Tropical Pacific Air-sea Interaction Processes and Biases in CESM2

Ho-Hsuan Wei1, Aneesh C Subramanian1, Kristopher B Karnauskas2, Charlotte A DeMott3, Matthew R Mazloff4 and Magdalena Alonso Balmaseda5, (1)University of Colorado Boulder, Boulder, CO, United States, (2)University of Colorado Boulder, Boulder, United States, (3)Colorado State University, Fort Collins, CO, United States, (4)Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA, United States, (5)European Centre for Medium-Range Weather Forecasts, Reading, United Kingdom
Abstract:
Variability in the location of the eastern edge of Indo/western Pacific Warm Pool and associated subsurface features is important for air-sea interactions involved in ENSO dynamics. In this study, we use the Ocean ReAnalysis System 5 (ORAS5) to identify mean-state biases in the NCAR Community Earth System Model version 2 (CESM2) with a particular focus on upper ocean properties and air-sea interaction processes. We seek to determine the physical mechanisms governing air-sea interactions in the region, and thus the impacts of model biases. We show that the CESM2 has warm and fresh surface biases in the western tropical Pacific Ocean and a barrier layer that is too thin, which may impact air-sea interaction processes involved in ENSO onset. For example, the onset of El Nino events is facilitated by the existence of a salinity barrier layer in this region, as this barrier layer makes conditions favorable for eastward propagation of the warm pool edge via reducing the efficacy of sea surface cooling by mixing during westerly wind burst events. We compare composites of El Nino events in ORAS5 and CESM2 and show that the biases of barrier layer thickness in the western Pacific is significant before the onset of the El Nino events. Other mechanisms leading to the development of the El Nino events in both ORAS5 and CESM2 are further examined. Guided by these results, we plan to analyze the CESM2 subseasonal forecast with different ocean initial conditions to identify the physical processes leading to these mean-state biases in the tropical Pacific. Our results help prioritize process studies and help better identify potential observational strategies in the region as part of TPOS 2020.