On the Harvest of Predictability From Land States in a Global Forecast Model

Abstract

Various land surface treatments in a suite of subseasonal-to-seasonal forecasts are applied to diagnose the degree to which potential predictability from the land surface is harvested, where breakdowns occur in the process chains that link land surface states to atmospheric phenomena, and the role played by memory in the climate system. Version 2 of the Coupled Forecast System (CFSv2) is used for boreal summer simulations spanning 28 years. Four types of retrospective forecasts are produced: those where land surface initial states are from the same date and year as the initial atmosphere and ocean states; ensembles where initial land states come from different years than the atmosphere and ocean; simulations where soil moisture is specified from an observationally constrained analysis; and simulations where an alternative triggering mechanism for convection is employed. The specified soil moisture allows estimation of an upper bound for land-driven predictability and prediction skill in boreal summer. Realistic land initialization represents the best possible case with this model in forecast mode, while the simulations with initial land states from different years isolate the impact of atmosphere and ocean initialization on forecasts. Harvested predictability is calculated, and its relationship to memory of initial anomalies is estimated. The pathway of land surface information through the energy and water cycles to the atmosphere, and ultimately its effects on precipitation, is traced, showing a robust propagation of useful signal through land surface fluxes, near-surface meteorological states, and boundary layer properties, but largely disappearing at precipitation, implying problems with the convective parameterization.