Reexamining the Relationship between Climate Sensitivity and the Southern Hemisphere Radiation Budget in CMIP Models

2015 ◽  
Vol 28 (23) ◽  
pp. 9298-9312 ◽  
Author(s):  
Kevin M. Grise ◽  
Lorenzo M. Polvani ◽  
John T. Fasullo
Keyword(s):  
Southern Ocean ◽  
Southern Hemisphere ◽  
Climate Sensitivity ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Radiation Budget ◽  
Cmip5 Models ◽  
Net Radiation ◽  
Subtropical Clouds

Abstract Recent efforts to narrow the spread in equilibrium climate sensitivity (ECS) across global climate models have focused on identifying observationally based constraints, which are rooted in empirical correlations between ECS and biases in the models’ present-day climate. This study reexamines one such constraint identified from CMIP3 models: the linkage between ECS and net top-of-the-atmosphere radiation biases in the Southern Hemisphere (SH). As previously documented, the intermodel spread in the ECS of CMIP3 models is linked to present-day cloud and net radiation biases over the midlatitude Southern Ocean, where higher cloud fraction in the present-day climate is associated with larger values of ECS. However, in this study, no physical explanation is found to support this relationship. Furthermore, it is shown here that this relationship disappears in CMIP5 models and is unique to a subset of CMIP models characterized by unrealistically bright present-day clouds in the SH subtropics. In view of this evidence, Southern Ocean cloud and net radiation biases appear inappropriate for providing observationally based constraints on ECS. Instead of the Southern Ocean, this study points to the stratocumulus-to-cumulus transition regions of the SH subtropical oceans as key to explaining the intermodel spread in the ECS of both CMIP3 and CMIP5 models. In these regions, ECS is linked to present-day cloud and net radiation biases with a plausible physical mechanism: models with brighter subtropical clouds in the present-day climate show greater ECS because 1) subtropical clouds dissipate with increasing CO2 concentrations in many models and 2) the dissipation of brighter clouds contributes to greater solar warming of the surface.

Trend analysis and transient climate sensitivity revealed by CMIP6

2020 ◽  
Author(s):  
Menghan Yuan ◽  
Thomas Leirvik
Keyword(s):  
Climate Sensitivity ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
Published Data ◽  
The Difference ◽  
Climate Forcings ◽  
The Moment

<p>CMIP6 (Coupled Model Intercomparison Project Version 6) is currently publishing updates on simulations for Global Climate Models (GCMs). In this paper, we focus on analyzing surface temperature and downward solar radiation (SDSR), which are two essential variables in estimating the transient climate sensitivity (TCS). We carry out the analysis for five GCMs that have published data at the moment. More GCMs will be included in the analysis when data is available. The research period dates from 1960 to 2014, providing the latest available projection for climate forcings. Temperature projections accord reasonably well with observations. This is no surprise, as data for CMIP5 was also aligned with observations.  On the other hand, a striking improvement has been observed with respect to SDSR. According to Storelvmo et al. (2018), CMIP5 models showed no statistically significant trend over time and revealed egregious mismatch with observations, casting major concerns about their fidelity. The data from CMIP6 models, however, this mismatch between simulations and observations is substantially alleviated. Not only is a negative trend recorded, but the significant fall around the beginning of the 1990s, due to the Mount Pinatubo eruption, is also reproduced, though with a slightly smaller scale compared to the observations in that period.<br>Based on the econometric framework from Phillips et al. (2019), we estimate the TCS for five GCMs. We find that the TCS estimates range from 2.03K to 2.65K. Each reported TCS for the five GCM’s are within it’s corresponding 95% confidence interval for the estimated TCS. It is worth noticing that a 25-year rolling window estimation indicates that average TCS for the GCMs varies greatly along time, though it has a significant upward trend from the beginning of the 1990s until 2009, and flattens, or even decreases, afterward.<br>We also compute the sample average of the TCS estimates. We find that for the period 1964-2005, which is used in Phillips et al. (2019), the average TCS is 1.82 for the CMIP5 models, and 2.07 for CMIP6. The difference is not significant. For the 1964-2014 period, however, the average TCS estimate for CMIP6 is 2.38, which is significantly higher than the average CMIP5 estimates. Since we find that the CMIP6 simulations reproduce observed trends in RSDS much better than the CMIP5 simulations, when compared to observations, this indicates both that the econometric framework of Phillips et al.(2019) is working very well and captures key drivers of the climate, and that the true TCS is most likely closer to the estimated TCS for observations.</p>

A simple approach to mimic the effect of active vegetation in hydrological models to better estimate hydrological variables under climate change

2021 ◽  
Author(s):  
Thedini Asali Peiris ◽  
Petra Döll
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Models ◽  
Potential Evapotranspiration ◽  
Global Climate ◽  
Global Climate Models ◽  
Hydrological Models ◽  
Net Radiation ◽  
Ensemble Mean ◽  
The Impact

<p>Unlike global climate models, hydrological models cannot simulate the feedbacks among atmospheric processes, vegetation, water, and energy exchange at the land surface. This severely limits their ability to quantify the impact of climate change and the concurrent increase of atmospheric CO<sub>2</sub> concentrations on evapotranspiration and thus runoff. Hydrological models generally calculate actual evapotranspiration as a fraction of potential evapotranspiration (PET), which is computed as a function of temperature and net radiation and sometimes of humidity and wind speed. Almost no hydrological model takes into account that PET changes because the vegetation responds to changing CO<sub>2</sub> and climate. This active vegetation response consists of three components. With higher CO<sub>2</sub> concentrations, 1) plant stomata close, reducing transpiration (physiological effect) and 2) plants may grow better, with more leaves, increasing transpiration (structural effect), while 3) climatic changes lead to changes in plants growth and even biome shifts, changing evapotranspiration. Global climate models, which include dynamic vegetation models, simulate all these processes, albeit with a high uncertainty, and take into account the feedbacks to the atmosphere.</p><p>Milly and Dunne (2016) (MD) found that in the case of RCP8.5 the change of PET (computed using the Penman-Monteith equation) between 1981- 2000 and 2081-2100 is much higher than the change of non-water-stressed evapotranspiration (NWSET) computed by an ensemble of global climate models. This overestimation is partially due to the neglect of active vegetation response and partially due to the neglected feedbacks between the atmosphere and the land surface.</p><p>The objective of this paper is to present a simple approach for hydrological models that enables them to mimic the effect of active vegetation on potential evapotranspiration under climate change, thus improving computation of freshwater-related climate change hazards by hydrological models. MD proposed an alternative approach to estimate changes in PET for impact studies that is only a function of the changes in energy and not of temperature and achieves a good fit to the ensemble mean change of evapotranspiration computed by the ensemble of global climate models in months and grid cells without water stress. We developed an implementation of the MD idea for hydrological models using the Priestley-Taylor equation (PET-PT) to estimate PET as a function of net radiation and temperature. With PET-PT, an increasing temperature trend leads to strong increases in PET. Our proposed methodology (PET-MD) helps to remove this effect, retaining the impact of temperature on PET but not on long-term PET change.</p><p>We implemented the PET-MD approach in the global hydrological model WaterGAP2.2d. and computed daily time series of PET between 1981 and 2099 using bias-adjusted climate data of four global climate models for RCP 8.5. We evaluated, computed PET-PT and PET-MD at the grid cell level and globally, comparing also to the results of the Milly-Dunne study. The global analysis suggests that the application of PET-MD reduces the PET change until the end of this century from 3.341 mm/day according to PET-PT to 3.087 mm/day (ensemble mean over the four global climate models).</p><p>Milly, P.C.D., Dunne K.A. (2016). DOI:10.1038/nclimate3046.</p>

scholarly journals Fidelity of the Observational/Reanalysis Datasets and Global Climate Models in Representation of Extreme Precipitation in East China

2018 ◽  
Vol 32 (1) ◽  
pp. 195-212 ◽  
Author(s):  
Sicheng He ◽  
Jing Yang ◽  
Qing Bao ◽  
Lei Wang ◽  
Bin Wang
Keyword(s):  
Extreme Precipitation ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Yangtze River Basin ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Global Ocean ◽  
East China ◽  
Cmip5 Models

AbstractRealistic reproduction of historical extreme precipitation has been challenging for both reanalysis and global climate model (GCM) simulations. This work assessed the fidelities of the combined gridded observational datasets, reanalysis datasets, and GCMs [CMIP5 and the Chinese Academy of Sciences Flexible Global Ocean–Atmospheric Land System Model–Finite-Volume Atmospheric Model, version 2 (FGOALS-f2)] in representing extreme precipitation over East China. The assessment used 552 stations’ rain gauge data as ground truth and focused on the probability distribution function of daily precipitation and spatial structure of extreme precipitation days. The TRMM observation displays similar rainfall intensity–frequency distributions as the stations. However, three combined gridded observational datasets, four reanalysis datasets, and most of the CMIP5 models cannot capture extreme precipitation exceeding 150 mm day−1, and all underestimate extreme precipitation frequency. The observed spatial distribution of extreme precipitation exhibits two maximum centers, located over the lower-middle reach of Yangtze River basin and the deep South China region, respectively. Combined gridded observations and JRA-55 capture these two centers, but ERA-Interim, MERRA, and CFSR and almost all CMIP5 models fail to capture them. The percentage of extreme rainfall in the total rainfall amount is generally underestimated by 25%–75% in all CMIP5 models. Higher-resolution models tend to have better performance, and physical parameterization may be crucial for simulating correct extreme precipitation. The performances are significantly improved in the newly released FGOALS-f2 as a result of increased resolution and a more realistic simulation of moisture and heating profiles. This work pinpoints the common biases in the combined gridded observational datasets and reanalysis datasets and helps to improve models’ simulation of extreme precipitation, which is critically important for reliable projection of future changes in extreme precipitation.

scholarly journals Evaluating Climate Model Simulations of the Radiative Forcing and Radiative Response at Earth’s Surface

2019 ◽  
Vol 32 (13) ◽  
pp. 4089-4102 ◽  
Author(s):  
Ryan J. Kramer ◽  
Brian J. Soden ◽  
Angeline G. Pendergrass
Keyword(s):  
Radiative Forcing ◽  
Climate Models ◽  
Climate Model ◽  
Hydrological Cycle ◽  
Global Climate ◽  
Global Climate Models ◽  
Lapse Rate ◽  
Radiation Budget ◽  
Model Simulations ◽  
Climate Model Simulations

Abstract We analyze the radiative forcing and radiative response at Earth’s surface, where perturbations in the radiation budget regulate the atmospheric hydrological cycle. By applying a radiative kernel-regression technique to CMIP5 climate model simulations where CO2 is instantaneously quadrupled, we evaluate the intermodel spread in surface instantaneous radiative forcing, radiative adjustments to this forcing, and radiative responses to surface warming. The cloud radiative adjustment to CO2 forcing and the temperature-mediated cloud radiative response exhibit significant intermodel spread. In contrast to its counterpart at the top of the atmosphere, the temperature-mediated cloud radiative response at the surface is found to be positive in some models and negative in others. Also, the compensation between the temperature-mediated lapse rate and water vapor radiative responses found in top-of-atmosphere calculations is not present for surface radiative flux changes. Instantaneous radiative forcing at the surface is rarely reported for model simulations; as a result, intermodel differences have not previously been evaluated in global climate models. We demonstrate that the instantaneous radiative forcing is the largest contributor to intermodel spread in effective radiative forcing at the surface. We also find evidence of differences in radiative parameterizations in current models and argue that this is a significant, but largely overlooked, source of bias in climate change simulations.

The Double-ITCZ Bias in CMIP6 Models

2020 ◽  
Author(s):  
Baijun Tian
Keyword(s):  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
Tropical Precipitation ◽  
Double Itcz ◽  
Intercomparison Project ◽  
Fully Coupled

<p>The double-Intertropical Convergence Zone (ITCZ) bias is one of the most outstanding problems in climate models. This study seeks to examine the double-ITCZ bias in the latest state-of-the-art fully coupled global climate models that participated in Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) in comparison to their previous generations (CMIP3 and CMIP5 models). To that end, we have analyzed the long-term annual mean tropical precipitation distributions and several precipitation bias indices that quantify the double-ITCZ biases in 75 climate models including 24 CMIP3 models, 25 CMIP3 models, and 26 CMIP6 models. We find that the double-ITCZ bias and its big inter-model spread persist in CMIP6 models but the double-ITCZ bias is slightly reduced from CMIP3 or CMIP5 models to CMIP6 models.</p>

Regionally Varying Assessments of Upper-Level Tropical Width in Reanalyses and CMIP5 Models Using a Tropopause Break Metric

2020 ◽  
Vol 33 (14) ◽  
pp. 5885-5903 ◽  
Author(s):  
Elinor R. Martin ◽  
Cameron R. Homeyer ◽  
Roarke A. McKinzie ◽  
Kevin M. McCarthy ◽  
Tao Xian
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
The Past ◽  
East Pacific ◽  
Change Climate ◽  
Upper Level ◽  
The Tropics

AbstractChanges in tropical width can have important consequences in sectors including ecosystems, agriculture, and health. Observations suggest tropical expansion over the past 30 years although studies have not agreed on the magnitude of this change. Climate model projections have also indicated an expansion and show similar uncertainty in its magnitude. This study utilizes an objective, longitudinally varying, tropopause break method to define the extent of the tropics at upper levels. The location of the tropopause break is associated with enhanced stratosphere–troposphere exchange and thus its structure influences the chemical composition of the stratosphere. The method shows regional variations in the width of the upper-level tropics in the past and future. Four modern reanalyses show significant contraction of the tropics over the eastern Pacific between 1981 and 2015, and slight but significant expansion in other regions. The east Pacific narrowing contributes to zonal mean narrowing, contradicting prior work, and is attributed to the use of monthly and zonal mean data in prior studies. Six global climate models perform well in representing the climatological location of the tropical boundary. Future projections show a spread in the width trend (from ~0.5° decade−1 of narrowing to ~0.4° decade−1 of widening), with a narrowing projected across the east Pacific and Northern Hemisphere Americas. This study illustrates that this objective tropopause break method that uses instantaneous data and does not require zonal averaging is appropriate for identifying upper-level tropical width trends and the break location is connected with local and regional changes in precipitation.

scholarly journals Effects of Convective Gravity Wave Drag in the Southern Hemisphere Winter Stratosphere

2013 ◽  
Vol 70 (7) ◽  
pp. 2120-2136 ◽  
Author(s):  
Hyun-Joo Choi ◽  
Hye-Yeong Chun
Keyword(s):  
Gravity Wave ◽  
Southern Hemisphere ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
Storm Track ◽  
Wave Drag ◽  
Global Climate Models ◽  
Systematic Biases ◽  
Hemisphere Winter

Abstract The excessively strong polar jet and cold pole in the Southern Hemisphere winter stratosphere are systematic biases in most global climate models and are related to underestimated wave drag in the winter extratropical stratosphere—namely, missing gravity wave drag (GWD). Cumulus convection is strong in the winter extratropics in association with storm-track regions; thus, convective GWD could be one of the missing GWDs in models that do not adopt source-based nonorographic GWD parameterizations. In this study, the authors use the Whole Atmosphere Community Climate Model (WACCM) and show that the zonal-mean wind and temperature biases in the Southern Hemisphere winter stratosphere of the model are significantly alleviated by including convective GWD (GWDC) parameterizations. The reduction in the wind biases is due to enhanced wave drag in the winter extratropical stratosphere, which is caused directly by the additional GWDC and indirectly by the increased existing nonorographic GWD and resolved wave drag in response to the GWDC. The cold temperature biases are alleviated by increased downwelling in the winter polar stratosphere, which stems from an increased poleward motion due to enhanced wave drag in the winter extratropical stratosphere. A comparison between two simulations separately using the ray-based and columnar GWDC parameterizations shows that the polar night jet with a ray-based GWDC parameterization is much more realistic than that with a columnar GWDC parameterization.

Simulation of Present-Day and Twenty-First-Century Energy Budgets of the Southern Oceans

2010 ◽  
Vol 23 (2) ◽  
pp. 440-454 ◽  
Author(s):  
Kevin E. Trenberth ◽  
John T. Fasullo
Keyword(s):  
Southern Hemisphere ◽  
Cloud Cover ◽  
Climate Models ◽  
Global Climate ◽  
Strong Relationship ◽  
Global Climate Models ◽  
Storm Tracks ◽  
Energy Budgets ◽  
Poleward Shift ◽  
Projected Changes

Abstract The energy budget of the modern-day Southern Hemisphere is poorly simulated in both state-of-the-art reanalyses and coupled global climate models. The ocean-dominated Southern Hemisphere has low surface reflectivity and therefore its albedo is particularly sensitive to cloud cover. In modern-day climates, mainly because of systematic deficiencies in cloud and albedo at mid- and high latitudes, too much solar radiation enters the ocean. Along with too little radiation absorbed at lower latitudes because of clouds that are too bright, unrealistically weak poleward transports of energy by both the ocean and atmosphere are generally simulated in the Southern Hemisphere. This implies too little baroclinic eddy development and deficient activity in storm tracks. However, projections into the future by coupled climate models indicate that the Southern Ocean features a robust and unique increase in albedo, related to clouds, in association with an intensification and poleward shift in storm tracks that is not observed at any other latitude. Such an increase in cloud may be untenable in nature, as it is likely precluded by the present-day ubiquitous cloud cover that models fail to capture. There is also a remarkably strong relationship between the projected changes in clouds and the simulated current-day cloud errors. The model equilibrium climate sensitivity is also significantly negatively correlated with the Southern Hemisphere energy errors, and only the more sensitive models are in the range of observations. As a result, questions loom large about how the Southern Hemisphere will actually change as global warming progresses, and a better simulation of the modern-day climate is an essential first step.

scholarly journals Hadley cell expansion in CMIP6 models

2020 ◽  
Author(s):  
Kevin M. Grise ◽  
Sean M. Davis
Keyword(s):  
Greenhouse Gases ◽  
Cell Expansion ◽  
Climate Models ◽  
Global Climate ◽  
Hadley Circulation ◽  
Global Climate Models ◽  
Hadley Cell ◽  
Cmip5 Models ◽  
Cell Edge ◽  
Poleward Shift

Abstract. In response to increasing greenhouse gases, the subtropical edges of Earth's Hadley circulation shift poleward in global climate models. Recent studies have found that reanalysis trends in the Hadley cell edge over the past 30–40 years are within the range of trends simulated by Coupled Model Intercomparison Project Phase 5 (CMIP5) models, and have documented seasonal and hemispheric asymmetries in these trends. In this study, we evaluate whether these conclusions hold for the newest generation of models (CMIP6). Overall, we find similar characteristics of Hadley cell expansion in CMIP5 and CMIP6 models. In both CMIP5 and CMIP6 models, the poleward shift of the Hadley cell edge in response to increasing greenhouse gases is 2–3 times larger in the Southern Hemisphere (SH), except during September–November. The trends from CMIP5 and CMIP6 models agree well with reanalyses, although prescribing observed coupled atmosphere-ocean variability allows the models to better capture reanalysis trends in the Northern Hemisphere (NH). We find two notable differences between CMIP5 and CMIP6 models. First, both CMIP5 and CMIP6 models contract the NH summertime Hadley circulation equatorward (particularly over the Pacific sector), but this contraction is larger in CMIP6 models due to their higher average climate sensitivity. Second, in recent decades, the poleward shift of the NH annual-mean Hadley cell edge is slightly larger in CMIP6 models. Increasing greenhouse gases drive similar trends in CMIP5 and CMIP6 models, so the larger recent NH trends in CMIP6 models point to the role of other forcings, such as aerosols.

scholarly journals Saharan Heat Low Biases in CMIP5 Models

2017 ◽  
Vol 30 (8) ◽  
pp. 2867-2884 ◽  
Author(s):  
Ross D. Dixon ◽  
Anne Sophie Daloz ◽  
Daniel J. Vimont ◽  
Michela Biasutti
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Climate Model ◽  
Global Climate ◽  
West African Monsoon ◽  
West African ◽  
Global Climate Models ◽  
Cmip5 Models ◽  
African Climate ◽  
Heat Low

Representing the West African monsoon (WAM) is a major challenge in climate modeling because of the complex interaction between local and large-scale mechanisms. This study focuses on the representation of a key aspect of West African climate, namely the Saharan heat low (SHL), in 22 global climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) multimodel dataset. Comparison of the CMIP5 simulations with reanalyses shows large biases in the strength and location of the mean SHL. CMIP5 models tend to develop weaker climatological heat lows than the reanalyses and place them too far southwest. Models that place the climatological heat low farther to the north produce more mean precipitation across the Sahel, while models that place the heat low farther to the east produce stronger African easterly wave (AEW) activity. These mean-state biases are seen in model ensembles with both coupled and fixed sea surface temperatures (SSTs). The importance of SSTs on West African climate variability is well documented, but this research suggests SSTs are secondary to atmospheric biases for understanding the climatological SHL bias. SHL biases are correlated across the models to local radiative terms, large-scale tropical precipitation, and large-scale pressure and wind across the Atlantic, suggesting that local mechanisms that control the SHL may be connected to climate model biases at a much larger scale.

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