Abstract
Ensembles of numerical forecasts based on perturbed initial conditions have long been used to improve estimates of both weather and climate forecasts. The Goddard Earth Observing System (GEOS) Atmosphere–Ocean General Circulation Model, Version 5 (GEOS-5 AOGCM) Seasonal-to-Interannual Forecast System has been used routinely by the GMAO since 2008, the current version since 2012. A coupled reanalysis starting in 1980 provides the initial conditions for the 9-month experimental forecasts. Once a month, sea surface temperature from a suite of 11 ensemble forecasts is contributed to the North American Multi-Model Ensemble (NMME) consensus project, which compares and distributes seasonal forecasts of ENSO events. Since June 2013, GEOS-5 forecasts of the Arctic sea-ice distribution were provided to the Sea-Ice Outlook project. The seasonal forecast output data includes surface fields, atmospheric and ocean fields, as well as sea ice thickness and area, and soil moisture variables. The current paper aims to document the characteristics of the GEOS-5 seasonal forecast system and to highlight forecast biases and skills of selected variables (sea surface temperature, air temperature at 2 m, precipitation and sea ice extent) to be used as a benchmark for the future GMAO seasonal forecast systems and to facilitate comparison with other global seasonal forecast systems.
Highlights
Deterministic numerical weather prediction forecasts have a forecasting window that is limited to about 15 days (e.g., Lorenz 1963, 1993)
The current paper aims to document the characteristics of the Goddard Earth Observing System (GEOS)-5 seasonal forecast system and to highlight forecast biases and skills of selected variables to be used as a benchmark for the future Global Modeling and Assimilation Office (GMAO) seasonal forecast systems and to facilitate comparison with other global seasonal forecast systems
As the system has progressed through several years within the North American Multi-Model Ensemble (NMME) near-operational mode, this paper critically examines recent performance
Summary
Deterministic numerical weather prediction forecasts have a forecasting window that is limited to about 15 days (e.g., Lorenz 1963, 1993). The GMAO system is based on its use and experience with data assimilation methods that have been developed for mission support and to enhance NASA’s program of earth observations. The development and use of the seasonal forecasting system enhances the use of NASA data and contributes to observing system science by improving assimilation systems and atmosphere and ocean modeling tools. To evaluate the model’s ability to simulate the Earth’s climate, it was validated against observational data and reanalysis products
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