However, it is also clear from the data that the overall OLS warming trend is not sustained but only the net result of violently changing 30-year warming and cooling trends with no evidence that these changes are related to fossil fuel emissions Correlation of Warming with Global Emissions
  2. “The heat-trapping nature of carbon dioxide and other gases was demonstrated in the mid-19th century”. This statement is false. Fourier, Tyndal, Arrhenius and others proposed a theory that this was the case but this effect was never demonstrated. Arrhenius used this theory to explain ice age cycles in very long time scales of millions of years but that theory has been discredited and is not the currently held theory of ice ages. The Milankovitch theory is the currently held view. That modern climate scientist need to lean so heavily on Arrhenius for this crucial relationship reveals the real weakness in climate science and that is the Equilibrium Climate Sensitivity (ECS)issue described in a related post  THE GREENHOUSE EFFECT OF ATMOSPHERIC CO2The testable implication of the greenhouse effect first proposed by Jule Charney is that there should be a linear relationship between surface temperature and the logarithm of atmospheric CO2 concentration. Although that regression can be carried out in the observational data and a regression coefficient computed, it’s instability and uncertainty is a major weakness in climate science and this issue has not been resolved.
  3. There is no question that increased levels of greenhouse gases must cause the Earth to warm in response”. There is no question that climate sensitivity is found in climate models but that is because it has been programmed into them. There is no question that the Charney Climate Sensitivity is not found in the observational data. Climate science has acknowledged the difficulty with the ECS issue and have proposed a new measure called Transient Climate Response or TCR based on a correlation between surface temperature and cumulative emissions. That climate science needs to resort to the TCR is a tacit admission of the failure of the ECS  and their failure to find the greenhouse effect of CO2 on which this whole science is based. The TCR is described in three related posts here SPURIOUS CORRELATIONS , here GREENHOUSE EFFECT OF ATMOSPHERIC CO2 and here TRANSIENT CLIMATE RESPONSE.
  4. Ice cores drawn from Greenland, Antarctica, and tropical mountain glaciers show that the Earth’s climate responds to changes in greenhouse gas levels“. The data show that warmer temperatures are associated with higher carbon dioxide levels. There is no data that show that temperature “responds” to carbon dioxide. That conclusion from the paleodata is a case of circular reasoning and confirmation bias because that which is to be proven is assumed in the way the data are interpreted. Causation and the direction of causation must be proven separately from the association. An association between X and Y in field data could mean that X causes Y or that Y causes X or that a third unobserved variable causes both X and Y or that the association has no causation interpretation. In fact, the NASA conclusion that that CO2 causes warming has been challenged by the alternate theory that warming causes CO2 because of the observed lag in the data.
  5. “paleoclimate, evidence reveals that current warming is occurring roughly ten times faster than the average rate of ice-age-recovery warming”. This statement is based on a comparison of warming trends in two very different time scales. It is true that in time scales of millions of millions of years the transitions between ice ages and interglacials are slow but within these slow moving changes are violent changes in the shorter time cycles just as I have shown that even the linear OLS warming since 1850 is simply the net result of violent 30-year periods of warming and cooling at much higher rates. Correlation of Warming with Global Emissions

 

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LINKS TO ALL FOUR SECTIONS OF THIS REVIEW 

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #1:

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #2:

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #3:

NASA EVIDENCE FOR HUMAN CAUSED CLIMATE CHANGE #4:

Source Document: Global Climate Change  

CLAIM: Scientific evidence for warming of the climate system is unequivocal according to the IPCC. The current warming trend is of particular significance because most of it is extremely likely (greater than 95 percent probability) to be the result of human activity since the mid-20th century and proceeding at a rate that is unprecedented over decades to millennia. [Source: IPCC Fifth Assessment Report, Summary for Policymakers, B.D. Santer et.al., “A search for human influences on the thermal structure of the atmosphere,” Nature vol 382, 4 July 1996, 39-46, Gabriele C. Hegerl, “Detecting Greenhouse-Gas-Induced Climate Change with an Optimal Fingerprint Method,” Journal of Climate, v. 9, October 1996, 2281-2306, V. Ramaswamy et.al., “Anthropogenic and Natural Influences in the Evolution of Lower Stratospheric Cooling,” Science 311 (24 February 2006), 1138-1141, B.D. Santer et.al., “Contributions of Anthropogenic and Natural Forcing to Recent Tropopause Height Changes,” Science vol. 301 (25 July 2003), 479-483.]

RESPONSE

  1. The IPCC opinion that warming is the “result of human activity since the mid-20th century” is inconsistent with the generally accepted theory of global warming which states that human caused global warming began with the Industrial Revolution which started long before 1950. It is variously dated by climate science as somewhere between 1800 and 1900 (Ruddiman, William. “The anthropogenic greenhouse era began thousands of years ago.” Climatic change, 2003), (Steffen, Will “The Anthropocene: conceptual and historical perspectives.” Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 2011), (Abram, Nerilie J., “Early onset of industrial-era warming across the oceans and continents.” Nature (2016). Abrams (2016) states that they use palaeoclimate records to show that “sustained industrial-era warming of the tropical oceans first developed during the mid-nineteenth century and was nearly synchronous with Northern Hemisphere continental warming”.
  2. The IPCC AR5 states that “The globally averaged combined land and ocean surface temperature data as calculated by a linear trend, show a warming of 0.85 [0.65 to 1.06]C over the period 1880 to 2012, when multiple independently produced datasets exist” but then adds that “Warming of the climate system is unequivocal, and since the 1950s, many of the observed changes are unprecedented over decades to millennia. The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, sea level has risen, and the concentrations of greenhouse gases have increased“. It appears that NASA climate scientists have misread the relevant IPCC AR5 sentence to mean that human caused global warming began in 1950. The NASA interpretation that the “mid 20th century” was the beginning of “unequivocal anthropogenic global warming confuses the AGW issue presented by climate science. It could mean that the theory is not “unequivocal” in the period prior to 1950 or perhaps that climate science is a construct created by unprofessional people not guided by generally accepted scientific methods.
  3. Regarding the IPCC AR5 sentence: “The atmosphere and ocean have warmed, the amounts of snow and ice have diminished, sea level has risen, and the concentrations of greenhouse gases have increased”, it should be noted that it implies that these similar changes imply correlation and therefore causation. This interpretation contains serious statistical errors as described in the post on SPURIOUS CORRELATIONS IN CLIMATE SCIENCE.
  4. Both Santer and Hegerl (details below) show a statistically significant relationship between fossil fuel emissions and warming by demonstrating that the observed warming is more likely in a world with fossil fuel emissions than in a world without such emissions. This evaluation was made with the use of climate models. Climate models are computer programs that are programmed to behave according to the theory to be tested containing both the greenhouse effect of carbon dioxide and the positive relationship between emissions and increases in atmospheric CO2 concentration. Therefore these these tests are statements about what the theory predicts and not empirical tests of theory. That climate science would present such computer results as tests of theory does not provide evidence to support the theory of anthropogenic global warming but rather disturbing evidence that climate scientists may not have been fully trained in the scientific method in which the independence of the empirical test of theory from the theory is critically important.
  5. Santer, Benjamin D., et al. “A search for human influences on the thermal structure of the atmosphere.” Nature 382.6586 (1996) says that “The observed spatial patterns of temperature change in the free atmosphere from 1963 to 1987 are similar to those predicted by state-of-the-art climate models incorporating various combinations of changes in carbon dioxide, anthropogenic sulphate aerosol and stratospheric ozone concentrations. The degree of pattern similarity between models and observations increases through this period. It is likely that this trend is partially due to human activities, although many uncertainties remain, particularly relating to estimates of natural variability.”
  6. Hegerl, Gabriele “Detecting greenhouse-gas-induced climate change with an optimal fingerprint method.” Journal of Climate 9.10 (1996) says that “A strategy using statistically optimal fingerprints to detect anthropogenic climate change is outlined and applied to near-surface temperature trends. The components of this strategy include observations, information about natural climate variability, and a “guess pattern” representing the expected time–space pattern of anthropogenic climate change. The expected anthropogenic climate change is identified through projection of the observations onto an appropriate optimal fingerprint, yielding a scalar-detection variable. The statistically optimal fingerprint is obtained by weighting the components of the guess pattern (truncated to some small-dimensional space) toward low-noise directions. The null hypothesis that the observed climate change is part of natural climate variability is then tested. This strategy is applied to detecting a greenhouse-gas-induced climate change in the spatial pattern of near-surface temperature trends defined for time intervals of 15–30 years. The expected pattern of climate change is derived from a transient simulation with a coupled ocean-atmosphere general circulation model. Global gridded near-surface temperature observations are used to represent the observed climate change. Information on the natural variability needed to establish the statistics of the detection variable is extracted from long control simulations of coupled ocean-atmosphere models and, additionally, from the observations themselves (from which an estimated greenhouse warming signal has been removed). While the model control simulations contain only variability caused by the internal dynamics of the atmosphere-ocean system, the observations additionally contain the response to various external forcings (e.g., volcanic eruptions, changes in solar radiation, and residual anthropogenic forcing). The resulting estimate of climate noise has large uncertainties but is qualitatively the best the authors can presently offer”. The authors conclude that “t is concluded that a statistically significant externally induced warming has been observed, but our caveat that the estimate of the internal climate variability is still uncertain is emphasized”.

 

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arctic-seaice

GLOBAL WARMING AND ARCTIC SEA ICE EXTENT: A BIBLIOGRAPHY

  1. 1999: Rothrock, Drew A., Yanling Yu, and Gary A. Maykut. “Thinning of the Arctic sea‐ice cover.” Geophysical Research Letters26.23 (1999): 3469-3472. Comparison of sea‐ice draft data acquired on submarine cruises between 1993 and 1997 with similar data acquired between 1958 and 1976 indicates that the mean ice draft at the end of the melt season has decreased by about 1.3 m in most of the deep water portion of the Arctic Ocean, from 3.1 m in 1958–1976 to 1.8 m in the 1990s. The decrease is greater in the central and eastern Arctic than in the Beaufort and Chukchi seas. Preliminary evidence is that the ice cover has continued to become thinner in some regions during the 1990s.
  2. 2000: Polyakov, Igor V., and Mark A. Johnson. “Arctic decadal and interdecadal variability.” Geophysical Research Letters 27.24 (2000): 4097-4100. Atmospheric and oceanic variability in the Arctic shows the existence of several oscillatory modes. The decadal‐scale mode associated with the Arctic Oscillation (AO) and a low‐frequency oscillation (LFO) with an approximate time scale of 60–80 years, dominate. Both modes were positive in the 1990s, signifying a prolonged phase of anomalously low atmospheric sea level pressure and above normal surface air temperature in the central Arctic. Consistent with an enhanced cyclonic component, the arctic anticyclone was weakened and vorticity of winds became positive. The rapid reduction of arctic ice thickness in the 1990s may be one manifestation of the intense atmosphere and ice cyclonic circulation regime due to the synchronous actions of the AO and LFO. Our results suggest that the decadal AO and multidecadal LFO drive large amplitude natural variability in the Arctic making detection of possible long‐term trends induced by greenhouse gas warming most difficult.
  3. 2003: Cavalieri, D. J., C. L. Parkinson, and K. Ya Vinnikov. “30‐Year satellite record reveals contrasting Arctic and Antarctic decadal sea ice variability.” Geophysical Research Letters30.18 (2003). A 30‐year satellite record of sea ice extents derived mostly from satellite microwave radiometer observations reveals that the Arctic sea ice extent decreased by 0.30 ± 0.03 × 106 km2/10 yr from 1972 through 2002, but by 0.36 ± 0.05 × 106km2/10yr from 1979 through 2002, indicating an acceleration of 20% in the rate of decrease. In contrast, the Antarctic sea ice extent decreased dramatically over the period 1973–1977, then gradually increased. Over the full 30‐year period, the Antarctic ice extent decreased by 0.15 ± 0.08 × 106 km2/10 yr. The trend reversal is attributed to a large positive anomaly in Antarctic sea ice extent in the early 1970’s, an anomaly that apparently began in the late 1960’s, as observed in early visible and infrared satellite images.
  4. 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.
  5. 2006: Divine, Dmitry V., and Chad Dick. “Historical variability of sea ice edge position in the Nordic Seas.” Journal of Geophysical Research: Oceans 111.C1 (2006). Historical ice observations in the Nordic Seas from April through August are used to construct time series of ice edge position anomalies spanning the period 1750–2002. While analysis showed that interannual variability remained almost constant throughout this period, evidence was found of oscillations in ice cover with periods of about 60 to 80 years and 20 to 30 years, superimposed on a continuous negative trend. The lower frequency oscillations are more prominent in the Greenland Sea, while higher frequency oscillations are dominant in the Barents. The analysis suggests that the recent well‐documented retreat of ice cover can partly be attributed to a manifestation of the positive phase of the 60–80 year variability, associated with the warming of the subpolar North Atlantic and the Arctic. The continuous retreat of ice edge position observed since the second half of the 19th century may be a recovery after significant cooling in the study area that occurred as early as the second half of the 18th century.
  6. 2008: Stroeve, Julienne, et al. “Arctic sea ice extent plummets in 2007.” Eos, Transactions American Geophysical Union 89.2 (2008): 13-14.Arctic sea ice declined rapidly to unprecedented low extents in the summer of 2007, raising concern that the Arctic may be on the verge of a fundamental transition toward a seasonal ice cover. Arctic sea ice extent typically attains a seasonal maximum in March and minimum in September. Over the course of the modern satellite record (1979 to present), sea ice extent has declined significantly in all months, with the decline being most pronounced in September. By mid‐July 2007, it was clear that a new record low would be set during the summer of 2007
  7. 2007: Stroeve, Julienne, et al. “Arctic sea ice decline: Faster than forecast.” Geophysical research letters 34.9 (2007). From 1953 to 2006, Arctic sea ice extent at the end of the melt season in September has declined sharply. All models participating in the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) show declining Arctic ice cover over this period. However, depending on the time window for analysis, none or very few individual model simulations show trends comparable to observations. If the multi‐model ensemble mean time series provides a true representation of forced change by greenhouse gas (GHG) loading, 33–38% of the observed September trend from 1953–2006 is externally forced, growing to 47–57% from 1979–2006. Given evidence that as a group, the models underestimate the GHG response, the externally forced component may be larger. While both observed and modeled Antarctic winter trends are small, comparisons for summer are confounded by generally poor model performance.
  8. 2008: Comiso, Josefino C., et al. “Accelerated decline in the Arctic sea ice cover.” Geophysical research letters 35.1 (2008). Satellite data reveal unusually low Arctic sea ice coverage during the summer of 2007, caused in part by anomalously high temperatures and southerly winds. The extent and area of the ice cover reached minima on 14 September 2007 at 4.1 × 106 km2 and 3.6 × 106 km2, respectively. These are 24% and 27% lower than the previous record lows, both reached on 21 September 2005, and 37% and 38% less than the climatological averages. Acceleration in the decline is evident as the extent and area trends of the entire ice cover (seasonal and perennial ice) have shifted from about −2.2 and −3.0% per decade in 1979–1996 to about −10.1 and −10.7% per decade in the last 10 years. The latter trends are now comparable to the high negative trends of −10.2 and −11.4% per decade for the perennial ice extent and area, 1979–2007.
  9. 2009: Kwok, Rothrock, and D. A. Rothrock. “Decline in Arctic sea ice thickness from submarine and ICESat records: 1958–2008.” Geophysical Research Letters 36.15 (2009). The decline of sea ice thickness in the Arctic Ocean from ICESat (2003–2008) is placed in the context of estimates from 42 years of submarine records (1958–2000) described by Rothrock et al. (1999, 2008). While the earlier 1999 work provides a longer historical record of the regional changes, the latter offers a more refined analysis, over a sizable portion of the Arctic Ocean supported by a much stronger and richer data set. Within the data release area (DRA) of declassified submarine sonar measurements (covering ∼38% of the Arctic Ocean), the overall mean winter thickness of 3.64 m in 1980 can be compared to a 1.89 m mean during the last winter of the ICESat record—an astonishing decrease of 1.75 m in thickness. Between 1975 and 2000, the steepest rate of decrease is −0.08 m/yr in 1990 compared to a slightly higher winter/summer rate of −0.10/−0.20 m/yr in the five‐year ICESat record (2003–2008). Prior to 1997, ice extent in the DRA was >90% during the summer minimum. This can be contrasted to the gradual decrease in the early 2000s followed by an abrupt drop to <55% during the record setting minimum in 2007. This combined analysis shows a long‐term trend of sea ice thinning over submarine and ICESat records that span five decades.
  10. 2009: Chylek, Petr, et al. “Arctic air temperature change amplification and the Atlantic Multidecadal Oscillation.” Geophysical Research Letters 36.14 (2009). Understanding Arctic temperature variability is essential for assessing possible future melting of the Greenland ice sheet, Arctic sea ice and Arctic permafrost. Temperature trend reversals in 1940 and 1970 separate two Arctic warming periods (1910–1940 and 1970–2008) by a significant 1940–1970 cooling period. Analyzing temperature records of the Arctic meteorological stations we find that (a) the Arctic amplification (ratio of the Arctic to global temperature trends) is not a constant but varies in time on a multi‐decadal time scale, (b) the Arctic warming from 1910–1940 proceeded at a significantly faster rate than the current 1970–2008 warming, and (c) the Arctic temperature changes are highly correlated with the Atlantic Multi‐decadal Oscillation (AMO) suggesting the Atlantic Ocean thermohaline circulation is linked to the Arctic temperature variability on a multi‐decadal time scale.
  11. 2010: Ho, Joshua. “The implications of Arctic sea ice decline on shipping.” Marine Policy 34.3 (2010): 713-715. Although a ‘blue’ Arctic Ocean is predicted in the summertime to occur from the middle of this century, current rates of warming indicate an earlier realization. Also, routes along the coast of Siberia will be navigable much earlier. However, before the Arctic routes can reliably be used on a large scale for transit by shipping along its passages, more investments are required on infrastructure and the provision of marine services to ensure the safe and secure transit of shipping with minimal environmental impact.
  12. 2010: Frankcombe, Leela M., Anna Von Der Heydt, and Henk A. Dijkstra. “North Atlantic multidecadal climate variability: an investigation of dominant time scales and processes.” Journal of climate 23.13 (2010): 3626-3638. The issue of multidecadal variability in the North Atlantic has been an important topic of late. It is clear that there are multidecadal variations in several climate variables in the North Atlantic, such as sea surface temperature and sea level height. The details of this variability, in particular the dominant patterns and time scales, are confusing from both an observational as well as a theoretical point of view. After analyzing results from observational datasets and a 500-yr simulation of an Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) climate model, two dominant time scales (20–30 and 50–70 yr) of multidecadal variability in the North Atlantic are proposed. The 20–30-yr variability is characterized by the westward propagation of subsurface temperature anomalies. The hypothesis is that the 20–30-yr variability is caused by internal variability of the Atlantic Meridional Overturning Circulation (MOC) while the 50–70-yr variability is related to atmospheric forcing over the Atlantic Ocean and exchange processes between the Atlantic and Arctic Oceans.
  13. 2010: Screen, James A., and Ian Simmonds. “The central role of diminishing sea ice in recent Arctic temperature amplification.” Nature 464.7293 (2010): 1334. The rise in Arctic near-surface air temperatures has been almost twice as large as the global average in recent decades1,2,3—a feature known as ‘Arctic amplification’. Increased concentrations of atmospheric greenhouse gases have driven Arctic and global average warming1,4; however, the underlying causes of Arctic amplification remain uncertain. The roles of reductions in snow and sea ice cover5,6,7 and changes in atmospheric and oceanic circulation8,9,10, cloud cover and water vapour11,12 are still matters of debate. A better understanding of the processes responsible for the recent amplified warming is essential for assessing the likelihood, and impacts, of future rapid Arctic warming and sea ice loss13,14. Here we show that the Arctic warming is strongest at the surface during most of the year and is primarily consistent with reductions in sea ice cover. Changes in cloud cover, in contrast, have not contributed strongly to recent warming. Increases in atmospheric water vapour content, partly in response to reduced sea ice cover, may have enhanced warming in the lower part of the atmosphere during summer and early autumn. We conclude that diminishing sea ice has had a leading role in recent Arctic temperature amplification. The findings reinforce suggestions that strong positive ice–temperature feedbacks have emerged in the Arctic15, increasing the chances of further rapid warming and sea ice loss, and will probably affect polar ecosystems, ice-sheet mass balance and human activities in the Arctic.
  14. 2010: Bhatt, Uma S., et al. “Circumpolar Arctic tundra vegetation change is linked to sea ice decline.” Earth Interactions 14.8 (2010): 1-20. Linkages between diminishing Arctic sea ice and changes in Arctic terrestrial ecosystems have not been previously demonstrated. Here, the authors use a newly available Arctic Normalized Difference Vegetation Index (NDVI) dataset (a measure of vegetation photosynthetic capacity) to document coherent temporal relationships between near-coastal sea ice, summer tundra land surface temperatures, and vegetation productivity. The authors find that, during the period of satellite observations (1982–2008), sea ice within 50 km of the coast during the period of early summer ice breakup declined an average of 25% for the Arctic as a whole, with much larger changes in the East Siberian Sea to Chukchi Sea sectors (>44% decline). The changes in sea ice conditions are most directly relevant and have the strongest effect on the villages and ecosystems immediately adjacent to the coast, but the terrestrial effects of sea ice changes also extend far inland. Low-elevation (<300 m) tundra summer land temperatures, as indicated by the summer warmth index (SWI; sum of the monthly-mean temperatures above freezing, expressed as °C month−1), have increased an average of 5°C month−1 (24% increase) for the Arctic as a whole; the largest changes (+10° to 12°C month−1) have been over land along the Chukchi and Bering Seas. The land warming has been more pronounced in North America (+30%) than in Eurasia (16%). When expressed as percentage change, land areas in the High Arctic in the vicinity of the Greenland Sea, Baffin Bay, and Davis Strait have experienced the largest changes (>70%). The NDVI has increased across most of the Arctic, with some exceptions over land regions along the Bering and west Chukchi Seas. The greatest change in absolute maximum NDVI occurred over tundra in northern Alaska on the Beaufort Sea coast [+0.08 Advanced Very High Resolution Radiometer (AVHRR) NDVI units]. When expressed as percentage change, large NDVI changes (10%–15%) occurred over land in the North America High Arctic and along the Beaufort Sea. Ground observations along an 1800-km climate transect in North America support the strong correlations between satellite NDVI observations and summer land temperatures. Other new observations from near the Lewis Glacier, Baffin Island, Canada, document rapid vegetation changes along the margins of large retreating glaciers and may be partly responsible for the large NDVI changes observed in northern Canada and Greenland. The ongoing changes to plant productivity will affect many aspects of Arctic systems, including changes to active-layer depths, permafrost, biodiversity, wildlife, and human use of these regions. Ecosystems that are presently adjacent to year-round (perennial) sea ice are likely to experience the greatest changes.
  15. 2010: Fauria, M. Macias, et al. “Unprecedented low twentieth century winter sea ice extent in the Western Nordic Seas since AD 1200.” Climate Dynamics 34.6 (2010): 781-795. We reconstructed decadal to centennial variability of maximum sea ice extent in the Western Nordic Seas for A.D. 1200–1997 using a combination of a regional tree-ring chronology from the timberline area in Fennoscandia and δ18O from the Lomonosovfonna ice core in Svalbard. The reconstruction successfully explained 59% of the variance in sea ice extent based on the calibration period 1864–1997. The significance of the reconstruction statistics (reduction of error, coefficient of efficiency) is computed for the first time against a realistic noise background. The twentieth century sustained the lowest sea ice extent values since A.D. 1200: low sea ice extent also occurred before (mid-seventeenth and mid-eighteenth centuries, early fifteenth and late thirteenth centuries), but these periods were in no case as persistent as in the twentieth century. Largest sea ice extent values occurred from the seventeenth to the nineteenth centuries, during the Little Ice Age (LIA), with relatively smaller sea ice-covered area during the sixteenth century. Moderate sea ice extent occurred during thirteenth–fifteenth centuries. Reconstructed sea ice extent variability is dominated by decadal oscillations, frequently associated with decadal components of the North Atlantic Oscillation/Arctic Oscillation (NAO/AO), and multi-decadal lower frequency oscillations operating at ~50–120 year. Sea ice extent and NAO showed a non-stationary relationship during the observational period. The present low sea ice extent is unique over the last 800 years, and results from a decline started in late-nineteenth century after the LIA.
  16. 2011: Kay, Jennifer E., Marika M. Holland, and Alexandra Jahn. “Inter‐annual to multi‐decadal Arctic sea ice extent trends in a warming world.” Geophysical Research Letters 38.15 (2011). A climate model (CCSM4) is used to investigate the influence of anthropogenic forcing on late 20th century and early 21st century Arctic sea ice extent trends. On all timescales examined (2–50+ years), the most extreme negative observed late 20th century trends cannot be explained by modeled natural variability alone. Modeled late 20th century ice extent loss also cannot be explained by natural causes alone, but the six available CCSM4 ensemble members exhibit a large spread in their late 20th century ice extent loss. Comparing trends from the CCSM4 ensemble to observed trends suggests that internal variability explains approximately half of the observed 1979–2005 September Arctic sea ice extent loss. In a warming world, CCSM4 shows that multi‐decadal negative trends increase in frequency and magnitude, and that trend variability on 2–10 year timescales increases. Furthermore, when internal variability counteracts anthropogenic forcing, positive trends on 2–20 year timescales occur until the middle of the 21st century.
  17. 2011: Stroeve, Julienne C., et al. “Sea ice response to an extreme negative phase of the Arctic Oscillation during winter 2009/2010.” Geophysical Research Letters 38.2 (2011). Based on relationships established in previous studies, the extreme negative phase of the Arctic Oscillation (AO) that characterized winter of 2009/2010 should have favored retention of Arctic sea ice through the 2010 summer melt season. The September 2010 sea ice extent nevertheless ended up as third lowest in the satellite record, behind 2007 and barely above 2008, reinforcing the long‐term downward trend. This reflects pronounced differences in atmospheric circulation during winter of 2009/2010 compared to the mean anomaly pattern based on past negative AO winters, low ice volume at the start of the melt season, and summer melt of much of the multiyear ice that had been transported into the warm southerly reaches of the Beaufort and Chukchi seas.
  18. 2011: Kay, Jennifer E., Marika M. Holland, and Alexandra Jahn. “Inter‐annual to multi‐decadal Arctic sea ice extent trends in a warming world.” Geophysical Research Letters 38.15 (2011). A climate model (CCSM4) is used to investigate the influence of anthropogenic forcing on late 20th century and early 21st century Arctic sea ice extent trends. On all timescales examined (2–50+ years), the most extreme negative observed late 20th century trends cannot be explained by modeled natural variability alone. Modeled late 20th century ice extent loss also cannot be explained by natural causes alone, but the six available CCSM4 ensemble members exhibit a large spread in their late 20th century ice extent loss. Comparing trends from the CCSM4 ensemble to observed trends suggests that internal variability explains approximately half of the observed 1979–2005 September Arctic sea ice extent loss. In a warming world, CCSM4 shows that multi‐decadal negative trends increase in frequency and magnitude, and that trend variability on 2–10 year timescales increases. Furthermore, when internal variability counteracts anthropogenic forcing, positive trends on 2–20 year timescales occur until the middle of the 21st century.
    Citing Literature
  19. 2011: Medhaug, Iselin, and Tore Furevik. “North Atlantic 20th century multidecadal variability in coupled climate models: Sea surface temperature and ocean overturning circulation.” (2011) Atlantic Multidecadal Oscillation (AMO) and sea surface temperature in the Bay of Biscay and adjacent regions. Output from a total of 24 state-of-the-art Atmosphere-Ocean General Circulation Models is analyzed. The models were integrated with observed forcing for the period 1850–2000 as part of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. All models show enhanced variability at multi-decadal time scales in the North Atlantic sector similar to the observations, but with a large intermodel spread in amplitudes and frequencies for both the Atlantic Multidecadal Oscillation (AMO) and the Atlantic Meridional Overturning Circulation (AMOC). The models, in general, are able to reproduce the observed geographical patterns of warm and cold episodes, but not the phasing such as the early warming (1930s–1950s) and the following colder period (1960s–1980s). This indicates that the observed 20th century extreme in temperatures are due to primarily a fortuitous phasing of intrinsic climate variability and not dominated by external forcing. Most models show a realistic structure in the overturning circulation, where more than half of the available models have a mean overturning transport within the observed estimated range of 13–24 Sverdrup. Associated with a stronger than normal AMOC, the surface temperature is increased and the sea ice extent slightly reduced in the North Atlantic. Individual models show potential for decadal prediction based on the relationship between the AMO and AMOC, but the models strongly disagree both in phasing and strength of the covariability. This makes it difficult to identify common mechanisms and to assess the applicability for predictions.
  20. 2011: Mahajan, Salil, Rong Zhang, and Thomas L. Delworth. “Impact of the Atlantic meridional overturning circulation (AMOC) on Arctic surface air temperature and sea ice variability.” Journal of Climate 24.24 (2011): 6573-6581. The simulated impact of the Atlantic meridional overturning circulation (AMOC) on the low-frequency variability of the Arctic surface air temperature (SAT) and sea ice extent is studied with a 1000-year-long segment of a control simulation of the Geophysical Fluid Dynamics Laboratory Climate Model version 2.1. The simulated AMOC variations in the control simulation are found to be significantly anticorrelated with the Arctic sea ice extent anomalies and significantly correlated with the Arctic SAT anomalies on decadal time scales in the Atlantic sector of the Arctic. The maximum anticorrelation with the Arctic sea ice extent and the maximum correlation with the Arctic SAT occur when the AMOC index leads by one year. An intensification of the AMOC is associated with a sea ice decline in the Labrador, Greenland, and Barents Seas in the control simulation, with the largest change occurring in winter. The recent declining trend in the satellite-observed sea ice extent also shows a similar pattern in the Atlantic sector of the Arctic in the winter, suggesting the possibility of a role of the AMOC in the recent Arctic sea ice decline in addition to anthropogenic greenhouse-gas-induced warming. However, in the summer, the simulated sea ice response to the AMOC in the Pacific sector of the Arctic is much weaker than the observed declining trend, indicating a stronger role for other climate forcings or variability in the recently observed summer sea ice decline in the Chukchi, Beaufort, East Siberian, and Laptev Seas.
  21. 2012: Liu, Jiping, et al. “Impact of declining Arctic sea ice on winter snowfall.” Proceedings of the National Academy of Sciences(2012). While the Arctic region has been warming strongly in recent decades, anomalously large snowfall in recent winters has affected large parts of North America, Europe, and east Asia. Here we demonstrate that the decrease in autumn Arctic sea ice area is linked to changes in the winter Northern Hemisphere atmospheric circulation that have some resemblance to the negative phase of the winter Arctic oscillation. However, the atmospheric circulation change linked to the reduction of sea ice shows much broader meridional meanders in midlatitudes and clearly different interannual variability than the classical Arctic oscillation. This circulation change results in more frequent episodes of blocking patterns that lead to increased cold surges over large parts of northern continents. Moreover, the increase in atmospheric water vapor content in the Arctic region during late autumn and winter driven locally by the reduction of sea ice provides enhanced moisture sources, supporting increased heavy snowfall in Europe during early winter and the northeastern and midwestern United States during winter. We conclude that the recent decline of Arctic sea ice has played a critical role in recent cold and snowy winters. (??)
  22. 2012: Garcia-Soto, Carlos, and Robin D. Pingree. “Atlantic Multidecadal Oscillation (AMO) and sea surface temperature in the Bay of Biscay and adjacent regions.” Journal of the Marine Biological Association of the United Kingdom 92.2 (2012): 213-234. The sea surface temperature (SST) variability of the Bay of Biscay and adjacent regions (1854–2010) has been examined in relation to the evolution of the Atlantic Multidecadal Oscillation (AMO), a major climate mode. The AMO index explains ~25% of the interannual variability of the annual SST during the last 150 years, while different indices of the North Atlantic Oscillation (NAO) explain ≤1% of the long-term record. NAO is a high frequency climate mode while AMO can modulate low frequency changes. Sixty per cent of the AMO variability is contained in periods longer than a decade. The basin-scale influence of NAO on SST over specific years (1995 to 1998) is presented and the SST anomalies explained. The period analysed represents an abrupt change in NAO and the North Atlantic circulation state as shown with altimetry and SST data. Additional atmospheric climate data over a shorter ~60 year period (1950–2008) show the influence on the Bay of Biscay SST of the East Atlantic (EA) pattern and the Scandinavia (SCA) pattern. These atmospheric teleconnections explain respectively ~25% and ~20% of the SST variability. The winter SST in the shelf-break/slope or poleward current region is analysed in relation to AMO. The poleward current shows a trend towards increasing SSTs during the last three decades as a result of the combined positive phase of AMO and global warming. The seasonality of this winter warm flow in the Iberian region is related to the autumn/winter seasonality of south-westerly (SW) winds. The SW winds are strengthened along the European shelf-break by the development of low pressure conditions in the region to the north of the Azores and therefore a negative NAO. AMO overall modulates multidecadal changes (~60% of the AMO variance). The long-term time-series of SST and SST anomalies in the Bay of Biscay show AMO-like cycles with maxima near 1870 and 1950 and minima near 1900 and 1980 indicating a period of 60–80 years during the last century and a half. Similar AMO-like variability is found in the Russell cycle of the Western English Channel (1924–1972). AMO relates at least to four mesozooplankton components of the Russell cycle: the abundance of the chaetognaths Parasagitta elegans and Parasagitta setosa (AMO −), the amount of the species Calanus helgolandicus (AMO −), the amount of the larvae of decapod crustaceans (AMO −) and the number of pilchard eggs (Sardine pilchardus; AMO +). In addition to AMO, the decadal to multidecadal (D2M) variability in the number of sunspots is analysed for the last 300 years. Several periodicities and a multi-secular linear increase are presented. There are secular minima near 1710, 1810, 1910 and 2010. The long term variability (>11 years) of the solar sunspot activity explains ~50% of the variance of the SST of the Bay of Biscay with periods longer than 11 years. AMO is finally compared with the Pacific Decadal Oscillation, the leading principal component of North Pacific SST anomalies.
  23. 2012: Day, J. J., et al. “Sources of multi-decadal variability in Arctic sea ice extent.” Environmental Research Letters 7.3 (2012): 034011. The observed dramatic decrease in September sea ice extent (SIE) has been widely discussed in the scientific literature. Though there is qualitative agreement between observations and ensemble members of the Third Coupled Model Intercomparison Project (CMIP3), it is concerning that the observed trend (1979–2010) is not captured by any ensemble member. The potential sources of this discrepancy include: observational uncertainty, physical model limitations and vigorous natural climate variability. The latter has received less attention and is difficult to assess using the relatively short observational sea ice records. In this study multi-centennial pre-industrial control simulations with five CMIP3 climate models are used to investigate the role that the Arctic oscillation (AO), the Atlantic multi-decadal oscillation (AMO) and the Atlantic meridional overturning circulation (AMOC) play in decadal sea ice variability. Further, we use the models to determine the impact that these sources of variability have had on SIE over both the era of satellite observation (1979–2010) and an extended observational record (1953–2010). There is little evidence of a relationship between the AO and SIE in the models. However, we find that both the AMO and AMOC indices are significantly correlated with SIE in all the models considered. Using sensitivity statistics derived from the models, assuming a linear relationship, we attribute 0.5–3.1%/decade of the 10.1%/decade decline in September SIE (1979–2010) to AMO driven variability.
  24. 2014: Beaugrand, Gregory, Xavier Harlay, and Martin Edwards. “Detecting plankton shifts in the North Sea: a new abrupt ecosystem shift between 1996 and 2003.” Marine Ecology Progress Series 502 (2014): 85-104. Global warming is now unequivocal, and studies suggest it has started to influence natural systems, including the oceans. Here, we quantify plankton changes in the North Sea for the period 1958 to 2007 using an approach we call Multi-Scale Multivariate Split Moving Window (MMS-SMW) analysis that we apply to 5 groups: (1) diatoms, (2) dinoflagellates, (3) copepods, (4) other holozooplankton and (5) meroplankton. Three temporally persistent shifts were identified in the 1960s, the 1980s and during the period 1996 to 2003. The present study therefore reveals for the first time an abrupt ecosystem shift between 1996 and 2003 in the North Sea, which had the same magnitude in terms of species response as the well-documented shift detected in the 1980s. All ecosystem shifts coincided with a significant change in hydro-climatic conditions and had consequences for the structure and the functioning of the ecosystems. We showed that the 3 shifts only impacted 40% of the plankton species or taxa considered in the analysis and that the timing of the shift varied according to the planktonic group and even among species within a group.
  25. 2014: Vihma, Timo. “Effects of Arctic sea ice decline on weather and climate: A review.” Surveys in Geophysics 35.5 (2014): 1175-1214. The areal extent, concentration and thickness of sea ice in the Arctic Ocean and adjacent seas have strongly decreased during the recent decades, but cold, snow-rich winters have been common over mid-latitude land areas since 2005. A review is presented on studies addressing the local and remote effects of the sea ice decline on weather and climate. It is evident that the reduction in sea ice cover has increased the heat flux from the ocean to atmosphere in autumn and early winter. This has locally increased air temperature, moisture, and cloud cover and reduced the static stability in the lower troposphere. Several studies based on observations, atmospheric reanalyses, and model experiments suggest that the sea ice decline, together with increased snow cover in Eurasia, favours circulation patterns resembling the negative phase of the North Atlantic Oscillation and Arctic Oscillation. The suggested large-scale pressure patterns include a high over Eurasia, which favours cold winters in Europe and northeastern Eurasia. A high over the western and a low over the eastern North America have also been suggested, favouring advection of Arctic air masses to North America. Mid-latitude winter weather is, however, affected by several other factors, which generate a large inter-annual variability and often mask the effects of sea ice decline. In addition, the small sample of years with a large sea ice loss makes it difficult to distinguish the effects directly attributable to sea ice conditions. Several studies suggest that, with advancing global warming, cold winters in mid-latitude continents will no longer be common during the second half of the twenty-first century. Recent studies have also suggested causal links between the sea ice decline and summer precipitation in Europe, the Mediterranean, and East Asia.
  26. 2014: Msadek, Rym, et al. “Importance of initial conditions in seasonal predictions of Arctic sea ice extent.” Geophysical Research Letters 41.14 (2014): 5208-5215. We present seasonal predictions of Arctic sea ice extent (SIE) over the 1982–2013 period using two suites of retrospective forecasts initialized from a fully coupled ocean‐atmosphere‐sea ice assimilation system. High skill scores are found in predicting year‐to‐year fluctuations of Arctic SIE, with significant correlations up to 7 month ahead for September detrended anomalies. Predictions over the recent era, which coincides with an improved observational coverage, outperform the earlier period for most target months. We find, however, a degradation of skill in September during the last decade, a period of sea ice thinning in observations. The two prediction models, Climate Model version 2.1 (CM2.1) and Forecast‐oriented Low Ocean Resolution (FLOR), share very similar ocean and ice component and initialization but differ by their atmospheric component. FLOR has improved climatological atmospheric circulation and sea ice mean state, but its skill is overall similar to CM2.1 for most seasons, which suggests a key role for initial conditions in predicting seasonal SIE fluctuations.
  27. 2014: Miles, Martin W., et al. “A signal of persistent Atlantic multidecadal variability in Arctic sea ice.” Geophysical Research Letters 41.2 (2014): 463-469. Satellite data suggest an Arctic sea ice‐climate system in rapid transformation, yet its long‐term natural modes of variability are poorly known. Here we integrate and synthesize a set of multicentury historical records of Atlantic Arctic sea ice, supplemented with high‐resolution paleoproxy records, each reflecting primarily winter/spring sea ice conditions. We establish a signal of pervasive and persistent multidecadal (~60–90 year) fluctuations that is most pronounced in the Greenland Sea and weakens further away. Covariability between sea ice and Atlantic multidecadal variability as represented by the Atlantic Multidecadal Oscillation (AMO) index is evident during the instrumental record, including an abrupt change at the onset of the early twentieth century warming. Similar covariability through previous centuries is evident from comparison of the longest historical sea ice records and paleoproxy reconstructions of sea ice and the AMO. This observational evidence supports recent modeling studies that have suggested that Arctic sea ice is intrinsically linked to Atlantic multidecadal variability. This may have implications for understanding the recent negative trend in Arctic winter sea ice extent, although because the losses have been greater in summer, other processes and feedbacks are also important.
  28. 2015: Peterson, K. Andrew, et al. “Assessing the forecast skill of Arctic sea ice extent in the GloSea4 seasonal prediction system.” Climate dynamics 44.1-2 (2015): 147-162. An assessment of the ability of the Met Office seasonal prediction system, GloSea4, to accurately forecast Arctic sea ice concentration and extent over seasonal time scales is presented. GloSea4 was upgraded in November 2010 to include the initialization of the observed sea ice concentration from satellite measurements. GloSea4 is one of only a few operational seasonal prediction systems to include both the initialization of observed sea ice followed by its prognostic determination in a coupled dynamical model of sea ice. For the forecast of the September monthly mean ice extent the best skill in GloSea4, as judged from the historical forecast period of 1996–2009, is when the system is initialized in late March and early April near to the sea ice maxima with correlation skills in the range of 0.6. In contrast, correlation skills using May initialization dates are much lower due to thinning of the sea ice at the start of the melt season which allows ice to melt too rapidly. This is likely to be due both to a systematic bias in the ice-ocean forced model as well as biases in the ice analysis system. Detailing the forecast correlation skill throughout the whole year shows that for our system, the correlation skill for ice extent at five to six months lead time is highest leading up to the September minimum (from March/April start dates) and leading up to the March maximum (from October/November start dates). Conversely, little skill is found for the shoulder seasons of November and May at any lead time.
  29. 2015: Zhang, Rong. “Mechanisms for low-frequency variability of summer Arctic sea ice extent.” Proceedings of the National Academy of Sciences (2015): 201422296. Satellite observations reveal a substantial decline in September Arctic sea ice extent since 1979, which has played a leading role in the observed recent Arctic surface warming and has often been attributed, in large part, to the increase in greenhouse gases. However, the most rapid decline occurred during the recent global warming hiatus period. Previous studies are often focused on a single mechanism for changes and variations of summer Arctic sea ice extent, and many are based on short observational records. The key players for summer Arctic sea ice extent variability at multidecadal/centennial time scales and their contributions to the observed summer Arctic sea ice decline are not well understood. Here a multiple regression model is developed for the first time, to the author’s knowledge, to provide a framework to quantify the contributions of three key predictors (Atlantic/Pacific heat transport into the Arctic, and Arctic Dipole) to the internal low-frequency variability of Summer Arctic sea ice extent, using a 3,600-y-long control climate model simulation. The results suggest that changes in these key predictors could have contributed substantially to the observed summer Arctic sea ice decline. If the ocean heat transport into the Arctic were to weaken in the near future due to internal variability, there might be a hiatus in the decline of September Arctic sea ice. The modeling results also suggest that at multidecadal/centennial time scales, variations in the atmosphere heat transport across the Arctic Circle are forced by anticorrelated variations in the Atlantic heat transport into the Arctic.
  30. 2015: Frey, Karen E., et al. “Divergent patterns of recent sea ice cover across the Bering, Chukchi, and Beaufort seas of the Pacific Arctic Region.” Progress in Oceanography 136 (2015): 32-49. Over the past three decades of the observed satellite record, there have been significant changes in sea ice cover across the Bering, Chukchi, and Beaufort seas of the Pacific Arctic Region (PAR). Satellite data reveal that patterns in sea ice cover have been spatially heterogeneous, with significant declines in the Chukchi and Beaufort seas, yet more complex multi-year variability in the Bering Sea south of St. Lawrence Island. These patterns in the Chukchi and Beaufort seas have intensified since 2000, indicating a regime shift in sea ice cover across the northern portion of the PAR. In particular, satellite data over 1979–2012 reveal localized decreases in sea ice presence of up to −1.64 days/year (Canada Basin) and −1.24 days/year (Beaufort Sea), which accelerated to up to −6.57 days/year (Canada Basin) and −12.84 days/year (Beaufort Sea) over the 2000–2012 time period. In contrast, sea ice in the Bering Sea shows more complex multi-year variability with localized increases in sea ice presence of up to +8.41 days/year since 2000. The observed increases in sea ice cover since 2000 in the southern Bering Sea shelf region are observed in wintertime, whereas sea ice losses in the Canada Basin and Beaufort Sea have occurred during summer. We further compare sea ice variability across the region with the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) wind and air temperature fields to determine the extent to which this recent variability is driven by thermal vs. wind-driven processes. Results suggest that for these localized areas that are experiencing the most rapid shifts in sea ice cover, those in the Beaufort Sea are primarily wind driven, those offshore in the Canada Basin are primarily thermally driven, and those in the Bering Sea are influenced by elements of both. Sea ice variability (and its drivers) across the PAR provides critical insight into the forcing effects of recent shifts in climate and its likely ultimate profound impacts on ecosystem productivity across all trophic levels.
  31. 2015: Swart, Neil C., et al. “Influence of internal variability on Arctic sea-ice trends.” Nature Climate Change 5.2 (2015): 86. Internal climate variability can mask or enhance human-induced sea-ice loss on timescales ranging from years to decades. It must be properly accounted for when considering observations, understanding projections and evaluating models.
  32. 2015: Liu, Jiping, et al. “Revisiting the potential of melt pond fraction as a predictor for the seasonal Arctic sea ice extent minimum.” Environmental Research Letters 10.5 (2015): 054017. The rapid change in Arctic sea ice in recent decades has led to a rising demand for seasonal sea ice prediction. A recent modeling study that employed a prognostic melt pond model in a stand-alone sea ice model found that September Arctic sea ice extent can be accurately predicted from the melt pond fraction in May. Here we show that satellite observations show no evidence of predictive skill in May. However, we find that a significantly strong relationship (high predictability) first emerges as the melt pond fraction is integrated from early May to late June, with a persistent strong relationship only occurring after late July. Our results highlight that late spring to mid summer melt pond information is required to improve the prediction skill of the seasonal sea ice minimum. Furthermore, satellite observations indicate a much higher percentage of melt pond formation in May than does the aforementioned model simulation, which points to the need to reconcile model simulations and observations, in order to better understand key mechanisms of melt pond formation and evolution and their influence on sea ice state..” Environmental Research Letters 10.5 (2015): 054017.
  33. 2015: Serreze, Mark C., and Julienne Stroeve. “Arctic sea ice trends, variability and implications for seasonal ice forecasting.” Phil. Trans. R. Soc. A 373.2045 (2015): 20140159. September Arctic sea ice extent over the period of satellite observations has a strong downward trend, accompanied by pronounced interannual variability with a detrended 1 year lag autocorrelation of essentially zero. We argue that through a combination of thinning and associated processes related to a warming climate (a stronger albedo feedback, a longer melt season, the lack of especially cold winters) the downward trend itself is steepening. The lack of autocorrelation manifests both the inherent large variability in summer atmospheric circulation patterns and that oceanic heat loss in winter acts as a negative (stabilizing) feedback, albeit insufficient to counter the steepening trend. These findings have implications for seasonal ice forecasting. In particular, while advances in observing sea ice thickness and assimilating thickness into coupled forecast systems have improved forecast skill, there remains an inherent limit to predictability owing to the largely chaotic nature of atmospheric variability.
  34. 2015: Hobbs, William Richard, Nathaniel L. Bindoff, and Marilyn N. Raphael. “New perspectives on observed and simulated Antarctic sea ice extent trends using optimal fingerprinting techniques.” Journal of Climate 28.4 (2015): 1543-1560. Using optimal fingerprinting techniques, a detection analysis is performed to determine whether observed trends in Southern Ocean sea ice extent since 1979 are outside the expected range of natural variability. Consistent with previous studies, it is found that for the seasons of maximum sea ice cover (i.e., winter and early spring), the observed trends are not outside the range of natural variability and in some West Antarctic sectors they may be partially due to tropical variability. However, when information about the spatial pattern of trends is included in the analysis, the summer and autumn trends fall outside the range of internal variability. The detectable signal is dominated by strong and opposing trends in the Ross Sea and the Amundsen–Bellingshausen Sea regions. In contrast to the observed pattern, an ensemble of 20 CMIP5 coupled climate models shows that a decrease in Ross Sea ice cover would be expected in response to external forcings. The simulated decreases in the Ross, Bellingshausen, and Amundsen Seas for the autumn season are significantly different from unforced internal variability at the 95% confidence level. Unlike earlier work, the authors formally show that the simulated sea ice response to external forcing is different from both the observed trends and simulated internal variability and conclude that in general the CMIP5 models do not adequately represent the forced response of the Antarctic climate system.
  35. 2016: Walsh, John E., and William L. Chapman. “Variability of sea ice extent over decadal and longer timescales.” Climate change: multidecadal and beyond. 2016. 203-217. Recent syntheses of sea ice and related proxy information have provided an improved picture of Arctic sea ice variability over decadal to century timescales. A spectrum of variability is superimposed on a recent decrease of Arctic sea ice. An outstanding feature is the correspondence with the Atlantic Multidecadal Oscillation, which has timescales of 50–120 years. The linkage appears to arise through the inflow of Atlantic Water to the Arctic Ocean. Less robust, and by all indications non-stationary, associations with atmospheric modes such as the North Atlantic Oscillation have also been documented, primarily in recent decades. One possible reason for the nonstationarity of such associations is that the centers of action of major atmospheric modes may change over the timescale of centuries or even less. While the recent decrease of summer ice in the Arctic appears to be unique in the past 1,450 years, paleo reconstructions also suggest a minimum in Arctic ice coverage during the early Holocene. Unlike the Arctic, Antarctic sea ice shows essentially no trend over the past 30 years. The absence of a trend has been attributed to wind forcing and possibly ocean interactions. Observational information on Antarctic sea ice variability is virtually nonexistent beyond the past 100–150 years, so proxy information provides the only clues to longer-term Antarctic sea ice variability. Such information obtained from ice cores suggests that wintertime ice extent in the East Antarctic sector has decreased by about 20% since 1950, and that multicentury variations also characterize Antarctic ice extent.
  36. 2016: Otto-Bliesner, Bette L., et al. “Climate variability and change since 850 CE: An ensemble approach with the Community Earth System Model.” Bulletin of the American Meteorological Society 97.5 (2016): 735-754. The climate of the past millennium provides a baseline for understanding the background of natural climate variability upon which current anthropogenic changes are superimposed. As this period also contains high data density from proxy sources (e.g., ice cores, stalagmites, corals, tree rings, and sediments), it provides a unique opportunity for understanding both global and regional-scale climate responses to natural forcing. Toward that end, an ensemble of simulations with the Community Earth System Model (CESM) for the period 850–2005 (the CESM Last Millennium Ensemble, or CESM-LME) is now available to the community. This ensemble includes simulations forced with the transient evolution of solar intensity, volcanic emissions, greenhouse gases, aerosols, land-use conditions, and orbital parameters, both together and individually. The CESM-LME thus allows for evaluation of the relative contributions of external forcing and internal variability to changes evident in the paleoclimate data record, as well as providing a longer-term perspective for understanding events in the modern instrumental period. It also constitutes a dynamically consistent framework within which to diagnose mechanisms of regional variability. Results demonstrate an important influence of internal variability on regional responses of the climate system during the past millennium. All the forcings, particularly large volcanic eruptions, are found to be regionally influential during the preindustrial period, while anthropogenic greenhouse gas and aerosol changes dominate the forced variability of the mid- to late twentieth century.
  37. 2017: Ding, Qinghua, et al. “Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice.” Nature Climate Change 7.4 (2017): 289. The Arctic has seen rapid sea-ice decline in the past three decades, whilst warming at about twice the global average rate. Yet the relationship between Arctic warming and sea-ice loss is not well understood. Here, we present evidence that trends in summertime atmospheric circulation may have contributed as much as 60% to the September sea-ice extent decline since 1979. A tendency towards a stronger anticyclonic circulation over Greenland and the Arctic Ocean with a barotropic structure in the troposphere increased the downwelling longwave radiation above the ice by warming and moistening the lower troposphere. Model experiments, with reanalysis data constraining atmospheric circulation, replicate the observed thermodynamic response and indicate that the near-surface changes are dominated by circulation changes rather than feedbacks from the changing sea-ice cover. Internal variability dominates the Arctic summer circulation trend and may be responsible for about 30–50% of the overall decline in September sea ice since 1979.
  38. 2017: Smedsrud, Lars H., et al. “Fram Strait sea ice export variability and September Arctic sea ice extent over the last 80 years.” The Cryosphere 11.1 (2017): 65-79. A new long-term data record of Fram Strait sea ice area export from 1935 to 2014 is developed using a combination of satellite radar images and station observations of surface pressure across Fram Strait. This data record shows that the long-term annual mean export is about 880 000 km2, representing 10 % of the sea-ice-covered area inside the basin. The time series has large interannual and multi-decadal variability but no long-term trend. However, during the last decades, the amount of ice exported has increased, with several years having annual ice exports that exceeded 1 million km2. This increase is a result of faster southward ice drift speeds due to stronger southward geostrophic winds, largely explained by increasing surface pressure over Greenland. Evaluating the trend onwards from 1979 reveals an increase in annual ice export of about +6 % per decade, with spring and summer showing larger changes in ice export (+11 % per decade) compared to autumn and winter (+2.6 % per decade). Increased ice export during winter will generally result in new ice growth and contributes to thinning inside the Arctic Basin. Increased ice export during summer or spring will, in contrast, contribute directly to open water further north and a reduced summer sea ice extent through the ice–albedo feedback. Relatively low spring and summer export from 1950 to 1970 is thus consistent with a higher mid-September sea ice extent for these years. Our results are not sensitive to long-term change in Fram Strait sea ice concentration. We find a general moderate influence between export anomalies and the following September sea ice extent, explaining 18 % of the variance between 1935 and 2014, but with higher values since 2004.
  39. 2017: Kirchmeier-Young, Megan C., Francis W. Zwiers, and Nathan P. Gillett. “Attribution of extreme events in Arctic sea ice extent.” Journal of Climate 30.2 (2017): 553-571. Arctic sea ice extent (SIE) has decreased over recent decades, with record-setting minimum events in 2007 and again in 2012. A question of interest across many disciplines concerns the extent to which such extreme events can be attributed to anthropogenic influences. First, a detection and attribution analysis is performed for trends in SIE anomalies over the observed period. The main objective of this study is an event attribution analysis for extreme minimum events in Arctic SIE. Although focus is placed on the 2012 event, the results are generalized to extreme events of other magnitudes, including both past and potential future extremes. Several ensembles of model responses are used, including two single-model large ensembles. Using several different metrics to define the events in question, it is shown that an extreme SIE minimum of the magnitude seen in 2012 is consistent with a scenario including anthropogenic influence and is extremely unlikely in a scenario excluding anthropogenic influence. Hence, the 2012 Arctic sea ice minimum provides a counterexample to the often-quoted idea that individual extreme events cannot be attributed to human influence.
  40. 2017: Walsh, John E., et al. “A database for depicting Arctic sea ice variations back to 1850.” Geographical Review 107.1 (2017): 89-107. Arctic sea ice data from a variety of historical sources have been synthesized into a database extending back to 1850 with monthly time‐resolution. The synthesis procedure includes interpolation to a uniform grid and an analog‐based estimation of ice concentrations in areas of no data. The consolidated database shows that there is no precedent as far back as 1850 for the 21st century’s minimum ice extent of sea ice on the pan‐Arctic scale. A regional‐scale exception to this statement is the Bering Sea. The rate of retreat since the 1990s is also unprecedented and especially large in the Beaufort and Chukchi Seas. Decadal and multidecadal variations have occurred in some regions, but their magnitudes are smaller than that of the recent ice loss. Interannual variability is prominent in all regions and will pose a challenge to sea ice prediction efforts.
  41. 2018: Slawinska, Joanna, and Alan Robock. “Impact of volcanic eruptions on decadal to centennial fluctuations of Arctic sea ice extent during the last millennium and on initiation of the Little Ice Age.” Journal of Climate 31.6 (2018): 2145-2167. This study evaluates different hypotheses of the origin of the Little Ice Age, focusing on the long-term response of Arctic sea ice and oceanic circulation to solar and volcanic perturbations. The authors analyze the Last Millennium Ensemble of climate model simulations carried out with the Community Earth System Model at the National Center for Atmospheric Research. The authors examine the duration and strength of volcanic perturbations, and the effects of initial and boundary conditions, such as the phase of the Atlantic multidecadal oscillation. They evaluate the impacts of these factors on decadal-to-multicentennial perturbations of the cryospheric, oceanic, and atmospheric components of the climate system. The authors show that, at least in the Last Millennium Ensemble, volcanic eruptions are followed by a decadal-scale positive response of the Atlantic multidecadal overturning circulation, followed by a centennial-scale enhancement of the Northern Hemispheric sea ice extent. It is hypothesized that a few mechanisms, not just one, may have to play a role in consistently explaining such a simulated climate response at both decadal and centennial time scales. The authors argue that large volcanic forcing is necessary to explain the origin and duration of Little Ice Age–like perturbations in the Last Millennium Ensemble. Other forcings might play a role as well. In particular, prolonged fluctuations in solar irradiance associated with solar minima potentially amplify the enhancement of the magnitude of volcanically triggered anomalies of Arctic sea ice extent.
  42. Responsiveness of Polar Sea Ice Extent to Air Temperature 1979-2016  Detrended correlation analysis of mean monthly sea ice extent with air temperature at an annual time scale in both Polar Oceans shows the expected negative correlation in 14 out of 36 cases studied. The other 22 cases, including the high profile case of September sea ice extent in the Arctic, show no evidence that temperature alone explains sea ice extent. We conclude that other factors such as wind, clouds, solar irradiance, and ocean circulation may be relevant in the study of differences in mean monthly sea ice extent for the same calendar month from year to year
  43. Trends in Polar Sea Ice Extent 1979-2015  A survey of trends in dispersed and concentrated sea ice extent in the Arctic in the northern summer and northern winter and in the Antarctic in the southern summer and southern winter for the period 1979-2015 shows a negative trend in dispersed and concentrated sea ice extent in the Arctic in the northern summer amid rising surface temperature in the northern hemisphere. The trend in concentrated sea ice extent in the Arctic summer is not uniform across the study period but mostly a phenomenon of the latter half from 1998-2014. A positive trend for dispersed sea ice extent in the Antarctic in winter amid rising winter temperature in the southern hemisphere is not matched by trends in concentrated sea ice extent and the degree of dispersion and is discounted as spurious. In the southern summer, we found no trends in sea ice extent in the Antarctic and no trend in mean surface temperature in the southern hemisphere. This work concerns only sea ice extent without considerations of the age, thickness, and total volume of sea ice.
  44. A General Linear Model for Sea Ice Extent  A general linear model is used for simultaneous identification of short term seasonal variations and long term trends in deseasonalized sea ice extent. It shows a sustained decline of Arctic sea ice extent over the entire study period from 1978 to 2014. No decline of sea ice extent is evident in the Antarctic.
  45. Does Global Warming Drive Changes in Arctic Sea Ice?

WUNSCHBOOK

 

DAVID ATTENBOROUGH: SALESMAN NOT SCIENTIST

plagueonearth

 

CARL WUNSCH: SCIENTIST NOT SALESMAN

carlwunsch

 

The source document

Wunsch, Carl. “Towards understanding the Paleocean.” Quaternary Science Reviews 29.17-18 (2010): 1960-1967.

With minor format and wording edits

 

  1. Anyone coming from the outside to the study of paleoceanography and paleoclimate has to be struck by the extreme lack of data as compared to the modern world–but where we still justifiably complain about under-sampling. Although there are many proxy data of diverse types (speleothems, tree rings, banded iron formations, terraces, etc.; e.g. Cronin, 2010) proxy data in ice cores provide much of the time series information about the climate system over roughly the last 100,000 to almost 1 million years. These are obtained from Greenland
    and Antarctica–regions hardly typical of the global climate, but nonetheless the records are commonly interpreted as being at least representative of the hemispheric state and commonly the entire globe.
  2. The much more numerous marine cores carry one back some tens of millions of years, but they are available only in narrow strips around the ocean where thick sediment layers exist (Wessel, 2010). Beyond 100 million years, one is reduced largely to inferences from the geochemical nature of scattered rock deposits with even poorer age controls in a system evolving over some 3.5GY.
  3. Thousands of papers do document regional changes in proxy concentrations, but almost everything is subject to debate including, particularly, the age models, geographical integrity of regional data, and the meaning of the apparent signals that are often transformed in complicated ways on their way through the atmosphere and the ocean to the sediments.
  4. From one point of view, scientific communities without adequate data have a distinct advantage because they can construct interesting and exciting stories and rationalizations with little or no risk of observational refutation. Colorful, sometimes charismatic, characters come to dominate the field, constructing their interpretations of a few intriguing, but indefinite observations that appeal to their followers, and which eventually emerge as “textbook truths.”
  5. The following characteristics are ascribed to one particularly notoriously data-poor field. (1) Tremendous self-confidence and a sense of entitlement and of belonging to an elite community of experts, (2) An unusually monolithic community, with a strong sense of consensus, whether driven by the evidence or not, and an unusual uniformity of views on open questions. These views seem related to the existence of a hierarchical structure in which the ideas of a few leaders dictate the viewpoint, strategy, and direction of the field. (3) In some cases a sense of identification with the group, akin to identification with a religious faith or political platform. (4) A strong sense of the boundary between that group of experts and the rest of the world. (5) A disregard for and disinterest in the ideas, opinions, and work of experts who are not part of the group, and a preference for talking only with other members of the community. (6) A tendency to interpret evidence optimistically, to believe exaggerated or incorrect statements of results and to disregard the possibility that the theory might be wrong. This is coupled with a tendency to believe results are true because they are widely believed, even if one has not checked (or even seen) the proof oneself. (7) A lack of appreciation for the extent to which a research program ought to involve risk.”
  6. The list is taken from Smolin (2006) which is about string theory in physics. Observers of the paleoclimate scene might recognize some common characteristics, even though paleoclimate may have better prospects for ultimately obtaining observational tests of its fundamental tenets. The group identification Smolin refers to, clearly exists in paleoclimate.
  7. Good scientists seek constantly to test the basic tenets of their field, not to buttress them. Routine science usually adds a trifling piece of support to everyone’s assumptions. Exciting, novel, important, science examines the basic underpinnings of those assumptions and either reports no conflict or, the contrary–that maybe it isn’t true. Imagine Darwin working hard to fit all of his observational data into the framework of Genesis.
  8. As both human beings and scientists, we always hope for explanations of the world that are conceptually simple yet with important predictive skills. Thus the strong desire that box models should explain climate change, or that simple orbital kinematics can explain the glacial cycles, or that climate change is periodic, is understandable. But some natural phenomena are intrinsically complex and attempts to represent them in oversimplified fashion are disastrous. “Everything should be made as simple as possible, but not simpler.
  9. In the climate context, one underlying question is “Under what circumstances can a three-dimensional, time-dependent, turbulent, flow of the atmosphere and ocean be reproduced usefully by a one- or two-dimensional steady circulation?” If it can be done, and understood, the result would be a most remarkable achievement in fluid dynamics, one that has eluded some of the most important mathematicians and physicists of the last three centuries.
  10. Yet the assumption that such a representation has been achieved, and even more remarkably, can be used to predict what would happen if the external parameters were disturbed (e.g., a change in insolation), underlies the great majority of discussions in climate science. Until recently (circa 1975), the ocean circulation was almost universally represented as a large-scale, almost unchanging, system, one that was best described in terms of laminar flow rather than the more chaotic turbulent flow. Oceans were thought of as more as geology than fluid mechanics.
  11. This picture was a necessary and inevitable consequence of the observational data available to oceanographers at the time. These data were limited almost exclusively to temperature and salinity and were studied as a function of position as compiled by hydrographers working on ships over many decades. They pieced together a dataset leading to the now ubiquitous hydrographic sections.
  12. Fortuitously, it was found that the thermohaline and related chemical properties of the ocean, occupying volumes spanning thousands of kilometers, were quasi-steady, and contour-able. It was inferred from these pictures that thousands of years would be required to communicate properties from the surface to and from the abyssal ocean. That one’s perception of a problem can be gravely distorted by the accident of which observations are available is plain.
  13. The Stommel quotation was a product of this era. The study of what came to be called “geophysical fluid dynamics” is directed at understanding the processes underlying real flow fields by reducing the systems to the most basic-barebones elements–thus exposing the essential ingredients. Much progress has been made that way.
  14. The pitfall, which has not always been avoided, is in claiming that because an essential element has been understood, that it necessarily explains what is seen in nature. An attractive theory of the simplified system is then applied far outside any plausible range of validity. The rather beautiful Stommel and Arons abyssal circulation theory (e.g., Stommel, 1958) is a good example. This theory is articularly beguiling because the mathematics are extremely simple (the linearized geostrophic balance equations plus mass conservation) and the result is counter-intuitive (implying e.g.„ that abyssal flows must be toward their sources).
  15. One sees published papers flatly asserting that the ocean abyssal circulation is what was described by Stommel-Arons. But there is essentially no evidence that the theory describes very much of the volume of the ocean (it does predict, qualitatively, the existence of deep western boundary currents–a triumph of GFD–but not always their average direction of flow). The inferred meridional flows are nowhere to be seen. The theory applies to a fluid flow in steady-state, very weak and linear, fed by a small number of isolated convective regions, on a flat-bottomed-ocean, with a vertical return flow assumed to be globally uniform, undisturbed by any other forces.
  16. Given the many assumptions, it is no surprise that one does not observe flows implied by the picture constructed by Stommel (1958). The physical insight–that interior geostrophic balance and the implied vorticity balance dominate, is truly fundamental to any understanding of the ocean circulation, and it is difficult to over-emphasize the importance of this simple model. But when it is claimed to describe the dominant flow field of the real ocean, the wish for beauty and simplicity are trumping the reality of observations.
  17. Extension of a simplified description or explanation outside of its domain of applicability is of little or no concern to anyone outside the academic community–unless it begins to control observational strategies or be used to make predictions about future behavior under disturbed conditions. One notes, for example, that there were essentially no measurements below 1000m of the hydrography of the Pacific Ocean until the middle 1960s, because “everyone knew” that the flows there were inconsequential.
  18. Meteorologists who assumed that the abyssal ocean was slow and steady, or accepted that the Sverdup et al. (1942) inference that the ocean could only carry about 10% of the meridional heat transport toward the poles (Wunsch, 2005) took a very long time to move away from their “swamp models” of the ocean for studying climate models that have still not disappeared.
  19. Conveyor Belts: Broecker 1991, building on a sketch of Gordon (1986), reduced the discussion of the paleocean circulation to that of a one-dimensional ribbon that he called the “great global conveyor.” Its rendering in color cartoon form in Natural History magazine has captured the imagination of a generation of scientists and non-technical writers alike. It is a vivid example of the power of a great graphic, having been used in at least two Hollywood films, and has found its way into essentially every existing textbook on climate, including those at a very elementary level. It is thus now a “fact” of oceanography and climate. Broecker himself originally referred to it as a “logo,” and it would have been well to retain that label.
  20. This ribbon contradicts known ocean physics. Most insidious, however, is the implication, from its wide acceptance, that the ocean circulation is intrinsically so simple that one can predict its behavior from what a one-dimensional ribbon flow would do. There are three recent examples of the way in which the complexity of the actual circulation is qualitatively at odds with the ribbon picture.
  21. (Bower, 2010) shows the trajectories of neutrally buoyant floats deployed in the western sub-polar gyre where the expectations from the conveyor, and those of the authors, was that the floats would largely move along the continental margin entering the subtropical gyre in the deep western boundary current. As is apparent, of the 40 floats deployed, only a single one followed the conveyor pathway. The remainder moved into the interior of the subpolar gyre to undergo a subsequent set of complex pathways.
  22. How when and if they ultimately enter the ocean farther south is far from apparent. Similarly (Brambilla and Talley, 2006) show surface drifters deployed in the subtropical gyre over a period of 12 years. These drifters apparently do not “know” that they were supposed to move into the subpolar gyre as part of the conveyor. (The simplest interpretation is probably that their trajectories are governed by the surface Ekman layer–whose net transport is southward in this region–an important flow structure entirely missing from the ribbon.)
  23. Most paleoclimate discussions of the North Atlantic circulation fail to even acknowledge the existence of such conflicting data sets. The ribbon conveyor postulates one region, the northern North Atlantic, where water sinks and fills the deep ocean, although even its partisans would likely agree that the Weddell and Ross Seas also contribute.
  24. Water that is at the surface anywhere in the ocean, ultimately moves elsewhere in the three-dimensional volume. (Gebbie and Huybers 2010) shows the fraction of the volume of the ocean that last was at the surface in each of all 4×4 degree boxes. Although some regions do make a higher than average contribution, none actually
    vanishes, and even the high latitude contributions orginate from a much more widespread area than one might have inferred from the obsession with the Labrador or Greenland Seas, or the Weddell or Ross Seas in the south.
  25. One might argue that the ribbon is a useful simplification employed mainly as a framework for discussing complex proxy data. The idea that the ocean transports mass, enthalpy around the world is indeed incontrovertible, as is the inference that heat, in particular, is “conveyed” from the tropics to high latitudes. But when the cartoon becomes a substitute for the reality, and is no longer the subject of questions and tests, it is time to raise the alarm.
  26. One eminent meteorologist once assured me that global ocean observations were unnecessary–as keeping track of the entire system could be done very simply and cheaply with expendable bathythermograph data in the North Atlantic, high latitude, branch of the “conveyor”. The large field programs now underway, intended to measure primarily the North Atlantic circulation, are a direct consequence of this notion.
  27. The conviction that the ribbon flow is reality, has clearly led to the extreme emphasis on supposed control of global climate by the North Atlantic Ocean. This narrow approach to the science is perhaps personified by the notorious “hosing” experiments. The Hosing Scenario Myriad hypotheses have been put forward as rationalizing some elements of the oceanic role in influencing climate–ranging over essentially all possible time scales out to the age of the ocean.
  28. Consider the popular hypothesis that the North Atlantic circulation largely controls the climate system, and that the surface salinity is the determining influence. Using the putative ribbon as a framework, it has been suggeted (Broecker (1990) that a meltwater pulse onto the North Atlantic would have had a major climate impact.
  29. The origin of this idea is not clear. Berger and Killingley (1981), attribute it to Worthington (1968) and there is a connection with Stommel’s (1961) one-dimensional fluid model displaying two stable states. Initially, the focus was on explaining the Younger Dryas, and it was later extended to numerous other events in the paleoclimate record, and then to predictions of what future global warming will bring. The suggestion is both a plausible and interesting one (see e.g., Bryan, 1987), and it was picked up by Manabe and Stouffer (1995) who showed with a coupled climate GCM that they could produce a marked disturbance in the North Atlantic circulation by imposing a “massive surface flux” of fresh water.
  30. It is a sensible avenue to explore in terms of fluid dynamics but despite the hundreds of papers discussing the idea, only a tiny minority has attempted to understand the underlying physics, and just as important, to analyze the possible conflicting evidence. Indeed, in the 15 years since their paper appeared, this
    hosing story has become essentially another “fact,” with most papers on the subject repeating variants of the initial story.
  31. Regarding freshwater input into the present-day world ocean, as best as we can determine them, by far the largest component is over-ocean precipitation, producing about 12Sv (1 Sverdrup=106m3/s≈ 109kg/s) of fresh water. Next is river-runoff of about 1Sv and possibly (Moore, 2010) another 0.1 Sverdrup from subsurface percolation. Of the runoff, modern Greenland is supposed to account for about 0.01Sv (Box et al., 2004), with a possible increment of 0.01Sv from recent excess ice loss (e.g., Velicogna, 2009). The equivalent values for Antarctica
    are (very roughly) 0.1Sv background with perhaps 0.01 Sv of recent excess net melting.
  32. Almost all of this injection of freshwater is balanced by net evaporation–but in a different regional pattern and with a different atmospheric physics. The residual is a global sea level rise of order of magnitude of 1mm/y (an excess of about 0.01Sv more freshwater entering than leaving). For an example, consider that (Stanford 2006) suggests that Meltwater Pulse 1a (MWP1a), occurring at approximately -14ky, reached a peak as large as 40mm/y (about 10 times the estimated recent sea level rise rate), superimposed on a background deglaciation rate of about 20mm/y. So the peak melting-ice value corresponds to about 0.2Sv on top of an also-increased background value of about 0.2Sv. How much of this represents northern rather
    than southern sources is the subject of some controversy.
  33. Evaluating the response of the ocean circulation to such an input disturbance raises a list of interesting questions that would need to be answered before one could claim understanding adequate to predict oceanic and climate behavior, be it past or future. In that list one would necessarily ask whether, given the relatively enormous modern precipitation rates, did the precipitation pattern shift, and if so, was the change small compared to 0.4Sv? If the background melt rate shifted for thousands of years from the estimated modern value of 1-3mm/y (0.01-0.03Sv) to 20mm/y (0.2Sv), how was the resulting circulation different from today’s–prior to MWP1a? How did the sea ice cover change with that excess of freshwater? How does that sea ice cover change influence the resulting circulation?
  34. This account is not intended to be a history of either the “hosing” hypothesis nor of the conveyor idea. With respect to (Våge et al, 2009), they showed that in the modern world, an increase in near-coastal ice cover in the Labrador and Irminger Seas, led to an increased convective response in the ocean because the atmosphere was much colder when it finally reached open water.
  35. Any important climate shift implies a wind-field change. As discussed by Huybers and Wunsch (2010), the overall strength of the ocean circulation is set by the magnitudes and patterns of the curl of the wind-stress. How did these change with the changing sea ice cover? Or with changes in height and albedo of the continental ice sheet? Or with the changes in sea surface and land temperatures?
  36. In the modern world, the high latitude North Atlantic meridional Ekman
    transport exceeds 1Sv in magnitude (Josey et al, 2002) which implies that a mere 10% change in the magnitude of the wind stress (not its curl) would change the surface layer transport by 0.1Sv. It is difficult to understand how such a potentially rapid and efficient mechanism for changing the transports of surface waters (fresh water and ice) can be ignored. Recall that ice cover directly influences the transmission of stress from atmosphere to ocean.
  37. At lower latitudes, the latitude of putative fresh water injection into the Gulf of Mexico through the Mississippi system, the Ekman transports are more than an order of magnitude larger–with consequent very large potential for moving and diverting surface water. Supposing that one does determine where (the Arctic, Greenland, the St. Lawrence Valley, the Mississippi, Antarctica,…) an excess of fresh water enters the ocean, a series of dynamical issues occur that will be peculiar to the particular region. Fresh water injection from the continents enters the ocean in some of the most complex of all oceanic regions, the continental
    margins, which are subject to strong tides, wind forcing, the local ambient circulation and in high latitudes, and to seasonal ice formation.
  38. If winds favor down-welling at the point of entry, one expects a very different distribution of salinity than if they favor up-welling. Consider fresh water input along a straight coastline . This problem is an example of the “Rossby adjustment problem.” The main result, known to all dynamicists, is that rotation tends to trap the fresh water near the coastline, over a distance dependent upon the rotation rate, the water depth, and the contrasting densities, but normally much less than 10km distance at high latitudes.
  39. Although global sea level (or bottom pressure) initially adjusts extremely rapidly, it can take many decades and longer for the freshwater to escape from the coastal area, depending upon the winds, the larger-scale general circulation, the water depth along and normal to the shore, the intensity of the oceanic eddy field, and the behavior of coastal ice if any. A rich literature exists on the influence of freshwater on the coastal circulation (Garvine and Whitney, 2006). Yet very few of the many papers on the paleoceanographic influence of fresh water sees fit to notice the possibility that it may be very difficult to overlay most of the subpolar gyre with freshwater.
  40. Many authors seem intent on bolstering the assumption that freshwater will simply overrun it, giving rise to weakening or “shut-down” of the meridional overturning circulation. Freshwater certainly does enter the ocean and convective mixing is a delicate process balanced between having the water freeze, and having it become dense enough to sink. But even if it does sink, it is far from obvious what the influence is on the larger-scale circulation. Using a model,
    Nilsson, et al. (2003) show that a reduced surface density gradient, perhaps from adding fresh water to the ocean, can increase the meridional overturning. In another modeling result, deBoer et al. (2010) also question whether the meridional density gradient is a determinant of the circulation rate, and there are other, similar, suggestions that the freshwater dynamics are complex.
  41. Eisenman et al. (2009) recognizes that variations in precipitation (mutatis mutandis, evaporation) might be considered as potential major influences on the circulation. Furthermore precipitation, unlike runoff, is injected in the open ocean more or less as the hosing story has it. The hosing experiments often lead to shifts in the climate of the North Atlantic region, most likely because the meridional oceanic heat transport is diminished.
  42. One rarely if ever sees the question raised as to how the global heat budget is then maintained? Does the atmosphere respond by increasing its transport and get warmer and/or wetter as in Bjerknes (1964) and Shaffrey&Sutton (2006)? Does the Pacific meridional enthalpy transport increase? Does the tropical albedo increase? Or is more heat transported poleward in the southern hemisphere?
  43. Questions such as these would lead to greater insights than merely rationalizing yet another data set in terms of “shutdown.” It is of course, possible that ice melt does control the major features of the North Atlantic circulation, and none of the complications listed above has any significant impact on that inference. But strikingly little attention has been paid to examining the basic physical elements of the usual assumptions in this evaluation.
  44. The original hosing story of the control of the Younger Dryas by the abrupt drainage of glacial Lake Agassiz into the St. Lawrence valley, seems finally on its way to abandonment because of the absence of any supporting geomorphological
    structure (Murton et al., 2010). It might have been regarded as suspect much earlier had the physics of the circulation been examined at the outset. Drainage through the now-favored Arctic Sea route would affect the wider ocean circulation very differently from the supposed St. Lawrence pathway.
  45. The Model Problem: Hosing experiments and many other climate discussions rely on complicated ocean general circulation models and their even more complex use as sub-components in coupled models involving, in addition, the atmosphere, cryosphere, and biosphere. Such models now dominate discussions of the behavior of the climate system. As with future climate, where no data exist
    at all, the models promise descriptions of climate change past and future without the painful necessity of supporting their conclusions with data.
  46. The apparent weight given to model behavior in discussions of paleoclimate arises, also, sometimes simply because they are sophisticated and difficult to understand, as well as appearing to substitute for missing data. (Huybers andWunsch 2010) discuss the issue of model credibility at some length. Here I note only that fully coupled climate models are among the most complicated pieces of machinery ever assembled, with upwards of a million lines of code. A machine that was fully realistic would be as complicated as the real system, and so the great power of models is their ability to simplify–so that one can come to understanding.
  47. But understanding a machine with “only” hundreds of thousands of interlinked elements is not so easy either. That models are incomplete representations of reality is their great power but they should never be mistaken for the real world. At every time-step, a model integration generates erroneous results, with those errors arising from a whole suite of approximations and omissions from
    uncertain or erroneous: initial conditions, boundary values, lack of resolution, missing physics, numerical representation of continuous differential operators, and ordinary coding errors.
  48. It is extremely rare to read any discussion at all of the error growth in models. Most errors are bounded in some way. The ocean is not permitted to boil or freeze over for example and lateral displacement errors cannot exceed half-the Earth’s circumference. Diffusion ultimately removes the effects of small initial condition errors although the time required to do so may be many thousands of years. A stopped clock never has an error exceeding six hours (on a twelve-hour system), but few would argue that it is a particularly useful model of the passage of time.
  49. An oceanic model run for five years might, with impunity, ignore errors tending to underestimate the amplitude of the annual sea ice cover change. But in a model run for 100 years, those errors may well dominate important aspects of the model-climate. Thus if one simulates with a coarse horizontal resolution, 20-layer vertical resolution, model for extended periods of time, one is implying (usually without mention), that the turbulence closure problems described above of the ocean circulation have been solved such that residual errors incurred are negligible
    after 100, 1000, or 1 million years. If that is correct, it is a truly remarkable breakthrough in fluid dynamics–one that should be celebrated everywhere as one of the major fluid dynamics accomplishments of the last 100 years. But alas no such breakthrough has been achieved.
  50. Some published model results indulge in a kind of psychological trick: the physics, chemistry, and biology are over-simplified, but the geometry of the continents, oceans and ice sheets is maintained in detail, lending the results a spurious air of being correct. Shouldn’t the geometric effects, which can be exceedingly complicated be simplified so as to permit understanding of what the governing elements really are? Would one willingly fly on an untested airplane designed using an aeronautical code of “intermediate complexity”–even if it sat, impressively, on the runway?
  51. Models used for hosing experiments are particularly vulnerable to resolution errors. As was noted, the dominant spatial scale of freshwater input, under the influence of Earth rotation, is the Rossby radius of deformation, which is typically less than 7 km at high latitudes. Movement of the fresh water, once it has escaped the unresolved coastal regions, will largely be determined by the detailed physics of the near-surface boundary layers (Ekman and turbulent mixed layers), and their interaction with the wind field, sea ice, and oceanic turbulence on all scales.
  52. The first coupled climate model written by Manabe and Stouffer (1995) used an oceanic model with resolution of 4.5◦ of longitude by 3.75◦ of latitude and 12 levels. If a model transports 0.1PW too much or too little heat meridionally, then after 100 years of integration, one has misplaced 3×1023J of energy, enough to melt or form 1018kg of ice, with all that implies.
  53. There is also a widespread notion that if errors are random that they “will average out.” But the phenomenon of a random walk shows that the inference can be quite wrong. Hecht and Smith (2008) discuss some of the myriad ways in which model results depend upon their (still) inadequate resolution. They question, in particular, whether the sensitivity of adequately resolved models will be at all like that of the low resolution models–which raises doubts about the manifold claims that GCMs display the same multiple states as do Stommel’s (1961) one-dimensional model and its kin.
  54. If a model fails to replicate the climate system over a few decades, the assumption that it is therefore skillful over thousands or millions of years is a non sequitur. Models use thousands of parameters that can be fine tuned and the ability to make them behave “reasonably” over long time intervals is not in doubt. That error estimates are not easy to make does not mean they are not necessary for interpretation and use of model extrapolations.
  55. An important issue is a widespread misuse of elementary statistical tests. A simple listing would include: (1) Use of a priori correlation statistics on time series data manipulated to produce high correlations. (2) Hypothesis tests using confidence limits that are sufficiently low to produce positive results (3) Confusing correlation with causation. For example, if Antarctic temperatures lag northern hemisphere ones it proves that northern hemisphere insolation caused southern hemisphere climate changes. (4) Use of implausible null hypotheses to demonstrate the existence of spectral peaks.  For example one could assume that climate is an AR(1) process–a two-parameter system. Estimated spectra are then claimed to have the wished-for “peaks”, when the proper inference is the expected one: that an AR(1) is an inadequate representation of an extremely complex system.
  56. This essay has indulged in a number of sweeping generalizations that will surely provoke and anger a number of readers, who can correctly point to published counter-examples. Nonetheless, scientific fields do develop their own cultures, and paleoclimate studies demonstrably have some widely-shared features that can be identified. The study of paleoclimate encompasses such a huge range of problems, methods, regions, phenomena, time and space scales, that no one has mastered it all. With that complexity, any science runs the risk of becoming so abstract, or so devoted to particular stories, or both, that they lose relevance to the physical world.
  57. As Chamberlin (1890) pointed out, it is essential to always be alert to alternative hypotheses. Some of the published exaggeration of the degree of understanding, and of over-simplification is best understood as a combination of human psychology and the pressures of fund-raising. Anyone who has struggled for several years to make sense of a complicated data set, only to conclude
    that “the data proved inadequate for this purpose” is in a quandary. Publishing such an inference would be very difficult, and few would notice if it were published. As the outcome of a funded grant, it is at best disappointing and at worst a calamity for a renewal or promotion.
  58. A parallel problem would emerge from a model calculation that produced no exciting” new behavior. Thus the temptation to over-interpret the data set is a very powerful one. Similarly, if the inference is that the data are best rationalized as an interaction of many factors of comparable amplitude described through the temporal and spatial evolution of a complicated fluid model, the story does not lend itself to a one-sentence, intriguing, explanation (“carbon dioxide was trapped in the abyssal ocean for thousands of years;” “millennial variability is controlled
    by solar variations”; “climate change is a bipolar seesaw”), and the near-impossibility of publishing in the near-tabloid science media (Science, Nature, Scientific American) with their consequent press conferences and celebrity.
  59. Amplifying this tendency is the relentlessly increasing use by ignorant or lazy administrators and promotion committees of supposed “objective” measures of scientific quality such as publication rates, citation frequencies, and impact factors. The pressures for “exciting” results, over-simplified stories, and notoriety, are evident throughout the climate and paleoclimate literature. The price being paid is not a small one. Often important technical details are omitted, and alternative hypotheses arbitrarily suppressed in the interests of telling a simple story. Some
    of these papers would not pass peer-review in conventional professional journals, but they lend themselves to headlines and simplistic stories written by non-scientist media people.
  60. What we see in climate science is the bizarre spectacle of technical discussions being carried on in the news columns of the New York Times and similar publications, not to speak of the dispiriting blog universe. In the long-term, this tabloid-like publication cannot be good for the science–which developed peer review in specialized journals over many decades beginning in the 17th Century–for very good reasons.
  61. Paleoclimate reconstruction and understanding presents some of the most intriguing data and problems in all of science. Progress clearly requires combining the remarkable achievements in producing proxy data with similar achievements in understanding dynamics, and in this context, oceanic physics. This combination does represent a rare, truly interdisciplinary, field in which individuals must have at least a working grasp of the powers and pitfalls of the data, and of the models and dynamical theories. Paleoclimate studies emerged out of geology and geochemistry. These are fields which historically did not attempt large-scale quantitative syntheses using time-evolving partial differential equations. In contrast, general circulation modeling emerged out of geophysical fluid dynamics and computer science–during a period when oceanographic data were few and far between; comparisons of the sparse, poorly understood data, with unrealistic numerical models led to a modeling community disconnected from understanding of the observational system. Paleoclimate study needs an open-minded, restrained, scientific community, one informed about both of these sub-fields–it is plainly primarily an issue of education for the coming generations of graduate students.

 

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