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- Author or Editor: Benjamin M. Sanderson x
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Abstract
One tool for studying uncertainties in simulations of future climate is to consider ensembles of general circulation models where parameterizations have been sampled within their physical range of plausibility. This study is about simulations from two such ensembles: a subset of the climateprediction.net ensemble using the Met Office Hadley Centre Atmosphere Model, version 3.0 and the new “CAMcube” ensemble using the Community Atmosphere Model, version 3.5. The study determines that the distribution of climate sensitivity in the two ensembles is very different: the climateprediction.net ensemble subset range is 1.7–9.9 K, while the CAMcube ensemble range is 2.2–3.2 K. On a regional level, however, both ensembles show a similarly diverse range in their mean climatology. Model radiative flux changes suggest that the major difference between the ranges of climate sensitivity in the two ensembles lies in their clear-sky longwave responses. Large clear-sky feedbacks present only in the climateprediction.net ensemble are found to be proportional to significant biases in upper-tropospheric water vapor concentrations, which are not observed in the CAMcube ensemble. Both ensembles have a similar range of shortwave cloud feedback, making it unlikely that they are causing the larger climate sensitivities in climateprediction.net. In both cases, increased negative shortwave cloud feedbacks at high latitudes are generally compensated by increased positive feedbacks at lower latitudes.
Abstract
One tool for studying uncertainties in simulations of future climate is to consider ensembles of general circulation models where parameterizations have been sampled within their physical range of plausibility. This study is about simulations from two such ensembles: a subset of the climateprediction.net ensemble using the Met Office Hadley Centre Atmosphere Model, version 3.0 and the new “CAMcube” ensemble using the Community Atmosphere Model, version 3.5. The study determines that the distribution of climate sensitivity in the two ensembles is very different: the climateprediction.net ensemble subset range is 1.7–9.9 K, while the CAMcube ensemble range is 2.2–3.2 K. On a regional level, however, both ensembles show a similarly diverse range in their mean climatology. Model radiative flux changes suggest that the major difference between the ranges of climate sensitivity in the two ensembles lies in their clear-sky longwave responses. Large clear-sky feedbacks present only in the climateprediction.net ensemble are found to be proportional to significant biases in upper-tropospheric water vapor concentrations, which are not observed in the CAMcube ensemble. Both ensembles have a similar range of shortwave cloud feedback, making it unlikely that they are causing the larger climate sensitivities in climateprediction.net. In both cases, increased negative shortwave cloud feedbacks at high latitudes are generally compensated by increased positive feedbacks at lower latitudes.
Abstract
Radiative kernels have become a common tool for evaluating and comparing radiative feedbacks to climate change in different general circulation models. However, kernel feedback calculations are inaccurate for simulations where the atmosphere is significantly perturbed from its base state, such as for very large forcing or perturbed physics simulations. In addition, past analyses have not produced kernels relating to prognostic cloud variables because of strong nonlinearities in their relationship to radiative forcing. A new methodology is presented that allows for fast statistical optimizing of existing kernels such that accuracy is increased for significantly altered climatologies. International Satellite Cloud Climatology Project (ISCCP) simulator output is used to relate changes in cloud-type histograms to radiative fluxes. With minimal additional computation, an individual set of kernels is created for each climate experiment such that climate feedbacks can be reliably estimated even in significantly perturbed climates.
This methodology is applied to successive generations of the Community Atmosphere Model (CAM). Increased climate sensitivity in CAM5 is shown to be due to reduced negative stratus and stratocumulus feedbacks in the tropics and midlatitudes, strong positive stratus feedbacks in the southern oceans, and a strengthened positive longwave cirrus feedback. Results also suggest that CAM5 exhibits a stronger surface albedo feedback than its predecessors, a feature not apparent when using a single kernel. Optimized kernels for CAM5 suggest weaker global-mean shortwave cloud feedback than one would infer from using the original kernels and an adjusted cloud radiative forcing methodology.
Abstract
Radiative kernels have become a common tool for evaluating and comparing radiative feedbacks to climate change in different general circulation models. However, kernel feedback calculations are inaccurate for simulations where the atmosphere is significantly perturbed from its base state, such as for very large forcing or perturbed physics simulations. In addition, past analyses have not produced kernels relating to prognostic cloud variables because of strong nonlinearities in their relationship to radiative forcing. A new methodology is presented that allows for fast statistical optimizing of existing kernels such that accuracy is increased for significantly altered climatologies. International Satellite Cloud Climatology Project (ISCCP) simulator output is used to relate changes in cloud-type histograms to radiative fluxes. With minimal additional computation, an individual set of kernels is created for each climate experiment such that climate feedbacks can be reliably estimated even in significantly perturbed climates.
This methodology is applied to successive generations of the Community Atmosphere Model (CAM). Increased climate sensitivity in CAM5 is shown to be due to reduced negative stratus and stratocumulus feedbacks in the tropics and midlatitudes, strong positive stratus feedbacks in the southern oceans, and a strengthened positive longwave cirrus feedback. Results also suggest that CAM5 exhibits a stronger surface albedo feedback than its predecessors, a feature not apparent when using a single kernel. Optimized kernels for CAM5 suggest weaker global-mean shortwave cloud feedback than one would infer from using the original kernels and an adjusted cloud radiative forcing methodology.
Abstract
The diverse set of Earth system models used to conduct the CMIP5 ensemble can partly sample the uncertainties in future climate projections. However, combining those projections is complicated by the fact that models developed by different groups share ideas and code and therefore biases. The authors propose a method for combining model results into single or multivariate distributions that are more robust to the inclusion of models with a large degree of interdependency. This study uses a multivariate metric of present-day climatology to assess both model performance and similarity in two recent model intercomparisons, CMIP3 and CMIP5. Model characteristics can be interpolated and then resampled in a space defined by independent climate properties. A form of weighting can be applied by sampling more densely in the region of the space close to the projected observations, thus taking into account both model performance and interdependence. The choice of the sampling distribution’s parameters is a subjective decision that should reflect the researcher’s prior assumptions as to the acceptability of different model errors.
Abstract
The diverse set of Earth system models used to conduct the CMIP5 ensemble can partly sample the uncertainties in future climate projections. However, combining those projections is complicated by the fact that models developed by different groups share ideas and code and therefore biases. The authors propose a method for combining model results into single or multivariate distributions that are more robust to the inclusion of models with a large degree of interdependency. This study uses a multivariate metric of present-day climatology to assess both model performance and similarity in two recent model intercomparisons, CMIP3 and CMIP5. Model characteristics can be interpolated and then resampled in a space defined by independent climate properties. A form of weighting can be applied by sampling more densely in the region of the space close to the projected observations, thus taking into account both model performance and interdependence. The choice of the sampling distribution’s parameters is a subjective decision that should reflect the researcher’s prior assumptions as to the acceptability of different model errors.
Abstract
The collection of Earth system models available in the archive of phase 5 of CMIP (CMIP5) represents, at least to some degree, a sample of uncertainty of future climate evolution. The presence of duplicated code as well as shared forcing and validation data in the multiple models in the archive raises at least three potential problems: biases in the mean and variance, the overestimation of sample size, and the potential for spurious correlations to emerge in the archive because of model replication. Analytical evidence is presented to demonstrate that the distribution of models in the CMIP5 archive is not consistent with a random sample, and a weighting scheme is proposed to reduce some aspects of model codependency in the ensemble. A method is proposed for selecting diverse and skillful subsets of models in the archive, which could be used for impact studies in cases where physically consistent joint projections of multiple variables (and their temporal and spatial characteristics) are required.
Abstract
The collection of Earth system models available in the archive of phase 5 of CMIP (CMIP5) represents, at least to some degree, a sample of uncertainty of future climate evolution. The presence of duplicated code as well as shared forcing and validation data in the multiple models in the archive raises at least three potential problems: biases in the mean and variance, the overestimation of sample size, and the potential for spurious correlations to emerge in the archive because of model replication. Analytical evidence is presented to demonstrate that the distribution of models in the CMIP5 archive is not consistent with a random sample, and a weighting scheme is proposed to reduce some aspects of model codependency in the ensemble. A method is proposed for selecting diverse and skillful subsets of models in the archive, which could be used for impact studies in cases where physically consistent joint projections of multiple variables (and their temporal and spatial characteristics) are required.
Abstract
Are equilibrium climate sensitivity and the associated radiative feedbacks a constant property of the climate system, or do they change with forcing magnitude and base climate? Using the radiative kernel technique, feedbacks and climate sensitivity are evaluated in a fully coupled general circulation model (GCM) for three successive doublings of carbon dioxide starting from present-day concentrations. Climate sensitivity increases by 23% between the first and third CO2 doublings. Increases in the positive water vapor and cloud feedbacks are partially balanced by a decrease in the positive surface albedo feedback and an increase in the negative lapse rate feedback. Feedbacks can be decomposed into a radiative flux change and a climate variable response to temperature change. The changes in water vapor and Planck feedbacks are due largely to changes in the radiative response with climate state. Higher concentrations of greenhouse gases and higher temperatures lead to more absorption and emission of longwave radiation. Changes in cloud feedbacks are dominated by the climate response to temperature change, while the lapse rate and albedo feedbacks combine elements of both. Simulations with a slab ocean model (SOM) version of the GCM are used to verify whether an SOM-GCM accurately reproduces the behavior of the fully coupled model. Although feedbacks differ in magnitude between model configurations (with differences as large as those between CO2 doublings for some feedbacks), changes in feedbacks between CO2 doublings are consistent in sign and magnitude in the SOM-GCM and the fully coupled model.
Abstract
Are equilibrium climate sensitivity and the associated radiative feedbacks a constant property of the climate system, or do they change with forcing magnitude and base climate? Using the radiative kernel technique, feedbacks and climate sensitivity are evaluated in a fully coupled general circulation model (GCM) for three successive doublings of carbon dioxide starting from present-day concentrations. Climate sensitivity increases by 23% between the first and third CO2 doublings. Increases in the positive water vapor and cloud feedbacks are partially balanced by a decrease in the positive surface albedo feedback and an increase in the negative lapse rate feedback. Feedbacks can be decomposed into a radiative flux change and a climate variable response to temperature change. The changes in water vapor and Planck feedbacks are due largely to changes in the radiative response with climate state. Higher concentrations of greenhouse gases and higher temperatures lead to more absorption and emission of longwave radiation. Changes in cloud feedbacks are dominated by the climate response to temperature change, while the lapse rate and albedo feedbacks combine elements of both. Simulations with a slab ocean model (SOM) version of the GCM are used to verify whether an SOM-GCM accurately reproduces the behavior of the fully coupled model. Although feedbacks differ in magnitude between model configurations (with differences as large as those between CO2 doublings for some feedbacks), changes in feedbacks between CO2 doublings are consistent in sign and magnitude in the SOM-GCM and the fully coupled model.
Abstract
Climate feedbacks vary strongly among climate models and continue to represent a major source of uncertainty in estimates of the response of climate to anthropogenic forcings. One method to evaluate feedbacks in global climate models is the radiative kernel technique, which is well suited for model intercomparison studies because of its computational efficiency. However, the usefulness of this technique is predicated on the assumption of linearity between top-of-atmosphere (TOA) radiative fluxes and feedback variables, limiting its application to simulations of small climate perturbations, where nonlinearities can be neglected. This paper presents an extension of the utility of this linear technique to large forcings, using global climate model simulations forced with CO2 concentrations ranging from 2 to 8 times present-day values. Radiative kernels depend on the model’s radiative transfer algorithm and climate base state. For large warming, kernels based on the present-day climate significantly underestimate longwave TOA flux changes and somewhat overestimate shortwave TOA flux changes. These biases translate to inaccurate feedback estimates. It is shown that a combination of present-day kernels and kernels computed using a large forcing climate base state leads to significant improvement in the approximation of TOA flux changes and increased reliability of feedback estimates. While using present-day kernels results in a climate sensitivity that remains constant, using the new kernels shows that sensitivity increases significantly with each successive doubling of CO2 concentrations.
Abstract
Climate feedbacks vary strongly among climate models and continue to represent a major source of uncertainty in estimates of the response of climate to anthropogenic forcings. One method to evaluate feedbacks in global climate models is the radiative kernel technique, which is well suited for model intercomparison studies because of its computational efficiency. However, the usefulness of this technique is predicated on the assumption of linearity between top-of-atmosphere (TOA) radiative fluxes and feedback variables, limiting its application to simulations of small climate perturbations, where nonlinearities can be neglected. This paper presents an extension of the utility of this linear technique to large forcings, using global climate model simulations forced with CO2 concentrations ranging from 2 to 8 times present-day values. Radiative kernels depend on the model’s radiative transfer algorithm and climate base state. For large warming, kernels based on the present-day climate significantly underestimate longwave TOA flux changes and somewhat overestimate shortwave TOA flux changes. These biases translate to inaccurate feedback estimates. It is shown that a combination of present-day kernels and kernels computed using a large forcing climate base state leads to significant improvement in the approximation of TOA flux changes and increased reliability of feedback estimates. While using present-day kernels results in a climate sensitivity that remains constant, using the new kernels shows that sensitivity increases significantly with each successive doubling of CO2 concentrations.
Abstract
Future climate change projections for phase 5 of the Coupled Model Intercomparison Project (CMIP5) are presented for the Community Earth System Model version 1 that includes the Community Atmospheric Model version 5 [CESM1(CAM5)]. These results are compared to the Community Climate System Model, version 4 (CCSM4) and include simulations using the representative concentration pathway (RCP) mitigation scenarios, and extensions for those scenarios beyond 2100 to 2300. Equilibrium climate sensitivity of CESM1(CAM5) is 4.10°C, which is higher than the CCSM4 value of 3.20°C. The transient climate response is 2.33°C, compared to the CCSM4 value of 1.73°C. Thus, even though CESM1(CAM5) includes both the direct and indirect effects of aerosols (CCSM4 had only the direct effect), the overall climate system response including forcing and feedbacks is greater in CESM1(CAM5) compared to CCSM4. The Atlantic Ocean meridional overturning circulation (AMOC) in CESM1(CAM5) weakens considerably in the twenty-first century in all the RCP scenarios, and recovers more slowly in the lower forcing scenarios. The total aerosol optical depth (AOD) changes from ~0.12 in 2006 to ~0.10 in 2100, compared to a preindustrial 1850 value of 0.08, so there is less negative forcing (a net positive forcing) from that source during the twenty-first century. Consequently, the change from 2006 to 2100 in aerosol direct forcing in CESM1(CAM5) contributes to greater twenty-first century warming relative to CCSM4. There is greater Arctic warming and sea ice loss in CESM1(CAM5), with an ice-free summer Arctic occurring by about 2060 in RCP8.5 (2040s in September) as opposed to about 2100 in CCSM4 (2060s in September).
Abstract
Future climate change projections for phase 5 of the Coupled Model Intercomparison Project (CMIP5) are presented for the Community Earth System Model version 1 that includes the Community Atmospheric Model version 5 [CESM1(CAM5)]. These results are compared to the Community Climate System Model, version 4 (CCSM4) and include simulations using the representative concentration pathway (RCP) mitigation scenarios, and extensions for those scenarios beyond 2100 to 2300. Equilibrium climate sensitivity of CESM1(CAM5) is 4.10°C, which is higher than the CCSM4 value of 3.20°C. The transient climate response is 2.33°C, compared to the CCSM4 value of 1.73°C. Thus, even though CESM1(CAM5) includes both the direct and indirect effects of aerosols (CCSM4 had only the direct effect), the overall climate system response including forcing and feedbacks is greater in CESM1(CAM5) compared to CCSM4. The Atlantic Ocean meridional overturning circulation (AMOC) in CESM1(CAM5) weakens considerably in the twenty-first century in all the RCP scenarios, and recovers more slowly in the lower forcing scenarios. The total aerosol optical depth (AOD) changes from ~0.12 in 2006 to ~0.10 in 2100, compared to a preindustrial 1850 value of 0.08, so there is less negative forcing (a net positive forcing) from that source during the twenty-first century. Consequently, the change from 2006 to 2100 in aerosol direct forcing in CESM1(CAM5) contributes to greater twenty-first century warming relative to CCSM4. There is greater Arctic warming and sea ice loss in CESM1(CAM5), with an ice-free summer Arctic occurring by about 2060 in RCP8.5 (2040s in September) as opposed to about 2100 in CCSM4 (2060s in September).
Abstract
A climate model emulator is developed using neural network techniques and trained with the data from the multithousand-member climateprediction.net perturbed physics GCM ensemble. The method recreates nonlinear interactions between model parameters, allowing a simulation of a much larger ensemble that explores model parameter space more fully.
The emulated ensemble is used to search for models closest to observations over a wide range of equilibrium response to greenhouse gas forcing. The relative discrepancies of these models from observations could be used to provide a constraint on climate sensitivity. The use of annual mean or seasonal differences on top-of-atmosphere radiative fluxes as an observational error metric results in the most clearly defined minimum in error as a function of sensitivity, with consistent but less well-defined results when using the seasonal cycles of surface temperature or total precipitation.
The model parameter changes necessary to achieve different values of climate sensitivity while minimizing discrepancy from observation are also considered and compared with previous studies. This information is used to propose more efficient parameter sampling strategies for future ensembles.
Abstract
A climate model emulator is developed using neural network techniques and trained with the data from the multithousand-member climateprediction.net perturbed physics GCM ensemble. The method recreates nonlinear interactions between model parameters, allowing a simulation of a much larger ensemble that explores model parameter space more fully.
The emulated ensemble is used to search for models closest to observations over a wide range of equilibrium response to greenhouse gas forcing. The relative discrepancies of these models from observations could be used to provide a constraint on climate sensitivity. The use of annual mean or seasonal differences on top-of-atmosphere radiative fluxes as an observational error metric results in the most clearly defined minimum in error as a function of sensitivity, with consistent but less well-defined results when using the seasonal cycles of surface temperature or total precipitation.
The model parameter changes necessary to achieve different values of climate sensitivity while minimizing discrepancy from observation are also considered and compared with previous studies. This information is used to propose more efficient parameter sampling strategies for future ensembles.
Abstract
Precipitation events cause disruption around the world and will be altered by climate change. However, different climate modeling approaches can result in different future precipitation projections. The corresponding “method uncertainty” is rarely explicitly calculated in climate impact studies and major reports but can substantially change estimated precipitation changes. A comparison across five commonly used modeling activities shows that, for changes in mean precipitation, less than half of the regions analyzed had significant changes between the present climate and 1.5°C global warming for the majority of modeling activities. This increases to just over half of the regions for changes between present climate and 2°C global warming. There is much higher confidence in changes in maximum 1-day precipitation than in mean precipitation, indicating the robust influence of thermodynamics in the climate change effect on extremes. We also find that none of the modeling activities captures the full range of estimates from the other methods in all regions. Our results serve as an uncertainty map to help interpret which regions require a multimethod approach. Our analysis highlights the risk of overreliance on any single modeling activity and the need for confidence statements in major synthesis reports to reflect this method uncertainty. Considering multiple sources of climate projections should reduce the risks of policymakers being unprepared for impacts of warmer climates relative to using single-method projections to make decisions.
Abstract
Precipitation events cause disruption around the world and will be altered by climate change. However, different climate modeling approaches can result in different future precipitation projections. The corresponding “method uncertainty” is rarely explicitly calculated in climate impact studies and major reports but can substantially change estimated precipitation changes. A comparison across five commonly used modeling activities shows that, for changes in mean precipitation, less than half of the regions analyzed had significant changes between the present climate and 1.5°C global warming for the majority of modeling activities. This increases to just over half of the regions for changes between present climate and 2°C global warming. There is much higher confidence in changes in maximum 1-day precipitation than in mean precipitation, indicating the robust influence of thermodynamics in the climate change effect on extremes. We also find that none of the modeling activities captures the full range of estimates from the other methods in all regions. Our results serve as an uncertainty map to help interpret which regions require a multimethod approach. Our analysis highlights the risk of overreliance on any single modeling activity and the need for confidence statements in major synthesis reports to reflect this method uncertainty. Considering multiple sources of climate projections should reduce the risks of policymakers being unprepared for impacts of warmer climates relative to using single-method projections to make decisions.
Abstract
Results are presented from experiments performed with the Community Climate System Model, version 4 (CCSM4) for the Coupled Model Intercomparison Project phase 5 (CMIP5). These include multiple ensemble members of twentieth-century climate with anthropogenic and natural forcings as well as single-forcing runs, sensitivity experiments with sulfate aerosol forcing, twenty-first-century representative concentration pathway (RCP) mitigation scenarios, and extensions for those scenarios beyond 2100–2300. Equilibrium climate sensitivity of CCSM4 is 3.20°C, and the transient climate response is 1.73°C. Global surface temperatures averaged for the last 20 years of the twenty-first century compared to the 1986–2005 reference period for six-member ensembles from CCSM4 are +0.85°, +1.64°, +2.09°, and +3.53°C for RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The ocean meridional overturning circulation (MOC) in the Atlantic, which weakens during the twentieth century in the model, nearly recovers to early twentieth-century values in RCP2.6, partially recovers in RCP4.5 and RCP6, and does not recover by 2100 in RCP8.5. Heat wave intensity is projected to increase almost everywhere in CCSM4 in a future warmer climate, with the magnitude of the increase proportional to the forcing. Precipitation intensity is also projected to increase, with dry days increasing in most subtropical areas. For future climate, there is almost no summer sea ice left in the Arctic in the high RCP8.5 scenario by 2100, but in the low RCP2.6 scenario there is substantial sea ice remaining in summer at the end of the century.
Abstract
Results are presented from experiments performed with the Community Climate System Model, version 4 (CCSM4) for the Coupled Model Intercomparison Project phase 5 (CMIP5). These include multiple ensemble members of twentieth-century climate with anthropogenic and natural forcings as well as single-forcing runs, sensitivity experiments with sulfate aerosol forcing, twenty-first-century representative concentration pathway (RCP) mitigation scenarios, and extensions for those scenarios beyond 2100–2300. Equilibrium climate sensitivity of CCSM4 is 3.20°C, and the transient climate response is 1.73°C. Global surface temperatures averaged for the last 20 years of the twenty-first century compared to the 1986–2005 reference period for six-member ensembles from CCSM4 are +0.85°, +1.64°, +2.09°, and +3.53°C for RCP2.6, RCP4.5, RCP6.0, and RCP8.5, respectively. The ocean meridional overturning circulation (MOC) in the Atlantic, which weakens during the twentieth century in the model, nearly recovers to early twentieth-century values in RCP2.6, partially recovers in RCP4.5 and RCP6, and does not recover by 2100 in RCP8.5. Heat wave intensity is projected to increase almost everywhere in CCSM4 in a future warmer climate, with the magnitude of the increase proportional to the forcing. Precipitation intensity is also projected to increase, with dry days increasing in most subtropical areas. For future climate, there is almost no summer sea ice left in the Arctic in the high RCP8.5 scenario by 2100, but in the low RCP2.6 scenario there is substantial sea ice remaining in summer at the end of the century.
