Global critical soil moisture thresholds of plant water stress.

During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θcrit). Better quantification of θcrit is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θcrit. We find an average global θcrit of 0.19 m3/m3, varying from 0.12 m3/m3 in arid ecosystems to 0.26 m3/m3 in humid ecosystems. θcrit simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θcrit reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θcrit, has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points.

A Gradient-Descent Optimization of CO2-CO-NOx Emissions over the Paris Megacity─The Case of the First SARS-CoV-2 Lockdown.

Urban greenhouse gas emissions monitoring is essential to assessing the impact of climate mitigation actions. Using atmospheric continuous measurements of air quality and carbon dioxide (CO2), we developed a gradient-descent optimization system to estimate emissions of the city of Paris. We evaluated our joint CO2-CO-NOx optimization over the first SARS-CoV-2 related lockdown period, resulting in a decrease in emissions by 40% for NOx and 30% for CO2, in agreement with preliminary estimates using bottom-up activity data yet lower than the decrease estimates from Bayesian atmospheric inversions (50%). Before evaluating the model, we first provide an in-depth analysis of three emission data sets. A general agreement in the totals is observed over the region surrounding Paris (known as Île-de-France) since all the data sets are constrained by the reported national and regional totals. However, the data sets show disagreements in their sector distributions as well as in the interspecies ratios. The seasonality also shows disagreements among emission products related to nonindustrial stationary combustion (residential and tertiary combustion). The results presented in this paper show that a multispecies approach has the potential to provide sectoral information to monitor CO2 emissions over urban areas enabled by the deployment of collocated atmospheric greenhouse gases and air quality monitoring stations.