Mean Surface Air Temperature Change Research Articles

The primary factors influencing the cooling effect of carbon dioxide removal

Carbon dioxide removal (CDR) is a crucial approach in achieving the goals set by the Paris Climate Agreement. However, understanding the cooling effect (CE) of CDR and its primary controlling factors has been challenging due to the limited number of models conducting the CDR simulation. To address this, we employed an energy balance model (EBM), which effectively captures or reproduces the global mean surface air temperature change to CO2 forcing. The outputs of the EBM revealed that even with the same CDR rate, the CE diverse significantly (σ=0.7 K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma =0.7{K}$$\\end{document}). Two main factors significantly affect the CE of CDR. The primary factor is the coefficient of the vertical heat exchange in the ocean [contributes to σCE=0.7 K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma \\left({CE}\\right)=0.7{K}$$\\end{document}], which governs the heat uptake and release of the deep ocean. It directly impacts the heat absorbed by the Earth’s surface and influences the magnitudes of the transient climate responses. The second factor is the estimation of effective radiative forcing (ERF) resulting from changes in CO2 concentration [contributes to σCE=0.6 K\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\sigma \\left({CE}\\right)=0.6{K}$$\\end{document}]. This estimation is directly associated with the amplitude of the CO2 radiative forcing and the responses of relevant energy processes, thereby influencing the temperature change. Regarding the timing of the CE emergence, the influences of processes within the EBM are quite small. Therefore, an accurate estimation of the vertical heat exchange in the ocean and ERF may favor designation of a better pathway to achieve the temperature goals outlined in the Paris Climate Agreement.

Presentation and Evaluation of the IPSL‐CM6A‐LR Ensemble of Extended Historical Simulations

AbstractThe Institut Pierre‐Simon Laplace Climate Modeling Center has produced an ensemble of extended historical simulations using the IPSL‐CM6A‐LR climate model. This ensemble (referred to as IPSL‐EHS) is composed of 32 members over the 1850–2059 period that share the same external forcings but differ in their initial conditions. In this study, we assess the simulated decadal to multidecadal climate variability in the IPSL‐EHS. In particular, we examine the global temperature evolution and recent warming trends, and their consistency with ocean heat content and sea ice cover. The model exhibits a large low‐frequency internal climate variability. In particular, a quasi‐bicentennial mode of internal climate variability is present in the model and is associated with the Atlantic Meridional Overturning Circulation. Such variability modulates the global mean surface air temperature changes over the historical period by about 0.1K. This modulation is found to be linked to the phase present in the initial condition state of each member. This variability appears to decrease during the 1850–2018 period in response to external forcings. The analysis of the ocean heat content reveals furthermore an overestimation of the ocean stratification, which likely leads to an overestimation of the recent warming rate on average.

Reducing Planetary Health Risks Through Short-Lived Climate Forcer Mitigation.

Global air pollution and climate change are major threats to planetary health. These threats are strongly linked through the short‐lived climate forcers (SLCFs); ozone (O3), aerosols, and methane (CH4). Understanding the impacts of ambitious SLCF mitigation in different source emission sectors on planetary health indicators can help prioritize international air pollution control strategies. A global Earth system model is applied to quantify the impacts of idealized 50% sustained reductions in year 2005 emissions in the eight largest global anthropogenic source sectors on the SLCFs and three indicators of planetary health: global mean surface air temperature change (∆GSAT), avoided PM2.5‐related premature mortalities and gross primary productivity (GPP). The model represents fully coupled atmospheric chemistry, aerosols, land ecosystems and climate, and includes dynamic CH4. Avoided global warming is modest, with largest impacts from 50% cuts in domestic (−0.085 K), agriculture (−0.034 K), and waste/landfill (−0.033 K). The 50% cuts in energy, domestic, and agriculture sector emissions offer the largest opportunities to mitigate global PM2.5‐related health risk at around 5%–7% each. Such small global impacts underline the challenges ahead in achieving the World Health Organization aspirational goal of a 2/3 reduction in the number of deaths from air pollution by 2030. Uncertainty due to natural climate variability in PM2.5 is an important underplayed dimension in global health risk assessment that can vastly exceed uncertainty due to the concentration‐response functions at the large regional scale. Globally, cuts to agriculture and domestic sector emissions are the most attractive targets to achieve climate and health co‐benefits through SLCF mitigation.

Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?

Abstract. An important metric for temperature projections is the equilibrium climate sensitivity (ECS), which is defined as the global mean surface air temperature change caused by a doubling of the atmospheric CO2 concentration. The range for ECS assessed by the Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report is between 1.5 and 4.5 K and has not decreased over the last decades. Among other methods, emergent constraints are potentially promising approaches to reduce the range of ECS by combining observations and output from Earth System Models (ESMs). In this study, we systematically analyze 11 published emergent constraints on ECS that have mostly been derived from models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) project. These emergent constraints are – except for one that is based on temperature variability – all directly or indirectly based on cloud processes, which are the major source of spread in ECS among current models. The focus of the study is on testing if these emergent constraints hold for ESMs participating in the new Phase 6 (CMIP6). Since none of the emergent constraints considered here have been derived using the CMIP6 ensemble, CMIP6 can be used for cross-checking of the emergent constraints on a new model ensemble. The application of the emergent constraints to CMIP6 data shows a decrease in skill and statistical significance of the emergent relationship for nearly all constraints, with this decrease being large in many cases. Consequently, the size of the constrained ECS ranges (66 % confidence intervals) widens by 51 % on average in CMIP6 compared to CMIP5. This is likely because of changes in the representation of cloud processes from CMIP5 to CMIP6, but may in some cases also be due to spurious statistical relationships or a too small number of models in the ensemble that the emergent constraint was originally derived from. The emergently- constrained best estimates of ECS also increased from CMIP5 to CMIP6 by 12 % on average. This can be at least partly explained by the increased number of high-ECS (above 4.5 K) models in CMIP6 without a corresponding change in the constraint predictors, suggesting the emergence of new feedback processes rather than changes in strength of those previously dominant. Our results support previous studies concluding that emergent constraints should be based on an independently verifiable physical mechanism, and that process-based emergent constraints on ECS should rather be thought of as constraints for the process or feedback they are actually targeting.

Role of Pacific trade winds in driving ocean temperatures during the recent slowdown and projections under a wind trend reversal

Interannual to decadal variability in the Pacific Ocean is a prominent feature of Earth’s climate system, with global teleconnections. Recent studies have identified Pacific decadal variability as a major driver of periods of rapid and slower global mean surface air temperature change. Here, we use an eddy-permitting global ocean model to investigate the role of the observed 1992–2011 trade wind intensification and concurrent trends in surface atmospheric variables over the Pacific associated with the negative phase of the Interdecadal Pacific Oscillation (IPO) in driving ocean circulation and heat content changes. We find a strengthening of the Equatorial Undercurrent (EUC) in response to strengthened winds, which brings cooler water to the surface of the eastern Pacific and an increase in the Pacific shallow overturning cells (PSOC), which in turn drives additional heat into the subsurface western Pacific. The wind acceleration also results in an increase in the strength and subsequent heat transport of the Indonesian throughflow (ITF), which transports some of the additional heat from the western Pacific into the Indian Ocean. The circulation changes result in warming of the subsurface western Pacific, cooling of the upper eastern Pacific Ocean and warming of the subsurface Indian Ocean, with an overall increase in Indo-Pacific heat content. Further experiments impose a symmetric reversal of the atmospheric state to examine how the ocean would behave if the winds (and other atmospheric variables) were to revert to their initial state. This mimics a return to the neutral phase of the IPO, characterised by a weakening of the Pacific trade winds. In response we find a slowdown of the EUC and the PSOC, which results in a return to climatological SST conditions in the western and eastern Pacific. The ITF also slows towards its original strength. However, the subsurface temperature, heat content and ITF responses are not symmetric due to an overall increase in the surface heat flux into the ocean associated with the cooler surface of the Pacific. There may also be irreversible heat transport across the thermocline via diapycnal mixing, further contributing to this asymmetry. The net result of the experiment is that the Indo-Pacific subsurface ocean is warmer than it was in its initial state.

The Sensitivity of the Proportionality between Temperature Change and Cumulative CO2 Emissions to Ocean Mixing

The ratio of global mean surface air temperature change to cumulative CO2 emissions, referred to as transient climate response to cumulative CO2 emissions (TCRE), has been shown to be approximately constant on centennial time scales. The mechanisms behind this constancy are not well understood, but previous studies suggest that compensating effects of ocean heat and carbon fluxes, which are governed by the same ocean mixing processes, could be one cause for this approximate constancy. This hypothesis is investigated by forcing different versions of the University of Victoria Earth System Climate Model, which differ in the ocean mixing parameterization, with an idealized scenario of 1% annually increasing atmospheric CO2 until quadrupling of the preindustrial CO2 concentration and constant concentration thereafter. The relationship between surface air warming and cumulative emissions remains close to linear, but the TCRE varies between model versions, spanning the range of 1.2°–2.1°C EgC−1 at the time of CO2 doubling. For all model versions, the TCRE is not constant over time while atmospheric CO2 concentrations increase. It is constant after atmospheric CO2 stabilizes at 1120 ppm, because of compensating changes in temperature sensitivity (temperature change per unit radiative forcing) and cumulative airborne fraction. The TCRE remains approximately constant over time even if temperature sensitivity, determined by ocean heat flux, and cumulative airborne fraction, determined by ocean carbon flux, are taken from different model versions with different ocean mixing settings. This can partially be explained with temperature sensitivity and cumulative airborne fraction following similar trajectories, which suggests ocean heat and carbon fluxes scale approximately linearly with changes in vertical mixing.

Assessment of aerosol effective radiative forcing and surface air temperature response over eastern China in CMIP5 models

AbstractThe effective radiative forcing (ERF) and associated surface air temperature change over eastern China are estimated using multi-model results from CMIP5 (Coupled Model Intercomparison Project Phase 5). The model results show that, relative to 1850, the multi-model and annual mean aerosol ERF for the year 2005 is −4.14 W m−2 at the top of the atmosphere over eastern China (20°–45°N, 105°–122.5°E). As a result of this ERF, the multi-model and annual mean surface air temperature change in eastern China during 1850–2005 is −1.05 °C, leading to a climate sensitivity of 0.24 °C/(W m−2) in this region.

Abrupt summer warming and changes in temperature extremes over Northeast Asia since the mid-1990s: Drivers and physical processes

Abstract This study investigated the drivers and physical processes for the abrupt decadal summer surface warming and increases in hot temperature extremes that occurred over Northeast Asia in the mid-1990s. Observations indicate an abrupt increase in summer mean surface air temperature (SAT) over Northeast Asia since the mid-1990s. Accompanying this abrupt surface warming, significant changes in some temperature extremes, characterized by increases in summer mean daily maximum temperature (Tmax), daily minimum temperature (Tmin), annual hottest day temperature (TXx), and annual warmest night temperature (TNx) were observed. There were also increases in the frequency of summer days (SU) and tropical nights (TR). Atmospheric general circulation model experiments forced by changes in sea surface temperature (SST)/sea ice extent (SIE), anthropogenic greenhouse gas (GHG) concentrations, and anthropogenic aerosol (AA) forcing, relative to the period 1964–93, reproduced the general patterns of observed summer mean SAT changes and associated changes in temperature extremes, although the abrupt decrease in precipitation since the mid-1990s was not simulated. Additional model experiments with different forcings indicated that changes in SST/SIE explained 76% of the area-averaged summer mean surface warming signal over Northeast Asia, while the direct impact of changes in GHG and AA explained the remaining 24% of the surface warming signal. Analysis of physical processes indicated that the direct impact of the changes in AA (through aerosol–radiation and aerosol–cloud interactions), mainly related to the reduction of AA precursor emissions over Europe, played a dominant role in the increase in TXx and a similarly important role as SST/SIE changes in the increase in the frequency of SU over Northeast Asia via AA-induced coupled atmosphere–land surface and cloud feedbacks, rather than through a direct impact of AA changes on cloud condensation nuclei. The modelling results also imply that the abrupt summer surface warming and increases in hot temperature extremes over Northeast Asia since the mid-1990s will probably sustain in the next few decades as GHG concentrations continue to increase and AA precursor emissions over both North America and Europe continue to decrease.

Transient Earth system responses to cumulative carbon dioxide emissions: linearities, uncertainties, and probabilities in an observation-constrained model ensemble

Abstract. Information on the relationship between cumulative fossil CO2 emissions and multiple climate targets is essential to design emission mitigation and climate adaptation strategies. In this study, the transient response of a climate or environmental variable per trillion tonnes of CO2 emissions, termed TRE, is quantified for a set of impact-relevant climate variables and from a large set of multi-forcing scenarios extended to year 2300 towards stabilization. An ∼ 1000-member ensemble of the Bern3D-LPJ carbon–climate model is applied and model outcomes are constrained by 26 physical and biogeochemical observational data sets in a Bayesian, Monte Carlo-type framework. Uncertainties in TRE estimates include both scenario uncertainty and model response uncertainty. Cumulative fossil emissions of 1000 Gt C result in a global mean surface air temperature change of 1.9 °C (68 % confidence interval (c.i.): 1.3 to 2.7 °C), a decrease in surface ocean pH of 0.19 (0.18 to 0.22), and a steric sea level rise of 20 cm (13 to 27 cm until 2300). Linearity between cumulative emissions and transient response is high for pH and reasonably high for surface air and sea surface temperatures, but less pronounced for changes in Atlantic meridional overturning, Southern Ocean and tropical surface water saturation with respect to biogenic structures of calcium carbonate, and carbon stocks in soils. The constrained model ensemble is also applied to determine the response to a pulse-like emission and in idealized CO2-only simulations. The transient climate response is constrained, primarily by long-term ocean heat observations, to 1.7 °C (68 % c.i.: 1.3 to 2.2 °C) and the equilibrium climate sensitivity to 2.9 °C (2.0 to 4.2 °C). This is consistent with results by CMIP5 models but inconsistent with recent studies that relied on short-term air temperature data affected by natural climate variability.

Adjusted radiative forcing and global radiative feedbacks in CNRM-CM5, a closure of the partial decomposition

This study provides a comprehensive global analysis of the climate radiative feedbacks and the adjusted radiative forcing for a CO2 increase perturbation in the CNRM-CM5 climate model using the partial radiative perturbations (PRP) method. Some methodological key points of the PRP are investigated, with a particular focus on the consideration of the effect of fast adjustments. First, the standard PRP method is applied by neglecting certain fast adjustments. The effect of the field decorrelation is highlighted by performing a PRP across two different periods of a control experiment and by analyzing second-order terms. Sensitivity tests to the field substitution frequency, the sampling period and the perturbed experiment used are performed. The impact of the definition of the top of the climate system (top-of-the-atmosphere or tropopause) in the feedback estimate is also discussed. Secondly, the fast adjustment processes are taken into account by combining the PRP framework with the method of linear regression of the partial net radiative flux change against the mean surface air temperature change using a step forcing experiment. This method allows us to quantify the contribution of the different constituents to the forcing adjustment and to improve the estimation of the radiative feedbacks. It is shown that such decomposition allows the retrieval of the adjusted radiative forcing, the radiative feedbacks and the climate sensitivity as estimated with the linear regression method with a high level of accuracy, validating the partial decomposition.

Abrupt CO2 experiments as tools for predicting and understanding CMIP5 representative concentration pathway projections

A fast simple climate modelling approach is developed for predicting and helping to understand general circulation model (GCM) simulations. We show that the simple model reproduces the GCM results accurately, for global mean surface air temperature change and global-mean heat uptake projections from 9 GCMs in the fifth coupled model inter-comparison project (CMIP5). This implies that understanding gained from idealised CO2 step experiments is applicable to policy-relevant scenario projections. Our approach is conceptually simple. It works by using the climate response to a CO2 step change taken directly from a GCM experiment. With radiative forcing from non-CO2 constituents obtained by adapting the Forster and Taylor method, we use our method to estimate results for CMIP5 representative concentration pathway (RCP) experiments for cases not run by the GCMs. We estimate differences between pairs of RCPs rather than RCP anomalies relative to the pre-industrial state. This gives better results because it makes greater use of available GCM projections. The GCMs exhibit differences in radiative forcing, which we incorporate in the simple model. We analyse the thus-completed ensemble of RCP projections. The ensemble mean changes between 1986–2005 and 2080–2099 for global temperature (heat uptake) are, for RCP8.5: 3.8 K (2.3 × 1024 J); for RCP6.0: 2.3 K (1.6 × 1024 J); for RCP4.5: 2.0 K (1.6 × 1024 J); for RCP2.6: 1.1 K (1.3 × 1024 J). The relative spread (standard deviation/ensemble mean) for these scenarios is around 0.2 and 0.15 for temperature and heat uptake respectively. We quantify the relative effect of mitigation action, through reduced emissions, via the time-dependent ratios (change in RCPx)/(change in RCP8.5), using changes with respect to pre-industrial conditions. We find that the effects of mitigation on global-mean temperature change and heat uptake are very similar across these different GCMs.

Projection of global mean surface air temperature changes in next 40 years: Uncertainties of climate models and an alternative approach

The Fourth Assessment Report (AR4) of the Intergovernmental Panel of Climate Change (IPCC) concluded that the climate projection using climate models that took account of both human and natural factors provided credible quantitative estimates of future climate change; however, the mismatches between the IPCC AR4 model ensembles and the observations, especially the multi-decadal variability (MDV), have cast shadows on the confidence of the model-based decadal projections of future climate. This paper reports an evaluation of many individual runs of AR4 models in the simulation of past global mean temperature. We find that most of the individual model runs fail to reproduce the MDV of past climate, which may have led to the overestimation of the projection of global warming for the next 40 years or so. Based on such an evaluation, we propose an alternative approach, in which the MDV signal is taken into account, to project the global mean temperature for the next 40 years and obtain that the global warming during 2011–2050 could be much smaller than the AR4 projection. decadal prediction, global warming, multi-decadal climate variability, the Ensemble Empirical Mode Decomposition, CMIP3 multi-model

Transient climate response estimated from radiative forcing and observed temperature change

Observations and simulations (using the HadCM3 AOGCM) of time‐dependent twentieth‐century climate change indicate a linear relationship F = ρΔT between radiative forcing F and global mean surface air temperature change ΔT. The same is a good description of ΔT from CMIP3 AOGCMs integrated with CO2 increasing at 1% per year compounded. The constant “climate resistance” ρ is related to the transient climate response (TCR, ΔT at the time of doubled CO2 under the 1% CO2 scenario). Disregarding any trend caused by natural forcing (volcanic and solar), which is small compared with the trend in anthropogenic forcing, we estimate that the real‐world TCR is 1.3–2.3 K (5–95% uncertainty range) from the data of 1970–2006, allowing for the effect of unforced variability on longer timescales. The climate response to episodic volcanic forcing cannot be described by the same relationship and merits further investigation; this constitutes a systematic uncertainty of the method. The method is quite insensitive to the anthropogenic aerosol forcing, which probably did not vary much during 1970–2006 and therefore did not affect the trend in ΔT. Our range is very similar to the range of recent AOGCM results for the TCR. Consequently projections for warming during the twenty‐first century under the SRES A1B emissions scenario made using the simple empirical relationship F = ρΔT agree with the range of AOGCM results for that scenario. Our TCR range is also similar to those from observationally constrained model‐based methods.

Urbanization Effects on Observed Surface Air Temperature Trends in North China

Abstract A dataset of 282 meteorological stations including all of the ordinary and national basic/reference surface stations of north China is used to analyze the urbanization effect on surface air temperature trends. These stations are classified into rural, small city, medium city, large city, and metropolis based on the updated information of total population and specific station locations. The significance of urban warming effects on regional average temperature trends is estimated using monthly mean temperature series of the station group datasets, which undergo inhomogeneity adjustment. The authors found that the largest effect of urbanization on annual mean surface air temperature trends occurs for the large-city station group, with the urban warming being 0.16°C (10 yr)−1, and the effect is the smallest for the small-city station group with urban warming being only 0.07°C (10 yr)−1. A similar assessment is made for the dataset of national basic/reference stations, which has been widely used in regional climate change analyses in China. The results indicate that the regional average annual mean temperature series, as calculated using the data from the national basic/reference stations, is significantly impacted by urban warming, and the trend of urban warming is estimated to be 0.11°C (10 yr)−1. The contribution of urban warming to total annual mean surface air temperature change as estimated with the national basic/reference station dataset reaches 37.9%. It is therefore obvious that, in the current regional average surface air temperature series in north China, or probably in the country as a whole, there still remain large effects from urban warming. The urban warming bias for the regional average temperature anomaly series is corrected. After that, the increasing rate of the regional annual mean temperature is brought down from 0.29°C (10 yr)−1 to 0.18°C (10 yr)−1, and the total change in temperature approaches 0.72°C for the period analyzed.

Probabilistic climate change predictions applying Bayesian model averaging

This study explores the sensitivity of probabilistic predictions of the twenty-first century surface air temperature (SAT) changes to different multi-model averaging methods using available simulations from the Intergovernmental Panel on Climate Change fourth assessment report. A way of observationally constrained prediction is provided by training multi-model simulations for the second half of the twentieth century with respect to long-term components. The Bayesian model averaging (BMA) produces weighted probability density functions (PDFs) and we compare two methods of estimating weighting factors: Bayes factor and expectation-maximization algorithm. It is shown that Bayesian-weighted PDFs for the global mean SAT changes are characterized by multi-modal structures from the middle of the twenty-first century onward, which are not clearly seen in arithmetic ensemble mean (AEM). This occurs because BMA tends to select a few high-skilled models and down-weight the others. Additionally, Bayesian results exhibit larger means and broader PDFs in the global mean predictions than the unweighted AEM. Multi-modality is more pronounced in the continental analysis using 30-year mean (2070-2099) SATs while there is only a little effect of Bayesian weighting on the 5-95% range. These results indicate that this approach to observationally constrained probabilistic predictions can be highly sensitive to the method of training, particularly for the later half of the twenty-first century, and that a more comprehensive approach combining different regions and/or variables is required.

A Multimodel Update on the Detection and Attribution of Global Surface Warming

Abstract This paper presents an update on the detection and attribution of global annual mean surface air temperature changes, using recently developed climate models. In particular, it applies a new methodology that permits the inclusion of many more general circulation models (GCMs) into the analysis, and it also includes more recent observations. This methodology involves fitting a series of energy balance models (EBMs) to the GCM output in order to estimate the temporal response patterns to the various forcings. Despite considerable spread in estimated EBM parameters, characteristics of model performance, such as the transient climate response, appear to be more constrained for each of the forcings. The resulting estimated response patterns are provided as input to the standard fingerprinting method used in previous studies. The estimated GCM responses to changes in greenhouse gases are detected in the observed record for all of the GCMs, and are generally found to be consistent with the observed changes; the same is generally true for the responses to changes in stratospheric aerosols from volcanic eruptions. GCM responses to changes in tropospheric sulfate aerosols and solar irradiance also appear consistent with the observed record, although the uncertainty is larger. Greenhouse gas and solar irradiance changes are found to have contributed to a best guess of ∼0.8 and ∼0.3 K warming over the 1901–2005 period, respectively, while sulfate aerosols have contributed a ∼0.4 K cooling. This analysis provides an observationally constrained estimate of future warming, which is found to be fairly robust across GCMs. By 2100, a warming of between about 1.5 and 4.5 K can be expected according to the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) A1B emissions scenario. These results indicate an emerging constraint for global mean surface temperature responses to external forcings across GCMs, which is corroborated in the observed record. This implies that observationally constrained estimates of past warming and predictions of future warming are indeed becoming robust.

A Bayesian Assessment of Climate Change Using Multimodel Ensembles. Part I: Global Mean Surface Temperature

Abstract A Bayesian approach is applied to the observed global surface air temperature (SAT) changes using multimodel ensembles (MMEs) of the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) simulations and single-model ensembles (SMEs) with the ECHO-G coupled climate model. A Bayesian decision method is used as a tool for classifying observations into given scenarios (or hypotheses). The prior probability of the scenarios, which represents a degree of subjective belief in the scenarios, is changed into the posterior probability through the likelihood where observations enter, and the posterior is used as a decision function. In the identical prior case the Bayes factor (or likelihood ratio) becomes a decision function and provides observational evidence for each scenario against a predefined reference scenario. Four scenarios are used to explain observed SAT changes: “CTL” (control or no change), “Nat” (natural forcing induced change), “GHG” (greenhouse gas–induced change), and “All” (natural plus anthropogenic forcing–induced change). Observed and simulated global mean SATs are decomposed into temporal components of overall mean, linear trend, and decadal variabilities through Legendre series expansions, coefficients of which are used as detection variables. Parameters (means and covariance matrices) needed to define the four scenarios are estimated from SMEs or MMEs. Taking the CTL scenario as reference one, application results for global mean SAT changes for the whole twentieth century (1900–99) show “decisive” evidence (logarithm of Bayes factor >5) for the All scenario only. While “strong” evidence (log of Bayes factor >2.5) for both the Nat and All scenarios are found in SAT changes for the first half (1900–49), there is decisive evidence for the All scenario for SAT changes in the second half (1950–99), supporting previous results. It is demonstrated that the Bayesian decision results for global mean SATs are largely insensitive to both intermodel uncertainties and prior probabilities.

A Bayesian approach to climate model evaluation and multi‐model averaging with an application to global mean surface temperatures from IPCC AR4 coupled climate models

A Bayesian approach is introduced to model evaluation and multi‐model averaging with a systematic consideration of model uncertainty, and its application to global mean surface air temperature (SAT) changes is shown from multi‐AOGCM ensembles of IPCC AR4 simulations. The Bayes factor or likelihood ratio of each model to the reference model (where mean is identical to the observation) provides a skill ranging from 0 to 1. Four categories of model skill are derived on the basis of the previous studies. Legendre series expansions are used to get a temporally refined model evaluation, which allow efficient analyses of time mean (scale) and linear trend. Application results show that all AOGCMs with natural plus anthropogenic forcing can simulate well the scale and trend of observed global mean SAT changes over the 20th century and its first and second halves. However, more than 50% of the models with anthropogenic‐only forcing cannot reproduce the observed warming reasonably. This indicates the important role of natural forcing although other factors like different climate sensitivity, forcing uncertainty, and a climate drift might be responsible for the discrepancy in anthropogenic‐only models. Besides, Bayesian and conventional skill comparisons demonstrate that a skill‐weighted average with the Bayes factors (Bayesian model averaging, BMA) overwhelms the arithmetic ensemble mean and three other weighted averages based on conventional statistics, illuminating future applicability of BMA to climate predictions.

Climate change hot‐spots

A Regional Climate Change Index (RCCI), is developed based on regional mean precipitation change, mean surface air temperature change, and change in precipitation and temperature interannual variability. The RCCI is a comparative index designed to identify the most responsive regions to climate change, or Hot‐Spots. The RCCI is calculated for 26 land regions from the latest set of climate change projections by 20 global climate models for the A1B, A2 and B1 IPCC emission scenarios. The Mediterranean and North Eastern European regions emerge as the primary Hot‐Spots, followed by high latitude northern hemisphere regions and by Central America, the most prominent tropical Hot‐Spot. The main African Hot‐Spots are Southern Equatorial Africa and the Sahara. Eastern North America is the prominent Hot‐Spot over the continental U.S. Different factors over different regions contribute to the magnitude of the RCCI, which is in fact greater than 0 for all regions.

Effects of Cloud Parameterization on the Simulation of Climate Changes in the GISS GCM. Part II: Sea Surface Temperature and Cloud Feedbacks

Abstract The influence of the sea surface temperature distribution on cloud feedbacks is studied by making two sets of doubled CO2 experiments with the Goddard Institute for Space Studies (GISS) GCM at 4° latitude × 5° longitude resolution. One set uses Q fluxes obtained by prescribing observed sea surface temperatures (MODELII′), and the other set uses Q fluxes obtained by prescribing the simulated sea surface temperature of a coupled ocean–atmosphere model (MODELIIO). The global and annual mean surface air temperature change (ΔTs) obtained in MODELII′ is reduced from 4.11° to 3.02°C in MODELIIO. This reduced sensitivity, aside from reduced sea ice/snow–albedo feedback, is mainly due to cloud feedback that becomes nearly neutral in MODELIIO. Furthermore, the negative effect on climate sensitivity of anvil clouds of large optical thickness identified by Yao and Del Genio changes its sign in MODELIIO primarily due to sharply reduced increases of cloud water in the tropical upper troposphere. Colder tropica...