Using
a perfect modeling framework, we set out to determine the upper limits of predictability for precipitation, soil moisture and forest fire risk in the US.
This approach allows us to neglect observational errors and model biases, and is therefore referred to as
the perfect modeling framework.
Based on
the perfect model framework, our results provide estimates of the maximum potential predictability of soil hydrological variability.
Not exact matches
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Applying the
framework of Delworth and Manabe (1988) to the more complex CESM system, we compare simple red noise null hypothesis
models for soil moisture variations at various depth levels with an ensemble of
perfect model forecasts conducted with the CESM.