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Land surface models are increasingly used to simulate land surface processes at hyper‐spatial resolutions (e.g., ∼1 km). As model resolution increases, grid‐scale topographic effects on surface radiation fluxes and their interactions between adjacent grids become more pronounced. However, current land surface models routinely neglect the fine‐scale topographic effects on surface radiation balance. This study developed physically‐based and computationally‐efficient parameterizations (fineTOP) that explicitly resolve fine‐scale topographic effects on downward shortwave and longwave radiation as well as land surface radiative properties. The newly developed parameterizations were implemented and tested in the Energy Exascale Earth System Model (E3SM) Land Model (ELM). Multi‐decadal km‐resolution ELM simulations over the California Sierra Nevada show that fine‐scale topography significantly impacts the surface energy balance and snow processes across seasons. Slope determines the magnitude of topographic effects, while aspect controls their sign. For slopes larger than 30°, topography‐induced change in annual surface temperature can be as large as 3.3 K. Regionally, the mean value and standard deviation of topography‐induced changes in annual surface temperature are −0.22 ± 0.38 K and +0.25 ± 0.37 K over north‐facing and south‐facing slopes, respectively. Topography‐induced changes in surface radiative properties account for 3.5% ± 13.8% of total topographic effects on annual net radiation. With fineTOP, ELM captures the aspect‐dependence of snow cover fraction, snow water equivalent, and land surface temperature found in MODIS satellite observations and a snow reanalysis data set, while the default ELM fails to capture this phenomenon. The enhanced capability to represent fine‐scale topographic effects on surface radiation balance can be used to advance understanding of the role of fine‐scale topography in land surface processes and land‐atmosphere interactions over mountainous regions. Plain Language Summary In mountainous regions, fine‐scale topographic features, like hills and valleys, can strongly impact how sunlight and thermal energy are distributed on the Earth's surface. While such fine‐scale topographic effects have been well recognized, they remain unresolved in global‐scale land surface models. We characterized the fine‐scale topographic effects using a set of pre‐computed topographic factors in a hyper‐resolution land surface model. We show that such a pre‐computation strategy is feasible in the California Sierra Nevada, and the improved model better captures the observed patterns of snow and surface temperature. Our study supports the viability of the pre‐computation strategy and builds confidence in its application for future large‐scale modeling efforts. Key Points Represent fine‐scale topographic effects on surface radiation balance in the Energy Exascale Earth System Model (E3SM) land model (ELM) Multi‐decadal km‐resolution ELM simulations with improved parameterizations capture the contrasts between north‐ and south‐facing slopes Improved ELM simulations also capture the aspect‐dependent patterns of snow and surface temperature revealed by the benchmark data sets

In recent winters, fog–haze events have occurred frequently over the North China Plain. To understand the characteristics of conventional meteorological conditions, the near-surface radiation balance, and the surface energy budget under different pollution levels, we analyzed data collected at an observation site in Gucheng, which is located in the Hebei province in North China, based on a campaign that ran from December 1 2016 to January 31 2017. We found that meteorological conditions with a lower wind speed, weakly unstable (stable) stratification, higher relative humidity, and lower surface pressure during the daytime (night-time) are associated with fog–haze events. On heavy pollution days (defined as days with a daily mean PM2.5 concentration > 150 μg m−3), the decrease in downward shortwave radiation (S↓) and the increase in downward longwave radiation (L↓) are significant. The mean S↓ (L↓) values on clean-air days (daily mean PM2.5 concentration < 75 μg m−3) and heavily polluted days was 222 (222) W m−2 and 124 (265) W m−2, respectively. Due to the negative (positive) radiative forcing of aerosols during the daytime (night-time), the daily maximum (night-time mean) net radiation (Rn) is negatively (positively) related to the daily mean PM2.5 concentration, the correlation coefficient between the daily maximum (night-time mean) Rn and daily mean PM2.5 concentration being − 0.47 (0.51). Diurnal variations in sensible heat flux (H) and latent heat flux (λE) are insignificant on heavily polluted days, the mean daily maximum H (λE) is only 40 (28) W m−2 on heavily polluted days, but reaches 90 (42) W m−2 on clean-air days. Additionally, the friction velocity, standard deviation of vertical velocity, and turbulent kinetic energy on heavily polluted days are also quantified.

Extensive urbanization around the world has resulted in the consumption of massive vegetated areas and natural resources. To this end, one strategy for urban development is to consolidate urban areas. In Balneário Camboriú/SC, Brazil, this trend has transformed the city into a vertical built-up area on its coastal strip, accommodating a large amount of buildings both in terms of quantity and number of floors. This research aims to quantify the thermo-hygrometric fluctuation on the waterfront of Balneário Camboriú, in negative radiation balance. To acquire the data on air temperature (Ta) and relative humidity (RH), two mobile transects and measuring at two fixed points were made in a situation of negative radiation balance on 26 August 2019, in the winter period of the Southern Hemisphere. The collection work began at 06:00:00 a.m. (before sunrise, the peak of the negative radiation balance), on Atlântica Avenue (waterfront) and Brasil Avenue (parallel to the waterfront). It was verified that the Ta varied from 16.0 °C to 19.0 °C, and the RH remained over 80% during the entire route. At the meteorological shelters, the temperature presented a variation from 14.4 °C to 17.7 °C, and the RH ranged from 79.6% to 91.3% between the two points. The spatial variability in the Ta and RH along the paths travelled and at the fixed points is directly related to the land cover, represented especially by the buildings’ verticalization and data collection time.

The Global Land Surface Satellite (GLASS) product suite currently contains 12 products, including leaf area index, fraction of absorbed photosynthetically active radiation, fraction of green vegetation coverage, gross primary production, broadband albedo, broadband longwave emissivity, downward shortwave radiation and photosynthetically active radiation, land surface temperature, downward and upwelling thermal radiation, all-wave net radiation, and evapotranspiration. These products are generated from the Advanced Very High Resolution Radiometer and Moderate Resolution Imaging Spectroradiometer satellite data. Their unique features include long-term temporal coverage (many from 1981 to the present), high spatial resolutions of the surface radiation products (1 km and 0.05°), spatial continuities without missing pixels, and high quality and accuracy based on extensive validation using in situ measurements and intercomparisons with other existing satellite products. Moreover, the GLASS products are based on robust algorithms that have been published in peer-reviewed literature. Herein, we provide an overview of the algorithm development, product characteristics, and some preliminary applications of these products. We also describe the next steps, such as improving the existing GLASS products, generating more climate data records (CDRs), broadening product dissemination, and fostering their wider utilization. The GLASS products are freely available to the public.

Terrestrial ecosystems are essential for food and water security and CO2 uptake. Ecosystem function is dependent on the availability of soil moisture, yet it is unclear how climate change will alter soil moisture limitation on vegetation. Here we use an ecosystem index that distinguishes energy and water limitations in Earth system model simulations to show a widespread regime shift from energy to water limitation between 1980 and 2100. This shift is found in both space and time. While this is mainly related to a reduction in energy-limited regions associated with increasing incoming shortwave radiation, the largest shift towards water limitation is found in regions where incoming shortwave radiation increases are accompanied by soil moisture decreases. We therefore demonstrate a widespread regime shift in ecosystem function that is stronger than implied by individual trends in incoming shortwave radiation, soil moisture and terrestrial evaporation, with important implications for future ecosystem services.Climate change is expected to impact moisture supply, which is critical for production of food and carbon uptake by terrestrial ecosystems. A shift from ecosystem energy to water limitation is predicted between 1980 and 2100, with implications for ecosystem function under climate change.

Soil moisture plays a crucial role for the energy partitioning at Earth’s surface. Changing fractions of latent and sensible heat fluxes caused by soil moisture variations can affect both near-surface air temperature and precipitation. In this study, a simple framework for the dependence of evaporative fraction (the ratio of latent heat flux over net radiation) on soil moisture is used to analyze spatial and temporal variations of land–atmosphere coupling and its effect on near-surface air temperature. Using three different data sources (two reanalysis datasets and one combination of different datasets), three key parameters for the relation between soil moisture and evaporative fraction are estimated: 1) the frequency of occurrence of different soil moisture regimes, 2) the sensitivity of evaporative fraction to soil moisture in the transitional soil moisture regime, and 3) the critical soil moisture value that separates soil moisture-and energy-limited evapotranspiration regimes. The results show that about 30%–60% (depending on the dataset) of the global land area is in the transitional regime during at least half of the year. Based on the identification of transitional regimes, the effect of changes in soil moisture on near-surface air temperature is analyzed. Typical soil moisture variations (standard deviation) can impact air temperature by up to 1.1–1.3 K, while changing soil moisture over its full range in the transitional regime can alter air temperature by up to 6–7 K. The results emphasize the role of soil moisture for atmosphere and climate and constitute a useful benchmark for the evaluation of the respective relationships in Earth system models.

Radiation‐induced lung injury (RILI) is one of the most common and fatal complications of thoracic radiotherapy. It is characterized with two main features including early radiation pneumonitis and fibrosis in later phase. This study was to investigate the potential radioprotective effects of polydatin (PD), which was shown to exert anti‐inflammation and anti‐oxidative capacities in other diseases. In this study, we demonstrated that PD‐mitigated acute inflammation and late fibrosis caused by irradiation. PD treatment inhibited TGF‐β1‐Smad3 signalling pathway and epithelial–mesenchymal transition. Moreover, radiation‐induced imbalance of Th1/Th2 was also alleviated by PD treatment. Besides its free radical scavenging capacity, PD induced a huge increase of Sirt3 in culture cells and lung tissues. The level of Nrf2 and PGC1α in lung tissues was also elevated. In conclusion, our data showed that PD attenuated radiation‐induced lung injury through inhibiting epithelial–mesenchymal transition and increased the expression of Sirt3, suggesting PD as a novel potential radioprotector for RILI.

Utility-scale solar power plants are a rapidly growing component of the renewable energy sector. While most agree that solar power can decrease greenhouse gas emissions, the effects of photovoltaic (PV) systems on surface energy exchanges and near-surface meteorology are not well understood. This study presents data from two eddy covariance observational towers, placed within and adjacent to a utility-scale PV array in southern Arizona. The observational period (October 2017–July 2018) includes the full range of annual temperature variation. Average daily maximum 1.5-m air temperature at the PV array was 1.3°C warmer than the reference (i.e., non-PV) site, whereas no significant difference in 1.5-m nocturnal air temperature was observed. PV modules captured the majority of solar radiation and were the primary energetically active surface during the day. Despite the removal of energy by electricity production, the modules increased daytime net radiation Q* available for partitioning by reducing surface albedo. The PV modules shift surface energy balance partitioning away from upward longwave radiation and heat storage and toward sensible heat flux QH because of their low emissivity, low heat capacity, and increased surface area and roughness, which facilitates more efficient QH from the surface. The PV modules significantly reduce ground heat flux QG storage and nocturnal release, as the soil beneath the modules is well shaded. Our work demonstrates the importance of targeted observational campaigns to inform process-based understanding associated with PV systems. It further establishes a basis for observationally based PV energy balance models that may be used to examine climatic effects due to large-scale deployment.

The presence of microplastics and nanoplastics (MnPs) in the atmosphere and their transport on a global scale has previously been demonstrated. However, little is known about their environmental impacts in the atmosphere. MnPs could act as cloud condensation nuclei (CCN) or ice-nucleating particles (INPs), affecting cloud formation processes. In sufficient quantities, they could change the cloud albedo, precipitation and lifetime, collectively impacting the Earth’s radiation balance and climate. In this Perspective, we evaluate the potential impact of MnPs on cloud formation by assessing their ability to act as CCN or INPs. Based on an analysis of their physicochemical properties, we propose that MnPs can act as INPs and potentially as CCN after environmental aging processes such as photochemical weathering and the sorption of macromolecules or trace soluble species onto the particle surface. The actual climate impact(s) of MnPs depend on their abundance relative to other aerosols. The concentration of MnPs in the atmosphere is currently low, so they are unlikely to make a substantial contribution to radiative forcing in regions exposed to other aerosols, either from natural sources or anthropogenic pollution. Nevertheless, MnPs will potentially cause non-negligible perturbations in unpolluted remote or marine clouds and generate local climate impacts, particularly in view of an increase in the release of MnPs to the environment in the future. Further measurements, coupled with better characterization of the physiochemical properties of MnPs, will enable a more accurate assessment of the climate impacts of MnPs acting as INPs and CCN. Microplastics and nanoplastics may affect cloud formation processes by acting as ice-nucleating particles and cloud condensation nuclei.

Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R2 < 0.5), ecosystem respiration (R2 > 0.6), gross primary production (R2> 0.7), latent heat (R2 > 0.7), sensible heat (R2 > 0.7), and net radiation (R2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.