Expansion of agricultural lands and inherent variability of climate can influence the water cycle in the Amazon basin, impacting numerous ecosystem services. However, these two influences do not work independently of each other. With two once-in-a-century-level droughts occurring in the Amazon in the past decade, it is vital to understand the feedbacks that contribute to altering the water cycle. The biogeophysical impacts of land cover change within the Amazon basin were examined under drought and pluvial conditions to investigate how land cover and drought jointly may have enhanced or diminished recent precipitation extremes by altering patterns and intensity. Using the Weather Research and Forecasting (WRF) Model coupled to the Noah land surface model, a series of April–September simulations representing drought, normal, and pluvial years were completed to assess how land cover change impacts precipitation and how these impacts change under varied rainfall regimes. Evaporative sources of water vapor that precipitate across the region were developed with a quasi-isentropic back-trajectory algorithm to delineate the extent and variability that terrestrial evaporation contributes to regional precipitation. A decrease in dry season latent heat flux and other impacts of deforestation on surface conditions were increased by drought conditions. Coupled with increases in dry season moisture recycling over the Amazon basin by ~7% during drought years, land cover change is capable of reducing precipitation and increasing the amplitude of droughts in the region.

Since World War II, the expansion of irrigation throughout the Great Plains has resulted in a significant decline in the water table of the Ogallala Aquifer, threatening its long-term sustainability. The addition of near-surface water for irrigation has previously been shown to impact the surface energy and water budgets by modifying the partitioning of latent and sensible heating. A strong increase in latent heating drives nearsurface cooling and an increase in humidity, which has opposing impacts on convective precipitation. In this study, the Weather Research and Forecasting Model (WRF) was modified to simulate the effects of irrigation on precipitation. Using a satellite-derived fractional irrigation dataset, grid cells were divided into irrigated and nonirrigated segments and the near-surface soil layer within irrigated segments was held at saturation. Nine April-October periods (three drought, three normal, and three pluvial) were simulated over the Great Plains. Averaging over all simulations, May-September precipitation increased by 4.97 mm (0.91%), with localized increases of up to 20%. The largest precipitation increases occurred during pluvial years (6.14 mm; 0.98%) and the smallest increases occurred during drought years (2.85 mm; 0.63%). Precipitation increased by 7.86 mm (1.61%) over irrigated areas from the enhancement of elevated nocturnal convection. Significant precipitation increases occurred over irrigated areas during normal and pluvial years, with decreases during drought years. This suggests that a soil moisture threshold likely exists whereby irrigation suppresses convection over irrigated areas when soil moisture is extremely low and enhances convection when antecedent soil moisture is relatively high.

The rapid expansion of irrigation in the Great Plains since World War II has resulted in significant water table declines, threatening the long-term sustainability of the Ogallala Aquifer. As discussed in Part I of this paper, the Weather Research and Forecasting Model (WRF) was modified to simulate the effects of irrigation at subgrid scales. Simulations of nine April-October periods (three drought, three normal, and three pluvial) over the Great Plains were completed to assess the full impact of irrigation on the water budget. Averaged over all simulated years, irrigation over the Great Plains contributes to May-September evapotranspiration increases of approximately 4% and precipitation increases of 1%, with localized increases of up to 20%. Results from these WRF simulations are used along with a backward trajectory analysis to identify where evapotranspiration from irrigated fields falls as precipitation (i.e., irrigation-induced precipitation) and how irrigation impacts precipitation recycling. On average, only 15.8% of evapotranspiration from irrigated fields falls as precipitation over the Great Plains, resulting in 5.11 mm of May-September irrigation-induced precipitation and contributing to 6.71 mm of recycled precipitation. Reductions in nonrecycled precipitation suggest that irrigation reduces precipitation of moisture advected into the region. The heaviest irrigationinduced precipitation is coincident with simulated and observed precipitation increases, suggesting that observed precipitation increases in north-central Nebraska are strongly related to evapotranspiration of irrigated water. Water losses due to evapotranspiration are much larger than irrigation-induced precipitation and recycled precipitation increases, confirming that irrigation results in net water loss over the Great Plains.

Climate change is expected to increase the frequency of hydrological extremes, producing more droughts and heavy rainfall events globally. How warm-season precipitation extremes will change over the Central U.S. is unclear because most coarse spatial resolution global climate models inadequately simulate hydrological extremes resulting from convective precipitation. However, the higher spatial resolution from dynamical downscaling potentially enables improved projections of future changes in extreme rainfall events. In this study, we downscaled two models from the Coupled Model Intercomparison Project-Phase 5 (CMIP5) using the Weather Research and Forecasting model for one historical period (1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999), two future periods (2040)(2041)(2042)(2043)(2044)(2045)(2046)(2047)(2048)(2049)(2090)(2091)(2092)(2093)(2094)(2095)(2096)(2097)(2098)(2099) in a midrange emissions scenario (Representative Concentration Pathway (RCP) 4.5), and one period (2090-2099) in a high emissions (RCP8.5) scenario. The diurnal cycle, extremes, and averages of precipitation in historical simulations compare well with observations. While the future change in the total amount of precipitation is unclear, model simulations suggest that summer rainfall will be less frequent, but more intense when precipitation does occur. Significant intensification of the heaviest rainfall events occurs in the models, with the greatest changes in the early warm season (April). Increases in total April-July rainfall and the enhancement of extreme rainfall events in the RCP8.5 2090s are related to a stronger Great Plains Low-Level Jet (GPLLJ) during those months. Conversely, late warm-season drying over the North Central U.S. is present in nearly all future simulations, with increased drought in August-September associated with a slight weakening of the GPLLJ. Simulated trends generally increase with stronger greenhouse gas forcing.

[1] Despite supporting exceptionally productive agricultural lands, the Central U.S. is susceptible to severe droughts and floods. Such precipitation extremes are expected to worsen with climate change. However, future projections are highly uncertain as global climate models (GCMs) generally fail to resolve precipitation extremes. In this study, we assess how well models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) simulate summer means, variability, extremes, and the diurnal cycle of Central U.S. summer rainfall. Output from a subset of historical CMIP5 simulations are used to drive the Weather Research and Forecasting model to determine whether dynamical downscaling improves the representation of Central U.S. rainfall. We investigate which boundary conditions influence dynamically downscaled precipitation estimates and identify GCMs that can reasonably simulate precipitation when downscaled. The CMIP5 models simulate the seasonal mean and variability of summer rainfall reasonably well but fail to resolve extremes, the diurnal cycle, and the dynamic forcing of precipitation. Downscaling to 30 km improves these characteristics of precipitation, with the greatest improvement in the representation of extremes. Additionally, sizeable diurnal cycle improvements occur with higher (10 km) resolution and convective parameterization disabled, as the daily rainfall peak shifts 4 h closer to observations than 30 km resolution simulations. This lends greater confidence that the mechanisms responsible for producing rainfall are better simulated. Because dynamical downscaling can more accurately simulate these aspects of Central U.S. summer rainfall, policymakers can have added confidence in dynamically downscaled rainfall projections, allowing for more targeted adaptation and mitigation.Citation: Harding, K. J., P. K. Snyder, and S. Liess (2013), Use of dynamical downscaling to improve the simulation of Central U.S. warm season precipitation in CMIP5 models,