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Extreme wildfires are becoming more common and increasingly affecting Earth’s climate. Wildfires in boreal forests have attracted much less attention than those in tropical forests, although boreal forests are one of the most extensive biomes on Earth and are experiencing the fastest warming. We used a satellite-based atmospheric inversion system to monitor fire emissions in boreal forests. Wildfires are rapidly expanding into boreal forests with emerging warmer and drier fire seasons. Boreal fires, typically accounting for 10% of global fire carbon dioxide emissions, contributed 23% (0.48 billion metric tons of carbon) in 2021, by far the highest fraction since 2000. 2021 was an abnormal year because North American and Eurasian boreal forests synchronously experienced their greatest water deficit. Increasing numbers of extreme boreal fires and stronger climate–fire feedbacks challenge climate mitigation efforts.

Southeast Australia experienced intensive and geographically extensive wildfires during the 2019–2020 summer season1,2. The fires released substantial amounts of carbon dioxide into the atmosphere3. However, existing emission estimates based on fire inventories are uncertain4, and vary by up to a factor of four for this event. Here we constrain emission estimates with the help of satellite observations of carbon monoxide5, an analytical Bayesian inversion6 and observed ratios between emitted carbon dioxide and carbon monoxide7. We estimate emissions of carbon dioxide to be 715 teragrams (range 517–867) from November 2019 to January 2020. This is more than twice the estimate derived by five different fire inventories8–12, and broadly consistent with estimates based on a bottom-up bootstrap analysis of this fire episode13. Although fires occur regularly in the savannas in northern Australia, the recent episodes were extremely large in scale and intensity, burning unusually large areas of eucalyptus forest in the southeast13. The fires were driven partly by climate change14,15, making better-constrained emission estimates particularly important. This is because the build-up of atmospheric carbon dioxide may become increasingly dependent on fire-driven climate–carbon feedbacks, as highlighted by this event16. The amount of carbon dioxide released by the Australian wildfires of 2019–2020 is uncertain, but is estimated here using satellite observations of carbon monoxide to be more than twice the amount suggested by fire inventories.

Southeast Australia experienced intensive and geographically extensive wildfires during the 2019-2020 summer season

The global fire emission inventories depend on ground and airborne measurements of species-specific emission factors (EFs), which translate dry matter losses due to fires to actual trace gas and aerosol emissions. The EFs of nitrogen oxides (NOx) and carbon monoxide (CO) can function as a proxy for combustion efficiency to distinguish flaming from smoldering combustion. The uncertainties on these EFs remain large as they are limited by the spatial and temporal representativeness of the measurements. The global coverage of satellite observations has the advantage to fill this gap, making these measurements highly complementary to ground-based or airborne data. We present a new analysis of biomass burning pollutants using space-borne data to investigate the spatiotemporal efficiency of fire combustion. Column measurements of nitrogen dioxide and carbon monoxide (XNO2 and XCO) from the TROPOspheric Monitoring Instrument (TROPOMI) are used to quantify the relative atmospheric enhancements of these species over different fire-prone regions around the world. We find spatial and temporal patterns in the ΔXNO2 / ΔXCO ratio that point to distinct differences in biomass burning behavior. Such differences are induced by the burning phase of the fire (e.g. high temperature flaming vs. low temperature smoldering combustion) and burning practice (e.g. the combustion of logs, coarse woody debris and soil organic matter vs. the combustion of fine fuels such as savanna grasses). The sampling techniques and the signal-to-noise of the retrieved ΔXNO2 / ΔXCO signals were quantified with WRF-CHEM experiments and showed similar distinct differences in combustion types. The TROPOMI measurements show that the fraction of surface smoldering combustion is much larger for the boreal forest fires in the upper northern hemisphere and peatland fires in Indonesia. These types of fires cause a much larger increase (3 to 6 times) in ΔXCO relative to ΔXNO2 than elsewhere in the world. The high spatial and temporal resolution of TROPOMI also enables the detection of spatial gradients in combustion efficiency at smaller regional scales. For instance, in the Amazon, we found higher combustion efficiency (up to 3-fold) for savanna fires than for the nearby tropical deforestation fires. Out of two investigated fire emission products, the TROPOMI measurements support the broad spatial pattern of combustion efficiency rooted in GFED4s. Meanwhile, TROPOMI data also add new insights on regional variability in combustion characteristics that are not well represented in the different emission inventories, which can help the fire modeling community to improve their representation of the spatiotemporal variability in EFs.

To improve our understanding of the global carbon balance and its representation in terrestrial biosphere models, we present here a first dual-species application of the CarbonTracker Data Assimilation System (CTDAS). The system's modular design allows for assimilating multiple atmospheric trace gases simultaneously to infer exchange fluxes at the Earth surface. In the prototype discussed here, we interpret signals recorded in observed carbon dioxide (CO2) along with observed ratios of its stable isotopologues 13CO2∕12CO2 (δ13C). The latter is in particular a valuable tracer to untangle CO2 exchange from land and oceans. Potentially, it can also be used as a proxy for continent-wide drought stress in plants, largely because the ratio of 13CO2 and 12CO2 molecules removed from the atmosphere by plants is dependent on moisture conditions.The dual-species CTDAS system varies the net exchange fluxes of both 13CO2 and CO2 in ocean and terrestrial biosphere models to create an ensemble of 13CO2 and CO2 fluxes that propagates through an atmospheric transport model. Based on differences between observed and simulated 13CO2 and CO2 mole fractions (and thus δ13C) our Bayesian minimization approach solves for weekly adjustments to both net fluxes and isotopic terrestrial discrimination that minimizes the difference between observed and estimated mole fractions.With this system, we are able to estimate changes in terrestrial δ13C exchange on seasonal and continental scales in the Northern Hemisphere where the observational network is most dense. Our results indicate a decrease in stomatal conductance on a continent-wide scale during a severe drought. These changes could only be detected after applying combined atmospheric CO2 and δ13C constraints as done in this work. The additional constraints on surface CO2 exchange from δ13C observations neither affected the estimated carbon fluxes nor compromised our ability to match observed CO2 variations. The prototype presented here can be of great benefit not only to study the global carbon balance but also to potentially function as a data-driven diagnostic to assess multiple leaf-level exchange parameterizations in carbon-climate models that influence the CO2, water, isotope, and energy balance.

North American ecosystems absorb more CO2 from the atmosphere during El Niño than during La Niña periods., Long-term atmospheric CO2 mole fraction and δ13CO2 observations over North America document persistent responses to the El Niño–Southern Oscillation. We estimate these responses corresponded to 0.61 (0.45 to 0.79) PgC year−1 more North American carbon uptake during El Niño than during La Niña between 2007 and 2015, partially offsetting increases of net tropical biosphere-to-atmosphere carbon flux around El Niño. Anomalies in derived North American net ecosystem exchange (NEE) display strong but opposite correlations with surface air temperature between seasons, while their correlation with water availability was more constant throughout the year, such that water availability is the dominant control on annual NEE variability over North America. These results suggest that increased water availability and favorable temperature conditions (warmer spring and cooler summer) caused enhanced carbon uptake over North America near and during El Niño.

The triple oxygen isotope signature Δ17O in atmospheric CO2, also known as its “17O excess,” has been proposed as a tracer for gross primary production (the gross uptake of CO2 by vegetation through photosynthesis). We present the first global 3‐D model simulations for Δ17O in atmospheric CO2 together with a detailed model description and sensitivity analyses. In our 3‐D model framework we include the stratospheric source of Δ17O in CO2 and the surface sinks from vegetation, soils, ocean, biomass burning, and fossil fuel combustion. The effect of oxidation of atmospheric CO on Δ17O in CO2 is also included in our model. We estimate that the global mean Δ17O (defined as Δ17O=ln(δ17O+1)−λRL·ln(δ18O+1) with λ RL = 0.5229) of CO2 in the lowest 500 m of the atmosphere is 39.6 per meg, which is ∼20 per meg lower than estimates from existing box models. We compare our model results with a measured stratospheric Δ17O in CO2 profile from Sodankylä (Finland), which shows good agreement. In addition, we compare our model results with tropospheric measurements of Δ17O in CO2 from Göttingen (Germany) and Taipei (Taiwan), which shows some agreement but we also find substantial discrepancies that are subsequently discussed. Finally, we show model results for Zotino (Russia), Mauna Loa (United States), Manaus (Brazil), and South Pole, which we propose as possible locations for future measurements of Δ17O in tropospheric CO2 that can help to further increase our understanding of the global budget of Δ17O in atmospheric CO2., Key Points This work presents a first view on possible spatial and temporal gradients of Δ17O in CO2 across the globeTropical, boreal, and Southern Hemisphere observations of Δ17O in CO2 could be of great interestWe implemented spatially and temporally explicit sources and sinks of Δ17O in CO2 in a 3‐D model framework

Understanding tropical rainforest carbon exchange and its response to heat and drought is critical for quantifying the effects of climate change on tropical ecosystems, including global climate-carbon feedbacks. Of particular importance for the global carbon budget is net biome exchange of CO2 with the atmosphere ( NBE), which represents nonfire carbon fluxes into and out of biomass and soils. Subannual and sub-Basin Amazon NBE estimates have relied heavily on process-based biosphere models, despite lack of model agreement with plot-scale observations. We present a new analysis of airborne measurements that reveals monthly, regional-scale (similar to 1-8 x 10(6) km(2)) NBE variations. We develop a regional atmospheric CO2 inversion that provides the first analysis of geographic and temporal variability in Amazon biosphere-atmosphere carbon exchange and that is minimally influenced by biosphere model-based first guesses of seasonal and annual mean fluxes. We find little evidence for a clear seasonal cycle in Amazon NBE but do find NBE sensitivity to aberrations from long-term mean climate. In particular, we observe increased NBE ( more carbon emitted to the atmosphere) associated with heat and drought in 2010, and correlations between wet season NBE and precipitation ( negative correlation) and temperature ( positive correlation). In the eastern Amazon, pulses of increased NBE persisted through 2011, suggesting legacy effects of 2010 heat and drought. We also identify regional differences in postdrought NBE that appear related to long-term water availability. We examine satellite proxies and find evidence for higher gross primary productivity ( GPP) during a pulse of increased carbon uptake in 2011, and lower GPP during a period of increased NBE in the 2010 dry season drought, but links between GPP and NBE changes are not conclusive. These results provide novel evidence of NBE sensitivity to short-term temperature and moisture extremes in the Amazon, where monthly and sub-Basin estimates have not been previously available.

Severe droughts in the Northern Hemisphere cause widespread decline of agricultural yield, reduction of forest carbon uptake, and increased CO2 growth rates in the atmosphere. Plants respond to droughts by partially closing their stomata to limit their evaporative water loss, at the expense of carbon uptake by photosynthesis. This trade-off maximizes their water-use efficiency, as measured for many individual plants under laboratory conditions and field experiments. Here we analyze the 13C/12C stable isotope ratio in atmospheric CO2 (reported as δ13C) to provide new observational evidence of the impact of droughts on the water-use efficiency across areas of millions of km2 and spanning one decade of recent climate variability. We find strong and spatially coherent increases in water-use efficiency along with widespread reductions of net carbon uptake over the Northern Hemisphere during severe droughts that affected Europe, Russia, and the United States in 2001-2011. The impact of those droughts on water-use efficiency and carbon uptake by vegetation is substantially larger than simulated by the land-surface schemes of six state-of-the-art climate models. This suggests that drought induced carbon-climate feedbacks may be too small in these models and improvements to their vegetation dynamics using stable isotope observations can help to improve their drought response.

We have implemented a regional carbon dioxide data assimilation system based on the CarbonTracker Data Assimilation Shell (CTDAS) and a high-resolution Lagrangian transport model, the Stochastic Time-Inverted Lagrangian Transport model driven by the Weather Forecast and Research meteorological fields (WRF-STILT). With this system, named CTDAS-Lagrange, we simultaneously optimize terrestrial biosphere fluxes and four parameters that adjust the lateral boundary conditions (BCs) against CO2 observations from the NOAA ESRL North America tall tower and aircraft programmable flask packages (PFPs) sampling program. Least-squares optimization is performed with a time-stepping ensemble Kalman smoother, over a time window of 10 days and assimilating sequentially a time series of observations. Because the WRF-STILT footprints are pre-computed, it is computationally efficient to run the CTDAS-Lagrange system. To estimate the uncertainties in the optimized fluxes from the system, we performed sensitivity tests with various a priori biosphere fluxes (SiBCASA, SiB3, CT2013B) and BCs (optimized mole fraction fields from CT2013B and CTE2014, and an empirical dataset derived from aircraft observations), as well as with a variety of choices on the ways that fluxes are adjusted (additive or multiplicative), covariance length scales, biosphere flux covariances, BC parameter uncertainties, and model–data mismatches. In pseudo-data experiments, we show that in our implementation the additive flux adjustment method is more flexible in optimizing net ecosystem exchange (NEE) than the multiplicative flux adjustment method, and our sensitivity tests with real observations show that the CTDAS-Lagrange system has the ability to correct for the potential biases in the lateral BCs and to resolve large biases in the prior biosphere fluxes. Using real observations, we have derived a range of estimates for the optimized carbon fluxes from a series of sensitivity tests, which places the North American carbon sink for the year 2010 in a range from −0.92 to −1.26 PgC yr−1. This is comparable to the TM5-based estimates of CarbonTracker (version CT2016, -0.91±1.10 PgC yr−1) and CarbonTracker Europe (version CTE2016, -0.91±0.31 PgC yr−1). We conclude that CTDAS-Lagrange can offer a versatile and computationally attractive alternative to these global systems for regional estimates of carbon fluxes, which can take advantage of high-resolution Lagrangian footprints that are increasingly easy to obtain.

Severe droughts in the Northern Hemisphere cause a widespread decline of agricultural yield, the reduction of forest carbon uptake, and increased CO2 growth rates in the atmosphere. Plants respond to droughts by partially closing their stomata to limit their evaporative water loss, at the expense of carbon uptake by photosynthesis. This trade-off maximizes their water-use efficiency (WUE), as measured for many individual plants under laboratory conditions and field experiments. Here we analyse the C-13/C-12 stable isotope ratio in atmospheric CO2 to provide new observational evidence of the impact of droughts on the WUE across areas of millions of square kilometres and spanning one decade of recent climate variability. We find strong and spatially coherent increases in WUE along with widespread reductions of net carbon uptake over the Northern Hemisphere during severe droughts that affected Europe, Russia and the United States in 2001-2011. The impact of those droughts on WUE and carbon uptake by vegetation is substantially larger than simulated by the land-surface schemes of six state-of-the-art climate models. This suggests that drought-induced carbon-climate feedbacks may be too small in these models and improvements to their vegetation dynamics using stable isotope observations can help to improve their drought response.

To improve our understanding of the global carbon balance and its representation in terrestrial biosphere models we present here a first multi-species application of the CarbonTracker Data Assimilation System (CTDAS). The system's modular design allows for assimilating multiple atmospheric trace gases simultaneously to infer exchange fluxes at the Earth surface. In the prototype discussed here we interpret signals recorded in observed carbon dioxide (CO 2 ) along with observed ratios of its stable isotopologues 13 CO 2 / 12 CO 2 (δ 13 C). The latter is in particular a valuable tracer to untangle CO 2 exchange from land and oceans. Potentially, it can also be used as a proxy for continent-wide drought stress in plants, largely because the ratio of 13 CO 2 and 12 CO 2 molecules removed from the atmosphere by plants is dependent on moisture conditions. The multi-species CTDAS system varies the net exchange fluxes of both 13 CO 2 and CO 2 in ocean and terrestrial biosphere models to create an ensemble of 13 CO 2 and CO 2 fluxes that propagates through an atmospheric transport model. Based on differences between observed and simulated 13 CO 2 and CO 2 mole fractions (and thus δ 13 C) our Bayesian minimization approach solves for weekly adjustments to both net fluxes and isotopic terrestrial discrimination that minimizes the difference between observed and estimated mole fractions. With this system we are able to estimate changes in terrestrial δ 13 C exchange on seasonal and continental scales in the Northern hemisphere where the observational network is most dense. Our results indicate a decrease in stomatal conductance on a continent-wide scale during a severe drought. These changes could only be detected after applying combined atmospheric CO 2 and δ 13 C constraints as done in this work. The additional constraints on surface CO 2 exchange from δ 13 C observations neither affected the estimated carbon fluxes, nor compromised our ability to match observed CO 2 variations. The prototype presented here can be of great benefit not only to study the global carbon balance but potentially also to function as a data driven diagnostic to assess multiple leaf-level exchange parameterizations in carbon-climate models that influence the CO 2 , water, isotope, and energy balance.

The terrestrial carbon cycle is currently the least constrained component of the global carbon budget. Large uncertainties stem from a poor understanding of plant carbon allocation, stocks, residence times, and carbon use efficiency. Imposing observational constraints on the terrestrial carbon cycle and its processes is, therefore, necessary to better understand its current state and predict its future state. We combine a diagnostic ecosystem carbon model with satellite observations of leaf area and biomass (where and when available) and soil carbon data to retrieve the first global estimates, to our knowledge, of carbon cycle state and process variables at a 1° × 1° resolution; retrieved variables are independent from the plant functional type and steady-state paradigms. Our results reveal global emergent relationships in the spatial distribution of key carbon cycle states and processes. Live biomass and dead organic carbon residence times exhibit contrasting spatial features (r = 0.3). Allocation to structural carbon is highest in the wet tropics (85-88%) in contrast to higher latitudes (73-82%), where allocation shifts toward photosynthetic carbon. Carbon use efficiency is lowest (0.42-0.44) in the wet tropics. We find an emergent global correlation between retrievals of leaf mass per leaf area and leaf lifespan (r = 0.64-0.80) that matches independent trait studies. We show that conventional land cover types cannot adequately describe the spatial variability of key carbon states and processes (multiple correlation median = 0.41). This mismatch has strong implications for the prediction of terrestrial carbon dynamics, which are currently based on globally applied parameters linked to land cover or plant functional types.