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Diurnal Cycle of Precipitation in the Amazon: Contrasting Observationally Constrained Cloud-System Resolving and Global Climate Models
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  • Sheng-Lun Tai,
  • Zhe Feng,
  • Po-Lun Ma,
  • Courtney Schumacher,
  • Jerome Fast
Sheng-Lun Tai
Pacific Northwest National Laboratory (DOE)

Corresponding Author:[email protected]

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Zhe Feng
Pacific Northwest National Laboratory (DOE)
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Po-Lun Ma
Pacific Northwest National Laboratory (DOE)
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Courtney Schumacher
TAMU
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Jerome Fast
PNNL
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Abstract

The ability of an observationally-constrained cloud-system resolving model (Weather Research and Forecasting; WRF, 4-km grid spacing) and a global climate model (Energy Exascale Earth System Model; E3SM, 1-degree grid spacing) to represent the precipitation diurnal cycle over the Amazon basin during the 2014 wet season is assessed. The month-long period is divided into days with and without the presence of observed propagating mesoscale convective systems (MCSs) over the central Amazon. The MCSs are strongly associated with rain amounts over the basin and also control the observed spatial variability of the diurnal rain rate. WRF model coupled with a 3-D variational data assimilation scheme reproduces the spatial variability of the precipitation diurnal cycle over the basin and the lifecycle of westward propagating MCSs initiated by the coastal sea-breeze front. In contrast, a single morning peak in rainfall is produced by E3SM for simulations with and without nudging the large-scale winds towards global reanalysis, indicating precipitation in E3SM is largely controlled by local convection associated with diurnal heating. Both models produce contrast in easterly wind profiles between days with and without MCS that are similar to data collected by U.S. DOE Atmospheric Radiation Measurement (ARM) facility during the Green Ocean Amazon (GoAmazon2014/5) campaign and other operational radiosondes. A multivariate perturbation analysis indicates the dryness of low-level air transported from ocean to inland has higher impact on the formation and maintenance of MCS in the Amazon than other processes.