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The US Northern Great Plains and the Canadian Prairies are known as the world's breadbaskets for their large spring wheat production and exports to the world. It is essential to accurately represent spring wheat growing dynamics and final yield and improve our ability to predict food production under climate change. This study attempts to incorporate spring wheat growth dynamics into the Noah-MP crop model for a long time period (13 years) and fine spatial scale (4 km). The study focuses on three aspects: (1) developing and calibrating the spring wheat model at a point scale, (2) applying a dynamic planting and harvest date to facilitate large-scale simulations, and (3) applying a temperature stress function to assess crop responses to heat stress amid extreme heat. Model results are evaluated using field observations, satellite leaf area index (LAI), and census data from Statistics Canada and the United States Department of Agriculture (USDA). Results suggest that incorporating a dynamic planting and harvest threshold can better constrain the growing season, especially the peak timing and magnitude of wheat LAI, as well as obtain realistic yield compared to prescribing a static province/state-level map. Results also demonstrate an evident control of heat stress upon wheat yield in three Canadian Prairies Provinces, which are reasonably captured in the new temperature stress function. This study has important implications in terms of estimating crop yields, modeling the land–atmosphere interactions in agricultural areas, and predicting crop growth responses to increasing temperatures amidst climate change. [ABSTRACT FROM AUTHOR]

Aerosols are complex compounds that greatly affect the global radiation balance and climate system and even human health; in addition, aerosols are currently a large source of uncertainty in the numerical simulation process. The arid and semi-arid areas have fragile ecosystems with abundant dust but lack related high-accuracy aerosol data. To solve these problems, we use the bagging trees ensemble model, based on 1 km aerosol optical depth (AOD) data and multiple environmental covariates, to produce a monthly advanced-performance, full-coverage, and high-resolution (250 m) AOD product (named FEC AOD, fusing environmental covariates AOD) covering the arid and semi-arid areas. Then, based on the FEC AOD products, we analyzed the spatiotemporal AOD pattern and further discussed the interpretation of environmental covariates to AOD. The results show that the bagging trees ensemble model has a good performance, with its verification R2 values always remaining at 0.90 and the R2 being 0.79 for FEC AOD compared with AERONET AOD product. The high-AOD areas are located in the Taklimakan Desert and on the Loess Plateau, and the low-AOD areas are concentrated in southern Qinghai province. The higher the AOD, the stronger the interannual variability. Interestingly, the AOD reflected a dramatic decrease on the Loess Plateau and an evident increase in the south-eastern Taklimakan Desert, while the southern Qinghai province AODs showed almost no significant change between 2000 and 2019. The annual variation characteristics show that the AOD was largest in spring (0.267±0.200) and smallest in autumn (0.147±0.089); the annual AOD variation pattern showed different features, with two peaks in March and August over Gansu province but only one peak in April in other provinces/autonomous regions. Farmlands and construction lands have high AOD levels compared to other land cover types. Meteorological factors demonstrate the maximum interpretation ability of the AODs on all set temporal scales, followed by the terrain factors, while surface properties have the smallest explanatory abilities; the corresponding average contributions are 77.1 %, 59.1 %, and 50.4 %, respectively. The capability of the environmental covariates to explain the AOD varies seasonally in the following sequence: winter (86.6 %) > autumn (80.8 %) > spring (79.9 %) > summer (72.5 %). In this research, we provide a pathbreaking high spatial resolution (250 m) and long time series (2000–2019) FEC AOD dataset covering arid and semi-arid regions to support atmospheric and related studies in northwest China; the full dataset is available at 10.5281/zenodo.5727119 (Chen et al., 2021b). [ABSTRACT FROM AUTHOR]

The study and control of air pollution involves measuring the structure of the atmospheric boundary layer (ABL) to understand the mechanisms of the interactions occurring between the atmospheric boundary layer and air pollution. Beijing, the capital of China, experienced heavy haze pollution in December 2016, and the city issued its first red-alert air pollution warning of the year (the highest PM 2.5 concentrations were later found to exceed 450 µgm-3). In this paper, the vertical profiles of wind, temperature, humidity and the extinction coefficient (reflecting aerosol concentrations), as well as ABL heights and turbulence quantities under heavy haze pollution conditions, are analyzed, with data collected from lidar, wind profile radar (WPR), radiosondes, a 325 m meteorological tower (equipped with a 7-layer ultrasonic anemometer and 15-layer low-frequency wind, temperature, and humidity sensors) and ground observations. The ABL heights obtained by three different methods based on lidar extinction coefficient data (Hc) are compared with the heights calculated from radiosonde temperature data (Hθ), and their correlation coefficient can reach 72 %. Our results show that Hθ measured on heavy haze pollution days was generally lower than that measured on clean days without pollution, but Hc increased from clean to heavy pollution days. The time changes in friction velocity (u*) and turbulent kinetic energy (TKE) were clearly inversely correlated with PM 2.5 concentration. Momentum and heat fluxes varied very little with altitude. The nocturnal sensible heat fluxes close to the Earth surface always stay positive. In the daytime of the haze pollution period, sensible heat fluxes were greatly reduced within 300 m of the ground. These findings will deepen our understanding of the boundary layer structure under heavy pollution conditions and improve the boundary layer parameterization in numerical models. [ABSTRACT FROM AUTHOR]