Surface flux response to interannual tropical Pacific sea surface temperature variability in AMIP models

Citation
R. Kleeman et al., Surface flux response to interannual tropical Pacific sea surface temperature variability in AMIP models, CLIM DYNAM, 17(8), 2001, pp. 627-641
Citations number
36
Categorie Soggetti
Earth Sciences
Journal title
CLIMATE DYNAMICS
ISSN journal
09307575 → ACNP
Volume
17
Issue
8
Year of publication
2001
Pages
627 - 641
Database
ISI
SICI code
0930-7575(200105)17:8<627:SFRTIT>2.0.ZU;2-4
Abstract
A systematic comparison of observed and modeled atmospheric surface heat an d momentum fluxes related to sea surface temperature (SST) variability on i nterannual time scales in the tropical Pacific is conducted. This is done t o examine the ability of atmospheric general circulation models (AGCMs) in the Atmospheric Model Intercomparison Project (AMIP) to simulate the surfac e fluxes important for driving the ocean on interannual time scales. In ord er to estimate the model and observed response to such SST variability, var ious regression calculations are made between a time series representing ob served ENSO SST variability in the tropical Pacific and the resulting surfa ce flux anomalies. The models exhibit a range of differences from the obser vations. Overall the zonal wind stress anomalies are most accurately simula ted while the solar radiation anomalies are the least accurately depicted. The deficiencies in the solar radiation are closely related to errors in cl oudiness. The total heat flux shows some cancellation of the errors in its components particularly in the central Pacific. The performance of the GCMs in simulating the surface flux anomalies seems to be resolution dependent and low-resolution models tend to exhibit weaker flux responses. The simula ted responses in the western Pacific are more variable than those of the ce ntral and eastern Pacific but in the west the observed estimates are less r obust as well. Further improvements in atmospheric GCM flux simulation thro ugh better physical parametrization is clearly required if such models are to be used to their full potential in coupled modeling and climate forecast ing.