TY - JOUR
T1 - Aligning Climate Models With Stakeholder Needs
T2 - Advances in Communicating Future Rainfall Uncertainties for South Florida Decision Makers
AU - Infanti, Johnna M.
AU - Kirtman, Ben P.
AU - Aumen, Nicholas G.
AU - Stamm, John
AU - Polsky, Colin
N1 - Funding Information:
The authors acknowledge funding from the Postdocs Applying Climate Expertise (PACE) Fellowship, administered by the University Corporation for Atmospheric Research (UCAR), and from the U. S. Geological Survey's (USGS) Greater Everglades Priority Ecosystem Sciences (GEPES) program. We thank two anonymous reviewers and USGS internal reviewers; their comments greatly improved the manuscript. We acknowledge the support of University of Miami (UM), Florida Atlantic University (FAU), USGS, and the National Oceanic and Atmospheric Administration (NOAA). Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - Changes in future precipitation are of great importance to climate data users in South Florida. A recent U.S. 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. These natural resource managers (hereafter broadly defined as “climate data users”) need more tailored output than is commonly provided by the climate modeling community. This study responds to these user needs by outlining and testing an adaptable methodology to select output from ensemble climate-model simulations based on user-defined precipitation drivers, using statistical methods common across scientific disciplines. This methodology is developed to provide a “decision matrix” that guides climate data users to specify the subset of models most important to their work based on each user's season (winter, summer, and annual) and the condition (dry, wet, neutral, and no threshold events) of interest. The decision matrix is intended to better communicate the subset of models best representing precipitation drivers. This information could increase users' confidence in climate models as a resource for natural resource planning and can be used to direct future dynamical downscaling efforts. This methodology is based in dynamical processes controlling precipitation via remote and local teleconnections. We also suggest that future climate studies in South Florida include high-resolution climate model runs (i.e., ocean eddy resolving) in conjunction with dynamical downscaling to adequately capture precipitation variability.
AB - Changes in future precipitation are of great importance to climate data users in South Florida. A recent U.S. 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. These natural resource managers (hereafter broadly defined as “climate data users”) need more tailored output than is commonly provided by the climate modeling community. This study responds to these user needs by outlining and testing an adaptable methodology to select output from ensemble climate-model simulations based on user-defined precipitation drivers, using statistical methods common across scientific disciplines. This methodology is developed to provide a “decision matrix” that guides climate data users to specify the subset of models most important to their work based on each user's season (winter, summer, and annual) and the condition (dry, wet, neutral, and no threshold events) of interest. The decision matrix is intended to better communicate the subset of models best representing precipitation drivers. This information could increase users' confidence in climate models as a resource for natural resource planning and can be used to direct future dynamical downscaling efforts. This methodology is based in dynamical processes controlling precipitation via remote and local teleconnections. We also suggest that future climate studies in South Florida include high-resolution climate model runs (i.e., ocean eddy resolving) in conjunction with dynamical downscaling to adequately capture precipitation variability.
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U2 - 10.1029/2019EA000725
DO - 10.1029/2019EA000725
M3 - Article
AN - SCOPUS:85088585177
VL - 7
JO - Earth and Space Science
JF - Earth and Space Science
SN - 2333-5084
IS - 7
M1 - e2019EA000725
ER -