Abstract
Abstract Forcing a land surface model (LSM) offline with realistic global fields of precipitation, radiation, and near-surface meteorology produces realistic fields (within the context of the LSM) of soil moisture, temperature, and other land surface states. These fields can be used as initial conditions for precipitation and temperature forecasts with an atmospheric general circulation model (AGCM). Their usefulness is tested in this regard by performing retrospective 1-month forecasts (for May through September, 1979–93) with the NASA Global Modeling and Assimilation Office (GMAO) seasonal prediction system. The 75 separate forecasts provide an adequate statistical basis for quantifying improvements in forecast skill associated with land initialization. Evaluation of skill is focused on the Great Plains of North America, a region with both a reliable land initialization and an ability of soil moisture conditions to overwhelm atmospheric chaos in the evolution of the meteorological fields. The land initialization does cause a small but statistically significant improvement in precipitation and air temperature forecasts in this region. For precipitation, the increases in forecast skill appear strongest in May through July, whereas for air temperature, they are largest in August and September. The joint initialization of land and atmospheric variables is considered in a supplemental series of ensemble monthly forecasts. Potential predictability from atmospheric initialization dominates over that from land initialization during the first 2 weeks of the forecast, whereas during the final 2 weeks, the relative contributions from the two sources are of the same order. Both land and atmospheric initialization contribute independently to the actual skill of the monthly temperature forecast, with the greatest skill derived from the initialization of both. Land initialization appears to contribute the most to monthly precipitation forecast skill.
Highlights
Numerical weather forecasts rely on atmospheric initialization—the accurate specification of atmospheric pressures, temperatures, winds, and humidities at the beginning of the forecast
The hope in seasonal forecasting is that this probability density function (PDF) can be reproduced accurately and can be narrowed significantly with the specification of slowly evolving boundary conditions in the ocean and land
The forecasts examined allow a first assessment of the impact of land initialization on 1-month forecast skill
Summary
Numerical weather forecasts rely on atmospheric initialization—the accurate specification of atmospheric pressures, temperatures, winds, and humidities at the beginning of the forecast. Such initialization may contribute to forecast skill at leads of up to 10 days. They must take advantage of slower modes of the climate system, modes with states that are not so quickly dissipated by chaos. To this end, operational centers supply seasonal atmospheric forecasts based largely on forecasts of ocean behavior. The idea is simple—if sea surface temperatures (SSTs) can be predicted months in advance, and if the atmosphere responds in predictable ways to the predicted SSTs, aspects of the atmosphere’s behavior can be predicted months in advance
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