Climate scientists have only recently become skillful at forecasting climatic events months in advance—a scientific advance that is fundamentally new in human history.3 Thus, only recently has it become possible for decision makers to achieve better outcomes by using information in climate forecasts than by ignoring them. Until recently, the best predictive rule available to water managers, land use planners, emergency managers, and many other decision makers for timescales beyond the reach of weather forecasting was to expect the future distribution of climate-driven events to mirror the past distribution for the same location and time of year.

It is important to emphasize that climate forecasting skill is still quite limited. It also varies considerably depending on lead time, geographic scale, location (e.g., the El Niño-Southern Oscillation [ENSO] effect is stronger in some places than others), time of year (for ENSO, the signal varies seasonally), phase of a climatic variation (e.g., forecasts are more skillful during the El Niño phase than during other parts of the ENSO cycle), the climate variable being predicted (e.g., temperature forecasts are typically more skillful than precipitation forecasts), and other factors. When forecasts combine climate information with information on other processes, such as the hydrological information in stream flow forecasts, the climate information may or may not increase forecasting skill. According to one recent assessment: “climate model forecasts presently suffer from a general lack of skill, [but] there may be locations, times of year and conditions (e.g., during El Niño or La Niña) for which they improve hydrological forecasts” relative to forecasts that do not use the climate information (Wood et al., 2005).

From the standpoint of potential climate forecast users, the current skill level of climate forecasts and related forecast products (e.g., hydrological forecasts that include climate) may or may not be sufficient to enable better decisions. Moreover, the same forecast may provide information that supports better decisions for some users but not others in the same geographic region: for example, hydropower producers may be able to use skillful forecasts of average conditions across a whole watershed,


Forecasting skill involves comparing a compilation of forecasts, such as might be derived from a climate model used to predict climate attributes at multiple times, with what was later observed at the forecasted times. When one says that climate scientists have become skillful, we mean that the skill of their forecasts for time periods months into the future is greater than the skill of a forecast consisting simply of historical averages for the forecasted times. Climate researchers often distinguish climate forecasts, which typically have lead times on the order of months (as in ENSO forecasts), from climate projections, which have lead times on the order of decades.

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