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Ocean Surface Flux Algorithm Effects on Tropical Indo-Pacific Intraseasonal Precipitation.

Chia-Wei HsuCharlotte A DeMottMark D BransonJ E Jack Reeves EyreXubin Zeng
Published in: Geophysical research letters (2022)
Surface latent heat fluxes help maintain tropical intraseasonal precipitation. We develop a latent heat flux diagnostic that depicts how latent heat fluxes vary with the near-surface specific humidity vertical gradient (Δ q ) and surface wind speed (|V|). Compared to fluxes estimated from |V| and Δ q measured at tropical moorings and the Coupled Ocean Atmosphere Response Experiment 3.0 (COARE3.0) algorithm, tropical latent heat fluxes in the National Center for Atmospheric Research CEMS2 and Department of Energy E3SMv1 models are significantly overestimated at |V| and Δ q extrema. Madden-Julian oscillation (MJO) sensitivity to surface flux algorithm is tested with offline and inline flux corrections. The offline correction adjusts model output fluxes toward mooring-estimated fluxes; the inline correction replaces the original bulk flux algorithm with the COARE3.0 algorithm in atmosphere-only simulations of each model. Both corrections indicate reduced latent heat flux feedback to intraseasonal precipitation, in better agreement with observations, suggesting that model-simulated fluxes are overly supportive for maintaining MJO convection.
Keyphrases
  • machine learning
  • deep learning
  • heat stress
  • climate change
  • neural network
  • quality improvement
  • tertiary care
  • carbon dioxide