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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 During boreal winter (December–February), the South American monsoon system (SAMS) reaches its annual maximum when upper‐tropospheric westerly winds prevail over the equatorial Atlantic. Atmospheric dynamic model simulations suggest that Rossby waves generated over South America can propagate to and potentially influence weather patterns in the Northern Hemisphere (NH). However, observational evidence has been absent previously. Here we focus on southeastern South American (SESA) precipitation anomalies, which can characterize intraseasonal rainfall variability of the SAMS. Since tropical “westerly duct” and convective heating are important factors for cross‐equatorial propagation of Rossby wave (CEPRW), we identify two groups of events based on the two factors. By comparing the events associated with both SESA rainfall and tropical westerlies to the events associated with tropical westerlies only, we find that an anomalous Rossby wave train is triggered by precipitation anomalies over SESA, propagates in the southwest–northeast direction, and subsequently crosses the equator. Over a period of 4 days, near‐surface temperature over northwestern Africa and western Europe becomes warmer, accompanied by increased surface downward longwave radiation and precipitable water. The equatorward propagating Eliassen–Palm flux anomalies originated from SESA support the evidence of CEPRW. Simulations using a time‐dependent linear barotropic model forced by prescribed divergence anomalies over SESA further confirm that SESA rainfall can influence the NH weather patterns through CEPRW. Knowledge of this study will help us better understand and model interhemispheric teleconnections over the American–Atlantic–African/European sector. 

Abstract Atlantic Niños dominate the equatorial Atlantic variability during boreal summer (June–August). The coupled ocean‐atmosphere processes associated with Atlantic Niños have been extensively documented. However, the role of atmospheric convectively coupled Kelvin waves (CCKWs), which are uncorrelated to those previously identified processes, in triggering Atlantic Niños has been unclear. Here we identify CCKWs using Wheeler‐Kiladis filtering based on 10°S–10°N averaged daily outgoing longwave radiation. CCKWs propagate eastward from South America and induce surface zonal wind anomalies over the equatorial Atlantic Ocean. Strong anomalous CCKWs during spring (March–May) and their associated surface westerly wind anomalies can trigger downwelling oceanic Kelvin waves that change the east–west slope of the thermocline, consequently leading to Atlantic Niño. A causal effect network reveals that interannual sea surface temperature (SST) anomalies in the Atlantic Niño Index area and CCKWs, both in spring, are uncorrelated, but both appear to influence SST anomalies over the Atlantic Niño Index area in summer. The CCKWs are also uncorrelated to other coupled ocean‐atmosphere sources, such as El Niño–Southern Oscillation and Atlantic Meridional Mode. Among a total of 15 Atlantic Niño/Niña events identified for the period of 1980–2017, two‐thirds of the events are linked to CCKWs. In particular, three Atlantic Niña events (1982, 1994, and 2005) are mainly triggered by CCKWs, under unfavorable SST preconditions. Thus, CCKWs in spring, due to atmospheric internal variability, provide another mechanism for triggering Atlantic Niños and probably weaken their predictability. 

Abstract South America, especially the Amazon region, is considered a hotspot of biosphere–atmosphere interactions and presents a unique challenge for regional climate modeling. Here, we evaluate the performance of a regional model in simulating the climate–vegetation system in South America and use the model to investigate the potential role of large‐scale warming in the recently observed trend of hydroclimate and vegetation. Compared with prescribing vegetation based on observational data, adding the predictive vegetation capacity to the regional climate model enabled the model to simulate the vegetation response to climate while sustaining the model performance in reproducing the mean, variability and extremes of the regional climate. The coupled vegetation–climate model captures the recent trends in hydroclimate and vegetation productivity and their spatial contrasts, including a trend toward warmer, drier, and less productive conditions in the Amazon and Nordeste regions and a trend toward cooler, wetter, and more productive condition in the La Plata region. Results from sensitivity experiment driven by detrended boundary forcing for the regional climate suggest that much of the trends in the Amazon and Nordeste regions can be attributed to the effects of large‐scale warming, but contribution from decadal variability also plays a role especially for the most recent decade. However, the trend in the La Plata region cannot be attenuated by the removal of the boundary forcing trend, indicating the role of large‐scale circulation pattern changes. The recent trends in vegetation productivity may be early manifestation of future changes in the Amazon and surrounding regions. 

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. 

Abstract Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to various societal applications. Here we evaluate seasonal forecasts of three climate variables, vapor pressure deficit (VPD), temperature, and precipitation, from operational dynamical models over the major cropland areas of South America; analyze their predictability from global and local circulation patterns, such as El Niño–Southern Oscillation (ENSO); and attribute the source of prediction errors. We show that the European Centre for Medium-Range Weather Forecasts (ECMWF) model has the highest quality among the models evaluated. Forecasts of VPD and temperature have better agreement with observations (average Pearson correlation of 0.65 and 0.70, respectively, among all months for 1-month-lead predictions from the ECMWF) than those of precipitation (0.40). Forecasts degrade with increasing lead times, and the degradation is due to the following reasons: 1) the failure of capturing local circulation patterns and capturing the linkages between the patterns and local climate; and 2) the overestimation of ENSO’s influence on regions not affected by ENSO. For regions affected by ENSO, forecasts of the three climate variables as well as their extremes are well predicted up to 6 months ahead, providing valuable lead time for risk preparedness and management. The results provide useful information for further development of dynamical models and for those who use seasonal climate forecasts for planning and management. Significance Statement Seasonal climate forecasts have socioeconomic value, and the quality of the forecasts is important to their applications. This study evaluated the quality of monthly forecasts of three important climate variables that are critical to agricultural management, risk assessment, and natural hazards warning. The findings provide useful information for those who use seasonal climate forecasts for planning and management. This study also analyzed the predictability of the climate variables and the attribution of prediction errors and thus provides insights for understanding models’ varying performance and for future improvement of seasonal climate forecasts from dynamical models. 

Abstract Atmospheric humidity and soil moisture in the Amazon forest are tightly coupled to the region’s water balance, or the difference between two moisture fluxes, evapotranspiration minus precipitation (ET-P). However, large and poorly characterized uncertainties in both fluxes, and in their difference, make it challenging to evaluate spatiotemporal variations of water balance and its dependence on ET or P. Here, we show that satellite observations of the HDO/H 2 O ratio of water vapor are sensitive to spatiotemporal variations of ET-P over the Amazon. When calibrated by basin-scale and mass-balance estimates of ET-P derived from terrestrial water storage and river discharge measurements, the isotopic data demonstrate that rainfall controls wet Amazon water balance variability, but ET becomes important in regulating water balance and its variability in the dry Amazon. Changes in the drivers of ET, such as above ground biomass, could therefore have a larger impact on soil moisture and humidity in the dry (southern and eastern) Amazon relative to the wet Amazon.