Potential Influence of Sea Surface Temperature Representation in Climate Model Simulations Over CORDEX-SEA Domain

• Main Authors: Magnaye, Angela Monina T, Narisma, Gemma T, Cruz, Faye T, Dado, Julie Mae B, Tangang, Fredolin T, Juneng, Liew, Ngo-Duc, Thanh, Phan-Van, Tan, Santisirisomboon, Jerasorn, Singhruck, Patama, Gunawan, Dodo, Aldrian, Edvin
• Format: text
• Published: Archīum Ateneo 2021
• Subjects: climate variability; CORDEX-SEA; regional climate modelling sea; surface temperature; Oceanography and Atmospheric Sciences and Meteorology; Physics
• Online Access: https://archium.ateneo.edu/physics-faculty-pubs/122; https://rmets.onlinelibrary.wiley.com/doi/10.1002/joc.7440
• Institution: Ateneo De Manila University

Regional climate simulations from the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment – Southeast Asia (SEA) indicated model biases in temperature and rainfall over SEA. Given the influence of sea surface temperature (SST) variability on SEA climate, this study examines SST representation in climate models to investigate its potential contribution to the resulting model biases over the Philippines. Observed SST over SEA is first characterized by its spatial patterns and temporal variability. An analysis of the SST representation over SEA and its potential influence on modelled climate over the Philippines in Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) is then conducted, followed by an assessment of the potential influence of SST representation in CMIP5 GCMs on downscaled regional climate output. Our results show that GCMs with well represented SSTs (i.e., low bias, well captured variability, and pattern) can produce climate simulations well over the Philippines. Whether or not the GCMs with poor SST representation can perform well is inconclusive. During boreal winter (summer), climate variables with high (low) spatial correlation with model SST get poor (better) spatial correlation with observed climate. Over west of the Philippines, where model SST seasonal variability is captured well, models also adequately simulate climate variables. Results suggest that the negative temperature biases, and positive precipitation and wind speed biases, in both GCMs and downscaled simulations, are associated with negative model SST biases. These findings give a better understanding on how SST potentially influences modelled climatology over the Philippines.