Thomas J. Phillips
The chaotic behavior of the continental climate of an atmospheric general circulation model is investigated from an ensemble of decadal simulations with common specifations of radiative forcings and monthly ocean boundary conditions, but different initial states of atmosphere and land. The variability structures of key model land-surface processes appear to agree sufficiently with observational estimates to warrant detailed examination of their predictability on seasonal time scales. This predictability is inferred from several novel measures of spatio-temporal reproducibility applied to eleven model variables. The reproducibility statistics are computed for variables in which the seasonal cycle is included or excluded, the former case being most pertinent to climate model simulations, and the latter to predictions of the seasonal anomalies. Because the reproducibility metrics in the latter case are determined in the context of a "perfectly" known ocean state, they are properly viewed as estimates of the potential predictability of seasonal climate. Inferences based on these reproducibility metrics are shown to be in general agreement with those derived from more conventional measures of potential predictability.
It is found that the land-surface variables which include the seasonal
cycle are impacted only marginally by changes in initial conditions; moreover,
their seasonal climatologies exhibit high spatial reproducibility. In contrast,
the reproducibility of a seasonal land-surface anomaly is generally low,
although it is considerably higher in the Tropics; its spatial reproducibility
also fluctuates in tandem with warm and cold phases of the El Niño/Southern
Oscillation phenomenon. However, the detailed sensitivities to initial
conditions depend somewhat on the land-surface process: pressure and temperature
anomalies exhibit the highest temporal reproducibilities, while hydrological
and turbulent flux anomalies show the highest spatial reproducibilities.
Implications of these results for model intercomparisons and seasonal forecasts
are elaborated.
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UCRL-MI-123395