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The hydroxyl radical (OH) is the main atmospheric oxidant and the primary sink of the greenhouse gas CH4. In an attempt to constrain atmospheric levels of OH, two recent studies combined a tropospheric two-box model with hemispheric-mean observations of methyl chloroform (MCF) and CH4. These studies reached different conclusions concerning the most likely explanation of the renewed CH4 growth rate, which reflects the uncertain and underdetermined nature of the problem. Here, we investigated how the use of a tropospheric two-box model can affect the derived constraints on OH due to simplifying assumptions inherent to a two-box model. To this end, we derived species- and time-dependent quantities from a full 3-D transport model to drive two-box model simulations. Furthermore, we quantified differences between the 3-D simulated tropospheric burden and the burden seen by the surface measurement network of the National Oceanic and Atmospheric Administration (NOAA). Compared to commonly used parameters in two-box models, we found significant deviations in the magnitude and time-dependence of the interhemispheric exchange rate, exposure to OH, and stratospheric loss rate. For MCF these deviations can be large due to changes in the balance of its sources and sinks over time. We also found that changes in the yearly averaged tropospheric burden of CH4 and MCF can be obtained within 0.96 ppb yr−1 and 0.14 % yr−1 by the NOAA surface network, but that substantial systematic biases exist in the interhemispheric mixing ratio gradients that are input to two-box model inversions. To investigate the impact of the identified biases on constraints on OH, we accounted for these biases in a two-box model inversion of MCF and CH4. We found that the sensitivity of interannual OH anomalies to the biases is modest (1 %–2 %), relative to the uncertainties on derived OH (3 %–4 %). However, in an inversion where we implemented all four bias corrections simultaneously, we found a shift to a positive trend in OH concentrations over the 1994–2015 period, compared to the standard inversion. Moreover, the absolute magnitude of derived global mean OH, and by extent, that of global CH4 emissions, was affected much more strongly by the bias corrections than their anomalies (∼10 %). Through our analysis, we identified and quantified limitations in the two-box model approach as well as an opportunity for full 3-D simulations to address these limitations. However, we also found that this derivation is an extensive and species-dependent exercise and that the biases were not always entirely resolvable. In future attempts to improve constraints on the atmospheric oxidative capacity through the use of simple models, a crucial first step is to consider and account for biases similar to those we have identified for the two-box model.

Despite the consensus on the overall downward trend in Amazon forest loss in the previous decade, estimates of yearly carbon emissions from deforestation still vary widely. Estimated carbon emissions are currently often based on data from local logging activity reports, changes in remotely sensed biomass, and remote detection of fire hotspots and burned area. Here, we use 16 years of satellite-derived carbon monoxide (CO) columns to constrain fire CO emissions from the Amazon Basin between 2003 and 2018. Through data assimilation, we produce 3 d average maps of fire CO emissions over the Amazon, which we verified to be consistent with a long-term monitoring programme of aircraft CO profiles over five sites in the Amazon. Our new product independently confirms a long-term decrease of 54 % in deforestation-related CO emissions over the study period. Interannual variability is large, with known anomalously dry years showing a more than 4-fold increase in basin-wide fire emissions relative to wet years. At the level of individual Brazilian states, we find that both soil moisture anomalies and human ignitions determine fire activity, suggesting that future carbon release from fires depends on drought intensity as much as on continued forest protection. Our study shows that the atmospheric composition perspective on deforestation is a valuable additional monitoring instrument that complements existing bottom-up and remote sensing methods for land-use change. Extension of such a perspective to an operational framework is timely considering the observed increased fire intensity in the Amazon Basin between 2019 and 2021.

Variations in the atmospheric oxidative capacity, largely determined by variations in the hydroxyl radical (OH), form a key uncertainty in many greenhouse and other pollutant budgets, such as that of methane (CH4). Methyl chloroform (MCF) is an often-adopted tracer to indirectly put observational constraints on large-scale variations in OH. We investigated the budget of MCF in a 4DVAR inversion using the atmospheric transport model TM5, for the period 1998–2018, with the objective to derive information on large-scale, interannual variations in atmospheric OH concentrations. While our main inversion did not fully converge, we did derive interannual variations in the global oxidation of MCF that bring simulated mole fractions of MCF within 1 %–2 % of the assimilated observations from the NOAA-GMD surface network at most sites. Additionally, the posterior simulations better reproduce aircraft observations used for independent validation compared to the prior simulations. The derived OH variations showed robustness with respect to the prior MCF emissions and the prior OH distribution over the 1998 to 2008 period. Although we find a rapid 8 % increase in global mean OH concentrations between 2010 and 2012 that quickly declines afterwards, the derived interannual variations were typically small (< 3 %/yr), with no significant long-term trend in global mean OH concentrations. The inverse system found strong adjustments to the latitudinal distribution of OH, relative to widely used prior distributions, with systematic increases in tropical and decreases in extra-tropical OH concentrations (both up to 30 %). These spatial adjustments were driven by intrahemispheric biases in simulated MCF mole fractions, which have not been identified in previous studies. Given the large amplitude of these adjustments, which exceeds spread between literature estimates, and a residual bias in the MCF intrahemispheric gradients, we suggest a reversal in the extratropical ocean sink of MCF in response to declining atmospheric MCF abundance (as hypothesized in Wennberg et al., 2004). This ocean source provides a more realistic explanation for the biases, possibly complementary to adjustments in the OH distribution. We identified significant added value in the use of a 3D transport model, since it implicitly accounts for variable transport and optimizes the observed spatial gradients of MCF, which is not possible in simpler models. However, we also found a trade-off in computational expense and convergence problems. Despite these convergence problems, the derived OH variations do result in an improved match with MCF observations relative to an interannually repeating prior for OH. Therefore, we consider that variations in OH derived from MCF inversions with 3D models can add value to budget studies of long-lived gases like CH4.

Earth systems models (ESMs), which can simulate the complex feedbacks between climate and fires, struggle to predict fires well for tropical rainforests. This study provides equations that predict historic carbon monoxide emissions from Amazon rainforest fires for 2003–2018, which could be implemented within ESMs’ current structures. We also include equations to convert the predicted emissions to burned area. Regressions of varying mathematical forms are fitted to one or both of two fire CO emission inventories. Equation accuracy is scored on r2, bias of the mean prediction, and ratio of explained variances. We find that one equation is best for studying smoke consequences that scale approximately linearly with emissions, or for a fully coupled ESM with online meteorology. Compared to the deforestation fire equation in the Community Land Model ver. 4.5, this equation’s linear-scale accuracies are higher for both emissions and burned area. A second equation, more accurate when evaluated on a log scale, may better support studies of certain health or cloud process consequences of fires. The most accurate recommended equation requires that meteorology be known before emissions are calculated. For all three equations, both deforestation rates and meteorological variables are key groups of predictors. Predictions nevertheless fail to reproduce most of the variation in emissions. The highest linear r2s for monthly and annual predictions are 0.30 and 0.41, respectively. The impossibility of simultaneously matching both emission inventories limits achievable fit. One key cause of the remaining unexplained variability appears to be noise inherent to pan-tropical data, especially meteorology.

In tropical South America there has been substantial progress in atmospheric monitoring capacity, but the region still has a limited number of continental atmospheric stations relative to its large area, hindering net carbon flux estimates using atmospheric inversions. In this study, we use dry-air CO2 mole fractions measured at the Amazon Tall Tower Observatory (ATTO) and airborne vertical CO2 profiles in an atmospheric inversion system to estimate net carbon exchange in tropical South America from 2010 to 2018. Given previous knowledge of a bias due to undried samples in the airborne vertical profiles, we calculate the effect of this systematic uncertainty in our inverse estimates and propose a water-vapor correction to the airborne CO2 profiles. We focus our analysis on the biogeographic Amazon and its neighboring “Cerrado and Caatinga” biomes. Including the water-vapor correction changes the posterior ensemble median from −0.33 to −0.04 PgC yr−1 with a posterior uncertainty of 0.33 PgC yr−1 for the Amazon and for the Cerrado and Caatinga from 0.31 to 0.50 PgC yr−1, with an uncertainty of 0.24 PgC yr−1. Our estimates of carbon exchange include the contributions from both net vegetation exchange and release from fires. Assuming that the correction brings the observational data closer to the truth implies that the Amazon is a weaker sink of carbon and that the Cerrado and Caatinga is a larger source. We do not find a strong spatial shift of fluxes within the biogeographic Amazon due to the correction, nor do we find a strong impact on the interannual variations. Finally, to further reduce the uncertainty in regional carbon balance estimates in tropical South America, we call for an expansion of the atmospheric monitoring network on the continent, mainly in the Amazon–Andes foothills.

In tropical South America there has been substantial progress in atmospheric monitoring capacity, but the region still has a limited number of continental atmospheric stations relative to its large area, hindering net carbon flux estimates using atmospheric inversions. In this study, we use dry-air CO.sub.2 mole fractions measured at the Amazon Tall Tower Observatory (ATTO) and airborne vertical CO.sub.2 profiles in an atmospheric inversion system to estimate net carbon exchange in tropical South America from 2010 to 2018. Given previous knowledge of a bias due to undried samples in the airborne vertical profiles, we calculate the effect of this systematic uncertainty in our inverse estimates and propose a water-vapor correction to the airborne CO.sub.2 profiles. We focus our analysis on the biogeographic Amazon and its neighboring \"Cerrado and Caatinga\" biomes. Including the water-vapor correction changes the posterior ensemble median from -0.33 to -0.04 PgC yr.sup.-1 with a posterior uncertainty of 0.33 PgC yr.sup.-1 for the Amazon and for the Cerrado and Caatinga from 0.31 to 0.50 PgC yr.sup.-1, with an uncertainty of 0.24 PgC yr.sup.-1 . Our estimates of carbon exchange include the contributions from both net vegetation exchange and release from fires. Assuming that the correction brings the observational data closer to the truth implies that the Amazon is a weaker sink of carbon and that the Cerrado and Caatinga is a larger source. We do not find a strong spatial shift of fluxes within the biogeographic Amazon due to the correction, nor do we find a strong impact on the interannual variations. Finally, to further reduce the uncertainty in regional carbon balance estimates in tropical South America, we call for an expansion of the atmospheric monitoring network on the continent, mainly in the Amazon-Andes foothills.

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 CO2 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 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 of 2010–2018. This is the longest period covered by a 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 Pg C yr−1, where 0.17 is the 1σ 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.

In tropical South America there has been substantial progress in atmospheric monitoring capacity, but the region still has a limited number of continental atmospheric stations relative to its large area, hindering net carbon flux estimates using atmospheric inversions. In this study, we use dry-air CO2 mole fractions measured at the Amazon Tall Tower Observatory (ATTO) and airborne vertical CO2 profiles in an atmospheric inversion system to estimate net carbon exchange in tropical South America from 2010 to 2018. Given previous knowledge of a bias due to undried samples in the airborne vertical profiles, we calculate the effect of this systematic uncertainty in our inverse estimates and propose a water-vapor correction to the airborne CO2 profiles. We focus our analysis on the biogeographic Amazon and its neighboring “Cerrado and Caatinga” biomes. Including the water-vapor correction changes the posterior ensemble median from -0.33 to -0.04 PgC yr−1 with a posterior uncertainty of 0.33 PgC yr−1 for the Amazon and for the Cerrado and Caatinga from 0.31 to 0.50 PgC yr−1, with an uncertainty of 0.24 PgC yr−1. Our estimates of carbon exchange include the contributions from both net vegetation exchange and release from fires. Assuming that the correction brings the observational data closer to the truth implies that the Amazon is a weaker sink of carbon and that the Cerrado and Caatinga is a larger source. We do not find a strong spatial shift of fluxes within the biogeographic Amazon due to the correction, nor do we find a strong impact on the interannual variations. Finally, to further reduce the uncertainty in regional carbon balance estimates in tropical South America, we call for an expansion of the atmospheric monitoring network on the continent, mainly in the Amazon–Andes foothills.

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 CO2 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 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 of 2010–2018. This is the longest period covered by a 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 Pg C yr-1, where 0.17 is the 1σ 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.

Venezuela has long been identified as an area with large methane emissions and intensive oil exploitation, especially in the Lake Maracaibo region, but production has strongly decreased in recent years. The area is notoriously difficult to observe from space due to its complex topography and persistent cloud cover. We use the unprecedented coverage of the TROPOspheric Monitoring Instrument (TROPOMI) methane observations in analytical inversions with the Integrated Methane Inversion (IMI) framework at the national scale and at the local scale with the Weather Research and Forecasting model with chemistry (WRF-Chem). In the IMI analysis, we find Venezuelan emissions of 7.5 (5.7–9.3) Tg a−1 in 2019, where about half of emissions can be informed by TROPOMI observations, and emissions from oil exploitation are a factor of ∼ 1.6 higher than in bottom-up inventories. Using WRF, we find emissions of 1.2 (1.0–1.5) Tg a−1 from the Lake Maracaibo area in 2019, close to bottom-up estimates. Our WRF estimate is ∼ 40 % lower than the result over the same region from the IMI due to differences in the meteorology used by the two models. We find only a small, non-significant trend in emissions between 2018 and 2020 around the lake, implying the area's methane emission intensity expressed against oil and gas production has doubled over the time period, to ∼ 20 %. This value is much higher than what has previously been found for other oil and gas production regions and indicates that there could be large emissions from abandoned infrastructure.