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Abstract Fully coupled global climate model experiments are performed using the Community Climate System Model version 4.0 (CCSM4) for preindustrial, present, and future climate to study the effects of realistic land surface initializations on subseasonal to seasonal climate forecasts. Model forecasts are verified against model control simulations (perfect model experiments), thus overcoming to some extent issues of uncertainties in the observations and/or model parameterizations. Findings suggest that realistic land surface initialization is important for climate predictability at subseasonal to seasonal time scales. We found the highest predictability for soil moisture, followed by evapotranspiration, temperature, and precipitation. The predictability is highest for the 16 to 30 days forecast period, and it progressively decreases for the second and third month forecasts. We found significant changes in the spatial distributions of temperature predictability in the present and future climate compared to the preindustrial climate, although the spatial average changes for North America were rather small (<10%). To attribute the potential cause of changes in land‐driven temperature predictability, they are correlated with the changes in land related climate metrics. The changes in temperature predictability are positively (0.40), and negatively (−0.35) correlated with the changes in nonrainy days evaporative fraction, and changes in dryness index respectively. From this result, the hypothesis arises that wetter conditions favor higher land‐driven temperature predictability in North America. We tested the hypothesis by rearranging the predictability experiment ensembles and found support for the hypothesis in the midlatitude regions and short‐term forecasts (16 to 30 days).
Kumar et al. (Mon,) studied this question.