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Description
Fortran source code developed to override wind stress with a climatology and output daily ocean variables in the Community Earth System Model (CESM) v. 1.2 from Luongo et al. (2024). Abstract of source study: Positive feedbacks in climate processes can make it difficult to identify the primary drivers of climate phenomena. Some recent global climate model (GCM) studies address this issue by controlling the wind stress felt by the surface ocean such that the atmosphere and ocean become mechanically decoupled. Most mechanical decoupling studies have chosen to override wind stress with an annual climatology. In this study we introduce an alternative method of interannually varying overriding which maintains higher frequency momentum forcing of the surface ocean. Using a GCM (NCAR CESM1), we then assess the size of the biases associated with these two methods of overriding by comparing with a freely evolving control integration. We find that overriding with a climatology creates sea surface temperature (SST) biases throughout the global oceans on the order of $pm1^circ$C. This is substantially larger than the biases introduced by interannually varying overriding, especially in the tropical Pacific. We attribute the climatological overriding SST biases to a lack of synoptic and subseasonal variability, which causes the mixed layer to be too shallow throughout the global surface ocean. This shoaling of the mixed layer reduces the effective heat capacity of the surface ocean such that SST biases excite atmospheric feedbacks. These results have implications for the reinterpretation of past climatological wind stress overriding studies: past climate signals attributed to momentum coupling may in fact be spurious responses to SST biases. NASA FINESST Fellowship 80NSSC22K1528 NSF grant OCE-2048590 Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp) Luongo, Matthew T.; Brizuela, Noel G.; Eisenman, Ian; Xie, Shang-Ping (2024). Code from: Retaining Short-Term Variability Reduces Mean State Biases in Wind Stress Overriding Simulations. In Data and Code from: Retaining Short-Term Variability Reduces Mean State Biases in Wind Stress Overriding Simulations. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0MP53GB
Type
dataset
Identifier
ark:/20775/bb2094010z
Language
English
Subject
Synoptic variability Global climate modeling decoupling Oceanography Earth sciences Wind stress overriding Nonlinear rectification Global
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