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The Impact of Interdecadal Variability on the Skill of Climate Models

The Impact of Interdecadal Variability on the Skill of Climate Models

Alice M. Grimm (1), A. K. Sahai (2), and C. F. Ropelewski (3)
(1) Department of Physics, Federal University of ParanᬠCuritiba, PR, Brazil.
(2) Indian Institute of Tropical Meteorology, Pune, India.
(3) International Institute for Climate and Society, Palisades, NY.

Global climate models forced by sea surface temperature are standard tools in seasonal climate prediction and projection of future climate change. It has been shown that models? performance is seasonally dependent, but there has always been the assumption that, for a given season, it is constant. Here, we demonstrate that there are periods when those models perform well and periods when they do not capture observed climate variability. We compare the seasonal responses of two AGCMs to observed SST with seasonal observed fields during 1950-1994. The model performance is assessed through simultaneous correlation between reanalysis data and model output, averaged over 20? latitude × 40? longitude regions. Seasonal correlations coefficients (CCs) are computed for winter and summer in 11-year sliding periods. The EOF analysis of these sliding CCs discloses the interdecadal variations of the models? skill all over the globe. The relationships between these variations and interdecadal modes of SST variability are examined by correlating SST with the two first Principal Components. The statistical significance is assessed through Monte Carlo procedure. The correlation patterns resemble closely those associated with well-known low-frequency variability (PDO and NAO), indicating that the modes of models? performance vary coherently with these interdecadal variability modes. These results suggest that there are un-modelled climate processes that affect seasonal climate prediction as well as scenarios of climate change, particularly regional climate-change projections. Their reliability may depend on the time slice being analyzed. More comprehensive assessments should include a verification of the long-term stability of the models? performance.

Acknowledgments. This work has been supported by the Interamerican Institute for Global Change Research (IAI -CRN055) and CNPq-Brazil.