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This paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes. Plain Language Summary: This paper presents a data‐driven method to improve rainfall‐runoff models used to generate future water resources scenario in climate change studies. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to monthly streamflow simulations over a large data set of catchments in South‐East Australia. Our results show that DAISI improves model performance for a wide range of metrics and increases the sensitivity of the model to climate inputs, which is critical in climate change scenarios. Finally, the improvements identified by DAISI take a simple mathematical form with distinct regional patterns in three sub‐regions of the study domain (Western Victoria, central region, and Northern New South Wales). Several improvements of DAISI are discussed including the inclusion of additional observed variables such as evapotranspiration to better constrain model simulations. Key Points: Data Assimilation Informed model Structure Improvement method diagnoses hydrological model structures by combining data assimilation with a polynomial update of state equationsThe method was applied to the GR2M rainfall‐runoff model with significantly improved streamflow simulations in 201 Australian catchmentsThe method identified updates to state equations with marked regional characteristics that could guide future improvement of GR2M [ABSTRACT FROM AUTHOR]

A reference or "no‐feedback" radiative response to warming is fundamental to understanding how much global warming will occur for a given change in greenhouse gases or solar radiation incident on the Earth. The simplest estimate of this radiative response is given by the Stefan‐Boltzmann law as −4σTe‾3≈−3.8 ${-}4\sigma {\overline{{T}_{e}}}^{3}\approx -3.8$ W m−2 K−1 for Earth's present climate, where Te‾ $\overline{{T}_{e}}$ is a global effective emission temperature. The comparable radiative response in climate models, widely called the "Planck feedback," averages −3.3 W m−2 K−1. This difference of 0.5 W m−2 K−1 is large compared to the uncertainty in the net climate feedback, yet it has not been studied carefully. We use radiative transfer models to analyze these two radiative feedbacks to warming, and find that the difference arises primarily from the lack of stratospheric warming assumed in calculations of the Planck feedback (traditionally justified by differing constraints on and time scales of stratospheric adjustment relative to surface and tropospheric warming). The Planck feedback is thus masked for wavelengths with non‐negligible stratospheric opacity, and this effect implicitly acts to amplify warming in current feedback analysis of climate change. Other differences between Planck and Stefan‐Boltzmann feedbacks arise from temperature‐dependent gas opacities, and several artifacts of nonlinear averaging across wavelengths, heights, and different locations; these effects partly cancel but as a whole slightly destabilize the Planck feedback. Our results point to an important role played by stratospheric opacity in Earth's climate sensitivity, and clarify a long‐overlooked but notable gap in our understanding of Earth's reference radiative response to warming. Plain Language Summary: Earth's climate is stable because a warmer planet loses more energy to space, at infrared wavelengths invisible to the naked eye. The rate of change of this energy loss as the planet warms provides an estimate how Earth's energy balance responds to warming, which is simple enough to write on a small piece of paper. When scientists investigate the warming predicted by climate models, they often start from a similar but not identical calculation of how Earth's energy balance responds to warming. This calculation, based on model output, is about 15% less stabilizing than the simple pencil‐and‐paper estimate. In this paper, we explore the causes of this 15% difference between the pencil‐and‐paper estimate and the calculations using climate models. We show that the difference is primarily caused by the lack of assumed warming in climate models high in Earth's atmosphere, where temperatures are not closely linked to surface warming. This lack of warming acts as a hidden destabilizing feedback in current analysis of climate models. Key Points: Earth's reference radiative response, or "Planck feedback," is ∼0.5 W m−2 K−1 less stabilizing than a Stefan‐Boltzmann estimateWe find this deviation is mostly due to the assumed lack of stratospheric warming in calculations of the Planck feedbackThe lack of stratospheric warming serves as an implicit positive feedback in analysis of climate model warming [ABSTRACT FROM AUTHOR]

This paper presents the response to anthropogenic forcing in the GISS‐E2.1 climate models for the 21st century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The experiments were performed using an updated and improved version of the NASA Goddard Institute for Space Studies (GISS) coupled general circulation model that includes two different versions for atmospheric composition: A non‐interactive version (NINT) with prescribed composition and a tuned aerosol indirect effect and the One‐Moment Aerosol model (OMA) version with fully interactive aerosols which includes a parameterized first indirect aerosol effect on clouds. The effective climate sensitivities are 3.0°C and 2.9°C for the NINT and OMA models, respectively. Each atmospheric version is coupled to two different ocean general circulation models: The GISS ocean model (E2.1‐G) and HYCOM (E2.1‐H). We describe the global mean responses for all future scenarios and spatial patterns of change for surface air temperature and precipitation for four of the marker scenarios: SSP1‐2.6, SSP2‐4.5, SSP4‐6.0, and SSP5‐8.5. By 2100, global mean warming ranges from 1.5°C to 5.2°C relative to 1,850–1,880 mean temperature. Two high‐mitigation scenarios SSP1‐1.9 and SSP1‐2.6 limit the surface warming to below 2°C by the end of the 21st century, except for the NINT E2.1‐H model that simulates 2.2°C of surface warming. For the high emission scenario SSP5‐8.5, the range is 4.6–5.2°C at 2100. Due to about 15% larger effective climate sensitivity and stronger transient climate response in both NINT and OMA CMIP6 models compared to CMIP5 versions, there is a stronger warming by 2100 in the SSP emission scenarios than in the comparable Representative Concentration Pathway (RCP) scenarios in CMIP5. Changes in sea ice area are highly correlated to global mean surface air temperature anomalies and show steep declines in both hemispheres, with the largest sea ice area decreases occurring during September in the Northern Hemisphere in both E2.1‐G (−1.21 × 106 km2/°C) and E2.1‐H models (−0.94 × 106 km2/°C). Both coupled models project decreases in the Atlantic overturning stream function by 2100. The largest decrease of 56%–65% in the 21st century overturning stream function is produced in the warmest scenario SSP5‐8.5 in the E2.1‐G model, comparable to the reduction in the corresponding CMIP5 GISS‐E2 RCP8.5 simulation. Both low‐end scenarios SSP1‐1.9 and SSP1‐2.6 also simulate substantial reductions of the overturning (9%–37%) with slow recovery of about 10% by the end of the 21st century (relative to the maximum decrease at the middle of the 21st century). Plain Language Summary: The projections of future climate change are uncertain because they are dependent on different possible scenarios of human‐caused emissions and their interaction with natural forcings, internal climate variability, and inter‐model differences. This paper presents the results of the climate model of the NASA Goddard Institute for Space Studies, GISS‐E2.1, for the anthropogenically forced climate response for the twenty first century Shared Socioeconomic Pathways emission scenarios within the Coupled Model Intercomparison Project Phase 6 (CMIP6). The sensitivity of the model response to different magnitudes of anthropogenic forcings in the twenty‐first‐century scenarios were performed using two different versions for the atmospheric composition and two different ocean general circulation models. Compared to CMIP5 GISS‐E2 versions, the CMIP6 GISS‐E2.1 climate model shows a stronger warming by 2100 in comparable scenarios due to larger effective climate sensitivity and transient climate response. Both climate models with two different ocean components project decreases in the Atlantic overturning stream function by 2100. Key Points: GISS E2.1 model with different configurations is used to carry out 134 Shared Socioeconomic Pathway (SSP) experimentsGISS‐E2.1 climate model shows a stronger warming by 2,100 in comparable Representative Concentration Pathway scenarios in CMIP5 due to larger effective climate sensitivity and stronger transient climate responseBoth coupled models, E2.1‐G and E2.1‐H, project decreases in the Atlantic overturning stream function by 2100 with the largest decrease in the warmest scenario SSP5‐8.5 in the E2.1‐G model [ABSTRACT FROM AUTHOR]

Characteristics of, and fundamental differences between, the radiative‐convective equilibrium (RCE) climate states following the Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) protocols in the Community Atmosphere Model version 5 (CAM5) and version 6 (CAM6) are presented. This paper explores the characteristics of clouds, moisture, precipitation and circulation in the RCE state, as well as the tropical response to surface warming, in CAM5 and CAM6 with different parameterizations. Overall, CAM5 simulates higher precipitation rates that result in larger global average precipitation, despite lower outgoing longwave radiation compared to CAM6. Differences in the structure of clouds, particularly the amount and vertical location of cloud liquid, exist between the CAM versions and can, in part, be related to distinct representations of shallow convection and boundary layer processes. Both CAM5 and CAM6 simulate similar peaks in cloud fraction, relative humidity, and cloud ice, linked to the usage of a similar deep convection parameterization. These anvil clouds rise and decrease in extent in response to surface warming. More generally, extreme precipitation, aggregation of convection, and climate sensitivity increase with warming in both CAM5 and CAM6. This analysis provides a benchmark for future studies that explore clouds, convection, and climate in CAM with the RCEMIP protocols now available in the Community Earth System Model. These results are discussed within the context of realistic climate simulations using CAM5 and CAM6, highlighting the usefulness of a hierarchical modeling approach to understanding model and parameterization sensitivities to inform model development efforts. Plain Language Summary: Clouds, circulations and rainfall in the tropics play an important role in Earth's climate. However, global climate models differ in their representation of these features, contributing to uncertainty in future climate projections. One useful tool to better understand model differences and inform efforts to improve models is to analyze idealized configurations. We explore two different numerical representations of an idealized atmosphere relevant to tropical regions to determine the impact on the characteristics of clouds, rainfall and circulations as well as the tropical atmospheric response to warming. We show that our idealized models mirror differences in the low‐cloud structure in the deep tropics of more realistic models. This work also finds similarities between the two numerical representations, such as a decrease in the spatial extent of high altitude clouds with warming. As the surface is warmed, both versions also show increases in the clustering of clouds, the likelihood of extreme precipitation rates, and an estimate of how much warming would occur in response to a doubling of carbon dioxide. Key Points: A new radiative‐convective equilibrium (RCE) configuration is added to the official release of Community Earth System Model (CESM) to facilitate the use of Radiative‐Convective Equilibrium Model Intercomparison Project (RCEMIP) in the CESM hierarchyThe Community Atmosphere Model version 5 and Community Atmosphere Model version 6 (CAM6) RCEMIP climate states and variation across SST experiments are documented in the context of the CESM hierarchyDiffering parameterizations lead to a reduction of low‐level clouds in CAM6, consistent with differences in more realistic simulations [ABSTRACT FROM AUTHOR]

The Alfred Wegener Institute Climate Model (AWI‐CM) participates for the first time in the Coupled Model Intercomparison Project (CMIP), CMIP6. The sea ice‐ocean component, FESOM, runs on an unstructured mesh with horizontal resolutions ranging from 8 to 80 km. FESOM is coupled to the Max Planck Institute atmospheric model ECHAM 6.3 at a horizontal resolution of about 100 km. Using objective performance indices, it is shown that AWI‐CM performs better than the average of CMIP5 models. AWI‐CM shows an equilibrium climate sensitivity of 3.2°C, which is similar to the CMIP5 average, and a transient climate response of 2.1°C which is slightly higher than the CMIP5 average. The negative trend of Arctic sea‐ice extent in September over the past 30 years is 20–30% weaker in our simulations compared to observations. With the strongest emission scenario, the AMOC decreases by 25% until the end of the century which is less than the CMIP5 average of 40%. Patterns and even magnitude of simulated temperature and precipitation changes at the end of this century compared to present‐day climate under the strong emission scenario SSP585 are similar to the multi‐model CMIP5 mean. The simulations show a 11°C warming north of the Barents Sea and around 2°C to 3°C over most parts of the ocean as well as a wetting of the Arctic, subpolar, tropical, and Southern Ocean. Furthermore, in the northern middle latitudes in boreal summer and autumn as well as in the southern middle latitudes, a more zonal atmospheric flow is projected throughout the year. Plain Language Summary: The Alfred Wegener Institute Helmholtz Centre for Polar and Marine Research (AWI) participates for the first time with a global climate model in the Coupled Model Intercomparison Project 6 (CMIP6). The results of CMIP6 and previous model comparison projects feed into the next assessment report of the Intergovernmental Panel on Climate Change (IPCC). The IPCC assessment reports include information on past and expected climate change in the future and is written for policy‐ and decision‐makers as well as for the general public. The main characteristics of the AWI climate model are described and compared to models from previous intercomparison projects. The projected global warming in AWI‐CM is similar to the average warming predicted by climate models in the previous intercomparison project. However, the Arctic sea‐ice extent declines faster than typical previous estimates. Areas that are wet in present‐day climate become wetter, and areas that are dry in present‐day climate become drier in the future—consistent with previous climate model simulations. The ocean currents remain rather stable in the AWI climate projections, which leads to a continued warm Gulf stream and therefore an only slightly reduced warming of the North Atlantic and parts of Europe compared to other middle‐latitude regions. Key Points: The paper describes contributions of AWI‐CM, which employs a sea‐ice ocean component formulated on unstructured meshes, to CMIP6Equilibrium climate sensitivity is similar to average of CMIP5 projections; transient climate response is slightly above averageResponse patterns are similar to CMIP5 with more pronounced Arctic sea ice loss and a more stable AMOC compared to other systems [ABSTRACT FROM AUTHOR]

This is the first paper that analyzes data from atmosphere model intercomparison project‐type climate simulations using a cloud‐system‐resolving global nonhydrostatic model without cumulus parameterization focussing particulaly on the relationship between clouds and circulation, and their changes due to global warming. The decrease in fractional coverage of low clouds is key to evaluating cloud radiative effects, because changes in shortwave cloud radiative effects overwhelm those of longwave cloud radiative effects. Thus, improved evaluation of low clouds is important, even in high‐resolution climate simulations. An analysis of heat redistribution by explicitly computed clouds revealed that column‐integrated heating rate due to phase changes correlates highly with vertical velocity at the altitude corresponding to 500 hPa and is closely linked to column water vapor, similar to the present climate result. Using data from year 1 to year 5, the effective climate sensitivity was evaluated to be 3.6−3.7°C. Possible convective aggregation is also examined using an index of modified subsidence fraction and characteristic changes in the number of cold pools. Despite previous idealized‐planet simulations showing more aggregated tropical convection under warmer conditions, here we show a decrease in the subsidence fraction and an increase in the number of smaller cold pools, suggesting that it is possible to realize less convective organization with warming under real atmospheric conditions. Key Points: We used global nonhydrostatic climate simulations to study how clouds and large‐scale circulations change in response to global warmingChanges in the shortwave cloud radiative effect are key to evaluating the net cloud radiative effect, even in high‐resolution simulationsWe found no evidence of an increased amount of tropical convective aggregation in response to global warming [ABSTRACT FROM AUTHOR]

We show that the seasonal cycles of clouds over the mid‐latitude oceans in the Northern Hemisphere are predictors of the responses of clouds to increasing sea‐surface temperatures globally. These regions are therefore "natural laboratories" in which the processes responsible for low‐cloud feedbacks on global scales are observed as seasonal changes in local cloud properties. We use an ensemble of configurations of a global‐climate model to show that the sensitivities of cloud‐radiative anomalies to surface temperature and lower‐tropospheric stability in the "laboratory" regions predict the models' global cloud‐radiative feedbacks. Models with greater changes in low‐clouds between seasons are shown to have stronger negative feedbacks in the mid‐latitudes, and stronger positive feedbacks from the subtropical stratocumulus. The biases in the simulated seasonal cycles, compared to observations, imply that both feedbacks are too weak in the model. The consequences of this for configuring our model to have a lower climate sensitivity are discussed. Plain Language Summary: As the Earth's climate warms, feedbacks from changes in clouds are crucial for determining the rate of warming. In some climate models, positive cloud feedbacks are known to contribute to projected temperature increases which fall outside the range that is considered plausible given our current understanding of the Earth's climate. Methods are needed which directly relate present‐day model errors to projected cloud feedbacks so that we can understand how to improve models and simultaneously obtain plausible feedback strengths. We show that in the Met Office Hadley Centres' climate model, the seasonal cycles of clouds in a few key regions, specifically the poleward ocean basins in the Northern Hemisphere, can be used to predict cloud feedbacks, globally, across a wide range of configurations of the model. These regions, particularly the North‐west Pacific, are "natural laboratories" in which the same processes that are responsible for global, cloud‐feedbacks onto global‐warming rates, occur annually as part of the local seasonal cycle. Studying clouds in these regions and model biases in predictions of these clouds will allow us to directly improve estimates of global climate change. Key Points: Seasonal cycles of clouds constrain cloud feedbacksThe northern ocean basins are natural laboratories for low‐cloud feedbacksThe hot‐model problem in HadGEM3 will not easily be solved by tuning [ABSTRACT FROM AUTHOR]

Climate models are becoming increasingly sophisticated as climate scientists continually work to improve the realism with which the processes influencing Earth's climate are represented. One example is the treatment of cloud microphysics: as complexity is added to cloud microphysical schemes, Earth's energy budget can respond to changes in climate forcings, such as carbon dioxide or aerosols, in new ways. This increase in degrees of freedom has illuminated larger spread in climate sensitivity across the latest generation of climate models participating Coupled Model Intercomparison Project, Phase 6, with more high climate sensitivity models (Zelinka et al., 2020, https://doi.org/10.1029/2019gl085782). Whilst the historical record gives us just over a century of data to apply toward climate sensitivity constraints (e.g., Nijsse et al., 2020, https://doi.org/10.5194/esd-11-737-2020), the ocean is still taking up much of the heat trapped by anthropogenic greenhouse gas emissions and the climate system is far from equilibrium which limits our understanding how climate sensitivity might change in response to long‐term forced climate change. Here we discuss the valuable tests that paleoclimate reconstructions can provide the latest generation of climate models, as demonstrated by the recent study of Zhu et al., 2022, https://doi.org/10.1029/2021ms002776. Their study provides an example of the benefits for climate model development when climate models are confronted with simulating climates very different from today. Ideally the climate model development stage under future iterations of CMIP will involve such tests as an effort to constrain global climate sensitivity and the regional patterns of climate, such as polar amplification and subtropical aridification. Plain Language Summary: In each successive generation of climate models the representation of climate processes becomes more realistic and more complex. The more sophisticated characterization of cloud processes has resulted in some models warming much more in response to increasing atmospheric carbon dioxide concentrations due to stronger cloud feedbacks; referred to as having a higher climate sensitivity. The Community Earth System Model version 2 (CESM2) is one such model. When used to simulate the Last Glacial Maximum (LGM), CESM2 has a global temperature around 5°C colder than surface temperature reconstructions and previous generations of this model. By investigating the updated cloud scheme, Zhu et al., 2022, https://doi.org/10.1029/2021ms002776 were able to identify and modify issues with the cloud microphysical scheme that were contributing to this problem. They subsequently produced a new version of CESM2 which can simulate the LGM more accurately without degrading the simulation of the 20th century climate. The Zhu et al., 2022, https://doi.org/10.1029/2021ms002776 study is an example of how a paleoclimate can be used to identify issues with cloud schemes not traditionally recognized in the model development and validation process. Incorporating paleoclimate simulations earlier in the model development and validation process may help constrain cloud feedbacks and subsequently climate sensitivity. Key Points: Paleoclimates provide valuable tests for the latest generation of climate modelsZhu et al., 2022, https://doi.org/10.1029/2021ms002776 provide an example of the paleoclimate benefits for climate model developmentFuture iterations of CMIP will ideally incorporate paleoclimate tests during model development [ABSTRACT FROM AUTHOR]

There is low confidence in projections of Antarctic sea ice area (SIA), due to deficiencies in climate model sea ice processes. Ensemble regression techniques can help to reduce this uncertainty. We investigate relationships between SIA climatology and 21st century change in the Coupled Model Intercomparison Project, phase 6 (CMIP6) multi‐model ensemble. In summer, under a strong forcing scenario, each model loses the majority of its sea ice. Therefore, models with greater historical SIA exhibit greater reductions, so the observed climatology of SIA strongly constrains projections. Ensemble spread in historical summer SIA is smaller than in CMIP phase 5 (CMIP5), and CMIP6 gives a more robust constraint on future SIA. In winter, by 2100 under a strong forcing scenario, 40% of SIA disappears on average, and ensemble spread in historical mean SIA explains approximately half the spread in projected change. A greater winter ice loss in CMIP6 than CMIP5 is explained by the higher climate sensitivities of some CMIP6 models. Plain Language Summary: Antarctic sea ice area is important for ecosystems, human activity, and for the dynamics of the atmosphere and ocean. However, major climate change assessments such as the Intergovernmental Panel on Climate Change Sixth Assessment Report have placed little confidence in projections of Antarctic sea ice area. This is because climate models struggle to simulate many aspects of sea ice area as observed by satellites. However, not all models should be treated equally. Models with relatively more sea ice area in the recent past project greater losses in the future, especially in summer. This suggests that models with historical sea ice area closest to what we have observed may be more reliable in their future simulations. We use this information in a simple statistical model to show that, firstly, the newest climate models largely show near‐total sea ice loss in summer by the end of the 21st century, which was not universally true for older climate models. Secondly, newer climate models lose more winter sea ice than their predecessors in the same time period, which is related to the known greater global warming response to given greenhouse gas concentrations in the newer models. Key Points: We compare Coupled Model Intercomparison Project (CMIP), phase 5 and the CMIP, phase 6 (CMIP6) projections of Antarctic sea ice loss, assessing drivers of uncertainty and the robustness of projectionsUnder strong forcing, CMIP6 models lose most Antarctic summer sea ice by 2100, so projections are constrained by historical climatologyIncreased winter sea ice loss in CMIP6 is related to increased equilibrium climate sensitivity [ABSTRACT FROM AUTHOR]

The persistent inter‐model spread in the response of global‐mean surface temperature to increased CO2 (known as the "Equilibrium Climate Sensitivity," or "ECS") is a crucial problem across model generations. This work examines the influence of the models' present‐day atmospheric circulation climatologies, and the accompanying climatological cloud radiative effects, in explaining that spread. We analyze the Coupled Model Intercomparison Project Phase 6 (CMIP6) models and find that they simulate a more poleward, and thus more realistic, edge of the Hadley cell (HC) in the Southern Hemisphere than the CMIP5 models, although the climatological shortwave cloud radiative effects are similar in the two generations of models. A few CMIP5 models with extreme equatorward biases in the HC edge exhibited high ECS due to strong Southern midlatitude shortwave cloud radiative warming in response to climate change, suggesting an ECS dependence on HC position. We find that such constraint no longer holds for the CMIP6 models, due to the absence of models with extreme HC climatologies. In spite of this, however, the CMIP6 models show an increased spread in ECS, with more models in the high ECS range. In addition, an improved representation of the climatological jet dynamics does not lead to a new emergent constraint in the CMIP6 models either. [ABSTRACT FROM AUTHOR]

During March 2021, large regions of Eastern Australia experienced prolonged heavy rainfall and extensive flooding. The maximum daily mean column integrated water vapor transport (IVT) over Sydney during this event was within the top 0.3% of all days since 1980, and the 10‐day mean IVT was in the top 0.2%. In this study, we have examined the change in frequency of extreme IVT events over Sydney in 16 climate models from the Coupled Model Intercomparison Project 6 under two Shared Socioeconomic Pathways (SSP245 and SSP585). Generalized Extreme Value modeling was used to further analyze the change in frequency of extreme IVT events. We found the probability of long duration high IVT events is projected to increase by 80% at the end of the century, but the future change in IVT is correlated to the rate of global and regional warming in each model. Plain Language Summary: During March 2021, large regions of Eastern Australia experienced prolonged heavy rainfall and extensive flooding. This was associated with strong horizontal water vapor transport over this region that persisted for approximately 10 days. The amount of water vapor transported over Sydney during this event was extreme and within the top 0.3% of all days since 1980. In this study, we used climate models to project how much more often events such as these may occur by the end of the twenty‐first century under two greenhouse gas emission scenarios. We found that the probability of long duration high water vapor transport over Sydney, as in March 2021, may increase by 80%. Key Points: Sydney's March 2021 floods were associated with persistent high water vapor transportThe probability of long‐duration high integrated water vapor transport events should increase by the end of the twenty‐first centuryDifferences in climate sensitivity within the CMIP6 ensemble may increase the spread of moisture flux projections [ABSTRACT FROM AUTHOR]

Many of the current, Coupled Model Intercomparison Project 6 (CMIP6), General Circulation Models (GCMs) show climate sensitivity higher than currently accepted uncertainty ranges. There is a weak correlation between increases in vertical resolution and in climate sensitivity from CMIP5. In particular, the MO Hadley Centre GCM's vertical resolution has more than doubled, and its climate sensitivity has also increased substantially. We therefore compare estimates of climate sensitivity from the CMIP6 model HadGEM3‐GC3.1‐LL, with 85 levels, and a version with 242 levels. This is far higher resolution than in any previously published simulations with a mainstream GCM, though still less than many scales important for clouds. The climate sensitivity and feedbacks, including the cloud feedback, change little. This suggests that vertical resolution did not drive recent increases in GCM climate sensitivity, though this result should be further tested in other GCMs and over a broader range of resolutions. Plain Language Summary: "Climate sensitivity" is how much climate is warmed, in the global and time average, by more CO2 in the air, largely from burning fossil fuels. However, it remains very uncertain. General Circulation Models (GCMs) are our most detailed and physically based models of climate, widely used in many sorts of study of possible climate change. "All models are wrong, but some are useful"—their complexity can make it hard to understand and so assess their behavior. They also have to approximate many physical processes on smaller scales—particularly cloud. More recent GCMs tend to have higher climate sensitivity, many higher than was ever thought likely. Cloud processes seem largely responsible. A possible cause is increased vertical resolution (calculations being done on more levels through the atmosphere) in the newer GCMs. There is a correlation between increased vertical resolution and increased climate sensitivity, but this could be by chance. We have therefore compared one new‐generation GCM with its standard vertical resolution and with 3 times higher (considerably higher than any previously published mainstream GCM work). The climate sensitivity barely changes, and the processes involved, including cloud processes, are clearly working almost identically. But other GCMs might be quite different. Key Points: Many Coupled Model Intercomparison Project 6 (CMIP6) General Circulation Models (GCMs) have climate sensitivities larger than the Intergovernmental Panel on Climate Change uncertainty range or other assessments, largely thanks to cloud feedbacksThis increase in sensitivity is correlated with an increase in vertical resolution—particularly important for cloud processesHowever, tripling the vertical resolution in one CMIP6 GCM, we found that climate sensitivity and feedbacks barely change [ABSTRACT FROM AUTHOR]

We analyze projected changes in climate extremes (extreme temperatures and heavy precipitation) in the multimodel ensembles of the fifth and sixth Coupled Model Intercomparison Projects (CMIP5 and CMIP6). The results reveal close similarity between both ensembles in the regional climate sensitivity of the projected multimodel mean changes in climate extremes, that is, their projected changes as a function of global warming. This stands in contrast to widely reported divergences in global (transient and equilibrium) climate sensitivity in the two multimodel ensembles. Some exceptions include higher warming in the South America monsoon region, lower warming in Southern Asia and Central Africa, and higher increases in heavy precipitation in Western Africa and the Sahel region in the CMIP6 ensemble. The multimodel spread in regional climate sensitivity is found to be large in both ensembles. In particular, it contributes more to intermodel spread in projected regional climate extremes compared with the intermodel spread in global climate sensitivity in CMIP6. Our results highlight the need to consider regional climate sensitivity as a distinct feature of Earth system models and a key determinant of projected regional impacts, which is largely independent of the models' response in global climate sensitivity. Plain Language Summary: Many articles analyze and compare global climate sensitivity in climate models, that is, how their global warming differs at a given level of CO2 concentrations. However, global warming is only one quantity affecting impacts. To assess human‐ and ecosystem‐relevant impacts, it is essential to evaluate the regional climate sensitivity of climate models, that is, how their regional climate features differ at a given level of global warming. We analyze here regional climate sensitivity in the new multimodel ensemble that will underlie the conclusions of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). This ensemble of model projections is called the "Sixth Coupled Model Intercomparison Project" or CMIP6. We find that differences in regional climate sensitivity between models in CMIP6 often contribute more to the uncertainty of regional extremes projections than the uncertainty in global mean warming between models. Overall, the regional climate sensitivity features in the CMIP6 models' projections ensemble are very similar to those of the prior ensemble (CMIP5), although the model ensembles have been highlighted to differ in their global climate sensitivity over the 21st century. Key Points: Changes in climate extremes as a function of global warming are quasilinear and determine a "regional climate sensitivity" in CMIP5 and CMIP6The regional climate sensitivity of climate extremes is found to be very similar in CMIP5 and CMIP6, unlike global climate sensitivityModel spread in regional climate sensitivity in CMIP6 contributes more to uncertainty of projected extremes than global climate sensitivity [ABSTRACT FROM AUTHOR]

Outputs from new state‐of‐the‐art climate models under the Coupled Model Inter‐comparison Project phase 6 (CMIP6) promise improvement and enhancement of climate change projections information for Australia. Here we focus on three key aspects of CMIP6: what is new in these models, how the available CMIP6 models evaluate compared to CMIP5, and their projections of the future Australian climate compared to CMIP5 focussing on the highest emissions scenario. The CMIP6 ensemble has several new features of relevance to policymakers and others, for example, the integrated matrix of socioeconomic and concentration pathways. The CMIP6 models show incremental improvements in the simulation of the climate in the Australian region, including a reduced equatorial Pacific cold tongue bias, slightly improved rainfall teleconnections with large‐scale climate drivers, improved representation of atmosphere and ocean extreme heat events, as well as dynamic sea level. However, important regional biases remain, evident in the excessive rainfall over the Maritime Continent and rainfall pattern biases in the nearby tropical convergence zones. Projections of Australian temperature and rainfall from the available CMIP6 ensemble broadly agree with those from CMIP5, except for a group of CMIP6 models with higher climate sensitivity and greater warming and increase in some extremes after 2050. CMIP6 rainfall projections are similar to CMIP5, but the ensemble examined has a narrower range of rainfall change in austral summer in Northern Australia and austral winter in Southern Australia. Overall, future national projections are likely to be similar to previous versions but perhaps with some areas of improved confidence and clarity. Key Points: CMIP6 shows incremental improvement of the simulation of Australian climate compared to CMIP5CMIP6 climate projections are similar to CMIP5 except for a group of higher sensitivity models with warmer projected change [ABSTRACT FROM AUTHOR]

The equilibrium climate sensitivity, that is, the global‐mean surface‐air temperature change in response to a doubling of the carbon dioxide concentration is a widely used metric in climate change studies. Its exact value is rarely known because its estimation requires a long integration time of several thousand years. We propose a method to estimate an accurate value of the equilibrium response from fully coupled climate models at a reasonable computational cost. Using this method, our state‐of‐the‐art climate model CNRM‐CM6‐1 reaches a stationary state after only few hundred of years of integration. This "Fast‐Forward" method consists of an optimal two‐step forcing pathway designed using the framework of a two‐layer energy balance model. It can be applied easily to any coupled climate model. Key Points: A simple method for estimating the equilibrium climate sensitivity is proposedThe method allows to simulate the stationary climate corresponding to any given radiative perturbation with a limited computational costThe method can be applied to any atmosphere‐ocean coupled climate model [ABSTRACT FROM AUTHOR]

This study explores the extent to which convective aggregation interacts with sea surface temperature (SST) and affects climate sensitivity. For this purpose, radiative‐convective equilibrium simulations are run with a general circulation model coupled to an ocean mixed layer, and several types of perturbations are imposed to the ocean‐atmosphere system. Convective aggregation turns out to be much more sensitive to temperature in coupled experiments than in prescribed SST experiments. But changes in convective aggregation induced by a doubling of the CO2 concentration are always smaller than changes associated with the transition from a non‐aggregated to an aggregated state. If aggregation changes were acting alone, they would exert a strong negative feedback on global mean surface temperature. However, in a coupled framework, aggregation changes interact with the SST and generate SST gradients that strengthen the positive low‐cloud feedback associated with changes in SST pattern. This overcompensates the negative feedback due to aggregation changes and leads to a larger equilibrium climate sensitivity than in the absence of SST gradients. Although this effect might be model specific, interactions between convective aggregation and the spatial distribution of SST appear crucial to assess the impact of convective aggregation on climate sensitivity. Key Points: Convective aggregation interacts strongly with surface temperature gradientsAggregation can increase climate sensitivity when coupled with an interactive slab ocean, rather than decreasing itThe impact of convective aggregation on climate should also be investigated in coupled frameworks [ABSTRACT FROM AUTHOR]

Land managers require information about the ongoing and potential effects of future climate to coordinate responses for ecosystems, species, and human communities at scales that are operationally meaningful. Our study focused on the vulnerability for all upland ecosystem types of Arizona and New Mexico in the southwestern United States. Local vulnerability across the two‐state area was represented by the level of departure for late 21st‐century climate from the characteristic pre‐1990 climate envelope of the ecosystem type at each given location, resulting in a probability surface of climate impacts for the two‐state area and an uncertainty assessment based on agreement in results among multiple global climate models. Though the results varied from one ecosystem type to the next, the majority of lands were forecast as high vulnerability and low uncertainty, reflecting significant agreement among climate model projections for the southwestern United States. We then tested our results in relation to ongoing ecological processes that have both regional and global change implications and discovered significant relationships with wildfire severity, upward tree species recruitment, and the encroachment of scrub into semidesert grassland. The testing helped determine the efficacy of the vulnerability surface, as a product of relatively high spatial and thematic resolution, in supporting local planning and management decisions. Most important, this study links climate and changes in vegetation by ecosystem processes that are already ongoing. The results affirm the value of climate model downscaling and show that this portable approach to correlative modeling has value in determining the location and magnitude of potential climate‐related impacts. [ABSTRACT FROM AUTHOR]