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The Coupled Model Intercomparison Project (CMIP) is one of the biggest international efforts aimed at better understanding the past, present, and future of climate changes in a multi-model context. A total of 21 model intercomparison projects (MIPs) were endorsed in its sixth phase (CMIP6), which included 190 different experiments that were used to simulate 40 000 years and produced around 40 PB of data in total. This paper presents the main findings obtained from the CPMIP (the Computational Performance Model Intercomparison Project), a collection of a common set of metrics, specifically designed for assessing climate model performance. These metrics were exclusively collected from the production runs of experiments used in CMIP6 and primarily from institutions within the IS-ENES3 consortium. The document presents the full set of CPMIP metrics per institution and experiment, including a detailed analysis and discussion of each of the measurements. During the analysis, we found a positive correlation between the core hours needed, the complexity of the models, and the resolution used. Likewise, we show that between 5 %–15 % of the execution cost is spent in the coupling between independent components, and it only gets worse by increasing the number of resources. From the data, it is clear that queue times have a great impact on the actual speed achieved and have a huge variability across different institutions, ranging from none to up to 78 % execution overhead. Furthermore, our evaluation shows that the estimated carbon footprint of running such big simulations within the IS-ENES3 consortium is 1692 t of CO 2 equivalent. As a result of the collection, we contribute to the creation of a comprehensive database for future community reference, establishing a benchmark for evaluation and facilitating the multi-model, multi-platform comparisons crucial for understanding climate modelling performance. Given the diverse range of applications, configurations, and hardware utilised, further work is required for the standardisation and formulation of general rules. The paper concludes with recommendations for future exercises aimed at addressing the encountered challenges which will facilitate more collections of a similar nature. [ABSTRACT FROM AUTHOR]

Systematic, routine, and comprehensive evaluation of Earth system models (ESMs) facilitates benchmarking improvement across model generations and identifying the strengths and weaknesses of different model configurations. By gauging the consistency between models and observations, this endeavor is becoming increasingly necessary to objectively synthesize the thousands of simulations contributed to the Coupled Model Intercomparison Project (CMIP) to date. The Program for Climate Model Diagnosis and Intercomparison (PCMDI) Metrics Package (PMP) is an open-source Python software package that provides quick-look objective comparisons of ESMs with one another and with observations. The comparisons include metrics of large- to global-scale climatologies, tropical inter-annual and intra-seasonal variability modes such as the El Niño–Southern Oscillation (ENSO) and Madden–Julian Oscillation (MJO), extratropical modes of variability, regional monsoons, cloud radiative feedbacks, and high-frequency characteristics of simulated precipitation, including its extremes. The PMP comparison results are produced using all model simulations contributed to CMIP6 and earlier CMIP phases. An important objective of the PMP is to document the performance of ESMs participating in the recent phases of CMIP, together with providing version-controlled information for all datasets, software packages, and analysis codes being used in the evaluation process. Among other purposes, this also enables modeling groups to assess performance changes during the ESM development cycle in the context of the error distribution of the multi-model ensemble. Quantitative model evaluation provided by the PMP can assist modelers in their development priorities. In this paper, we provide an overview of the PMP, including its latest capabilities, and discuss its future direction. [ABSTRACT FROM AUTHOR]

Regional climate models (RCMs) and reanalysis datasets provide valuable information for assessing the vulnerability of ice shelves to collapse over Antarctica, which is important for future global sea level rise estimates. Within this context, this paper examines variability in snowfall, near-surface air temperature and melt across products from the Met Office Unified Model (MetUM), Regional Atmospheric Climate Model (RACMO) and Modèle Atmosphérique Régional (MAR) RCMs, as well as the ERA-Interim and ERA5 reanalysis datasets. Seasonal and trend decomposition using LOESS (STL) is applied to split the monthly time series at each model grid cell into trend, seasonal and residual components. Significant systematic differences between outputs are shown for all variables in the mean and in the seasonal and residual standard deviations, occurring at both large and fine spatial scales across Antarctica. Results imply that differences in the atmospheric dynamics, parametrisation, tuning and surface schemes between models together contribute more significantly to large-scale variability than differences in the driving data, resolution, domain specification, ice sheet mask, digital elevation model and boundary conditions. Despite significant systematic differences, high temporal correlations are found for snowfall and near-surface air temperature across all products at fine spatial scales. For melt, only moderate correlation exists at fine spatial scales between different RCMs and low correlation between RCM and reanalysis outputs. Root mean square deviations (RMSDs) between all outputs in the monthly time series for each variable are shown to be significant at fine spatial scales relative to the magnitude of annual deviations. Correcting for systematic differences results in significant reductions in RMSDs, suggesting the importance of observations and further development of bias-correction techniques. [ABSTRACT FROM AUTHOR]

Philosophers have yet to provide a systematic analysis of the relationship between historical science and model-based science. In this paper I argue that prototypical model-based sciences exhibit features understood to be central to historical science. Philosophers of science have argued that historical scientists are distinctly concerned with inference to the best explanation, that explanations in historical science tend to increase in complexity over time, and that the explanations take the form of narratives. Using general circulation models in climate science as a reference, I illustrate how simulation models in model-based science share these features exhibited by historical science. That model based sciences share these features raises important philosophical questions about how we should understand prototypical types of scientific enquiry, including the relationship between experimental science, historical science, and model-based science. I conclude by exploring several options for how to accommodate the noted similarities within a more general taxonomy of the sciences. [ABSTRACT FROM AUTHOR]

Philosophers of science have paid close attention to climate simulations as means of projecting the severity and effects of climate change, but have neglected the full diversity of methods in climate science. This paper shows the philosophical richness of another method in climate science: the practice of using paleoclimate analogues to inform our climate projections. First, I argue that the use of paleoclimate analogues can offer important insights to philosophers of the historical sciences. Rather than using the present as a guide to the past, as is common in the historical sciences, paleoclimate analogues involve using the past as a guide to the future. I thereby distinguish different methods in the historical sciences and argue that these distinctions bear on debates over whether the historical sciences can produce generalizations or predictions. Second, I suggest that paleoclimate analogues might actually be considered a type of climate model, and, as such, their use expands on common characterizations of models to include those that are full-scale, naturally occurring, and non-manipulable. [ABSTRACT FROM AUTHOR]

Climate model selection stands as a critical process in climate science and research. It involves choosing the most appropriate climate models to address specific research questions, simulating climate behaviour, or making projections about future climate conditions. This paper proposes a new approach, using spatial functional data analysis, to asses which of the 18 EURO CORDEX simulation models work better for predicting average temperatures in the Campania region (Italy). The method involves two key steps: first, using functional data analysis to process climate variables and select optimal models by a hierarchical clustering procedure; second, validating the chosen models by proposing a new conformal prediction approach to the anomalies associated to each cluster. [ABSTRACT FROM AUTHOR]

We developed a new version of the Alfred Wegener Institute Climate Model (AWI-CM3), which has higher skills in representing the observed climatology and better computational efficiency than its predecessors. Its ocean component FESOM2 (Finite-volumE Sea ice–Ocean Model) has the multi-resolution functionality typical of unstructured-mesh models while still featuring a scalability and efficiency similar to regular-grid models. The atmospheric component OpenIFS (CY43R3) enables the use of the latest developments in the numerical-weather-prediction community in climate sciences. In this paper we describe the coupling of the model components and evaluate the model performance on a variable-resolution (25–125 km) ocean mesh and a 61 km atmosphere grid, which serves as a reference and starting point for other ongoing research activities with AWI-CM3. This includes the exploration of high and variable resolution and the development of a full Earth system model as well as the creation of a new sea ice prediction system. At this early development stage and with the given coarse to medium resolutions, the model already features above-CMIP6-average skills (where CMIP6 denotes Coupled Model Intercomparison Project phase 6) in representing the climatology and competitive model throughput. Finally we identify remaining biases and suggest further improvements to be made to the model. [ABSTRACT FROM AUTHOR]

Perturbations in extreme precipitation characteristics are investigated over the Volta Basin (VB) and its three subdomains (Sahel, Soudano-Sahel and Guinea Coast) for the early-21st (2030–2053) and mid-twenty-first centuries (2057–2080) under representative concentration pathways (RCPs) 4.5 and 8.5. Seven climate indices from the Expert Team on Climate Change Detection and Indices were selected to examine future extreme precipitation features. Owing to its performance over the West African sub region, CanESM2 model results were used with Global Precipitation Climatology Centre (GPCC v7) dataset serving as reference data. Results generally show lowering trends in extreme precipitation events over the VB. The declines in extremes were dominant in the Sahel and Soudano-Sahel zones with some degree of upsurges observed in the Guinea Coast. Spatially over the basin, wet spells (CWD) were projected to shorten under RCP 8.5 (~ 7–27 days/year) relative to RCP 4.5 (~ 8–30 days/year). Similar pattern was observed for dry spells (CDD) with ranges of ~ 64–198 days/year and ~ 61–186 days/year respectively for RCPs 4.5 and 8.5. As revealed, future alterations in precipitation events have the propensity to cause alternating drought or flood events. In this line, sustainable adaptation measures and coping strategies need to be devised in time to minimize the consequences of these events, particularly those on water resources availability and ecosystem functions and services. [ABSTRACT FROM AUTHOR]

Climate models struggle to produce sea surface temperature (SST) gradient trends in the tropical Pacific comparable to those seen recently in nature. Here, we find that the magnitude of the cloud‐SST feedback in the subtropical Southeast Pacific is correlated across models with the magnitude of Eastern Pacific multi‐decadal SST variability. A heat‐budget analysis reveals coupling between cloud‐radiative effects, circulation, and SST gradients in driving multi‐decadal variability in the Eastern Pacific. Using this relationship and observed feedback estimates, we find that internal Eastern Pacific SST variability is underestimated in most models. Adjusting for model bias increases the likelihood of generating a cooling trend at least as large as observations in preindustrial control simulations by ∼ ${\sim} $56% on average. If models underestimate climate "noise," as our results suggest, this bias should be accounted for when attributing the relative importance of forced versus unforced changes in the climate. Plain Language Summary: In recent decades, observed sea surface temperatures (SSTs) have cooled in the eastern tropical Pacific and warmed in the western tropical Pacific. Historical simulations using state‐of‐the‐art climate models fail to reproduce this pattern. We find that the feedback between low‐lying clouds and SSTs is related to the magnitude of naturally occurring SST variability in the Southeast Pacific. On average, climate models that have too weak a cloud‐SST feedback in the Southeast Pacific underestimate the likelihood of multi‐decadal cooling in the eastern Pacific in preindustrial simulations. Our results suggest that biases in cloud feedbacks may be causing models to underestimate internal SST variability. Key Points: Multi‐decadal Southeast Pacific sea surface temperature trends are related to the strength of the subtropical cloud feedbackCloud radiative effects amplify multi‐decadal Pacific sea surface temperature trends by impacting circulation and surface energy fluxesCorrecting model biases in cloud feedback raises the likelihood of internally producing Southeast Pacific cooling as large as observations [ABSTRACT FROM AUTHOR]

Climate variability in the growing season is well suited for testing adaptation measures. Adaptation to adverse events, such as heatwaves and droughts, increases the capacity of players in agri-food systems, not only producers but also transporters and food manufacturers, to prepare for production disruptions due to seasonal extremes and climate change. Climate impact models (e.g., crop models) can be used to develop adaptation responses. To run these models, historical records and climate forecasts need to be combined as a single daily time series. We introduce the daily 0.5° global hybrid reanalysis-forecast meteorological forcing dataset from 2010 to 2021. The dataset consists of the Japanese 55-yr Reanalysis (JRA55) and the Japan Meteorological Agency/Meteorological Research Institute Coupled Prediction System version 2 (JMA/MRI-CPS2) 5-member ensemble forecast. Both are bias-corrected using the Delta method and integrated with a baseline climatology derived from the Environmental Research and Technology Development Fund's Strategic Research 14 Meteorological Forcing Dataset (S14FD). The dataset is called JCDS (JRA55-CPS2-Delta-S14FD) and offers a framework for monitoring and forecasting applications towards adaptation. [ABSTRACT FROM AUTHOR]

Terrestrial, aquatic, and marine ecosystems regulate climate at local to global scales through exchanges of energy and matter with the atmosphere and assist with climate change mitigation through nature‐based climate solutions. Climate science is no longer a study of the physics of the atmosphere and oceans, but also the ecology of the biosphere. This is the promise of Earth system science: to transcend academic disciplines to enable study of the interacting physics, chemistry, and biology of the planet. However, long‐standing tension in protecting, restoring, and managing forest ecosystems to purposely improve climate evidences the difficulties of interdisciplinary science. For four centuries, forest management for climate betterment was argued, legislated, and ultimately dismissed, when nineteenth century atmospheric scientists narrowly defined climate science to the exclusion of ecology. Today's Earth system science, with its roots in global models of climate, unfolds in similar ways to the past. With Earth system models, geoscientists are again defining the ecology of the Earth system. Here we reframe Earth system science so that the biosphere and its ecology are equally integrated with the fluid Earth to enable Earth system prediction for planetary stewardship. Central to this is the need to overcome an intellectual heritage to the models that elevates geoscience and marginalizes ecology and local land knowledge. The call for kilometer‐scale atmospheric and ocean models, without concomitant scientific and computational investment in the land and biosphere, perpetuates the geophysical view of Earth and will not fully provide the comprehensive actionable information needed for a changing climate. Plain Language Summary: Terrestrial ecosystems provide a natural solution to planetary warming by storing carbon, dissipating surface heating through evapotranspiration, and other processes. That forests, in particular, influence climate is a centuries‐old premise, but its potential for planetary stewardship has not been realized. In an acrimonious controversy spanning several centuries, managing forests to purposely change climate was advocated, legislated, and resoundingly dismissed as unscientific. Similar intellectual bias is evident in today's Earth system science and the associated Earth system models, which are the state‐of‐the‐art models used to inform climate policy. The popular characterization of Earth system science lauds its interdisciplinary melding of physics, chemistry, and biology, but the models emphasize the physics and fluid dynamics of the atmosphere and oceans and present a limited perspective of terrestrial ecosystems in the Earth system. Ecologists studying the living world increasingly have a voice in Earth system science as we move beyond the physical basis for climate change to Earth system prediction for planetary stewardship. As we once again look to forests to solve a climate problem, we must surmount the disciplinary narrowness that failed to answer the forest‐climate question in the past and that continues to limit the interdisciplinary potential of Earth system science. Key Points: Nature‐based climate solutions have been advocated for centuries, but have been distorted by academic bias and colonialist prejudiceEarth system science, while recognizing the climate services of the biosphere, has a geophysical bias in interdisciplinary collaborationTo realize the potential for planetary stewardship, Earth system models must embrace the living world equally with the fluid world [ABSTRACT FROM AUTHOR]

Clouds are a key player in the global climate system, affecting the atmospheric water and energy budgets, and they are strongly coupled to the large-scale atmospheric circulation. Here, we examine the co-variability of the atmospheric energy and water budget imbalances in three different global model configurations-radiative-convective equilibrium, aqua-planet, and global simulations with land. The gradual increase in the level of complexity of the model configuration enables an investigation of the effects of rotation, meridional temperature gradient, land-sea contrast, and seasonal cycle on the co-variability of the water and energy imbalances. We demonstrate how this co-variability is linked to both the large-scale tropical atmospheric circulation and to cloud properties. Hence, we propose a co-variability-based framework that connects cloud properties to the large-scale tropical circulation and climate system and is directly linked to the top-down constrains on the system--the water and energy budgets. In addition, we examine how the water and energy budget imbalances co-variability depends on the temporal averaging scale, and explain its dependency on how stationary the circulation is in the different model configurations. Finally, we demonstrate the effect of an idealized global warming and convective aggregation on this co-variability. Plain Language Summary Understanding the link between clouds and the atmospheric circulation is considered as a major challenge in climate science. Both atmospheric circulation and clouds are intimately linked to Earth's water and energy budgets. We show that looking at how the residuals of these budgets depend on each other and on cloud properties can provide a new framework with which to study the properties of clouds, circulations, and their respective impacts on the climate system. In this paper, we present this framework, and demonstrate its usefulness for a range of different climate model complexities. We show examples of how the framework could be used to investigate the effects of climate change on clouds and circulation. Finally, we present an application for convective aggregation (the clustering of deep convective clouds in idealized climate models). [ABSTRACT FROM AUTHOR]

Ice clouds are a crucial component of the Earth's weather system, and their representation remains a principal challenge for current weather and climate models. Several past and future satellite missions were explicitly designed to provide observations offering new insights into cloud processes, but these specialized cloud sensors are limited in their spatial and temporal coverage. Geostationary satellites have been observing clouds for several decades and can ideally complement the sparse measurements from specialized cloud sensors. However, the geostationary observations that are continuously and globally available over the full observation record are restricted to a small number of wavelengths, which limits the information they can provide on clouds. The Chalmers Cloud Ice Climatology (CCIC) is a novel cloud-property dataset that aims to provide an improved climate record of ice hydrometeor concentrations by applying state-of-the-art machine-learning techniques to retrieve ice cloud properties from globally gridded, single-channel geostationary observations that are readily available from 1980 onwards. CCIC offers a novel perspective on the record of geostationary IR observations by providing spatially and temporally continuous retrievals of the vertically integrated and vertically resolved concentrations of frozen hydrometeors, typically referred to as ice water path (IWP) and ice water content (IWC). In addition to that, CCIC provides 2D and 3D cloud masks and a 3D cloud classification. A fully convolutional quantile regression neural network constitutes the core of the CCIC retrieval, providing probabilistic estimates of IWP and IWC. The network is trained against CloudSat retrievals using 3.5 years of global collocations. Assessed on a held-out test dataset, the CCIC-provided IWP and IWC estimates achieve correlations exceeding 0.7 and 0.6, respectively, and biases better than - 5 % and - 2 % demonstrating considerable skill in estimating both IWP and IWC. In addition, CCIC is extensively validated against both in situ and remote sensing measurements from two flight campaign series and a ground-based radar. The results of this independent validation confirm the ability of CCIC to retrieve IWP and IWC. CCIC thus ideally complements temporally and spatially more limited measurements from dedicated cloud sensors by providing spatially and temporally continuous estimates of ice cloud properties. The CCIC network and its associated software are made accessible to the scientific community. [ABSTRACT FROM AUTHOR]

Present study examined the overall performance of 12 CMIP6 GCMs for rainfall, maximum and minimum temperatures for rice crop-growing seasons i.e., Boro (January to May) and Kharif (June to October) over the new alluvial zone of West Bengal. A wide range of indices i.e., index of agreement, error indices and bias estimators were utilized to put more confidence on the results. Results indicated that CMIP6 models were able to reproduce observed mean climatology and inter-annual variability of maximum and minimum temperature adequately for both seasons while a smaller number of models (3-4 models) out of a total of 12 GCM-CMIP6 models showed satisfactory performance for rainfall. The ranks assigned to the models revealed that CNRM–ESM2–1 was the best-performing model for Kharif and MRI-ESM2-0 showed the highest skill for Boro. ACCESS-CM2 and MPI-ESM1-2-LR performed worst for Kharif and Boro seasons respectively. Further, CNRM–ESM2–1 and MRIESM2-0 were used to project the future climate for Kharif and Boro seasons respectively under both moderate (SSP2-4.5) and extreme scenarios (SSP5-8.5). Higher warming was projected during Boro season than Kharif. Projections revealed increasing rainfall during Kharif season but decreasing rainfall in Boro season in both the moderate and extreme future scenarios. [ABSTRACT FROM AUTHOR]

A leading goal for climate science and weather risk management is to accurately model both the physics and statistics of extreme events. These two goals are fundamentally at odds: the higher a computational model's resolution, the more expensive are the ensembles needed to capture accurate statistics in the tail of the distribution. Here, we focus on events that are localized in space and time, such as heavy precipitation events, which can start suddenly and decay rapidly. We advance a method for sampling such events more efficiently than straightforward climate model simulation. Our method combines elements of two existing approaches: adaptive multilevel splitting (AMS), a rare event algorithm that generates rigorous statistics but fails to enhance the sampling of sudden, transient extremes; and "ensemble boosting," which generates physically plausible storylines of these events but not their statistics. We modify AMS by splitting trajectories well in advance of the event's onset, following the approach of ensemble boosting. Early splitting requires a rejection step that reduces efficiency, but it is critical for amplifying and diversifying simulated events in tests with the Lorenz‐96 model, for which we demonstrate improved sampling of extreme local energy fluctuations by approximately a factor of 10 relative to direct sampling. Our method is related to previous algorithms, including subset simulation and anticipated AMS, but is distinctly tailored to handle bursting events caused by chaotic traveling waves. Our work makes progress toward the goal of efficiently sampling such transient local extremes in atmospheric models. Plain Language Summary: What is the strongest rainstorm that we can expect in a given thousand‐year period? To augment the available ∼100 years of historical data and to account for climate change, computer simulations are a useful, but expensive, tool to answer such questions. A model must run for many millennia to deliver an answer with statistical confidence. Rare event algorithms provide a promising alternative simulation protocol, in which an ensemble of short simulations is biased to produce more extreme events and reweighting is used to correct for the bias when calculating statistics. However, a classical rare event algorithm fails when the events of interest are short and "bursty" (like heavy rainstorms) instead of long and slow‐moving (like anomalously hot summers). We modify the rare event algorithm to make it amenable to events resembling heavy precipitation in an idealized dynamical system with chaotic traveling waves. Key Points: Rare event algorithms may help address the challenge of simulating extreme weather events and quantifying their probabilityWhen the event of interest is sudden and transient, perturbed ensembles diversify too slowly for standard rare event algorithms to workUsing the Lorenz‐96 model as a prototype for midlatitude weather, we use early perturbation and a rejection step to gain a speedup [ABSTRACT FROM AUTHOR]

Emulators, or reduced complexity climate models, are surrogate Earth system models (ESMs) that produce projections of key climate quantities with minimal computational resources. Using time‐series modeling or more advanced machine learning techniques, data‐driven emulators have emerged as a promising avenue of research, producing spatially resolved climate responses that are visually indistinguishable from state‐of‐the‐art ESMs. Yet, their lack of physical interpretability limits their wider adoption. In this work, we introduce FaIRGP, a data‐driven emulator that satisfies the physical temperature response equations of an energy balance model. The result is an emulator that (a) enjoys the flexibility of statistical machine learning models and can learn from data, and (b) has a robust physical grounding with interpretable parameters that can be used to make inference about the climate system. Further, our Bayesian approach allows a principled and mathematically tractable uncertainty quantification. Our model demonstrates skillful emulation of global mean surface temperature and spatial surface temperatures across realistic future scenarios. Its ability to learn from data allows it to outperform EBMs, while its robust physical foundation safeguards against the pitfalls of purely data‐driven models. We also illustrate how FaIRGP can be used to obtain estimates of top‐of‐atmosphere radiative forcing and discuss the benefits of its mathematical tractability for applications such as detection and attribution or precipitation emulation. We hope that this work will contribute to widening the adoption of data‐driven methods in climate emulation. Plain Language Summary: Emulators are simplified climate models that can be used to rapidly explore climate scenarios—they can run in less than a minute on an average computer. They are key tools used by the Intergovernmental Panel on Climate Change to explore the diversity of possible future climates. Data‐driven emulators use advanced machine learning techniques to produce climate predictions that look very similar to the predictions of complex climate models. However, they are not easy to interpret, and therefore to trust in practice. In this work, we introduce FaIRGP, a data‐driven emulator based on physics. The emulator is flexible and can learn from data to improve its predictions, but is also grounded on physical energy balance relationships, which makes it robust and interpretable. The model performs well in predicting future global and local temperatures under realistic future scenarios, outperforming purely physics‐driven or purely data‐driven models. Further, the probabilistic nature of our model allows for mathematically tractable uncertainty quantification. By gaining trust in such a data‐driven yet physically grounded model, we hope the climate science community can benefit more widely from their potential. Key Points: We introduce FaIRGP, a Bayesian machine learning emulator for global and local mean surface temperatures that builds upon a physically based simple climate modelThe model improves upon both purely physically‐driven and purely data‐driven baseline emulators on several metrics across realistic future scenariosThe model is fully mathematically tractable, which makes it a convenient and easy‐to‐use probabilistic tool for the emulation of surface temperatures, but also for downstream applications such as detection and attribution or precipitation emulation [ABSTRACT FROM AUTHOR]

We present the new Cloud Class Climatology (CCClim) dataset, quantifying the global distribution of established morphological cloud types over 35 years. CCClim combines active and passive sensor data with machine learning (ML) and provides a new opportunity for improving the understanding of clouds and their related processes. CCClim is based on cloud property retrievals from the European Space Agency's (ESA) Cloud_cci dataset, adding relative occurrences of eight major cloud types, designed to be similar to those defined by the World Meteorological Organization (WMO) at 1° resolution. The ML framework used to obtain the cloud types is trained on data from multiple satellites in the afternoon constellation (A-Train). Using multiple spaceborne sensors reduces the impact of single-sensor problems like the difficulty of passive sensors to detect thin cirrus or the small footprint of active sensors. We leverage this to generate sufficient labeled data to train supervised ML models. CCClim's global coverage being almost gapless from 1982 to 2016 allows for performing process-oriented analyses of clouds on a climatological timescale. Similarly, the moderate spatial and temporal resolutions make it a lightweight dataset while enabling straightforward comparison to climate models. CCClim creates multiple opportunities to study clouds, of which we sketch out a few examples. Along with the cloud-type frequencies, CCClim contains the cloud properties used as inputs to the ML framework, such that all cloud types can be associated with relevant physical quantities. CCClim can also be combined with other datasets such as reanalysis data to assess the dynamical regime favoring the occurrence of a specific cloud type in association with its properties. Additionally, we show an example of how to evaluate a global climate model by comparing CCClim with cloud types obtained by applying the same ML method used to create CCClim to output from the icosahedral nonhydrostatic atmosphere model (ICON-A). CCClim can be accessed via the following digital object identifier: 10.5281/zenodo.8369202. [ABSTRACT FROM AUTHOR]

There are indications that the reference climatology underlying meteorological drought has shown nonstationarity at seasonal, decadal, and centennial timescales, impacting the calculation of drought indices and potentially having ecological and economic consequences. Analyzing these trends in meteorological drought climatology beyond 100 years, a time frame which exceeds the available period of observation data, contributes to a better understanding of the nonstationary changes, ultimately determining whether they are within the range of natural variability or outside this range. To accomplish this, our study introduces a novel approach to integrate unevenly scaled tree-ring proxy data from the North American Seasonal Precipitation Atlas (NASPA) with instrumental precipitation datasets by first temporally downscaling the proxy data to produce a regular time series and then modeling climate nonstationarity while simultaneously correcting model-induced bias. This new modeling approach was applied to 14 sites across the continental United States using the 3-month standardized precipitation index (SPI) as a basis. The findings showed that certain locations have experienced recent rapid shifts towards drier or wetter conditions during the instrumental period compared to the past 1000 years, with drying trends generally found in the west and wetting trends in the east. This study also found that seasonal shifts have occurred in some regions recently, with seasonality changes most notable for southern gauges. We expect that our new approach provides a foundation for incorporating various datasets to examine nonstationary variability in long-term precipitation climatology and to confirm the spatial patterns noted here in greater detail. [ABSTRACT FROM AUTHOR]

A comprehensive and robust dataset of tropospheric aerosol properties is important for understanding the effects of aerosol–radiation feedback on the climate system and reducing the uncertainties of climate models. The "Third Pole" of Earth (Tibetan Plateau, TP) is highly challenging for obtaining long-term in situ aerosol data due to its harsh environmental conditions. Here, we provide the more reliable new vertical aerosol index (AI) parameter from the spaceborne-based lidar CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) on board CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) for daytime and nighttime to investigate the aerosol's climatology over the TP region during 2007–2020. The calculated vertical AI was derived from the aerosol extinction coefficient (EC), which was rigorously quality-checked and validated for passive satellite sensors (MODIS) and ground-based lidar measurements. Generally, our results demonstrated that there was agreement of the AI dataset with the CALIOP and ground-based lidar. In addition, the results showed that, after removing the low-reliability aerosol target signal, the optimized data can obtain the aerosol characteristics with higher reliability. The data also reveal the patterns and concentrations of high-altitude vertical structure characteristics of the tropospheric aerosol over the TP. They will also help to update and make up the observational aerosol data in the TP. We encourage climate modelling groups to consider new analyses of the AI vertical patterns, comparing the more accurate datasets, with the potential to increase our understanding of the aerosol–cloud interaction (ACI) and aerosol–radiation interaction (ARI) and their climate effects. Data described in this work are available at 10.11888/Atmos.tpdc.300614 (Huang, 2023). [ABSTRACT FROM AUTHOR]

To better understand the regional changes in summertime temperatures across the conterminous United States (CONUS), we adopt a recently developed machine learning framework that can be used to reveal the timing of emergence of forced climate signals from the noise of internal climate variability. Specifically, we train an artificial neural network (ANN) on seasonally averaged temperatures across the CONUS and then task the ANN to output the year associated with an individual map. In order to correctly identify the year, the ANN must therefore learn time‐evolving patterns of climate change amidst the noise of internal climate variability. The ANNs are first trained and tested on data from large ensembles and then evaluated using observations from a station‐based data set. To understand how the ANN is making its predictions, we leverage a collection of ad hoc feature attribution methods from explainable artificial intelligence (XAI). We find that anthropogenic signals in seasonal mean minimum temperature have emerged by the early 2000s for the CONUS, which occurred earliest in the Eastern United States. While our observational timing of emergence estimates are not as sensitive to the spatial resolution of the training data, we find a notable improvement in ANN skill using a higher resolution climate model, especially for its early twentieth century predictions. Composites of XAI maps reveal that this improvement is linked to temperatures around higher topography. We find that increases in spatial resolution of the ANN training data may yield benefits for machine learning applications in climate science. Plain Language Summary: While temperatures around the world continue to warm due to human‐caused climate change, some areas have observed smaller temperature trends than others. Understanding this regional variability in the rate of warming is important when assessing future projections. One location that has observed less warming is across the United States during their summer season. To evaluate temperature variability in this region using real‐world observations and climate model simulations, we use a statistical method from artificial intelligence called neural networks. The goal of the neural network setup is to learn temperature patterns across the United States and then identify whether climate change effects have exceeded the range of natural variability that has occurred in the past. This is called the timing of emergence (ToE), which is the first year that the effect has clearly appeared. We find that the average United States minimum temperature increase has already emerged in historical records. However, we find no ToE for the average maximum temperature, other than in the Western United States. Another important finding of this study is that by using higher resolution climate model data (i.e., more latitude and longitude points), we find better accuracy in the neural network predictions. Key Points: Forced temperature changes have emerged in observations during summer in the United States as detected by an artificial neural networkIncreasing spatial resolution improves neural network skill for predicting the year of a given summer temperature mapWestern United States land surface climate properties contribute to earlier timing of emergence predictions for the SPEAR climate model [ABSTRACT FROM AUTHOR]

The Earth's mesopause region between about 75 and 105 km is characterised by chemiluminescent emission from various lines of different molecules and atoms. This emission was and is important for the study of the chemistry and dynamics in this altitude region at nighttime. However, our understanding is still very limited with respect to molecular emissions with low intensities and high line densities that are challenging to resolve. Based on 10 years of data from the astronomical X-shooter echelle spectrograph at Cerro Paranal in Chile, we have characterised in detail this nightglow (pseudo-)continuum in the wavelength range from 300 to 1800 nm. We studied the spectral features, derived continuum components with similar variability, calculated climatologies, studied the response to solar activity, and even estimated the effective emission heights. The results indicate that the nightglow continuum at Cerro Paranal essentially consists of only two components, which exhibit very different properties. The main structures of these components peak at 595 and 1510 nm. While the former was previously identified as the main peak of the FeO "orange arc" bands, the latter is a new discovery. Laboratory data and theory indicate that this feature and other structures between about 800 and at least 1800 nm are caused by emission from the low-lying A′′ and A′ states of HO2. In order to test this assumption, we performed runs with the Whole Atmosphere Community Climate Model (WACCM) with modified chemistry and found that the total intensity, layer profile, and variability indeed support this interpretation, where the excited HO2 radicals are mostly produced from the termolecular recombination of H and O2. The WACCM results for the continuum component that dominates at visual wavelengths show good agreement for FeO from the reaction of Fe and O3. However, the simulated total emission appears to be too low, which would require additional mechanisms where the variability is dominated by O3. A possible (but nevertheless insufficient) process could be the production of excited OFeOH by the reaction of FeOH and O3. [ABSTRACT FROM AUTHOR]

This work shows a 3-year climatology of the horizontal components of the solar diurnal tide, obtained from wind measurements made by a multistatic specular meteor radar (SIMONe) located in Jicamarca, Peru (12 ∘ S, 77 ∘ W). Our observations show that the meridional component is more intense than the zonal component, and that it exhibits its maxima shifted with respect to the equinox times (i.e., the largest peak occurs in August–September, and the second one in April–May). The zonal component only shows a clear maximum in August–September. This observational climatology is compared to a climatology obtained with the Whole Atmosphere Community Climate Model with thermosphere and ionosphere extension (WACCM-X). Average comparisons indicate that the model amplitudes are 50% smaller than the observed ones. The WACCM-X results are also used in combination with observed altitude profiles of the tidal phases to understand the relative contributions of migrating and non-migrating components. Based on this, we infer that the migrating diurnal tide (DW1) dominates in general, but that from June until September (November until July) the DE3 (DW2) may have a significant contribution to the zonal (meridional) component. Finally, applying wavelet analysis to the complex amplitude of the total diurnal tide, modulating periods between 5 and 80 days are observed in the SIMONe measurements and the WACCM-X model. These modulations might be associated to planetary waves and intraseasonal oscillations in the lower tropical atmosphere. [ABSTRACT FROM AUTHOR]

Climate models are basic tools to obtain reliable estimates of future climate change and its effects on the water resources and agriculture in given basin. However, all climate models are not equally valuable for all areas. Therefore, determining the most appropriate climate model for a specific study area is essential. This study examines the performance of 10 CORDEX-AFRICA-220 Regional Climate Models (RCMs), three downscaling institutional based ensembles mean (Reg ensemble, CCLM ensemble and REMOO ensemble) and the multi-model ensemble mean. The models were evaluated based on their ability in replicating the seasonal and annual rainfall, minimum and maximum temperature and inter-annual variability for the period of 1986–2005 using statistical metrics such as BIAS, Root Mean Square Error (RMSE), Pearson correlation coefficient (r), coefficient of variation (CV), Kling Gupta Efficiency (KGE) and Taylor diagram. The findings indicated that HadREMOO, MPI-Reg4-7, HadReg4-7, Reg ensemble, and multi-model ensemble mean performed relatively better in representing the mean annual observed rainfall at the Adiramets, Debarik Ketema, Niguse Maystebri, and Zarima stations, respectively. Whereas, NorESM-CCLM, MPI-CCLM, NorESM-Reg4-7, and NorESM-REMOO exhibited a weak performance in reproducing the observed mean annual rainfall at the Adiramets, Debarik Ketema Niguse, Maystebri, and Zarima stations, respectively. Similarly, RCMs generally capture the mean annual maximum temperature of climatic stationsof Zarima subbasin well. Specifically, the MPI-Reg4-7 simulation performs well in representing the mean annual observed maximum temperature at Adiramets and Maytsebri stations, while the Debarik and Ketema Niguse stations exhibit superior performance in the HadReg4-7 simulation and the Zarima station shows better representation in the CCLM ensemble simulations. The majority of the model simulations exhibit good representation of mean annual minimum temperature at Adiramets, Debarik, and Zarima stations. Specifically, CanESM-RCM, HadReg4-7, REMOOensemble, multi-model ensemble, and Regensemble simulations perform better at Adiramets, Debarik, Ketema niguse, Maystebri and Zarima stations respectively. This suggests that these models may have biases or shortcomings in capturing the temperature values in the subbasin. Furthermore, NorESM-CCLM at Adiramets, Ketema niguse, and Zarima stations, NorESM-REMOO at Debarik station, and HadReg4-7 at Maystebri station demonstrate poor performance in representing the observed mean minimum temprature. Majority of the RCMs, all institutional based ensemble means and the multi-model ensemble mean simulations overestimate the observed mean annual rainfall of the Zarima subbasin with minimum bias of 0.02 mm at Ketema niguse HadReg4-7and maximum bias of 2.81 mm at Maytsebri MPI-CCLM simulation. Similarly, HadReg4-7 simulation of Ketama Niguse MPI-CCLM showed a minimum 0.02 mm and Maytsebri simulation kiremit season mean rainfall showed a maximum bias of and 2.99 mm. Regarding mean annual and kiremit season maximum and minimum temperature of the Zarima subbasin were overestimated by majority of the simulation and the ensemble means. The correlation (r) of observed and model simulated mean annual and kiremit season rainfall was strong (0.60–0.79) and very strong (0.80–0.99) in the majority of the simulations except Ketema niguse station mean annual and kiremit season rainfall simulations of MPI-REMOO, NorESM-Reg4-7; Debarik station kiremit season rainfall of NorESM-CCLM and NorESM-REMOO, MPI-Reg4-7 and MPI-REMOO, which showed moderate correlation. The performance of the RCMs, institutional based ensemble means and multi-model ensemble mean were different in statistical metrics (BIAS, RMSE, r, CV and KGE) and Taylor diagram. Among the simulations and ensemble means, the multi-model ensemble mean was superiors in two or more of statistical metrics at each station of the Zarima subbasin except Maytsebri station kiremit season rainfall, where the CCLM ensemble was better. Consistently, the Taylor diagram showed that the multi-model ensemble was better in the replication of the areal annual and kiremit season rainfall, maximum and minimum temperature of the subbasin. This finding evidenced that selecting the best RCMs and ensemble mean is necessary for climate projection and climate change impact assessment study. [ABSTRACT FROM AUTHOR]

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Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article describes a learner-centered, tutorial-based approach to climate modeling. Using these tutorials, students in a general education course in global climate change taught in the physics department engaged with compelling, "real-world" questions by drawing on foundational physics concepts along with their prior experiences and conceptual resources in the process of knowledge construction. Students used simulations, energy tracking diagrams, and algebraic analysis to construct climate models that address challenging quantitative questions and reveal key concepts in climate science. Special emphasis was given to the learning goals, pre-requisite knowledge, and conceptual challenges associated with these activities. Instructional materials are provided for educators who wish to adapt these tutorials for use in their own educational context. The supplementary materials are appropriate for both non-science majors and upper-level physics majors. [ABSTRACT FROM AUTHOR]

The Geoengineering Model Intercomparison Project (GeoMIP) is a coordinating framework, started in 2010, that includes a series of standardized climate model experiments aimed at understanding the physical processes and projected impacts of solar geoengineering. Numerous experiments have been conducted, and numerous more have been proposed as "test-bed" experiments, spanning a variety of geoengineering techniques aimed at modifying the planetary radiation budget: stratospheric aerosol injection, marine cloud brightening, surface albedo modification, cirrus cloud thinning, and sunshade mirrors. To date, more than 100 studies have been published that used results from GeoMIP simulations. Here we provide a critical assessment of GeoMIP and its experiments. We discuss its successes and missed opportunities, for instance in terms of which experiments elicited more interest from the scientific community and which did not, and the potential reasons why that happened. We also discuss the knowledge that GeoMIP has contributed to the field of geoengineering research and climate science as a whole: what have we learned in terms of intermodel differences, robustness of the projected outcomes for specific geoengineering methods, and future areas of model development that would be necessary in the future? We also offer multiple examples of cases where GeoMIP experiments were fundamental for international assessments of climate change. Finally, we provide a series of recommendations, regarding both future experiments and more general activities, with the goal of continuously deepening our understanding of the effects of potential geoengineering approaches and reducing uncertainties in climate outcomes, important for assessing wider impacts on societies and ecosystems. In doing so, we refine the purpose of GeoMIP and outline a series of criteria whereby GeoMIP can best serve its participants, stakeholders, and the broader science community. [ABSTRACT FROM AUTHOR]

In this work, a stochastic representation based on a physical transport principle is proposed to account for mesoscale eddy effects on the large‐scale oceanic circulation. This stochastic framework arises from a decomposition of the Lagrangian velocity into a smooth‐in‐time component and a highly oscillating noise term. One important characteristic of this random model, without any external forcing and damping, is that it conserves the total energy of the resolved flow for any realization. The proposed stochastic formulation is successfully implemented in a well established multi‐layered quasi‐geostrophic dynamical core. The empirical spatial correlation of the unresolved noise is calibrated from the eddy‐resolving simulation data. In particular, a stationary correction drift can be introduced in the noise through Girsanov transformation. This non‐intuitive term appears to be important in reproducing on a coarse mesh the eastward jet of the wind‐driven double‐gyre circulation. In addition, a projection method has been proposed to constrain the noise to act along the iso‐surfaces of the vertical stratification. The resulting noise enables us to improve the intrinsic low‐frequency variability of the large‐scale current. Plain Language Summary: Accurate numerical simulation of the ocean and the atmosphere is important for understanding complex patterns in climate science. Unfortunately, the high‐dimensional geophysical fluid dynamics contain a wide range of spatio‐temporal scales with complex interactions. In order to maintain affordable computational time, only large‐scale representations of the geophysical flows of interest are simulated. However, the effect of the unresolved scales on the resolved component must be carefully taken into account to minimize the errors of truncation. In the present work, we stick to a specific stochastic formulation that represents the physical principles of the resolved large scales and parameterizes the statistical properties of the unresolved small scales. Such random formulation is successfully tested in numerical simulations of a simplified ocean mesoscale model, in which the structure of the unresolved flow is calibrated from a finite sequence of reference data. The resulting model enables to represent accurately the statistics of the resolved flow component. In addition, numerical results show that an adequate modeling of the unresolved dynamics leads to a significant improvement of the intrinsic variability for the large‐scale ocean models. These findings are important for future developments of ocean and atmosphere climate models. Key Points: A stochastic transport formulation is used to parameterize the effects of the unresolved eddies on the resolved flowThe structure of unresolved flow is calibrated from data and a projection method is proposed to parameterize its dynamicsThe proposed random models on coarse resolutions enable to reproduce the eastward jet and the intrinsic variability of ocean wind‐driven circulation [ABSTRACT FROM AUTHOR]

Based on the 18 realizations of three state-of-the-art climate models participated in the Coupled Model Intercomparison Project phase 6 (CMIP6), this paper examines the changes in climatology and trends of winter diurnal temperature range (DTR) over East Asia in response to natural and anthropogenic forcings. In the observation, the climatology of DTR during 1979–2014 displays a horizontal tile from southwest to northeast over East Asia. In addition, the observed DTR also presents an obviously declining trend in recent decades, with the magnitude of roughly −0.10 °C decade−1. The historical all-forcing simulations well reproduce the geographical distributions and magnitude of climatology and trends of winter DTR over East Asia in 1979–2014. Furthermore, the winter DTR exhibits a continuous declining trend from the historical scenario to the future SSP245 scenario over East Asia, with the most significant declining during the first half of twenty-first century (i.e., 2021–2060). Based on the individual forcing experiments, it suggests that the greenhouse gases forcing plays a primary role in driving the climatology and trends of DTR over East Asia, especially for the SSP245 scenario. Further analysis indicates that the increasing precipitation under greenhouse gases forcing largely explain the declining DTR trends over East Asia, especially over the northern East Asia both in historical and SSP245 scenarios. • The observed decreasing DTR trends are reasonably captured in the all-forcing historical simulations in CMIP6 models. • The greenhouse gas forcing plays a primary role in driving the declining DTR trends over East Asia • The declining of DTR is partly attributed to the increasing precipitation over East Asia in the greenhouse gas forcing. [ABSTRACT FROM AUTHOR]

Although collaborative efforts have been made to retrieve climate data from instrumental observations and paleoclimate records, there is still a large amount of valuable information in historical archives that has not been utilized for climate reconstruction. Due to the qualitative nature of these datasets, historical texts have been compiled and studied by historians aiming to describe the climate impact in socioeconomic aspects of human societies, but the inclusion of this information in past climate reconstructions remains fairly unexplored. Within this context, we present a novel approach to assimilate climate information contained in chronicles and annals from the 15th century to generate robust temperature and precipitation reconstructions of the Burgundian Low Countries, taking into account uncertainties associated with the descriptions of narrative sources. After data assimilation, our reconstructions present a high seasonal temperature correlation of ∼0.8 independently of the climate model employed to estimate the background state of the atmosphere. Our study aims to be a first step towards a more quantitative use of available information contained in historical texts, showing how Bayesian inference can help the climate community with this endeavor. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A new version of the AWI Coupled Prediction System is developed based on the Alfred Wegener Institute Climate Model v3.0. Both the ocean and the atmosphere models are upgraded or replaced, reducing the computation time by a factor of 5 at a given resolution. This allowed us to increase the ensemble size from 12 to 30, maintaining a similar resolution in both model components. The online coupled data assimilation scheme now additionally utilizes sea‐surface salinity and sea‐level anomaly as well as temperature and salinity profile observations. Results from the data assimilation demonstrate that the sea‐ice and ocean states are reasonably constrained. In particular, the temperature and salinity profile assimilation has mitigated systematic errors in the deeper ocean, although issues remain over polar regions where strong atmosphere‐ocean‐ice interaction occurs. One‐year‐long sea‐ice forecasts initialized on 1 January, 1 April, 1 July and 1 October from 2003 to 2019 are described. To correct systematic forecast errors, sea‐ice concentration from 2011 to 2019 is calibrated by trend‐adjusted quantile mapping using the preceding forecasts from 2003 to 2010. The sea‐ice edge raw forecast skill is within the range of operational global subseasonal‐to‐seasonal forecast systems, outperforming a climatological benchmark for about 2 weeks in the Arctic and about 3 weeks in the Antarctic. The calibration is much more effective in the Arctic: Calibrated sea‐ice edge forecasts outperform climatology for about 45 days in the Arctic but only 27 days in the Antarctic. Both the raw and the calibrated forecast skill exhibit strong seasonal variations. Plain Language Summary: Ocean data sparseness and systematic model errors pose problems for the initialization of coupled seasonal forecasts, especially in polar regions. Our global forecast system follows a seamless approach with refined ocean resolution in the Arctic. The new version presented here features higher computational efficiency and utilizes more ocean and sea‐ice observations. Ice‐edge forecasts outperform a climatological benchmark for about 1 month, comparable to established systems. Key Points: We describe an upgrade of the AWI Coupled Prediction System with new ocean and atmosphere models and more observations assimilatedIndependent evaluations show advances in the new version on the analysis of the sea‐ice and ocean states against the old oneCalibrated sea‐ice edge forecasts outperform a climatological benchmark for around 1 month in both hemispheres [ABSTRACT FROM AUTHOR]

The climate science and applications communities need a broad and demand‐driven concept to assess physical climate conditions that are relevant for impacts on human and natural systems. Here, we augment the description of the "climatic impact‐driver" (CID) approach adopted in the Working Group I (WGI) contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report. CIDs are broadly defined as "physical climate system conditions (e.g., means, events, and extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions." We give background information on the IPCC Report process that led to the development of the 7 CID types (heat and cold, wet and dry, wind, snow and ice, coastal, open ocean, and other) and 33 distinct CID categories, each of which may be evaluated using a variety of CID indices. This inventory of CIDs was co‐developed with WGII to provide a useful collaboration point between physical climate scientists and impacts/risk experts to assess the specific climatic phenomena driving sectoral responses and identify relevant CID indices within each sector. The CID Framework ensures that a comprehensive set of climatic conditions informs adaptation planning and risk management and may also help prioritize improvements in modeling sectoral dynamics that depend on climatic conditions. CIDs contribute to climate services by increasing coherence and neutrality when identifying and communicating relevant findings from physical climate research to risk assessment and planning activities. Plain Language Summary: Climatic impact‐drivers (CIDs) are climate conditions that affect the things we care about in nature and society. We deepen the motivation and definitions that allowed the Intergovernmental Panel on Climate Change to identify 33 distinct CID categories including extreme heat, hydrological drought, severe wind storm, permafrost, relative sea level, marine heatwaves, and air pollution weather. Each CID category may be analyzed with specific indices that inform adaptation, mitigation and risk management. The CID Framework allows us to avoid universally labeling a climate condition as a "hazard," recognizing that the same physical condition may be detrimental for some and beneficial or inconsequential for others. This approach allows climate scientists to engage with impacts and risk experts to target specific tolerance thresholds that are system‐ and sector‐dependent. This more comprehensive description of the CID Framework provides a practical foundation for climate research, climate and impact model development, risk assessments and climate service product creation. Key Points: Deepens explanation of Climatic Impact‐Driver (CID) Framework utilized in Intergovernmental Panel on Climate Change Sixth Assessment ReportsDistinguishes practical CID types and categories that allows climate information to target conditions that affect the things we care aboutNeutral Framework does not pre‐judge beneficial, detrimental or neutral outcomes which are system‐ and sector‐dependent [ABSTRACT FROM AUTHOR]

Atmospheric rivers (ARs), referring to long and narrow filamentary bands of intense water vapor transport in the atmosphere, can cause extreme precipitation, floods, and drought events. Their variability has been linked to various climate modes, such as El Niño/Southern Oscillation, Pacific Decadal Oscillation, and Pacific‐North America pattern. Understanding and improving simulation of this linkage can provide the potential to predict ARs at subseasonal‐to‐decadal timescales. Up to now, the extent to which climate model simulations of ARs are dependent on model resolutions has not been fully assessed. Here, we compare and evaluate ARs in a pair of high‐resolution (HR) and a low‐resolution (LR) Community Earth System Model (CESM) simulations against the observations. The results show that within this CESM framework, climatological AR strength and associated precipitation are severely underestimated by LR compared to the observations, and their relationships with major modes of climate variability are also poorly reproduced. These deficiencies in LR are alleviated to a significant extent in HR. Using a linear regression analysis, we show that HR is able to capture with high fidelity the observed relationships between ARs and major climate modes in the Northern Hemisphere at seasonal‐to‐decadal time scales. These results suggest that the use of HR CESM may potentially lead to an improved forecast skill of ARs at season‐to‐decadal time scales. Plain Language Summary: Atmospheric rivers (ARs) can trigger extreme rainfall and snowfall events, causing high risk of flooding. Meanwhile, they are sometimes responsible for the occurrence and break of drought events. So, it is of great importance for weather and climate forecast models to accurately represent ARs. Studies have shown that AR variability is linked to various climate modes, such as El Niño/Southern Oscillation, Pacific Decadal Oscillation, Pacific‐North America pattern, providing the potential for predicting ARs at subseasonal‐to‐decadal timescales. In this study, we examine the impact of climate model resolution on AR simulations and their relationship with major climate modes by comparing a pair of high‐resolution and a low‐resolution (LR) Community Earth System Model (CESM) simulations against the observations. The results reveal major deficiencies in LR that include underestimated climatological AR strength and associated precipitation, and inaccurate representations of ARs relationships with climate modes. Significant improvements are observed by increasing CESM horizontal resolution, suggesting a path for more accurate prediction of ARs. Key Points: Simulated atmospheric rivers (ARs) are dependent on horizontal resolutions of coupled Community Earth System Model (CESM)High‐resolution (HR) CESM simulates more realistic ARs' strength and associated rainfall than low‐resolution (LR)ARs' response to large‐scale climate modes is better simulated in HR CESM than in LR CESM [ABSTRACT FROM AUTHOR]

The evolution of hydroclimate in East Asia during the Pliocene—a potential analog of the future greenhouse climate—remains highly uncertain. Here we reconstruct changes in the hydroclimate of Northern China during this epoch. Our results reveal previously undocumented severe drought conditions that persisted through the early Pliocene with a significantly greater magnitude than suggested by other records. Using a broad hierarchy of climate simulations, we find that reduction in the Pacific meridional sea surface temperature (SST) gradient has particularly strong impacts on precipitation over Northern China, which suggests its key role in maintaining drought conditions during the early Pliocene. The weaker Pacific meridional SST gradient causes the weakening of the East Asian summer monsoon precipitation over this region via a reduction of atmospheric northward moisture transport from the ocean, which enhances aridity inland. Our results highlight the fundamental control of the tropical ocean on the extra‐tropical hydrological cycle. Plain Language Summary: Understanding East Asian rainfall changes with global warming is an important issue of climate sciences. The Pliocene epoch provides a potential analog to the future greenhouse climate. Here, for the first time we reconstruct high‐resolution aridity changes over the Chinese Loess Plateau (CLP) in northern East Asia during the Pliocene. Our results indicate persistent drought conditions over CLP during the early Pliocene. Analyzing a broad range of climate model simulations, we find that the reduced meridional sea surface temperature gradient is a critical factor in the weakening of the East Asian summer monsoon over Northern China and in maintaining drought conditions during the early Pliocene relative to the late Pliocene. Our results expand the Asian inland paleoclimate records during the Pliocene, providing new information on past Asian monsoon precipitation variations during the warm Pliocene with potential implications for future hydroclimate changes under greenhouse warming in East Asia. Key Points: Severe drought conditions persisted throughout the early Pliocene epoch over Northern ChinaWe link these conditions to the reduced meridional temperature gradient in the Pacific OceanThe reduced Pacific meridional temperature gradient acts to weaken the summer East Asian monsoon precipitation over Northern China [ABSTRACT FROM AUTHOR]

The South China Sea summer monsoon (SCSSM) is crucial for the East Asian monsoon system, which has been detected from plenty of aspects, while its prediction has been relatively less investigated on the subseasonal timescale. The 1–31-day predictions of SCSSM, including fundamental dynamic and thermodynamic characteristics, indices, onset date and associated circulations, are examined and diagnosed for different climate systems, i.e., T106 and T106 × T255 (with a nudging process added) in the Chinese Academy of Meteorological Sciences climate system model (CAMS-CSM). The results indicate the general decreasing prediction skills of the model with the growing lead times. For lead times of 1–10 days, zonal winds at the lower (850 hPa) and higher (200 hPa) levels can be reasonably predicted, as well as the pseudo-equivalent potential temperatures at 850 hPa. Meanwhile, the prediction skill for the higher level generally shows a better performance than that for the lower level. The prediction capability is relatively weak during the circulation adjustment period before the monsoon onset, while a significant enhancement occurs after that. During the analyzed period of 2011–2020, the prediction of SCSSM onset date is mainly skillful in most years, while the year of 2015 shows a prediction result with at least six pentads earlier than the observation, which is subsequently taken as a failure case for further investigation. At the lower level, the model could not effectively predict the weakening and eastward withdrawal of the Western Pacific subtropical high and the shift in wind field during the SCSSM onset. As for the upper level, the rapid northward movement of the South Asia high and its establishment in the Indochina Peninsula are neither well captured. In addition, the models of T106 and T106 × T255 do not show significant differences in most cases, but the latter tends to be more skillful on the continent. [ABSTRACT FROM AUTHOR]

The global climate models (GCMs) of Coupled Model Intercomparison Project phase 6 (CMIP6) were used for spatiotemporal projections of precipitation and temperature over Afghanistan for three shared socioeconomic pathways (SSP1-2.6, 2–4.5, and 5–8.5) and two future time horizons, early (2020–2059) and late (2060–2099). The compromise programming (CP) approach was employed to order the GCMs based on their skill to replicate precipitation and temperature climatology for the reference period (1975–2014). Three models, namely, ACCESS-CM2, MPI-ESM1-2-LR, and FIO-ESM-2–0, showed the highest skill in simulating all three variables (precipitation, maximum temperature, and minimum temperature), and therefore, were chosen for the future projections. The ensemble mean of the GCMs showed an increase in maximum temperature by 1.5 − 2.5 °C, 2.7 − 4.3 °C, and 4.5 − 5.3 °C and minimum temperature by 1.3 − 1.8 °C, 2.2 − 3.5 °C, and 4.6 − 5.2 °C for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively, in the later period. Meanwhile, the changes in precipitation in the range of − 15 − 18%, − 36 − 47%, and − 40 − 68% for SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. The temperature and precipitation were projected to increase in the highlands and decrease over the deserts, indicating dry regions would be drier and wet regions wetter. [ABSTRACT FROM AUTHOR]

This study investigates present and future European heat wave magnitudes, represented by the Heat Wave Magnitude Index-daily (HWMId), for regional climate models (RCMs) and the driving global climate models (GCMs) over Europe. A subset of the large EURO-CORDEX ensemble is employed to study sources of uncertainties related to the choice of GCMs, RCMs, and their combinations. We initially compare the evaluation runs of the RCMs driven by ERA-interim reanalysis to E-OBS (observation-based estimates), finding that the RCMs can capture most of the observed spatial and temporal features of HWMId. With their higher resolution compared to GCMs, RCMs can reveal spatial features of HWMId associated with small-scale processes (e.g., orographic effects); moreover, RCMs represent large-scale features of HWMId satisfactorily (e.g., by reproducing the general pattern revealed by E-OBS with high values at western coastal regions and low values at the eastern part). Our results indicate a clear added value of the RCMs compared to the driving GCMs. Forced with the emission scenario RCP8.5, all the GCM and RCM simulations consistently project a rise in HWMId at an exponential rate. However, the climate change signals projected by the GCMs are generally attenuated when downscaled by the RCMs, with the spatial pattern also altered. The uncertainty in a simulated future change of heat wave magnitudes following global warming can be attributed almost equally to the difference in model physics (as represented by different RCMs) and to the driving data associated with different GCMs. Regarding the uncertainty associated with RCM choice, a major factor is the different representation of the orographic effects. No consistent spatial pattern in the ensemble spread associated with different GCMs is observed between the RCMs, suggesting GCM uncertainties are transformed by RCMs in a complex manner due to the nonlinear nature of model dynamics and physics. In summary, our results support the use of dynamical downscaling for deriving regional climate realization regarding heat wave magnitudes. [ABSTRACT FROM AUTHOR]

Measurements of the atmospheric boundary layer (ABL) structure were performed for three years (October 2017–August 2020) at the Russian observatory "Ice Base Cape Baranova" (79.280° N, 101.620° E) using SODAR (Sound Detection And Ranging). These measurements were part of the YOPP (Year of Polar Prediction) project "Boundary layer measurements in the high Arctic" (CATS_BL) within the scope of a joint German–Russian project. In addition to SODAR-derived vertical profiles of wind speed and direction, a suite of complementary measurements at the observatory was available. ABL measurements were used for verification of the regional climate model COSMO-CLM (CCLM) with a 5 km resolution for 2017–2020. The CCLM was run with nesting in ERA5 data in a forecast mode for the measurement period. SODAR measurements were mostly limited to wind speeds <12 m/s since the signal was often lost for higher winds. The SODAR data showed a topographical channeling effect for the wind field in the lowest 100 m and some low-level jets (LLJs). The verification of the CCLM with near-surface data of the observatory showed good agreement for the wind and a negative bias for the 2 m temperature. The comparison with SODAR data showed a positive bias for the wind speed of about 1 m/s below 100 m, which increased to 1.5 m/s for higher levels. In contrast to the SODAR data, the CCLM data showed the frequent presence of LLJs associated with the topographic channeling in Shokalsky Strait. Although SODAR wind profiles are limited in range and have a lot of gaps, they represent a valuable data set for model verification. However, a full picture of the ABL structure and the climatology of channeling events could be obtained only with the model data. The climatological evaluation showed that the wind field at Cape Baranova was not only influenced by direct topographic channeling under conditions of southerly winds through the Shokalsky Strait but also by channeling through a mountain gap for westerly winds. LLJs were detected in 37% of all profiles and most LLJs were associated with channeling, particularly LLJs with a jet speed ≥ 15 m/s (which were 29% of all LLJs). The analysis of the simulated 10 m wind field showed that the 99%-tile of the wind speed reached 18 m/s and clearly showed a dipole structure of channeled wind at both exits of Shokalsky Strait. The climatology of channeling events showed that this dipole structure was caused by the frequent occurrence of channeling at both exits. Channeling events lasting at least 12 h occurred on about 62 days per year at both exits of Shokalsky Strait. [ABSTRACT FROM AUTHOR]

Sudden stratospheric warmings (SSWs) are a major mode of variability of the winter stratosphere. In recent years, climate models have improved their ability to simulate SSWs. However, the representation of the frequency and temporal distribution of SSWs in models depends on many factors and remains challenging. The vertical resolution of a model might be one such factor. Therefore, here we analyse the impact of increased vertical resolution on the simulation of major sudden stratospheric warmings (SSWs) in the Whole Atmosphere Community Climate Model (WACCM). We compare two versions of the model, WACCM3.5 and WACCM4. We find that the frequency of occurrence of SSWs is improved in the newer version and closer to that obtained using reanalysis. Furthermore, simulations with a coupled ocean best reproduce the behaviour of temperature during these events. Increasing vertical resolution increases the number of occurrences; however, it does not produce significantly different results than standard resolution. WACCM4 also does not reproduce vortex split events well, generating far fewer of these than observed. Finally, the ratio between polar vortex splits and displacement events in the model is slightly better for non-ocean-coupled simulations. We conclude that, at least for WACCM4, the use of the high vertical resolution configuration is not cost-effective for the study of SSWs. [ABSTRACT FROM AUTHOR]

The ability of any climate model to reproduce present-day climate fields is very important for the confidence in the projections of the future state of the same fields. It is against this background that the performance of the Global Coupled configuration 2 (GC2) of the Unified Model (UM), a Global Climate Model (GCM), was evaluated over the West African region (a climate-sensitive region) for two periods on 3 climatological time scales for precipitation (using the Global Precipitation Climatology Center data) and 2-m temperature where the Climate Research Unit Time Series dataset was used. The GC2's performance is also compared with 4 Coupled Model Intercomparison Project phase 6 (CMIP6) GCMs. Results indicate that spatial biases are of similar patterns across the domain for the two study periods for both variables, demonstrating the consistency of the model in reproducing the two climate variables over West Africa (WA). The simulation of temperature by the UM is better than precipitation. Biases are larger in the UM and the CMIP6 GCMs than the levels of observational uncertainties for precipitation, but for temperature, biases in all the models are within the levels of uncertainties found in the observations. Also, the holistic performance of the UM in the two fields is relatively better than the 4 CMIP6 GCMs. The two variables are also simulated better in the southern parts of the region than the northern parts of the domain by the UM. The analysis of the UM suggests a strong linkage between the two variables and therefore, a detailed investigation in how the model reproduces precipitation and temperature-based processes over WA is recommended in order to enhance the model's simulations of these climate variables. Also, the model is recommended for future investigations of precipitation and temperature climatologies over WA. [ABSTRACT FROM AUTHOR]

Central Asia is one of the most important sources of mineral saline dust worldwide. A comprehensive understanding of Central Asian dust transport is essential for evaluating its impacts on human health, ecological safety, weather and climate. This study first puts forward an observation-based climatology of Central Asian dust transport flux by using the 3-D dust detection of Cloud-Aerosol LiDAR with Orthogonal Polarization (CALIOP). The seasonal difference of transport flux and downstream contribution are evaluated and compared with those of the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2). Central Asian dust can be transported not only southward in summer under the effect of the South Asian summer monsoon, but also eastward in other seasons under the control of the westerly jet. Additionally, the transport of Central Asian dust across the Pamir Plateau to the Tibetan Plateau is also non-negligible, especially during spring (with a transport flux rate of 150 kg m−1 day−1). The annual CALIOP-based downstream contribution of Central Asian dust to South Asian (164.01 Tg) is 2.1 times that to East Asia (78.36 Tg). This can be attributed to the blocking effect of the higher terrain between Central and East Asia. Additionally, the downstream contributions to South and East Asia from MERRA-2 are only 0.36 and 0.84 times that of CALIOP, respectively. This difference implies the overestimation of the wet and dry depositions of the model, especially in the low latitude zone. The quantification of the Central Asian dust transport allows a better understanding of the Central Asian dust cycle, and supports the calibration/validation of aerosol-related modules of regional and global climate models. [ABSTRACT FROM AUTHOR]

The stratosphere can be a source of predictability for surface weather on timescales of several weeks to months. However, the potential predictive skill gained from stratospheric variability can be limited by biases in the representation of stratospheric processes and the coupling of the stratosphere with surface climate in forecast systems. This study provides a first systematic identification of model biases in the stratosphere across a wide range of subseasonal forecast systems. It is found that many of the forecast systems considered exhibit warm global mean temperature biases from the lower to middle stratosphere, too strong/cold wintertime polar vortices, and too cold extratropical upper troposphere/lower stratosphere regions. Furthermore, tropical stratospheric anomalies associated with the Quasi-Biennial Oscillation tend to decay toward each system's climatology with lead time. In the Northern Hemisphere (NH), most systems do not capture the seasonal cycle of extreme vortex event probabilities, with an underestimation of sudden stratospheric warming events and an overestimation of strong vortex events in January. In the Southern Hemisphere (SH), springtime interannual variability of the polar vortex is generally underestimated, but the timing of the final breakdown of the polar vortex often happens too early in many of the prediction systems. These stratospheric biases tend to be considerably worse in systems with lower model lid heights. In both hemispheres, most systems with low-top atmospheric models also consistently underestimate the upward wave driving that affects the strength of the stratospheric polar vortex. We expect that the biases identified here will help guide model development for sub-seasonal to seasonal forecast systems, and further our understanding of the role of the stratosphere for predictive skill in the troposphere. [ABSTRACT FROM AUTHOR]

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

We examine the historical evolution and projected changes in the hydrography of the deep basin of the Arctic Ocean in 23 climate models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6). The comparison between historical simulations and observational climatology shows that the simulated Atlantic Water (AW) layer is too deep and thick in the majority of models, including the multi‐model mean (MMM). Moreover, the halocline is too fresh in the MMM. Overall our findings indicate that there is no obvious improvement in the representation of the Arctic hydrography in CMIP6 compared to CMIP5. The climate change projections reveal that the sub‐Arctic seas are outstanding warming hotspots, causing a strong warming trend in the Arctic AW layer. The MMM temperature increase averaged over the upper 700 m at the end of the 21st century is about 40% and 60% higher in the Arctic Ocean than the global mean in the SSP245 and SSP585 scenarios, respectively. Salinity in the upper few hundred meters is projected to decrease in the Arctic deep basin in the MMM. However, the spread in projected salinity changes is large and the tendency toward stronger halocline in the MMM is not simulated by all the models. The identified biases and projection uncertainties call for a concerted effort for major improvements of coupled climate models. Plain Language Summary: Coupled climate models are crucial tools for understanding and projecting climate change, especially for the Arctic where the climate is changing at unprecedented rates. A cold fresh layer of water (aka halocline) has been protecting sea‐ice at the surface from the warm layer of water (aka Atlantic Water layer) which flows underneath and could potentially accelerate sea ice melting from below. Climate change disturbs this vertical structure by changing the temperature and salinity of the Arctic Ocean (in a process known as Atlantification and Pacification) which may lead to additional sea ice basal melting and accelerate sea ice decline. We examined the simulated temperature and salinity in the Arctic Ocean deep basin in state‐of‐the‐art climate model simulations which provided the basis for the IPCC Assessment Report. We found that although there are persistent inaccuracies in the representation of Arctic temperature and salinity, the Arctic Ocean below 100 m is subject to much stronger warming than the average global ocean. On the other hand, the upper Arctic Ocean salinity is projected to decrease, which on average may strengthen the isolation of sea ice from Atlantic Water heat in the Arctic deep basin area. Key Points: A too deep and thick Arctic Atlantic Water layer continues to be a major issue in contemporary climate models contributing to the CMIP6The Arctic Ocean below the halocline is subject to much stronger warming than the global mean during the 21st centuryThe multi‐model mean upper ocean salinity is projected to decrease in the future but with high uncertainty [ABSTRACT FROM AUTHOR]

This study compared precipitation projections of CMIP5 and CMIP6 GCMs over Yulin City, China. The performance of CMIP5 and CMIP6 GCMs in replicating Global Precipitation Climatology Centre (GPCC) precipitation climatology of the city was evaluated using different statistical metrics. The best performing GCMs common to both CMIP5 and CMIP6 were finally selected and subsequently downscaled to GPCC resolution using linear scaling method to assess spatiotemporal changes in precipitation in the basin. The study revealed BCC.CSM1.1(m), IPSL.CM5A.LR, MRI.CGCM3, and MIROC5 of CMIP5 and their equivalents BCC-CSM2-MR, IPSL-CM6A-LR, MRI.ESM2.0, and MIRCO6 of CMIP6 as the most suitable GCMs for the projection of precipitation in Yulin. This study revealed changes in precipitation in the range of −14.0 to 0.0% and −22.0 to 0.2% during 2021−2060 for RCP4.5 and SSP245 scenarios, respectively. The precipitation was projected to decrease more during 2061–2100 for both the scenarios. The highest decrease of −29.7 to −22.0% was projected by MRI-ESM-2-0 for SSP2-45, while −28.0 to −20.0% by MIROC5 for RCP4.5. For RCP8.5 and SSP5-85 scenarios, precipitation was projected to decrease in the range of −17.0 to −2.0% and −32.0 to 0.0%, respectively, during 2021–2060 by most of the GCMs. An increase in precipitation up to 12.3% was projected only by IPSL-CM5A-LR for RCP85 for this period. A further decrease in precipitation was projected by all GCMs during 2061−2100 for both RCP8.5 and SSP5-85 scenarios. The highest decrease was projected by MIROC5 (−40.2 to −29.0%) for RCP8.5 and IPSL-CM6A-LR (−40.2 to −26.0%) for SSP5-85. Overall, the results revealed a higher decrease in precipitation in Yulin City by CMIP6 GCMs compared to that projected by their corresponding GCMs of CMIP5 for both scenarios. This study can be of significance in the planning and mitigation of climate change as it gives insight into the expected changes in precipitation and the possibility of the choices of the best performing GCMs. [ABSTRACT FROM AUTHOR]

Summer 2010 saw two simultaneous extremes linked by an atmospheric wave train: a record-breaking heatwave in Russia and severe floods in Pakistan. Here, we study this wave event using a large ensemble climate model experiment. First, we show that the circulation in 2010 reflected a recurrent wave train connecting the heatwave and flooding events. Second, we show that the occurrence of the wave train is favored by three drivers: (1) 2010 sea surface temperature anomalies increase the probability of this wave train by a factor 2-to-4 relative to the model's climatology, (2) early-summer soil moisture deficit in Russia not only increases the probability of local heatwaves, but also enhances rainfall extremes over Pakistan by forcing an atmospheric wave response, and (3) high-latitude land warming favors wave-train occurrence and therefore rainfall and heat extremes. These findings highlight the complexity and synergistic interactions between different drivers, reconciling some seemingly contradictory results from previous studies. [ABSTRACT FROM AUTHOR]

This study evaluates the precipitation over East Asia simulated by the variable resolution CAM‐MPAS featuring the Model for Prediction Across Scales (MPAS) atmospheric dynamical core coupled with the physics parameterizations of the Community Atmosphere Model (CAM) version 5.4. Two CAM‐MPAS experiments, one with a global quasi‐uniform resolution mesh of 120 km (MPAS‐UR) and the other with a variable resolution mesh of 30–120 km refined over East Asia (MPAS‐VR), are conducted from 1989 to 2005 following the Atmospheric Model Intercomparison Project protocol. Two regional climate model simulations at ∼25 km resolution from the Coordinated Regional Downscaling Experiment East Asia second plan are also analyzed for comparison. Results show that CAM‐MPAS performs better than the selected regional models in simulating the precipitation climatology over East Asia. Compared with MPAS‐UR, MPAS‐VR with refinement over East Asia better simulates the precipitation over and around the Tibetan Plateau (TP), the upper‐level circulation, the precipitation frequency and intensity in various regions of China, and the interannual precipitation pattern associated with the East Asian summer monsoon. Both decreased grid spacing and the use of finer‐scale terrain information contribute to the improvements in MPAS‐VR relative to MPAS‐UR. However, decreased grid spacing is more essential for simulating the precipitation magnitude over TP and the location of peak precipitation south of TP, likely due to the better resolved physical and dynamical processes associated with orographic precipitation. The 30–120 km CAM‐MPAS model is shown to be a promising tool for precipitation simulation over East Asia. Plain Language Summary: Precipitation and associated cloud and radiation processes can affect the energy budgets and water supply over land and thus the livelihoods of billions of people over East Asia. However, traditional low‐resolution global climate models still have difficulty in realistically simulating the mean‐state and variability of precipitation over East Asia, partly due to their insufficient resolution to resolve the complex terrain and associated synoptic‐scale systems. In this work, we perform model experiments using the Community Atmosphere Model coupled with the atmospheric dynamical core from the Model for Prediction Across Scales with a variable resolution mesh to assess the impact of regional refinement on the precipitation simulation over East Asia. Experiment with higher resolution (30 vs. 120 km) over East Asia better simulates the total precipitation amount over and around the Tibetan Plateau (TP), as well as precipitation frequency, intensity, and interannual variability associated with the East Asian summer monsoon. The finer grid spacing is found to contribute more to the improvements in areas adjacent to TP than the use of finer terrain information, likely due to better resolved physical and dynamical processes associated with orographic precipitation. Key Points: The precipitation climatology over East Asia can be reasonably reproduced by a global climate model CAM‐MPASRegional refinement over East Asia improves the precipitation simulation over East Asia in various aspectsFiner grid spacing is more essential for precipitation simulation around large‐scale terrain than using finer‐scale terrain information [ABSTRACT FROM AUTHOR]

During strong El Niño events, below-average rainfall is expected in large parts of southern Africa. The 1992 El Niño season was associated with one of the worst drought episodes in large parts of South Africa. Using reanalysis data set from NCEP-NCAR, this study examined circulation types (CTs) in Africa south of the equator that are statistically related to the El Niño signal in the southwest Indian Ocean and the implication of this relationship during the 1992 drought episode in South Africa. A statistically significant correlation was found between the above-average Nino 3.4 index and a CT that features widespread cyclonic activity in the tropical southwest Indian Ocean, coupled with a weaker state of the south Indian Ocean high-pressure. During the analysis period, it was found that the El Niño signal enhanced the amplitude of the aforementioned CT. The impacts of the El Niño signal on CTs in southern Africa, which could have contributed to the 1992 severe drought episode in South Africa, were reflected in (i) robust decrease in the frequency of occurrence of the austral summer climatology pattern of atmospheric circulation that favors southeasterly moisture fluxes, advected by the South Indian Ocean high-pressure; (ii) modulation of easterly moisture fluxes, advected by the South Atlantic Ocean high-pressure, ridging south of South Africa; (iii) and enhancement of the amplitude of CTs that both enhances subsidence over South Africa, and associated with the dominance of westerlies across the Agulhas current. Under the ssp585 scenario, the analyzed climate models suggested that the impact of radiative heating on the CT significantly related to El Niño might result in an anomalous increase in surface pressure at the eastern parts of South Africa. [ABSTRACT FROM AUTHOR]