Terrestrial gross primary productivity (GPP) varies greatly over time and space. A better understanding of this variability is necessary for more accurate predictions of the future climate-carbon cycle feedback. Recent studies have suggested that variability in GPP is driven by a broad range of biotic and abiotic factors operating mainly through changes in vegetation phenology and physiological processes. However, it is still unclear how plant phenology and physiology can be integrated to explain the spatiotemporal variability of terrestrial GPP. Based on analyses of eddy-covariance and satellite-derived data, we decomposed annual terrestrial GPP into the length of the CO 2 uptake period (CUP) and the seasonal maximal capacity of CO 2 uptake (GPP max ). The product of CUP and GPP max explained >90% of the temporal GPP variability in most areas of North America during 2000-2010 and the spatial GPP variation among globally distributed eddy flux tower sites. It also explained GPP response to the European heatwave in 2003 (r 2 = 0.90) and GPP recovery after a fire disturbance in South Dakota (r 2 = 0.88). Additional analysis of the eddy-covariance flux data shows that the interbiome variation in annual GPP is better explained by that in GPP max than CUP. These findings indicate that terrestrial GPP is jointly controlled by ecosystem-level plant phenology and photosynthetic capacity, and greater understanding of GPP max and CUP responses to environmental and biological variations will, thus, improve predictions of GPP over time and space. ecosystem carbon uptake | growing season length | photosynthetic capacity | spatiotemporal variability | climate extreme L arge variability exists among estimates of terrestrial carbon sequestration, resulting in substantial uncertainty in modeled dynamics of atmospheric CO 2 concentration and predicted future climate change (1). The variability in carbon sequestration is partially caused by variation in terrestrial gross primary productivity (GPP) (2), which is the cumulative rate over time of gross plant Significance Terrestrial gross primary productivity (GPP), the total photosynthetic CO 2 fixation at ecosystem level, fuels all life on land. However, its spatiotemporal variability is poorly understood, because GPP is determined by many processes related to plant phenology and physiological activities. In this study, we find that plant phenological and physiological properties can be integrated in a robust index-the product of the length of CO 2 uptake period and the seasonal maximal photosynthesis-to explain the GPP variability over space and time in response to climate extremes and during recovery after disturbance.

Various environmental factors influence the outbreak and spread of epidemic or even pandemic events which, in turn, may cause feedbacks on the environment. The novel coronavirus disease (COVID-19) was declared a pandemic on 13 March 2020 and its rapid onset, spatial extent and complex consequences make it a once-in-a-century global disaster. Most countries responded by social distancing measures and severely diminished economic and other activities. Consequently, by the end of April 2020, the COVID-19 pandemic has led to numerous environmental impacts, both positive such as enhanced air and water quality in urban areas, and negative, such as shoreline pollution due to the disposal of sanitary consumables. This study presents an early overview of the observed and potential impacts of the COVID-19 on the environment. We argue that the effects of COVID-19 are determined mainly by anthropogenic factors which are becoming obvious as human activity diminishes across the planet, and the impacts on cities and public health will be continued in the coming years.

a b s t r a c tAll-wave net surface radiation is greatly needed in various scientific research and applications. Satellite data have been used to estimate incident shortwave radiation, but hardly to estimate all-wave net radiation due to the inference of clouds on longwave radiation. A practical solution is to estimate all-wave net radiation empirically from shortwave radiation and other ancillary information. Since existing models were developed using a limited number of ground observations, a comprehensive evaluation of these models using a global network of representative measurements is urgently required. In this study, we developed a new day-time net radiation estimation model and evaluated it against seven commonly used existing models using radiation measurements obtained from 326 sites around the world from 1991 to 2010. MERRA re-analysis products from which the meteorological data were derived and remotely sensed products during the same period were also used. Model evaluations were performed in both global mode (all data were used to fit the models) and conditional mode (the data were divided into four subsets based on the surface albedo and vegetation index, and the models were fitted separately). Besides, the factors (i.e., albedo, air temperature, and NDVI) that may impact the estimation of all-wave net radiation were also extensively explored. Based on these evaluations, the fitting RMSE of the new developed model was approximately 40.0 Wm −2 in the global mode and varied between 18.2 and 54.0 Wm −2 in the conditional mode. We found that it is better to use net shortwave radiation (including surface albedo) than the incident shortwave radiation nearly in all models. Overall, the new model performed better than other existing linear models.

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Abstract. Microbial aerosols (mainly composed of bacterial and fungal cells) may constitute up to 74 % of the total aerosol volume. These biological aerosols are not only relevant to the dispersion of pathogens, but they also have geochemical implications. Some bacteria and fungi may, in fact, serve as cloud condensation or ice nuclei, potentially affecting cloud formation and precipitation and are active at higher temperatures compared to their inorganic counterparts. Simulations of the impact of microbial aerosols on climate are still hindered by the lack of information regarding their emissions from ground sources. This present work tackles this knowledge gap by (i) applying a rigorous micrometeorological approach to the estimation of microbial net fluxes above a Mediterranean grassland and (ii) developing a deterministic model (the PLAnET model) to estimate these emissions on the basis of a few meteorological parameters that are easy to obtain. The grassland is characterized by an abundance of positive net microbial fluxes and the model proves to be a promising tool capable of capturing the day-to-day variability in microbial fluxes with a relatively small bias and sufficient accuracy. PLAnET is still in its infancy and will benefit from future campaigns extending the available training dataset as well as the inclusion of ever more complex and critical phenomena triggering the emission of microbial aerosol (such as rainfall). The model itself is also adaptable as an emission module for dispersion and chemical transport models, allowing further exploration of the impact of landcover-driven microbial aerosols on the atmosphere and climate.