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The Southeast Asian Monsoon (SEAM) is strongly affected by the complex topography and land–sea interface over Southeast Asia (SEA), which combine to make simulation of the SEAM technically challenging. To adequately assess the regional climate change signal, we have employed the Weather Research and Forecasting (WRF) Model to dynamically downscale a global climate change projection produced with the Community Earth System Model using different physics configurations in WRF, constituting a 5-member physics mini-ensemble. All ensemble members consistently project an increase in average SEAM rainfall and an increase in the frequency of extreme events. A regional ocean model based upon the Coastal and Regional Ocean Community model system was then incorporated into the dynamical downscaling pipeline and this has also contributed to significantly further improving the simulations of both sea surface temperature and SEAM rainfall. Since the Tibetan Plateau (TP) is widely considered to act as an elevated heat source which contributes to driving the Asian monsoon system, a coupled dynamically downscaled simulation with flattened plateau has also been performed so as to investigate the role of TP in the SEAM at a higher spatial resolution than has previously been investigated. Significant decrease of precipitation and winds over SEA, as well as a later monsoon onset by 1 month, are documented for the no TP experiment. Extreme precipitation is less affected than average precipitation. Such changes are more important for the northern part of the domain and are significantly amplified in the dynamically downscaled WRF simulations when compared with the global simulations that employ significantly coarser resolution.

Arctic winter warming is stronger than in summer, but its driving mechanisms remain debated, particularly the roles of local processes, like sea-ice loss, versus remote factors, like atmospheric heat transport. Here we introduce a novel decomposition framework that characterizes Arctic warming as a function of historical atmospheric circulation, sea ice concentration, and carbon dioxide changes using observational and reanalysis data. We show that sea ice changes explain about 55% of the winter Arctic near-surface temperature trend during 1959–2015, after removing the effects directly connected to atmospheric circulation. Dynamically induced warming accounts for about 20% at surface and up to 80% in mid-troposphere. The remaining ~25% is attributed to the increase in carbon dioxide, though it also indirectly affects sea-ice loss and circulation-related warming. These findings highlight the dominant role of sea ice loss and change in atmospheric dynamics in affecting the historical Arctic winter warming. About 55% of the increase in winter Arctic surface temperature over 1959-2015 is explained by changes in sea ice concentration, while energy transport toward the poles and increased carbon dioxide account for about 20% and 25%, respectively, according to a statistical analysis of climate data.

Earth system models are essential tools for climate projections, but coarse resolutions limit regional accuracy, especially in the Arctic. Regionally refined meshes (RRMs) enhance resolution in key areas while maintaining computational efficiency. This paper provides an overview of the United States (U.S.) Department of Energy's (DOE's) Energy Exascale Earth System Model version 2.1 with an Arctic RRM, hereafter referred to as E3SMv2.1‐Arctic, for the atmosphere (25 km), land (25 km), and ocean/ice (10 km) components. We evaluate the atmospheric component and its interactions with land, ocean, and cryosphere by comparing the RRM (E3SM2.1‐Arctic) historical simulations (1950–2014) with the uniform low‐resolution (LR) counterpart, reanalysis products, and observational data sets. The RRM generally reduces biases in the LR model, improving simulations of Arctic large‐scale mean fields, such as precipitation, atmospheric circulation, clouds, atmospheric river frequency, and sea ice thickness. However, it introduces a seasonally dependent surface air temperature bias, reducing the LR cold bias in summer but enhancing the LR warm bias in winter, which contributes to the underestimated winter sea ice area and volume. Radiative feedback analysis shows similar climate feedback strengths in both model configurations, with the RRM exhibiting a more positive surface albedo feedback and contributing to a stronger surface warming than LR. These findings underscore the importance of high‐resolution modeling for advancing our understanding of Arctic climate changes and their broader global impacts, although some persistent biases appear to be independent of model resolution at 10–100 km scales. Plain Language Summary Earth system models (ESMs) are essential tools for understanding the climate system and projecting future changes, but standard coarse model resolutions often fail to realistically represent regional processes and topography, particularly in the polar regions, while uniform high‐resolution grids are too computationally expensive. To address this, regionally refined meshes (RRMs) have been developed within ESMs to provide high‐resolution simulations in target areas, improving the accuracy of regional climate simulations while maintaining computational efficiency. This study looks at how well the U.S. DOE's RRM model, E3SMv2.1‐Arctic, performs in simulating Arctic climate. The RRM, which focuses on the Arctic region, is compared to a uniform low‐resolution version, as well as to observations and reanalysis data. The RRM does a better job of simulating important Arctic climate features like precipitation, atmospheric circulation, clouds, sea ice and atmospheric rivers compared to the low‐resolution model. However, it shows some seasonal temperature biases, reducing the cold bias in summer but increasing the warm bias in winter. The RRM also underestimates winter sea ice, consistent with the warm winter bias. While the study demonstrates the advantages of using high‐resolution models to better understand Arctic climate changes, it also notes that some biases remain despite the increased resolution. Key Points Evaluation of atmosphere‐land‐ocean‐ice fully coupled E3SM‐Arctic historical simulations with regionally refined meshes for the Arctic E3SM‐Arctic reduces bias in precipitation, clouds and atmospheric rivers due to better topography compared to its low‐resolution counterpart Higher resolution leads to an increased albedo feedback and more accurate sea ice area, but with faster sea ice melting due to a warm bias

In this paper we address the question whether variability in the Atlantic Meridional Overturning Circulation (AMOC) and its associated heat transport at mid‐latitudes impacts the Arctic Earth system. To that end, we perform coherence analysis on time series of ocean heat transport, AMOC strength, and Arctic climate metrics across a large number of models from the CMIP6 ensemble. We find that, on multidecadal timescales, the majority of CMIP6 models indeed display a statistically significant relationship between AMOC at mid‐latitudes (45°^{\\circ}$ N) and metrics like Arctic surface air temperatures and sea ice. However, our results do not support the narrative that heat transport anomalies at lower latitudes propagate northward toward the Arctic. Instead, our results confirm that variability in meridional ocean heat transport arises in the subpolar North Atlantic and propagates southward and northward.

The extreme concentration of population over South Asia makes it critical to accurately understand the global warming impact on the South Asian monsoon (SAM), but the complex orography of the region makes future projections of monsoon intensity technically challenging. Here we describe a series of climate projections constructed using the Weather Research and Forecasting (WRF) Model for South Asia to dynamically downscale a global warming simulation constructed using the Community Earth System Model under the representative concentration pathway 8.5 (RCP8.5) scenario. A physics-based miniensemble is employed to investigate the sensitivity of the projected change of the SAM to the implementation of different parameterization schemes in WRF. We analyze not only the changes in mean seasonal precipitation but also the impact of the warming process on precipitation extremes. All projections are characterized by a consistent increase in average monsoon precipitation and a fattening of the tail of the daily rainfall distribution (more than a 50% decrease in the return periods of 50-yr extreme rainfall events by the end of the twenty-first century). Further analysis based on one of the WRF physics ensemble members shows that both the average rainfall intensity changes and the extreme precipitation increases are projected to be slightly larger than expectations based upon the Clausius–Clapeyron thermodynamic reference of 7% °C−1 of surface warming in most parts of India. This further increase can be primarily explained by the fact that the surface warming is projected to be smaller than the warming in the midtroposphere, where a significant portion of rain originates, and dynamical effects play only a secondary role.

An extreme warming event near the North Pole, with 2 m temperature rising above 0 °C, was observed in late December 2015. This specific event has been attributed to cyclones and their associated moisture intrusions. However, little is known about the characteristics and drivers of similar events in the historical record. Here, using data from European Centre for Medium-Range Weather Forecasts Reanalysis, version 5 (ERA5), we study these winter extreme warming events with 2 m temperature over a grid point above 0 °C over the high Arctic (poleward of 80° N) that occurred during 1980–2021. In ERA5, such wintertime extreme warming events can only be found over the Atlantic sector. They occur rarely over many grid points, with a total absence during some winters. Furthermore, even when occurring, they tend to be short-lived, with the majority of the events lasting for less than a day. By examining their surface energy budget, we found that these events transition with increasing latitude from a regime dominated by turbulent heat flux into the one dominated by downward longwave radiation. Positive sea level pressure anomalies which resemble blocking over northern Eurasia are identified as a key ingredient in driving these events, as they can effectively deflect the eastward propagating cyclones poleward, leading to intense moisture and heat intrusions into the high Arctic. Using an atmospheric river (AR) detection algorithm, the roles of ARs in contributing to the occurrence of these extreme warming events defined at the grid-point scale are explicitly quantified. The importance of ARs in inducing these events increases with latitude. Poleward of about 83° N, 100 % of these events occurred under AR conditions, corroborating that ARs were essential in contributing to the occurrence of these events. Over the past 4 decades, both the frequency, duration, and magnitude of these events have been increasing significantly. As the Arctic continues to warm, these events are likely to increase in both frequency, duration, and magnitude, with great implications for the local sea ice, hydrological cycle, and ecosystem.

The complex orography of South Asia, including both the Himalayas and the Tibetan Plateau, renders the regional climate complex. How this climate, especially the monsoon circulations, will respond to the global warming process is important given the large population of the region. In a first step toward a contribution to the understanding of the expected impacts, a series of dynamically downscaled instrumental-era climate simulations for the Indian subcontinent are described and will serve as a basis for comparison against global warming simulations. Global simulations based upon the Community Earth System Model (CESM) are employed to drive a dynamical downscaling pipeline in which the Weather Research and Forecasting (WRF) Model is employed as regional climate model, in a nested configuration with two domains at 30- and 10-km resolution, respectively. The entire ensemble was integrated for 15 years (1980–94), with the global model representing a complete integration from the onset of Northern Hemisphere industrialization. Compared to CESM, WRF significantly improves the representation of orographic precipitation. Precipitation extremes are also characterized using extreme value analysis. To investigate the sensitivity of the South Asian summer monsoon simulation to different parameterization schemes, a small physics ensemble is employed. The Noah multiphysics (Noah-MP) land surface scheme reduces the summer warm bias compared to the Noah land surface scheme. Compared with the Kain–Fritsch cumulus scheme, the Grell-3 scheme produces an increased moisture bias at the first western rain barrier, whereas the Tiedtke scheme produces less precipitation over the subcontinent than observed. Otherwise the improvement of fit to the observations derived from applying the downscaling methodology is highly significant.

The Tibetan Plateau (TP) contains the headwaters of major Asian rivers that sustain billions of people and plays an important role in both regional and global climate through thermal and mechanical forcings. Understanding the characteristics and changes to the hydrological regimes on the TP during the mid-Holocene (MH) will help in understanding the expected future changes. Here, an analysis of the hydroclimates over the headwater regions of three major rivers originating in the TP, namely the Yellow, Yangtze, and Brahmaputra rivers, is presented, using dynamically downscaled climate simulations constructed using the Weather Research and Forecasting Model (WRF) coupled to the hydrological model WRF-Hydro. Green Sahara (GS) boundary conditions have also been incorporated into the global model so as to capture the remote feedbacks between the Saharan vegetation and the river hydrographs over the TP. Model–data comparisons show that the dynamically downscaled simulations significantly improve the regional climate simulations over the TP in both the modern day and the MH, highlighting the crucial role of downscaling in both present-day and past climates. TP precipitation is also strongly affected by the greening of the Sahara, with a particularly large increase over the southern TP, as well as a delay in the monsoon withdrawal. The simulation results were first validated over the upper basins of the three rivers before the hydrological responses to the MH forcing for the three basins were quantified. Both the upper Yellow and Yangtze rivers exhibit a decline in streamflow during the MH, especially in summer, which is a combined effect of less snowmelt and stronger evapotranspiration. The GS forcing caused a rise in temperature during the MH, as well as larger rainfall but less snowfall and greater evaporative water losses. The Brahmaputra River runoff is simulated to increase in the MH due to greater net precipitation.

Proxy records suggest that the Northern Hemisphere during the mid-Holocene (MH), to be assumed herein to correspond to 6000 years ago, was generally warmer than today during summer and colder in the winter due to the enhanced seasonal contrast in the amount of solar radiation reaching the top of the atmosphere. The complex orography of both South and Southeast Asia (SA and SEA), which includes the Himalayas and the Tibetan Plateau (TP) in the north and the Western Ghats mountains along the west coast of India in the south, renders the regional climate complex and the simulation of the intensity and spatial variability of the MH summer monsoon technically challenging. In order to more accurately capture important regional features of the monsoon system in these regions, we have completed a series of regional climate simulations using a coupled modeling system to dynamically downscale MH global simulations. This regional coupled modeling system consists of the University of Toronto version of the Community Climate System Model version 4 (UofT-CCSM4), the Weather Research and Forecasting (WRF) regional climate model, and the 3D Coastal and Regional Ocean Community model (CROCO). In the global model, we have taken care to incorporate Green Sahara (GS) boundary conditions in order to compare with standard MH simulations and to capture interactions between the GS and the monsoon circulations in India and SEA. Comparison of simulated and reconstructed climates suggest that the dynamically downscaled simulations produce significantly more realistic anomalies in the Asian monsoon than the global climate model, although they both continue to underestimate the inferred changes in precipitation based upon reconstructions using climate proxy information. Monsoon precipitation over SA and SEA is also greatly influenced by the inclusion of a GS, with a large increase particularly being predicted over northern SA and SEA, and a lengthening of the monsoon season. Data–model comparisons with downscaled simulations outperform those with the coarser global model, highlighting the crucial role of downscaling in paleo data–model comparison.

The extreme concentration of population over South and Southeast Asia makes it critical to accurately understand the global warming impact on the South and Southeast Asian monsoon (SAM and SEAM) while the complex orography of the regions makes future projections of monsoon intensity technically challenging. Here we describe a series of climate projections constructed using the Weather Research and Forecasting (WRF) Model to dynamically downscale a global warming simulation generated using the Community Earth System Model. All projections are characterized by a consistent increase in average monsoon precipitation and daily extreme precipitation. A regional ocean model based upon the Coastal and Regional Ocean Community model system was then incorporated into the dynamical downscaling pipeline and this has also contributed to significantly improving the simulations of both sea surface temperature and rainfall. Since the Tibetan Plateau (TP) is widely considered to act as an elevated heat source which contributes to driving the Asian monsoon system, a coupled dynamically downscaled simulation with flattened plateau has also been performed so as to investigate the role of TP in the SEAM at a higher spatial resolution than has previously been investigated. We also have completed a series of regional paleoclimate simulations using the coupled modeling system to dynamically downscale global simulations during the mid-Holocene (MH), a time period featuring generally warmer summers than present in the Northern Hemisphere. In the global model, we have taken care to incorporate Green Sahara (GS) boundary conditions so as investigate the interactions between the GS and the Asian monsoon circulations. SAM and SEAM precipitation is greatly enhanced by the inclusion of a GS. Data–model comparisons with downscaled simulations outperform those with the coarser global model, highlighting the crucial role of downscaling in paleo data–model comparison. The TP also contains the headwaters of major Asian rivers and understanding the characteristics and changes of the hydrological regimes on the TP during the MH will help understand the expected future changes. Thus, an analysis of hydroclimates in three major river basins in the TP are also presented, based on dynamically downscaled climate simulations constructed using coupled WRF-WRF-Hydro.