Equilibrium Climate Sensitivity in AWI-ESM: Mechanisms and Effects

<p><span>The global-mean surface air temperature change due to a doubled carbon dioxide concentration in the atmosphere (equilibrium climate sensitivity, ECS) is an important measure to quantify the impact of predicted anthropogenic climate change. The latest climate modeling intercomparison project (CMIP6) exhibits a higher ECS compared to the previous climate model generation (1.8 to 5.6 K for CMIP6 versus 1.5 to 4.5 K for CMIP5). The increase in ECS is likely due to decreases in extratropical low cloud coverage and albedo, caused by improvements in the numerical aerosol schemes. Our state-of-the-art Earth system model AWI-ESM, developed at the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, yields an ECS of 3.59-3.62 K, which is close to the CMIP5 mean. Using a set of varying model configurations, we identify dynamic vegetation and model resolution as the primary driving factors which influence the modeled global response to an increased greenhouse gas forcing.</span></p>

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Including the efficacy of land ice changes in deriving climate sensitivity from paleodata

10.5194/esd-2018-88 ◽

2018 ◽

Author(s):

Lennert B. Stap ◽

Peter Köhler ◽

Gerrit Lohmann

Keyword(s):

Climate Sensitivity ◽

Radiative Forcing ◽

Climate Models ◽

Climate Model ◽

Temperature Anomaly ◽

Global Temperature ◽

Radiative Forcings ◽

Intercomparison Project ◽

The Impact

Abstract. The influence of long-term processes in the climate system, such as land ice changes, has to be compensated for when comparing climate sensitivity derived from paleodata with equilibrium climate sensitivity (ECS) calculated by climate models, which is only generated by a CO2 change. Several recent studies found that the impact these long-term processes have on global temperature cannot be quantified directly through the global radiative forcing they induce. This renders the approach of deconvoluting paleotemperatures through a partitioning based on radiative forcings inaccurate. Here, we therefore implement an efficacy factor ε[LI], that relates the impact of land ice changes on global temperature to that of CO2 changes, in our calculation of climate sensitivity from paleodata. We apply our new approach to a proxy-inferred paleoclimate dataset, and find an equivalent ECS of 5.6 ± 1.3 K per CO2 doubling. The substantial uncertainty herein is generated by the range in ε[LI] we use, which is based on a multi-model assemblage of simulated relative influences of land ice changes on the Last Glacial Maximum (LGM) temperature anomaly (46 ± 14 %). The low end of our ECS estimate, which concurs with estimates from other approaches, tallies with a large influence for land ice changes. To separately assess this influence, we analyse output of the PMIP3 climate model intercomparison project. From this data, we infer a functional intermodel relation between global and high-latitude temperature changes at the LGM with respect to the pre-industrial climate, and the temperature anomaly caused by a CO2 change. Applying this relation to our dataset, we find a considerable 64 % influence for land ice changes on the LGM temperature anomaly. This is even higher than the range used before, and leads to an equivalent ECS of 3.8 K per CO2 doubling. Together, our results suggest that land ice changes play a key role in the variability of Late Pleistocene temperatures.

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Could the Pliocene constrain the equilibrium climate sensitivity?

Climate of the Past ◽

10.5194/cp-12-1591-2016 ◽

2016 ◽

Vol 12 (8) ◽

pp. 1591-1599 ◽

Cited By ~ 8

Author(s):

J. C. Hargreaves ◽

J. D. Annan

Keyword(s):

Climate Sensitivity ◽

Atmospheric Carbon Dioxide ◽

Climate Models ◽

Climate Model ◽

Temperature Anomaly ◽

Model Intercomparison ◽

Equilibrium Climate Sensitivity ◽

Intercomparison Project ◽

First Time ◽

Industrial State

Abstract. The mid-Pliocene Warm Period (mPWP) is the most recent interval in which atmospheric carbon dioxide was substantially higher than in modern pre-industrial times. It is, therefore, a potentially valuable target for testing the ability of climate models to simulate climates warmer than the pre-industrial state. The recent Pliocene Model Intercomparison Project (PlioMIP) presented boundary conditions for the mPWP and a protocol for climate model experiments. Here we analyse results from the PlioMIP and, for the first time, discuss the potential for this interval to usefully constrain the equilibrium climate sensitivity. We observe a correlation in the ensemble between their tropical temperature anomalies at the mPWP and their equilibrium sensitivities. If the real world is assumed to also obey this relationship, then the reconstructed tropical temperature anomaly at the mPWP can in principle generate a constraint on the true sensitivity. Directly applying this methodology using available data yields a range for the equilibrium sensitivity of 1.9–3.7 °C, but there are considerable additional uncertainties surrounding the analysis which are not included in this estimate. We consider the extent to which these uncertainties may be better quantified and perhaps lessened in the next few years.

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Seasonal Surface Air Temperature and Precipitation in the FSU Climate Model Coupled to the CLM2

Journal of Climate ◽

10.1175/jcli3470.1 ◽

2005 ◽

Vol 18 (16) ◽

pp. 3217-3228 ◽

Cited By ~ 16

Author(s):

D. W. Shin ◽

S. Cocke ◽

T. E. LaRow ◽

James J. O’Brien

Keyword(s):

Heat Flux ◽

Air Temperature ◽

Climate Model ◽

Initial Conditions ◽

Sensible Heat Flux ◽

Surface Air Temperature ◽

Community Land Model ◽

Temperature And Precipitation ◽

The Impact ◽

Convective Scheme

Abstract The current Florida State University (FSU) climate model is upgraded by coupling the National Center for Atmospheric Research (NCAR) Community Land Model Version 2 (CLM2) as its land component in order to make a better simulation of surface air temperature and precipitation on the seasonal time scale, which is important for crop model application. Climatological and seasonal simulations with the FSU climate model coupled to the CLM2 (hereafter FSUCLM) are compared to those of the control (the FSU model with the original simple land surface treatment). The current version of the FSU model is known to have a cold bias in the temperature field and a wet bias in precipitation. The implementation of FSUCLM has reduced or eliminated this bias due to reduced latent heat flux and increased sensible heat flux. The role of the land model in seasonal simulations is shown to be more important during summertime than wintertime. An additional experiment that assimilates atmospheric forcings produces improved land-model initial conditions, which in turn reduces the biases further. The impact of various deep convective parameterizations is examined as well to further assess model performance. The land scheme plays a more important role than the convective scheme in simulations of surface air temperature. However, each convective scheme shows its own advantage over different geophysical locations in precipitation simulations.

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

Earth System Dynamics ◽

10.5194/esd-11-1233-2020 ◽

2020 ◽

Vol 11 (4) ◽

pp. 1233-1258

Author(s):

Manuel Schlund ◽

Axel Lauer ◽

Pierre Gentine ◽

Steven C. Sherwood ◽

Veronika Eyring

Keyword(s):

Climate Sensitivity ◽

Statistical Significance ◽

Coupled Model ◽

Surface Air Temperature ◽

Co2 Concentration ◽

Intergovernmental Panel ◽

Equilibrium Climate Sensitivity ◽

Emergent Constraints ◽

Statistical Relationships ◽

Emergent Constraint

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.

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On the Choice of Ensemble Mean for Estimating the Forced Signal in the Presence of Internal Variability

Journal of Climate ◽

10.1175/jcli-d-17-0662.1 ◽

2018 ◽

Vol 31 (14) ◽

pp. 5681-5693 ◽

Cited By ~ 14

Author(s):

Leela M. Frankcombe ◽

Matthew H. England ◽

Jules B. Kajtar ◽

Michael E. Mann ◽

Byron A. Steinman

Keyword(s):

Regional Differences ◽

Climate Model ◽

Surface Air Temperature ◽

Internal Variability ◽

Single Model ◽

Sea Level Pressure ◽

Model Simulations ◽

Ensemble Mean ◽

Climate Model Simulations ◽

The Impact

Abstract In this paper we examine various options for the calculation of the forced signal in climate model simulations, and the impact these choices have on the estimates of internal variability. We find that an ensemble mean of runs from a single climate model [a single model ensemble mean (SMEM)] provides a good estimate of the true forced signal even for models with very few ensemble members. In cases where only a single member is available for a given model, however, the SMEM from other models is in general out-performed by the scaled ensemble mean from all available climate model simulations [the multimodel ensemble mean (MMEM)]. The scaled MMEM may therefore be used as an estimate of the forced signal for observations. The MMEM method, however, leads to increasing errors further into the future, as the different rates of warming in the models causes their trajectories to diverge. We therefore apply the SMEM method to those models with a sufficient number of ensemble members to estimate the change in the amplitude of internal variability under a future forcing scenario. In line with previous results, we find that on average the surface air temperature variability decreases at higher latitudes, particularly over the ocean along the sea ice margins, while variability in precipitation increases on average, particularly at high latitudes. Variability in sea level pressure decreases on average in the Southern Hemisphere, while in the Northern Hemisphere there are regional differences.

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The Strength of Low-Cloud Feedbacks and Tropical Climate: A CESM Sensitivity Study

Journal of Climate ◽

10.1175/jcli-d-18-0551.1 ◽

2019 ◽

Vol 32 (9) ◽

pp. 2497-2516 ◽

Cited By ~ 4

Author(s):

Ehsan Erfani ◽

Natalie J. Burls

Keyword(s):

Climate Sensitivity ◽

Cloud Cover ◽

Hydrological Cycle ◽

Tropical Climate ◽

Cloud Feedback ◽

Cloud Feedbacks ◽

Feedback Strength ◽

Low Cloud ◽

Low Cloud Cover ◽

The Impact

Abstract Variability in the strength of low-cloud feedbacks across climate models is the primary contributor to the spread in their estimates of equilibrium climate sensitivity (ECS). This raises the question: What are the regional implications for key features of tropical climate of globally weak versus strong low-cloud feedbacks in response to greenhouse gas–induced warming? To address this question and formalize our understanding of cloud controls on tropical climate, we perform a suite of idealized fully coupled and slab-ocean climate simulations across which we systematically scale the strength of the low-cloud-cover feedback under abrupt 2 × CO2 forcing within a single model, thereby isolating the impact of low-cloud feedback strength. The feedback strength is varied by modifying the stratus cloud fraction so that it is a function of not only local conditions but also global temperature in a series of abrupt 2 × CO2 sensitivity experiments. The unperturbed decrease in low cloud cover (LCC) under 2 × CO2 is greatest in the mid- and high-latitude oceans, and the subtropical eastern Pacific and Atlantic, a pattern that is magnified as the feedback strength is scaled. Consequently, sea surface temperature (SST) increases more in these regions as well as the Pacific cold tongue. As the strength of the low-cloud feedback increases this results in not only increased ECS, but also an enhanced reduction of the large-scale zonal and meridional SST gradients (structural climate sensitivity), with implications for the atmospheric Hadley and Walker circulations, as well as the hydrological cycle. The relevance of our results to simulating past warm climate is also discussed.

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Estimating the Uncertainty in a Regional Climate Model Related to Initial and Lateral Boundary Conditions

Journal of Climate ◽

10.1175/jcli-3293.1 ◽

2005 ◽

Vol 18 (7) ◽

pp. 917-933 ◽

Cited By ~ 72

Author(s):

Wanli Wu ◽

Amanda H. Lynch ◽

Aaron Rivers

Keyword(s):

Boundary Conditions ◽

Climate Model ◽

Initial Conditions ◽

Climate Modeling ◽

Regional Climate ◽

Regional Climate Modeling ◽

Regional Model ◽

Lateral Boundary ◽

Lateral Boundary Conditions ◽

The Impact

Abstract There is a growing demand for regional-scale climate predictions and assessments. Quantifying the impacts of uncertainty in initial conditions and lateral boundary forcing data on regional model simulations can potentially add value to the usefulness of regional climate modeling. Results from a regional model depend on the realism of the driving data from either global model outputs or global analyses; therefore, any biases in the driving data will be carried through to the regional model. This study used four popular global analyses and achieved 16 driving datasets by using different interpolation procedures. The spread of the 16 datasets represents a possible range of driving data based on analyses to the regional model. This spread is smaller than typically associated with global climate model realizations of the Arctic climate. Three groups of 16 realizations were conducted using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) in an Arctic domain, varying both initial and lateral boundary conditions, varying lateral boundary forcing only, and varying initial conditions only. The response of monthly mean atmospheric states to the variations in initial and lateral driving data was investigated. Uncertainty in the regional model is induced by the interaction between biases from different sources. Because of the nonlinearity of the problem, contributions from initial and lateral boundary conditions are not additive. For monthly mean atmospheric states, biases in lateral boundary conditions generally contribute more to the overall uncertainty than biases in the initial conditions. The impact of initial condition variations decreases with the simulation length while the impact of variations in lateral boundary forcing shows no clear trend. This suggests that the representativeness of the lateral boundary forcing plays a critical role in long-term regional climate modeling. The extent of impact of the driving data uncertainties on regional climate modeling is variable dependent. For some sensitive variables (e.g., precipitation, boundary layer height), even the interior of the model may be significantly affected.

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Simulation of black carbon in snow and its climate impact in the Canadian Global Climate Model

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-18839-2015 ◽

2015 ◽

Vol 15 (13) ◽

pp. 18839-18882 ◽

Cited By ~ 1

Author(s):

M. Namazi ◽

K. von Salzen ◽

J. N. S. Cole

Keyword(s):

Black Carbon ◽

Climate Model ◽

Global Climate ◽

Global Climate Model ◽

Surface Air Temperature ◽

Climate Impact ◽

Radiative Forcings ◽

Mixing Ratios ◽

Emission Changes ◽

The Impact

Abstract. A new physically-based parameterization of black carbon (BC) in snow was developed and implemented in the Canadian Atmospheric Global Climate Model (CanAM4.2). Simulated BC snow mixing ratios and BC snow radiative forcings are in good agreement with measurements and results from other models. Simulations with the improved model yield considerable trends in regional BC concentrations in snow and BC snow radiative forcings during the time period from 1950–1959 to 2000–2009. Increases in radiative forcings for Asia and decreases for Europe and North America are found to be associated with changes in BC emissions. Additional sensitivity simulations were performed in order to study the impact of BC emission changes between 1950–1959 and 2000–2009 on surface albedo, snow cover fraction, and surface air temperature. Results from these simulations indicate that impacts of BC emission changes on snow albedos between these two decades are small and not significant. Overall, changes in BC concentrations in snow have much smaller impacts on the cryosphere than the net warming surface air temperatures during the second half of the 20th century.

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