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Abstract This study explores the impact of coupling cumulus and planetary boundary layer (PBL) parameterizations on diurnal precipitation forecasting during the plum rainy season in Jiangsu Province, China, using a double grid‐nesting approach. Results show that coherent coupling of cumulus (only in the 15 km grid outer domain [O]) and PBL parameterizations leads to improved forecasting of diurnal variations in the morning, afternoon, and the evening. Increasing the frequency of the Kain‐Fritsch (KF) cumulus scheme in [O] enhances subgrid precipitation while reducing grid‐scale precipitation, resulting in a more accurate representation of daytime convective activities and a reduction in over‐forecasting of evening valley and early‐morning precipitation. Additionally, coupling a suitable PBL scheme mitigates the overpredicted afternoon peak by facilitating turbulent mixing to penetrate higher altitudes with a thicker layer, thereby reducing instability energy accumulation. A higher KF frequency in [O] retains less low tropospheric moisture, reducing moisture convergence into the 1 km grid inner domain [I] and decreasing overpredicted daytime precipitation in [I]. Various PBL schemes produce distinct vertical distributions of turbulent moisture and heat transport, impacting convection and precipitation in [I] resolved by cloud microphysics processes. The coherent coupling of these parameterizations maintains a balanced supply of convective energy and water vapor, significantly improving diurnal precipitation forecasts in [I]. Isolating these parameterizations between nested grids may undermine this improvement. 

Abstract This study investigates skill enhancement in operational seasonal forecasts of Beijing Climate Center’s Climate System Model through regional Climate–Weather Research and Forecasting (CWRF) downscaling and improved land initialization in China. The downscaling mitigates regional climate biases, enhancing precipitation pattern correlations by 0.29 in spring and 0.21 in summer. It also strengthens predictive capabilities for interannual anomalies, expanding skillful temperature forecast areas by 6% in spring and 12% in summer. Remarkably, during 7 of 10 years with relatively high predictability, the downscaling increases average seasonal precipitation anomaly correlations by 0.22 and 0.25. Additionally, the substitution of initial land conditions via a Common Land Model integration reduces snow cover and cold biases across the Tibetan Plateau and Mongolia–northeast China, consistently contributing to CWRF’s overall enhanced forecasting capabilities. Improved downscaling predictive skill is attributed to CWRF’s enhanced physics representation, accurately capturing intricate regional interactions and associated teleconnections across China, especially linked to the Tibetan Plateau’s blocking and thermal effects. In summer, CWRF predicts an intensified South Asian high alongside a strengthened East Asian jet compared to CSM, amplifying cold air advection and warm moisture transport over central to northeast regions. Consequently, rainfall distributions and interannual anomalies over these areas experience substantial improvements. Similar enhanced circulation processes elucidate skill improvement from land initialization, where the accurate specification of initial snow cover and soil temperature within sensitive regions persists in influencing local and remote circulations extending beyond two seasons. Our findings emphasize the potential of improving physics representation and surface initialization to markedly enhance regional climate predictions. 

Abstract Bioenergy with carbon capture and geological storage (BECCS) is considered one of the top options for both offsetting CO₂emissions 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 CO₂storage 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 CO₂per year, which would offset about 11% of current total U.S. emission. Further, this biomass approximates the net primary CO₂productivity 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 Climate change is impacting global crop productivity, and agricultural land suitability is predicted to significantly shift in the future. Responses to changing conditions and increasing yield variability can range from altered management strategies to outright land use conversions that may have significant environmental and socioeconomic ramifications. However, the extent to which agricultural land use changes in response to variations in climate is unclear at larger scales. Improved understanding of these dynamics is important since land use changes will have consequences not only for food security but also for ecosystem health, biodiversity, carbon storage, and regional and global climate. In this study, we combine land use products derived from the Moderate Resolution Imaging Spectroradiometer with climate reanalysis data from the European Centre for Medium-Range Weather Forecasts Reanalysis v5 to analyze correspondence between changes in cropland and changes in temperature and water availability from 2001 to 2018. While climate trends explained little of the variability in land cover changes, increasing temperature, extreme heat days, potential evaporation, and drought severity were associated with higher levels of cropland loss. These patterns were strongest in regions with more cropland change, and generally reflected underlying climate suitability—they were amplified in hotter and drier regions, and reversed direction in cooler and wetter regions. At national scales, climate response patterns varied significantly, reflecting the importance of socioeconomic, political, and geographic factors, as well as differences in adaptation strategies. This global-scale analysis does not attempt to explain local mechanisms of change but identifies climate-cropland patterns that exist in aggregate and may be hard to perceive at local scales. It is intended to supplement regional studies, providing further context for locally-observed phenomena and highlighting patterns that require further analysis. 

Climate anomalies and changes have complex and critical impacts on agriculture. Given global warming, the scientific community has dramatically increased research on these impacts. During 1996–2022, over 3,000 peer-reviewed papers in the Web of Science Core Collection database have investigated the fields. This study conducted a bibliometric analysis of these papers for systematic mapping and inductive understanding to comprehensively review the research’s status, focus, network, and funding. After almost 30 years, the research is now centered in quantifying climate impacts on crop yields and agriculture productivity while seeking effective adaptation solutions. The hot keywords recently emerged include poverty, food security, water resource, climate service, climate-smart agriculture, sustainability, and policy. They suggest increasing concerns on global food and water shortage and pressing needs for action to adapt to climate change and sustain agricultural productivity. Given the uncertainty of climate change and the complexity of agriculture systems, most current studies are interdisciplinary research combining various agricultural fields with climate, environmental, and socioeconomic sciences. The United States, as the world’s leading food commodity producer, has the most diverse funding agencies and provides the largest number of awards to support the research. Future priority research should take the coupled earth system approach with the food-energy-water nexus principles to provide effective, actionable decision supports at local-regional scales to sustain national agricultural productivity and quantify climate-smart agricultural practices to mitigate global warming. 

Groundwater use for irrigation has a major influence on agricultural productivity and local water resources. This study evaluated the groundwater irrigation schemes, SWAT auto-irrigation scheduling based on plant water stress (Auto-Irr), and prescribed irrigation based on well pumping rates in MODFLOW (Well-Irr), in the U.S. Northern High Plains (NHP) aquifer using coupled SWAT-MODFLOW model simulations for the period 1982–2008. Auto-Irr generally performed better than Well-Irr in simulating groundwater irrigation volume (reducing the mean bias from 86 to −30%) and groundwater level (reducing the normalized root-mean-square-error from 13.55 to 12.47%) across the NHP, as well as streamflow interannual variations at two stations (increasing NSE from 0.51, 0.51 to 0.55, 0.53). We also examined the effects of groundwater irrigation on the water cycle. Based on simulation results from Auto-Irr, historical irrigation led to significant recharge along the Elkhorn and Platte rivers. On average over the entire NHP, irrigation increased surface runoff, evapotranspiration, soil moisture and groundwater recharge by 21.3%, 4.0%, 2.5% and 1.5%, respectively. Irrigation improved crop water productivity by nearly 27.2% for corn and 23.8% for soybean. Therefore, designing sustainable irrigation practices to enhance crop productivity must consider both regional landscape characteristics and downstream hydrological consequences.