model hindcasts of past MJO events. The National Multimodel Ensemble project2 has generated a series of seasonal-interannual hindcasts (and real-time forecasts) by several U.S. and international global climate models.

The unified modeling approach is much more scientifically and organizationally challenging to implement, but it has considerable additional benefits. For weather prediction models, two potential benefits are reduction of systematic errors due to meanstate drift and more skillful data assimilation. Weather forecast models typically suffer from mean-state drifts as they are run out for periods of a few days or longer and drift toward their biased internal climatology, creating forecast errors. In a weather forecast model also designed and tested as a climate model, minimizing such climatological biases will be a development priority; ultimately this should lead to better mediumrange forecasts. Some quantities such as soil moisture affect weather forecasts but are not routinely measured. They therefore are particularly susceptible to large errors due to model drift. Again, model testing in a climate mode should expose such drifts; reducing them can lead to more skillful forecasts and also allow more effective assimilation of observations taken near the land surface, such as near-surface humidity and temperature. A unified model with fewer systematic biases may support more accurate data assimilation and better analyses and reanalyses, which can help in testing of other climate models.

A unified model would foster the development of parameterizations that work well across a range of grid spacings and time scales. Combining weather and climate model resources for development of parameterizations and other modeling infrastructure might ultimately be more efficient and lead to intellectual cross-fertilization between weather and climate model research and development.

Finding 11.1: One useful form of seamless prediction is the testing of climate models in a weather forecast mode. Unified weather-climate modeling has further potential benefits, including improved weather forecasts, data assimilation, and reanalysis, and more efficient use of resources.


This section addresses the scientific and technical challenges for unified modeling, and the management and organizational challenges are discussed later in this chapter.


2 (accessed October 11, 2012).

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