Communicating climate information consistently: bridging the gap between seasonal climate forecasts and climate change projections

Although climate information is becoming increasingly available to society, additional tools are needed to translate climate change information into secondary products that are useful for local assessments and decisions. However, one hurdle to translating climate information stems from the fact that it is not communicated consistently, making it more difficult to develop translation tools that are general, flexible, and useful for end-users. For example, routine seasonal climate forecasts are provided in an A:N:B format, where A indicates the likelihood of above-normal precipitation, N indicates near-normal precipitation, and B indicates below-normal precipitation, where the above and below normal categories are based on the terciles. This probabilistic format is useful in that it can be used to develop climate ensembles that express the associated uncertainty. On the other hand, to express the uncertainty in climate change projections for a climate model, probability density functions (PDFs) can be used. However, because many end-users are not familiar with PDFs, climate information is often expressed in terms of shifts in averages – which does not communicate the uncertainty. Here, a method is provided to translate climate shifts from a PDF to the A:N:B format already utilized by seasonal climate forecasts. This provides a “future climate forecast” for managers, which can be interpreted in the same way as seasonal climate forecasts. Once the climate change information is translated into the A:N:B format, it can be utilized to develop climate ensembles. Here, a conditional resampling technique is employed, where the “future climate forecast” is used to condition, or bias, the resample so as to reflect the changes likely under climate change. Here, the technique will be demonstrated using climate change model output from the CESM, but the method could be easily extended to other climate change models. This also allows the variability from climate change to be examined in light of natural variability, which is simulated by using an A:N:B = 33:33:33. This is demonstrated for precipitation shifts under climate change that are relevant to natural resource managers in the Northern Rockies.