Browsing by Subject "Dynamical downscaling"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Dynamically downscaled CMIP5 climate projection data for Minnesota(2022-01-25) Liess, Stefan; Twine, Tracy E; Snyder, Peter K; Hutchison, William D; Konar-Steenberg, Gabriel; Keeler, Bonnie L; Brauman, Kate A; liess@umn.edu; Liess, StefanThis dataset contains climate projections over Minnesota at 10 km horizontal resolution. Eight CMIP5 global climate models have been dynamically downscaled with the regional WRF model for the periods 1980-1999, 2040-2059, and 2080-2099, with the latter being represented as a moderate (RCP4.5) and also as an extreme "business as usual" scenario (RCP8.5). The projections suggest ongoing warming in all seasons, especially in winter, as well as reduced snow depth and fewer days with snow cover. Significant increases in spring and early summer heavy precipitation events are expected. The other variables in this dataset are daily max. and min. temperatures, relative humidity, latent heat flux (as proxy for evaporation), sensible heat flux, ground heat flux, incoming solar radiation, total radiation, snow depth, and wind speed. Temperatures, precipitation, and snow depth are also available as bias adjusted. Results indicate a climate near the end of the 21st century that is significantly different from what has been observed by the end of the 20th century. Winters and summers are expected to be up to 6C and 4C warmer, respectively, and spring precipitation may increase by more than 1 mm per day over northern Minnesota. Winter snow depth is projected to decrease by more than 12 cm and the number of days per year with snow depth of more than 2.54 cm (one inch) is expected to decrease by up to 55. These results are expected to influence regional decision-making related to agriculture, infrastructure, water resources, and other sectors.Item Examining the drivers of current and future changes in Central U.S. warm-season rainfall(2014-09) Harding, Keith John IliffWarm-season precipitation in the Central U.S. is highly variable, as severe droughts and flooding often occur in consecutive years or simultaneously. Some of the most highly productive agricultural lands are present within the region despite susceptibility to warm-season rainfall extremes. Climate change is expected to increase precipitation extremes globally, but how warm-season Central U.S. precipitation will be affected is unclear. In this study, I examine the drivers of current and future warm-season precipitation in the region as well as how the basic characteristics of summer rainfall may be affected by climate change through the use of gridded observations, reanalysis datasets, and dynamical downscaling of global climate models (GCMs). It is demonstrated that the negative phase of the Pacific-North American (PNA) teleconnection pattern enhances heavy precipitation events over the Upper Midwest by modulating the strength of the Great Plains Low Level Jet (GPLLJ), possibly enabling greater medium range prediction of Midwest heavy rain events. Similarly, I aim to reduce uncertainty in long-term projections of how precipitation may be affected by climate change by examining shortfalls in GCM-simulated warm-season precipitation and demonstrating improvement with dynamical downscaling. Using the Weather Research and Forecasting (WRF) model, two GCMs are dynamically downscaled in one historical and three future timeslices with varying anthropogenic forcing. Future warm-season precipitation in these simulations is more intense, less frequent, and occurs with more days between rain events, similar to trends in observations that show large increases in extreme rainfall events and rainfall intensity. The intensification of extreme rainfall events in future simulations is the strongest during the April-July, associated with a strengthening of the GPLLJ during those months. Heavier rainfall rates during extreme precipitation events are related to a stronger cold pool and mesohigh, which force stronger moisture convergence above the cold pool in the presence of additional low-level moisture and a drier mid-troposphere. Overall, the identification of plausible physical mechanisms that might contribute to the enhancement of heavy rainfall events in the region enables greater confidence in future projections of extreme rainfall events.