J3.8 Using a hierarchy of climate models to assess predictability on decadal timescales

Tuesday, 18 June 2013: 9:45 AM
Viking Salons ABC (The Hotel Viking)
R. Saravanan, Texas A&M University, College Station, Texas; and X. Zhu, P. Chang, and L. Ji

Decadal climate predictions are the “no man's land” between seasonal climate predictions, dominated by natural modes of variability (like the ENSO) and centennial climate projections, where anthropogenic radiative forcing dominates. Assessing the relative importance of natural and anthropogenic contribution to decadal prediction skill is made difficult by the fact that global coupled general circulation models (CGCMs) used to make the predictions exhibit significant climate drift on these time scales. The use of statistical models can circumvent the model bias issue, but the statistical approach suffers from the lack of sufficient training data to characterize an unprecedented event like anthropogenic climate change.

In this study, we evaluate the use of models simpler than CGCMs to predict decadal climate variations. We have carried out ensembles of century-long climate predictions with a global atmospheric general circulation model coupled to a slab ocean model (AGCM-SOM), with simple extrapolative prescriptions of future greenhouse gas concentrations and other external forcings. The use of the slab ocean model mitigates the problem of climate drift and reduces the impact of initial condition errors in the ocean. In addition to the control integration using the standard AGCM-SOM configuration, we have carried out additional sensitivity experiments where we have eliminated the Wind-Evaporation-SST (WES) feedback, and also allowed the surface heat flux correction (QFLUX), which acts a proxy for oceanic heat transport, to vary over time. Additionally, we have constructed a simple statistical model (a variant of the Hasselmann model) to assess the skill of empirical approaches to decadal climate prediction. Our results suggest that (i) the AGCM-SOM configuration can capture a significant portion of the predictive skill on decadal time scales; (ii) the WES feedback serves to decrease the signal-to-noise ratio over many regions, and (iii) the choice of QFLUX can affect the decadal prediction skill.

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