ABSTRACT The 2023 Canadian forest fires have been extreme in scale and intensity with more than seven times the average annual area burned compared to the previous four decades1. Here, we

quantify the carbon emissions from these fires from May to September 2023 on the basis of inverse modelling of satellite carbon monoxide observations. We find that the magnitude of the

carbon emissions is 647 TgC (570–727 TgC), comparable to the annual fossil fuel emissions of large nations, with only India, China and the USA releasing more carbon per year2. We find that

widespread hot–dry weather was a principal driver of fire spread, with 2023 being the warmest and driest year since at least 19803. Although temperatures were extreme relative to the

forests as a carbon sink5,6,7,8. SIMILAR CONTENT BEING VIEWED BY OTHERS MULTI-DECADAL INCREASE OF FOREST BURNED AREA IN AUSTRALIA IS LINKED TO CLIMATE CHANGE Article Open access 26 November

2021 PROJECTED INCREASES IN WESTERN US FOREST FIRE DESPITE GROWING FUEL CONSTRAINTS Article Open access 02 November 2021 FIRE WEAKENS LAND CARBON SINKS BEFORE 1.5 °C Article Open access 03

October 2024 MAIN Canadian forests cover a vast area of nearly 362 million ha (ref. 9), amounting to 8.5% of the global forested area10. These forests are an important sink of carbon,

absorbing fossil carbon dioxide (CO2) from the atmosphere and slowing the pace of climate warming11,12. However, climate change is increasing forest fire activity, acting to suppress the

carbon uptake capacity of these forests13. Although more frequent fires have been widespread, 2023 has seen forest fires on an extreme scale. With 15 million ha of Canadian forests burned

(about 4% of forest area)1, 2023 saw more than seven times (8 _σ_) the average burned area over the preceding 40 years (1983–2022 mean, 2.2 million ha; range, 0.2–7.1 million ha)1. The

adverse societal impacts of these fires are clear: 232,000 evacuations and poor air quality affecting millions14. However, the carbon emissions from the fire events remain uncertain. In this

study, we quantify these emissions through inverse modelling of satellite observations of carbon monoxide (CO). Then, we examine concurrent climate anomalies and projected changes in the

prevalence of hot–dry weather under climate change. Finally, we discuss the implications of fires for the Canadian carbon budget. FIRE EMISSIONS Fire carbon emissions can be tracked from

space using bottom-up and top-down approaches. Bottom-up approaches use satellite observations to track fire activity, such as burned area15 or fire radiative power16. Emissions of CO2, CO

and other trace gases are then estimated by combining the estimates of fire activity with quantities such as fuel loads and emission factors. Although these bottom-up estimates are

continually improving, inventories can vary significantly in global and regional trace gas and aerosol emission estimates15,17. Top-down approaches provide a method for refining bottom-up

trace gas emission estimates by optimally scaling emission estimates to be consistent with the observed concentrations of trace gases in fire plumes. A strength of this approach is that it

integrates emissions from both flaming and smouldering combustion to capture net emissions. In this study, we perform top-down estimates of CO emissions from the 2023 Canadian fires based on

observational constraints from the TROPOspheric monitoring instrument (TROPOMI) space-based CO retrievals (Fig. 1a,b). These estimates are performed using three different bottom-up fire

emission inventories: the global fire emissions database (GFED4.1s)15, the global fire assimilation system v.1.2 (GFAS)16 and the quick fire emissions dataset v.2.6r1 (QFED)18. For each

inversion, the combined carbon emissions released as CO and CO2 (CO2 + CO) are then estimated using the CO2/CO emission factors from the same bottom-up database. The CO2/CO emission ratios

can be highly variable, adding uncertainty to our analysis. We incorporate some of this uncertainty here as each bottom-up database has different mean emission ratios for Canadian forests

(range, 7.7–10.8 gC of CO2 per gC of CO2). Details for these inversions are provided in the methods and a description of the inversion results and evaluation of the performance of the

top-down estimates are provided in Supplementary Information sections 1 and 2). We find the top-down estimates are relatively insensitive to choices about inversion configuration but do show

sensitivity to prescribed hydroxyl radical (OH) abundances19, which determine the atmospheric lifetime of the CO emitted (Supplementary Information section 1 and Supplementary Fig. 1).

Figure 1c shows the bottom-up and top-down CO2 + CO carbon emissions from fires during May–September 2023. The bottom-up datasets show large differences, ranging from 234 to 735 TgC (mean of

469 TgC). This range is reduced by 69% in the top-down estimates (570–727 TgC), which also give a larger mean estimate of 647 TgC. Emissions during 2023 far exceed typical Canadian forest

fire emissions, with 2010–2022 average emissions of 29–82 TgC for the bottom-up inventories and 121 TgC for top-down estimates (Supplementary Fig. 2). To contextualize these numbers, we

compare the top-down estimates to annual national fossil fuel emissions for the ten largest emitters (Fig. 1d). The 5 month 2023 emissions are more than four times larger than Canadian

annual fossil fuel emissions (149 TgC yr−1) and comparable to India’s annual emissions (740 TgC yr−1). Fire activity is affected by several complex drivers, including fuel traits20 and

ignition probability21. However, fire weather—hot and dry conditions—has been shown to be extremely important in driving fire behaviour22. Climate data show an exceptionally hot and dry fire

season for Canadian forests during 2023 (Fig. 2). This was the driest January–September period for Canadian forests since at least 1980, with about 86% of forested area having below-average

precipitation and about 52% being more than 1 s.d. below the 2003–2022 average (Supplementary Fig. 4). May–September 2023 was the warmest since at least 1980, with about 100% of the forest

area above average and about 90% being more than 1 s.d. above the 2003–2022 average. Similarly, the vapour pressure deficit (VPD), which is closely associated with fire activity22,23,24, was

the third highest since 1980, including 85% of the forest area being above average and about 54% being more than 1  s.d. above the 2003–2022 average. Although hot–dry conditions were

widespread across Canadian forests, there are two notable regional patterns. Western Quebec (49°–55° N, 72°–80° W), which is typically relatively wet (Supplementary Fig. 5a), had

exceptionally dry conditions during 2023, with precipitation through September being 23.7 cm (49%) below average. Coupled with extreme heat and VPD during June–July, fire emissions in this

region contributed about 15% of the national total (Supplementary Fig. 6). The other notable region was northwestern Canada near the Great Slave Lake (57°–62° N, 110°−125° W). This region is

drier than western Quebec on average, with about half the annual precipitation. However, 2023 was exceptional, with both a large precipitation deficit of 8.1 cm (27% of January–September

total) and exceptionally warm conditions throughout May–September (+2.6 °C) (Supplementary Fig. 6). This region contributed about 61% of the total Canadian forest fire emissions. FIRES AND

CLIMATE The relationship between climate variability and fire emissions for Canadian forests is examined in Fig. 3, which shows fire emissions as a function of temperature and precipitation

_Z_-scores over 2003–2023 for the 0.5° × 0.625° grid cells, in which _Z_-scores are the anomalies divided by the standard deviation. May–September emissions are lowest for combined cool–wet

conditions (5.2 gC m−2), whereas emissions increase when either temperature is above average (19.5 gC m−2) or precipitation is below average (9.2 gC m−2). However, emissions are largest for

combined warm–dry conditions (35.7 gC m−2). In particular, fire emissions are much increased during exceptionally hot and dry conditions (99.6 gC m−2, temperature _Z_ > 1 and

precipitation _Z_ < −1). These hot–dry conditions were much more prevalent in 2023 than in preceding years, with a mean May–September T2M _Z_-score of 2.3 and a precipitation _Z_-score of

−1.1 across grid cells, explaining why fire emissions were extreme during 2023. Notably, the number of individual fires during 2023 was not unusual, with 6,623 relative to a 10 yr average

of 5,597 (ref. 25). Yet, probably primarily driven by these hot–dry conditions24, many of these fires grew to enormous sizes with hundreds of megafires (greater than 10,000 ha) recorded.

Next, we examine future climate conditions in the region and how they compare to the concurrent climate conditions that led to the massive fires. Figure 3 shows the decadal mean temperature

and precipitation _Z_-scores for the median of 27 models from the coupled model intercomparison project phase 6 (CMIP6)26 under the moderate-warming shared socioeconomic pathway (SSP) 2–4.5

(ref. 4). Large projected temperature increases are found to occur, with average temperatures in the 2050s similar to 2023. More modest increases in precipitation are projected, indicating a

‘speeding up’ of the water cycle, in which both evaporation and precipitation rates increase (Supplementary Fig. 12 shows ensemble distribution). Studies indicate that the combined effect

will result in regional increases in moisture deficits for Canadian forests through the end of the twenty-first century6,27,28. Beyond the 2050s, average temperature and precipitation

conditions are projected to exceed the historical range. These changes will impact the boreal carbon cycle in many ways, such as changing fuel loads and species composition, which

complicates projections of future fire activity. However, increases in boreal fire emissions linked to warming have been reported over recent decades13,27,29,30 and several studies have

projected further increases in Canadian fire activity with future warming5,6,7,8. Thus, we find that warming, coupled with regionally increasing moisture deficits, is likely to drive

increased fire carbon emissions from Canadian forests. CANADIAN CARBON BUDGET IMPLICATIONS As a party to the Paris Agreement, Canada is obligated to track economy-wide greenhouse gas (GHG)

emissions and removals in a national GHG inventory (NGHGI). This includes tracking emissions and removals from ‘managed’ lands, for which human interventions and practices have been applied

to perform production, ecological or social functions31. However, the 2006 Intergovernmental Panel on Climate Change (IPCC) guidelines for national GHG inventories31 and Canadian NGHGI32

differ in how emissions and removals over managed lands are categorized. The IPCC guidelines treat all emissions and removals on managed land as anthropogenic, whereas the Canadian NGHGI

treats ‘natural disturbances’ as non-anthropogenic. This difference in categorization leads to large differences between the Canadian NGHGI and an estimate using the IPCC guideline

definitions. Figure 4 shows that NGHGI removals on managed forest land are almost exactly compensated by emissions from harvested wood products, such that the total CO2 emissions for Canada

are dominated by the energy sector (more than 90% of net emissions). However, we see that natural disturbances are shown to be of considerable magnitude, amounting to nearly 60% of total CO2

emissions in 2021. The 2023 CO + CO2 fire emissions across managed Canadian forests (see section on ‘Managed land’) are estimated to be 421 (388–461) TgC, amounting to 2.5–3 years of

economy-wide CO2 emissions. Regardless of their characterization, fire carbon emissions will affect the growth rate atmospheric CO2. As such, monitoring changes in the carbon budget across

both managed and unmanaged land is important. Including all land in the Canadian carbon budget, top-down estimates find that Canadian ecosystems are a sink of CO2 when constrained by either

in situ or space-based CO2 observations. Using both data types, an ensemble of atmospheric CO2 inversion systems report that Canadian carbon stocks increased 366 ± 88.6 TgC yr−1 over

2015–202011, contributing about 30% of the net land carbon sink. Similarly, space-based biomass estimates find carbon accumulation in Canadian boreal forests, although smaller in

magnitude13,33,34. Thus, Canadian forests play an important role in mitigating anthropogenic emissions, slowing the rise of atmospheric CO2. The large carbon release resulting from the 2023

Canadian fires puts into question the durability of this sink. Others13 showed that fires have acted to suppress the carbon uptake potential of Canadian forests over the past 30 years.

Although Canadian forests have historically experienced large stand-replacing fires at infrequent intervals of 30 to more than 100 years35,36,37, increases in fire frequency will probably

reduce biomass recovery and affect species composition37,38,39,40. It has also been argued that fire, insects and droughts may already be driving Canadian forests into a carbon source41,42.

In the extreme case that expansive fires, such as that of 2023, become the norm (burning 4% of Canadian forest area), all Canadian forests could burn every 25 years. So, although the

magnitude is uncertain, it is likely that increasing fire activity in Canadian forests will reduce the capacity of these Canadian forests to continue to act as a carbon sink. The role of

Canada’s fire management strategy in managing fire carbon emissions also deserves some discussion. Fire management strategies require balancing several considerations, including

socioeconomic costs, ecological impacts and carbon emissions. Canada’s present strategy adopts a risk-based approach, for which decisions on whether or not to suppress fires are made on a

fire-by-fire basis43, with differing priorities across provinces and territories. Understanding how fire regimes will change with climate change is thus of high importance, for future

decision criteria and costing. CONCLUSIONS The 2023 fire season was the warmest and driest for Canadian forests since at least 1980, resulting in vast carbon emissions from forest fires.

Using TROPOMI CO retrievals, we estimate the total May–September CO2 + CO emissions from these fires to be 647 TgC (range 570–727 TgC), comparable in magnitude to India’s annual fossil fuel

CO2 emissions. The 2023 warmth was exceptional based on the last 44 years but CMIP6 climate models project that the temperatures of 2023 will become normal by the 2050s. Such changes are

likely to increase fire activity5,6,7,8, risking the carbon uptake potential of Canadian forests. This will impact allowable emissions for reaching warming targets, as reduced carbon

sequestration by ecosystems must be compensated for by adjusting anthropogenic emissions reductions. METHODS CLIMATE DATA Precipitation estimates were derived from Climate Prediction Center

(CPC) global unified gauge-based analysis of daily precipitation data provided by the National Oceanic and Atmospheric Administration from their website at https://psl.noaa.gov (refs.

44,45). MERRA-2 2 m temperature (T2M) and dew point temperature at 2 m (T2MDEW) were obtained from the single-level diagnostics file3. VPD was calculated from these quantities using:

$${\rm{VPD}}={\rm{e}}{\rm{s}}-{\rm{e}}{\rm{a}},$$ where es is the saturation vapour pressure and ea is the vapour pressure, calculated from T2MDEW and T2M, respectively, using the

formulation of ref. 46. The _Z_-scores for precipitation, T2M and VPD were calculated relative to the 20-year baseline of 2003–2022; for example, for T2M this is calculated as:

$${Z-{\rm{score}}}_{{\rm{year}}}=\frac{{{\rm{T2M}}}_{{\rm{year}}}-{\rm{mean}}\left({\rm{T}}{\rm{2}}{{\rm{M}}}_{2003-2022}\right)}{{\rm{std}}\left({\rm{T}}{\rm{2}}{{\rm{M}}}_{2003-2022}\right)},$$

where T2Myear is the May–September mean T2M for a given year and T2M2003–2022 is the 20-element ensemble of May–September mean T2Ms during 2003–2022. CMIP6 data were downloaded from the

Canadian Climate Data and Scenarios website (https://climate-scenarios.canada.ca/?page=cmip6-scenarios). We examine the ensemble median of 27 models provided on a 1° × 1° grid (technical

documentation at https://climate-scenarios.canada.ca/?page=pred-cmip6-notes). The models included are based on data availability and are tabulated at

https://climate-scenarios.canada.ca/?page=cmip6-model-list. T2M and precipitation are analysed for the historical and future scenarios. We combine the historical simulation with SSP 2–4.5

(ref. 4), shown in the main text or SSP 5–8.5 as shown in Supplementary Fig. 7. The _Z_-scores are calculated from the median of the CMIP6 ensemble by calculating the temporal mean of the

median model mean over 2000–2019 for a given grid cell, whereas the reanalysis data are used to estimate internal variability. Therefore, the T2M _Z_-score is calculated as:

$$Z-{{\rm{score}}}_{{\rm{year}}}=\,\frac{{{\rm{T2M}}}_{{\rm{C}}{\rm{M}}{\rm{I}}{\rm{P}}{\rm{year}}}-{\rm{m}}{\rm{e}}{\rm{a}}{\rm{n}}\left({\rm{T}}{\rm{2}}{{\rm{M}}}_{{\rm{CMIP}}{\rm{2000}}-{\rm{2019}}}\right)}{{\rm{std}}\left({\rm{T}}{\rm{2}}{{\rm{M}}}_{{\rm{MERRA}}{\rm{2003}}-{\rm{2022}}}\right)}$$

SOURCES AND SINKS FOSSIL CO EMISSIONS Anthropogenic CO emissions are obtained from the community emissions data system (CEDS) for historical emissions47; specifically we use version

CEDS-2021-04-21 (ref. 48). PRIOR FIRE CO2 AND CO EMISSIONS Fire CO2 and CO emissions are obtained from the GFED, GFAS and QFED databases. GFED4.1s15 provides estimates of biomass burning

using a biogeochemical model ingesting MODIS 500 m burned area49 in combination with 1 km thermal anomalies and 500 m surface reflectance observations to estimate burned area associated with

small fires using a statistical model50. These data were downloaded from https://www.globalfiredata.org/. GFAS v.1.2 provides estimates of daily biomass burning emissions by assimilating

MODIS fire radiative power observations16. These data were downloaded from the atmosphere data store (https://ads.atmosphere.copernicus.eu). We use v.2.6 of the QFED gridded emission

estimates18. These data were downloaded from https://portal.nccs.nasa.gov/datashare/iesa/aerosol/emissions/QFED/v2.6r1/0.25/QFED/. For all biomass burning datasets, we release fire emissions

at the model surface but incorporate a 3 hourly diurnal cycle based on ref. 51. Year-specific emissions are used for the prior in the atmospheric CO inversions. BIOGENIC EMISSIONS,

ATMOSPHERIC CO PRODUCTION AND OH DATA Biogenic emissions, atmospheric CO production and OH data were all derived from the outputs of the MOMO-Chem chemical data assimilation52. An updated

version of the tropospheric chemistry reanalysis v.2 (TCR-2)53 produced using MOMO-Chem is used to evaluate the atmospheric production and loss of CO. The reanalysis is produced through the

assimilation of several satellite measurements of ozone, CO, NO2, HNO3 and SO2. The chemical loss of CO was estimated using the reanalysis OH fields. Because of the multiconstituent data

assimilation, the reanalysis OH shows improved agreements in global distributions over remote oceans in comparison with the ATom aircraft measurements from the surface to the upper

troposphere53. Constraints obtained for OH profiles have a large potential to influence the chemistry of the entire troposphere, including oxidation of non-methane hydrocarbons (NMHCs) to

estimate the chemical production of CO. The biogenic emissions at the surface were obtained from the model of emissions of gases and aerosols from Nature v.2.1 (MEGAN2.1)54. Year-specific

fields were only available through 2018 and estimates for that year are repeated for more recent years. We also perform a supplemental sensitivity analysis for the impact of prescribed OH

abundances on inferred emissions using the fields of ref. 55, which are commonly used for GEOS-Chem methane inversions56. CO RETRIEVALS TROPOMI TROPOMI is a grating spectrometer aboard the

ESA Sentinel-5 Precursor (S5P) satellite which measures Earth-reflected radiances57. CO total column densities are retrieved in the shortwave infrared (around 2.3 μm) using the shortwave

infrared CO retrieval algorithm58,59. TROPOMI CO retrievals60 were downloaded from the Copernicus data space ecosystem (https://dataspace.copernicus.eu/). We use S5P RPRO L2 CO (processor

v.2.4.0) through 25 July 2022, then switch to S5P OFFL L2 CO for more recent data (processor v.2.5.0 or 2.5.0). Retrieved CO total column densities are then converted to dry-air mole

fractions of CO (_X_CO) using the dry-air surface pressure and hypsometric equation. The column averaging kernel is similarly converted to mole-fraction space. Individual retrievals (quality

flag ≥ 0.5) from each orbit are aggregated into super-observations using the model grid (2° × 2.5°). The retrieval uncertainty on super-observations is taken to be the mean uncertainty on

all retrievals in a given super-observation. This approach is used because systematic errors may exist between retrievals, such that assuming random errors would underestimate the true

retrieval error. For assimilation into NASA carbon monitoring system-flux (CMS-Flux), we calculate observational errors that incorporate error in the atmospheric transport model. For this,

we follow the approach of ref. 61. First, we perform a forward model simulation with the prior fluxes for 2019–2023. Then we take the observational uncertainty to be the standard deviation

between the simulated and real TROPOMI super-observations over a moving window of 30° latitude, 30° longitude and 30 days (across all years). The uncertainties estimated using this approach

range over 3.5–14.3 ppb (5–95 percentiles), whereas retrieval errors range over 1.4–4.9 ppb. Thus, the observational errors are dominated by representativeness errors. MOPITT We use the

MOPITT (measurements of pollution in the troposphere) satellite thermal-infrared–near-infrared (TIR–NIR) CO retrieval. Version 9 (L2V19.9.3)62 is used from 2009 to 31 October 2022, whereas

L2V19.10.3.beta is used from 1 November 2022 onwards. These data were downloaded from the EarthData ASDC (https://asdc.larc.nasa.gov/data/MOPITT/). As with TROPOMI, profile retrievals were

converted into dry-air mole fractions of CO (_X_CO) for assimilation; however, unlike TROPOMI, we do not generate super-observations but instead assimilate individual observations. This is

because the footprint of MOPITT retrievals (22 × 22 km2) is much coarser than TROPOMI retrievals (3 × 7 km2). TCCON The total carbon column observing network (TCCON) consists of ground-based

Fourier transform spectrometers which retrieve _X_CO, _X_CO2 and other species from observations of solar radiation63. In this study, we examine GGG2020 (ref. 64) TCCON data from Park

Falls65 and East Trout Lake66. These data were obtained from the TCCON Data Archive hosted by CaltechDATA at https://tccondata.org. Super-observations are created for each site as hourly

averages; we only include hours with five or more observations. ATMOSPHERIC CO INVERSIONS We perform a series of CO inversion analyses using the CMS-Flux atmospheric inversion system. This

inversion model is descended from the GEOS-Chem adjoint model67 and has been used for CO2 (refs. 68,69) and CO inversion analyses70. The inversions in this study are all performed globally

at 2° × 2.5° spatial resolution using MERRA-2 reanalysis. CEDS anthropogenic emissions, biogenic atmospheric CO production, direct biogenic CO emissions and fire emissions (from GFED4.1s,

GFAS or QFED) and atmospheric OH fields are all prescribed in the forward simulations (see section on ‘Sources and sinks’). Four-dimensional variational data assimilation (4D-Var) is used to

optimize scaling factors on the net surface flux for each grid cell (combined anthropogenic, fire and direct biogenic CO flux). The posterior CO fluxes are then decomposed into

anthropogenic, fire and biogenic fluxes using the fractional contribution of the prior (an approach widely used for CO inversions). A series of MOPITT _X_CO inversions are performed over

2010–2021. Weekly fluxes are optimized over the period 7 November of the preceding year (YYYY − 1) to 1 February of the next year (YYYY + 1), the optimized fluxes in the desired year are

retained (YYYY) and the fluxes outside this period are discarded as spin-up or spin-down. These inversions are performed using the GFED4.1s fire inventory. Prior uncertainties on emissions

are assumed to be proportional to the emissions, with a scale factor uncertainty of 200%. TROPOMI _X_CO inversions are performed over 2019–2023. These inversions are performed over a

truncated period of 1 April to 30 September, with April then being discarded as spin-up. Several different inversion configurations are used to quantify the uncertainty in posterior fluxes

due to both Bayesian posterior uncertainties and systematic choices about error specification and inversion configuration, both of which have been shown to contribute significantly to

inversion error estimates11. Three ensembles of inversions are performed on the basis of the three different prior fire inventories: GFED4.1s, GFAS or QFED (Extended Data Fig. 1a). Each

prior inventory was subjected to four different experimental configurations (Extended Data Fig. 1b). In one case, the _X_CO super-observations error is taken to be the mean retrieval

uncertainty across all retrievals included in a given super-observation. This approach typically gives an uncertainty of 1.3–4.9 ppb. The other case uses an observational error estimate that

incorporates representativeness errors (see section on ‘TROPOMI’), which are typically between 3.5 and 14.3 ppb. The experimental configurations also differ by the treatment of prior

uncertainties on the fluxes. These uncertainties are not well known a priori, thus we use two very different approaches. In the first approach, we assume that the errors on fluxes are equal

to 200% of the prior flux estimate. In the second approach, we assume that flux uncertainties are near constant in flux units (scale factor uncertainty times control flux is constant, this

is truncated to scale factors uncertainties between 0.25 and 1,000). Finally, we also vary the temporal optimization to either 3 or 7 days. As with the prior flux uncertainties, there are

many possible choices for temporal optimization, so we choose two reasonable estimates to quantify the sensitivity to this choice. The spread in maximum a posteriori estimates across these

different set-ups gives an indication of the uncertainty in estimated fluxes due to the set-up decisions. We also estimate the Bayesian posterior uncertainty (Extended Data Fig. 1c), which

derives from uncertainties in the prior fluxes and observations. This uncertainty is estimated using the Monte Carlo method introduced by ref. 71 and formalized by ref. 72 We perform the

experiment during 2023 for each prior inventory and use 40 inversion ensemble members using the inversion configuration with TROPOMI _X_CO representativeness errors and 7 day optimization.

Finally, for each prior inventory, we calculate the posterior best estimates and uncertainties from the experiments described above (Extended Data Fig. 1d). The best estimate is taken to be

the mean across the four different inversion configurations. The uncertainty on this estimate is taken as the square-root of the sum of the variances resulting from the different inversion

configurations and Monte Carlo posterior covariance estimate. The overall best estimate is taken to be the average across the best estimates for the prior inventory ensembles and the overall

uncertainty is taken to be the range of 1 _σ_ uncertainties across the three prior inventory ensembles. We estimate posterior CO2 fluxes from the posterior CO emissions using the CO2/CO

emission ratios provided by the prior GFED4.1s, GFAS and QFED inventories. Each inventory has different CO2/CO emission; thus, we use the emission ratio to estimate the posterior CO2 from

the same inventory that was used as the prior inventory. This incorporates some uncertainty CO2/CO emission ratio into the CO2 emission estimates. REGIONAL MASKS FOREST AREA Forest area is

defined using v.6.1 of the MODIS MCD12C1 product73. On the basis of the type 1 majority land cover, we define forests to include the categories evergreen needleleaf forests, evergreen

broadleaf forests, deciduous needleleaf forests, deciduous broadleaf forests, mixed forests, woody savannas and savannas. MANAGED LAND The map of managed lands74 was accessed through

personal communication with M. Hafer and A. Dyk (the map was only created for cartographic communication purposes). The extent of land considered managed forest in Canada for the purposes of

GHG reporting to the United Nations Framework Convention on Climate Change cannot be mapped in detail. That information comes from provincial/territorial forest inventories that are not

spatially explicit and cannot be mapped. Supplementary Fig. 13 shows the managed land map and the fractional managed/unmanaged for 2° × 2.5° grid cells. DATA AVAILABILITY The dataset

produced for this study can be accessed at JPL Open Repository, https://doi.org/10.48577/jpl.V5GR9F. CODE AVAILABILITY The Python and Bash codes used in this study are available at Zenodo

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bkabyrne/2023CanadaFires: v1.0.0. _Zenodo_ https://doi.org/10.5281/zenodo.12709398 (2024). Download references ACKNOWLEDGEMENTS The research carried out at the Jet Propulsion Laboratory,

California Institute of Technology, was under a contract with the National Aeronautics and Space Administration. Resources supporting this work were provided by the NASA High-End Computing

programme through the NASA Advanced Supercomputing Division at Ames Research Center. Authors B.B., A.C., J.L. and K.B. acknowledge the support from NASA Orbiting Carbon Observatory Science

Team Program and the Carbon Monitoring System Program (grant no. NNH20ZDA001N-CMS). We acknowledge the World Climate Research Programme, which, through its Working Group on Coupled

Modelling, coordinated and promoted CMIP6. We thank the climate modelling groups for producing and making available their model output, the Earth System Grid Federation (ESGF) for archiving

the data and providing access and the many funding agencies who support CMIP6 and ESGF. GFAS is generated using Copernicus Atmosphere Monitoring Service Information 2020; neither the

European Commission nor ECMWF is responsible for any use that may be made of the information it contains. The East Trout Lake TCCON station is funded through an infrastructure grant from the

Canada Foundation for Innovation (grant no. 35278) and the Ontario Research Fund (grant no. 35278). The Park Falls TCCON site was supported by NASA (grant no. 80NSSC22K1066). We thank J. L.

Laughner for guidance with the TCCON data. We thank M. Hafer and A. Dyk for providing information on Canada’s managed land. And we thank L. Baskaran for help in rasterizing these data.

AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA Brendan Byrne, Junjie Liu, Kevin W. Bowman, Madeleine

Pascolini-Campbell, Abhishek Chatterjee, Sudhanshu Pandey & Kazuyuki Miyazaki * Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA

Junjie Liu, Paul O. Wennberg & Coleen M. Roehl * Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, CA, USA Kevin W. Bowman *

Meteorology & Air Quality Group, Wageningen University and Research, Wageningen, The Netherlands Guido R. van der Werf * Department of Physics, University of Toronto, Toronto, Ontario,

Canada Debra Wunch * Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA Paul O. Wennberg * Department of Energy, Environmental, and Chemical

Engineering, Washington University in St. Louis, St. Louis, MO, USA Saptarshi Sinha Authors * Brendan Byrne View author publications You can also search for this author inPubMed Google

Scholar * Junjie Liu View author publications You can also search for this author inPubMed Google Scholar * Kevin W. Bowman View author publications You can also search for this author

inPubMed Google Scholar * Madeleine Pascolini-Campbell View author publications You can also search for this author inPubMed Google Scholar * Abhishek Chatterjee View author publications You

can also search for this author inPubMed Google Scholar * Sudhanshu Pandey View author publications You can also search for this author inPubMed Google Scholar * Kazuyuki Miyazaki View

author publications You can also search for this author inPubMed Google Scholar * Guido R. van der Werf View author publications You can also search for this author inPubMed Google Scholar *

Debra Wunch View author publications You can also search for this author inPubMed Google Scholar * Paul O. Wennberg View author publications You can also search for this author inPubMed 

Google Scholar * Coleen M. Roehl View author publications You can also search for this author inPubMed Google Scholar * Saptarshi Sinha View author publications You can also search for this

author inPubMed Google Scholar CONTRIBUTIONS B.B., J.L., K.W.B., M. P.-C., A.C. and S.P. conceptualized and designed the study. K.M. provided atmospheric CO production and OH estimates.

G.R.v.d.W extended the GFED4.1s dataset for this experiment. D.W., P.O.W. and C.M.R. provided TCCON data. S.S. provided MERRA-2 reanalysis for the model. B.B. conducted the analysis and

wrote the paper, with input from all authors. CORRESPONDING AUTHOR Correspondence to Brendan Byrne. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. PEER

REVIEW PEER REVIEW INFORMATION _Nature_ thanks Mike Flannigan, Johannes Kaiser and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer

reports are available. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. EXTENDED

DATA FIGURES AND TABLES EXTENDED DATA FIG. 1 SCHEMATIC DIAGRAM OF THE TROPOMI XCO INVERSION PROCEDURE. (a) Ensembles of inversions are performed based on three different flux inventories.

(b) To quantify the sensitivity to systematic error sources, four inversions are performed that differ in observational error constraints, prior error constraints, and temporal optimization

frequency. (c) Bayesian posterior error estimates are estimate for 2023 by following the Monte Carlo approach for 4D-Var of Chevalier et al.71. (d) The posterior best estimates are taken as

the average maximum a posteriori estimate across inversion configurations while the uncertainty is taken to be the sum-of-squares of the error components estimated in (b) and (c).

SUPPLEMENTARY INFORMATION SUPPLEMENTARY INFORMATION Supplementary sections 1 and 2, Figs. 1–14, Tables 1–3 and references. PEER REVIEW FILE RIGHTS AND PERMISSIONS OPEN ACCESS This article is

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copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Byrne, B., Liu, J., Bowman, K.W. _et al._ Carbon

emissions from the 2023 Canadian wildfires. _Nature_ 633, 835–839 (2024). https://doi.org/10.1038/s41586-024-07878-z Download citation * Received: 29 November 2023 * Accepted: 25 July 2024 *

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