The folks at RealClimate provide a report from the meeting of the European Meteorological Society in Amsterdam. In this post, the focus is on a talk by Tim Palmer of the European Centre for Medium-Range Weather Forecasts, and consistent with that groups title, he addressed “the idea of using one system to predict atmospheric conditions on time scales varying from hours to decades.” This he called “seamless prediction.”
Much of the foot-dragging that holds local governments and communities from committing to adaptive planning comes from the lack of dependable forecasting that’s longer-range than the local weather and more immediate than climate models. If you have confidence that the next decade is going to bring you above-the-historical-average precipitation, you’re more likely to approve infrastructure improvements that will mitigate damage from increased flooding, for example.
You don’t have to understand the more technical discussion at RealClimate in order to take away the importance of this new approach. The following paragraphs I found to be educational without being over my head:
Due to historical and practical reasons, day-to-day weather forecasts tend to be performed on different systems than seasonal forecasts and climate change scenarios. Whereas the former can take the oceanic state to be approximately constant for the next few days, slow changes may have a greater impact for the latter two.
Numerical weather prediction (NWP – i.e. the daily operational weather forecast) and climatology communities have drifted apart for a while, but Palmer argued that there is a need to a convergence of the communities. He also proposed using global climate models (GCMs) the way NWP models are used for weather forecasting to test their quality. By looking at the initial part of their evolution, he reckoned it may be possible to get some idea of how good they are. Thus, he proposed a way to weigh the different GCMs up against each other. Time will show if this strategy will work.