{'fraction_non_alphanumeric': 0.017877094972067038, 'fraction_numerical': 0.004469273743016759, 'mean_word_length': 5.56043956043956, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Nonlocal impacts of soil moisture variability in South America: linking two land-atmosphere coupling hot spots


The land-atmosphere interactions play an important role in modulating climate variability at different spatial and temporal scales. In South America, two recognized hot spots of soil moisture-atmosphere coupling are located in southeastern South America (SESA) and eastern Brazil. Soil moisture variability may not only alter the climate locally, but also have nonlocal impacts through changes in the regional circulation. Here we explore how these two local coupling hot spots interact with each other, how soil moisture variability modulates the regional circulation, and which is the consequent nonlocal impact on precipitation. To this end, we analyze numerical experiments performed with a regional climate model for the period October-March 1983-2012, which allow us to isolate the influence of the soil moisture interannual variability on the regional climate. When the soil moisture-atmosphere interaction is enabled, we find a nonlocal coupling mechanism that links both hot spots at different temporal scales, favoring precipitation in eastern Brazil in detriment of the precipitation in SESA through shifts in the regional circulation, compared to a simulation with constrained soil moisture-atmosphere interaction. In northeastern Argentina, a sub-region of SESA located at the exit of the South American low-level jet, it was found that the amount of nighttime precipitation is modulated by the proposed nonlocal coupling mechanism. A better understanding of the variability of precipitation due to the influence of land-atmosphere interaction processes may contribute to improving the predictability of precipitation and the interpretation of climate projections.