Atmosphere-ocean Modeling: Coupling And Couplers by Mechoso Carlos Roberto;An Soon-il;Valcke Sophie;

Author:Mechoso, Carlos Roberto;An, Soon-il;Valcke, Sophie;
Language: eng
Format: epub
Publisher: World Scientific Publishing Company
Published: 2021-10-15T00:00:00+00:00

Figure 7.5. Anomaly correlation skill for October–December average SST for ensemble predictions starting on 1 February performed with (a) a fully global CGCM, and (b) the same model except for Atlantic SST restored to observations. The prediction period is 1980–2005. In (b), regions in color and non-stippled indicate where using observed Atlantic SST leads to a significant increase in skill at the 5% level. (Adapted from Figure 1 in Keenlyside et al. [2013].)

7.5 Long-term predictions

CGCMs are indispensable tools for predicting and studying a variety of atmospheric phenomena from day-to-day weather changes to long-term climate variations. If an uncoupled AGCM is to be used for NWP, then surface conditions have to be prescribed from some additional source. An obvious first choice has been to keep the SST and sea-ice state constant during the forecast or taken from an observed climatology. One can ask to what extent in time can interactions with the ocean be neglected. The answer is a resounding “no” in reference to timescales beyond the medium range (about 10 days). It has been recognized that the ability to predict climate on seasonal timescales arises from the interaction of the atmosphere with the slower varying components of the climate system [Keenlyside et al., 2021]. The case for seasonal forecasts has been made by the usefulness of seasonal forecasting in the tropics. Predictions of ENSO using both dynamical and statistical models have been examined in Ch. 3. This usefulness has allowed for development of a system for seasonal forecasts using an AGCM with boundary conditions over the tropical oceans provided by other predictions systems. Such a methodology is referred to as a “two-tier forecast system”. The reader is referred to Misra et al. [2013] and Li and Misra [2013] for examples of this methodology and its performance.

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