Satellite data reveal widespread changes in Earth’s vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change, and consequent disturbances such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at the biome level that can be scaled to a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies for vegetation changes and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying causal counterfactual theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). The probabilistic attribution method clearly identifies the CO2 fertilization effect as the dominant driver in only two biomes, the temperate forests and cool grasslands, challenging the view of a dominant global-scale effect. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last 2 decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slowdown in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century.

We synthesized N2O emissions over North America using 17 bottom-up (BU) estimates from 1980-2016 and five top-down (TD) estimates from 1998-2016. The BU-based total emission shows a slight increase owing to U.S. agriculture, while no consistent trend is shown in TD estimates. During 2007-2016, North American N2O emissions are estimated at 1.7 (1.0-3.0) Tg N yr-1 (BU) and 1.3 (0.9-1.5) Tg N yr-1 (TD). Anthropogenic emissions were twice larger than natural fluxes from soil and water. Direct agricultural and industrial activities accounted for 68% of total anthropogenic emissions, 71% of which was contributed by the U.S. Our estimates of U.S. agricultural emissions are comparable to the EPA greenhouse gas (GHG) inventory, which includes estimates from IPCC tier 1 (emission factor) and tier 3 (process-based modeling) approaches. Conversely, our estimated agricultural emissions for Canada and Mexico are twice as large as the respective national GHG inventories based on tier 1 approaches.

Abiotic and biotic releases of nitrous acid (HONO) from soils contribute substantially to the missing source of tropospheric HONO and hydroxyl radicals (OH). However, global and regional patterns of soil HONO emissions are rarely quantified, and the contributions of such emissions to atmospheric oxidization capacity are unclear. Here, we present that the best estimate of global soil HONO emissions in 2017 is 9.67 with a range of 7.36-11.99 Tg N yr⁻1, where cropland soils accounted for ~ 79%. The analyses also indicate that regional soil HONO emissions enhanced ground OH concentrations by 10-60% and ozone concentrations by 0.5-1.5 ppb at daytime in the ambient area of Shanghai, China. The impact of soil HONO emissions on OH budgets were more important in rural than urban areas. These findings suggest that the global soil HONO emissions, especially from cropland, could quicken photochemical reactions and aggravate air pollution in rural areas.

A key to better constraining estimates of the ocean sink for fossil fuel emissions of carbon dioxide is reducing uncertainties in coastal carbon fluxes. A contributing factor in uncertainties in coastal carbon fluxes stems from the under sampling of seasonality and spatial heterogeneity. Our objectives were to i) assess satellite-based approaches that would expand the spatial and temporal coverage of the surface ocean pCO2 and sea-air CO2 flux for the northern Gulf of Mexico, and ii) investigate the seasonal and interannual variations in CO2 dynamics and possible environmental drivers. Regression tree analysis was effective in directly relating surface ocean pCO2 to satellite-retrieved (MODIS Aqua) products including chlorophyll, sea surface temperature, and dissolved/detrital absorption. Satellite-based assessments of sea surface pCO2 were made spanning the period from 2006-2010 and were used in conjunction with estimates of wind fields and atmospheric pCO2 to produce regional-scale estimates of air-sea fluxes. Seasonality was evident in air-sea fluxes of CO2, with an estimated annual average CO2 flux for the study region of -4.3 + 1.1 Tg C y-1, confirming prior findings that the Gulf of Mexico was a net CO2 sink. Interannual variability in fluxes was related to Mississippi River dissolved inorganic nitrogen inputs, an indication that human- and climate-related changes in river exports will impact coastal carbon budgets. This is the first multi-year assessment of pCO2 and air-sea flux of CO2 using satellite-derived environmental data for the northern Gulf of Mexico.

The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one-third of anthropogenic CO2 emissions during the 1959-2019 period. This sink-estimate is produced by an ensemble of terrestrial biosphere models collectively referred to as the TRENDY ensemble and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well TRENDY models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation-based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference datasets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter-model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties.