Using a model for weather prediction requires a methodology for initialization of the model. For real-time forecasting, this typically involves a data assimilation system, which is a major effort, and requires a substantial infrastructure. Thus, one challenge is to optimize the information gained by using a new model to make short-term predictions, while not being overwhelmed with the necessary infrastructure development associated with data assimilation and model initialization.

For weather prediction, detailed analyses of the observed state of the atmosphere are required, but uncertainties in this initial state grow rapidly over several days. Other components of the climate system are typically fixed as observed. For climate predictions, the initial state of the atmosphere makes less difference, but the initial states of other climate system components are necessary. For predictions of a season to a year or so, the upper ocean state, sea-ice extent, soil moisture, snow cover, and state of surface vegetation over land can all be important. For the decadal prediction problem, a full-depth global ocean initial state could be essential (Meehl et al., 2009; Shukla, 2009; Smith et al., 2007; Trenberth, 2008). Initial conditions for the global ocean could conceivably be provided by existing ocean data assimilation exercises. However, hindcast predictions for the 20th century, which are desirable to test models, are severely hampered by poor salinity reconstructions prior to the early 2000s when Argo floats began to provide much better depictions of temperature and salinity in the upper 2,000 m of the near-global ocean. Challenging research tasks are to develop optimal methods for initializing climate model predictions with the current observational network and identifying an optimal set of ocean observations to use for initializing climate predictions (Hurrell et al., 2009).

The mass, extent, thickness, and state of sea ice and snow cover are key climate variables at high latitudes. The states of soil moisture and surface vegetation are especially important in understanding and predicting warm season precipitation and temperature anomalies along with other aspects of the land surface, but they are difficult to quantify. The errors induced by incorrect initial conditions should become less apparent as the simulations evolve as systematic “boundary” and external influences become more important, but they could still be evident through the course of the simulations (Hurrell et al., 2009). Any information on systematic changes to the atmosphere (especially its composition and influences from volcanic eruptions) as well as external forcings, such as from changes in the sun, are also needed; otherwise these are specified as fixed at climatological average values.

Finding 11.2: Current observations are insufficient for complete initialization of climate models, especially for seasonal to decadal forecasts; poorly observed fields will be subject to more initialization bias and uncertainty.

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement