Does Model Sensitivity to Changes in CO2 Provide a Measure of Sensitivity to Other Forcings?

2006 ◽  
Vol 19 (13) ◽  
pp. 3294-3306 ◽  
Author(s):  
Andrei P. Sokolov
Keyword(s):  
General Circulation ◽  
Climate Model ◽  
Co2 Concentration ◽  
General Circulation Models ◽  
Cloud Feedback ◽  
Future Climate Change ◽  
Massachusetts Institute Of Technology ◽  
Model Sensitivity ◽  
Sea Ice Extent ◽  
Surface Warming

Abstract Simulation of both the climate of the twentieth century and a future climate change requires taking into account numerous forcings, while climate sensitivities of general circulation models are defined as the equilibrium surface warming due to a doubling of atmospheric CO2 concentration. A number of simulations with the Massachusetts Institute of Technology (MIT) climate model of intermediate complexity with different forcings have been carried out to study to what extent sensitivity to changes in CO2 concentration (SCO2) represent sensitivities to other forcings. The MIT model, similar to other models, shows a strong dependency of the simulated surface warming on the vertical structure of the imposed forcing. This dependency is a result of “semidirect” effects in the simulations with localized tropospheric heating. A method for estimating semidirect effects associated with different feedback mechanisms is presented. It is shown that forcing that includes these effects is a better measure of expected surface warming than a forcing that accounts for stratospheric adjustment only. Simulations with the versions of the MIT model with different strengths of cloud feedback show that, for the range of sensitivities produced by existing GCMs, SCO2 provides a good measure of the model sensitivity to other forcings. In the case of strong cloud feedback, sensitivity to the increase in CO2 concentration overestimates model sensitivity to both negative forcings, leading to the cooling of the surface and “black carbon”–like forcings with elevated heating. This is explained by the cloud feedback being less efficient in the case of increasing sea ice extent and snow cover or by the above-mentioned semidirect effects, which are absent in the CO2 simulations, respectively.

scholarly journals PLASIM-ENTSem: a spatio-temporal emulator of future climate change for impacts assessment

2013 ◽  
Vol 6 (2) ◽  
pp. 3349-3380 ◽  
Author(s):  
P. B. Holden ◽  
N. R. Edwards ◽  
P. H. Garthwaite ◽  
K. Fraedrich ◽  
F. Lunkeit ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
General Circulation ◽  
Coupled Model ◽  
Co2 Concentration ◽  
General Circulation Models ◽  
Climate Impacts ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Data ◽  
Spatio Temporal

Abstract. Many applications in the evaluation of climate impacts and environmental policy require detailed spatio-temporal projections of future climate. To capture feedbacks from impacted natural or socio-economic systems requires interactive two-way coupling but this is generally computationally infeasible with even moderately complex general circulation models (GCMs). Dimension reduction using emulation is one solution to this problem, demonstrated here with the GCM PLASIM-ENTS. Our approach generates temporally evolving spatial patterns of climate variables, considering multiple modes of variability in order to capture non-linear feedbacks. The emulator provides a 188-member ensemble of decadally and spatially resolved (~ 5° resolution) seasonal climate data in response to an arbitrary future CO2 concentration and radiative forcing scenario. We present the PLASIM-ENTS coupled model, the construction of its emulator from an ensemble of transient future simulations, an application of the emulator methodology to produce heating and cooling degree-day projections, and the validation of the results against empirical data and higher-complexity models. We also demonstrate the application to estimates of sea-level rise and associated uncertainty.

scholarly journals How Well Do the CMIP5 Models Simulate the Antarctic Atmospheric Energy Budget?

2015 ◽  
Vol 28 (20) ◽  
pp. 7933-7942 ◽  
Author(s):  
Michael Previdi ◽  
Karen L. Smith ◽  
Lorenzo M. Polvani
Keyword(s):  
Sea Ice ◽  
Energy Budget ◽  
General Circulation ◽  
Climate Model ◽  
General Circulation Models ◽  
Cmip5 Models ◽  
Secondary Importance ◽  
Sea Ice Extent ◽  
Atmospheric Energy ◽  
The Antarctic

Abstract The authors evaluate 23 coupled atmosphere–ocean general circulation models from phase 5 of CMIP (CMIP5) in terms of their ability to simulate the observed climatological mean energy budget of the Antarctic atmosphere. While the models are shown to capture the gross features of the energy budget well [e.g., the observed two-way balance between the top-of-atmosphere (TOA) net radiation and horizontal convergence of atmospheric energy transport], the simulated TOA absorbed shortwave (SW) radiation is too large during austral summer. In the multimodel mean, this excessive absorption reaches approximately 10 W m−2, with even larger biases (up to 25–30 W m−2) in individual models. Previous studies have identified similar climate model biases in the TOA net SW radiation at Southern Hemisphere midlatitudes and have attributed these biases to errors in the simulated cloud cover. Over the Antarctic, though, model cloud errors are of secondary importance, and biases in the simulated TOA net SW flux are instead driven mainly by biases in the clear-sky SW reflection. The latter are likely related in part to the models’ underestimation of the observed annual minimum in Antarctic sea ice extent, thus underscoring the importance of sea ice in the Antarctic energy budget. Finally, substantial differences in the climatological surface energy fluxes between existing observational datasets preclude any meaningful assessment of model skill in simulating these fluxes.

The Simulation of Moisture Processes in Climate Models and Climate Sensitivity

2005 ◽  
Vol 18 (13) ◽  
pp. 2172-2193 ◽  
Author(s):  
Haijun Hu ◽  
Robert J. Oglesby ◽  
Susan Marshall
Keyword(s):  
Boundary Layer ◽  
General Circulation ◽  
Climate Models ◽  
General Circulation Models ◽  
Atmospheric Moisture ◽  
Model Sensitivity ◽  
Surface Warming ◽  
Wide Range ◽  
Modeling Techniques ◽  
Moisture Performance

Abstract General circulation models (GCMs) designed for projecting climatic change have exhibited a wide range of sensitivity. Therefore, projected surface warming with increasing CO2 varies considerably depending on which model is used. Despite notable advances in computing power and modeling techniques that have occurred over the past decade, uncertainties of model sensitivity have not been reduced accordingly. The sensitivity issue is investigated by examining two GCMs of very different modeling techniques and sensitivity, with attention focused on how moisture processes are treated in these models, how moisture simulations are affected by these processes, and how well these simulations compare to the observed and analyzed moisture field. Both GCMs predict increases of atmospheric moisture with doubled CO2, but the increment predicted by one model is substantially higher (approximately twice) than that predicted by the other. This same difference is seen in responses of the boundary layer diffusive moistening rate. Calculations with a radiative–convective model indicate that the differences in predicted equilibrium atmospheric moisture, including both column amount and vertical distribution, have contributed to the largest differences in model sensitivity between the two models. We argue that in order for climate models to be credible for prediction purposes, they must possess credible skills of simulating surface and boundary layer processes, which likely holds the key to overall moisture performance, its response to external forcing, and in turn to model sensitivity.

scholarly journals The Monash Simple Climate Model Experiments (MSCM-DB v1.0): An interactive database of mean climate, climate change and scenario simulations

2018 ◽  
Author(s):  
Dietmar Dommenget ◽  
Kerry Nice ◽  
Tobias Bayr ◽  
Dieter Kasang ◽  
Christian Stassen ◽  
...  
Keyword(s):  
General Circulation ◽  
Climate Model ◽  
Model Simulation ◽  
Co2 Concentration ◽  
General Circulation Models ◽  
Simple Climate Model ◽  
Model Simulations ◽  
Sensitivity Experiments ◽  
The Mean ◽  
Mean Climate

Abstract. This study introduces the Monash Simple Climate Model (MSCM) experiment database. The model simulations are based on the Globally Resolved Energy Balance (GREB) model. They provide a basis to study three different aspects of climate model simulations: (1) understanding the processes that control the mean climate, (2) the response of the climate to a doubling of the CO2 concentration, and (3) scenarios of external CO2 concentration and solar radiation forcings. A series of sensitivity experiments in which elements of the climate system are turned off in various combinations are used to address (1) and (2). This database currently provides more than 1,300 experiments and has an online web interface for fast analysis of the experiments and for open access to the data. We briefly outline the design of all experiments, give a discussion of some results, and put the findings into the context of previously published results from similar experiments. We briefly discuss the quality and limitations of the MSCM experiments and also give an outlook on possible further developments. The GREB model simulation of the mean climate processes is quite realistic, but does have uncertainties in the order of 20–30 %. The GREB model without flux corrections has a root mean square error in mean state of about 10 °C, which is larger than those of general circulation models (2 °C). However, the MSCM experiments show good agreement to previously published studies. Although GREB is a very simple model, it delivers good first-order estimates, is very fast, highly accessible, and can be used to quickly try many different sensitivity experiments or scenarios.

scholarly journals PLASIM-ENTSem v1.0: a spatio-temporal emulator of future climate change for impacts assessment

2014 ◽  
Vol 7 (1) ◽  
pp. 433-451 ◽  
Author(s):  
P. B. Holden ◽  
N. R. Edwards ◽  
P. H. Garthwaite ◽  
K. Fraedrich ◽  
F. Lunkeit ◽  
...  
Keyword(s):  
Radiative Forcing ◽  
General Circulation ◽  
Coupled Model ◽  
Co2 Concentration ◽  
General Circulation Models ◽  
Climate Impacts ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Data ◽  
Spatio Temporal

Abstract. Many applications in the evaluation of climate impacts and environmental policy require detailed spatio-temporal projections of future climate. To capture feedbacks from impacted natural or socio-economic systems requires interactive two-way coupling, but this is generally computationally infeasible with even moderately complex general circulation models (GCMs). Dimension reduction using emulation is one solution to this problem, demonstrated here with the GCM PLASIM-ENTS (Planet Simulator coupled with the efficient numerical terrestrial scheme). Our approach generates temporally evolving spatial patterns of climate variables, considering multiple modes of variability in order to capture non-linear feedbacks. The emulator provides a 188-member ensemble of decadally and spatially resolved (~ 5° resolution) seasonal climate data in response to an arbitrary future CO2 concentration and non-CO2 radiative forcing scenario. We present the PLASIM-ENTS coupled model, the construction of its emulator from an ensemble of transient future simulations, an application of the emulator methodology to produce heating and cooling degree-day projections, the validation of the simulator (with respect to empirical data) and the validation of the emulator (with respect to high-complexity models). We also demonstrate the application to estimates of sea-level rise and associated uncertainty.

scholarly journals Natural variability contributes to model–satellite differences in tropical tropospheric warming

2021 ◽  
Vol 118 (13) ◽  
pp. e2020962118
Author(s):  
Stephen Po-Chedley ◽  
Benjamin D. Santer ◽  
Stephan Fueglistaler ◽  
Mark D. Zelinka ◽  
Philip J. Cameron-Smith ◽  
...  
Keyword(s):  
Climate Sensitivity ◽  
General Circulation ◽  
Climate Model ◽  
Coupled Model ◽  
General Circulation Models ◽  
Satellite Observations ◽  
Sea Surface ◽  
Eastern Pacific ◽  
Surface Warming ◽  
The Tropics

A long-standing discrepancy exists between general circulation models (GCMs) and satellite observations: The multimodel mean temperature of the midtroposphere (TMT) in the tropics warms at approximately twice the rate of observations. Using a large ensemble of simulations from a single climate model, we find that tropical TMT trends (1979–2018) vary widely and that a subset of realizations are within the range of satellite observations. Realizations with relatively small tropical TMT trends are accompanied by subdued sea-surface warming in the tropical central and eastern Pacific. Observed changes in sea-surface temperature have a similar pattern, implying that the observed tropical TMT trend has been reduced by multidecadal variability. We also assess the latest generation of GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). CMIP6 simulations with muted warming over the central and eastern Pacific also show reduced tropical tropospheric warming. We find that 13% of the model realizations have tropical TMT trends within the observed trend range. These simulations are from models with both small and large climate sensitivity values, illustrating that the magnitude of tropical tropospheric warming is not solely a function of climate sensitivity. For global averages, one-quarter of model simulations exhibit TMT trends in accord with observations. Our results indicate that even on 40-y timescales, natural climate variability is important to consider when comparing observed and simulated tropospheric warming and is sufficiently large to explain TMT trend differences between models and satellite data.

scholarly journals Variability and extremes: Statistical validation of the AWI-ESM

2021 ◽  
Author(s):  
Justus Contzen ◽  
Thorsten Dickhaus ◽  
Gerrit Lohmann
Keyword(s):  
General Circulation ◽  
Clustering Algorithm ◽  
Climate Models ◽  
Climate Model ◽  
Extreme Values ◽  
Global Climate Model ◽  
General Circulation Models ◽  
Value Theory ◽  
Future Climate Change ◽  
Data Set

Abstract. Coupled general circulation models are of paramount importance to assess quantitatively the magnitude of future climate change. Usual methods for validating climate models include the evaluation of mean values and covariances, but less attention is directed to the evaluation of extremal behaviour. This is a problem because many severe consequences of climate changes are due to climate extremes. We present a method for model validation in terms of extreme values based on classical extreme value theory. We further discuss a clustering algorithm to detect spacial dependencies and tendencies for concurrent extremes. To illustrate these methods, we analyse precipitation extremes of the AWI-ESM global climate model compared to the reanalysis data set CRU TS4.04. The methods presented here can also be used for the comparison of model ensembles, and there may be further applications in palaeoclimatology.

scholarly journals Evaluating climate emulation: fundamental impulse testing of simple climate models

2019 ◽  
Vol 10 (4) ◽  
pp. 729-739 ◽  
Author(s):  
Adria K. Schwarber ◽  
Steven J. Smith ◽  
Corinne A. Hartin ◽  
Benjamin Aaron Vega-Westhoff ◽  
Ryan Sriver
Keyword(s):  
General Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Chemical Species ◽  
Co2 Concentration ◽  
General Circulation Models ◽  
Climate System ◽  
Physically Based ◽  
Mean Climate ◽  
The Impact

Abstract. Simple climate models (SCMs) are numerical representations of the Earth's gas cycles and climate system. SCMs are easy to use and computationally inexpensive, making them an ideal tool in both scientific and decision-making contexts (e.g., complex climate model emulation, parameter estimation experiments, climate metric calculations, and probabilistic analyses). Despite their prolific use, the fundamental responses of SCMs are often not directly characterized. In this study, we use fundamental impulse tests of three chemical species (CO2, CH4, and black carbon – BC) to understand the fundamental gas cycle and climate system responses of several comprehensive (Hector v2.0, MAGICC 5.3, MAGICC 6.0) and idealized (FAIR v1.0, AR5-IR) SCMs. We find that while idealized SCMs are widely used, they fail to capture the magnitude and timescales of global mean climate responses under emissions perturbations, which can produce biased temperature results. Comprehensive SCMs, which have physically based nonlinear forcing and carbon cycle representations, show improved responses compared to idealized SCMs. Even the comprehensive SCMs, however, fail to capture the response timescales to BC emission perturbations seen recently in two general circulation models. Some comprehensive SCMs also generally respond faster than more complex models to a 4×CO2 concentration perturbation, although this was not evident for lower perturbation levels. These results suggest where improvements should be made to SCMs. Further, we demonstrate here a set of fundamental tests that we recommend as a standard evaluation suite for any SCM. Fundamental impulse tests allow users to understand differences in model responses and the impact of model selection on results.

scholarly journals Evaluation of General Circulation Models over the Upper Ouémé River Basin in the Republic of Benin

Hydrology ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 11 ◽  
Author(s):  
André Attogouinon ◽  
Agnidé E. Lawin ◽  
Jean-François Deliège
Keyword(s):  
Climate Change ◽  
River Basin ◽  
General Circulation ◽  
Climate Model ◽  
Coupled Model ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
General Circulation Models ◽  
Future Climate Change ◽  
Data Set

This study assessed the performance of eight general circulation models (GCMs) implemented in the upper Ouémé River basin in Benin Republic (West Africa) during the Fifth Assessment Report on Climate Change. Historical rainfall simulations of the climate model of Rossby Regional Centre (RCA4) driven by eight Coupled Model Intercomparison Project (CMIP5) GCMs over a 55-year period (1951 to 2005) are evaluated using the observational data set. Apart from daily rainfall, other rainfall parameters calculated from observed and simulated rainfall were compared. U-test and other statistical criteria (R2, MBE, MAE, RMSE and standard of standard deviations) were used. According to the results, the simulations correctly reproduce the interannual variability of precipitation in the upper Ouémé River basin. However, the models tend to produce drizzle. Especially, the overestimation of April, May and November rains not only explains the overestimation of seasonal and annual cumulative rainfall but also the early onset of the rainy season and its late withdrawal. However, we noted that this overestimation magnitude varies from one model to another. As for extreme rainfall indices, the models reproduced them poorly. The CanESM2, CNRM-CM5 and EC-EARTH models perform well for daily rainfall. A trade-off is formulated to select the common MPI-ESM-LR, GFDL-ESM2M, NorESM1-M and CanESM2 models for different rainfall parameters for the reliable projection of rainfall in the area. However, the MPI-ESM-LR model is a valuable tool for studying future climate change.

How do polar clouds and precipitation affect global warming?

2021 ◽  
Author(s):  
Jennifer Kay
Keyword(s):  
Global Warming ◽  
Climate Model ◽  
Research Problem ◽  
Cloud Feedback ◽  
Polar Regions ◽  
Unsolved Research Problem ◽  
Cloud Feedbacks ◽  
Surface Warming ◽  
Polar Clouds ◽  
Radiative Feedbacks

<p>Understanding the influence of clouds and precipitation on global warming remains an important unsolved research problem. This talk presents an overview of this topic, with a focus on recent observations, theory, and modeling results for polar clouds. After a general introduction, experiments that disable cloud radiative feedbacks or “lock the clouds” within a state‐of‐the‐art,  well‐documented, and observationally vetted climate model will be presented. Through comparison of idealized greenhouse warming experiments with and without cloud locking, the sign and magnitude cloud feedbacks can be quantified. Global cloud feedbacks increase both global and Arctic warming by around 25%. In contrast, disabling Arctic cloud feedbacks has a negligible influence on both Arctic and global surface warming. Do observations and theory support a positive global cloud feedback and a weak Arctic cloud feedback?  How does precipitation affect polar cloud feedbacks? What are the implications especially for climate change in polar regions?  </p>

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