Program  
 
Ocean-atmosphere interactions and multi-scale climate variability in a changing climate
 
 
 
Poster
On the simulations of global oceanic latent heat flux in CMIP5 multi-model ensemble
P-P2-12
Rongwang Zhang* , State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences
Xin Wang, State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences
Chunzai Wang, State Key Laboratory of Tropical Oceanography, South China Sea Institute of Oceanology, Chinese Academy of Sciences
Presenter Email: rwzhang@scsio.ac.cn

Simulations of the global oceanic latent heat flux (LHF) in CMIP5 multi-model ensemble (MME) were evaluated in comparison with 11 LHF products. The results show that the mean state of LHF in MME coincides well with that in the observations, except for a slight overestimation in the tropical regions. The reproduction of the seasonal cycle of LHF in MME is in good agreement with that in the observations. However, biases are relatively obvious in the coastal regions. A prominent upward trend in global mean LHF is confirmed with all of the LHF products during the period of 1979 to 2005. Despite the consistent increase of LHF in CMIP5 models, the rates of increase are much weaker than those in the observations, with an average of approximate 1/9 of that in the observations. The findings show that the rate of increase of near-surface specific humidity (qa) in MME is nearly six times that in the observations, while the rate of increase of the near-surface wind speed (U) is less than 1/2 of that in the observations. The faster increase of qa and the slower increase of U could both suppress evaporation, and thus latent heat released by the ocean, which may be one of the reasons that the upward trend of LHF in MME is nearly one order lower than that in the observations.

It is worth noting that the very weak trend in LHF in all the models suggests that the LHF values are nearly conserved in the models. This discrepancy with respect to the observations may have an important impact on the energy budget in the models, and further studies are needed.

 
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