Gross Discrepancies between Observed and Simulated Twentieth-to-Twenty-First-Century Precipitation Trends in Southeastern South America

Abstract

Southeastern South America (SESA; encompassing Paraguay, Southern Brazil, Uruguay, and northern Argentina) experienced a 27% increase in austral summer precipitation from 1902-2019, one of the largest observed trends in seasonal precipitation globally. Previous research identifies Atlantic Multidecadal Variability and anthropogenic forcing from stratospheric ozone depletion and greenhouse gas emissions as key factors contributing to the positive precipitation trends in SESA. We analyze multi-model ensemble simulations from Phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP) and find that not only do Earth System Models simulate positive SESA precipitation trends that are much weaker over the historical interval, but some models persistently simulate negative SESA precipitation trends under historical forcings. Similarly, 16-member ensembles from two atmospheric models forced with observed historical sea surface temperatures never simulate precipitation trends that even reach the lower bound of the observed trend’s range of uncertainty. Moreover, while future 21st-century projections from CMIP6 yield positive ensemble mean precipitation trends over SESA that grow with increasing greenhouse-gas emissions, the mean forced response never exceeds the observed historical trend. Pre-industrial control runs from CMIP6 indicate that some models do occasionally simulate centennial-scale trends in SESA that fall within the observational range, but most models do not. Results point to significant uncertainties in the attribution of anthropogenically forced influences on the observed increases in precipitation over SESA, while also suggesting that internal decadal-to-centennial variability of unknown origin and not present in state-of-the-art models may have also played a large role in generating the 20th-21st-century SESA precipitation trend.

Influence of the South American Low‐Level Jet on the Austral Summer Precipitation Trend in Southeastern South America

“…Increases in SESA precipitation driven by increased humidity fluxes through the jet region from 1951 to 2020 are therefore likely robust. This result points to a potential assessment metric in climate models, which should be evaluated in light of their consistent inability to simulate historical SESA precipitation trends as large as those observed (e.g., Varuolo-Clarke et al, 2021). More work is therefore needed to further vet data set uncertainties, consider longer datasets, diagnose unaccounted factors driving SESA precipitation, and understand the role of the SALLJ in state-of-the-art climate models.…”

“…This precipitation trend has had implications for agriculture over the last several decades and coincided with a 210% expansion in Argentina's soy cultivation area (Barreiro et al., 2014; Lucas et al., 2018; Modernel et al., 2016). Despite SESA precipitation increases and related variables being well documented in observations (e.g., Carvalho, 2020; Dai, 2021; de Barros Soares et al., 2017; Ferrero et al., 2015; Haylock et al., 2006; Liebmann et al., 2004; Zilli et al., 2017), climate models struggle to simulate similar precipitation trends in the region, including fully coupled historical simulations and simulations forced with sea surface temperature (SST) from the Coupled Model Intercomparison Project Phase 3 (CMIP3), CMIP5, and CMIP6 archives (i.e., Díaz et al., 2021; Gonzalez et al., 2014; Seager et al., 2010; Varuolo‐Clarke et al., 2021; Zhang et al., 2016). Climate models nevertheless project precipitation to increase in SESA as a consequence of continued anthropogenic emissions (e.g., Cook et al., 2020; Varuolo‐Clarke et al., 2021), making it critical to better understand key drivers of the trend over the historical interval and the reasons why climate models fail to reproduce it.…”

“…Precipitation over southeastern South America (SESA; southern Brazil, Paraguay, Uruguay, and northern Argentina) increased 27% from 1902 to 2020 during austral summer [December‐February (DJF)], one of the largest observed precipitation trends globally (see Varuolo‐Clarke et al., 2021 for a comprehensive reference list). While precipitation has also increased in other seasons, a focus on austral summer is critical because it is SESA's rainy season and the season with the largest trend (e.g., Gonzalez et al., 2014; Varuolo‐Clarke et al., 2021). This precipitation trend has had implications for agriculture over the last several decades and coincided with a 210% expansion in Argentina's soy cultivation area (Barreiro et al., 2014; Lucas et al., 2018; Modernel et al., 2016).…”

“…In contrast, Zhang et al (2016), showed precipitation increases over SESA were attributable to increased GHG emissions but not to aerosols or stratospheric ozone depletion in the Geophysical Fluid Dynamics Lab model. Disagreement among models in terms of how external forcings affect SESA precipitation, in addition to the widespread model failure in simulating the magnitude or direction of observed historical SESA precipitation trends, highlight model discrepancies across simulations and attributions of SESA precipitation trends (e.g., Barros & Doyle, 2018;Díaz et al, 2021;Varuolo-Clarke et al, 2021).…”

Evaluation of long‐term changes in precipitation over Bolivia based on observations and Coupled Model Intercomparison Project models

“…Paradoxically, the overestimation of the negative precipitation trends in the lowlands of Bolivia is more acute in the most recent CMIP6 experiments, characterized by higher spatial resolution and improved physics (Eyring et al ., 2016). The uncertainty in reproducing long‐term precipitation trends using of climate models has been already documented in previous studies on the global and regional scales (van Oldenborgh et al ., 2013; Abadi et al ., 2018; Peña‐Angulo et al ., 2020), as well as some subtropical regions of the North and South hemispheres and in high latitudes of the North hemisphere (Kumar et al ., 2013; Vicente‐Serrano et al ., 2021) and in South America (Varuolo‐Clarke et al ., 2021). In other regions of the world characterized by data scarcity, the assessment of the goodness of the model performance is much more difficult.…”