Thongchai Thailand

Correlation of CMIP5 Forcings with Temperature

Posted on: August 31, 2018



















The author acknowledges the kind assistance and encouragement of Ashley Francs of Salisbury, England in carrying out this work.

This work is a correlation analysis that evaluates proposed “forcings” that should determine surface temperature according to theory against  observed and predicted temperatures. The Coupled Model Intercomparison Project of 2008 (CMIP5) was a meeting of the IPCC Working Group for Coupled Modeling (WGCM) with Atmosphere-Ocean General Circulation Models (AOGCM). The meeting was held to prepare for the IPCC Fifth Assessment Report AR5 because it was expected that some of the scientific questions that arose during preparation of the IPCCAR4 would be addressed by CMIP5. These scientific questions arose because climate science acknowledged “gaps in the understanding” of how the climate system works particularly having to do with forcings, feedbacks, and ocean uptake.

The CMIP5 has reconciled differences among general circulation models and coupled ocean-atmosphere models to standardize climate forcings. These parameters have been estimated from theoretical considerations in conjunction with data on atmospheric composition, emissions, aerosols, and other relevant factors and tested with climate  models against historical data both paleo and reconstructions of the instrumental record (Taylor, 2012). It follows that historical temperature reconstructions by NASA-GISS and the Hadley Centre, both being participants in CMIP5, should be consistent with the CMIP5 forcings published by NASA-GISS and used by the Hadley Center.

This analysis uses the NASA GISS-2 forcings (Figure 1) against the NASA-GISTEMP. Additional temperature series studied include the HadCRUT4 global temperature reconstructions from the Hadley Climate Research Unit. In additon the RCP8.5 theoretical hindcast of temperatures under the “business as usual” scenario (where no climate action is taken) is also presented and the three series are compared.

The end year in this study is 2012. It is constrained by the availability of NASA GISS-2 CMIP5 forcings data. In addition to CO2-GHG forcing (WMGHG), eight additional forcings are published, They include ozone, land use, snow albedo, aerosols (both tropospheric and stratospheric) and solar. NASA-GISS also publishes the net total forcing from all sources both natural and human caused, as a separate parameter called “ALLFORCINGS”. Only the CO2 forcing series (WMGHG) and the total forcing series (ALLFORCINGS) 1851-2012 are used in this correlation analysis. The correlation between WMGHG and ALLFORCINGS is also presented.

The analysis considers not only the full span correlations in the source data but also its decomposition into contributions from shared trends and contributions from responsiveness at an annual time scale with the use of detrended fluctuation analysis. Discrepancies between source data and detrended correlations are examined for instability with split half tests and also with correlations in a moving 30-year window. These issues are discussed in greater detail in related posts on Spurious Correlations in Climate Science and ECS: Equilibrium Climate SensitivityIn short, unstable correlations are normally spurious and have no interpretation in terms of cause and effect in the study of field data.

The forcings are available for the time span 1851-2012 but global temperature data are available in three different spans that begin in 1851 (Hadcrut), 1861 (RCP 8.5), and 1880 (NASA-GISTEMP). Because differences in the span are known to change correlation value (Time Series Analysis by Box and Jenkins), the data with longer time spans are also studied at the shorter time spans. Thus, for the three temperature time series used, there are actually six different time series in the correlation analysis. They are Hadcrut 1851, HadCrut 1861, Hadcrut 1880, RCP8.5 1861, RCP8.5 1880, and GISTEMP 1880.

The full span correlations between the WMGHG /ALLFORCINGS data and HADCRUT4 NASA GISTEMP global temperature reconstructions, and RCP8.5 temperature hindcasts, as well as the correlation between the two forcing functions, are tabulated in Figure 4. With one exception, they show a strong and statistically significant correlation of temperature with both forcing measures as well as between the two forcing measures. Strong correlations in the source data between r=[0.74-0.94] survive into the detrended series as statistically significant responsiveness at an annual time scale with r=[0.33-0.79]. The critical values of correlation for statistical significance at alpha=0.001 are listed in the column marked CRITCOR. Rows labeled as FOR1851, FOR1861, and FOR1880 contain correlations between the two forcing functions WMGHG and ALLFORCINGS; and all of these correlations are strong with statistical significance surviving into the detrended series. The sole exception here is GISTEMP 1880-2012 global temperature reconstruction. It shows a strong correlation of r=0.74 with WMGHG but fails to show a statistically significant detrended correlation with ALLFORCINGS. As implied in (Lacis &Schmidt “Atmospheric CO2: Principal control knob governing Earth’s temperature.” Science 330.6002 (2010): 356-359.), it seems that CO2 alone is almost as good as if not better than the sum of all forcings in explaining temperatures at these time spans.

As strong as the full span correlations are, the table and chart of 30-year moving correlations in the lower portion of Figure 4 may reveal a weakness in this result although the loss of correlation at short time spans may be interpreted in terms of the internal climate variability issue described in a related post:

In the chart we see that moving 30-year correlations between temperature and the two forcing measures vary over a large range above and below the horizontal statistical significance line and also above and below the zero-line such that both insignificant and negative correlations are seen in the table of 30-year correlations above the chart. The table of 30-year moving correlations also presents a comparison of the two forcing metrics. The correlations between the two forcings is presented in the rows labeled FOR1851, FOR1861, and FOR1880. They contain data for 30-year moving correlations between the two forcing measures WMGHG and ALLFORCINGS published by NASA-GISS. They show good evidence that the two forcing metrics are correlated in support of the strong full span correlations in the first table of Figure 4. In comparing the second and third charts of Figure 3, it appears that the correlation of warming with the two CMIP5 forcings is strong at high warming rates and weak at low warming rates. Periods of high 30-year warming rates (second chart of Figure 3) correspond with periods of strong 30-year correlation above the line demarcating statistical significance (third chart of Figure 3). In summary, the moving 30-year correlations show a large range of values with about half of them below the horizontal line of statistical significance. This pattern is indicative of an unstable correlation between forcings and temperature.

The stability of the correlation is therefore tested with a split half test in Figures 5&6. Each time span is cut in half and results for the first half of the time span are presented in Figure 5 and those for the second half in Figure 6. The comparison of the split half correlations and detrended correlations confirms the instability of correlations between temperature and the two forcings. The first half of the data show generally weak, statistically insignificant and more negative correlations whereas the second half of the time series show generally strong correlations as in the full span of the data.



  1. CORRELATION BETWEEN THE TWO MEASURES OF FORCING WMGHG & ALLFORCINGS: {FULL SPAN} Strong correlations between CO2 forcing and all forcings with supporting detrended correlations are seen in the full span of the data particularly so in the longer time spans. The observed correlations and detrended correlations between WMGHG & ALLFORCINGS are [corr, detcorr] = [0.844, 0.489], [0.841, 0.476], [0.838, 0.334] in the three time spans of 162, 152, and 133 years. All correlations are statistically significant. The averages for all three time spans are [corr, detcorr] = [0.841, 0.433] in the full span, [0.494, 0.078] in the first half, and [0.801, 0.420] in the second half. The two forcings are strongly correlated in the most recent portions of the time series that begin later than 1931 but that correlation is  not found in time series that end earlier than 1947. This behavior is anomalous.
  2. {MOVING WINDOW} More than 50% of the 30-year moving average correlations between WMGHG & ALLFORCINGS are found to be statistically significant in the 152-year and 133-year time spans. The significance percentage is just shy of 50% when the oldest decade is included in the 162-year time span 1851-2012.
  3. {SPLIT HALF} No statistically significant correlation between WMGHG & ALLFORCINGS is found in the first half of any of the three time spans studied. The second half of all three time spans shows statistically significant correlations between the two forcing measures.
  4. In conclusion, the two measures of climate forcing are found to be correlated but with some anomalous behavior in the oldest portions of the time series studied.
  5. COMPARISON OF THE EXPLANATORY POWER OF THE TWO FORCING MEASURES: ALLFORCINGS shows a higher average correlation of r=0.471 in the 30-year moving correlations across the full span of the data with a lower proportion of negative correlations at 5%. However, WMGHG shows a higher proportion of statistically significant moving correlations at 52% compared with 38% for ALLFORCINGS. Average correlations with temperature in the full span are [corr, detcorr] = [0.880, 0.544] for WMGHG against [0.824, 0.501] for ALLFORCINGS. In the first half of the data series, [corr, detcorr] = [0.407, 0.226] for WMGHG against [0.477, 0.370] for ALLFORCINGS. In the second half of the data series, [corr, detcorr] = [0.832, 0.413] for WMGHG against [0.787, 0.485] for ALLFORCINGS. The two measures appear to be comparable with no clear winner although ALLFORCINGS may be somewhat better in terms of detrended correlation.
  6. The comparison at the bottom of Figure 7 above is a confirmation of this conclusion as it shows that the averages of all full span and half span correlations with temperature are approximately the same for the two forcings.
  7. In conclusion, the two measures of forcing study appear to be similar in their ability to explain temperature. ALLFORCINGS does not show a greater correlation with temperature than WMGHG alone.
  8. COMPARISON OF CORRELATIONS OF THE THREE TEMPERATURE SERIES WITH FORCINGS. The comparison is presented in Figure 7. It shows that when all full span and half span correlations are averaged, the the strongest correlation is seen with the RCP8.5 global mean temperature projections. The average of all correlations of the RCP8.5 series are [corr, detcorr] = [0.857, 0.663] for with WMGHG forcing and [0.752, 0.739] with ALLFORCINGS. The corresponding correlations for the two global temperature reconstructions are HadCRUT4 [corr, detcorr] = [0.729, 0.553] with WMGHG and [0.605, 0.359] with ALLFORCINGS and for GISTEMP they are [corr, detcorr] = [0.460, -0.136] with WMGHG and [0.424, 0.074] with ALLFORCINGS.
  9. The close correspondence between temperature projections according to theory and forcings is simply verification that the theory was accurately applied in the construction of the RCP8.5 projection and their lower correlations with observations are indicative of the degree of departure of data from theory.

SUMMARY: A correlation analysis is carried out with NASA GISS-2 CMIP5 climate forcings against two global surface temperature reconstructions and the RCP8.5 “business as usual” temperature projection based on CMIP5. Two climate forcings are used. They are WMGHG, mostly a contribution of human action, and ALLFORCINGS, that includes natural factors.

All three temperature series show anomalous differences in correlation with forcings according to location along the temperature time series. Among the temperature data, the RCP8.5 series shows the strongest correlation with forcings as expected since this series was derived from forcings in climate models.

The observational data show weaker correlations and these differences may be interpreted in terms of departure from theory. No significant difference in correlation was found between the anthropogenic WMGHG forcing and the ALLFORCING series that includes natural forcing. This finding supports Andrew Lacis who had written that human emissions alone explain warming (Lacis &Schmidt “Atmospheric CO2: Principal control knob governing Earth’s temperature.” Science 330.6002 (2010): 356-359). 


  1. 2011: Bellouin, Nicolas, et al. “Aerosol forcing in the Climate Model Intercomparison Project (CMIP5) simulations by HadGEM2‐ES and the role of ammonium nitrate.” Journal of Geophysical Research: Atmospheres 116.D20 (2011). The latest Hadley Centre climate model, HadGEM2‐ES, includes Earth system components such as interactive chemistry and eight species of tropospheric aerosols. It has been run for the period 1860–2100 in support of the fifth phase of the Climate Model Intercomparison Project (CMIP5). Anthropogenic aerosol emissions peak between 1980 and 2020, resulting in a present‐day all‐sky top of the atmosphere aerosol forcing of −1.6 and −1.4 W m−2 with and without ammonium nitrate aerosols, respectively, for the sum of direct and first indirect aerosol forcings. Aerosol forcing becomes significantly weaker in the 21st century, being weaker than −0.5 W m−2 in 2100 without nitrate. However, nitrate aerosols become the dominant species in Europe and Asia and decelerate the decrease in global mean aerosol forcing. Considering nitrate aerosols makes aerosol radiative forcing 2–4 times stronger by 2100 depending on the representative concentration pathway, although this impact is lessened when changes in the oxidation properties of the atmosphere are accounted for. Anthropogenic aerosol residence times increase in the future in spite of increased precipitation, as cloud cover and aerosol‐cloud interactions decrease in tropical and midlatitude regions. Deposition of fossil fuel black carbon onto snow and ice surfaces peaks during the 20th century in the Arctic and Europe but keeps increasing in the Himalayas until the middle of the 21st century. Results presented here confirm the importance of aerosols in influencing the Earth’s climate, albeit with a reduced impact in the future, and suggest that nitrate aerosols will partially replace sulphate aerosols to become an important anthropogenic species in the remainder of the 21st century.
  2. 2012: Ahlström, Anders, et al. “Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections.” Environmental Research Letters 7.4 (2012): 044008. We have investigated the spatio-temporal carbon balance patterns resulting from forcing a dynamic global vegetation model with output from 18 climate models of the CMIP5 (Coupled Model Intercomparison Project Phase 5) ensemble. We found robust patterns in terms of an extra-tropical loss of carbon, except for a temperature induced shift in phenology, leading to an increased spring uptake of carbon. There are less robust patterns in the tropics, a result of disagreement in projections of precipitation and temperature. Although the simulations generally agree well in terms of the sign of the carbon balance change in the middle to high latitudes, there are large differences in the magnitude of the loss between simulations. Together with tropical uncertainties these discrepancies accumulate over time, resulting in large differences in total carbon uptake over the coming century (−0.97–2.27 Pg C yr−1 during 2006–2100). The terrestrial biosphere becomes a net source of carbon in ten of the 18 simulations adding to the atmospheric CO2 concentrations, while the remaining eight simulations indicate an increased sink of carbon.
  3. 2012: Andrews, Timothy, et al. “Forcing, feedbacks and climate sensitivity in CMIP5 coupled atmosphere‐ocean climate models.” Geophysical Research Letters 39.9 (2012). We quantify forcing and feedbacks across available CMIP5 coupled atmosphere‐ocean general circulation models (AOGCMs) by analysing simulations forced by an abrupt quadrupling of atmospheric carbon dioxide concentration. This is the first application of the linear forcing‐feedback regression analysis of Gregory et al. (2004) to an ensemble of AOGCMs. The range of equilibrium climate sensitivity is 2.1–4.7 K. Differences in cloud feedbacks continue to be important contributors to this range. Some models show small deviations from a linear dependence of top‐of‐atmosphere radiative fluxes on global surface temperature change. We show that this phenomenon largely arises from shortwave cloud radiative effects over the ocean and is consistent with independent estimates of forcing using fixed sea‐surface temperature methods. We suggest that future research should focus more on understanding transient climate change, including any time‐scale dependence of the forcing and/or feedback, rather than on the equilibrium response to large instantaneous forcing.
  4. 2012: Booth, Ben BB, et al. “Aerosols implicated as a prime driver of twentieth-century North Atlantic climate variability.” Nature484.7393 (2012): 228. Systematic climate shifts have been linked to multidecadal variability in observed sea surface temperatures in the North Atlantic Ocean1. These links are extensive, influencing a range of climate processes such as hurricane activity2 and African Sahel3,4,5 and Amazonian5 droughts. The variability is distinct from historical global-mean temperature changes and is commonly attributed to natural ocean oscillations6,7,8,9,10. A number of studies have provided evidence that aerosols can influence long-term changes in sea surface temperatures11,12, but climate models have so far failed to reproduce these interactions6,9 and the role of aerosols in decadal variability remains unclear. Here we use a state-of-the-art Earth system climate model to show that aerosol emissions and periods of volcanic activity explain 76 per cent of the simulated multidecadal variance in detrended 1860–2005 North Atlantic sea surface temperatures. After 1950, simulated variability is within observational estimates; our estimates for 1910–1940 capture twice the warming of previous generation models but do not explain the entire observed trend. Other processes, such as ocean circulation, may also have contributed to variability in the early twentieth century. Mechanistically, we find that inclusion of aerosol–cloud microphysical effects, which were included in few previous multimodel ensembles, dominates the magnitude (80 per cent) and the spatial pattern of the total surface aerosol forcing in the North Atlantic. Our findings suggest that anthropogenic aerosol emissions influenced a range of societally important historical climate events such as peaks in hurricane activity and Sahel drought. Decadal-scale model predictions of regional Atlantic climate will probably be improved by incorporating aerosol–cloud microphysical interactions and estimates of future concentrations of aerosols, emissions of which are directly addressable by policy actions.
  5. 2012: Taylor, Karl E., Ronald J. Stouffer, and Gerald A. Meehl. “An overview of CMIP5 and the experiment design.” Bulletin of the American Meteorological Society 93.4 (2012): 485-498. The fifth phase of the Coupled Model Intercomparison Project (CMIP5) will produce a state-of-the- art multimodel dataset designed to advance our knowledge of climate variability and climate change. Researchers worldwide are analyzing the model output and will produce results likely to underlie the forthcoming Fifth Assessment Report by the Intergovernmental Panel on Climate Change. Unprecedented in scale and attracting interest from all major climate modeling groups, CMIP5 includes “long term” simulations of twentieth-century climate and projections for the twenty-first century and beyond. Conventional atmosphere–ocean global climate models and Earth system models of intermediate complexity are for the first time being joined by more recently developed Earth system models under an experiment design that allows both types of models to be compared to observations on an equal footing. Besides the longterm experiments, CMIP5 calls for an entirely new suite of “near term” simulations focusing on recent decades and the future to year 2035. These “decadal predictions” are initialized based on observations and will be used to explore the predictability of climate and to assess the forecast system’s predictive skill. The CMIP5 experiment design also allows for participation of stand-alone atmospheric models and includes a variety of idealized experiments that will improve understanding of the range of model responses found in the more complex and realistic simulations. An exceptionally comprehensive set of model output is being collected and made freely available to researchers through an integrated but distributed data archive. For researchers unfamiliar with climate models, the limitations of the models and experiment design are described.
  6. 2012Morice, Colin P., et al. “Quantifying uncertainties in global and regional temperature change using an ensemble of observational estimates: The HadCRUT4 data set.” Journal of Geophysical Research: Atmospheres 117.D8 (2012). Recent developments in observational near‐surface air temperature and sea‐surface temperature analyses are combined to produce HadCRUT4, a new data set of global and regional temperature evolution from 1850 to the present. This includes the addition of newly digitized measurement data, both over land and sea, new sea‐surface temperature bias adjustments and a more comprehensive error model for describing uncertainties in sea‐surface temperature measurements. An ensemble approach has been adopted to better describe complex temporal and spatial interdependencies of measurement and bias uncertainties and to allow these correlated uncertainties to be taken into account in studies that are based upon HadCRUT4. Climate diagnostics computed from the gridded data set broadly agree with those of other global near‐surface temperature analyses. Fitted linear trends in temperature anomalies are approximately 0.07°C/decade from 1901 to 2010 and 0.17°C/decade from 1979 to 2010 globally. Northern/southern hemispheric trends are 0.08/0.07°C/decade over 1901 to 2010 and 0.24/0.10°C/decade over 1979 to 2010. Linear trends in other prominent near‐surface temperature analyses agree well with the range of trends computed from the HadCRUT4 ensemble members.
  7. 2013: Jones, Gareth S., Peter A. Stott, and Nikolaos Christidis. “Attribution of observed historical near‒surface temperature variations to anthropogenic and natural causes using CMIP5 simulations.” Journal of Geophysical Research: Atmospheres118.10 (2013): 4001-4024. We have carried out an investigation into the causes of changes in near‒surface temperatures from 1860 to 2010. We analyze the HadCRUT4 observational data set which has the most comprehensive set of adjustments available to date for systematic biases in sea surface temperatures and the CMIP5 ensemble of coupled models which represents the most sophisticated multi‒model climate modeling exercise yet carried out. Simulations that incorporate both anthropogenic and natural factors span changes in observed temperatures between 1860 and 2010, while simulations of natural factors do not warm as much as observed. As a result of sampling a much wider range of structural modeling uncertainty, we find a wider spread of historic temperature changes in CMIP5 than was simulated by the previous multi‒model ensemble, CMIP3. However, calculations of attributable temperature trends based on optimal detection support previous conclusions that human‒induced greenhouse gases dominate observed global warming since the mid‒20th century. With a much wider exploration of model uncertainty than previously carried out, we find that individually the models give a wide range of possible counteracting cooling from the direct and indirect effects of aerosols and other non‒greenhouse gas anthropogenic forcings. Analyzing the multi‒model mean over 1951–2010 (focusing on the most robust result), we estimate a range of possible contributions to the observed warming of approximately 0.6 K from greenhouse gases of between 0.6 and 1.2 K, balanced by a counteracting cooling from other anthropogenic forcings of between 0 and −0.5 K.
  8. 2013: Gillett, Nathan P., et al. “Constraining the ratio of global warming to cumulative CO2 emissions using CMIP5 simulations.” Journal of Climate 26.18 (2013): 6844-6858. The ratio of warming to cumulative emissions of carbon dioxide has been shown to be approximately independent of time and emissions scenarios and directly relates emissions to temperature. It is therefore a potentially important tool for climate mitigation policy. The transient climate response to cumulative carbon emissions (TCRE), defined as the ratio of global-mean warming to cumulative emissions at CO2doubling in a 1% yr−1 CO2 increase experiment, ranges from 0.8 to 2.4 K EgC−1 in 15 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5)—a somewhat broader range than that found in a previous generation of carbon–climate models. Using newly available simulations and a new observational temperature dataset to 2010, TCRE is estimated from observations by dividing an observationally constrained estimate of CO2-attributable warming by an estimate of cumulative carbon emissions to date, yielding an observationally constrained 5%–95% range of 0.7–2.0 K EgC−1.
  9. 2013: Forster, Piers M., et al. “Evaluating adjusted forcing and model spread for historical and future scenarios in the CMIP5 generation of climate models.” Journal of Geophysical Research: Atmospheres 118.3 (2013): 1139-1150. We utilize energy budget diagnostics from the Coupled Model Intercomparison Project phase 5 (CMIP5) to evaluate the models’ climate forcing since preindustrial times employing an established regression technique. The climate forcing evaluated this way, termed the adjusted forcing (AF), includes a rapid adjustment term associated with cloud changes and other tropospheric and land‐surface changes. We estimate a 2010 total anthropogenic and natural AF from CMIP5 models of 1.9 ± 0.9 W m−2 (5–95% range). The projected AF of the Representative Concentration Pathway simulations are lower than their expected radiative forcing (RF) in 2095 but agree well with efficacy weighted forcings from integrated assessment models. The smaller AF, compared to RF, is likely due to cloud adjustment. Multimodel time series of temperature change and AF from 1850 to 2100 have large intermodel spreads throughout the period. The intermodel spread of temperature change is principally driven by forcing differences in the present day and climate feedback differences in 2095, although forcing differences are still important for model spread at 2095. We find no significant relationship between the equilibrium climate sensitivity (ECS) of a model and its 2003 AF, in contrast to that found in older models where higher ECS models generally had less forcing. Given the large present‐day model spread, there is no indication of any tendency by modelling groups to adjust their aerosol forcing in order to produce observed trends. Instead, some CMIP5 models have a relatively large positive forcing and overestimate the observed temperature change.
  10. 2013: Knutti, Reto, and Jan Sedláček. “Robustness and uncertainties in the new CMIP5 climate model projections.” Nature Climate Change 3.4 (2013): 369. Estimates of impacts from anthropogenic climate change rely on projections from climate models. Uncertainties in those have often been a limiting factor, in particular on local scales. A new generation of more complex models running scenarios for the upcoming Intergovernmental Panel on Climate Change Fifth Assessment Report (IPCC AR5) is widely, and perhaps naively, expected to provide more detailed and more certain projections. Here we show that projected global temperature change from the new models is remarkably similar to that from those used in IPCC AR4 after accounting for the different underlying scenarios. The spatial patterns of temperature and precipitation change are also very consistent. Interestingly, the local model spread has not changed much despite substantial model development and a massive increase in computational capacity. Part of this model spread is irreducible owing to internal variability in the climate system, yet there is also uncertainty from model differences that can potentially be eliminated. We argue that defining progress in climate modelling in terms of narrowing uncertainties is too limited. Models improve, representing more processes in greater detail. This implies greater confidence in their projections, but convergence may remain slow. The uncertainties should not stop decisions being made.
  11. 2013: Vial, Jessica, Jean-Louis Dufresne, and Sandrine Bony. “On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates.” Climate Dynamics 41.11-12 (2013): 3339-3362. This study diagnoses the climate sensitivity, radiative forcing and climate feedback estimates from eleven general circulation models participating in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5), and analyzes inter-model differences. This is done by taking into account the fact that the climate response to increased carbon dioxide (CO2) is not necessarily only mediated by surface temperature changes, but can also result from fast land warming and tropospheric adjustments to the CO2 radiative forcing. By considering tropospheric adjustments to CO2 as part of the forcing rather than as feedbacks, and by using the radiative kernels approach, we decompose climate sensitivity estimates in terms of feedbacks and adjustments associated with water vapor, temperature lapse rate, surface albedo and clouds. Cloud adjustment to CO2 is, with one exception, generally positive, and is associated with a reduced strength of the cloud feedback; the multi-model mean cloud feedback is about 33 % weaker. Non-cloud adjustments associated with temperature, water vapor and albedo seem, however, to be better understood as responses to land surface warming. Separating out the tropospheric adjustments does not significantly affect the spread in climate sensitivity estimates, which primarily results from differing climate feedbacks. About 70 % of the spread stems from the cloud feedback, which remains the major source of inter-model spread in climate sensitivity, with a large contribution from the tropics. Differences in tropical cloud feedbacks between low-sensitivity and high-sensitivity models occur over a large range of dynamical regimes, but primarily arise from the regimes associated with a predominance of shallow cumulus and stratocumulus clouds. The combined water vapor plus lapse rate feedback also contributes to the spread of climate sensitivity estimates, with inter-model differences arising primarily from the relative humidity responses throughout the troposphere. Finally, this study points to a substantial role of nonlinearities in the calculation of adjustments and feedbacks for the interpretation of inter-model spread in climate sensitivity estimates. We show that in climate model simulations with large forcing (e.g., 4 × CO2), nonlinearities cannot be assumed minor nor neglected. Having said that, most results presented here are consistent with a number of previous feedback studies, despite the very different nature of the methodologies and all the uncertainties associated with them.
  12. 2013: Kumar, Sanjiv, et al. “Evaluation of temperature and precipitation trends and long-term persistence in CMIP5 twentieth-century climate simulations.” Journal of Climate26.12 (2013): 4168-4185. The authors have analyzed twentieth-century temperature and precipitation trends and long-term persistence from 19 climate models participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). This study is focused on continental areas (60°S–60°N) during 1930–2004 to ensure higher reliability in the observations. A nonparametric trend detection method is employed, and long-term persistence is quantified using the Hurst coefficient, taken from the hydrology literature. The authors found that the multimodel ensemble–mean global land–average temperature trend (0.07°C decade−1) captures the corresponding observed trend well (0.08°C decade−1). Globally, precipitation trends are distributed (spatially) at about zero in both the models and in the observations. There are large uncertainties in the simulation of regional-/local-scale temperature and precipitation trends. The models’ relative performances are different for temperature and precipitation trends. The models capture the long-term persistence in temperature reasonably well. The areal coverage of observed long-term persistence in precipitation is 60% less (32% of land area) than that of temperature (78%). The models have limited capability to capture the long-term persistence in precipitation. Most climate models underestimate the spatial variability in temperature trends. The multimodel ensemble–average trend generally provides a conservative estimate of local/regional trends. The results of this study are generally not biased by the choice of observation datasets used, including Climatic Research Unit Time Series 3.1; temperature data from Hadley Centre/Climatic Research Unit, version 4; and precipitation data from Global Historical Climatology Network, version 2.
  13. 2014: Santer, Benjamin D., et al. “Volcanic contribution to decadal changes in tropospheric temperature.” Nature Geoscience 7.3 (2014): 185. Despite continued growth in atmospheric levels of greenhouse gases, global mean surface and tropospheric temperatures have shown slower warming since 1998 than previously1,2,3,4,5. Possible explanations for the slow-down include internal climate variability3,4,6,7, external cooling influences1,2,4,8,9,10,11 and observational errors12,13. Several recent modelling studies have examined the contribution of early twenty-first-century volcanic eruptions1,2,4,8 to the muted surface warming. Here we present a detailed analysis of the impact of recent volcanic forcing on tropospheric temperature, based on observations as well as climate model simulations. We identify statistically significant correlations between observations of stratospheric aerosol optical depth and satellite-based estimates of both tropospheric temperature and short-wave fluxes at the top of the atmosphere. We show that climate model simulations without the effects of early twenty-first-century volcanic eruptions overestimate the tropospheric warming observed since 1998. In two simulations with more realistic volcanic influences following the 1991 Pinatubo eruption, differences between simulated and observed tropospheric temperature trends over the period 1998 to 2012 are up to 15% smaller, with large uncertainties in the magnitude of the effect. To reduce these uncertainties, better observations of eruption-specific properties of volcanic aerosols are needed, as well as improved representation of these eruption-specific properties in climate model simulations.
  14. 2014: Wuebbles, Donald, et al. “CMIP5 climate model analyses: climate extremes in the United States.” Bulletin of the American Meteorological Society 95.4 (2014): 571-583. This is the fourth in a series of four articles on historical and projected climate extremes in the United States. Here, we examine the results of historical and future climate model experiments from the phase 5 of the Coupled Model Intercomparison Project (CMIP5) based on work presented at the World Climate Research Programme (WCRP) Workshop on CMIP5 Climate Model Analyses held in March 2012. Our analyses assess the ability of CMIP5 models to capture observed trends, and we also evaluate the projected future changes in extreme events over the contiguous Unites States. Consistent with the previous articles, here we focus on model-simulated historical trends and projections for temperature extremes, heavy precipitation, large-scale drivers of precipitation variability and drought, and extratropical storms. Comparing new CMIP5 model results with earlier CMIP3 simulations shows that in general CMIP5 simulations give similar patterns and magnitudes of future temperature and precipitation extremes in the United States relative to the projections from the earlier phase 3 of the Coupled Model Intercomparison Project (CMIP3) models. Specifically, projections presented here show significant changes in hot and cold temperature extremes, heavy precipitation, droughts, atmospheric patterns such as the North American monsoon and the North Atlantic subtropical high that affect interannual precipitation, and in extratropical storms over the twenty-first century. Most of these trends are consistent with, although in some cases (such as heavy precipitation) underestimate, observed trends
  15. 2014: Friedlingstein, Pierre, et al. “Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks.” Journal of Climate27.2 (2014): 511-526. In the context of phase 5 of the Coupled Model Intercomparison Project, most climate simulations use prescribed atmospheric CO2 concentration and therefore do not interactively include the effect of carbon cycle feedbacks. However, the representative concentration pathway 8.5 (RCP8.5) scenario has additionally been run by earth system models with prescribed CO2 emissions. This paper analyzes the climate projections of 11 earth system models (ESMs) that performed both emission-driven and concentration-driven RCP8.5 simulations. When forced by RCP8.5 CO2 emissions, models simulate a large spread in atmospheric CO2; the simulated 2100 concentrations range between 795 and 1145 ppm. Seven out of the 11 ESMs simulate a larger CO2 (on average by 44 ppm, 985 ± 97 ppm by 2100) and hence higher radiative forcing (by 0.25 W m−2) when driven by CO2 emissions than for the concentration-driven scenarios (941 ppm). However, most of these models already overestimate the present-day CO2, with the present-day biases reasonably well correlated with future atmospheric concentrations’ departure from the prescribed concentration. The uncertainty in CO2 projections is mainly attributable to uncertainties in the response of the land carbon cycle. As a result of simulated higher CO2 concentrations than in the concentration-driven simulations, temperature projections are generally higher when ESMs are driven with CO2 emissions. Global surface temperature change by 2100 (relative to present day) increased by 3.9° ± 0.9°C for the emission-driven simulations compared to 3.7° ± 0.7°C in the concentration-driven simulations. Although the lower ends are comparable in both sets of simulations, the highest climate projections are significantly warmer in the emission-driven simulations because of stronger carbon cycle feedbacks.
  16. 2014: Huber, Markus, and Reto Knutti. “Natural variability, radiative forcing and climate response in the recent hiatus reconciled.” Nature Geoscience 7.9 (2014): 651. Global mean surface warming over the past 15 years or so has been less than in earlier decades and less than simulated by most climate models1. Natural variability2,3,4, a reduced radiative forcing5,6,7, a smaller warming response to atmospheric carbon dioxide concentrations8,9 and coverage bias in the observations10 have been identified as potential causes. However, the explanations of the so-called ‘warming hiatus’ remain fragmented and the implications for long-term temperature projections are unclear. Here we estimate the contribution of internal variability associated with the El Niño/Southern Oscillation (ENSO) using segments of unforced climate model control simulations that match the observed climate variability. We find that ENSO variability analogous to that between 1997 or 1998 and 2012 leads to a cooling trend of about −0.06 °C. In addition, updated solar and stratospheric aerosol forcings from observations explain a cooling trend of similar magnitude (−0.07 °C). Accounting for these adjusted trends we show that a climate model of reduced complexity with a transient climate response of about 1.8 °C is consistent with the temperature record of the past 15 years, as is the ensemble mean of the models in the Coupled Model Intercomparison Project Phase 5 (CMIP5). We conclude that there is little evidence for a systematic overestimation of the temperature response to increasing atmospheric CO2 concentrations in the CMIP5 ensemble.
  17. 2015: Sharmila, S., et al. “Future projection of Indian summer monsoon variability under climate change scenario: An assessment from CMIP5 climate models.” Global and Planetary Change 124 (2015): 62-78. In this study, the impact of enhanced anthropogenic greenhouse gas emissionson the possible future changes in different aspects of daily-to-interannual variability of Indian summer monsoon (ISM) is systematically assessed using 20 coupled models participated in the Coupled Model Inter-comparison ProjectPhase 5. The historical (1951–1999) and future (2051–2099) simulations under the strongest Representative Concentration Pathway have been analyzed for this purpose. A few reliable models are selected based on their competence in simulating the basic features of present-climate ISM variability. The robust and consistent projections across the selected models suggest substantial changes in the ISM variability by the end of 21st century indicating strong sensitivity of ISM to global warming. On the seasonal scale, the all-India summer monsoon mean rainfall is likely to increase moderately in future, primarily governed by enhanced thermodynamic conditions due to atmospheric warming, but slightly offset by weakened large scale monsoon circulation. It is projected that the rainfall magnitude will increase over core monsoon zone in future climate, along with lengthening of the season due to late withdrawal. On interannual timescales, it is speculated that severity and frequency of both strong monsoon (SM) and weak monsoon (WM) might increase noticeably in future climate. Substantial changes in the daily variability of ISM are also projected, which are largely associated with the increase in heavy rainfall events and decrease in both low rain-rate and number of wet days during future monsoon. On the subseasonal scale, the model projections depict considerable amplification of higher frequency (below 30 day mode) components; although the dominant northward propagating 30–70 day mode of monsoon intraseasonal oscillationsmay not change appreciably in a warmer climate. It is speculated that the enhanced high frequency mode of monsoon ISOs due to increased GHG induced warming may notably modulate the ISM rainfall in future climate. Both extreme wet and dry episodes are likely to intensify and regionally extend in future climate with enhanced propensity of short active and long break spells. The SM (WM) could also be more wet (dry) in future due to the increment in longer active (break) spells. However, future changes in the spatial pattern during active/break phase of SM and WM are geographically inconsistent among the models. The results point out the growing climate-related vulnerability over Indian subcontinent, and further suggest the requisite of profound adaptation measures and better policy making in future.
  18. 2015: Slangen, Aimée BA, et al. “The sea level response to external forcings in historical simulations of CMIP5 climate models.” Journal of Climate 28.21 (2015): 8521-8539. Changes in Earth’s climate are influenced by internal climate variability and external forcings, such as changes in solar radiation, volcanic eruptions, anthropogenic greenhouse gases (GHG), and aerosols. Although the response of surface temperature to external forcings has been studied extensively, this has not been done for sea level. Here, a range of climate model experiments for the twentieth century is used to study the response of global and regional sea level change to external climate forcings. Both the global mean thermosteric sea level and the regional dynamic sea level patterns show clear responses to anthropogenic forcings that are significantly different from internal climate variability and larger than the difference between models driven by the same external forcing. The regional sea level patterns are directly related to changes in surface winds in response to the external forcings. The spread between different realizations of the same model experiment is consistent with internal climate variability derived from preindustrial control simulations. The spread between the different models is larger than the internal variability, mainly in regions with large sea level responses. Although the sea level responses to GHG and anthropogenic aerosol forcing oppose each other in the global mean, there are differences on a regional scale, offering opportunities for distinguishing between these two forcings in observed sea level change.
  19. 2016: Atwood, A. R., et al. “Quantifying climate forcings and feedbacks over the last millennium in the CMIP5–PMIP3 models.” Journal of Climate 29.3 (2016): The role of radiative forcings and climate feedbacks on global cooling over the last millennium is quantified in the CMIP5–PMIP3 transient climate model simulations. Changes in the global energy budget over the last millennium are decomposed into contributions from radiative forcings and climate feedbacks through the use of the approximate partial radiative perturbation method and radiative kernels. Global cooling occurs circa 1200–1850 CE in the multimodel ensemble mean with pronounced minima corresponding with volcanically active periods that are outside the range of natural variability. Analysis of the global energy budget during the last millennium indicates that Little Ice Age (LIA; 1600–1850 CE) cooling is largely driven by volcanic forcing (comprising an average of 65% of the total forcing among models), while contributions due to changes in land use (13%), greenhouse gas concentrations (12%), and insolation (10%) are substantially lower. The combination of these forcings directly contributes to 47% of the global cooling during the LIA, while the remainder of the cooling arises from the sum of the climate feedbacks. The dominant positive feedback is the water vapor feedback, which contributes 29% of the global cooling. Additional positive feedbacks include the surface albedo feedback (which contributes 7% of the global cooling and arises owing to high-latitude sea ice expansion and increased snow cover) and the lapse rate feedback (which contributes an additional 7% of the global cooling and arises owing to greater cooling near the surface than aloft in the middle and high latitudes).1161-1178.
  20. 2017: Stouffer, Ronald J., et al. “CMIP5 scientific gaps and recommendations for CMIP6.” Bulletin of the American Meteorological Society 98.1 (2017): 95-105.The Coupled Model Intercomparison Project (CMIP) is an ongoing coordinated international activity of numerical experimentation of unprecedented scope and impact on climate science. Its most recent phase, the fifth phase (CMIP5), has created nearly 2 PB of output from dozens of experiments performed by dozens of comprehensive climate models available to the climate science research community. In so doing, it has greatly advanced climate science. While CMIP5 has given answers to important science questions, with the help of a community survey we identify and motivate three broad topics here that guided the scientific framework of the next phase of CMIP, that is, CMIP6: How does the Earth system respond to changes in forcing? What are the origins and consequences of systematic model biases? How can we assess future climate changes given internal climate variability, predictability, and uncertainties in scenarios? CMIP has demonstrated the power of idealized experiments to better understand how the climate system works. We expect that these idealized approaches will continue to contribute to CMIP6. The quantification of radiative forcings and responses was poor, and thus it requires new methods and experiments to address this gap. There are a number of systematic model biases that appear in all phases of CMIP that remain a major climate modeling challenge. These biases need increased attention to better understand their origins and consequences through targeted experiments. Improving understanding of the mechanisms’ underlying internal climate variability for more skillful decadal climate predictions and long-term projections remains another challenge for CMIP6.
  21. 2017: Power, Scott, et al. “Apparent limitations in the ability of CMIP5 climate models to simulate recent multi-decadal change in surface temperature: implications for global temperature projections.” Climate Dynamics 49.1-2 (2017): 53-69. Observed surface temperature trends over the period 1998–2012/2014 have attracted a great deal of interest because of an apparent slowdown in the rate of global warming, and contrasts between climate model simulations and observations of such trends. Many studies have addressed the statistical significance of these relatively short-trends, whether they indicate a possible bias in the model values and the implications for global warming generally. Here we re-examine these issues, but as they relate to changes over much longer-term changes. We find that on multidecadal time scales there is little evidence for any change in the observed global warming rate, but some evidence for a recent temporary slowdown in the warming rate in the Pacific. This multi-decadal slowdown can be partly explained by a cool phase of the Interdecadal Pacific Oscillation and a short-term excess of La Niña events. We also analyse historical and projected changes in 38 CMIP climate models. All of the model simulations examined simulate multi-decadal warming in the Pacific over the past half-century that exceeds observed values. This difference cannot be fully explained by observed internal multi-decadal climate variability, even if allowance is made for an apparent tendency for models to underestimate internal multi-decadal variability in the Pacific. Models which simulate the greatest global warming over the past half-century also project warming that is among the highest of all models by the end of the twenty-first century, under both low and high greenhouse gas emission scenarios. Given that the same models are poorest in representing observed multi-decadal temperature change, confidence in the highest projections is reduced.
  22. 2018: Hao, Mingju, et al. “Narrowing the surface temperature range in CMIP5 simulations over the Arctic.” Theoretical and Applied Climatology 132.3-4 (2018): 1073-1088. Much uncertainty exists in reproducing Arctic temperature using different general circulation models (GCMs). Therefore, evaluating the performance of GCMs in reproducing Arctic temperature is critically important. In our study, 32 GCMs in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) during the period 1900–2005 are used, and several metrics, i.e., bias, correlation coefficient (R), and root mean square error (RMSE), are applied. The Cowtan data set is adopted as the reference data. The results suggest that the GCMs used can reasonably reproduce the Arctic warming trend during the period 1900–2005, as observed in the observational data, whereas a large variation of inter-model differences exists in modeling the Arctic warming magnitude. With respect to the reference data, most GCMs have large cold biases, whereas others have weak warm biases. Additionally, based on statistical thresholds, the models MIROC-ESM, CSIRO-Mk3-6-0, HadGEM2-AO, and MIROC-ESM-CHEM (bias ≤ ±0.10 °C, R ≥ 0.50, and RMSE ≤ 0.60 °C) are identified as well-performingGCMs. The ensemble of the four best-performing GCMs (ES4), with bias, R, and RMSE values of −0.03 °C, 0.72, and 0.39 °C, respectively, performs better than the ensemble with all 32 members, with bias, R, and RMSE values of −0.04 °C, 0.64, and 0.43 °C, respectively. Finally, ES4 is used to produce projections for the next century under the scenarios of RCP2.6, RCP4.5, and RCP8.0. The uncertainty in the projected temperature is greater in the higher emissions scenarios. Additionally, the projected temperature in the cold half year has larger variations than that in the warm half year.
  23. 2018: Palmer, Matthew D., Glen R. Harris, and Jonathan M. Gregory. “Extending CMIP5 projections of global mean temperature change and sea level rise due to thermal expansion using a physically-based emulator.” Environmental Research Letters13.8 (2018): 084003. We present a physically-based emulator approach to extending 21st century CMIP5 model simulations of global mean surface temperature (GMST) and global thermal expansion (TE) to 2300. A two-layer energy balance model that has been tuned to emulate the CO2 response of individual CMIP5 models is combined with model-specific radiative forcings to generate an emulated ensemble to 2300 for RCP2.6, RCP4.5 and RCP8.5. Errors in the emulated time series are quantified using a subset of CMIP5 models with data available to 2300 and factored into the ensemble uncertainty. The resulting projections show good agreement with 21st century ensemble projections reported in IPCC AR5 and also compare favourably with individual CMIP5 model simulations post-2100. There is a tendency for the two-layer model simulations to overestimate both GMST rise and TE under RCP2.6, which is suggestive of a systematic error in the applied radiative forcings. Overall, the framework shows promise as a basis for extending process-based projections of global sea level rise beyond the 21st century time horizon that typifies CMIP5 simulations. The results also serve to illustrate the differing responses of GMST and Earth’s energy imbalance (EEI) to reductions in greenhouse gas emissions. GMST responds relatively quickly to changes in emissions, leading to a negative trend post-2100 for RCP2.6, although temperature remains substantially elevated compared to present day at 2300. In contrast, EEI remains positive under all RCPs, and results in ongoing sea level rise from TE.

This post was initiated by Ashley Francis of Salisbury, England who had kindly forwarded the information, encouragement, and data needed to carry out this work.


Stratospheric Cooling

The Anomalies in Temperature Anomalies

The Greenhouse Effect of Atmospheric CO2

ECS: Equilibrium Climate Sensitivity

Climate Sensitivity Research: 2014-2018

TCR: Transient Climate Response

Peer Review of Climate Research: A Case Study

Spurious Correlations in Climate Science

Antarctic Sea Ice: 1979-2018

Arctic Sea Ice 1979-2018

Global Warming and Arctic Sea Ice: A Bibliography

Carbon Cycle Measurement Problems Solved with Circular Reasoning

NASA Evidence of Human Caused Climate Change

Event Attribution Science: A Case Study

Event Attribution Case Study Citations

Global Warming Trends in Daily Station Data

The Trend Profile of Temperature Data

History of the Global Warming Scare

The dearth of scientific knowledge only adds to the alarm

Nonlinear Dynamics: Is Climate Chaotic?

The Anthropocene

Eco-Fearology in the Anthropocene

Carl Wunsch Assessment of Climate Science: 2010

Gerald Marsh, A Theory of Ice Ages

History of the Ozone Depletion Scare

Empirical Test of Ozone Depletion

Ozone Depletion Chemistry

Brewer-Dobson Circulation Bibliography

Elevated CO2 and Crop Chemistry

Little Ice Age Climatology: A Bibliography

Sorcery Killings, Witch Hunts, & Climate Action

Climate Impact of the Kuwait Oil Fires: A Bibliography

Noctilucent Clouds: A Bibliography

Climate Change Denial Research: 2001-2018

Climate Change Impacts Research

Tidal Cycles: A Bibliography

Climate Change: Late Bronze Age Collapse

21 Responses to "Correlation of CMIP5 Forcings with Temperature"

[…] series is a generated by climate models with CMIP5 forcings (discussed in a related post: Correlation of CMIP5 Forcings with Temperature ). Here we compare these temperature projections of climate change theory with the observational […]

[…] Correlation of CMIP5 Forcings with Temperature […]

[…] Correlation of CMIP5 Forcings with Temperature […]

[…] Correlation of CMIP5 Forcings with Temperature […]

[…] Correlation of CMIP5 Forcings with Temperature […]

[…] Correlation of CMIP5 Forcings with Temperature […]

[…] Correlation of CMIP5 Forcings with Temperature […]

[…] Correlation of CMIP5 Forcings with Temperature […]

[…] Correlation of CMIP5 Forcings with Temperature […]

[…] Correlation of CMIP5 Forcings with Temperature […]

[…] reconstructions. This issue is also presented in some related posts on this site  [LINK]  [LINK]  [LINK]  […]

[…] The important contribution of this work to the AGW discussion is that it may encourage a greater attention to solar variability in the understanding of climate change that now relies solely on the Lacis principle that climate change can and must be understood solely in terms of fossil fuel emissions and CO2 forcing.  Related posts on this site are : [LINK] [LINK] [LINK] [LINK][LINK] […]

[…] The important contribution of this work to the AGW discussion is that it may encourage a greater attention to solar variability in the understanding of climate change that now relies solely on the Lacis principle that climate change can and must be understood solely in terms of fossil fuel emissions and CO2 forcing.  Related posts on this site are : [LINK] [LINK] [LINK] [LINK][LINK] [LINK]  […]

I don’t see any discussion of autocorrelation in the description of the statistical analysis. Therefore, the statistical validity of the r values is suspect.

The correlations reported are those between different time series over the same time span.

Autocorrelation refers to correlations among different time spans of the same time series.

Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s

%d bloggers like this: