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The spatial heterogeneity related to complex topography in California demands high-resolution (< 5 km) modeling, but global convection-permitting climate models are computationally too expensive to run multi-decadal simulations. We developed a 3.25 km California climate modeling framework by leveraging regional mesh refinement (CARRM) using the U.S. Department of Energy (DOE)'s global Simple Cloud-Resolving E3SM Atmosphere Model (SCREAM) version 0. Four 5-year time periods (2015–2020, 2029–2034, 2044–2049, and 2094–2099) were simulated by nudging CARRM outside California to 1° coupled simulation of E3SMv1 under the Shared Socioeconomic Pathways (SSP)5-8.5 future scenario. The 3.25 km grid spacing adds considerable value to the prediction of the California climate changes, including more realistic high temperatures in the Central Valley and much improved spatial distributions of precipitation and snowpack in the Sierra Nevada and coastal stratocumulus. Under the SSP5-8.5 scenario, CARRM simulation predicts widespread warming of 6–10 °C over most of California, a 38 % increase in statewide average 30 d winter–spring precipitation, a near-complete loss of the alpine snowpack, and a sharp reduction in shortwave cloud radiative forcing associated with marine stratocumulus by the end of the 21st century. We note a climatological wet precipitation bias for the CARRM and discuss possible reasons. We conclude that SCREAM RRM is a technically feasible and scientifically valid tool for climate simulations in regions of interest, providing an excellent bridge to global convection-permitting simulations. [ABSTRACT FROM AUTHOR]

Exploring the intensive planting scheme to improve the agricultural productivity of coastal salinized farmland, the field positioning trial conducted in Kenli district, Shandong province was carried out since 2015, and 4 treatments were set up by considering fertilizer application amount, fertilizer application period, planting density, soil amendment and other measures including farmer's practice (FP), improved soil management (ISM), improved crops management (ICM), integrated soil crop management (ISCM). Track the population dynamic changes in the key growth period of wheat, analyzed the dry matter accumulation and nitrogen, phosphorus and potassium nutrient absorption of wheat and maize during harvest, investigated the changes of wheat and maize yield and yield components, and compared the net income of crop production, soil sodium removal rate and sodium adsorption ratio in the 0--20 cm soil layer after harvest. The results showed that compared with FP treatment, ISM significantly increased the number of ears during wheat and maize harvest, resulting in a 25.2% and 10.0% increase in yield, and a 7.8% increase in net income, respectively. In addition, the soil sodium removal rate of ISM treatment reached 78.6%. The ICM treatment significantly increased the dry matter accumulation of wheat and maize, with a yield increase of 17.8% and 10.7%, respectively, compared to the FP treatment, and the net income increase of 19.3% . The ISCM treatment obtained the highest wheat yield of 11.96 and 10.83 t · hm-2, respectively, the net income increased by 20.8% compared to the FP treatment, and the soil sodium removal rate reached 82.9%. The ISM treatment could significantly reduce the sodium ion content of salinized soil, improve the nutrient absorption capacity of wheat and maize, and increase yield. Although the yield increase effect of ICM treatment was lower than that of ISM treatment, lower input cost could obtain higher net benefit. On the whole, the ISCM treatment could simultaneously achieve soil quality improvement, crop yield increase and economic benefits, which was the optimal management practice scheme under the conditions of this study. This research result could provide technical and theoretical support and demonstration models for improving the production capacity of medium to low yield fields in the region. [ABSTRACT FROM AUTHOR]

The spatial heterogeneity related to complex topography in California demands high-resolution (<5 km) modeling, but global convection-permitting climate models are computationally too expensive to run multi-decadal simulations. We developed a 3.25 km California regionally refined model (CARRM) using the U.S. Department of Energy's (DOE) global Simple Cloud Resolution E3SM Atmospheric Model (SCREAM) version 0. Four 5-wateryear time periods (2015–2020, 2029–2034, 2044–2049, 2094–2099) were simulated by nudging CARRM outside California to 1° coupled simulation of E3SMv1 under the SSP5-8.5 future scenario. The 3.25 km grid spacing adds considerable value to the prediction of the California climate changes, including more realistic high temperatures in the Central Valley, much improved spatial distributions of precipitation and snow in the Sierra Nevada and coastal stratocumulus. Under the SSP5-8.5 scenario, CARRM simulation predicts widespread warming of 6–10 °C over most of California, a 38 % increase in statewide average 30-day winter-spring precipitation, a near complete loss of the alpine snowpack, and a sharp reduction in shortwave cloud radiative forcing associated with marine stratocumulus by the end of the 21st century. We note a climatological wet precipitation bias for the CARRM and discuss possible reasons. We conclude that SCREAM-RRM is a technically feasible and scientifically valid tool for climate simulations in regions of interest, providing an excellent bridge to global convection-permitting simulations. [ABSTRACT FROM AUTHOR]

In this paper, the safe optimal control method for continuous-time (CT) nonlinear safety-critical systems with asymmetric input constraints and unmatched disturbances based on the adaptive dynamic programming (ADP) is investigated. Initially, a new non-quadratic form function is implemented to effectively handle the asymmetric input constraints. Subsequently, the safe optimal control problem is transformed into a two-player zero-sum game (ZSG) problem to suppress the influence of unmatched disturbances, and a new Hamilton–Jacobi–Isaacs (HJI) equation is introduced by integrating the control barrier function (CBF) with the cost function to penalize unsafe behavior. Moreover, a damping factor is embedded in the CBF to balance safety and optimality. To obtain a safe optimal controller, only one critic neural network (CNN) is utilized to tackle the complex HJI equation, leading to a decreased computational load in contrast to the utilization of the conventional actor–critic network. Then, the system state and the parameters of the CNN are uniformly ultimately bounded (UUB) through the application of the Lyapunov stability method. Lastly, two examples are presented to confirm the efficacy of the presented approach. [ABSTRACT FROM AUTHOR]