Aligning Climate Models with Stakeholder Needs: A Decision Tool for Communicating Future Rainfall Uncertainties to South Florida Decision-Makers

Changes in future precipitation are of great importance to climate data users in south Florida due to the restriction and redirection of freshwater flows in the Florida Everglades - State and Federal agencies have spent decades and millions of dollars devising plans to help restore some of the historical flows to mitigate adverse effects on historical alterations, ultimately to regain the health of Everglades ecosystems. These plans were not designed with the consideration of future changes in climate, but there is now recognition that planning must include and consider the potential for changes in spatial and temporal patterns in precipitation. However, there is considerable uncertainty in south Florida climate projections, in particular in determining which models are the best predictors of future conditions, and it can be difficult for non-climatologists to interpret and use large climate datasets, leading to under-utilization of climate model output. A recent United States Geological Survey workshop, “Increasing Confidence in Precipitation Projections for Everglades Restoration,” highlighted a gap between standard climate model outputs and the climate information needs of some key Florida natural resource managers, where natural resource managers and decision makers need more tailored output than is commonly provided by the climate modeling community. One requested output from this workshop was to determine a subset of models that outperforms others across all seasons and parameters, though a singular subset of models is – at best – difficult to determine as all models have their strengths and weaknesses. In response, we develop a decision support tool that provides a subset of models based on user-defined areas of interest. This “decision matrix” guides climate data users to specify the subset of models most important to their work based on each user’s season (winter, summer, annual) and the condition (dry, wet, neutral, and no threshold events) of interest. We create this decision matrix using an adaptable methodology to select relevant output from ensemble climate-model simulations based on relevant user-defined precipitation drivers, using statistical methods common across scientific disciplines. This approach seeks to select models that simulate observed Florida precipitation well, in addition to representing two sources of precipitation variability – SST and 2-meter temperature – allowing for greater confidence that this subset is capturing the precipitation for the right reasons. The intention of the decision matrix tool is that a user who is most interested in say, winter season dry events and how they might change in the future, could use a specific subset of models that represents the physical drivers of winter season wet events particularly well. We envision that this decision matrix tool can be used as guidance for decision makers and users desiring more information on future changes in the parameters of precipitation, and for those looking for a subset of data to drive hydrologic or ecological models.