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Why does SCREAM exhibit an L-shaped Relative Humidity profile in the tropics?

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Abstract

Year-long integrations of the Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) have recently been carried out for present day as well as a hypothetical +4K future run. A striking feature in our simulated tropical profiles is a lack of moisture in the mid-troposphere and an excess of moisture in the upper troposphere. The model also exhibits a positive temperature bias in the upper troposphere. In Relative Humidity (RH) space, this translates to a negative RH bias in both the mid and upper troposphere leading to an L-shaped tropical RH profile. This profile is inconsistent with the emblematic C-shaped profile simulated by many models at various scales (Romps, 2014). We hypothesize that this bias in rooted in SCREAM’s chronic “popcorn convection” problem which results in overly efficient vertical transport of moisture to the upper troposphere with little detrainment in the middle troposphere. While this should lead to a stronger than usual C-shaped tropical profile, we hypothesize that the resultant overly dense anvil clouds radiatively warm the upper troposphere leading a high-level positive temperature bias and a negative RH bias. This presentation will present evidence for this argument, our attempts to reproduce the problem in idealized simulations, and possible remedies.

References

Romps, D. M. (2014). An Analytical Model for Tropical Relative Humidity. Journal of Climate, 27(19), 7432–7449. https://doi.org/10.1175/JCLI-D-14-00255.1

Lawrence Livermore National Laboratory is operated by Lawrence Livermore National Security, LLC, for the U.S. Department of Energy, National Nuclear Security Administration under Contract DE-AC52 07NA27344 IM Release number LLNL-ABS- 865080. This research was supported as part of the Energy Exascale Earth System Model (E3SM) project, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research.

Category
Water Cycle and Hydroclimate
Model Uncertainties, Model Biases, and Fit-for-Purpose
Funding Program Area(s)