Assessment of uncertainty in multi‐model means of downscaled South Florida precipitation for projected (2019–2099) climate

Abstract

South Florida resource management, particularly the Everglades restoration effort, is beginning to consider projections of precipitation from multiple climate models for decision‐making. Because precipitation changes can significantly affect the Everglades ecosystem, characterization of precipitation projection uncertainty is important for resource management decisions, and reduction of uncertainty is desired for better decision‐making. Though uncertainty of precipitation projections has been characterized for many regions, uncertainty has not been sufficiently quantified for South Florida. This study builds upon prior results for projected Florida precipitation by considering recent climate model simulations, seasonal and spatial information, and uncertainty quantification and reduction. We identify the multi‐model mean change in South Florida precipitation and characterize the uncertainty of 37 statistically downscaled Coupled Model Intercomparison Project Phase 5 models. For 2019–2045, there is a likely (over 60% of ensemble members) increase in South Florida annual mean precipitation owing to a likely to very likely (near 90% of ensemble members) increase in dry season (November, December, and January) precipitation, while wet season (June, July, and August) shows a more likely than not (over 50% of ensemble members) decrease in precipitation in the southern region and increase in precipitation in the northern region of South Florida. As South Florida agencies are on the verge of including precipitation projections in their upcoming planning horizon, this information will aid South Florida climate data users in decisions influenced by future rainfall.

Aligning Climate Models With Stakeholder Needs: Advances in Communicating Future Rainfall Uncertainties for South Florida Decision Makers

“…We also note that the no threshold changes in AMJJAS are overshadowed by ONDJFM; thus, the annual mean projected change is wet. These projected changes in the nonsubsetted and in the subsetted suite agree with those in the Intergovernmental Panel on Climate Change (IPCC) report and Infanti et al (2019).…”

“…The AMJJAS change is weak (compared to annual mean and NDJFM changes) and displays large variations across models, particularly in the location of the shift from dry to wet. This change is discussed in more detail in Infanti et al (2019). The leading hypothesis for this pattern of change is that interocean temperature differences cause Caribbean drying (Lee et al, 2010;Rauscher et al, 2010).…”

“…We used monthly Bias Corrected Spatially Disaggregated (BCSD) and daily Localized Constructed Analogue (LOCA) precipitation from Representative Concentration Pathway (RCP) 4.5 (moderate emissions) and RCP8.5 (high emissions) in this study. Remaining RCPs (RCP2.6 and 6.0) have higher uncertainty or unrealistic future information (Infanti et al, 2019), and we have elected not to include these here. For more information on RCPs, see van Vuuren et al (2011).…”

“…There is now a recognition that planning of this scale must consider the potential for changes in spatial and temporal patterns of precipitation among other aspects of climate. Climate projections for South Florida, however, have been variable-some projecting wetter future conditions, some drier, and there is considerable uncertainty in which models are best predictors of future conditions (Infanti et al, 2019;Obeysekera et al, 2011). Climate data users desire increased confidence in climate projections as restoration plans are costly and can take years to implement.…”

“…The ecosystem of the Florida Everglades and the built environment of South Florida can be heavily impacted by changing precipitation (Aumen et al, 2015;Obeysekera et al, 2011). Though we now have better understanding of potential South Florida precipitation changes to 2,100, the associated uncertainties remain high (Infanti et al, 2019). Moreover, it can be difficult for non-climatologists to interpret and use large climate data sets.…”