The Pacific–Indian Ocean associated mode in CMIP5 models

• Published in: Ocean Science
• Main Authors: M. Yang, X. Li, W. Shi, C. Zhang, J. Zhang
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2020
• Subjects: Geography. Anthropology. Recreation; G; Environmental sciences; GE1-350; Indian; Pacific; Sea ice
• Online Access: https://doi.org/10.5194/os-16-469-2020; https://doaj.org/article/a64fcd53b4cc4edf8da8e820709a873c

The Pacific–Indian Ocean associated mode (PIOAM), defined as the first dominant mode (empirical orthogonal function, EOF1) of sea surface temperature anomalies (SSTAs) in the Pacific–Indian Ocean between 20 ∘ S and 20 ∘ N, is the product of the tropical air–sea interaction at the cross-basin scale and the main mode of ocean variation in the tropics. Evaluating the capability of current climate models to simulate the PIOAM and finding the possible factors that affect the simulation results are beneficial in the pursuit of more accurate future climate change prediction. Based on the 55-year Hadley Centre Global Sea Ice and Sea Surface Temperature (HadISST) dataset and the output data from 21 Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) models, the PIOAM in these CMIP5 models is assessed. Instead of using the time coefficient (PC1) of the PIOAM as its index, we chose to utilize the alternative PIOAM index (PIOAMI), defined with SSTA differences in the boxes, to describe the PIOAM. It is found that the explained variance of the PIOAM in almost all 21 CMIP5 models is underestimated. Although all models reproduce the spatial pattern of the positive sea surface temperature anomaly in the eastern equatorial Pacific well, only one-third of these models successfully simulate the El Niño–Southern Oscillation (ENSO) mode with the east–west inverse phase in the Pacific Ocean. In general, CCSM4, GFDL-ESM2M and CMCC-CMS have a stronger capability to capture the PIOAM than the other models. The strengths of the PIOAM in the positive phase in less than one-fifth of the models are slightly greater, and very close to the HadISST dataset, especially CCSM4. The interannual variation of the PIOAM can be measured by CCSM4, GISS-E2-R and FGOALS-s2.