Live woody vegetation is the largest reservoir of biomass carbon, with its restoration considered one of the most effective natural climate solutions. However, terrestrial carbon fluxes remain the largest uncertainty in the global carbon cycle. Here, we develop spatially explicit estimates of carbon stock changes of live woody biomass from 2000 to 2019 using measurements from ground, air, and space. We show that live biomass has removed 4.9 to 5.5 PgC year−1 from the atmosphere, offsetting 4.6 ± 0.1 PgC year−1 of gross emissions from disturbances and adding substantially (0.23 to 0.88 PgC year−1) to the global carbon stocks. Gross emissions and removals in the tropics were four times larger than temperate and boreal ecosystems combined. Although live biomass is responsible for more than 80% of gross terrestrial fluxes, soil, dead organic matter, and lateral transport may play important roles in terrestrial carbon sink.

[1] This study is concerned with the quantitative prediction of dust storms in real time. An integrated wind erosion modeling system is used for 24-, 48-, and 72-hour forecasts of northeast Asian dust events for March and April 2002. The predictions are validated with synoptic records from the meteorological network and dust concentration measurements at 12 stations in China, Japan, and Korea. The predicted spatial patterns and temporal evolutions of dust events and the predicted near-surface dust concentrations are found to agree well with the observations. The validation confirms the capacity of the modeling system in quantitative forecasting of dust events in real time. On the basis of the predictions, dust activities in northeast Asia are examined using quantities such as dust emission, deposition, and load. During an individual dust episode, dust sources and intensities vary in space and time, but on average the Gobi Desert, the Hexi (Yellow River West) Corridor, the Chaidam Basin, the Tulufan Basin, and the fringes of the Talimu and Zhunge'er Basins are identified to be the main source regions. The Gobi Desert is the strongest dust source, where the maximum dust emission reaches 5000 mg m À2 s À1 and the net dust emission reaches 16 t km À2 d À1 in March and April 2002. Net dust deposition covers a large area, with the Loess Plateau receiving about 1.6 to 4.3 t km À2 d À1. A zone of high dust load exists along the northern boundary of the Tibet Plateau, with a maximum of around 2 t km À2 situated over the Gobi Desert. The total dust emission, total dust deposition, and total dust load for the domain of the simulation are estimated. The average (maximum) total dust emission is 11.5 Â 10 6 (65.7 Â 10 6 ) t d À1 , the average (maximum) total dust deposition is 10.8 Â 10 6 (51.4 Â 10 6 ) t d À1 , and the average (maximum) total dust load is 5.5 Â 10 6 (15.9 Â 10 6 ) t.

Amazon forests have experienced frequent and severe droughts in the past two decades. However, little is known about the large-scale legacy of droughts on carbon stocks and dynamics of forests. Using systematic sampling of forest structure measured by LiDAR waveforms from 2003 to 2008, here we show a significant loss of carbon over the entire Amazon basin at a rate of 0.3 ± 0.2 (95% CI) PgC yr−1 after the 2005 mega-drought, which continued persistently over the next 3 years (2005–2008). The changes in forest structure, captured by average LiDAR forest height and converted to above ground biomass carbon density, show an average loss of 2.35 ± 1.80 MgC ha−1 a year after (2006) in the epicenter of the drought. With more frequent droughts expected in future, forests of Amazon may lose their role as a robust sink of carbon, leading to a significant positive climate feedback and exacerbating warming trends.

In a recent paper (Mitchard et al. 2014, Global Ecology and Biogeography, 23, 935-946) a new map of forest biomass based on a geostatistical model of field data for the Amazon (and surrounding forests) was presented and contrasted with two earlier maps based on remotesensing data Saatchi et al. (2011; RS1) and Baccini et al. (2012; RS2). Mitchard et al. concluded that both the earlier remote-sensing based maps were incorrect because they did not conform to Mitchard et al. interpretation of the field-based results. In making their case, however, they misrepresented the fundamental nature of primary field and remote-sensing data and committed critical errors in their assumptions about the accuracy of research plots, the interpolation methodology and the statistical analysis. By ignoring the large uncertainty associated with ground estimates of biomass and the significant under-sampling and spatial bias of research plots, Mitchard et al. reported erroneous trends and artificial patterns of biomass over Amazonia. Because of these misrepresentations and methodological flaws, we find their critique of the satellite-derived maps to be invalid.

Determining the seasonality of terrestrial carbon exchange with the atmosphere remains a challenge in tropical forests because of the heterogeneity of ecosystem and climate. The magnitude and spatial variability of this flux are unknown, particularly in Amazonia where empirical upscaling approaches from spatially sparse in situ measurements and simulations from process-based models have been challenged in recent scientific literature. Here, we use satellite proxy observations of canopy structure, skin temperature, water content, and optical properties over a period of 10 years (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009) to constrain and quantify the spatial pattern and seasonality of carbon exchange of Amazonian forests. We identify nine regions through an optimized cluster approach with distinct leaf phenology synchronized with either water or light availability and corresponding seasonal cycles of gross primary production (GPP), covering more than 600 million ha of remaining old growth forests of Amazonia. We find South and Southwestern regions show strong seasonality of GPP with a peak in the wet season; while from Central Western to Northeastern Amazonia cover three regions with rising GPP in the dry season. The remaining four regions have significant but weak seasonality. These patterns agree with satellite florescence observations, a better proxy for photosynthetic activity. Our results suggest that only one-third of the patterns can be explained by the spatial autocorrelation caused by intra-annual variability of climate over Amazonia. The remaining twothirds of variations are due to biogeography of the Amazon basin driven by forest composition, structure, and nutrients. These patterns, for the first time, provide a complex picture of seasonal changes of tropical forests related to photosynthesis and influenced by water, light, and stomatal responses of trees that can improve modeling of regional carbon cycle and future prediction of impacts of climate change.