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Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a significant methodological challenge. It is therefore of high societal value to synthetically simulate TC tracks and winds to assess potential impacts along with their probability distributions for example, land use planning and financial risk management. A common approach to generate TC tracks is to apply storm detection methodologies to climate model output, but such an approach is sensitive to the method and parameterization used and tends to underestimate intense TCs. We present a global TC model (the UQAM‐TCW model thereafter) that melds statistical modeling, to capture historical risk features, with a climate model large ensemble, to generate large samples of physically coherent TC seasons. Integrating statistical and physical methods, the model is probabilistic and consistent with the physics of how TCs develop. The model includes frequency and location of cyclogenesis, full trajectories with maximum sustained winds and the entire wind structure along each track for the six typical cyclogenesis basins from IBTrACS. Being an important driver of TCs globally, we also integrate ENSO effects in key components of the model. The global TC model thus belongs to a recent strand of literature that combines probabilistic and physical approaches to TC track generation. As an application of the model, we show global hazard maps for direct and indirect hits expressed in terms of return periods. The global TC model can be of interest to climate and environmental scientists, economists and financial risk managers. Plain Language Summary: Tropical cyclones (TCs) are among the most destructive natural hazards and yet, quantifying their financial impacts remains a difficult task. Being able to randomly simulate TCs and their features (such as wind speed) with mathematical models is therefore critical to build scenarios (and their corresponding probability) for land use planning and financial risk management. A common approach is to simulate TCs by tracking them directly in climate model outputs but this often underestimates the frequency of intense TCs while being computationally costly overall to generate a large number of events. For these reasons, many authors have looked into alternative approaches that replicate key physical features of TCs but rather using statistical models that are much less computationally demanding. This paper therefore presents a global TC model that leverages the strengths of both statistical and climate models to simulate a large number of TCs whose features are consistent with the physics and observations. As an important global phenomenon that affects TCs globally, we also integrate in our model the effects of El Niño. The paper focuses on the methodology and validation of each model component and concludes with global hazard maps for direct and indirect hits. Key Points: We present a global tropical cyclone (TC) wind model built upon a climate model large ensemble that can be used for risk analysisWe integrate ENSO into our model since it is a strong driver of storm annual frequency, cyclogenesis, trajectories, and intensityWe present global hazard maps consistent with statistical features of TC components and coherent with a global climate model [ABSTRACT FROM AUTHOR]

The cloud feedback may result in amplification or damping of Arctic warming. Two common techniques used to diagnose the top‐of‐the‐atmosphere cloud feedback are the Adjusted Cloud Radiative Effect (AdjCRE) method and the Cloud Radiative Kernel (CRK) method. We apply both to CMIP5 and CMIP6 model data, finding that the AdjCRE calculated Arctic shortwave cloud feedback is twice as correlated with sea ice loss in CMIP5, and four times in CMIP6, as the CRK method. We find that the CRK method produces Arctic all‐sky residual percentages exceeding 20% in 15 of 18 models. We use the CRK method to decompose the feedback in CMIP5 and CMIP6 finding that its median value changed from negative to positive driven by a less‐negative cloud optical depth feedback. Despite its lack of closure, we conclude that the CRK method is better suited for Arctic SW feedbacks as it is less impacted by surface albedo changes. Plain Language Summary: The cloud feedback is the process by which cloud property changes in a warming climate can either further enhance warming or damp it. The Arctic is warming faster than the rest of the globe, and one of the largest sources of uncertainty in its climate projections is the cloud feedback. There are two popular methods to calculate the cloud feedback: the Adjusted Cloud Radiative Effect technique, and the Cloud Radiative Kernel technique. In this paper we compare the two methods in a suite of climate models by considering the extent to which changes in Arctic sea ice impact the cloud feedbacks. From this analysis we conclude that the Cloud Radiative Kernel method is less affected by sea ice loss. We then apply the Cloud Radiative Kernel technique to data from the two most recent generations of global climate models to investigate how polar day Arctic cloud feedbacks have changed between these generations. We find that the median value of these Arctic feedbacks is slightly positive in the newest generation of models, a change from slightly negative in the previous generation that is largely fueled by a weakening of the feedback associated with changes in cloud optical depth. Key Points: The Cloud Radiative Kernel method is less sensitive to surface albedo changes than the Adjusted Cloud Radiative Effect techniqueThe Cloud Radiative Kernel method provides poor radiative closure in a suite of global climate modelsThe median shortwave Arctic cloud feedback in recent climate models is slightly positive due to a weakened cloud optical depth feedback [ABSTRACT FROM AUTHOR]

In spite of massive efforts directed to development of climate models over recent decades, accurate climate simulations and prediction remain a grand challenge. Large sea surface temperature model bias is one of the key indicators of the problem, among others. Qiao and his team suggested the concept of surface wave‐induced turbulence and elaborated the way for such coupling, through turbulent processes at both sides of the air‐ocean interface. These are the wave mixing in the ocean and wave modulation of air‐sea fluxes. This wave‐ocean coupled approach led to essential improvements of performance of ocean circulation and climate models, essentially introducing the next generation of large‐scale air‐sea interaction models. This Commentary reviews three recent JGR publications where the Qiao theory was implemented. The first paper (Huang & Qiao, 2021; https://doi.org/10.1029/2020JC016839) reported that the momentum gain by the ocean could be larger than the wind stress input, due to the ocean waves. The second paper (Chen et al., 2022; https://doi.org/10.1029/2021JC018360) demonstrated that, also due to waves, the drag coefficients in the atmospheric boundary layer have a spatial asymmetry during tropical cyclones. The third paper (Zhao et al., 2022; https://doi.org/10.1029/2022JC019015), based on combination of wave‐coupled effects on both sides of air‐sea interface, demonstrates their impact on simulation of tropical cyclone intensity. All papers are based on unique in situ measurements and their data analysis. The Commentary is further used as an opportunity to outline and review the current state of the small‐large scale coupling topic with respect to ocean, weather and climate modeling. Plain Language Summary: Understanding the nature of climate change and predicting its trends have been one of the highest priorities for scientific communities and of great importance for the public. Climate simulations, however, have been facing challenges which persist for decades, and apparently mean that some of the physical processes in their description are missing. A large set of such processes rest with the coupled nature of small‐ and large‐scale air‐sea interaction phenomena. Since the spatio‐temporal scales of surface waves, around 100 m and 10 s, are too far from those in the climate system, that is, thousands to tens thousands kilometers and years to decades, their coupling have never been incorporated by climate models due to lack of understanding of physics of such interactions and computing capabilities. Recent publications indicate that surface waves can both change the upper ocean through non‐breaking wave‐induced mixing and, on the atmospheric side, air‐sea fluxes through modulating the interface of ocean and atmosphere. As a result, ocean and climate predictions can be essentially improved by considering surface waves in climate models. The Commentary mainly discusses three recent papers dedicated to this topic. Key Points: This Commentary reviews three recent JGR publications dedicated to wave‐coupled approaches to large‐scale air‐sea systemsThis wave‐ocean coupled approach led to essential improvements of performance of ocean circulation and climate modelsThe Commentary is further used as an opportunity to outline and review the current state of the small‐large scale coupling topic [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]

This is a summary of the papers published in the Special Section titled "Fast Physics in Climate Models: Parameterization, Evaluation, and Observation" (https://agupubs.onlinelibrary.wiley.com/doi/toc/10.1002/(ISSN)2169‐8996.FASTPHYS1), as requested by the JGR‐Atmosphere Chief Editor, Professor Minghua Zhang. Key Points: Motivation for this special section is presentedPapers in this special issue are summarizedFuture challenges facing fast physics in climate models are discussed [ABSTRACT FROM AUTHOR]

This study investigates some of the uncertainties sources associated with the pseudo global warming (PGW) approach which was employed to project future patterns of tropical cyclones (TCs) over the Arabian Sea (AS). First, the climate variables controlling the patterns of tropical cyclones were extracted from reanalysis datasets of ERA5, ERAI, CFSR, and NCEP/NCAR. Then, each dataset was evaluated against long‐term measurements to identify the best‐performing reanalysis dataset. ERA5 showed the best performance for most of the variables. Outputs of 20 CMIP5 global climate models (GCMs) were then evaluated against the ERA5 data resulting in an ensemble of the best performing GCMs. A PGW framework was then used to project the changes in patterns of three significant historical cyclones: Gonu, Phet, and Ashobaa. In doing so, the signals of future climate variables were extracted from the GCMs ensemble to modify the initial and boundary conditions of the WRF model which was previously tuned for reproducing the historical TCs. Different tests were conducted to address the sources of uncertainty in the PGW approach, including the selection of the climate variables contributing to the computation of the signals, the selection of GCMs, and the spatial variation of signals. A considerable sensitivity of the projected track and intensity of TCs to the choice of GCMs was observed, acknowledging the importance of GCMs evaluation before calculating the signals. Moreover, it was found that among all variables, signals of sea surface temperature and air temperature have major effects on the cyclone's track and intensity. Apart from that, when the signals were applied to the domain of the WRF model uniformly, compared to applying spatially varying signals, different tracks and intensities for future TCs were also observed. Overall, the findings of this paper challenge the reliability of the projected changes in TCs patterns obtained from PGW. [ABSTRACT FROM AUTHOR]

Antarctic sea ice trends have to date been linked to surface winds, through sea ice motion and atmospheric thermal advection. This paper analyzes sea ice volume in 10 Coupled Model Intercomparison Project Phase 5 (CMIP5) model configurations under pre‐industrial and historical climate forcings, to compare the relative importance of ice motion and thermodynamic processes. We find that the models' responses to historical forcings is dependent on their sea ice motion formulation; models with low‐magnitude sea ice motion tend to have historical trends that are dominated by thermodynamic processes, while sea ice models with higher‐magnitude motion have more spatially variable relative contributions from dynamic and thermodynamic processes. Trends at the sea ice edge during the season of sea ice advance are generally dominated by dynamic processes, whereas during retreat thermodynamic trends dominate. The models show more disagreement in the sea ice interior. This analysis highlights the different estimates and patterns of sea ice volume among global climate models and offers insight into the drivers of sea ice volume change as well as the subsequent implications for simulated atmosphere‐sea ice‐ocean interactions. Plain Language Summary: Increasing observed Antarctic sea ice is thought to be largely driven by surface winds, through driving sea ice motion and advecting warm air southward to melt ice in some regions. However, global climate models do not reproduce observed sea ice trends. This paper analyzes the sea ice volume budget in 10 climate models to see how much simulated sea ice change is driven by dynamic processes such as wind‐driven sea ice motion, and how much is due to thermodynamic freeze and melt processes. We find that the models generally agree when it comes to mean sea ice volume change, with dynamic processes dominating the sea ice edge and thermodynamic processes dominating in the interior of the sea ice pack. However, the models disagree about trends of sea ice volume. In the sea ice interior, each process dominates approximately half the models in all sea ice sectors, and thermodynamic processes tend to dominate in models with low‐magnitude average sea ice motion. These results provide insight into the estimates and patterns of Antarctic sea ice in global climate models, helping to understand the disparity between simulated and observed sea ice trends. Key Points: Thermodynamic drivers tend to dominate trends of Antarctic sea ice volume in models with low‐magnitude mean sea ice mass transportDrivers of sea ice volume trends in models with higher‐magnitude mean sea ice mass transport have large spatial and seasonal variabilityBiases in mean Antarctic sea ice concentration are unrelated to the dominant processes driving sea ice volume trends [ABSTRACT FROM AUTHOR]

Using the depth (z) and density (ϱ) frameworks, we analyze local contributions to AMOC variability in a 900‐year simulation with the AWI climate model. Both frameworks reveal a consistent interdecadal variability; however, the correlation between their maxima deteriorates on year‐to‐year scales. We demonstrate the utility of analyzing the spatial patterns of sinking and diapycnal transformations through depth levels and isopycnals. The success of this analysis relies on the spatial binning of these maps which is especially crucial for the maps of vertical velocities which appear to be too noisy in the main regions of upwelling and downwelling because of stepwise bottom topography. Furthermore, we show that the AMOC responds to fast (annual or faster) fluctuations in atmospheric forcing associated with the NAO. This response is more obvious in the ϱ than in the z framework. In contrast, the link between AMOC and deep water production south of Greenland is found for slower fluctuations and is consistent between the frameworks. Plain Language Summary: The variability and trends in Atlantic meridional overturning circulation (AMOC) are some of the most important characteristics of an ocean model simulation that are relevant for climate. Usually, the AMOC is computed as a meridional transport (from bottom to top) of zonally integrated flow along constant depth levels (z‐AMOC). However, water in the ocean generally flows along the density surfaces. In low latitudes, they are nearly flat and the general picture given by z‐AMOC is highly relevant. In high latitudes, where the most important processes of dense water formation and sinking take place, density surfaces are sloping, and the z‐AMOC gives a distorted image of the real circulation. This is why recent studies underlined the importance of computing the AMOC as a function of density in addition to AMOC as the function of vertical coordinate. In this paper, we analyze the fundamental differences with respect to AMOC variability in both frameworks in a 900‐year run with the AWI climate model. We demonstrate that the latitudinal position of overturning maxima, amplitude, and variability show substantial differences between the frameworks. We suggest that the ϱ‐AMOC and watermass transformation framework should be used routinely in standard analyses, including forthcoming intercomparison projects. Key Points: Patterns of sinking and diapycnal transformation across depth levels and isopycnals, respectively, can be used to study AMOC variabilityThe AMOC subpolar maximum is largely driven by internal transformationsThe density framework illustrates the interplay between surface buoyancy flux and interior‐mixing [ABSTRACT FROM AUTHOR]

The need for high‐precision calculations with 64‐bit or 32‐bit floating‐point arithmetic for weather and climate models is questioned. Lower‐precision numbers can accelerate simulations and are increasingly supported by modern computing hardware. This paper investigates the potential of 16‐bit arithmetic when applied within a shallow water model that serves as a medium complexity weather or climate application. There are several 16‐bit number formats that can potentially be used (IEEE half precision, BFloat16, posits, integer, and fixed‐point). It is evident that a simple change to 16‐bit arithmetic will not be possible for complex weather and climate applications as it will degrade model results by intolerable rounding errors that cause a stalling of model dynamics or model instabilities. However, if the posit number format is used as an alternative to the standard floating‐point numbers, the model degradation can be significantly reduced. Furthermore, mitigation methods, such as rescaling, reordering, and mixed precision, are available to make model simulations resilient against a precision reduction. If mitigation methods are applied, 16‐bit floating‐point arithmetic can be used successfully within the shallow water model. The results show the potential of 16‐bit formats for at least parts of complex weather and climate models where rounding errors would be entirely masked by initial condition, model, or discretization error. Plain Language Summary: 64‐bit floating‐point numbers are the standard number format for scientific computing in fluid dynamics, which allows for very precise calculations with negligible rounding errors. The need for calculations at this precision level has been questioned for weather and climate models, as errors are caused primarily by insufficient observations or deficiencies of the models themselves. Reducing numerical precision can accelerate simulations and low‐precision number formats are increasingly supported by modern computers. This paper investigates the potential of low numerical precision with numbers that only use 16 bit of information, when applied within simulations of weather and climate. The different number formats are applied in a two‐dimensional oceanic or atmospheric circulation model. There are several 16‐bit number formats that can potentially be used, all of which have considerably larger rounding errors than the standard 64‐bit numbers. A simple change to 16 bits for all calculations will not be possible as it will degrade simulation results. However, if mitigation methods are applied, 16‐bit calculations can be used successfully within the applications of this paper. The results show the potential of 16‐bit number formats for at least parts of complex weather and climate models. Key Points: Posit numbers reduce rounding errors compared to floating‐point numbers, enabling reliable shallow water simulations with 16‐bit arithmeticErrors caused by 16‐bit arithmetic are reduced with critical computations in 32 bit, which can be implemented on present‐day hardware16‐ or even 8‐bit communication between processors introduces negligible errors, providing a perspective for reduced data communication [ABSTRACT FROM AUTHOR]

The results of the sixth phase of the coupled model intercomparison project (CMIP) are currently being analyzed and will form the basis of the IPCC Sixth Assessment Report. Since its creation in the mid‐1990s, CMIP has had an increasing influence on climate research. While the principle behind it has always remained the same—comparing different climate models under similar conditions—its design and motivations have evolved significantly over the phases of the project. This evolution is closely linked to that of the IPCC since, historically as well as today, the results of CMIP have played a major role in the Panel's reports. This role increased the visibility of CMIP. Over time, more and more people started to be interested in CMIP and to analyze its results. Despite this success, the way CMIP is used today raises methodological issues. In fact, CMIP has promoted a particular way of doing climate research, centered on a single tool–Global Coupled Models (GCMs)–and creating a gap between model developers and model users. Due to the debates regarding the interpretation of multi‐model ensembles and the validation of GCMs, whether the emphasis on this particular way of studying climate is serving the progress of climate science is questionable. This article is categorized under:Climate Models and Modeling > Knowledge Generation with Models [ABSTRACT FROM AUTHOR]

Regional hydrological sensitivity (i.e., precipitation change per degree local surface warming) contributes substantially to the uncertainty in future precipitation projections over tropical oceans. Here, we investigate the sensitivity of relative precipitation (P*, precipitation divided by the basin average precipitation) to local sea surface temperature (SST) change by dissecting it into three components, namely the sensitivity of P* to relative SST (SSTrel, SST minus the tropical mean SST) changes, the sensitivity of P* to surface convergence changes, and the sensitivity of surface convergence to SST gradient changes. We show that the relationships between P* and SSTrel, and between P*, surface convergence, and SST gradients are largely constant during climate change. This allows us to constrain regional hydrological sensitivity based on present‐day SST‐precipitation relationships. The sensitivity of surface convergence to SST gradient changes is a main source of uncertainty in regional hydrological sensitivity and is likely underestimated in GCMs. Plain Language Summary: Understanding how precipitation will change over tropical oceans is important because these changes influence the atmospheric circulation, which in turn affects the global climate and weather patterns. Climate models disagree on their projections of precipitation changes over tropical oceans in part due to a lack of understanding on how precipitation should respond to a given amount of local surface warming. We find that the sensitivity of precipitation to future changes in local sea surface temperature (which is commonly referred to as regional hydrological sensitivity) largely depends on the present‐day relationship between precipitation and local sea surface temperature, as well as that between precipitation and the spatial gradient in sea surface temperature, and both relationships are observable and thus can serve as constraints. We find that inter‐model differences in regional hydrological sensitivity result primarily from differences in the response of surface winds to sea surface temperature gradient changes. Key Points: Regional hydrological sensitivity is an important source of uncertainty in rainfall projections over tropical oceansRegional hydrological sensitivity can be constrained by components of rainfall‐temperature relationship that stay constant during warmingUncertainty in regional hydrological sensitivity originates largely from surface convergence sensitivity to temperature gradient changes [ABSTRACT FROM AUTHOR]

The globally averaged diurnal temperature range (DTR) has shrunk since the mid‐20th century, and climate models project further shrinking. Observations indicate a slowdown or reversal of this trend in recent decades. Here, we show that DTR has a minimum for average temperatures close to 0°C. Observed DTR shrinks strongly at colder temperature, where warming shifts the average temperature toward the DTR minimum, and expands at warmer temperature, where warming shifts the average temperature away from the DTR minimum. Most, but not all climate models reproduce the minimum DTR close to average temperatures of 0°C and a stronger DTR shrinking at colder temperature. In models that reproduce the DTR minimum, DTR shrinking slows down significantly in recent decades. Models project that the global‐mean DTR will shrink over the 21st century, and models with a DTR minimum close to 0°C project slower shrinking than other models. Plain Language Summary: The diurnal temperature range, that is, the difference between daily maximum and minimum temperatures, affects both human health and plant development. Global data sets have shown a shrinking diurnal temperature range since the 1950s, but a recent study found that the diurnal temperature range had expanded again. In this study, we investigate how the diurnal temperature range behaves at different mean temperatures. We find that the diurnal temperature range is particularly small for temperatures close to the melting point of water (0°C), and larger for both colder and warmer temperatures. Due to the latent heat of freezing/melting, more energy is required to warm the soil from −1 to +1°C than from −3 to −1°C or from 1 to 3°C. The diurnal temperature range shrinks in regions and seasons with negative average temperature, where global warming pushes the average closer to 0°C and expands for warmer temperature. Most, but not all climate models also show a narrow diurnal temperature range near 0°C, shrinking of the temperature range at colder temperature and a slower reduction in the diurnal temperature range in recent decades. Models suggest that the currently observed expansion of the diurnal temperature range is a transient phenomenon. Key Points: The diurnal temperature range (DTR) has a local minimum near average temperatures of 0°CDTR in observations shrinks strongly for average temperatures below 0°C and expands for warmer temperaturesClimate models that reproduce the local DTR minimum near 0°C show a significant slowdown of DTR shrinking in recent decades [ABSTRACT FROM AUTHOR]

The UK contribution to the Detection and Attribution Model Intercomparison Project (DAMIP), part of the sixth phase of the Climate Model Intercomparison Project (CMIP6), is described. The lower atmosphere and ocean resolution configuration of the latest Hadley Centre global environmental model, HadGEM3‐GC3.1, is used to create simulations driven either with historical changes in anthropogenic well‐mixed greenhouse gases, anthropogenic aerosols, or natural climate factors. Global mean near‐surface air temperatures from the HadGEM3‐GC31‐LL simulations are consistent with CMIP6 model ensembles for the equivalent experiments. While the HadGEM3‐GC31‐LL simulations with anthropogenic and natural forcing factors capture the overall observed warming, the lack of marked simulated warming until the 1990s is diagnosed as due to aerosol cooling mostly offsetting the well‐mixed greenhouse gas warming until then. The model has unusual temperature variability over the Southern Ocean related to occasional deep convection bringing heat to the surface. This is most prominent in the model's aerosol only simulations, which have the curious feature of warming in the high southern latitudes, while the rest of the globe cools, a behavior not seen in other CMIP6 models. This has implications for studies that assume model responses, from different climate drivers, can be linearly combined. While DAMIP was predominantly designed for detection and attribution studies, the experiments are also very valuable for understanding how different climate drivers influence a model, and thus for interpretating the responses of combined anthropogenic and natural driven simulations. We recommend institutions provide model simulations for the high priority DAMIP experiments. Plain Language Summary: We describe the UK submission to the Detection and Attribution Model Intercomparison Project (DAMIP), using the HadGEM3‐GC3.1 climate model. The model's near‐surface temperature responses to different human and natural climate drivers are compared with other climate models and observed temperature changes. The experiments help to understand the evolution of the model's simulated historical global temperatures. One of the more interesting model features is the variability in the Southern Ocean which manifests itself as occasional surface warming due to deep ocean heat coming to the surface. This behavior, which occurs more often in simulations that cool than in simulations that warm, appears to be unusual compared to other models. The investigation of this model behavior demonstrates that DAMIP model experiments are not just useful for climate change detection and attribution, but also for understanding how a model responds to different climate drivers. Climate model participation in DAMIP is encouraged. Key Points: The UK's contribution to the Detection and Attribution Model Intercomparison project (DAMIP) is describedThe climate model's global temperature response to different anthropogenic and natural drivers is examined and compared to other modelsSouthern Ocean temperature variability is unusual and sensitive to climate driver [ABSTRACT FROM AUTHOR]

In this paper we present a novel, flexible, and multi-purpose class of designs for initial exploration of the parameter spaces of computer models, such as those used to study many features of the environment. The idea applies existing technology aimed at expanding a Latin Hypercube (LHC) in order to generate initial LHC designs that are composed of many smaller LHCs. The resulting design and its component parts are designed so that each is approximately orthogonal and maximises a measure of coverage of the parameter space. Designs of the type advocated for in this paper are particularly useful when we want to simultaneously quantify parametric uncertainty and any uncertainty due to the initial conditions, boundary conditions, or forcing functions required to run the model. This makes the class of designs particularly suited to environmental models, such as climate models that contain all of these features. The proposed designs are particularly suited to initial exploratory ensembles whose goal is to guide the design of further ensembles aimed at, for example, calibrating the model. We introduce a new emulator diagnostic that exploits the structure of the advocated ensemble designs and allows for the assessment of structural weaknesses in the statistical modelling. We provide illustrations of the method through a simple example and describe a 400 member ensemble of the Nucleus for European Modelling of the Ocean (NEMO) ocean model designed using the method. We build an emulator for NEMO using the created design to illustrate the use of our emulator diagnostic test. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Recent increases in resolution of coupled ocean‐atmosphere models have the potential to improve the representation of poleward heat transport within the climate system. Here we examine the interplay between model resolution‐dependent changes in Atlantic Ocean heat transport (AOHT) and surface heat fluxes. The different roles of changes in atmospheric and ocean resolution are isolated using three different climate models (The Centro Euro‐Mediterraneo sui Cambiamenti Climatici Climate Model 2, Hadley Centre Global Environmental Model 3 – Global Coupled configuration 2, and European Community Earth‐System Model 3.1) and comparing runs in which (a) only the ocean resolution changes, (b) only the atmosphere resolution changes, and (c) both change. Enhancing ocean resolution from eddy parameterized to eddy permitting increases the AOHT throughout the basin, values changing from 1.0 to 1.2 PW at 26°N, bringing the AOHT into the range of estimates from the RAPID observing array. This increase in AOHT is associated with higher North Atlantic sea surface temperatures and increased ocean heat loss to the atmosphere. Increasing the atmospheric resolution alone has little impact on the AOHT due to regionally compensating changes in the components of the net heat flux. Finally, in a fourth experiment the impact of resolution changes in both components and the transition to an eddy‐resolving ocean is assessed. This additional resolution increase is accompanied by a further change in the AOHT and improves agreement with observations in the tropics but not the subpolar regions. However, unlike with the increase to the eddy‐permitting ocean, when the greatest AOHT change occurs in the subtropics and subpolar region, the most significant increase now occurs in the tropics. Plain Language Summary: The ocean and atmosphere export large amounts of heat from the tropics toward more poleward latitudes. In the Northern Hemisphere about 25% of this export is accomplished by the movement of water in the Atlantic Ocean. It is therefore important for climate models to accurately simulate this Atlantic Ocean heat transport (AOHT). However, historically climate models have typically underestimated AOHT relative to our best observations. This paper investigates how changing the size of the grid cells in the model changes both AOHT and the ocean‐to‐atmosphere exchange of heat in the model. Reducing the size of the grid cells in the models is referred to as increasing the resolution. Increasing the resolution of the ocean component of the model to 1/4° latitude and longitude increases AOHT, making agreement with observations better in the subtropics but slightly worse in subpolar regions. Increasing the resolution of the atmospheric part of the model had little effect on AOHT, instead bringing changes to different parts of the ocean‐to‐atmosphere heat exchange that compensate each other. Finally, we examined the AOHT in one of the newest climate models with even higher resolution (1/12° ocean and 25‐km atmosphere). Additional improvements in the simulation of AOHT were found. Key Points: Model resolution‐dependent changes in Atlantic Ocean heat transport and surface heat fluxes are determined using three climate modelsIncreasing ocean resolution to eddy‐permitting raises Atlantic heat transport; changing atmospheric resolution only impacts surface fluxesEnhancing ocean resolution to eddy‐resolving results in a further increase in Atlantic Heat Transport and the best observational agreement [ABSTRACT FROM AUTHOR]

Abstract: Many theoretical methods for evaluating robustness have been developed in the last decades. Especially probability‐based robustness quantification promises to provide a rational basis for decision‐making. To allow for this type of robustness evaluation, the probability of failure (or the corresponding reliability index) of the structural system in the damaged and the undamaged state has to be determined. In this paper, the Probability Density Evolution Method (PDEM) is used to evaluate the dynamic reliability, taking into account the randomness which comes both from the excitation and from the stochastic system properties. The efficiency of the PDEM for robustness quantification is evaluated through a simple cargo crane example case. In addition, the instant equivalent extreme‐value event, which is quite different from the equivalent extreme‐value event for one variable, is applied to obtain the extreme value of reaction forces for each sample at instant time. [ABSTRACT FROM AUTHOR]

Abstract: Climate change could significantly affect climatic actions, so influencing not only existing structures, designed in accordance to the provisions of past Codes, but also updating of structural Codes. In the paper a new probabilistic technique for bias correction and downscaling of climate projections is presented to assess uncertainties in the future trends of climate extremes at the local scale. Referring to the relevant historical period, climate model outputs at relevant weather stations are compared with recorded daily series of maximum and minimum temperatures and water precipitations to derive appropriate monthly error probability density functions. Subsequently, new climate data series are generated by adding to the climate models output a random error term sampled from the monthly error PDFs. An extreme value analysis is finally carried out for each generated series according different time windows to assess how characteristic values (50 years return period) vary over time. The results for snow loads at the investigated weather stations confirm that the proposed methodology is suitable to reproduce recorded past climate extremes, so suggesting that it could be a useful tool to improve the climate change forecasts derived from climate models. [ABSTRACT FROM AUTHOR]

A machine learning method is used to identify sources of long‐term ENSO predictability in the ocean (sea surface temperature (SST) and heat content) and the atmosphere (near‐surface zonal wind (U10)). Tropical SST represents the primary source of predictability skill. While U10 does not increase the skill when associated with SST, our analysis suggests U10 alone has apredictive skill comparable to that of SST between 11 and 21 months in advance, from late fall up to late spring. The long‐lead signal originates from coupled wind‐SST interactions across the Indian Ocean (IO) and propagates across the Pacific via an atmospheric bridge mechanism. A linear correlation analysis supports this mechanism, suggesting a precursor link between anomalies in SST in the western and wind in the eastern IO. Our results have important implications for ENSO predictions beyond 1 year ahead and identify the key role of U10 over the IO. Plain Language Summary: Many extreme events, such as floods or droughts, can be attributed to the El Niño Southern Oscillation, a mode of large‐scale ocean‐atmosphere coupled variability in the tropical Pacific Ocean occurring with a period of approximately 4 years. In this analysis, we use a machine learning methodology to disentangle the key atmospheric and oceanic ENSO components' relative contribution to its predictability, particularly the role of near‐surface 10‐m zonal wind. We quantify the potential for improved ENSO predictions for up to 2 years in advance and present a mechanistic understanding of the location of the sources of predictability. While equatorial sea surface temperature represents the primary source of ENSO predictability, the equatorial U10 plays a vital role from late spring to fall, from 1 to 2 years in advance. The enhanced predictability skill is shown to be linked to an SST anomaly originating in the Indian Ocean. The ML model used provides a new way to get new insights into the sources of predictability for ENSO and can be used as a simple but powerful tool to improve the underlying mechanistic understanding. Key Points: A deep learning‐based approach suggests near‐surface 10 m wind to play a significant role in providing skills for long‐term ENSO predictabilityThe skill of the 10‐m zonal wind is large from 12 months up to 23 months in advance (depending on the season)The signal is generated by coupled wind‐SST interactions in the Indian Ocean and later propagates across the Pacific [ABSTRACT FROM AUTHOR]

Subgrid‐scale processes, such as atmospheric gravity waves (GWs), play a pivotal role in shaping the Earth's climate but cannot be explicitly resolved in climate models due to limitations on resolution. Instead, subgrid‐scale parameterizations are used to capture their effects. Recently, machine learning (ML) has emerged as a promising approach to learn parameterizations. In this study, we explore uncertainties associated with a ML parameterization for atmospheric GWs. Focusing on the uncertainties in the training process (parametric uncertainty), we use an ensemble of neural networks to emulate an existing GW parameterization. We estimate both offline uncertainties in raw NN output and online uncertainties in climate model output, after the neural networks are coupled. We find that online parametric uncertainty contributes a significant source of uncertainty in climate model output that must be considered when introducing NN parameterizations. This uncertainty quantification provides valuable insights into the reliability and robustness of ML‐based GW parameterizations, thus advancing our understanding of their potential applications in climate modeling. Plain Language Summary: Climate models are unable to resolve processes that vary on length and time scales smaller than the model resolution and timestep. For example, atmospheric gravity waves (GWs), which are waves created when winds encounter disturbances to the flow, such as mountains, convection and fronts, can have wavelengths smaller than the spacing between grid cells. Climate models use "parameterizations" to capture the effect of these processes. Machine learning based parameterizations are becoming popular because they can learn relationships purely from data. However, we do not have a good understanding of the uncertainties introduced through machine learning parameterizations. This study estimates uncertainties associated with training a neural network (NN) GW parameterization. We explore uncertainties in the NN output, as well as the uncertainties in the climate model output, when the NN is used for the GW parameterization. Key Points: Using ensembles of neural networks, we learn parametric uncertainties associated with an emulator of a gravity wave parameterizationWhen coupled to the climate model, the ensemble of neural networks reveals increased climate variabilityParametric uncertainty dominates the Quasi‐Biennial Oscillation statistics, although polar vortex properties remain robust to parameters [ABSTRACT FROM AUTHOR]

The last glacial inception (LGI) marks the transition from the interglacial warm climate to the glacial period with extensive Northern Hemisphere ice sheets and colder climate. This transition is initiated by decreasing boreal summer insolation but requires positive feedbacks to stimulate the appearance of perennial snow. We perform simulations of LGI with climate model AWI‐ESM‐2.1, forced by the astronomical and greenhouse gas forcing of 115,000 years before present. To compare with the preindustrial (PI) simulation, we use a consistent definition of the seasons during the LGI and the PI and evaluate model output on an angular astronomical calendar. Our study reveals a prominent role of the sea‐ice albedo feedback to amplify the delayed climate signal at polar latitudes. Through a radiative budget analysis, we examine that the ice‐albedo feedback exceeds the shortwave radiative forcing, contributing to the cooling and high latitude snow built‐up during LGI. Plain Language Summary: The onset of the last ice age marks the transition from the interglacial warm climate to the ice age with extensive Northern Hemisphere (NH) ice sheets and colder climate. This transition is initiated by decreasing boreal summer irradiance and reinforced by positive feedbacks. We perform climate simulations under the astronomical and greenhouse gas forcing, and use a consistent definition of seasons. Our study shows that a delayed ice‐albedo feedback plays an important role in generating a cold NH climate. Key Points: Climate simulations reveal year‐round cooling in the Northern Hemisphere high latitudes during the last glacial inceptionA refined angular calendar provides a tool to analyze the seasonal dynamicsWe identify the crucial role of the albedo feedback on the high‐latitude radiative budget [ABSTRACT FROM AUTHOR]

Global climate change is often thought of as a steady and approximately predictable physical response to increasing forcings, which then requires commensurate adaptation. But adaptation has practical, cultural and biological limits, and climate change may pose unanticipated global hazards, sudden changes or other surprises–as may societal adaptation and mitigation responses. These poorly known factors could substantially affect the urgency of mitigation as well as adaptation decisions. We outline a strategy for better accommodating these challenges by making climate science more integrative, in order to identify and quantify known and novel physical risks including those arising from interactions with ecosystems and society. We need to do this even–or especially–when they are highly uncertain, and to explore risks and opportunities associated with mitigation and adaptation responses by engaging across disciplines. We argue that upcoming climate assessments need to be more risk‐aware, and suggest ways of achieving this. These strategies improve the chances of anticipating potential surprises and identifying and communicating "safe landing" pathways that meet UN Sustainable Development Goals and guide humanity toward a better future. Plain Language Summary: Global risks may arise from many places, not all of which are being considered at the moment in the usual projections of future climate. Some risks are being overlooked, sometimes by most of the physical science community itself, or else by others who need information about future climate. We think physical climate sciences can do a lot to remedy this situation by looking more systematically at a broader array of risks and possible futures, narrowing them down, and collaboratively seeking better ways to express them. In this perspective we argue why this is important and explain a few ways it can be done. Key Points: Climate science needs to adjust to account systematically for low‐likelihood, high‐impact risks and comprehensive pathway interactionsAchieving this requires new strategies; several are outlined here, including increased model coupling and scenario explorationSuccess will require collaboration between physical sciences and other disciplines [ABSTRACT FROM AUTHOR]

In 2023, Antarctic sea‐ice extent (SIE) reached record lows, with winter SIE falling to 2.5Mkm2 below the satellite era average. With this multi‐model study, we investigate the occurrence of anomalies of this magnitude in latest‐generation global climate models. When these anomalies occur, SIE takes decades to recover: this indicates that SIE may transition to a new, lower, state over the next few decades. Under internal variability alone, models are extremely unlikely to simulate these anomalies, with return period >1000 years for most models. The only models with return period <1000 years for these anomalies have likely unrealistically large interannual variability. Based on extreme value theory, the return period is reduced from 2650 years under internal variability to 580 years under a strong climate change forcing scenario. Plain Language Summary: In 2023, the area of winter Antarctic sea ice fell to the lowest measured since satellite records began in late 1978. It is still under debate how far this low can be explained by natural variations, and how much can be explained by climate change. Global climate models are tools used to study past and predict future global change. We show that, without climate change, the latest generation of these models are extremely unlikely to simulate a sea‐ice reduction from the mean as large as observed in winter 2023. Including strong climate change quadruples the chance of such a reduction, but the chance is still very low. When these rare reductions are simulated, sea ice takes around 10 years to recover to a new, lower, area: this indicates that Antarctic sea ice may transition to a new, lower, state over the next few decades. Key Points: The latest generation of global climate models rarely simulate an Antarctic sea‐ice extent anomaly as negative as observed in winter 2023The return period for such an anomaly is 2650 years under internal variability, decreasing to 580 years under strong climate change forcingAfter the anomaly occurs, sea‐ice extent recovers within a decade to a new, lower state [ABSTRACT FROM AUTHOR]

The equilibrium climate sensitivity estimated from different sources is inconsistent due to its dependence on the surface warming pattern. Cloud feedbacks have been identified as the major contributor to this so‐called pattern effect. We find a large unforced pattern effect in CERES data, with cloud feedback estimated from two consecutive 125‐month periods (March 2000–July 2010 and August 2010–December 2020) changing from −0.45 ± 0.85 to +1.2 ± 0.78 W/m2/K. When comparing to models, 27% of consecutive 10‐year segments in CMIP6 control runs have differences similar to the observations. We also compare the spatial patterns in the CERES data to those in climate models and find they are similar, with the East Pacific playing a key role. This suggests that the impact of the unforced pattern effect can be significant and that models are capable of reproducing its global‐average magnitude. Plain Language Summary: Climate sensitivity, the amount of surface warming in response to increasing carbon dioxide, is often used as a metric to quantify the severity of climate change. The estimates of climate sensitivity from different sources, however, are not consistent. The main reason is that the response of clouds to surface temperature changes, called cloud feedback, depends on the spatial pattern of surface warming. We investigate the impact of this so‐called pattern effect on cloud feedback in observations and climate models. Here we find that the observed cloud feedbacks calculated from two consecutive decades have significant differences in both global mean value and spatial pattern, and the climate models produce similar results. This highlights the fact that differences of surface warming patterns can have large influences on cloud feedbacks, and climate models have the ability to reproduce what happened in the real world. Key Points: The difference in cloud feedback between two ∼10‐year periods inferred from CERES is 1.6 ± 1.1 W/m2/KThe distinct difference in cloud feedback verifies the existence of a large unforced pattern effectCMIP6 models produce unforced pattern effects of similar magnitude and spatial structure to those seen in the observations [ABSTRACT FROM AUTHOR]

As sea ice disappears, the emergence of open ocean deep convection in the Arctic, which would enhance ice loss, has been suggested. Here, using 36 state‐of‐the‐art climate models and up to 50 ensemble members per model, we show that Arctic deep convection is rare under the strongest warming scenario. Only five models have convection by 2100, while 11 have had convection by the middle of the run. For all, the deepest mixed layers are in the eastern Eurasian basin. When that region undergoes a salinification and increasing wind speeds, the models convect; yet most models are freshening. The models that do not convect have the strongest halocline and most stable sea ice, but those that lose their ice earliest ‐because of their strongly warming Atlantic Water‐ do not have a persistent deep convection: it shuts down mid‐century. Halocline and Atlantic Water changes urgently need to be better constrained in models. Plain Language Summary: Both observations and modeling simulations suggest that deep vertical mixing (or deep convection) in winter may become the new normal in the Arctic as sea ice disappears, which further accelerates that disappearance. These simulations are often done using only one model, so here we used all models available that participated in the Climate Model Intercomparison Project Phase 6 (CMIP6), for the strongest warming scenario. We show that after removing those that are already inaccurate in the present, and even with a restrictive threshold, most models have no deep convection in the Arctic, or extremely rarely. Only five still had deep convection by the time the run finishes in 2100. We found that deep convection regions and periods are associated with a saltier, windier surface, while the rest of the Arctic and/or run freshens. The subsurface properties were crucial too: a deep halocline, that is, strongly stratified model, leads to less ice loss and no deep convection. In contrast, models with a shallow halocline and strongly warming Atlantic Water lose their ice and convect earliest, although it does not persist. As water mass properties are poorly represented in CMIP6 models, these need improving to better constrain Arctic deep convection and sea ice projections. Key Points: Oceanic deep convection does not emerge and persist in the Arctic in the majority of Climate Model Intercomparison Project Phase 6 models, despite a drop in the Nordic SeasArctic deep convection occurs only when both surface salinity and winds are increasing, year round, yet most models are fresheningA deep halocline and stable ice cover hinder deep convection, while early ice loss due to warmer Atlantific water can also stop it rapidly [ABSTRACT FROM AUTHOR]

This study examines the impact of ocean advection and surface freshwater flux on the non‐seasonal, upper‐ocean salinity variability in two climate model simulations with eddy‐resolving and eddy‐parameterized ocean components (HR and LR, respectively). We assess the realism of each simulation by comparing their sea surface salinity (SSS) variance with satellite and Argo float estimates. In the extratropics, the HR variance is about five times larger than that in LR and agrees with Argo. In turn, the extratropical satellite SSS variance is smaller than that from HR and Argo by about a factor of two, potentially caused by the insufficient resolution of radiometers to capture mesoscale features and their low sensitivity to SSS in cold waters. Using a simplified salinity conservation equation for the upper‐50‐m ocean, we find that the advection‐driven variance in HR is, on average, 10 times larger than the surface flux‐driven variance, reflecting the action of mesoscale processes. Plain Language Summary: This study explores the importance of ocean currents, evaporation, and rainfall for driving changes in the salt concentration in the upper ocean (known as salinity) in two climate model simulations with differing ocean resolutions. The high‐resolution model (HR) simulates ocean currents with dimensions of tens of km, while the low‐resolution model (LR) can only simulate currents with hundreds of km in size. When comparing their simulated sea surface salinity variations with those captured by satellites and autonomous floats from the Argo array, the salinity variability in the high‐resolution model is similar to the Argo data at mid to high latitudes and about five times stronger than that in the low‐resolution model. The satellite data show a variability two times smaller than HR and Argo in the same regions, potentially due to their insufficient spatial resolution at higher latitudes and their low sensitivity to the surface salinity in cold waters. Using a simple equation describing the conservation of salinity in the upper ocean, we have shown that small‐scale ocean currents drive most of the salinity variability in HR, while in LR, ocean currents play a much smaller role. Key Points: We investigate how advection and surface flux affect upper‐50‐m salinity variance in eddy‐resolving and eddy‐parameterized climate modelsThe extratropical variance in the eddy‐resolving run matches Argo and is much larger than in the eddy‐parameterized run and satellite dataThe larger upper‐ocean salinity variance in the eddy‐resolving run is predominantly driven by mesoscale ocean processes [ABSTRACT FROM AUTHOR]

The additional water from the Antarctic ice sheet and ice shelves due to climate‐induced melt can impact ocean circulation and global climate. However, the major processes driving melt are not adequately represented in Coupled Model Intercomparison Project phase 6 (CMIP6) models. Here, we analyze a novel multi‐model ensemble of CMIP6 models with consistent meltwater addition to examine the robustness of the modeled response to meltwater, which has not been possible in previous single‐model studies. Antarctic meltwater addition induces a substantial weakening of open‐ocean deep convection. Additionally, Antarctic Bottom Water warms, its volume contracts, and the sea surface cools. However, the magnitude of the reduction varies greatly across models, with differing anomalies correlated with their respective mean‐state climatology, indicating the state‐dependency of the climate response to meltwater. A better representation of the Southern Ocean mean state is necessary for narrowing the inter‐model spread of response to Antarctic meltwater. Plain Language Summary: The melting of the Antarctic ice sheet and ice shelves can have significant impacts on ocean circulation and thermal structure, but current climate models do not fully capture these effects. In this study, we analyze seven climate models to understand how they respond to the addition of meltwater from Antarctica. We find that the presence of Antarctic meltwater leads to a significant weakening of deep convection in the open ocean. The meltwater also causes Antarctic Bottom Water to warm and its volume to decrease, while the sea surface cools and sea ice expands. However, the magnitude of the response to meltwater varies across models, suggesting that the mean‐state conditions of the Southern Ocean play a role. A better representation of the mean state and the inclusion of Antarctic meltwater in climate models will help reduce uncertainties and improve our understanding of the impact of Antarctic meltwater on climate. Key Points: Antarctic meltwater substantially reduces the strength of simulated Southern Ocean deep convection in climate modelsThe additional meltwater induces Antarctic Bottom Water warming and contraction, with dense water classes converting to lighter onesDifferences in the magnitude of these responses between models can be partly attributed to their different base states [ABSTRACT FROM AUTHOR]

Species distribution models (SDMs) are common tools for assessing the potential impact of climate change on species ranges. Uncertainty in SDM output occurs due to differences among alternate models, species characteristics and scenarios of future climate. While considerable effort is being devoted to identifying and quantifying the first two sources of variation, a greater understanding of climate scenarios and how they affect SDM output is also needed. Climate models are complex tools: variability occurs among alternate simulations, and no single ‘best’ model exists. The selection of climate scenarios for impacts assessments should not be undertaken arbitrarily - strengths and weakness of different climate models should be considered. In this paper, we provide bioclimatic modellers with an overview of emissions scenarios and climate models, discuss uncertainty surrounding projections of future climate and suggest steps that can be taken to reduce and communicate climate scenario-related uncertainty in assessments of future species responses to climate change. [ABSTRACT FROM AUTHOR]

Bioclimatic models are the primary tools for simulating the impact of climate change on species distributions. Part of the uncertainty in the output of these models results from uncertainty in projections of future climates. To account for this, studies often simulate species responses to climates predicted by more than one climate model and/or emission scenario. One area of uncertainty, however, has remained unexplored: internal climate model variability. By running a single climate model multiple times, but each time perturbing the initial state of the model slightly, different but equally valid realizations of climate will be produced. In this paper, we identify how ongoing improvements in climate models can be used to provide guidance for impacts studies. In doing so we provide the first assessment of the extent to which this internal climate model variability generates uncertainty in projections of future species distributions, compared with variability between climate models. We obtained data on 13 realizations from three climate models (three from CSIRO Mark2 v3.0, four from GISS AOM, and six from MIROC v3.2) for two time periods: current (1985–1995) and future (2025–2035). Initially, we compared the simulated values for each climate variable ( P, Tmax, Tmin, and Tmean) for the current period to observed climate data. This showed that climates simulated by realizations from the same climate model were more similar to each other than to realizations from other models. However, when projected into the future, these realizations followed different trajectories and the values of climate variables differed considerably within and among climate models. These had pronounced effects on the projected distributions of nine Australian butterfly species when modelled using the BIOCLIM component of DIVA-GIS. Our results show that internal climate model variability can lead to substantial differences in the extent to which the future distributions of species are projected to change. These can be greater than differences resulting from between-climate model variability. Further, different conclusions regarding the vulnerability of species to climate change can be reached due to internal model variability. Clearly, several climate models, each represented by multiple realizations, are required if we are to adequately capture the range of uncertainty associated with projecting species distributions in the future. [ABSTRACT FROM AUTHOR]

A new detection and attribution method is presented and applied to the global mean surface air temperature (GSAT) from 1900 to 2014. The method aims at attributing the climate changes to the variations of greenhouse gases, anthropogenic aerosols, and natural forcings. A convolutional neural network (CNN) is trained using the simulated GSAT from historical and single‐forcing simulations of 12 climate models. Then, we perform a backward optimization with the CNN to estimate the attributable GSAT changes. Such a method does not assume additivity in the effects of the forcings. The uncertainty in the attributable GSAT is estimated by sampling different starting points from single‐forcing simulations and repeating the backward optimization. To evaluate this new method, the attributable GSAT changes are also calculated using the regularized optimal fingerprinting (ROF) method. Using synthetic non‐additive data, we first find that the neural network‐based method estimates attributable changes better than ROF. When using GSAT data from climate model, the attributable anomalies are similar for both methods, which might reflect that the influence of forcing is mainly additive for the GSAT. However, we found that the uncertainties given both methods are different. The new method presented here can be adapted and extended in future work, to investigate the non‐additive changes found at the local scale or on other physical variables. Plain Language Summary: In order to design effective adaptation policies, it is essential to have reliable estimates of the effect of anthropogenic activities on the climate. For that purpose, a new attribution method based on a neural network is designed and evaluated. The method estimates the past global mean surface air temperatures anomalies caused by the changes in the greenhouse gases concentration, the variation of anthropogenic aerosols, and the variations driven by naturally occurring phenomena. To build this estimation, the data from observations and climate models are used. This methodology is compared with another state‐of‐the‐art method. The results of both methods are evaluated and discussed. The proposed method provides better estimations in the case of large non‐additivity of the causes of climate change and can be applied to other physical variables or at the regional scale. In the case of the global mean surface air temperature, the method presented provides estimation similar to other methods. Key Points: We present a non linear method based on neural network to attribute the global mean surface air temperature variability to different forcingsWe use a convolutional neural network associated with a backward optimization to estimate the climate response to the different external forcingsThe attributable forcings are consistent with those obtained using another state‐of‐the‐art method [ABSTRACT FROM AUTHOR]

Volcanic forcing, a major natural source of climate variability, represents a challenge for current climate modeling because of the unpredictability and specificity of individual eruptions, and because of the complexity of processes linking the eruption to the climate response. Volcanic forcing is largely underrepresented in available future climate projections, which is a critical problem. The study by Man Mei Chim and Colleagues (Chim et al., 2023, https://doi.org/10.1029/2023GL103743) tackles this known unknown and reveals how climatically relevant volcanic activity may be stronger than currently thought in a future warmer climate, enhancing uncertainty of climate projections. The study exemplifies the profound implications of inaccuracies within simplified climate scenarios and motivates new research on volcanically forced climate variability. It also arouses some thoughts on climate uncertainty communication. Plain Language Summary: Volcanic eruptions are a critical part of the earth system, capable of causing large climatic fluctuations over periods from years to decades. Available scenario experiments typically neglect volcanic forcing, which may cause systematic errors in future climate projections. Man Mei Chim and Colleagues (Chim et al., 2023, https://doi.org/10.1029/2023GL103743) use a series of statistical and mathematical models to comprehensively simulate how volcanic eruptions may affect climate during the 21st century. The results indicate that a more realistic representation of volcanic eruptions yields slightly less future warming and larger uncertainty than standard projections. In other words, we are less confident of future climate changes once we account for the (unpredictable) volcanic forcing. By considering future volcano‐climate interactions in their whole complexity and revealing a substantial role for small‐to‐moderate eruptions, the study places a new important piece in the volcano‐climate puzzle. It will motivate new coordinated research to improve the simulated representation of volcano‐climate interactions not only in the ambit of future climate scenarios but in paleoclimatic investigations and idealized volcanic experiments well. The research also reminds us of limitations inherent in climate model experiments and should foster improved communication of natural climate variability and associated uncertainties. Key Points: The study by Man Mei Chim and Colleagues shows that volcanic forcing can strongly affect future climate projectionsThe study reaffirms the importance of natural variability for future climates, stimulating research on volcano‐climate interactionsVolcanic forcing remains unpredictable, which rises the question of how to communicate irreducible climate projection uncertainties [ABSTRACT FROM AUTHOR]

The North Tropical Atlantic (NTA) mode is a dominant component of sea surface temperature (SST) variability over the northern tropical Atlantic, which is crucial to the occurrence of El Niño‐Southern Oscillation (ENSO). This study utilizes the historical simulation of the Coupled Model Intercomparison Project Phase 5 and 6 (CMIP5 and CMIP6) to evaluate the simulations of the NTA mode and NTA–ENSO connection. We found that the CMIP5 and CMIP6 could reproduce the spatial pattern of the NTA mode similarly. Most of them could reasonably reproduce the spatial pattern of the NTA mode similar to the observation. Still, there are some differences in the central values and position of the NTA mode. In addition, the analysis further suggests that the CMIP5/CMIP6 models have a large diversity in the NTA–ENSO connection, which may be due to their differences in simulating the spring climatological mean Atlantic Intertropical Convergence Zone (ITCZ) precipitation and spring‐to‐summer NTA SST persistence. Models with stronger spring climatological mean Atlantic ITCZ precipitation in favor of the spring NTA‐related SST anomalies stimulate local precipitation anomalies, resulting in a more powerful atmospheric circulation reaction over the tropical Pacific. Additionally, the stronger spring‐to‐summer NTA SST persistence for sustaining the teleconnection, which constantly transmits impacts from the tropical Atlantic into the tropical Pacific. The CMIP6 models show improvements to the CMIP5 models regarding simulations of the spring climatological mean Atlantic ITCZ precipitation and spring‐to‐summer NTA SST persistence. Therefore, the CMIP6 models simulate the NTA–ENSO connection better than the CMIP5 models. Plain Language Summary: El Niño‐Southern Oscillation (ENSO) significantly affects global climate variability through teleconnections. In particular, previous studies have suggested that ENSO impacts sea surface temperature (SST) variability over the North Tropical Atlantic (NTA). In addition, previous studies also showed that the NTA SST might feed back to the development of ENSO events and as a predictor for improving ENSO prediction. Therefore, evaluating the capability to capture the NTA mode and NTA–ENSO connection in the state‐of‐the‐art CMIP climate models gives some implications to improve the understanding of inter‐basin interactions and the ability of climate models to predict El Niño. The results show that the CMIP5 and CMIP6 could reproduce the spatial pattern of the NTA mode similarly. In addition, the CMIP5/CMIP6 models have a large diversity in the NTA–ENSO connection, which may be due to their differences in simulating the spring climatological mean Atlantic ITCZ precipitation and spring‐to‐summer NTA SST persistence. The CMIP6 models simulate the NTA–ENSO connection better than the CMIP5 models because the CMIP6 models show improvements to the CMIP5 models regarding simulations of the above two factors. Key Points: CMIP5 and CMIP6 could reproduce the spatial pattern of the North Tropical Atlantic (NTA) mode similarlyCMIP5/CMIP6 have large diversity in the NTA‐El Niño‐Southern Oscillation (ENSO) connection, CMIP6 simulate the connection better than CMIP5Differences in simulating the Atlantic Intertropical Convergence Zone (ITCZ) precipitation and NTA sea surface temperature persistence cause the bias in the NTA–ENSO connection [ABSTRACT FROM AUTHOR]

Improved seasonal precipitation forecasts can enable more effective water resource management decisions in a number of sectors, including municipal supply, agriculture, hydropower generation, and tourism. This study develops an effective straightforward statistical approach to enhance the quality of seasonal precipitation forecasts through the utilization of El Niño–Southern Oscillation (ENSO) information projected by Coupled General Climate Models (CGCMs). A stochastic weather generation (WG) model is developed to predict seasonal precipitation condition on ENSO condition. The WG model links a nonhomogeneous Markov Chain representing ENSO occurrence model to a bivariate normal distribution for seasonal precipitation conditioned on ENSO phase. Two verification metrics are suggested to measure the degree of predictability of raw, calibrated and climatological seasonal precipitation forecasts over northwest Costa Rica as a case study. Results indicate the potential to narrow the uncertainty of seasonal precipitation forecasts by incorporating CGCMs ENSO cycle information. Precipitation during the late part of the wet season (LS) has more predictability than precipitation in the early part of the wet season (ES). In addition, the degree of predictability decreases with an increase in lead time for a given forecast. A lead time of 1 year maintains a moderate level of predictability likely to support tangible benefits to various decision‐making processes. [ABSTRACT FROM AUTHOR]

Local, short‐duration extreme precipitation events can cause floodings and have massive economic consequences. The climate change impact on such events is of great interest, but due to the small spatio‐temporal scales involved, these are challenging to properly represent in climate models. This study analyses a new sub‐kilometre (750 m) HARMONIE‐Climate model simulation driven by ERA5 reanalysis data. Three convection‐permitting models at 750 m, 3 and 5 km grid distance are analysed and compared with driving reanalyses, intermediate model simulations and a dense rain gauge network. The representation of convective events is analysed by a range of metrics categorised as spatial, temporal and event‐focused. Precipitation events are analysed at both hourly and sub‐hourly scales and a clear difference between model performance on these scales is found. Overall, we find a better performance for HCLIM750m for most metrics, yet the added benefits of the computationally intensive sub‐kilometre scale simulation seems limited compared to the convection‐permitting models at 3 and 5 km. [ABSTRACT FROM AUTHOR]

As the planet warms, the atmosphere's water vapor holding capacity rises, leading to more intense precipitation extremes. River floods with high peak discharge or long duration can increase the likelihood of infrastructure failure and enhance ecosystem vulnerability. However, changes in the peak and duration of floods and corresponding socioeconomic exposure under climate change are still poorly understood. This study employs a bivariate framework to quantify changes in flood risks and their socioeconomic impacts in China between the past (1985–2014) and future (2071–2100) in 204 catchments. Future daily river streamflow is projected by using a cascade modeling chain based on the outputs of five bias‐corrected global climate models (GCMs) under three shared socioeconomic CMIP6 pathways (SSP1‐26, SSP3‐70, and SSP5‐85), a machine learning model and four hydrological models. We also utilize the copula function to build the joint distribution of flood peak and duration, and calculate the joint return periods of the bivariate flood hazard. Finally, the exposure of population and regional gross domestic product to floods are investigated at the national scale. Our results indicate that flood peak and duration are likely to increase in the majority of catchments by 25%–100% by the late 21st century depending on the shared socioeconomic pathway. China is projected to experience a significant increase in bivariate flood risks even under the lowest emission pathway, with 24.0 million dollars/km2 and 608 people/km2 exposed under a moderate emissions scenario (SSP3‐70). These findings have direct implications for hazard mitigation and climate adaptation policies in China. Plain Language Summary: Our planet is becoming increasingly vulnerable to the dangers of river flooding, frequently causing significant environmental damage and economic losses. In a warming climate, precipitation extremes are projected to intensify, increasing flood risk as well as its environmental impacts. However, changes in the peak and duration of floods and corresponding socioeconomic exposure under climate change is little studied, particularly in China. By the late 21st century, the exposure of the population and China's total economic outputs/prospects is projected to reach 24.0 million dollars/km2 and 608 people/km2, even under considerable greenhouse gas emission. In order to mitigate the potential socioeconomic exposure and vulnerability, the implementation of the Sendai Framework for Disaster Risk Reduction is necessary and urgent. Our findings provide valuable information for mitigating hazards and developing climate adaptation policies in China. Key Points: We project future daily river streamflow using a hybrid approach that combines machine learning, hydrological models and climate modelsWe evaluate shifts in future flood risks within a bivariate copula‐based frameworkChina might experience a doubling of flood risk by the late 21st century under SSP3‐70, with over a billion people being affected [ABSTRACT FROM AUTHOR]

In hydrology, projected climate change impact assessment studies typically rely on ensembles of downscaled climate model outputs. Due to large modeling uncertainties, the ensembles are often averaged to provide a basis for studying the effects of climate change. A key issue when analyzing averages of a climate model ensemble is whether to weight all models in the ensemble equally, often referred to as the equal‐weights or unweighted approach, or to use a weighted approach, where, in general, each model would have a different weight. Many studies have advocated for the latter, based on the assumption that models that are better at simulating the past, that is, the models with higher hindcast accuracy, will give more accurate forecasts for the future and thus should receive higher weights. To examine this issue, observed and modeled daily precipitation frequency (PF) estimates for three urban areas in the United States, namely Boston, Massachusetts; Houston, Texas; and Chicago, Illinois, were analyzed. The comparison used the raw output of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. The PFs from these models were compared with the observed PFs for a specific historical training period to determine model weights for each area. The unweighted and weighted averaged model PFs from a more recent testing period were then compared with their corresponding observed PFs to determine if weights improved the estimates. These comparisons indeed showed that the weighted averages were closer to the observed values than the unweighted averages in nearly all cases. The study also demonstrated how weights can help reduce model spread in future climate projections by comparing the unweighted and weighted ensemble standard deviations in these projections. In all studied scenarios, the weights actually reduced the standard deviations compared to the equal‐weights approach. Finally, an analysis of the results' sensitivity to the areal reduction factor used to allow comparisons between point station measurements and grid‐box averages is provided. [ABSTRACT FROM AUTHOR]

Using potentially best available rainfall data sets for the entire country of Japan (spatial scales of 1‐ and 20‐km), we analyze the 1–24 hr and city‐scale (1–400 km2) extreme rainfalls for both current (2006–2020) and future periods (2081–2109) at 1.5 K global warming scenario, complementing previous work that focuses on either coarse spatial and temporal scales or other warming scenarios (e.g., RCP and 2 K warming scenarios). A peak‐over‐threshold (POT)‐based approach is applied to compute areal reduction factor (ARF) for subsequently establishing intensity‐duration‐area‐frequency (IDAF) curves. Our results reveal that ARF values generally decrease with increasing area size and increase with longer duration and are affected by multiple underlying physical phenomena. Moreover, we find a greater increase in the rainfall intensities for shorter durations and higher return periods, ranging from 9.4% (1‐hr) to 6.2% (24‐hr), averaged for all return periods and 8.3% (25‐year) to 7.3% (2‐year), averaged for all rainfall durations. Spatially, extreme rainfall intensities are projected to increase by 8.9% in northern Japan (albeit with a less intense rainfall intensity in the current period), which is greater than the rest of the country (6.8%), underscoring the need to focus more on infrastructure designs in northern Japan. The projected IDAF curves further display an increase in the frequency of extreme events at city‐scale, for example, 25‐year extreme rainfall events in 2006–2020 would likely be 5‐10‐year events in 2081–2109. Our results with the state‐of‐the‐art data and implementable approach can be utilized for policymaking to reduce the warming‐induced risks in Japan and beyond. Key Points: For the first time, we analyze the city‐scale and sub‐daily extreme rainfall for the entire country of Japan by using high‐quality data setsAreal reduction factor values generally decrease with increasing area size and increase with longer duration and are affected by multiple physical phenomenaUnder 1.5 K warming, extreme rainfall will increase more for shorter durations and higher return periods, especially in northern Japan [ABSTRACT FROM AUTHOR]

As groundwater found in aquifers is the main reservoir of freshwater for human activity, knowledge of the future response of groundwater to climate change is key for improving water management adaptation plans. We analyze the climate‐driven evolution of future levels of unconfined aquifers in the 218 world's major groundwater basins in global climate simulations following the latest Intergovernmental Panel on Climate Change scenarios, run with models able to capture feedbacks among climate, land use and groundwater. We find a rising of groundwater levels on global average, which is consistent with the projected global intensification of precipitation. This signal presents large regional disparities which mostly match the patterns of precipitation changes. As the climate models we used do not simulate human groundwater withdrawals (irrigation as well as domestic and industrial uses) which represent the other main driver of groundwater levels evolution, we also use Food and Agriculture Organization maps of present‐day irrigated areas and projections of population in 2100 to identify regions where groundwater withdrawals could exacerbate the projected depletion, or even reverse a projected rise into a depletion. Depending on the scenario, we then find a rise (respectively a depletion) of groundwater levels in 2100 over 33%[28–39]% to 42%[41–45]% (respectively 26%[25–32]% to 37%[36–40]%) of the area covered by the 218 world's major groundwater basins. And we estimate that 31%[29–36]% to 43%[42–44]% of the world's population could be affected by these groundwater changes, facing either water scarcity issues (for 29%[27–33]% to 40%[39–40]% of the population), or increased risks of flooding (for 1.7%[1.5–2.2]% to 2.2%[2.2–2.4]% of the population). Key Points: The impact of climate change on water table depth (WTD) in the world's major groundwater basins is assessed using the sixth phase of the Coupled Model Intercomparison Project (CMIP6) global simulationsProjections run with four shared socioeconomic pathways scenarios show a global rising of groundwater by 2100, with the occurrence of a depletion in numerous regionsIn 2100, 31%–43% of the world's population could face water scarcity issues or flood risks worsened by these WTD changes [ABSTRACT FROM AUTHOR]

The parameterization scheme that represents gravity waves due to convection in LMDz‐6A, the atmospheric components of the IPSL coupled climate model (IPSLCM6), is directly compared to Strateole‐2 balloon observations made in the lower tropical stratosphere from November 2019 to February 2020. The input meteorological fields necessary to run the parameterization offline are extracted from the ERA5 reanalysis and correspond to the instantaneous meteorological conditions found underneath the balloons. In general, we find a fair agreement between measurements of the momentum fluxes due to waves with periods less than 1 hr and the parameterization. The correlation of the daily values between the observations and the results of the parameterization is around 0.4, which is statistically elevated considering that we analyze around 600 days of data and surprisingly good considering that the parameterization has not been tuned: the scheme is just the standard one that helps producing a quasi‐biennial oscillation (QBO) in the IPSLCM6 model. Online simulations also show that the measured values of momentum fluxes are well representative of the zonally and averaged values of momentum fluxes needed in LMDz‐6A to simulate a QBO. The observations also show that longer waves with periods smaller than a day carry about twice as much flux as waves with periods smaller than an hour, which is a challenge since the low period waves that make the difference are potentially in the "gray zone" of most climate models. Plain Language Summary: In most large‐scale atmospheric models, gravity wave (GW) parameterizations are based on well‐understood but simplified theories and parameters which are keyed to reduce systematic errors on the planetary scale winds. In the equatorial regions, the most challenging errors concern the quasi‐biennial oscillation. Although it has never been verified directly, it is expected that the parameterizations tuned this way should transport a realistic amount of momentum flux in both the eastward and westward directions when compared to direct observations. Here, we show that it is the case, to a certain extent, using constant‐level balloon observations at 20 km altitude. The method consists in comparing directly, each day and at the location of the balloon the measured momentum fluxes and the estimation of a GW parameterization using observed values of the large‐scale meteorological conditions of wind, temperature, and precipitation. Key Points: A non‐orographic parameterization tuned to produce a realistic tropical quasi‐biennial oscillation is used to predict in‐situ observationsParameterized gravity waves (GWs) needed in large‐scale models have realistic amplitudes in the tropical lower stratosphereDay‐to‐day variations of the estimated GW momentum fluxes correlate well with observations [ABSTRACT FROM AUTHOR]

We examine multiple factors in the representation of satellite‐retrieved atmospheric temperature diagnostics in historical simulations of climate change during the satellite era (specifically 1979–2021) using GISS ModelE contributions to the Coupled Model Intercomparison Project (Phase 6) (CMIP6). The tropospheric and stratospheric trends in these diagnostics are affected by greenhouse gases (notably carbon dioxide and ozone), coupling with the ocean, volcanic aerosols, solar activity and compositional and dynamic feedbacks. We explore the impacts of internal variability, changing forcing specifications, composition interactivity, the quality of the stratospheric circulation, vertical resolution, and possible impacts of the mis‐specification of volcanic aerosol optical depths. Overall temperature trends throughout the satellite period are well captured, but discrepancies at all levels exist and have multiple distinct causes. We find that stratospheric comparisons (using Stratospheric Sounding Unit (SSU) retrievals and successor instruments) are most affected by variations in the representation of ozone depletion and feedbacks, followed by the volcanic signals. Tropospheric skill (using the Microwave Sounding Unit (MSU) retrievals) is affected by the trends in ocean heat uptake and tropospheric aerosols, but also by the representation of stratospheric processes through the impact of the Brewer‐Dobson circulation on the height of the tropical tropopause. We demonstrate that no single factor is the dominant cause of the discrepancies and that almost all observations lie within the broad envelope of structural uncertainty. Plain Language Summary: The assessment of the ability of climate models to match trends and variability in the real world is a key factor in building the credibility of model projections under future scenarios. We focus on satellite‐derived atmosphere temperatures from the surface to the stratosphere whose trends at different levels reflect different processes and drivers. The satellite retrievals are weighted averages of atmospheric temperatures and so the vertical structure of the model trends matter in the comparison. We find that overall the trends throughout the atmosphere are well‐captured by the GISS models but that discrepancies can occur due to misspecified forcings, chaotic weather variability, and model structure. Key Points: Changes in forcing configurations have significant impact on agreement with satellite dataTropospheric model‐observation agreement is linked to ocean heat uptake, aerosols, sensitivity, and internal variabilityStratospheric discrepancies are mainly related to volcanic aerosol modeling and representations of ozone climatology and trends [ABSTRACT FROM AUTHOR]

We evaluate the performance of climate models from the Coupled Model Intercomparison Project phases 5 (CMIP5) and 6 (CMIP6) in simulating the weather regimes over East Asia during the period from 1961 to 2005. The circulation patterns, frequencies and transition probabilities of the weather regimes are obtained by categorizing the daily 500‐hPa geopotential height (Z500) anomalies using the k‐means clustering algorithm. We identify four winter (WWR1‐4) and three summer weather regimes (SWR1‐3) in both reanalysis datasets and CMIP5/6 models. Significant increasing (declining) frequency trends in WWR3 (WWR4) and SWR2 (SWR1) are found in the ERA5 and NCEP/NCAR reanalysis datasets. On the whole, both generations of models demonstrate reasonable abilities to reproduce the circulation patterns of weather regimes based on the Z500 anomaly fields but overall underestimate the intensities of weather regime patterns, underestimate the occurrence frequencies of WWR3, SWR2, and SWR3, and overestimate the frequencies of WWR1, WWR2, WWR4, and SWR1. In terms of both the ensemble‐mean and pairwise comparisons, the CMIP6 models generally perform better than their CMIP5 counterparts in reproducing the circulation pattern intensities, occurrence frequencies during the whole period and the linear frequency trends and transition probabilities of the weather regimes both in winter and summer. Further analysis shows a highly positive correlation between the total frequency and transition performances but a lower correlation between the biases in the total frequency and intensity of weather regime patterns. The improved skill of CMIP6 over the CMIP5 models in reproducing the weather regimes over East Asia may be partly related to the increased vertical atmospheric resolution in the model. [ABSTRACT FROM AUTHOR]

The effects of aridity on ecosystems and water cycles are pronounced and have received considerable attention. However, aridity changes due to future warming and its regional variability over China remain uncertain. This paper aims to identify the spatiotemporal variations in aridity and its key influencing factors over China in the mid-21st century based on five general circulationmodels (GCMs) and four representative concentration pathway (RCP) scenarios. An aridity index (AI), defined as the ratio of reference evapotranspiration (ETo) to precipitation (P), was calculated. We show that the GCMensemble means are able to reproduce the variation of aridity during the baseline period. Generally, ETo anomalies are consistently positive. Other than for the RCP2.6 low-emission scenario, precipitation and aridity are both projected to increase. There are pronounced regional differences in aridity changes; i.e. wetter across most of western China and drier across most of eastern China in the mid-21st century. Negative AI anomalies in western China can be attributed mainly to the projected increase in precipitation. In eastern China, the AI was higher despite positive precipitation anomalies, due mainly to the greater effect of climate change on increasing atmospheric moisture demand. This suggests that evapotranspiration demand should be incorporated into aridity changes under future warming. [ABSTRACT FROM AUTHOR]

Observations from the RAPID array at 26.5°N suggest a weakening in the Atlantic meridional overturning circulation between 2004 and 2020. The shortness of this time series raises the question of whether this weakening should be thought of as natural variability or as related to climate change. To estimate natural variability, preindustrial control runs of the Coupled Model Intercomparison Project Phase 6 are used and the observed weakening is found to fall within the distribution of naturally occurring trends. These trends result from model variability on periods longer than the observational window, and variation between models at these frequencies is especially pronounced. Finally, adding noise to inflate model variability also inflates model trends; however, the effect on trends is sensitive only to the low frequency portion of the noise spectrum. Plain Language Summary: The Atlantic meridional overturning circulation (AMOC) is a major ocean circulation system that transports heat northward in the Atlantic basin thus contributing to the regulation of the climate system. Fluctuations in the AMOC have been linked with changes in Arctic sea ice and to variations in the climate of Europe and North America. The longest direct observational record of the AMOC spans only 15.8 years and has shown a weakening of the circulation. Whether this weakening is due to natural oscillations in the climate system or is a response to anthropogenic greenhouse gases in the atmosphere is unclear. To assess whether the ocean circulation produces spontaneously weakening signals of such intensity, long simulations of climate models run under a preindustrial scenario are used to estimate the natural variability of the AMOC. These models suggest that the current weakening of the AMOC is consistent with natural variability. An accurate representation of slow timescale fluctuations in the AMOC is what matters most to the detection of an anthropogenically driven AMOC trend. This variability, however, differs markedly across models. Key Points: The 2004–2020 observed Atlantic meridional overturning circulation (AMOC) trend is not significantly relative to Coupled Model Intercomparison Project Phase 6 (CMIP6) model natural variabilityCMIP6 models largely disagree on AMOC variability, including at decadal and longer periodsIt is periods longer than the observational window that set AMOC trend detectability thresholds [ABSTRACT FROM AUTHOR]

We investigate the dependence of radiative feedback on the pattern of sea‐surface temperature (SST) change in 14 Atmospheric General Circulation Models (AGCMs) forced with observed variations in SST and sea‐ice over the historical record from 1871 to near‐present. We find that over 1871–1980, the Earth warmed with feedbacks largely consistent and strongly correlated with long‐term climate sensitivity feedbacks (diagnosed from corresponding atmosphere‐ocean GCM abrupt‐4xCO2 simulations). Post 1980, however, the Earth warmed with unusual trends in tropical Pacific SSTs (enhanced warming in the west, cooling in the east) and cooling in the Southern Ocean that drove climate feedback to be uncorrelated with—and indicating much lower climate sensitivity than—that expected for long‐term CO2 increase. We show that these conclusions are not strongly dependent on the Atmospheric Model Intercomparison Project (AMIP) II SST data set used to force the AGCMs, though the magnitude of feedback post 1980 is generally smaller in nine AGCMs forced with alternative HadISST1 SST boundary conditions. We quantify a "pattern effect" (defined as the difference between historical and long‐term CO2 feedback) equal to 0.48 ± 0.47 [5%–95%] W m−2 K−1 for the time‐period 1871–2010 when the AGCMs are forced with HadISST1 SSTs, or 0.70 ± 0.47 [5%–95%] W m−2 K−1 when forced with AMIP II SSTs. Assessed changes in the Earth's historical energy budget agree with the AGCM feedback estimates. Furthermore satellite observations of changes in top‐of‐atmosphere radiative fluxes since 1985 suggest that the pattern effect was particularly strong over recent decades but may be waning post 2014. Key Points: Post 1980 the Earth warmed with feedbacks uncorrelated with—and indicating much lower equilibrium climate sensitivity than—that expected for long‐term CO2 increaseSatellite observations of changes in top‐of‐atmosphere radiative fluxes since 1985 are in agreement with the modelsThe pattern effect may be waning post 2014 [ABSTRACT FROM AUTHOR]

Projected changes in the Northern Hemisphere stratospheric polar vortex are analyzed using Climate Model Intercomparison Project Phase 6 experiments. Previous studies showed that projections of the wintertime zonally averaged polar vortex strength diverge widely between climate models with no agreement on the sign of change, and that this uncertainty contributes to the regional climate change uncertainty. Here, we show that there remains large uncertainty in the projected strength of the polar vortex in experiments with global warming levels ranging from moderate (SSP245 runs) to large (Abrupt‐4xCO2 runs), and that the uncertainty maximizes in winter. Partitioning of the uncertainty in wintertime polar vortex strength projections reveals that, by the end of the 21st century, model uncertainty contributes half of the total uncertainty, with scenario uncertainty contributing only 10%. Regression analysis shows that up to 20% of the intermodel spread in projected precipitation over the Iberian Peninsula and northwestern US, and 20%–30% in near‐surface temperature over western US and northern Eurasian, can be associated with the spread in vortex strength projections after accounting for global warming. While changes in the magnitude and sign of the zonally averaged vortex strength are uncertain, most models (>95%) predict an eastward shift of the vortex by 8°–20° degrees in longitude relative to its historical location with the magnitude of the shift increasing for larger global warming levels. There is less agreement across models on a latitudinal shift, whose direction and magnitude correlate with changes in the zonally averaged vortex strength so that vortex weakening/strengthening corresponds to a southward/poleward shift. Plain Language Summary: Previous studies showed that changes in the strength of the winds in the Northern Hemisphere wintertime stratosphere, the so‐called polar vortex, can affect near‐surface winds and precipitation on various timescales. However, climate models do not agree on whether the polar vortex will weaken or strengthen during the 21st century. Here, we use Climate Model Intercomparison Project Phase 6 experiments to better understand how the polar vortex will respond to future greenhouse gas emissions. We show that half of the uncertainty in the vortex strength projections by the end of the 21st century is due to climate model errors (model uncertainty). We show that the uncertainty in the vortex strength projections is linked to the uncertainty in projected precipitation over the Iberian Peninsula and northwestern US and projected near‐surface temperature over the western US and northern Eurasia. Most models predict an eastward shift of the vortex relative to its historical location but we do not detect any influence of the vortex longitudinal shift on surface precipitation and temperatures. There is less agreement across models on a latitudinal shift, whose direction and magnitude correlate with changes in the vortex strength so that vortex weakening/strengthening corresponds to a southward/poleward shift of the vortex. Key Points: Model uncertainty contributes half of the total uncertainty in the projected strength of the Northern winter stratospheric polar vortexUncertainty in the projected polar vortex strength is linked to uncertainty in projected regional surface temperature and precipitationMost climate models project an eastward shift of the Northern winter stratospheric polar vortex [ABSTRACT FROM AUTHOR]

Tropical cyclones generate large waves that physically damage coral communities and are commonly cited as a worsening threat to coral reefs under climate change. However, beyond projections of ocean basin‐scale changes in cyclone intensity, the other determinants of future coral reef damage such as cyclone size and duration remain uncertain. Here, we determine the extent to which downscaled cyclones represent observed cyclone characteristics that influence wave damage to Australian coral reef regions. We then investigate mid‐century (2040–2060) and end of century (2080–2100) downscaled tracks to assess whether cyclone characteristics will change with future warming under a high‐emissions scenario. We find that spatial uncertainties in downscaled cyclogenesis and track positions limit estimates of reef damage for individual coral reefs and regions. Further, the models are unable to reproduce the most reef damaging cyclones for any of the regions. The downscaled tracks capture observed cyclone characteristics, such as size, impacting the Great Barrier Reef well, but perform poorly for the Northern Territory, with mixed performance for the Coral Sea and Western Australia. We find no clear evidence that cyclones will cause more damage to Australian coral reef regions in the future, at least based on the climate models and downscaling approach examined here. There is increasing interest in using tropical cyclone projections to assess future coral reef exposure to damaging waves. We recommend caution when interpreting such projections due to large uncertainty in the mechanisms that influence reef damaging tropical cyclone characteristics and how these will change with future warming. Plain Language Summary: Tropical cyclone intensity, size and duration together determine the extent to which their waves damage coral reefs. Increased tropical cyclone intensity with climate change is often cited as evidence that tropical cyclones will cause more damage to coral reefs in the future but changes to size and duration remain uncertain. Here, we determine whether tropical cyclones simulated from climate models can represent the observed tropical cyclone characteristics that are important for estimating wave damage to coral reefs and assess how these characteristics might change in the future. We find that the tropical cyclones simulated from climate models capture the observed cyclone characteristics well for those impacting the Great Barrier Reef (with the exception of the most damaging cyclones) with mixed to poor performance for other regions. The projections of future reef damage are uncertain with some models projecting increases and others decreases. Tropical cyclone projections are used in conservation planning to identify and protect the coral reefs least exposed to future tropical cyclones. However, we find that the simulated tropical cyclone tracks explored here are unlikely to represent future reef damaging tropical cyclone characteristics well if used in conservation decision making. Key Points: Wave damage to coral communities from tropical cyclones depends on their intensity, size and duration while tracking near coral reefsDownscaled tracks capture observed cyclone characteristics impacting the Great Barrier Reef with mixed to poor performance in other regionsProjections vary by region and cyclone characteristic with some models projecting increases and others decreases in future reef damage [ABSTRACT FROM AUTHOR]

While the annual mean Arctic Amplification (AA) index varied between two and three during the 1970–2000 period, it reached values exceeding four during the first two decades of the 21st century. The AA did not change in a continuous fashion but rather in two sharp increases around 1986 and 1999. During those steps the mean global surface air temperature trend remained almost constant, while the Arctic trend increased. Although the "best" CMIP6 models reproduce the increasing trend of the AA in 1980s they do not capture the sharply increasing trend of the AA after 1999 including its rapid step‐like increase. We propose that the first sharp AA increase around 1986 is due to external forcing, while the second step close to 1999 is due to internal climate variability, which models cannot reproduce in the observed time. Plain Language Summary: The rate of Arctic warming varied in time, while the rate of global warming was nearly constant over the period 1960–2020. This led to a variable Arctic Amplification (AA) defined as a ratio of the Arctic warming trend to mean global warming trend. In addition, the Arctic experienced sudden changes in the rate of warming with step‐like changes in annual mean Arctic Amplification around the years 1986 and 1999. Climate models do not capture the second observed sharp increase of the AA. We suggest that the first AA increase around 1986 is primarily due to external forcing (increasing concentration of carbon dioxide) while the second sharp increase around 1999 is dominated by internal climate variability which current models cannot capture accurately within the time scale of the physical phenomena. Key Points: Annual mean Arctic Amplification (AA) within the period 1970–2020 changed in steep steps around 1986 and 1999. It reached values over 4.0Even those CMIP6 models best at reproducing the AA did not reproduce the second sudden increase near 1999We conjecture that the first AA sharp increase is due to external forcing, while the second is due to internal climate variability [ABSTRACT FROM AUTHOR]

Assessing forced climate change requires the extraction of the forced signal from the background of climate noise. Traditionally, tools for extracting forced climate change signals have focused on one atmospheric variable at a time, however, using multiple variables can reduce noise and allow for easier detection of the forced response. Following previous work, we train artificial neural networks to predict the year of single‐ and multi‐variable maps from forced climate model simulations. To perform this task, the neural networks learn patterns that allow them to discriminate between maps from different years—that is, the neural networks learn the patterns of the forced signal amidst the shroud of internal variability and climate model disagreement. When presented with combined input fields (multiple seasons, variables, or both), the neural networks are able to detect the signal of forced change earlier than when given single fields alone by utilizing complex, nonlinear relationships between multiple variables and seasons. We use layer‐wise relevance propagation, a neural network explainability tool, to identify the multivariate patterns learned by the neural networks that serve as reliable indicators of the forced response. These "indicator patterns" vary in time and between climate models, providing a template for investigating inter‐model differences in the time evolution of the forced response. This work demonstrates how neural networks and their explainability tools can be harnessed to identify patterns of the forced signal within combined fields. Plain Language Summary: Using machine learning tools called neural networks, we identify patterns of the changing climate within climate model data. Changes in the climate can be identified earlier when detecting patterns within maps of multiple variables and seasons than for single maps alone. By visualizing the patterns learned by the neural networks, we can identify which regions, variables, and seasons are most important for detecting climate change. These patterns offer insight into how climate change is represented in different climate models, and how the patterns of climate change will evolve over time. Key Points: Neural networks and their explainability tools can be harnessed to identify patterns of forced change within combined fieldsCombined fields of input allow for earlier detection of the emergence of a forced climate responseExplainable AI techniques can be used to identify patterns that describe the emergence and evolution of forced climate change [ABSTRACT FROM AUTHOR]

Understanding polar amplification (PA) and its underlying processes is key to accurately predicting the climate system's response to increasing anthropogenic forcings. We examine the amplified warming in the Arctic and Antarctic in 17 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) against satellite data. Large hemispheric differences in PA strength was found in the CMIP6 models. Changes in surface temperature and strength of PA is closely coupled to changes in albedo. The planetary albedo of Earth (αp) is partitioned into a component associated with surface albedo (defined as surface contribution to planetary albedo, αsurfp ${\alpha }_{\mathit{surf}}^{p}$), and a component associated with atmospheric albedo (atmospheric contribution to planetary albedo, αatmp ${\alpha }_{\mathit{atm}}^{p}$). To assess the hemispheric differences in PA strengths, the relative importance of αsurfp ${\alpha }_{\mathit{surf}}^{p}$ and αatmp ${\alpha }_{\mathit{atm}}^{p}$ were investigated. The surface reflection looks different as seen at the surface (defined as surface albedo, αsurf) compared to αsurfp ${\alpha }_{\mathit{surf}}^{p}$ (as seen at the top of the atmosphere). We find a stronger correlation between surface temperature and αsurf in the Arctic than in the Antarctic, with correlation coefficients of −0.94 and −0.88, respectively. Interestingly, the correlation for surface temperature and αsurfp ${\alpha }_{\mathit{surf}}^{p}$ is stronger in the Antarctic than in the Arctic with correlation coefficients of −0.93 and −0.90, respectively. In the southern high latitudes, albedo changes at the surface are more important than changes in the atmosphere, while the opposite applies in the northern high latitudes. Surface temperature changes in the low‐ and mid‐latitudes are strongly associated with changes in αatmp ${\alpha }_{\mathit{atm}}^{p}$, dominated by changes in cloud properties. Plain Language Summary: The temperature is increasing faster in high latitudes compared to other regions of Earth. This is known as polar amplification (PA). The amount of light reflected from a surface is called albedo. Using 17 climate models and satellite data, this study compares the effects of albedo from surface and atmosphere on PA. We find a large spread in PA among models, with stronger PA in the northern hemisphere than in the southern hemisphere. Surface albedo looks different when looking from the surface compared to when looking from the top of the atmosphere (TOA), because of the effects from the atmosphere when looking from the TOA. We find greater correlation between surface temperature and albedo at the surface in the Arctic than the Antarctic. Interestingly, from the TOA we see greater correlation between surface temperature and surface albedo in the Antarctic than in the Arctic. We also find that in Antarctica, changes in surface albedo as seen from the TOA are more sensitive to changes in surface temperature than changes in atmospheric albedo are, while the opposite applies in the Arctic. These differences may provide part of the answer to why there is greater PA in the northern than in the southern hemisphere. Key Points: A strong correlation between CO2‐induced temperature change and planetary albedo is found in all regionsThere is a large spread in polar amplification between modelsCorrelation between CO2‐induced temperature change and contribution from surface albedo to total planetary albedo is clear in the Antarctic, but less clear in the Arctic [ABSTRACT FROM AUTHOR]

Tropical upper‐troposphere clouds are expected to rise under global warming, contributing a positive radiative feedback. Here we show that Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals over 2002–2021 from the Terra and Aqua satellites report a tropical‐mean high‐cloud altitude increase of 6.9 ± 2.7 m yr−1 (±2σ), while atmospheric models report 5.6 ± 3.0 m yr−1 over 1979–2014. During their common overlap period, however, the observed trend is greater than that of the models. The atmospheric models also show a tropically confined cloud‐height response to El Nino, whereas the observed cloud height response peak extends into the subtropics. Poleward of the mid‐latitudes, Aqua and Terra MODIS have cloud‐height drifts relative to each other, highlighting the need for deeper investigation of potential time‐dependent biases in MODIS cloud retrievals. Both sensors report significant tropical cloud height increases since 2002, which are statistically consistent with those simulated by climate models. Plain Language Summary: Theory has long predicted that the highest‐altitude tropical clouds would rise as Earth warms, a feedback that amplifies warming. Previous work used multi‐satellite products to report that tropical clouds are getting higher, but there were large uncertainties likely caused by combining data from different satellites. Here we use data from two Moderate Resolution Imaging Spectroradiometer (MODIS) sensors on different satellites that have been flying together since 2002 and use data through 2021. Compared with other satellite cloud‐height data, the MODIS instruments provide a longer data record than radar or lidar, and have a larger swath so provide more data than multi‐angle imaging. Both MODIS sensors support that the average altitude of tropical upper‐troposphere cloud tops has risen at approximately 7 ± 3 m yr−1. This is new evidence for a tropical cloud‐height change, although the net feedback will also be determined by changes in cloud fraction and thickness. Key Points: We estimate high‐altitude cloud height trends and El Nino response from Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and the latest atmospheric climate model simulationsTropical clouds rose during 2002–2021 according to MODIS, and the observed El Nino cloud‐height response differs from modelsModels simulate rising clouds 1979–2014, when their runs end, but during the MODIS overlap period 2002–2014 they do not show a trend [ABSTRACT FROM AUTHOR]

We propose a parameterization scheme of convective organization effects based on a moisture‐distribution approach, which can reflect aggregation of convective cells within a model grid as well as the interaction between convection and spatial heterogeneity in free‐troposphere moisture. With this concept, convective cells in an aggregated state are surrounded by air that is moister than the grid‐mean condition, which provides a "shielding" effect favorable for deeper convective updrafts. Such effects are represented quantitatively via utilizing a schematic diagram about the geometric interpretation of convective cluster, dry area, and their contact area, in which both the convective cluster size and dry area fraction increase during the development of convection. Our sensitivity analyses indicate that the new scheme performs well in capturing the mean precipitation features. Excluding the convective organization effects in the model leads to a considerable reduction in the simulated precipitation magnitude. Notably, there exists an inverse relationship between background mean moisture condition and sub‐grid moisture variability for a given rain rate, suggesting that with a high spatial heterogeneity in free‐troposphere moisture, convection can still develop and maintain its strength under relatively dry background conditions, consistent with the convection‐permitting model simulation. Overall, our new parameterization of convective organization effects can successfully reproduce the relationship between precipitation and sub‐grid moisture variability, which is a missing element in traditional convection parameterization schemes but important for the simulations of precipitation variability at various scales in climate models. Plain Language Summary: In nature, convection experiences spontaneous organization, which is important for precipitation variability at various scales but hard to represent in climate models. Here we propose a parameterization scheme of convective organization effects based on a moisture‐distribution approach, aiming to quantitatively represent aggregation of convective cells within a model grid as well as the interaction between convection and spatial heterogeneity in free‐troposphere moisture. With this concept, convective cells in an aggregated state are surrounded by air that is moister than the background mean condition, providing a "shielding" effect favorable for deeper convective updrafts. Our model results indicate that the new scheme successfully reproduces the relationship between precipitation and sub‐grid moisture variability. By taking into account the effect of spatial heterogeneity in moisture, convection can develop and maintain its strength even under relatively dry background conditions, consistent with the results from a convection‐permitting model simulation. Key Points: λ We propose a parameterization scheme of convective organization effects based on a moisture‐distribution approachλ Convection can interact with spatial variability in free‐troposphere moisture, providing a "shielding" effect on convective updraftsλ The precipitation‐moisture connections simulated with the new scheme are evaluated against a convection‐permitting model simulation [ABSTRACT FROM AUTHOR]