Abstract Interactions between the tropical Pacific and the tropical Atlantic (TP–TA) play a crucial role in tropical Pacific climate variability and predictability on a broad range of time scales. However, the ability of state-of-the-art climate models to simulate these cross-basin interactions remains uncertain. This study systematically evaluates the representation of TP–TA interactions in 34 climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6). While models generally reproduce the spatial patterns of key tropical climate modes, significant biases are found in their amplitudes, seasonality (particularly for the equatorial Atlantic mode), and spectral characteristics. Notably, most models substantially underestimate Atlantic impacts on spatiotemporal aspects of El Niño–Southern Oscillation (ENSO). To disentangle the bidirectional coupling mechanisms, we employ a linear inverse model (LIM) that allows selective isolation of Atlantic-to-Pacific versus Pacific-to-Atlantic coupling. Our analysis reveals two key aspects of TP–TA interactions: 1) internal Atlantic variability enhances Pacific climate variance across interannual and decadal time scales and 2) Pacific-driven Atlantic variability reduces tropical Pacific low-frequency variability. Although these influences qualitatively agree with observations, their simulated intensities are markedly weaker. Furthermore, we identify considerable intermodel spread in representing TA impacts on TP variability, highlighting persistent challenges in achieving robust model consensus. Our findings underscore the need to improve the representation of TP–TA interactions in climate models, particularly through more realistic simulations of tropical Atlantic dynamics and their seasonal evolution, to make progress in seasonal-to-decadal climate predictions.

• Extreme precipitation patterns across different regions of WNP are evaluated. • Two concentrated precipitation belts are observed throughout the four seasons. • Mid-21st century projections are derived from HighResMIP ensemble means. • Future precipitation tends to get wetter in high intensity region, and vice versa. Extreme precipitation in the western North Pacific (WNP) is jointly controlled by complex circulation systems, which can be better captured by enhancing model resolution. Despite progress in evaluating precipitation in the Coupled Model Intercomparison Project Phase 6 (CMIP6), most assessments have focused on land monsoon regions or individual river basins, leaving the land-ocean transitional WNP insufficiently investigated. This study, leveraging the climate model outputs from the High-Resolution Model Intercomparison Project (HighResMIP) of the CMIP6, examines the spatiotemporal characteristics of precipitation in the WNP and evaluates the simulation biases across different models. The results indicate that most HighResMIP models are capable of simulating spatial patterns of double‑banded precipitation zones that are consistent with observations, with higher skill scores compared to their lower‑resolution counterparts. Among these models, ECMWF-IFS-HR and EC-Earth3P-HR exhibit superior performance in capturing both annual mean precipitation and extreme precipitation characteristics. The multi-model ensemble means from optimal models reduce uncertainty significantly by much narrowed inter-model spread. Precipitation projections in the mid-21st century (2031–2050) exhibit a "wet-get-wetter, dry-get-drier" divergent development characteristic, indicating an eastward expansion and an increased influence of extreme precipitation toward lower- and higher-latitude regions under future warming.

Abstract This study examines future changes in precipitation and temperature across Myanmar using bias-corrected multimodel ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6) archive under shared socioeconomic pathway 2-4.5 (SSP2-4.5) and SSP5-8.5 scenarios. Thirteen general circulation models (GCMs) were evaluated against observed data from 38 meteorological stations (1985–2014), and seven models were selected based on performance metrics. Bias correction was applied using the power transformation method for precipitation and variance scaling for temperature, and its performance was assessed for reproducing both baseline climate and extreme indices. Decomposition of uncertainty sources for the baseline period revealed that model and temporal variability are the dominant contributors to projection uncertainty. Future projections were analyzed for near-future (2021–50), mid-future (2051–80), and far-future (2081–2100) periods. Results indicate increasing precipitation across most regions, particularly during the rainy season, with the central dry zone and coastal areas showing the most notable changes. Minimum temperatures show a consistent warming trend across all scenarios and regions. In contrast, maximum temperatures exhibit mixed trends under SSP2-4.5 but a pronounced increase under SSP5-8.5, with the northern hilly region projected to warm by up to 4.9°C by the end of the century. Extreme indices also show a clear intensification of extremes toward the end of the century, especially under SSP5-8.5. These findings offer essential insights into Myanmar’s future climate risks and provide a scientific foundation for developing region-specific climate adaptation and resilience strategies. Significance Statement This study applies CMIP6 multimodel ensembles to project long-term precipitation and temperature changes across Myanmar under shared socioeconomic pathway 2-4.5 (SSP2-4.5) and SSP5-8.5. By bias-correcting model outputs and selecting high-performing general circulation models (GCMs) and analyzing both mean climate and extreme indices, we provide robust regional and seasonal climate projections through 2100. The decomposition of uncertainty sources for the base period and projection period shows how model and temporal variability affect the projections. The results reveal significant warming and shifting precipitation patterns, particularly affecting the dry and hilly zones. These changes have critical implications for agriculture, water resources, and climate risk management. Our work addresses a regional knowledge gap in climate projection literature and offers actionable information for adaptation planning in one of Southeast Asia’s most climate-vulnerable countries.

Anthropogenic climate forcing is altering ocean circulation and water mass distribution across the Southern Ocean, reshaping the habitat of circumpolar marine predators such as threatened crested ( Eudyptes ) penguins. Understanding species vulnerability remains challenging due to substantial uncertainties in climate projections. Here, we integrate two state-of-the-art climate assessment tools—storylines and time of emergence—to evaluate the vulnerability of crested penguins to ocean warming while explicitly addressing projection uncertainties. Using this framework, we select a discrete set of projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that capture qualitatively different global circulation responses and climate sensitivity. Uncertainty in global atmospheric circulation responses, particularly the degree of intensification of the Westerlies, strongly influences both the magnitude and spatial pattern of projected sea surface temperature (SST) warming within penguin foraging habitats. Storylines and climate sensitivity explain a greater proportion of overall projection uncertainty compared to conventional CMIP6 scenario ensembles. We identify two groups of SST sensitivity among crested penguins: (1) highly sensitive species, including Northern Rockhoppers ( E. moseleyi ) and Aotearoa/New Zealand endemic species, and (2) species with broader distributions, such as Southern and Eastern Rockhoppers ( E. chrysocome ) and Macaroni/Royal penguins ( E. chrysolophus/E. schlegeli ), which exhibit spatially heterogeneous exposure and sensitivity. Spatial variability in exposure among widely distributed species highlights opportunities for targeted monitoring to detect early climate change impacts. However, limited data on population dynamics, gene flow, and foraging ecology constrain vulnerability assessments, emphasizing the need for expanded ecological and tracking studies coupled with environmental monitoring. We advocate for interdisciplinary, uncertainty-aware approaches and transparent workflows, including open data and code sharing, to strengthen future climate vulnerability assessments for threatened species.

Abstract. The Land and Land Ice Theme in the Coupled Model Intercomparison Project Phase 7 (CMIP7) represents the current understanding of physical processes in land surface ecosystems, hydrology, cryosphere, and their physical interactions with other Earth system components. Simulations from Earth system models (ESMs) could provide crucial information for assessing planetary safety, such as critical tipping elements, and be used to inform climate risks for improving climate impact assessments and policy decisions. This paper presents a collaborative effort to identify scientific opportunities in the Land and Land Ice Theme of the CMIP7 Data Request. The proposed opportunities build upon advances in ESMs, including new freshwater system and land ice processes being included in CMIP7, as well as the scientific community's demand for high-frequency and sub-grid-scale land surface outputs. In total, 25 variable groups that contain 716 variables have been identified to be potentially available to the broad scientific audience for performing analysis in land–atmosphere coupling, hydrological processes and freshwater systems, glacier and ice sheet mass balance and their influence on the sea levels, land use, and plant phenology. Key reflections from this data request effort include advocacy for closer engagement between the user community and modeling groups, reduction in the technical barriers to tracking existing parameters and defining new variables, and more streamlined variable management. These will be essential to enhance the usability and reliability of CMIP7 outputs for climate and Earth system research and applications to a broad audience that relies on the CMIP7 endeavor.

Abstract. The terrestrial biosphere absorbs about one third of anthropogenic CO2 emissions, thereby significantly slowing human-induced climate change. Its capacity to act as a carbon sink strongly depends on climate conditions, particularly soil moisture (SM), which can constrain plant growth and amplify land–atmosphere feedbacks. Therefore, accurately capturing these effects in Earth System Models (ESMs) is critical. Using dedicated experiments of the Land Feedback Intercomparison Project (LFMIP, an experiment within the Land Surface, Snow, and Soil Moisture Model Intercomparison Project, LS3MIP) from the latest generation of ESMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we show that projected SM changes substantially reduce the land carbon sink by the end of the century (2070–2099). This reduction is mainly driven by SM variability, highlighting the importance of SM extremes, which are projected to become more frequent and intense under climate change. Our results confirm those of the previous model generation based on the Global Land–Atmosphere Climate Experiment-Coupled Model Intercomparison Project phase 5 (GLACE-CMIP5). The results show that the strong negative impact of SM changes on the land carbon sink shown for GLACE-CMIP5 is less severe in LFMIP. A more in-depth analysis reveals that this is due at least in part to the specific ESM sampling of the respective experiments, with participating ESMs from CMIP5 generally showing a stronger drying trend. Despite agreement on the negative impact of SM on the land carbon sink in most tropical and mid-latitude ecosystems in both sets of multi-model experiments, there are large intermodel differences in the projected magnitudes. As SM can influence land carbon uptake both directly and indirectly via land–atmosphere coupling, we conduct a contribution analysis on the impact of direct and indirect SM effects on carbon uptake, which reveals that SM–atmosphere interaction dominate SM-induced changes globally. However, models show disagreement on the magnitude of these effects. Intermodel differences arise mainly from varying sensitivities of GPP to SM-related direct and indirect effects, suggesting that differences likely stem from varying representations of water-stress related processes across ESMs. Our findings highlight SM–atmosphere coupling as a critical factor for future land carbon uptake. Improving the representation of water stress processes, plant hydraulics, and vegetation characteristics in ESMs is essential for reducing uncertainty in projections. Maintaining and possibly extending the experimental setup to a larger set of models in future CMIP generations will be key to advancing our understanding of SM-carbon interactions and consequently of the evolution of the land carbon sink under human-induced climate change.

Climate models show considerable discrepancies in their future projections around the Atlantic, mainly due to uncertainties in the fate of the Atlantic Meridional Overturning Circulation (AMOC). Climate models suggest a reduction in AMOC strength of 32 ± 37% by 2100 (90% probability, Shared Socioeconomic Pathways 2-4.5 scenario, Coupled Model Intercomparison Project Phase 6). To refine this estimate and reduce its uncertainty, we use four different observational constraint methods. The best one, which provides the lowest leave-one-out error, integrates a large set of observable variables using ridge-regularized linear regression-a method unusual in climate science. It gives an estimate of the AMOC slowdown of 51 ± 8% (90% probability), i.e., a weakening ∼ 60% stronger than suggested by the multimodel mean. This refinement mainly results from correcting a bias in South Atlantic surface salinity, consistent with recent studies emphasizing its role in the proximity to an AMOC tipping point. This more substantial AMOC weakening has key implications for future adaptation strategies.

Abstract This study evaluates the ability of the Coupled Model Intercomparison Project phase 6 (CMIP6) climate models to simulate the observed effects of tropical Pacific and Indian Ocean sea surface temperature anomalies (SSTAs) on Indian summer monsoon rainfall (ISMR) variability. Using observational data and the large ensemble historical simulations of seven CMIP6 models from 1950 to 2014, we applied a cyclostationary linear inverse model (CS-LIM) to isolate the impacts of tropical Pacific SSTAs, Indian Ocean SSTAs, and their interaction on the interannual variability of ISMR. Overall, these CMIP6 models well reproduced the observed enhanced (reduced) ISMR variability from Pacific SSTAs (Indian Ocean SSTAs and the Indo-Pacific interaction), though with varying spatial patterns and magnitudes. Among them, CESM2 and Energy Exascale Earth System Model version 2.0 (E3SM-2-0) showed the best agreement with observations for the effects of Pacific SSTAs and the Indo-Pacific interaction, respectively. Composite analysis of ISMR anomalies during the developing phases of pure and co-occurring El Niño–Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) events revealed that the impacts from Pacific SSTAs were captured reasonably well by E3SM-2-0, CESM2, MIROC6, and MPI-ESM1-2-LR, while E3SM-2-0 also showed the best agreement with observations for the effects from the Indo-Pacific interaction. However, all seven models exhibited substantial biases in simulating the Indian Ocean SSTA impacts on ISMR, particularly during pure El Niño events. Overall, this study provides new insights into how individual CMIP6 models simulate the isolated impacts from the tropical Pacific and Indian Oceans, which have important applications for improving ISMR predictions and interpreting ISMR future projections.

Abstract. This paper presents a comprehensive overview of the Coupled Model Intercomparison Project Phase 7 (CMIP7) request for data pertaining to Earth systems science, and provides justification for the resources needed to produce this data. Topics within the CMIP7 Earth System (CMIP7-ES) theme centre around tracking of flows of energy, carbon, water and other fluxes across domains, and constraining feedbacks between these cycles and the climate system. These topics are summarized in this paper as scientific “opportunities” describing specific model intercomparison experiments and use cases for next-generation Earth System Model (ESM) output. These opportunities were submitted by modelling groups and scientific consortia following an extended public consultation process. Contained within each opportunity are requests for groups of Climate & Forecasting (CF) variables, which are bundled into variable groups representing all data required to address the opportunities' needs. Novel opportunities in CMIP7 compared with previous phases will include running `emissions-driven' simulations that integrate carbon emissions and removal scenarios with updated representations of the global carbon cycle, expanded variable groups needed to model marine trophic interactions and biogeochemistry, and data needed to understand the risk of global tipping points, among others. The production of these variables will close key gaps and uncertainties identified during previous rounds of CMIP, and support the 7th Intergovernmental Panel on Climate Change Assessment Report (AR7). We argue that CMIP7-ES data will be broadly used by scientific, policy, governmental, industry, and other communities that rely on climate model projections for research and decision making. As an author group we also reflect on the evolution of the CMIP7-ES data request as a part of a deliberative process in support of the global CMIP program.

Abstract Rapid changes and increasing climatic variability across the widely varied Köppen-Geiger regions of northern Europe generate significant needs for adaptation. Regional planning needs high-resolution projected temperatures. This work presents an integrative statistical bias correction framework that incorporates Vision Transformer (ViT), Convolutional Long Short-Term Memory (ConvLSTM), and Geospatial Spatiotemporal Transformer with Attention and Imbalance-Aware Network (GeoStaNet) models. The framework is evaluated with a multicriteria decision system, Deep Learning-TOPSIS (DL-TOPSIS), for ten strategically chosen meteorological stations encompassing the temperate oceanic (Cfb), subpolar oceanic (Cfc), warm-summer continental (Dfb), and subarctic (Dfc) climate regions. Norwegian Earth System Model (NorESM2-LM) Coupled Model Intercomparison Project Phase 6 (CMIP6) outputs were bias-corrected during the 1951–2014 period and subsequently validated against independent historical observations (1951–2014) of day-to-day temperature metrics, extreme value distributions (99th percentile), and thermodynamic coupling (Diurnal Temperature Range). The ViT showed improved performance (Root Mean Squared Error (RMSE): 1.01 °C; R 2 : 0.92), allowing for production of credible bias-corrected projections. Under the SSP5-8.5 scenario, the Dfc and Dfb climate zones are projected to warm by 4. 8 °C and 3. 9 °C (Summer T m a x ), respectively, by 2100, with expansion in the diurnal temperature range by more than 1. 5 °C. The Time of Emergence signal first appears in subarctic winter seasons (Dfc: ~ 2032), signifying an urgent need for adaptation measures. The presented framework offers station-based, high-resolution estimates of uncertainties and extremes, with direct uses for adaptation policy over high-latitude regions with fast environmental change.

Abstract Tropical South American summer precipitation is primarily controlled by the intensity and position of the South American monsoon and the intertropical convergence zone, both of which respond to sea surface temperature anomalies over the surrounding tropical oceans. Our analysis examines how well contemporary, high-complexity Earth system models from the Coupled Model Intercomparison Project phase 6 (CMIP6) simulate the summer precipitation distribution and its interannual variability under preindustrial climate conditions. Specifically, we investigate how El Niño–Southern Oscillation (ENSO) and Atlantic Niño—two major zonal modes of variability in the tropical ocean—shape tropical South American precipitation through remote atmospheric teleconnections. The quality of the simulated climatological mean state and interannual variability across models is primarily evaluated using pattern correlations with the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) product. The three models with the highest and the three models with the lowest field correlation with the ERA5 reference are selected for a more detailed study of their representation of major modes of variability and associated teleconnection patterns. We show that models with large discrepancies in the location and abundance of core monsoon precipitation also typically fail to accurately represent atmospheric deep convection and teleconnections associated with the zonal modes. Differences in the ability to simulate South American summer precipitation, even under preindustrial forcings, emphasize the importance of selecting an appropriate model for studying the regional hydroclimate. Our study further calls for future research using high-resolution models that explicitly resolve deep convection to realistically capture South American monsoon rainfall. Significance Statement Tropical South America receives abundant rainfall from December through February, which is dominated by the South American monsoon system and the deep convection in the intertropical convergence zone. Naturally occurring climate modes in the surrounding tropical oceans, such as El Niño–Southern Oscillation and Atlantic Niño, also drive large variability in summer rainfall. Accurately representing how rainfall over tropical South America responds to modes of climate variability in preindustrial simulations is essential for evaluating the robustness and realism of climate models. Our study shows that models with more realistic atmospheric convection (upward vertical motion in the lower to midtroposphere) better depict the observed rainfall patterns and year-to-year changes over tropical South America, including the rainfall patterns shaped by the modes of variability. Models that better replicate these influences can be instrumental not only in understanding the future of South American rainfall but also in attribution studies of extreme events like droughts and floods.

ABSTRACT Global warming increases the potential risks of hydrological extremes, such as extreme precipitation and flood. Limited attention has been given to the integrated effects of climate change, land‐use change, and socioeconomic advancement on flood risk under global warming of 1.5°C and 2.0°C threshold outlined in the Paris Agreement. Here, utilizing the latest coupled model Intercomparison Project 6 (CMIP6), the new shared socioeconomic pathway scenarios (SSPs), hydrological model and future land use simulation (FLUS) model, we perform a comprehensive assessment of the flood risk in the Huai River Basin (HRB) under the global warming of 1.5°C and 2.0°C scenarios. The results reveal that (1) more intense extreme precipitation events will occur in the HRB under two global warming scenarios. The increases in extreme precipitation are approximately twice as high under 2.0°C than under 1.5°C global warming scenario; (2) under global warming of 1.5°C and 2.0°C scenarios, future 100‐year floods will increase by 18.4% and 19.2%, respectively, in the HRB; and (3) high flood‐risk areas are expected to primarily locate in regions with unfavorable flood regimes, with increases of 4.3% and 17.8%, and very high flood‐risk areas are projected to expand by 2% and 4.3%, respectively. Considering the holistic effects of future environmental changes on the flood risk, it is imperative to incorporate flood control management and prevention measures into regional adaptation strategies.

ABSTRACT Compound wind‐precipitation extremes (CWPE) are among the most impactful compound climate extremes under climate change. Using three CWPE definitions (strict co‐occurrence, CWPE; temporal offset, CWPE_day_offset; and spatial offset, CWPE_space_offset), we systematically analyse the spatiotemporal evolution of CWPE over China during 1980–2022. Based on multi‐model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6), we further evaluate historical simulations, project future changes under SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5, and quantify the sources of projection uncertainty. CWPE shows a pronounced “more in the east, less in the west” pattern, with hotspots in eastern Sichuan and coastal South China. Nationally, CWPE decreases during 1980–2010 but shifts to an increasing tendency during 2011–2022. Introducing temporal and spatial offsets markedly increases annual‐mean CWPE frequency and expands the affected area, with a stronger enhancement from spatial offsets. CWPE is strongly seasonal, occurring mainly in spring (MAM) and summer (JJA) and least in winter (DJF). CMIP6 models reproduce the large‐scale spatial pattern but generally overestimate CWPE magnitude, and biases are generally larger when offsets are considered. Projections indicate persistent increases in CWPE frequency under all scenarios, strongest under SSP5‐8.5, with central and eastern China as the main hotspots and larger increases toward later decades and higher emissions. Uncertainty decomposition indicates that internal variability dominates projection uncertainty in the early period, but its relative contribution gradually weakens over time. In the mid‐to‐late period, model uncertainty becomes the dominant source and continues to increase, while scenario uncertainty remains the smallest contributor. This systematic assessment of CWPE helps inform climate‐risk management in China under climate change. Our results show that CWPE definitions strongly affect trend estimates and spatial identification; introducing spatiotemporal offsets increases the number of identified events and their spatial coverage, thereby providing a clearer signal of potential risk changes for climate‐impact assessment.

The intensity and frequency of fatal landslides in Western Ghats of India are adversely influenced by human-induced land-use modifications and climate change. Understanding these factors are vital for effective disaster risk reduction strategy for coming days. This study evaluates landslide susceptibility and risk for the year 2050 by incorporating land-use/land-cover (LULC) and future rainfall patterns. The study integrates a Random Forest (RF)-based landslide susceptibility model with an ensemble of Coupled Model Intercomparison Project Phase 6 (CMIP6) rainfall projections, future LULC simulation using a Multilayer Perceptron-Cellular Automata (MLP-CA) framework. It further incorporates an exposure-based Landslide Risk Index (LRI) assessment under multiple Shared Socioeconomic Pathway (SSP) scenarios. The findings show a significant increase in rainfall intensity and urban development by 2050, resulting in an increased risk of landslides. Under the SSP5-8.5 scenario, the very-high susceptibility class is projected to increase from 8.85% in 2024 to 13.25% by 2050, which is approximately 1980km². In same period and SSP scenario, very-high risk area is expected to increase from 1.18% to 2.73%, which is about 697km². The most affected areas lie along major transport corridors and densely populated areas of Uttara Kannada, Kodagu and Chikkamagaluru districts in Karnataka state. These results show the combined impact of LULC and climate change on landslides. The study provides crucial information for the development of climate-resilient land-use planning and disaster risk reduction strategies considering probable future scenarios in the Western Ghats region of India.

ABSTRACT Conceptual model illustrating three steps: estimating future PCC from temperature, combining contextual data for water demand components, and integrating both via the water balance equation to generate OPT and PES scenarios. Urban areas face increasing pressures due to climate change, population growth, and aging infrastructures. In this challenging context, municipalities need to foresee future water demand to plan investments and ensure a reliable water supply. This study proposes a simple model to estimate future urban water demand and applies it to a city located in Southern Quebec, Canada. The model estimates future water demand under different climate projections based on Coupled Model Intercomparison Project Phase 6 models by considering residential consumption alongside factors such as leakage rates, industrial-commercial-institutional consumption, demographic growth, and conservation strategies. A combination of regression and scenario-based analysis is used to explore water demand under different future conditions in this data-scarce study area. Population growth and rising temperatures, particularly under high radiative forcing scenarios, are projected to increase water demand. However, the study finds that implementing effective conservation measures, such as reducing system leakage and introducing conservation measures, can significantly offset these increases. The study underscores the importance of integrating climate, demographic trends, and behavioral factors into long-term water planning and puts forth a simple approach for municipalities to anticipate urban water demand based on limited available data to support strategic decision-making.

Abstract Equatorial subsurface ocean temperature ( Tsub ) strongly influences tropical Pacific climate variability, yet the Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) models exhibit persistent Tsub cold biases and substantial inter‐model uncertainty. Here we diagnose both systematic biases and inter‐model spread in historical Tsub by applying a linear decomposition based on three key parameters: warm‐pool temperature, thermocline depth, and thermocline sharpness. Cooler Tsub tends to occur in models with a cooler warm pool, shallower thermocline, and sharper stratification. These parameters explain most of the systematic biases and inter‐model spread in Tsub among the CMIP historical simulations. Thermocline sharpness consistently dominates the systematic Tsub biases, whereas thermocline depth emerges as the primary source of inter‐model spread in CMIP6. Our method provides an efficient empirical diagnosis of Tsub biases and model uncertainty that can help guide future model development.

Abstract Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space–time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation. Significance Statement Computer models that simulate Earth system, known as climate models, are important tools for understanding how climate processes work and provide estimates of the climate system in the past and the future. Policy decisions regarding how to adapt to and limit the impact of climate change rely on these models, and it is, therefore, important to know how well they capture the real world. Real-world observations play an important role in understanding how well climate models represent Earth’s climate system. We describe various aspects of using observations for model evaluation and suggest best practices to make this process more efficient, accurate, and inclusive of a wider number of observed phenomena. Considerations and best practices are presented for the use of observations in climate model evaluation. The discussion centers on practices to support next-generation evaluation for Coupled Model Intercomparison Project simulations.

Abstract Antarctic sea ice expanded for the first 35 years of the satellite record but in the past 10 years has dropped to record lows. Recent studies argue that the recent decline brings climate models into better agreement with observed Antarctic sea ice trends. Here, we assess this claim by considering Antarctic sea ice area and concentration across a large number of Coupled Model Intercomparison Project phase 6 (CMIP6) simulations, and we reach a different conclusion. Even with many models that sample a large range of internal variability, we find that very few capture the observed multidecadal expansion or the large decline after 2014. Further analysis reveals that models with trends similar to observations typically have poor simulation of climatological sea ice, its variability, and its regional patterns. The continued inability of CMIP-class models to simulate Antarctic sea ice and its observed trends severely limits our ability to understand past changes and to confidently project its evolution in the coming decades.

Abstract To enhance the skill of climate projections, researchers have developed various weighting schemes based on the performance of models in simulating historical climate. However, the capacity of models with these weighting methods to accurately reproduce future climate change remains uncertain. In this study, models from the Coupled Model Intercomparison Project phase 5 (CMIP5) with reliability ensemble averaging (REA) scheme and performance and independence (PI)-weighted scheme are employed to project near-term temperature changes over China. By comparing observations with the weighted ensemble averages of models, we evaluate the projection skill of CMIP5 ensembles using these two weighting methods for the near-term projections. Our results indicate that, compared to the equal-weighted ensemble mean, PI-weighted projections based on the performance in reproducing historical temperature trends at the gridcell level display overall improvements in spring, summer, and autumn. Meanwhile, REA-weighted projections perform slightly worse on both annual and seasonal scales than the equal-weighted ensembles. Of importance is that the projection uncertainties, measured by the range of 5th–95th percentiles, estimated by the two unequal-weighted schemes are generally lower than those of the equal-weighted approach. However, the small projection uncertainty with large biases in REA-weighted ensembles increases the undesirable risk of observations falling outside the uncertainty range. As reducing the projection uncertainty also extends to the next two decades by weighting, the PI-weighted ensemble serves as a reliable alternative for future temperature projections over China.

Abstract Climate models exhibit an approximately invariant surface warming pattern in typical end-of-century projections. This feature has been used extensively in climate impact assessments for fast calculations of local temperature anomalies, with a linear procedure known as pattern scaling . At the same time, emerging research has also shown that time-varying warming patterns are necessary to explain the time evolution of effective climate sensitivity in coupled models, a mechanism that is known as the pattern effect and that seemingly challenges the pattern scaling understanding. Here, we present a simple theory based on local energy balance arguments to reconcile this apparent contradiction. Specifically, we show that the pattern invariance arises from the combination of exponential forcing, linear feedbacks, a constant forcing pattern, and linear changes in heat transport. These conditions are approximately met in typical Coupled Model Intercomparison Project phase 6 (CMIP6) shared socioeconomic pathways (SSPs), except in the Arctic where nonlinear feedbacks are important and in regions where different aerosol projections alter the forcing pattern. In idealized experiments where concentrations of carbon dioxide (CO 2 ) are abruptly increased, such as those used to study the pattern effect, the warming pattern evolves considerably over time because of spatially inhomogeneous ocean heat uptake, even in the absence of nonlinear feedbacks. Our results illustrate why typical future projections are amenable to pattern scaling and provide a plausible explanation of why more complicated approaches, such as nonlinear emulators, have only shown marginal improvements in accuracy over simple linear calculations. Significance Statement In typical end-of-century climate projections from comprehensive models, the ratio between local and global surface temperature anomalies is approximately time and scenario invariant. This feature has enabled fast calculations of local temperature changes by scaling the global average with a constant pattern. At the same time, idealized quadrupling of CO 2 (4xCO 2 ) experiments show a different behavior and a considerable time evolution of the warming pattern. We present a simple theory based on local energy balance to reconcile this apparent contradiction. Specifically, we show that the pattern invariance arises under a set of conditions that are approximately satisfied typical end-of-century scenarios. Our findings clarify why scaling the global average to calculate local temperature anomalies is effective for most future projections.