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Abstract. The Congo Basin in Central Africa is one of three convective centers in the tropics, characterized by a high proportion of precipitation produced by mesoscale convective systems (MCSs). However, process-level understanding of these systems and their relationship to environmental factors over the Congo Basin remains unclear, largely due to scarce in-situ observations. This study employs the Model for Prediction Across Scales–Atmosphere (MPAS-A), a global cloud-resolving model, to investigate MCSs in this region. Compared to satellite-observed brightness temperature (Tb), MPAS-A realistically simulates key MCS features, allowing a detailed comparison between two mesoscale convective complex (MCC) cases: one over the southern mountainous region (MCC-south) and the other over the northern lowland forests (MCC-north). MCC-south is larger, longer-lived, and moves a longer distance than MCC-north. Our analysis shows that MCC-south is supported by higher thermodynamic energy and more favorable vertical wind shear ahead of the system. The shear extends up to 400 km, explains up to 65 % of the Tb variance, and is well balanced by a moderately strong cold pool. In contrast, MCC-north features weaker, localized shear near the center and a stronger cold pool. The African Easterly Jet helps maintain the shear in both cases, but an overly strong jet may suppress low-level westerlies and weaken convection. These results show how latitude and topography modulate environmental influences on Congo Basin MCS developments. The findings underscore the value of global cloud-resolving models in data-sparse regions for understanding convective systems and their impacts on weather extremes and societal risks. 

Abstract The Congo basin hosts one of the largest terrestrial precipitation centers. Yet, the mechanisms that start the rainy seasons in Congo have not been studied systematically. We show that the transition from the dry to the rainy season over the southern Congo is initiated by a decrease in moisture export towards the Sahel, about three to four months before the rainy season onset (RSO), referred to as the pre-transition period. During this period, evapotranspiration (ET) is low due to low surface solar radiation, resulting from low insolation and high amounts of low-level clouds. In the early transition period, one to three months before the RSO, column water vapor increases due to increased oceanic moisture transport. Meanwhile, ET starts increasing due to increases in surface radiation and vegetation photosynthesis, despite a lack of soil moisture increases. Finally, in the late transition period, about one month before the RSO, ET continues to increase, contributing equally to atmospheric moisture needed for deep convection as advected oceanic moisture. Additionally, the formation of the African Easterly-Jet South and the southward movement of the Congo Air Boundary increase vertical wind shear and provide large-scale dynamic lifting of the warm and humid air from Congo. The frequency of deep convection increases rapidly, leading to the start of the rainy season. Therefore, the RSO over southern-hemispheric Congo basin is a result of combined large-scale atmospheric circulation change driven by increasing land–ocean surface temperature gradient and vegetation response to the seasonal change of insolation. 

Abstract The Pacific–South American (PSA) pattern is a key mode of climate variability in the mid-to-high latitudes of the Southern Hemisphere, impacting circulation and rainfall anomalies over South America. However, the effect of South American rainfall on the PSA has not been previously explored. This study focuses on the impact of rainfall over southeastern South American (SESA) during the austral summer (December–February). Observational analyses reveal that the PSA pattern remains confined to higher southern latitudes when SESA rainfall anomalies are weak. In contrast, strong SESA rainfall anomalies can generate a quasi-stationary Rossby wave train, which represents a cross-equatorial extension of the PSA. This wave train propagates along a southwest–northeast great circle path from higher latitudes, crosses the equator, and reaches the tropical Atlantic, southern Europe, and northern Africa, inducing a wet and cool weather condition over western and southern Europe. The observed wave train can be reproduced by the linear baroclinic model (LBM) simulations. Given the PSA’s connection to tropical forcing over the central Pacific, we examine differences in the wave response to central Pacific forcing alone versus combined central Pacific and SESA forcings. By incorporating SESA forcing, the wave train originally triggered by central Pacific forcing is amplified and extended. Our findings confirm the significant role of SESA rainfall anomalies in extending the PSA pattern to the Northern Hemisphere and highlight the South American continent as a land bridge that links circulation anomalies across the Pacific and Atlantic Oceans and the Southern and Northern Hemispheres. 

Historically, meteorological drought in the western United States (WUS) has been driven primarily by precipitation deficits. However, our observational analysis shows that, since around 2000, rising surface temperature and the resulting high evaporative demand have contributed more to drought severity (62%) and coverage (66%) over the WUS than precipitation deficit. This increase in evaporative demand during droughts, mostly attributable to anthropogenic warming according to analyses of both observations and climate model simulations, is the main cause of the increased drought severity and coverage. The unprecedented 2020–2022 WUS drought exemplifies this shift in drought drivers, with high evaporative demand accounting for 61% of its severity, compared to 39% from precipitation deficit. Climate model simulations corroborate this shift and project that, under the fossil-fueled development scenario (SSP5-8.5), droughts like the 2020–2022 event will transition from a one-in-more-than-a-thousand-year event in the pre-2022 period to a 1-in-60-year event by the mid-21st century and to a 1-in-6-year event by the late-21st century. 

Abstract Large spatio‐temporal gradients in the Congo basin vegetation and rainfall are observed. However, its water‐balance (evapotranspiration minus precipitation, orET − P) is typically measured at basin‐scales, limited primarily by river‐discharge data, spatial resolution of terrestrial water storage measurements, and poorly constrainedET. We use observations of the isotopic composition of water vapor to quantify the spatio‐temporal variability of net surface water fluxes across the Congo Basin between 2003 and 2018. These data are calibrated at basin scale using satellite gravity and total Congo river discharge measurements and then used to estimate time‐varyingET − Pover four quadrants representing the Congo Basin, providing first estimates of this kind for the region. We find that the multi‐year record, seasonality, and interannual variability ofET − Pfrom both the isotopes and the gravity/river discharge based estimates are consistent. Additionally, we use precipitation and gravity‐based estimates with our water vapor isotope‐basedET − Pto calculate time and space averagedETand net river discharge within the Congo Basin. These quadrant‐scale moisture flux estimates indicate (a) substantial recycling of moisture in the Congo Basin (temporally and spatially averagedET/P > 70%), consistent with models and visible light‐basedETestimates, and (b) net river outflow is largest in the Western Congo where there are more rivers and higher flow rates. Our results confirm the importance ofETin modulating the Congo water cycle relative to other water sources. 

Abstract. The Great Plains and southwest regions of the US are highly vulnerable to precipitation-related climate disasters such as droughts and floods. In this study, we propose a self-organizing map–analogue (SOMA) approach to empirically quantify the contribution of atmospheric moist circulation (mid-tropospheric geopotential and column moisture transport) to the regional precipitation anomalies, variability, and multi-decadal changes. Our results indicate that moist circulation contributes significantly to short-term precipitation variability, accounting for 54 %–61 % of the total variance in these regions, though these contributions vary significantly across seasons. As indicated in previous research, Pacific Decadal Oscillation (PDO) is one of the major climate modes influencing the long-term multi-decadal variation in precipitation. By contrasting three multi-decadal periods (1950–1976, 1977–1998, 1999–2021) with shifting PDO phases and linking the phase shift to self-organizing map (SOM) nodes, we found that circulation changes contribute considerably to the multi-decadal changes in precipitation anomaly in terms of the mean and days of dry and wet extremes, especially for the southern Great Plains (GP) and southwest. However, these circulation-induced changes are not totally related to the PDO phase shift (mostly less than half) since internal variability or anthropogenically induced changes in circulation can also be potential contributors. Our approach improves upon flow analogue and SOM-based methods and provides insights into the contribution of atmospheric circulation to regional precipitation anomalies and variability. 

Abstract. ​​​​​​​Land–atmosphere coupling (LAC) has long been studied, focusing on land surface and atmospheric boundary layer processes. However, the influence of humidity in the lower troposphere (LT), especially that above the planetary boundary layer (PBL), on LAC remains largely unexplored. In this study, we use radiosonde observations from the US Southern Great Plains (SGP) site and an entrained parcel buoyancy model to investigate the impact of LT humidity on LAC there during the warm season (May–September). We quantify the effect of LT humidity on convective buoyancy by measuring the difference between the 2–4 km vertically integrated buoyancy with the influence of background LT humidity and that without it. Our results show that, under dry soil conditions, anomalously high LT humidity is necessary to produce the buoyancy profiles required for afternoon precipitation events (APEs). These APEs under dry soil moisture cannot be explained by commonly used local LAC indices such as the convective triggering potential and low-level humidity index (CTP / HILow), which do not account for the influence of the LT humidity. On the other hand, consideration of LT humidity is unnecessary to explain APEs under wet soil moisture conditions, suggesting that the boundary layer moisture alone could be sufficient to generate the required buoyancy profiles. These findings highlight the need to consider the impact of LT humidity, which is often decoupled from the humidity near the surface and is largely controlled by moisture transport, in understanding land–atmospheric feedbacks under dry soil conditions, especially during droughts or dry spells over the SGP. 

In regions of the world where topography varies significantly with distance, most global climate models (GCMs) have spatial resolutions that are too coarse to accurately simulate key meteorological variables that are influenced by topography, such as clouds, precipitation, and surface temperatures. One approach to tackle this challenge is to run climate models of sufficiently high resolution in those topographically complex regions such as the North American Regionally Refined Model (NARRM) subset of the Department of Energy’s (DOE) Energy Exascale Earth System Model version 2 (E3SM v2). Although high-resolution simulations are expected to provide unprecedented details of atmospheric processes, running models at such high resolutions remains computationally expensive compared to lower-resolution models such as the E3SM Low Resolution (LR). Moreover, because regionally refined and high-resolution GCMs are relatively new, there are a limited number of observational datasets and frameworks available for evaluating climate models with regionally varying spatial resolutions. As such, we developed a new framework to quantify the added value of high spatial resolution in simulating precipitation over the contiguous United States (CONUS). To determine its viability, we applied the framework to two model simulations and an observational dataset. We first remapped all the data into Hierarchical Equal-Area Iso-Latitude Pixelization (HEALPix) pixels. HEALPix offers several mathematical properties that enable seamless evaluation of climate models across different spatial resolutions including its equal-area and partitioning properties. The remapped HEALPix-based data are used to show how the spatial variability of both observed and simulated precipitation changes with resolution increases. This study provides valuable insights into the requirements for achieving accurate simulations of precipitation patterns over the CONUS. It highlights the importance of allocating sufficient computational resources to run climate models at higher temporal and spatial resolutions to capture spatial patterns effectively. Furthermore, the study demonstrates the effectiveness of the HEALPix framework in evaluating precipitation simulations across different spatial resolutions. This framework offers a viable approach for comparing observed and simulated data when dealing with datasets of varying spatial resolutions. By employing this framework, researchers can extend its usage to other climate variables, datasets, and disciplines that require comparing datasets with different spatial resolutions.