Amazon boundary layer aerosol concentration sustained by vertical transport during rainfall

International audienceThe nucleation of atmospheric vapours is an important source of new aerosol particles that can subsequently grow to form cloud condensation nuclei in the atmosphere 1. Most field studies of atmospheric aerosols over continents are influenced by atmospheric vapours of anthropogenic origin (for example, ref. 2) and, in consequence, aerosol processes in pristine, terrestrial environments remain poorly understood. The Amazon rainforest is one of the few continental regions where aerosol particles and their precursors can be studied under near-natural conditions 3–5 , but the origin of small aerosol particles that grow into cloud condensation nuclei in the Amazon boundary layer remains unclear 6–8. Here we present aircraft-and ground-based measurements under clean conditions during the wet season in the central Amazon basin. We find that high concentrations of small aerosol particles (with diameters of less than 50 nanometres) in the lower free troposphere are transported from the free troposphere into the boundary layer during precipitation events by strong convective downdrafts and weaker downward motions in the trailing stratiform region. This rapid vertical transport can help to maintain the population of particles in the pristine Amazon boundary layer, and may therefore influence cloud properties and climate under natural conditions

Global-scale evaluation of 22 precipitation datasets using gauge observations and hydrological modeling

We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76,086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the conceptual model HBV against streamflow records for each of 9053 small to medium-sized (<50,000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence, the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite- and reanalysis-based P estimates

The CMS Collaboration

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