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A regional climate model (RCM) is being developed for U.S. Midwest applications on the basis of the newly released Pennsylvania State University–NCAR Fifth-Generation Mesoscale Model (MM5), version 3.3. This study determines the optimal RCM domain and effective data assimilation technique to accurately integrate lateral boundary conditions (LBCs) across the buffer zones. The LBCs are constructed from both the NCEP–NCAR and ECMWF reanalyses to depict forcing uncertainties. The RCM domain was chosen to correctly represent the governing physical processes while minimizing LBC errors. Sensitivity experiments are conducted for the Midwest 1993 summer flood to investigate buffer zone treatment impacts on RCM performance. The results demonstrate the superiority of the buffer zone treatment that consists of the physically based domain choice and revised assimilation technique. Given this treatment, the RCM realistically simulates both temporal variations and spatial distributions in the major flood area (MFA). This success is identified with the accurate representation of both the midlatitude upper-level jet stream and Great Plains low-level jet (LLJ). The RCM reproduces different climate regimes, where observed rainfall was identified with the periodic (5 day) passage of midlatitude cyclones in June and persistent synoptic circulations in July. The model also correctly simulates the MFA rainfall diurnal cycle (with the peak amount at 0900 UTC), which follows the LLJ cycle by approximately 3 h. On the other hand, RCM performance is substantially reduced when the southern buffer zone extends to the Tropics, where large forcing errors exist. In particular, the RCM generates a weaker LLJ and, as a consequence, a decreased amount and delayed diurnal cycle of the MFA rainfall. In addition, the MM5 default LBC data assimilation technique produces considerable model biases, whereas the revised technique improves overall RCM performance and reduces sensitivi... [ABSTRACT FROM AUTHOR]

Perennial grasses can reduce soil erosion, restore carbon stocks, and provide feedstocks for biofuels and bioproducts. Here, we show an additional benefit, amelioration of regional climate warming, and drying. Growing Miscanthus × giganteus, an example of perennial biomass crops, on US marginal land cools the Midwest Heartland summer by up to 1°C as predicted by a new coupled climate‐crop modeling system. This cooling is mainly caused by the increased duration and size of the Miscanthus × giganteus leaf canopy when compared with the existing vegetations on marginal land, resulting in larger solar reflection, more evapotranspiration, and decreased sensible heat transfer. Summer rainfall is increased through mesoscale circulation responses by 23–29 mm (14%–15%) and water vapor pressure deficit reduced by 5%–13%, lowering potential transpiration for all Midwest crops. Similar but weaker effects are simulated in the Southern Heartland. This positive feedback through the climate–crop interaction and teleconnection leads to 4%–8% more biomass production and potentially 12% higher corn and soybean yields, with greater yield stability. Growing perennials on marginal land could be a feasible solution to climate change mitigation and adaptation by strengthening food security and providing sustainable alternatives to fossil‐based products. [ABSTRACT FROM AUTHOR]

Bioenergy with carbon capture and geological storage (BECCS) is considered one of the top options for both offsetting CO2 emissions and removing atmospheric CO2. BECCS requires using limited land resources efficiently while ensuring minimal adverse impacts on the delicate food‐energy‐water nexus. Perennial C4 biomass crops are productive on marginal land under low‐input conditions avoiding conflict with food and feed crops. The eastern half of the contiguous U.S. contains a large amount of marginal land, which is not economically viable for food production and liable to wind and water erosion under annual cultivation. However, this land is suitable for geological CO2 storage and perennial crop growth. Given the climate variation across the region, three perennials are major contenders for planting. The yield potential and stability of Miscanthus, switchgrass, and energycane across the region were compared to select which would perform best under the recent (2000–2014) and future (2036–2050) climates. Miscanthus performed best in the Midwest, switchgrass in the Northeast and energycane in the Southeast. On average, Miscanthus yield decreased from present 19.1 t/ha to future 16.8 t/ha; switchgrass yield from 3.5 to 2.4 t/ha; and energycane yield increased from 14 to 15 t/ha. Future yield stability decreased in the region with higher predicted drought stress. Combined, these crops could produce 0.6–0.62 billion tonnes biomass per year for the present and future. Using the biomass for power generation with CCS would capture 703–726 million tonnes of atmospheric CO2 per year, which would offset about 11% of current total U.S. emission. Further, this biomass approximates the net primary CO2 productivity of two times the current baseline productivity of existing vegetation, suggesting a huge potential for BECCS. Beyond BECCS, C4 perennial grasses could also increase soil carbon and provide biomass for emerging industries developing replacements for non‐renewable products including plastics and building materials. [ABSTRACT FROM AUTHOR]

Although the Soil and Water Assessment Tool (SWAT) model has been widely used to assess the environmental impacts of growing perennial grasses for bioenergy production, its utility is limited by not explicitly accounting for shoot and root biomass development. In this study, we integrated the DAYCENT model's grass growth algorithms into SWAT (SWAT–GRASS D) and further modified it by considering the impact of leaf area index (LAI) on potential biomass production (SWAT–GRASS M). Based on testing at eight sites in the US Midwest, we found that SWAT–GRASS M generally outperformed SWAT and SWAT–GRASS D in simulating switchgrass biomass yield and the seasonal development of LAI. Additionally, SWAT–GRASS M can more realistically represent root development, which is key for the allocation of accumulated biomass and nutrients between aboveground and belowground biomass pools. These improvements are critical for credible assessment of agronomic and environmental impacts of growing perennial grasses for biomass production. • We developed the SWAT-GRASS model by integrating the grass growth module from the DAYCENT model into SWAT. • We further enhanced SWAT-GRASS to better represent the effects of LAI on biomass accumulation. • The new model achieved better simulation of switchgrass yield across multiple sites. • The new model can explicitly and reasonably represent root and shoot development of switchgrass. • A new tool for comprehensive assessment of agronomic and environmental impacts of growing switchgrass. [ABSTRACT FROM AUTHOR]