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Amazonia hosts the Earth's largest tropical forests and has been shown to be an important carbon sink over recent decades.sup.1-3. This carbon sink seems to be in decline, however, as a result of factors such as deforestation and climate change.sup.1-3. Here we investigate Amazonia's carbon budget and the main drivers responsible for its change into a carbon source. We performed 590 aircraft vertical profiling measurements of lower-tropospheric concentrations of carbon dioxide and carbon monoxide at four sites in Amazonia from 2010 to 2018.sup.4. We find that total carbon emissions are greater in eastern Amazonia than in the western part, mostly as a result of spatial differences in carbon-monoxide-derived fire emissions. Southeastern Amazonia, in particular, acts as a net carbon source (total carbon flux minus fire emissions) to the atmosphere. Over the past 40 years, eastern Amazonia has been subjected to more deforestation, warming and moisture stress than the western part, especially during the dry season, with the southeast experiencing the strongest trends.sup.5-9. We explore the effect of climate change and deforestation trends on carbon emissions at our study sites, and find that the intensification of the dry season and an increase in deforestation seem to promote ecosystem stress, increase in fire occurrence, and higher carbon emissions in the eastern Amazon. This is in line with recent studies that indicate an increase in tree mortality and a reduction in photosynthesis as a result of climatic changes across Amazonia.sup.1,10. Aircraft observations of atmospheric carbon dioxide and monoxide concentrations in Brazil show higher carbon emissions in eastern Amazonia than in the western part, which are linked to increased ecosystem stress and fire occurrence., Author(s): Luciana V. Gatti [sup.1] [sup.2] , Luana S. Basso [sup.1] , John B. Miller [sup.3] , Manuel Gloor [sup.4] , Lucas Gatti Domingues [sup.1] [sup.2] [sup.5] , Henrique L. [...]

Inverse estimation of carbon dioxide (CO2) sources and sinks uses atmospheric CO2 observations, mostly made near the Earth's surface. However, transport models used in such studies lack perfect representation of atmospheric dynamics and thus often fail to produce unbiased forward simulations. The error is generally larger for observations over the land than those over the remote/marine locations. The range of this error is estimated by using multiple transport models (16 are used here). We have estimated the remaining differences in CO2 fluxes due to the use of ocean-only versus all-sites (i.e., over ocean and land) observations of CO2 in a time-independent inverse modeling framework. The fluxes estimated using the ocean-only networks are more robust compared to those obtained using all-sites networks. This makes the global, hemispheric, and regional flux determination less dependent on the selection of transport model and observation network.

Spatial and temporal variations of atmospheric CO2 concentrations contain information about surface sources and sinks, which can be quantitatively interpreted through tracer transport inversion. Previous CO2 inversion calculations obtained differing results due to different data, methods and transport models used. To isolate the sources of uncertainty, we have conducted a set of annual mean inversion experiments in which 17 different transport models or model variants were used to calculate regional carbon sources and sinks from the same data with a standardized method. Simulated transport is a significant source of uncertainty in these calculations, particularly in the response to prescribed “background” fluxes due to fossil fuel combustion, a balanced terrestrial biosphere, and air–sea gas exchange. Individual model-estimated fluxes are often a direct reflection of their response to these background fluxes. Models that generate strong surface maxima near background exchange locations tend to require larger uptake near those locations. Models with weak surface maxima tend to have less uptake in those same regions but may infer small sources downwind. In some cases, individual model flux estimates cannot be analyzed through simple relationships to background flux responses but are likely due to local transport differences or particular responses at individual CO2 observing locations. The response to the background biosphere exchange generates the greatest variation in the estimated fluxes, particularly over land in the Northern Hemisphere. More observational data in the tropical regions may help in both lowering the uncertain tropical land flux uncertainties and constraining the northern land estimates because of compensation between these two broad regions in the inversion. More optimistically, examination of the model-mean retrieved fluxes indicates a general insensitivity to the prior fluxes and the prior flux uncertainties. Less uptake in the Southern Ocean than implied by oceanographic observations, and an evenly distributed northern land sink, remain in spite of changes in this aspect of the inversion setup.]

Information about regional carbon sources and sinks can be derived from variations in observed atmospheric CO2 concentrations via inverse modelling with atmospheric tracer transport models. A consensus has not yet been reached regarding the size and distribution of regional carbon fluxes obtained using this approach, partly owing to the use of several different atmospheric transport models. Here we report estimates of surface-atmosphere CO2 fluxes from an intercomparison of atmospheric CO2 inversion models (the TransCom 3 project), which includes 16 transport models and model variants. We find an uptake of CO2 in the southern extratropical ocean less than that estimated from ocean measurements, a result that is not sensitive to transport models or methodological approaches. We also find a northern land carbon sink that is distributed relatively evenly among the continents of the Northern Hemisphere, but these results show some sensitivity to transport differences among models, especially in how they respond to seasonal terrestrial exchange of CO2. Overall, carbon fluxes integrated over latitudinal zones are strongly constrained by observations in the middle to high latitudes. Further significant constraints to our understanding of regional carbon fluxes will therefore require improvements in transport models and expansion of the CO2 observation network within the tropics.

Tropical forests such as the Amazonian rainforests play an important role for climate, are large carbon stores and are a treasure of biodiversity. Amazonian forests have been exposed to large-scale deforestation and degradation for many decades. Deforestation declined between 2005 and 2012 but more recently has again increased with similar rates as in 2007–2008. The resulting forest fragments are exposed to substantially elevated temperatures in an already warming world. These temperature and land cover changes are expected to affect the forests, and an important diagnostic of their health and sensitivity to climate variation is their carbon balance. In a recent study based on CO 2 atmospheric vertical profile observations between 2010 and 2018, and an air column budgeting technique used to estimate fluxes, we reported the Amazon region as a carbon source to the atmosphere, mainly due to fire emissions. Instead of an air column budgeting technique, we use an inverse of the global atmospheric transport model, TOMCAT, to assimilate CO 2 observations from Amazon vertical profiles and global flask measurements. We thus estimate inter- and intra-annual variability in the carbon fluxes, trends over time and controls for the period of 2010–2018. This is the longest period covered by a Bayesian inversion of these atmospheric CO 2 profile observations to date. Our analyses indicate that the Amazon is a small net source of carbon to the atmosphere (mean 2010–2018 = 0.13 ± 0.17 Pg C yr -1 , where 0.17 is the 1 σ uncertainty), with the majority of the emissions coming from the eastern region (77 % of total Amazon emissions). Fire is the primary driver of the Amazonian source (0.26 ± 0.13 Pg C yr -1), while forest carbon uptake removes around half of the fire emissions to the atmosphere (- 0.13 ± 0.20 Pg C yr -1). The largest net carbon sink was observed in the western-central Amazon region (72 % of the fire emissions). We find larger carbon emissions during the extreme drought years (such as 2010, 2015 and 2016), correlated with increases in temperature, cumulative water deficit and burned area. Despite the increase in total carbon emissions during drought years, we do not observe a significant trend over time in our carbon total, fire and net biome exchange estimates between 2010 and 2018. Our analysis thus cannot provide clear evidence for a weakening of the carbon uptake by Amazonian tropical forests. [ABSTRACT FROM AUTHOR]

Anthropogenic emissions of methane (CH 4) have made a considerable contribution towards the Earth's changing radiative budget since pre-industrial times. This is because large amounts of methane are emitted from human activities, and the global warming potential of methane is high. The majority of anthropogenic fossil methane emissions to the atmosphere originate from a large number of small (point) sources. Thus, detection and accurate, rapid quantification of such emissions are vital to enable the reduction of emissions to help mitigate future climate change. There exist a number of instruments on satellites that measure radiation at methane-absorbing wavelengths, which have sufficiently high spatial resolution that can be used for detecting plumes of highly spatially localised methane "point sources" (areas on the order of m 2 to km 2). Searching for methane plumes in methane-sensitive satellite images using classical methods, such as thresholding and clustering, can be useful but is time-consuming and often involves empirical decisions. Here, we develop a deep neural network to identify and quantify methane point source emissions from hyperspectral imagery from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite with 30 m spatial resolution. The moderately high spectral and spatial resolution, as well as considerable global coverage and free access to data, makes PRISMA a good candidate for methane plume detection. The neural network was trained with simulated synthetic methane plumes generated with the large eddy simulation extension of the Weather Research and Forecasting model (WRF-LES), which we embedded into PRISMA images. The deep neural network was successful at locating plumes with a F1 score, precision, and recall of 0.95, 0.96, and 0.92, respectively, and was able to quantify emission rates with a mean error of 24 %. The neural network was furthermore able to locate several plumes in real-world images. We have thus demonstrated that our method can be effective in locating and quantifying methane point source emissions in near-real time from 30 m resolution satellite data, which can aid us in mitigating future climate change. [ABSTRACT FROM AUTHOR]

Tropical forests such as the Amazonian rainforests play an important role for climate, are large carbon stores and are a treasure of biodiversity. Amazonian forests are being exposed to large scale deforestation and degradation for many decades which declined between 2005 and 2012 but more recently has again increased with similar rates as in the 2007/2008. The resulting forest fragments are exposed to substantially elevated temperatures in an already warming world. These changes are expected to affect the forests and an important diagnostic of their health and sensitivity to climate variation is their carbon balance. In a recent study based on CO2 atmospheric vertical profile observations between 2010 and 2018, and an air column budgeting technique to estimate fluxes, we reported the Amazon region as a carbon source to the atmosphere, mainly due to fire emissions. Instead of an air column budgeting technique, we use here an inverse of the global atmospheric transport model, TOMCAT, to assimilate CO2 observations from Amazon vertical profiles and global flask measurements. We thus estimate inter- and intra-annual variability in the carbon fluxes, trends over time and controls for the period 2010–2018. This represents the longest Bayesian inversion of these atmospheric CO2 profile observations to date. Our analyses indicate that the Amazon is a small net source of carbon to the atmosphere (mean 2010–2018 = 0.13 ± 0.17 PgC y−1, where 0.17 is the 1-σ uncertainty), with the majority of the emissions coming from the eastern region (77 % of total Amazon emission). Fire is the primary driver of the Amazonian source (0.26 ± 0.13 PgC y−1), however the forest uptake likely removes around half of the fire emissions to the atmosphere (−0.13 ± 0.20 PgC y−1). The largest net carbon sink was observed in the western-central Amazon region (72 % of the fire emissions). We find larger carbon emissions during the extreme drought years (such as 2010, 2015 and 2016), correlated with increases in temperature, cumulative water deficit and burned area. Despite the increase in total carbon emissions during drought years, we do not observe a significant trend over time in our carbon total, fire and net biome exchange estimates between 2010 and 2018. Our analysis thus cannot provide clear evidence for a weakening of the carbon uptake by Amazonian tropical forests. [ABSTRACT FROM AUTHOR]

Anthropogenic emissions of methane (CH4) make up a considerable contribution towards the Earth's radiative budget since pre-industrial times. This is because large amounts of methane are emitted from human activities and the global warming potential of methane is high. The majority of anthropogenic fossil methane emissions to the atmosphere originate from a large number of small (point) sources. Thus, detection and accurate, rapid quantification of such emissions is vital to enable the reduction of emissions to help mitigate future climate change. There exist a number of instruments on satellites that measure radiation at methane-absorbing wavelengths, which have sufficiently high spatial resolution that can be used for detecting highly spatially localised methane 'point sources' (areas on the order of km2). Searching for methane plumes in methane sensitive satellite images using classical methods, such as thresholding and clustering, can be useful but are time-consuming and often inaccurate. Here, we develop a deep neural network to identify and quantify methane point source emissions from hyperspectral imagery from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) satellite with 30-m spatial resolution. The moderately high spectral and spatial resolution as well as considerable global coverage and free access to data make PRISMA a good candidate for methane plume detection. The neural network was trained with simulated synthetic methane plumes generated with the Large Eddy Simulation extension of the Weather Research and Forecasting model (WRF-LES), which we embedded into PRISMA images. The deep neural network was successful at locating plumes with F1-score, precision and recall of 0.95, 0.96 and 0.92, respectively, and was able to quantify emission rates with a mean error of 24 %. The neural network was furthermore able to locate several plumes in real-world images. We have thus demonstrated that our method can be effective in locating and quantifying methane point source emissions in near real time from 30-m resolution satellite data which can aid us in mitigating future climate change. [ABSTRACT FROM AUTHOR]

The response of terrestrial vegetation to a globally changing environment is central to predictions of future levels of atmospheric carbon dioxide (1,2). The role of tropical forests is critical because they are carbon-dense and highly productive (3,4). Inventory plots across Amazonia show that old-growth forests have increased in carbon storage over recent decades (5-7), but the response of one-third of the world's tropical forests in Africa (8) is largely unknown owing to an absence of spatially extensive observation networks (9,10). Here we report data from a ten-country network of long-term monitoring plots in African tropical forests. We find that across 79 plots (163 ha) above-ground carbon storage in live trees increased by 0.63 Mg C [ha.sup.-1] [yr.sup.-1] between 1968 and 2007 (95% confidence interval (CI), 0.22-0.94; mean interval, 1987-96). Extrapolation to unmeasured forest components (live roots, small trees, necromass) and scaling to the continent implies a total increase in carbon storage in African tropical forest trees of 0.34 Pg C [yr.sup.-1] (CI, 0.15-0.43). These reported changes in carbon storage are similar to those reported for Amazonian forests per unit area (6,7), providing evidence that increasing carbon storage in old-growth forests is a pan-tropical phenomenon. Indeed, combining all standardized inventory data from this study and from tropical America and Asia (5,6,11) together yields a comparable figure of 0.49 Mg C [ha.sup.-1] [yr.sup.-1] (n = 156; 562 ha; CI, 0.29-0.66; mean interval, 1987-97). This indicates a carbon sink of 1.3 Pg C [yr.sup.-1] (CI, 0.8-1.6) across all tropical forests during recent decades. Taxon-specific analyses of African inventory and other data (12) suggest that widespread changes in resource availability, such as increasing atmospheric carbon dioxide concentrations, may be the cause of the increase in carbon stocks (13), as some theory (14) and models (2,10,15) predict., Tropical forests cover 7-10% of the global land area, store 40-50% of carbon in terrestrial vegetation and annually process approximately six times as much carbon via photosynthesis and respiration as [...]

Light and water availability are likely to vary over the lifespan of closed-canopy forest trees, with understory trees experiencing greater limitations to growth by light and canopy trees greater limitation due to drought. As drought and shade have opposing effects on isotope discrimination (Δ13C), paired measurement of ring width and Δ13C can potentially be used to differentiate between water and light limitations on tree growth. We tested this approach for Cedrela trees from three tropical forests in Bolivia and Mexico that differ in rainfall and canopy structure. Using lifetime ring width and Δ13C data for trees of up to and over 200 years old, we assessed how controls on tree growth changed from understory to the canopy. Growth and Δ13C are mostly anti-correlated in the understory, but this anti-correlation disappeared or weakened when trees reached the canopy, especially at the wettest site. This indicates that understory growth variation is controlled by photosynthetic carbon assimilation due to variation in light levels. Once trees reached the canopy, inter-annual variation in growth and Δ13C at one of the dry sites showed positive correlations, indicating that inter-annual variation in growth is driven by variation in water stress affecting stomatal conductance. Paired analysis of ring widths and carbon isotopes provides significant insight in what environmental factors control growth over a tree's life; strong light limitations for understory trees in closed-canopy moist forests switched to drought stress for (sub)canopy trees in dry forests. We show that combined isotope and ring width measurements can significantly improve our insights in tree functioning and be used to disentangle limitations due to shade from those due to drought. [ABSTRACT FROM AUTHOR]