Arctic condensed aromatic carbon budget reveals efficient fluvial transfer and shelf storage

Arctic condensed aromatic carbon budget reveals efficient fluvial transfer and shelf storage | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Arctic condensed aromatic carbon budget reveals efficient fluvial transfer and shelf storage Melissa Schwab, Alysha Coppola, Max van Gerrevink, Clément Bertin, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9476681/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Arctic–Boreal ecosystems hold vast carbon stocks, yet intensifying wildfires threaten to shift them from net sinks to net sources. Fires produce condensed aromatic carbon (ConAC), a chemically stable form of pyrogenic carbon that accumulates in terrestrial and marine reservoirs. However, ConAC mobilization, retention, and export along the riverine continuum remain poorly constrained. Here, we present a comprehensive source-to-sink ConAC budget for the Mackenzie River–Beaufort Sea system, quantifying wildfire production, riverine transport, and shelf accumulation. Between 2001 and 2017, fires burned 9.7% of the basin and generated 36.5 Tg ConAC, while soils stored ~4 Pg ConAC with millennial-scale turnover. Fluvial export averaged 0.42 Tg ConAC yr –1 . The dissolved fraction reflected modern pyrogenic sources, in contrast to particulate ConAC dominated by radiocarbon-depleted carbon derived from sedimentary rocks. On the Beaufort Shelf, waters retained ~1.2 Tg ConAC with a ~6-year residence time, whereas sediments accumulated ~0.14 Tg ConAC yr –1 . This budget reveals that basin properties including lithology, geomorphic routing, and sediment trapping control the composition and efficiency of land-to-ocean ConAC transfer. It establishes a quantitative baseline for assessing how ongoing environmental change will reshape ConAC cycling across high-latitude systems. Earth and environmental sciences/Biogeochemistry/Carbon cycle Earth and environmental sciences/Climate sciences/Biogeochemistry/Carbon cycle Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Arctic-Boreal ecosystems constitute one of the largest terrestrial carbon reservoirs, yet escalating fire activity increasingly threatens a transition toward net carbon sources 1–5 . Fires act as both a major contributor to atmospheric carbon emissions and as a disturbance that catalyzes the destabilization of carbon-rich permafrost (permanently frozen) soils 3,5–7 . Model projections indicate that the frequency, extent, and severity of Arctic-Boreal fires will intensify under continued climate warming 8–11 . The 2023 Canadian fire season exemplifies this trajectory, with approximately 15 million ha burned and 570–727 Tg C emitted to the atmosphere 12 , rivaling annual fossil fuel emissions from major industrialized nations 13 . Such events underscore the growing susceptibility of northern high-latitude ecosystems to fire-driven carbon mobilization and the amplification of carbon–climate feedbacks mediated by hydroclimatic change. Amid shifting fire dynamics, pyrogenic carbon (PyC) represents a quantitatively important, under-characterized component of the post-fire carbon budget. Formed through incomplete combustion, PyC consists predominantly of condensed aromatic carbon (ConAC), a chemically resistant material that accumulates within terrestrial and aquatic environments and can persist for centuries to millennia 14–21 . However, in addition to its pyrogenic formation, ConAC can also originate from non-pyrogenic pathways, including low-temperature oxidation of biomass 22,23 , soil humification 24 , and the erosion-driven input of thermally mature 25,26 and lithified sedimentary organic matter 27–29 . This diversity of sources creates a central challenge for interpreting ConAC dynamics and attributing observed fluxes to contemporary fire activity. Pyrogenic and non‑pyrogenic ConAC reach soils, floodplains, and aquatic systems at different times, from different source areas, and through distinct transport and storage pathways. Consequently, the ConAC pool reflects cumulative landscape processing rather than solely recent combustion. Aromatic carbon persistence and radiocarbon composition integrate fire recurrence, geomorphic routing, lithological inputs, and multi‑stage storage across terrestrial and aquatic environments 30,31 . As a result, basin‑scale ConAC export can diverge sharply from recent wildfire activity, complicating its use as a proxy for modern fire intensity and underscoring its role as a long‑term integrator of carbon mobilization across northern landscapes. Current estimates report global ConAC production of 116–385 Tg ConAC yr –1 (refs. 32–35 ). In contrast, non-pyrogenic inputs contribute 163−182 Tg ConAC yr –1 (ref. 23 ), reflecting significant overlap among sources and uncertainty in attribution. Once produced, ConAC is subjected to biotic and abiotic decomposition, yielding a global loss of ~248 Tg ConAC yr –1 (ref. 34 ). Residual particulate ConAC (pConAC) stocks span 54–450 Pg ConAC 31,33,36 in soils and 100–1,440 Pg ConAC 31,33 in the upper 1 m of marine sediments. Dissolved ConAC (dConAC) provides an additional 12–145 Tg ConAC 19,20,33,37 to the marine carbon pool. Despite these substantial stocks, the processes controlling ConAC mobilization, transport, and long-term sequestration along the land–ocean continuum remain insufficiently resolved 30,38 , highlighting the scarcity of basin-scale observations linking production, riverine export, and marine burial. River networks serve as the main conduits connecting terrestrial ConAC reservoirs to the ocean 17,30,35,39,40 . Particulate ConAC accounts for ~16% 17 of fluvial particulate organic carbon (POC), but global flux estimates vary widely (17–80 Tg ConAC yr –1 ) 17,31,33,35,41,42 owing to uncertainties in scaling and analytical heterogeneity. Flux-weighted pConAC radiocarbon ( 14 C) ages average 3,700 ± 400 14 C yr globally 17 , whereas Arctic rivers transport pConAC ranging from modern to 17,000 14 C yr, indicating extensive pre-aging through repeated deposition–resuspension cycles and storage in soils, floodplains, and lakes 17,43 . Dissolved organic carbon (DOC) contains ~9% dConAC 35 , corresponding to a flux of 12–28 Tg ConAC yr –1 (refs. 35,44 ). High-latitude rivers export proportionally more dConAC, comprising 21 ± 6% (2.6–3.8 Tg ConAC yr –1 ) 35,45 , with largely modern 14 C signatures indicative of strong coupling to contemporary biomass burning 46,47 . Nevertheless, the degree to which riverine ConAC export reflects recent fire activity versus long-term storage and remobilization remains unconstrained, particularly in large Arctic watersheds where fire regimes, permafrost dynamics, and lithological inputs interact across broad spatial and temporal scales. The Mackenzie River basin provides a uniquely powerful setting in which to resolve this uncertainty. It integrates extensive wildfire activity, pronounced latitudinal permafrost gradients, widespread lake interception, and heterogeneous lithology within a single drainage network, features that typify much of the Arctic-Boreal domain. The basin stores vast amounts of carbon, with 4.4 ± 0.1 Pg C held in living plant biomass and detritus 48,49 and 58.5 ± 3.6 Pg C in the top 1 m of soil 48,49 . This expansive reservoir sustains frequent and widespread wildfire activity, rendering the basin a potential hotspot for fire-derived CO 2 emissions and ConAC production. Concurrent shifts in hydrological regimes 50,51 and accelerating permafrost degradation 52,53 are expected to reshape ConAC mobilization pathways and enhance delivery to coastal margins. These characteristics position the Mackenzie as an ideal natural laboratory for examining how pyrogenic and non-pyrogenic ConAC interact with permafrost dynamics, hydrological routing, and sedimentary inputs during land–ocean transfer. Inferences from this system therefore establish a baseline against which shifts in ConAC cycling across high-latitude watersheds can be assessed under accelerating climate forcing. Here, we present an integrated, basin-scale ConAC budget for the Mackenzie River–Beaufort Sea system, tracing source-to-sink dynamics across a complete terrestrial–marine continuum. Our analysis synthesizes both published and newly generated data on pConAC and dConAC concentrations and associated 14 C values from soils, rivers, and marine environments. We quantified ConAC production from wildfires across seven sub-basins of the Mackenzie River by combining data from the Arctic Boreal Vulnerability Experiment Fire Emissions Database (ABoVE-FED) 54 with ConAC production-to-carbon emissions ratios 33 . Riverine ConAC export and coastal accumulation were estimated through partitioning-based flux calculations, while the contribution of dConAC to the air-sea CO 2 flux was computed using a regional ocean-ice-biogeochemistry model (ECCO-Darwin) 55 . Our findings establish a quantitative framework for assessing how ConAC is mobilized and sequestered in a rapidly changing Arctic. RESULTS AND DISCUSSION From fire to soil: ConAC accumulation and persistence Wildfires burned over 175,000 km 2 (9.7%) of the Mackenzie River Basin between 2001 and 2017, with fire activity strongly concentrated in the southeastern watershed ( Fig. 1a, Extended Data Table 1). The Athabasca and Slave River Basins accounted for more than two-thirds (68.4%) of the total burned area, indicating that southern sub-catchments experience more frequent and extensive fires and therefore contribute disproportionately to total fire emissions. The largest wildfire during the study period was recorded in 2014, affecting approximately 34,000 km 2 . Fire activity clustered near waterways, with 35–44% of total burned area occurring within 1 km of streams (Strahler order 1–3) and 39–70% within 10 km of larger rivers (Strahler order 4–9), after which burned area declined sharply with distance ( Fig. 1b, Extended Data Table 2). Fire density exhibited a similar proximity-driven pattern, though with greater spatial variability across northern sub-basins (Extended Data Table 2). Riparian fire prevalence reflects high fuel loads maintained by dense biomass 56 . Although typically moist, these areas can dry sufficiently to reduce fuel moisture and elevate flammability 4,57 . Microtopographic variability and channelized winds along river corridors can further enhance fire propagation 58 . Wildfire activity in the Mackenzie River Basin is a significant source of carbon emissions, exhibiting substantial interannual variability. The ABoVE-FED 54 provided a framework for quantifying its regional-scale carbon cycle impacts. Between 2001 and 2017, wildfires released approximately 680 Tg C to the atmosphere, with a median annual emission rate of 35.2 Tg C yr –1 (22.6, 45.1) (median (interquartile range)). Using ConAC production-to-carbon emissions ratios 59 , we estimate a total ConAC production of 36.5 Tg, corresponding to a median annual rate of 2.05 Tg ConAC yr –1 (0.98, 2.39) ( Fig. 2 ) 54 . The area-normalized ConAC production was 1.14 t ConAC km –2 yr –1 (0.55, 1.33). Peak ConAC production occurred in 2014 and 2015, driven by extensive wildfire activity that generated 6.55 ± 5.08 Tg ConAC yr –1 (mean ± uncertainty) and 4.96 ± 3.75 Tg ConAC yr –1 , respectively. The highest ConAC production rates were concentrated in the southern Mackenzie River Basin, comprising the Athabasca (0.30 Tg ConAC yr –1 (0.20, 1.02)), Peace (0.33 Tg ConAC yr –1 (0.14, 0.55)), and Slave (0.30 Tg ConAC yr –1 (0.15, 0.62)) sub-catchments (Extended Data Fig. 1, Extended Data Table 1). The highest ConAC production rate was recorded in the Slave Basin in 2014, reaching 3.45 ± 2.70 Tg ConAC yr –1 . In contrast, ConAC production was notably lower in the northern sub-basins, including the Liard (0.09 Tg ConAC yr –1 (0.04, 0.47)), Main Mackenzie (0.11 Tg ConAC yr –1 (0.03, 0.20)), Arctic Red (0.0004 Tg ConAC yr –1 (0.0000, 0.0023)), and Peel Rivers (0.01 Tg ConAC yr –1 (0.00, 0.04)). These patterns highlight a strong south-to-north gradient in fire-derived carbon production, shaped by vegetation structure, climate, and fire regimes. Following wildfire, a portion of ConAC is retained in soils, contributing to long-term carbon storage. Within the top 100 cm of soil across the Mackenzie River Basin, ConAC constitutes approximately 6.9% of total soil organic carbon 18 , amounting to 4.0 ± 0.4 Pg ConAC. This reservoir is nearly equivalent to the total plant biomass carbon stock across the basin, emphasizing the role of ConAC as a major, persistent terrestrial carbon pool. The computed steady-state turnover of ~1950 yr aligns with the younger range of observed 14 C-ConAC ages in soils, reflecting millennial-scale residence times that generally exceed those of bulk soil organic carbon 15,18,31 . Older ConAC ages occur under continuous permafrost, suggesting thermal stabilization and limited microbial accessibility 18 . The magnitude and longevity of soil ConAC storage indicate that terrestrial reservoirs act as a first-order buffer against short-term variability in wildfire activity, decoupling production pulses from downstream transport 18,60 . Nonetheless, 37–40% of ConAC remains susceptible to decomposition 60,61 and lateral export via hydrological and erosional pathways 45,62–64 transfers ConAC into aquatic networks. From basin to shelf: mobilization and loss of ConAC The dConAC content as a proportion of DOC in the northern Mackenzie River system, measured prior to its discharge into the Arctic Ocean, spans 3.9–27.8% ( Fig. 3a ), with a flow-weighted mean of 14.2 ± 6.3% (n = 7). This exceeds values observed by Stubbins et al. 45 of 10.9 ± 1.3% and Jones et al. 33 of 9.6 ± 1.5% for major high-latitude rivers. The estimated annual dConAC export is 0.20 ± 0.10 Tg ConAC yr –1 , which is considerably higher than the previously reported flux of 0.13 ± 0.02 Tg ConAC yr –1 (ref. 45 ). The export of dConAC is intricately linked to DOC dynamics, groundwater, fluvial transport, and the cumulative in-transit processing. The majority of dConAC originates in the southern Mackenzie River basin, where approximately 78% of wildfire-derived ConAC is produced. Given that the dissolved fraction of ConAC is not retained by lakes or other intermittent storage systems, it can travel unimpeded through the river system to the mouth. However, extended transit times expose dConAC to photochemical 65,66 and microbial degradation 34,67 , potentially resulting in significant losses before reaching the Arctic Ocean. As a result, the shorter hydrological residence times in northern tributaries may partially modify dConAC signals from the southern basins. Source attribution remains uncertain, owing to the limited number of observations, and further sampling and study are necessary to clarify these dynamics. dConAC-F 14 C values from the Arctic Red River display moderately aged signatures likely reflecting inputs from both recent wildfire residues and groundwater flow through the upper active layer (seasonally thawed permafrost) ( Fig. 3 ). The content of pConAC as a percentage of the POC fraction exhibits notable variability, ranging from 3.9% in the Arctic Red River to 27.6% in the Peel River. The flow-weighted mean pConAC content across the Mackenzie River system is 14.9 ± 3.0% (n = 27) ( Fig. 3a ). The average pConAC flux across the entire basin is 0.22 ± 0.14 Tg ConAC yr –1 , consistent with estimates from Elmquist et al. 42 and Coppola et al. 17 , which range from 0.099 to 0.296 Tg ConAC yr -1 at Tsiigehtshik. Despite extensive fire activity in the southern Mackenzie Basin, we expect the contribution of pConAC to fluvial downstream transport to be minimal. The region's low-relief shield terrain results in limited surface erosion, thereby restricting pConAC mobilization into river networks 68,69 . Additionally, significant sediment trapping in Lake Athabasca (~45%) 70 and Great Slave Lake (~61%) 70 further reduces pConAC transfer to the main stem of the Mackenzie River. The geomorphic and limnological constraints sharply limit the downstream propagation of pConAC from southern sources. In contrast, northern basins, while producing lower quantities of ConAC from wildfires, export a larger proportion of pConAC to the Arctic Ocean. The Liard River alone accounts for approximately 40% of the total sediment and POC load reaching the Mackenzie River Delta 71,72 . The Peel and Arctic Red Rivers drain steeper upland terrain, where higher erosion, surface runoff, and sediment yields promote increased downstream pConAC flux 72 . pConAC-F 14 C signatures in the northern Mackenzie River system exceed 14 C ages expected for pyrogenic carbon produced by contemporary wildfire regimes. Values range from 0.01 to 0.22 (flow-weighted mean 0.10 ± 0.06; 14 C age 18,000 ± 11,600 yr) and are substantially lower than the corresponding bulk POC-F 14 C of 0.41 ± 0.08 (7,300 ± 1,400 14 C yr; Fig. 3 ) 72,73 . This systematic age offset between pConAC and bulk organic matter echoes spot measurements in Mackenzie River basin soils 18,74 . Such extreme 14 C depletion cannot be reconciled with any post-glacial biogenic source. pConAC-F 14 C age constraints are inconsistent with permafrost soils formed following the Laurentide Ice Sheet retreat (~18 kyr BP) 75 . Pleistocene permafrost deposits (yedoma) are negligible within the watershed 76,77 . The majority of the pConAC is therefore likely sourced from weathering of sedimentary bedrock or deposition of fossil-fuel-derived soot, which are inherently 14 C depleted. The applied benzene polycarboxylic acid (BPCA) method is known to detect non-pyrogenic aromatic carbon originating from shale, coal, and other geologic substrates 29 . Independent measurements of Mackenzie River pConAC using chemo-thermal oxidation (CTO) yielded comparable 14 C age values (0.185 ± 0.003; ~13,600 14 C yr) 42 , corroborating the presence of ancient carbon in the ConAC fraction. Although CTO efficiently removes common petroleum source rock constituents 29 , geologically preserved macerals (e.g., pyrofusinites) may survive thermal oxidation and contribute fossil 14 C signatures to the measured ConAC pool 42 . In agreement with these observations, polycyclic aromatic hydrocarbons (PAHs) in Mackenzie River suspended and bank sediments exhibit source-diagnostic ratios indicative of petrogenic rather than pyrogenic inputs 42,78 . Extensive Devonian-Carboniferous coal and shale deposits of the Interior Platform and Cordilleran foreland serve as large reservoirs for petrogenic carbon 79,80 . Particularly in the Liard and Peel catchments, erosive, barren, and mountainous terrain facilitates the delivery of fossil carbon into the river system 72,73,81 . Although reworked Pleistocene soils and fossil fuel-derived soot 42,82,83 cannot be excluded as secondary contributors, bedrock weathering as the dominant source of aged pConAC. The combined 14 C, molecular, and geomorphic evidence reveals a systematic decoupling between dConAC and pConAC pools during fluvial transport. The dConAC fraction retains a distinctly contemporary pyrogenic signature, mobilized rapidly through the river network without significant storage. In contrast, pConAC is dominated by 14 C-depleted, non-pyrogenic material sourced primarily from sedimentary lithologies. This divergence arises from erosion efficiency, sediment trapping, and the distribution of carbon-rich substrates within the basin. Geomorphic and lithological controls overprint wildfire signals in the particulate phase, rendering exported pConAC a tracer of watershed structure rather than a record of basin-wide fire activity. From plume to permanence: fate of ConAC across the shelf system We estimated dConAC stocks within the Mackenzie plume region (~60,000 km 2 , 0–1,800 m depth, salinity <27) 55 by applying the reported range of dConAC content (1.5–8.6%) 21,84 from adjacent Arctic shelves to kriged DOC concentrations 85,86 . This yielded a stock estimate of 1.2 ± 0.5 Tg ConAC, embedded within a DOC pool of 23.6 ± 0.6 Tg C. Dividing the computed stock by the annual riverine dConAC flux translates to a shelf residence time of approximately six years. This residence time implies that the Mackenzie Shelf is not a passive transition zone but an intermediate reservoir. The shelf operates either as a net accumulator, with fluvial inputs exceeding degradation and offshore export, or as a quasi-steady state system with balanced inputs and losses. However, the limited on-shelf aging of dConAC is insufficient to explain the ~1,500 yr discrepancy between Western Beaufort Sea (~3,740 m; F 14 C: 0.72 ± 0.28 21 ) and the Arctic Red River dConAC ( Fig. 3b ). This divergence is likely shaped by several controls on transport mechanisms, carbon sourcing, and shelf-basin exchange. Seasonal bias may partly reflect this age difference, as the Arctic Red River sample was collected during the spring freshet. Shallow subsurface flow and minimal infiltration through deeper soil horizons favor the delivery of more modern dConAC. The Beaufort Sea offshore samples closely match corresponding DOC-F 14 C values, indicating a shared, aged terrestrial origin 84,87 . Although the Canadian and Alaskan ecosystems are broadly similar, rivers on the Alaskan North Slope may export older permafrost- and yedoma-derived ConAC to the coastal margin, contributing to the aged offshore signal. The shift toward more depleted offshore 14 C signatures can further be attributed to advection of aged water masses from the Chukchi and East Siberian Seas, selective preservation of refractory dConAC, and dissolution of severely aged pConAC and fossil fuel residues. A sample collected near Barrow Canyon (~150 m) exhibits an extreme 14 C age of ~14,000 years, likely reflecting localized upwelling of legacy carbon 20,21 . The removal of dConAC on the Mackenzie Shelf encompasses multiple pathways, including photochemical and microbial degradation 20,40,65,67,88 , sedimentation 16,20,37 , and export to the interior ocean 19,21 . To assess oxidative loss, we modeled CO 2 outgassing from dConAC using a regional ECCO-Darwin configuration 55 . The resulting flux (0.007 ± 0.001 Tg ConAC yr –1 ) is negligible relative to modeled DOC emission fluxes of up to 0.13 Tg C yr –1 (Extended Data Fig. 3) 55 , indicating that outgassing represents a minor removal pathway for dConAC on the Mackenzie Shelf. The model, however, excludes photochemical degradation. As condensed aromatic structures are highly susceptible to photooxidation 66,89,90 , its exclusion likely leads to an underestimation of total oxidative losses. Dissolved ConAC can be further removed from the water column by aggregation, flocculation, and particle interactions 16,37,40 , including complexation with iron 91 . In the Mackenzie–Beaufort system, the particle-mediated removal of DOC, ranging from 45 to 87%, is driven by strong salinity gradients that develop along the estuarine transition 92–94 . Whether dConAC experiences losses of comparable magnitude remains unknown, as direct observations on the Mackenzie Shelf are scarce. In the absence of direct observations, the only large-scale constraint is provided by a transect spanning the Bering Sea to the Canada Basin, where linear mixing models suggest that 47–97% of terrestrial dConAC is lost during shelf transfer, with less than 23% reaching the interior ocean 84 . Despite the potentially significant role of biological and physicochemical removal processes, the six-year shelf residence time of dConAC suggests that riverine inputs accumulate on the Mackenzie Shelf. This accumulation may result from a combination of molecularly resistant compounds and sustained fluxes that outpace decomposition and export 65 . Physical retention is limited, as surface stratification and wind-driven advection promote the offshore transport of plume waters rather than prolonged residence 95 . The sparse and spatially variable dConAC concentrations and 14 C signatures highlight the need for expanded sampling across Arctic river and shelf systems to resolve the sources, dispersal, and fate of dConAC in coastal environments. A substantial portion of pConAC is retained within Mackenzie Shelf sediments, forming a long-term carbon reservoir. Published estimates place POC sedimentation rates for the Mackenzie plume region between 0.22 and 0.52 Tg C yr –1 (refs. 72,96–99 ), while pConAC sediment accumulation rates, constrained by 137 Cs and 210 Pb profiles 100 , amount to 0.14 ± 0.01 Tg C yr –1 . The upper 100 cm of these sediments contains 500 ± 73 Tg C 101 , representing 800–1,300 years of accumulation 84 . Given an observed pConAC content of 17.8 ± 4.3% (n = 3) 100 , this yields a total ConAC stock of 89 ± 22 Tg. Mass balance calculations imply that ~64% of the riverine pConAC export is deposited within the plume area. Yet, the retention efficiency likely overestimates true pConAC burial as dConAC flocculation during estuarine mixing contributes additional ConAC to sediments beyond the original fluvial particulate fraction 16,102,103 . The deposited pConAC is strongly 14 C-depleted (F 14 C = 0.14), consistent with its predominantly petrogenic origin. Comparable F 14 C values in Chukchi Sea sediments (F 14 C = 0.11–0.30) 102 point to a regional pattern of aged pConAC accumulation across Arctic margin depocenters, underscoring the preservation of refractory pConAC in marine sediments. The Mackenzie–Beaufort Sea system efficiently transfers rock-derived pConAC to long-term marine storage, limiting its exposure to oxidation during land–ocean transport and reducing the potential return of petrogenic CO 2 to the atmosphere. From source to signal: implications and uncertainties This study presents a quantitative, source-to-sink budget of ConAC fluxes across the Mackenzie River–Beaufort Sea continuum, linking wildfire-driven production, fluvial mobilization, and marine sequestration within a single integrated framework ( Fig. 4 ). Three findings emerge from this synthesis. First, the Mackenzie River Basin functions as an active regulator of ConAC dynamics rather than a passive conduit, with geomorphic routing and sediment trapping governing the efficiency with which fire-derived carbon reaches coastal margins. Second, the dissolved and particulate fractions export carbon from fundamentally different landscape reservoirs. dConAC is derived from modern, pyrogenic sources and mobilized rapidly through the fluvial network, while pConAC is dominated by ancient, non-pyrogenic carbon sourced from shales and coal-bearing siliciclastics. In watersheds underlain by carbon-rich sedimentary rocks, lithology is a first-order control that reduces the application of pConAC as a proxy for fire activity. Third, the Mackenzie Shelf operates not as a passive transition zone but as an active intermediate reservoir, retaining riverine inputs over multiyear timescales, efficiently transferring radiocarbon-depleted pConAC to long-term marine sediment storage, and limiting the re-oxidation of ancient petrogenic carbon to atmospheric CO 2 . These findings position the Mackenzie–Beaufort system as a reference point for land–ocean ConAC coupling across the Arctic-Boreal domain. Beyond documenting stocks and fluxes, this budget provides a quantitative framework for constraining the dominant fluxes in the ConAC cycle. The difference between wildfire production and riverine export brackets the magnitude of terrestrial ConAC retention and loss, while shelf residence time and sediment accumulation rates define the marine sink. As a diagnostic tool, the budget identifies the flux pathways where targeted observations and process-level understanding are most required. The largest unquantified flux is the export of ConAC from soils to fluvial networks, particularly in permafrost terrain where thermal erosion amplifies lateral carbon delivery to streams 103,104 . During fluvial transport, losses through sedimentation in lakes, floodplains, and deltas 105,106 , and through oxidative decomposition 107 , remain largely unconstrained ( Fig. 4 ). Wildfire production estimates incorporate uncertainty from burn severity and fuel consumption assumptions, but are expected to improve as combustion models and emission databases are refined. The processing of ConAC during estuarine mixing persists as the key unresolved marine pathway, with limited data on the balance between degradation, aggregation, and offshore export. The budget provides a roadmap informing future sampling design and targeted field observations to characterize ConAC mobilization, transformation, and sequestration. It further establishes a baseline for detecting and attributing changes driven by amplifying fire activity 9,10 , permafrost degradation 53,104 , precipitation 108–110 and hydrological regime shift 111 . With Arctic-Boreal systems undergoing the most rapid environmental change on Earth 112,113 , the fate of ConAC represents an unaccounted but consequential term in the global carbon budget. Online Methods Sample collection. Sediment and water samples were collected in 2009, 2010, 2011, 2017, and 2018 from the Mackenzie River and Delta and three tributaries, the Liard, Arctic Red, and the Peel Rivers 72,73,114 . Freshly deposited bank sediments were collected with a metal spoon, while riverbed sediments were obtained using a metal bucket. Surface water was collected using a pre-rinsed bucket and transferred to cubitainers or sterilized plastic bags. Surface water was subsequently filtered through 0.2 µm 90-142 mm polyethersulfone (PES) filters using pressure filtration towers. Bank, bed, and suspended sediments were immediately frozen post-collection and stored frozen until freeze-drying. For each dConAC sample, one liter of filtered water was acidified to pH 2 with hydrochloric acid and slowly passed through four columns (each 250 mL) of styrene divinyl benzene copolymer resin (Sigma Aldrich Diaion 13605, HP-20, pore size 200Å) to concentrate dConAC 115,116 . Cartridges were wrapped in combusted aluminum and preserved at -18ºC. pConAC and dConAC analysis and radiocarbon measurements. Suspended sediments, along with the <63 µm fraction of channel sediments—considered equivalent to suspended sediments—were used to quantify pConAC. Clay and silica matrices can interfere with 14 C analysis of pConAC by adsorbing extraneous organic carbon, thus obscuring the 14 C signal of pConAC, particularly in low-organic-carbon samples 117 . To isolate pConAC, the <63 µm fraction was treated sequentially: first with 1 N hydrochloric acid for 24 hours to remove carbonates and salts, followed by 10% hydrofluoric acid to remove silicate minerals. The treatment was repeated five times, with the sample rinsed ten times with Milli-Q water to remove residual hydrofluoric acid and salts. Before eluting the DOC fraction containing dConAC, salts were removed by rinsing all columns with 0.01 mol L –1 hydrochloric acid. The DOC was then eluted with 30 mL of methanol. Samples of pConAC and dConAC were isolated using the benzene polycarboxylic acid (BPCA) method 115,116,118 , which oxidizes condensed aromatic structures to yield distinct BPCA compounds: benzene tricarboxylic acids (B3CA, specifically 1,2,3-B3CA, and 1,2,4-B3CA), benzene tetracarboxylic acid (B4CA, specifically 1,2,4,5-B4CA), benzene pentacarboxylic acid (B5CA), and benzene hexacarboxylic acid (B6CA) 119 . For BPCA production, concentrated nitric acid was added to each sample within a pressurized digestion chamber, maintained at 170°C for 8 hours. Following oxidation, samples were filtered under vacuum using glass syringes equipped with glass fiber filters (Chromabond®, 0.7 µm pore size). The filtered solution was then pipetted onto a cation exchange resin (Dowex® 50WX8) for initial purification and subsequently processed by solid phase extraction (Discovery® DSC-18, 500 mg columns) for further refinement. BPCAs were separated and collected using preparative high-performance liquid chromatography (HPLC; Agilent 1290 Infinity LC system). Chromatographic separation was performed on a reverse-phase 2.7 µm Poroshell 120 C-18 column with two mobile phases: pH 2 Milli-Q water (adjusted with 1.7% H 3 PO 4 ) and high-purity acetonitrile (>99.98%, Scharlau, F 14 C <0.004). BPCA quantification was performed via seven-point calibration curves (2–200 ng µL –1 ) with commercially available BPCA standards, including pentacarboxylic acid (Aldrich S437107) and hexacarboxylic acid (Aldrich M2705). BPCA concentrations were calculated from peak areas observed in chromatographs generated by the diode array detector (60 mm path length). To convert BPCAs to ConAC estimates and allow comparison with published values, a ConAC recovery factor of 23.2 ± 0.4% was applied 120,121 . The HPLC fraction collector was used to collect B3CA through B6CA marker compounds, including nitrated B3CAs and B4CAs 116 . Notably, B2CA markers were excluded from the collection, as they can also derive from aromatic compounds of non-combusted origin (e.g., lignin). BPCAs were oxidized to CO 2 using a wet chemical oxidation method 116,117,122 . Samples containing 30 µgC and 4 mL of purified sodium persulfate were transferred into gas-tight 12 mL borosilicate Exetainer vials. The mixture was purged with ultrahigh-purity helium (100 mL min -1 for 8 min) and oxidized to CO 2 at 95 °C for 1 hr. Radiocarbon measurements were conducted on a Mini Carbon Dating System (MICADAS) Accelerator Mass Spectrometer at the ETH Zurich Ion Beam Laboratory, using a modified carbonate handling system with a sparging needle. To control for procedural carbon contamination, 14 C-dead ConAC (F 14 C = 0.003 ± 0.001), modern ConAC (F 14 C = 1.149 ± 0.004) wood char standards, marine sediment standard NIST 1941b, and procedural blanks were processed alongside samples 29,123 . ConAC samples were corrected for extraneous carbon using these standards 117,123 . 14 C values are reported as fraction modern (F 14 C) 124 . Estimating riverine ConAC export fluxes. Riverine export fluxes of dConAC and pConAC were estimated by multiplying measured ConAC contents with corresponding DOC and POC fluxes. We used ConAC content data synthesized from the Mackenzie River system, including published values from Stubbins et al. (2015) 45 and Coppola et al. (2018) 17 , along with 4 new dConAC and 25 new pConAC measurements from this study. Flow-weighted mean ConAC contents were computed for the Mackenzie River and its tributaries using discharge records from the Water Survey of Canada (https://wateroffice.ec.gc.ca/). For the Mackenzie River Delta, discharge was estimated as the sum of the Mackenzie and Arctic Red Rivers at Tsiigehtshik and the Peel River at Teetł’it Zheh, assuming negligible losses (e.g., evaporation). ConAC fluxes were calculated by multiplying the flow-weighted dConAC and pConAC content by independent estimates of DOC and POC fluxes, respectively. To propagate uncertainty in both ConAC content and riverine carbon flux, we employed a Monte Carlo simulation (n = 10,000) using a truncated normal distribution for ConAC content (to constrain values to physically realistic, non-negative ranges) and a uniform distribution for DOC and POC fluxes (Extended Data Fig. 2). DOC fluxes were assumed to range from 1.04 to 1.76 Tg C yr –1 , and POC fluxes from 0.3 to 2.6 Tg C yr –1 , based on available literature 50,51,72,96,125–132 . Fire Proximity to Rivers Across Sub-basins. We used QGIS (version 3.40.4-Bratislava; QGIS Development Team) for all spatial analyses, including stream classification, buffer creation, and burned area calculations. HydroRIVERS (HydroSHEDS) waterways were categorized into streams (Strahler order 1–3) and rivers (Strahler order 4–9) 133 . Buffer zones were defined as 0–1 km, 1–2 km, 2–3 km, 3–4 km, 4–5 km, and >5 km for streams, and 0–10 km, 10–20 km, 20–30 km, 30–40 km, 40–50 km, and >50 km for rivers. These buffers were overlaid with burned area data from the National Burned Area Composite (NBAC), provided by Natural Resources Canada and the Canadian Forest Service through the Fire Monitoring, Accounting, and Reporting System (FireMARS) ( https://cwfis.cfs.nrcan.gc.ca/datamart ). The burned area within each buffer was extracted using vector intersection and expressed as a percentage of the total burned area within each sub-basin. Analyses were conducted using the equal area projection NAD83(CSRS) / Canada Atlas Lambert (EPSG: 3979). High-resolution estimation of fire-derived ConAC production. We estimated ConAC production from wildfires across seven sub-basins of the Mackenzie River by integrating data from the Arctic Boreal Vulnerability Experiment Fire Emissions Database (ABoVE-FED) 54,134 and the Global Fire Emissions Database version 4s+ConAC (GFED4s+PyC; http://www.globalfiredata.org) 33 . The ABoVE-FED provided carbon emissions and burned area fraction at a high spatial resolution of 500 m, while the GFED4s+PyC supplied ConAC production-to-carbon emission ratios at a 0.25º resolution. To align the spatial resolution of these datasets, we applied nearest neighbor interpolation to resample GFED4s+PyC data to match the 500 m resolution of the ABoVE-FED. ConAC production was computed by multiplying the interpolated ratios by carbon emissions and the burned fraction of each pixel. Uncertainties in ConAC production were derived by propagating the uncertainties from the carbon emissions data 54 . Finally, we aggregated the ConAC production estimates for each of the seven sub-basins and the entire Mackenzie River basin. Estimating organic carbon and pyrogenic carbon stocks in the Mackenzie River system. We estimated organic carbon stocks in plant biomass and soils across the Mackenzie River basin using model outputs and associated uncertainties from Sothe et al. 48,49 For marine sediments in the Mackenzie plume region, we used the gridded carbon stock and uncertainty data from Atwood et al. 101 To quantify marine DOC stocks on the Mackenzie Shelf, we performed ordinary kriging on in situ DOC concentration measurements (n = 573) 85,86 . All spatial datasets were projected to EPSG:3979 to ensure consistency in distance and area calculations. We first fit an empirical semivariogram to DOC concentrations using a spherical model, with a maximum lag set to 66% of the maximum pairwise distance and 15 lags. To robustly estimate spatially explicit uncertainties, we performed leave-one-out cross-validation. For each iteration, a single point was withheld from kriging, and the prediction error (difference between observed and predicted DOC) was recorded. The absolute cross-validation errors were then modeled with an exponential variogram, which tends to better capture the structure of stochastic residuals. We conducted the final kriging over a regular 300×300 grid covering the study domain, generating both predicted DOC concentrations and spatially explicit uncertainty surfaces. Absolute uncertainties were derived by kriging the cross-validation errors, while relative uncertainties (%) were calculated as the ratio of absolute uncertainty to predicted DOC concentration. Model performance was assessed with root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R 2 ), based on cross-validation predictions. DOC stock was estimated by multiplying the kriged mean DOC concentration (µmol L –1 ) by the total water volume of the plume (13,249.53 km 3 ) and converting it to Tg C using a molar mass of 12.0107 g mol –1 . To estimate total carbon stocks, rasters of plant biomass and soil organic carbon, along with their uncertainty layers, were clipped to the Mackenzie River basin extent. Marine sediment carbon and kriged DOC concentrations, with associated uncertainties, were clipped to the Mackenzie Plume extent. Pixel-level values were summed and weighted by pixel area. To propagate uncertainty while accounting for spatial autocorrelation, we applied block bootstrapping to the uncertainty rasters. Block size was set based on the median correlation length estimated from 30 bootstrapped semivariograms of the uncertainty field, using exponential models fit to spatially random subsets (≤4000 points). During each of the 1000 bootstrap iterations, spatially contiguous blocks of pixels were resampled with replacement. Errors were drawn from the uncertainty surface within each sampled block and added to the original carbon values. The total stock was recalculated for each realization, producing an empirical distribution from which mean, standard deviation, and 95% confidence intervals were derived. ConAC stocks in soils, marine sediments, and marine DOC were estimated using published ConAC fractions applied to the corresponding carbon stock components. Uncertainty was propagated using Monte Carlo simulations (n = 10,000). The lower bound of the resulting 95% confidence intervals was truncated at zero to ensure physical plausibility. Soil ConAC in the Mackenzie River basin was estimated using a mean fraction of 6.9% ± 0.5%, based on Schiedung et al. 18 . Marine sediment ConAC was estimated from two values reported by Yang and Guo 100 , along with one additional value from this study. Marine dConAC content was approximated using a representative range (1.5–8.6%) observed across the Beaufort and Chukchi Seas and the Canada Basin 21,84 . All calculations were performed using the skgstat and rasterio libraries in Python. Determining the contribution of dConAC to the air-sea CO 2 flux using ECCO-Darwin . We estimated the contribution of dConAC to the air-sea CO 2 flux in the Mackenzie region using the ECCO-Darwin biogeochemistry state estimate for the Southeastern Beaufort Sea (ED-SBS) 55 . The ED-SBS is a regional variant of the global model, which combines a physical ocean circulation and sea ice model (Estimating the Circulation and Climate of the Ocean, ECCO), the Massachusetts Institute of Technology general circulation model (MITgcm) 135 , with an ocean ecology model, Darwin 136 . ECCO-Darwin assimilates in situ and remotely-sensed observations from 1992 to 2019, allowing for an ocean state estimate for physics and biogeochemistry at global and regional scales 136–138 . Building on the ECCO-LLC270 global-ocean and sea-ice configuration 139 , the ED-SBS regional model integrates the global-ocean biogeochemistry state estimate by Carroll et al. 140,141 . This configuration simulates four Arctic Ocean plankton functional types: diatoms, large eukaryotes, and small and large zooplankton. The biogeochemical component of the model explicitly simulates the cycling of carbon, nitrogen, phosphorus, silica, iron, and oxygen as they transition between inorganic, living, and dead organic pools 142 , and includes a simulation of the carbonate cycle 143 . Daily land-to-ocean fluxes of freshwater and six biogeochemical tracers (DON, DOP, DSi, DOC, DIC, Alk) are prescribed in the model based on estimates relying on the chemodynamic relationship (flux of nutrients as a function of discharge) 132,144 . Given that this manuscript focuses on ConAC and its export from the terrestrial biosphere to the marine reservoir, we adapted the ED-SBS model to include a third DOC pool specifically for dConAC (DOC bc ). This adaptation was essential to simulate the fate and reactivity of dConAC within the riverine freshwater plume on the shelf. In addition to this newly introduced DOC bc pool, the model simulates two oceanic pools of DOC following Bertin et al. 55 : a semi-labile pool (DOC sl ) with a residence time of t = 1 month and a semi-refractory pool (DOC sr ) with a residence time of t = 10 years. For DOC bc , a residence time of t = 1 year was assigned to represent the behavior of ConAC exported by the Mackenzie River. The Mackenzie River terrestrial DOC flux was allocated among the three pools, with 12% designated for the DOC bc and the remaining 88% equally distributed between DOC sr and DOC sl 55 . We conducted two simulations: one considering only the DOC sr and DOC sl pools, forced with 88% of the total riverine DOC flux, and another including the DOC bc pool. By comparing the results of these simulations, we estimated the portion of CO 2 originating from dConAC. This contribution was calculated within the river plume region, defined by the isohaline threshold of S = 27, which differentiates between fresh and marine water 95 . Declarations Data Availability All data used in this study are publicly accessible. Discharge records were obtained from the Water Survey of Canada (https://wateroffice.ec.gc.ca/). River networks were derived from HydroRIVERS (https://www.hydrosheds.org/products/hydrorivers). Burned area data for Canada were obtained from the National Burned Area Composite (NBAC), provided by Natural Resources Canada and the Canadian Forest Service through FireMARS (https://cwfis.cfs.nrcan.gc.ca/datamart), and for Alaska from the Alaska Interagency Coordination Center (https://fire.ak.blm.gov/predsvcs/maps.php). Fire emissions and activity were obtained from the Arctic Boreal Vulnerability Experiment Fire Emissions Database (ABoVE-FED; https://doi.org/10.3334/ORNLDAAC/2063) and the Global Fire Emissions Database version 4s+ConAC (GFED4s+PyC; http://www.globalfiredata.org). Forest and soil carbon stock maps and associated uncertainties were obtained from https://doi.org/10.4121/14572929.v1 and https://doi.org/10.4121/16686154.v3, respectively. Marine sediment carbon stocks for the Mackenzie plume region are available at https://figshare.com/articles/Global_marine_sedimentary_carbon_stock/11956356. Model code and instructions for ED-SBS simulations are available at https://doi.org/10.5281/zenodo.7417828, with model forcing files accessible from https://ecco.jpl.nasa.gov/drive/files/ECCO2/LLC270/Mac_Delta. All data generated for this study are submitted to Zenodo and will be publicly available upon publication. Acknowledgments We thank Mathieu Dellinger, Christina Larkin, and Edwin Amos for their invaluable field support in the collection of samples from the Mackenzie River, conducted under Northwest Territories Research Licenses 14557 and 15288. We also acknowledge B.B., a former master’s student under A.I.C, for his significant contributions to sample processing. A.I.C is grateful for funding from the Swiss National Science Foundation Ambizione Research Grant (PZ00P2_185835). M.S.S. acknowledges support from the NASA Postdoctoral Program at the Jet Propulsion Laboratory, administered by Oak Ridge Associated Universities under contract with NASA. M.vG and S.V.’s contributions were funded by the European Research Council (ERC) through a Consolidator Grant under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 101000987) awarded to S.V. Fieldwork was partially funded by an ERC Starting Grant (ROC-CO2, 678779) awarded to R.G.H., who also received support from an ERC Consolidator Grant (RIV-ESCAPE, 101002563. M.W.J. received support from the UK Natural Environment Research Council (NE\V01417X\1). We also appreciate the constructive feedback on the manuscript provided by Carlos Sierra and David Nielsen. Author contributions R.G.H., V.G., and M.S.S. collected samples from the Mackenzie River Basin. A.I.C., T.I.E., R.G.H., and V.G. contributed resources. Samples were processed and analyzed by A.I.C. and B.B. M. vG. calculated fire-derived ConAC fluxes, while air-sea ConAC-CO 2 was computed by C.M.B. N.H. performed radiocarbon measurements. 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Comprehensive radiocarbon analysis of benzene polycarboxylic acids (BPCAs) derived from pyrogenic carbon in environmental samples. Radiocarbon 59 , 1103–1116 (2017). Glaser, B., Haumaier, L., Guggenberger, G. & Zech, W. Black carbon in soils: The use of benzenecarboxylic acids as specific markers. Org. Geochem. 29 , 811–819 (1998). Dittmar, T. The molecular level determination of black carbon in marine dissolved organic matter. Org. Geochem. 39 , 396–407 (2008). Ziolkowski, L. A. & Druffel, E. R. M. The feasibility of isolation and detection of fullerenes and carbon nanotubes using the benzene polycarboxylic acid method. Mar. Pollut. Bull. 59 , 213–218 (2009). Ziolkowski, L. A., Chamberlin, A. R., Greaves, J. & Druffel, E. R. M. Quantification of black carbon in marine systems using the benzene polycarboxylic acid method: A mechanistic and yield study. Limnol. Oceanogr. Methods 9 , 140–140 (2011). Lang, S. Q., Früh-Green, G. L., Bernasconi, S. M. & Wacker, L. Isotopic (δ13C, Δ14C) analysis of organic acids in marine samples using wet chemical oxidation. Limnol. Oceanogr. Methods 11 , 161–175 (2013). Coppola, A. I., Ziolkowski, L. A. & Druffel, E. R. M. Extraneous Carbon Assessments in Radiocarbon Measurements of Black Carbon in Environmental Matrices. Radiocarbon 55 , 1631–1640 (2013). Reimer, P. J., Brown, T. A. & Reimer, R. W. Discussion: Reporting and Calibration of Post-Bomb 14C Data. Radiocarbon 46 , 1299–1304 (2004). Le Fouest, V., Babin, M. & Tremblay, J. E. The fate of riverine nutrients on Arctic shelves. Biogeosciences 10 , 3661–3677 (2013). McClelland, J. W. et al. Particulate organic carbon and nitrogen export from major Arctic rivers. Global Biogeochem. Cycles 30 , 629–643 (2016). Lacroix, F., Ilyina, T. & Hartmann, J. Oceanic CO2 outgassing and biological production hotspots induced by pre-industrial river loads of nutrients and carbon in a global modeling approach. Biogeosciences 17 , 55–88 (2020). Behnke, M. I. et al. 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Global river hydrography and network routing: Baseline data and new approaches to study the world’s large river systems. Hydrol. Process. 27 , 2171–2186 (2013). Potter, S. et al. ABoVE: Burned Area, Depth, and Combustion for Alaska and Canada, 2001-2019. ORNL DAAC, Oak Ridge, Tennessee, USA , https://doi.org/10.3334/ORNLDAAC/2063 (2022). Marshall, J., Adcroft, A., Hill, C., Perelman, L. & Heisey, C. A finite-volume, incompressible navier stokes model for, studies of the ocean on parallel computers. J. Geophys. Res. Oceans 102 , 5753–5766 (1997). Dutkiewicz, S. et al. Capturing optically important constituents and properties in a marine biogeochemical and ecosystem model. Biogeosciences 12 , 4447–4481 (2015). Dutkiewicz, S., Hickman, A. E. & Jahn, O. Modelling ocean-colour-derived chlorophyll a. Biogeosciences 15 , 613–630 (2018). Dutkiewicz, S. et al. Ocean colour signature of climate change. Nat. Commun. 10 , 578 (2019). Zhang, H., Menemenlis, D. & Fenty, I. ECCO LLC270 Ocean-Ice State Estimate . http://hdl.handle.net/1721.1/119821%0Ahttps://dspace.mit.edu/handle/1721.1/119821 (2018). Carroll, D. et al. Attribution of Space-Time Variability in Global-Ocean Dissolved Inorganic Carbon. Global Biogeochem. Cycles 36 , e2021GB007162 (2022). Carroll, D. et al. The ECCO-Darwin Data-Assimilative Global Ocean Biogeochemistry Model: Estimates of Seasonal to Multidecadal Surface Ocean pCO2 and Air-Sea CO2 Flux. J. Adv. Model. Earth Syst. 12 , e2019MS001888 (2020). Dutkiewicz, S., Scott, J. R. & Follows, M. J. Winners and losers: Ecological and biogeochemical changes in a warming ocean. Global Biogeochem. Cycles 27 , 463–477 (2013). Follows, M. J., Ito, T. & Dutkiewicz, S. On the solution of the carbonate chemistry system in ocean biogeochemistry models. Ocean Model. (Oxf). 12 , 290–301 (2006). Tank, S. E. et al. Landscape-level controls on dissolved carbon flux from diverse catchments of the circumboreal. Global Biogeochem. Cycles 26 , 1–15 (2012). Raymond, P. A. et al. Global carbon dioxide emissions from inland waters. Nature 503 , 355–359 (2013). Liu, S. et al. The importance of hydrology in routing terrestrial carbon to the atmosphere via global streams and rivers. Proceedings of the National Academy of Sciences, 119 , (2022). Arrigo, K. R., Pabi, S., Van Dijken, G. L. & Maslowski, W. Air-sea flux of CO2 in the Arctic Ocean, 1998-2003. J. Geophys. Res. Biogeosci. 115 , (2010). Manizza, M. et al. Changes in the Arctic Ocean CO2 sink (1996-2007): A regional model analysis. Global Biogeochem. Cycles 27 , 1108–1118 (2013). Evans, W. et al. Sea-air CO2 exchange in the western Arctic coastal ocean. Global Biogeochem. Cycles 29 , 1190–1209 (2015). Manizza, M., Menemenlis, D., Zhang, H. & Miller, C. E. Modeling the Recent Changes in the Arctic Ocean CO2 Sink (2006–2013). Global Biogeochem. Cycles 33 , 420–438 (2019). Terhaar, J., Orr, J. C., Ethé, C., Regnier, P. & Bopp, L. Simulated Arctic Ocean Response to Doubling of Riverine Carbon and Nutrient Delivery. Global Biogeochem. Cycles 33 , 1048–1070 (2019). Ouyang, Z. et al. Summertime evolution of net community production and CO 2 flux in the western Arctic Ocean . Global Biogeochem. Cycles 35 , 1–24 (2021). Telang, S. A. et al. Carbon and mineral transport in major north American, Russian, Arctic, and Siberian Rivers : the St Lawrence, the Mackenzie, the Yukon, the Arctic Alaskan rivers, the Arctic Basin rivers in the Soviet Union, and the Yenisey. in Biogeochemistry of Major World Rivers, SCOPE 42 (eds. Degens, E. T., Kempe, S. & Richey, J. E.) 75–105 (Wiley, New York, 1991). Additional Declarations There is NO Competing Interest. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9476681","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":632632812,"identity":"c4d2b91d-8042-477f-82a6-9e59159c7bc5","order_by":0,"name":"Melissa Schwab","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIiWNgGAWjYFAC5oYDYFoCyGSoIEoLI7KWM1DBAwS0MMC1MLYRoUW3vbHx4I8/dvb8s3sMPxfOq402OMB78PEHPFrMzhxsOMzblpw4484ZY+mZ247nzmzgSzbAZ4vZjcSGw4wNzAkGErkbpHm3HcvtZ+Axk8Cr5f7DBqDD6u2BWjb/5p1zLLeNgcf8B35bgCHGw3aYcYNE7jZp3oYasC14vW92JhHkl+OJM27kf7OecexA7sxmHmOJM/i0HD98+OOPP9X2/DPSkm8X1NTlbjjeY/iBuCiFgMOgCCUN1JGofhSMglEwCkYCAAAQkVgzO32XyAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-5600-4439","institution":"Dalhousie University","correspondingAuthor":true,"prefix":"","firstName":"Melissa","middleName":"","lastName":"Schwab","suffix":""},{"id":632632813,"identity":"0d21cca0-11c4-4c17-808e-584fd6a2be87","order_by":1,"name":"Alysha Coppola","email":"","orcid":"","institution":"ETH Zurich","correspondingAuthor":false,"prefix":"","firstName":"Alysha","middleName":"","lastName":"Coppola","suffix":""},{"id":632632814,"identity":"3c08168a-f6a0-4988-a862-f763d46775be","order_by":2,"name":"Max van Gerrevink","email":"","orcid":"https://orcid.org/0000-0002-5202-1263","institution":"Wageningen University and Research","correspondingAuthor":false,"prefix":"","firstName":"Max","middleName":"van","lastName":"Gerrevink","suffix":""},{"id":632632815,"identity":"72e0e940-86a2-4845-a396-bcb8c37d9a19","order_by":3,"name":"Clément Bertin","email":"","orcid":"https://orcid.org/0000-0002-1097-3856","institution":"NASA Jet Propulsion Laboratory","correspondingAuthor":false,"prefix":"","firstName":"Clément","middleName":"","lastName":"Bertin","suffix":""},{"id":632632816,"identity":"001835f4-5166-4a6a-ae08-6a89cdb00c74","order_by":4,"name":"Negar Haghipour","email":"","orcid":"","institution":"ETH 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Anglia","correspondingAuthor":false,"prefix":"","firstName":"Matthew","middleName":"","lastName":"Jones","suffix":""},{"id":632632820,"identity":"b59bfc14-1807-4c95-8cca-475bce961c86","order_by":8,"name":"Sander Veraverbeke","email":"","orcid":"https://orcid.org/0000-0003-1362-5125","institution":"Vrije Universiteit Amsterdam","correspondingAuthor":false,"prefix":"","firstName":"Sander","middleName":"","lastName":"Veraverbeke","suffix":""},{"id":632632821,"identity":"4965588c-2712-4c33-8249-0b6cdc92edce","order_by":9,"name":"Timothy Eglinton","email":"","orcid":"","institution":"ETH Zürich","correspondingAuthor":false,"prefix":"","firstName":"Timothy","middleName":"","lastName":"Eglinton","suffix":""}],"badges":[],"createdAt":"2026-04-20 22:20:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9476681/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9476681/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108380737,"identity":"822cdaa0-489b-4286-b3d6-5373d4a8a032","added_by":"auto","created_at":"2026-05-04 04:50:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":575966,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFire history and burned area distribution across riverine distance intervals in the Mackenzie River Basin.\u003c/strong\u003e a) Wildfire activity in the Mackenzie River Basin from 2001 to 2017, retrieved from the National Burned Area Composite (NBAC) via the Fire Monitoring, Accounting and Reporting System (FireMARS) (Natural Resources Canada and Canadian Forest Service, \u003ca href=\"https://cwfis.cfs.nrcan.gc.ca/datamart\" target=\"_new\"\u003ehttps://cwfis.cfs.nrcan.gc.ca/datamart\u003c/a\u003e) and the Alaska Interagency Coordination Center (https://fire.ak.blm.gov/predsvcs/maps.php). b) Bar plots depicting the proportion of the total burned area across river distance intervals, expressed as a percentage of total fire in each sub-basin, for streams (Strahler order 1–3) and rivers (Strahler order 4–9). The bars are color-coded to represent stream distance intervals (0–5 km, \u0026gt;5 km) and river distance intervals (0–50 km, \u0026gt;50 km).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-9476681/v1/e58dd02ebee21bc0081701d9.png"},{"id":108493063,"identity":"e74f916b-816c-4ba3-8b67-41712ed73584","added_by":"auto","created_at":"2026-05-05 09:59:18","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":112152,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnnual pyrogenic carbon production (ConAC) rates in the Mackenzie River Basin.\u003c/strong\u003e a) Modeled annual ConAC production rates (Tg ConAC) for the Mackenzie River Basin from 2001 to 2017, with corresponding gray bars representing the burned area (in 10\u003csup\u003e3\u003c/sup\u003e km\u003csup\u003e2\u003c/sup\u003e) on the secondary y-axis. b) Cumulative plot of the annual modeled ConAC production rates, with the 2001–2017 average indicated by a dashed line.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9476681/v1/75e0e0613f6d065159b71e6d.png"},{"id":108380741,"identity":"22b70d93-2dba-490a-8b69-f38ead501f65","added_by":"auto","created_at":"2026-05-04 04:50:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":159451,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRadiocarbon signatures of condensed aromatic carbon (ConAC) and bulk organic carbon (OC) in the Mackenzie River–Western Arctic Ocean system.\u003c/strong\u003e a) Relationship between ConAC content (%) and its radiocarbon activity (ConAC-F\u003csup\u003e14\u003c/sup\u003eC). b) Radiocarbon activity of bulk OC (OC-F\u003csup\u003e14\u003c/sup\u003eC) plotted against ConAC-F\u003csup\u003e14\u003c/sup\u003eC. Radiocarbon values are additionally expressed as \u003csup\u003e14\u003c/sup\u003eC ages (kyr Before Present) to aid interpretation of carbon source age. Marker style indicates material type; color denotes sampling location. Data include samples from this study and published datasets (Coppola et al.\u003csup\u003e21\u003c/sup\u003e, Fang et al.\u003csup\u003e84\u003c/sup\u003e, Yang and Guo\u003csup\u003e100\u003c/sup\u003e, Ren et al.\u003csup\u003e102\u003c/sup\u003e, Schiedung et al.\u003csup\u003e18\u003c/sup\u003e, Stubbins et al.\u003csup\u003e45\u003c/sup\u003e).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-9476681/v1/c66e958545868272db7185b0.png"},{"id":108380742,"identity":"adbee73c-4f88-4206-8788-bf72bbe86f96","added_by":"auto","created_at":"2026-05-04 04:50:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":207479,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConceptual model of pyrogenic carbon flux from terrestrial to marine reservoirs in the Mackenzie system.\u003c/strong\u003eSchematic representation of the pyrogenic carbon (ConAC) cycle, illustrating its production by wildfire in boreal forests, subsequent storage in soils, mobilization via fluvial transport, and eventual deposition in Arctic shelf sediments. Quantitative estimates of ConAC stocks and fluxes along this pathway are provided in Extended Data Table 3.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-9476681/v1/791654544f8f15acb4b8dc76.png"},{"id":108493918,"identity":"1ba66c4b-6fa2-47c4-ba97-2d3d655308be","added_by":"auto","created_at":"2026-05-05 10:02:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1572862,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9476681/v1/5bb66610-7e06-431a-9d3c-782b35a6af27.pdf"},{"id":108493061,"identity":"723b0a90-69da-49c5-a210-8cc2bcb8a72d","added_by":"auto","created_at":"2026-05-05 09:59:17","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":30538,"visible":true,"origin":"","legend":"Dataset","description":"","filename":"SourceData.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9476681/v1/4ddaa7920a302bce0b5b27e2.pdf"},{"id":108380738,"identity":"c3a5f55e-e325-4630-afa7-c17bf4a7106e","added_by":"auto","created_at":"2026-05-04 04:50:32","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1442274,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedData.docx","url":"https://assets-eu.researchsquare.com/files/rs-9476681/v1/7cfb6bfa41c015b1fc7b51f4.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Arctic condensed aromatic carbon budget reveals efficient fluvial transfer and shelf storage","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eArctic-Boreal ecosystems constitute one of the largest terrestrial carbon reservoirs, yet escalating fire activity increasingly threatens a transition toward net carbon sources\u003csup\u003e1\u0026ndash;5\u003c/sup\u003e. Fires act as both a major contributor to atmospheric carbon emissions and as a disturbance that catalyzes the destabilization of carbon-rich permafrost (permanently frozen) soils\u003csup\u003e3,5\u0026ndash;7\u003c/sup\u003e. Model projections indicate that the frequency, extent, and severity of Arctic-Boreal fires will intensify under continued climate warming\u003csup\u003e8\u0026ndash;11\u003c/sup\u003e. The 2023 Canadian fire season exemplifies this trajectory, with approximately 15 million ha burned and 570\u0026ndash;727\u0026nbsp;Tg\u0026nbsp;C emitted to the atmosphere\u003csup\u003e12\u003c/sup\u003e, rivaling annual fossil fuel emissions from major industrialized nations\u003csup\u003e13\u003c/sup\u003e. Such events underscore the growing susceptibility of northern high-latitude ecosystems to fire-driven carbon mobilization and the amplification of carbon\u0026ndash;climate feedbacks mediated by hydroclimatic change.\u003c/p\u003e\n\u003cp\u003eAmid shifting fire dynamics, pyrogenic carbon (PyC) represents a quantitatively important, under-characterized component of the post-fire carbon budget. Formed through incomplete combustion, PyC consists predominantly of condensed aromatic carbon (ConAC), a chemically resistant material that accumulates within terrestrial and aquatic environments and can persist for centuries to millennia\u003csup\u003e14\u0026ndash;21\u003c/sup\u003e. However, in addition to its pyrogenic formation, ConAC can also originate from non-pyrogenic pathways, including low-temperature oxidation of biomass\u003csup\u003e22,23\u003c/sup\u003e, soil humification\u003csup\u003e24\u003c/sup\u003e, and the erosion-driven input of thermally mature\u003csup\u003e25,26\u003c/sup\u003e and lithified sedimentary organic matter\u003csup\u003e27\u0026ndash;29\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThis diversity of sources creates a central challenge for interpreting ConAC dynamics and attributing observed fluxes to contemporary fire activity. Pyrogenic and non‑pyrogenic ConAC reach soils, floodplains, and aquatic systems at different times, from different source areas, and through distinct transport and storage pathways. Consequently, the ConAC pool reflects cumulative landscape processing rather than solely recent combustion. Aromatic carbon persistence and radiocarbon composition integrate fire recurrence, geomorphic routing, lithological inputs, and multi‑stage storage across terrestrial and aquatic environments\u003csup\u003e30,31\u003c/sup\u003e. As a result, basin‑scale ConAC export can diverge sharply from recent wildfire activity, complicating its use as a proxy for modern fire intensity and underscoring its role as a long‑term integrator of carbon mobilization across northern landscapes.\u003c/p\u003e\n\u003cp\u003eCurrent estimates report global ConAC production of 116\u0026ndash;385\u0026nbsp;Tg\u0026nbsp;ConAC yr\u003csup\u003e\u0026ndash;1\u0026nbsp;\u003c/sup\u003e(refs.\u0026nbsp;\u003csup\u003e32\u0026ndash;35\u003c/sup\u003e). In contrast, non-pyrogenic inputs contribute 163\u0026minus;182 Tg ConAC yr\u003csup\u003e\u0026ndash;1\u0026nbsp;\u003c/sup\u003e(ref.\u003csup\u003e\u0026nbsp;\u003c/sup\u003e\u003csup\u003e23\u003c/sup\u003e), reflecting significant overlap among sources and uncertainty in attribution. Once produced, ConAC is subjected to biotic and abiotic decomposition, yielding a global loss of ~248 Tg ConAC yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (ref.\u0026nbsp;\u003csup\u003e34\u003c/sup\u003e). Residual particulate ConAC (pConAC) stocks span 54\u0026ndash;450\u0026nbsp;Pg\u0026nbsp;ConAC\u003csup\u003e31,33,36\u003c/sup\u003e in soils and 100\u0026ndash;1,440\u0026nbsp;Pg\u0026nbsp;ConAC\u003csup\u003e31,33\u003c/sup\u003e in the upper 1 m of marine sediments. Dissolved ConAC (dConAC) provides an additional 12\u0026ndash;145\u0026nbsp;Tg\u0026nbsp;ConAC\u003csup\u003e19,20,33,37\u003c/sup\u003e to\u0026nbsp;the marine carbon pool. Despite these substantial stocks, the processes controlling ConAC mobilization, transport, and long-term sequestration along the land\u0026ndash;ocean continuum remain insufficiently resolved\u003csup\u003e30,38\u003c/sup\u003e, highlighting the scarcity of basin-scale observations linking production, riverine export, and marine burial.\u003c/p\u003e\n\u003cp\u003eRiver networks serve as the main conduits connecting terrestrial ConAC reservoirs to the ocean\u003csup\u003e17,30,35,39,40\u003c/sup\u003e. Particulate ConAC accounts for ~16%\u003csup\u003e17\u003c/sup\u003e of fluvial particulate organic carbon (POC), but global flux estimates vary widely (17\u0026ndash;80 Tg ConAC yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)\u003csup\u003e17,31,33,35,41,42\u003c/sup\u003e owing to uncertainties in scaling and analytical heterogeneity. Flux-weighted pConAC radiocarbon (\u003csup\u003e14\u003c/sup\u003eC) ages average 3,700 \u0026plusmn; 400 \u003csup\u003e14\u003c/sup\u003eC\u0026nbsp;yr globally\u003csup\u003e17\u003c/sup\u003e, whereas Arctic rivers transport pConAC ranging from modern to 17,000 \u003csup\u003e14\u003c/sup\u003eC\u0026nbsp;yr, indicating extensive pre-aging through repeated deposition\u0026ndash;resuspension cycles and storage in soils, floodplains, and lakes\u003csup\u003e17,43\u003c/sup\u003e. Dissolved organic carbon (DOC) contains ~9% dConAC\u003csup\u003e35\u003c/sup\u003e, corresponding to a flux of 12\u0026ndash;28\u0026nbsp;Tg\u0026nbsp;ConAC yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (refs. \u003csup\u003e35,44\u003c/sup\u003e). High-latitude rivers export proportionally more dConAC, comprising 21\u0026nbsp;\u0026plusmn;\u0026nbsp;6% (2.6\u0026ndash;3.8\u0026nbsp;Tg\u0026nbsp;ConAC\u0026nbsp;yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e)\u003csup\u003e35,45\u003c/sup\u003e, with largely modern \u003csup\u003e14\u003c/sup\u003eC signatures indicative of strong coupling to contemporary biomass burning\u003csup\u003e46,47\u003c/sup\u003e. Nevertheless, the degree to which riverine ConAC export reflects recent fire activity versus long-term storage and remobilization remains unconstrained, particularly in large Arctic watersheds where fire regimes, permafrost dynamics, and lithological inputs interact across broad spatial and temporal scales.\u003c/p\u003e\n\u003cp\u003eThe Mackenzie River basin provides a uniquely powerful setting in which to resolve this uncertainty. It integrates extensive wildfire activity, pronounced latitudinal permafrost gradients, widespread lake interception, and heterogeneous lithology within a single drainage network, features that typify much of the Arctic-Boreal domain. The basin stores vast amounts of carbon, with 4.4\u0026nbsp;\u0026plusmn;\u0026nbsp;0.1 Pg\u0026nbsp;C held in living plant biomass and detritus\u003csup\u003e48,49\u003c/sup\u003e and 58.5 \u0026plusmn; 3.6\u0026nbsp;Pg\u0026nbsp;C in the top 1\u0026nbsp;m of soil\u003csup\u003e48,49\u003c/sup\u003e. This expansive reservoir sustains frequent and widespread wildfire activity, rendering the basin a potential hotspot for fire-derived CO\u003csub\u003e2\u003c/sub\u003e emissions and ConAC production. Concurrent shifts in hydrological regimes\u003csup\u003e50,51\u003c/sup\u003e and accelerating permafrost degradation\u003csup\u003e52,53\u003c/sup\u003e are expected to reshape ConAC mobilization pathways and enhance delivery to coastal margins. These characteristics position the Mackenzie as an ideal natural laboratory for examining how pyrogenic and non-pyrogenic ConAC interact with permafrost dynamics, hydrological routing, and sedimentary inputs during land\u0026ndash;ocean transfer. Inferences from this system therefore establish a baseline against which shifts in ConAC cycling across high-latitude watersheds can be assessed under accelerating climate forcing.\u003c/p\u003e\n\u003cp\u003eHere, we present an integrated, basin-scale ConAC budget for the Mackenzie River\u0026ndash;Beaufort Sea system, tracing source-to-sink dynamics across a complete terrestrial\u0026ndash;marine continuum. Our analysis synthesizes both published and newly generated data on pConAC and dConAC concentrations and associated \u003csup\u003e14\u003c/sup\u003eC values from soils, rivers, and marine environments. We quantified ConAC production from wildfires across seven sub-basins of the Mackenzie River by combining data from the Arctic Boreal Vulnerability Experiment Fire Emissions Database (ABoVE-FED)\u003csup\u003e54\u003c/sup\u003e with ConAC production-to-carbon emissions ratios\u003csup\u003e33\u003c/sup\u003e. Riverine ConAC export and coastal accumulation were estimated through partitioning-based flux calculations, while the contribution of dConAC to the air-sea CO\u003csub\u003e2\u003c/sub\u003e flux was computed using a regional ocean-ice-biogeochemistry model (ECCO-Darwin)\u003csup\u003e55\u003c/sup\u003e. Our findings establish a quantitative framework for assessing how ConAC is mobilized and sequestered in a rapidly changing Arctic.\u003c/p\u003e"},{"header":" RESULTS AND DISCUSSION","content":"\u003cp\u003e\u003cstrong\u003eFrom fire to soil: ConAC accumulation and persistence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWildfires burned over 175,000 km\u003csup\u003e2\u003c/sup\u003e (9.7%) of the Mackenzie River Basin between 2001 and 2017, with fire activity strongly concentrated in the southeastern watershed (\u003cstrong\u003eFig. 1a,\u003c/strong\u003e Extended Data Table 1). The Athabasca and Slave River Basins accounted for more than two-thirds (68.4%) of the total burned area, indicating that southern sub-catchments experience more frequent and extensive fires and therefore contribute disproportionately to total fire emissions. The largest wildfire during the study period was recorded in 2014, affecting approximately 34,000 km\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eFire activity clustered near waterways, with 35\u0026ndash;44% of total burned area occurring within 1 km of streams (Strahler order 1\u0026ndash;3) and 39\u0026ndash;70% within 10 km of larger rivers (Strahler order 4\u0026ndash;9), after which burned area declined sharply with distance (\u003cstrong\u003eFig. 1b,\u0026nbsp;\u003c/strong\u003eExtended Data Table 2). Fire density exhibited a similar proximity-driven pattern, though with greater spatial variability across northern sub-basins (Extended Data Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRiparian fire prevalence reflects high fuel loads maintained by dense biomass\u003csup\u003e56\u003c/sup\u003e. Although typically moist, these areas can dry sufficiently to reduce fuel moisture and elevate flammability\u003csup\u003e4,57\u003c/sup\u003e. Microtopographic variability and channelized winds along river corridors can further enhance fire propagation\u003csup\u003e58\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWildfire activity in the Mackenzie River Basin is a significant source of carbon emissions, exhibiting substantial interannual variability. The ABoVE-FED\u003csup\u003e54\u003c/sup\u003e provided a framework for quantifying its regional-scale carbon cycle impacts. Between 2001 and 2017, wildfires released approximately 680 Tg C to the atmosphere,\u0026nbsp;with a median annual emission rate of 35.2 Tg C\u0026nbsp;yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (22.6, 45.1) (median (interquartile range)). Using ConAC production-to-carbon emissions ratios\u003csup\u003e59\u003c/sup\u003e, we estimate a total ConAC production of 36.5\u0026nbsp;Tg, corresponding to a median annual rate of 2.05 Tg\u0026nbsp;ConAC\u0026nbsp;yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (0.98, 2.39) (\u003cstrong\u003eFig. 2\u003c/strong\u003e)\u003csup\u003e54\u003c/sup\u003e. The area-normalized ConAC production was 1.14 t\u0026nbsp;ConAC\u0026nbsp;km\u003csup\u003e\u0026ndash;2\u003c/sup\u003e yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (0.55, 1.33). Peak ConAC production occurred in 2014 and 2015, driven by extensive wildfire activity that generated 6.55 \u0026plusmn; 5.08 Tg ConAC yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (mean \u0026plusmn; uncertainty) and 4.96 \u0026plusmn; 3.75 Tg ConAC yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe highest ConAC production rates were concentrated in the southern Mackenzie River Basin, comprising the Athabasca (0.30 Tg\u0026nbsp;ConAC\u0026nbsp;yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (0.20, 1.02)), Peace (0.33 Tg ConAC yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (0.14, 0.55)), and Slave (0.30 Tg ConAC yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (0.15, 0.62)) sub-catchments (Extended Data Fig. 1,\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eExtended Data Table 1). The highest ConAC production rate was recorded in the Slave Basin in 2014, reaching 3.45\u0026nbsp;\u0026plusmn;\u0026nbsp;2.70 Tg\u0026nbsp;ConAC\u0026nbsp;yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast, ConAC production was notably lower in the northern sub-basins, including the Liard (0.09 Tg\u0026nbsp;ConAC\u0026nbsp;yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (0.04, 0.47)), Main Mackenzie (0.11 Tg ConAC yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (0.03, 0.20)), Arctic Red (0.0004 Tg ConAC yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (0.0000, 0.0023)), and Peel Rivers (0.01 Tg ConAC yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (0.00, 0.04)). These patterns highlight a strong south-to-north gradient in fire-derived carbon production, shaped by vegetation structure, climate, and fire regimes.\u003c/p\u003e\n\u003cp\u003eFollowing wildfire, a portion of ConAC is retained in soils, contributing to long-term carbon storage. Within the top 100 cm of soil across the Mackenzie River Basin, ConAC constitutes approximately 6.9% of total soil organic carbon\u003csup\u003e18\u003c/sup\u003e, amounting to 4.0 \u0026plusmn; 0.4 Pg ConAC. This reservoir is nearly equivalent to the total plant biomass carbon stock across the basin, emphasizing the role of ConAC as a major, persistent terrestrial carbon pool.\u003c/p\u003e\n\u003cp\u003eThe computed steady-state turnover of ~1950 yr aligns with the younger range of observed \u003csup\u003e14\u003c/sup\u003eC-ConAC ages in soils, reflecting millennial-scale residence times that generally exceed those of bulk soil organic carbon\u003csup\u003e15,18,31\u003c/sup\u003e. Older ConAC ages occur under continuous permafrost, suggesting thermal stabilization and limited microbial accessibility\u003csup\u003e18\u003c/sup\u003e. The magnitude and longevity of soil ConAC storage indicate that terrestrial reservoirs act as a first-order buffer against short-term variability in wildfire activity, decoupling production pulses from downstream transport\u003csup\u003e18,60\u003c/sup\u003e. Nonetheless, 37\u0026ndash;40% of ConAC remains susceptible to decomposition\u003csup\u003e60,61\u003c/sup\u003e and lateral export via hydrological and erosional pathways\u003csup\u003e45,62\u0026ndash;64\u003c/sup\u003e transfers ConAC into aquatic networks.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFrom basin to shelf: mobilization and loss of ConAC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dConAC content as a proportion of DOC in the northern Mackenzie River system, measured prior to its discharge into the Arctic Ocean, spans 3.9\u0026ndash;27.8% (\u003cstrong\u003eFig. 3a\u003c/strong\u003e), with a flow-weighted mean of 14.2 \u0026plusmn; 6.3% (n = 7). This exceeds values observed by Stubbins et al.\u003csup\u003e45\u003c/sup\u003e of 10.9 \u0026plusmn; 1.3% and Jones et al.\u003csup\u003e33\u003c/sup\u003e of 9.6 \u0026plusmn; 1.5% for major high-latitude rivers. The estimated annual dConAC export is 0.20 \u0026plusmn; 0.10 Tg ConAC yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, which is considerably higher than the previously reported flux of 0.13 \u0026plusmn; 0.02 Tg ConAC yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (ref. \u003csup\u003e45\u003c/sup\u003e).\u003c/p\u003e\n\u003cp\u003eThe export of dConAC is intricately linked to DOC dynamics, groundwater, fluvial transport, and the cumulative in-transit processing. The majority of dConAC originates in the southern Mackenzie River basin, where approximately 78% of wildfire-derived ConAC is produced. Given that the dissolved fraction of ConAC is not retained by lakes or other intermittent storage systems, it can travel unimpeded through the river system to the mouth. However, extended transit times expose dConAC to photochemical\u003csup\u003e65,66\u003c/sup\u003e and microbial degradation\u003csup\u003e34,67\u003c/sup\u003e, potentially resulting in significant losses before reaching the Arctic Ocean. As a result, the shorter hydrological residence times in northern tributaries may partially modify dConAC signals from the southern basins.\u003c/p\u003e\n\u003cp\u003eSource attribution remains uncertain, owing to the limited number of observations, and further sampling and study are necessary to clarify these dynamics. dConAC-F\u003csup\u003e14\u003c/sup\u003eC values from the Arctic Red River display moderately aged signatures likely reflecting inputs from both recent wildfire residues and groundwater flow through the upper active layer (seasonally thawed permafrost) (\u003cstrong\u003eFig. 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eThe content of pConAC as a percentage of the POC fraction exhibits notable variability, ranging from 3.9% in the Arctic Red River to 27.6% in the Peel River. The flow-weighted mean pConAC content across the Mackenzie River system is 14.9 \u0026plusmn; 3.0% (n = 27) (\u003cstrong\u003eFig. 3a\u003c/strong\u003e). The average pConAC flux across the entire basin is 0.22 \u0026plusmn; 0.14 Tg ConAC yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, consistent with estimates from Elmquist et al.\u003csup\u003e42\u003c/sup\u003e and Coppola et al.\u003csup\u003e17\u003c/sup\u003e, which range from 0.099 to 0.296 Tg ConAC yr\u003csup\u003e-1\u003c/sup\u003e at Tsiigehtshik.\u003c/p\u003e\n\u003cp\u003eDespite extensive fire activity in the southern Mackenzie Basin, we expect the contribution of pConAC to fluvial downstream transport to be minimal. The region\u0026apos;s low-relief shield terrain results in limited surface erosion, thereby restricting pConAC mobilization into river networks\u003csup\u003e68,69\u003c/sup\u003e. Additionally, significant sediment trapping in Lake Athabasca (~45%)\u003csup\u003e70\u003c/sup\u003e and Great Slave Lake (~61%)\u003csup\u003e70\u003c/sup\u003e further reduces pConAC transfer to the main stem of the Mackenzie River. The geomorphic and limnological constraints sharply limit the downstream propagation of pConAC from southern sources.\u003c/p\u003e\n\u003cp\u003eIn contrast, northern basins, while producing lower quantities of ConAC from wildfires, export a larger proportion of pConAC to the Arctic Ocean. The Liard River alone accounts for approximately 40% of the total sediment and POC load reaching the Mackenzie River Delta\u003csup\u003e71,72\u003c/sup\u003e. The Peel and Arctic Red Rivers drain steeper upland terrain, where higher erosion, surface runoff, and sediment yields promote increased downstream pConAC flux\u003csup\u003e72\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003epConAC-F\u003csup\u003e14\u003c/sup\u003eC signatures in the northern Mackenzie River system exceed \u003csup\u003e14\u003c/sup\u003eC ages expected for pyrogenic carbon produced by contemporary wildfire regimes. Values range from 0.01 to 0.22 (flow-weighted mean 0.10 \u0026plusmn; 0.06; \u003csup\u003e14\u003c/sup\u003eC age 18,000 \u0026plusmn; 11,600 yr) and are substantially lower than the corresponding bulk POC-F\u003csup\u003e14\u003c/sup\u003eC of 0.41 \u0026plusmn; 0.08 (7,300 \u0026plusmn; 1,400 \u003csup\u003e14\u003c/sup\u003eC yr; \u003cstrong\u003eFig. 3\u003c/strong\u003e)\u003csup\u003e72,73\u003c/sup\u003e. This systematic age offset between pConAC and bulk organic matter echoes spot measurements in Mackenzie River basin soils\u003csup\u003e18,74\u003c/sup\u003e. Such extreme \u003csup\u003e14\u003c/sup\u003eC depletion cannot be reconciled with any post-glacial biogenic source. pConAC-F\u003csup\u003e14\u003c/sup\u003eC\u0026nbsp;age constraints are inconsistent with permafrost soils formed following the Laurentide Ice Sheet retreat (~18 kyr BP)\u003csup\u003e75\u003c/sup\u003e. Pleistocene permafrost deposits (yedoma) are negligible within the watershed\u003csup\u003e76,77\u003c/sup\u003e. The majority of the pConAC is therefore likely sourced from weathering of sedimentary bedrock or deposition of fossil-fuel-derived soot, which are\u0026nbsp;inherently \u003csup\u003e14\u003c/sup\u003eC depleted.\u003c/p\u003e\n\u003cp\u003eThe applied benzene polycarboxylic acid (BPCA) method is known to detect non-pyrogenic aromatic carbon originating from shale, coal, and other geologic substrates\u003csup\u003e29\u003c/sup\u003e. Independent measurements of Mackenzie River pConAC using chemo-thermal oxidation (CTO) yielded comparable \u003csup\u003e14\u003c/sup\u003eC age values (0.185 \u0026plusmn; 0.003; ~13,600 \u003csup\u003e14\u003c/sup\u003eC yr)\u003csup\u003e42\u003c/sup\u003e, corroborating the presence of ancient carbon in the ConAC fraction. Although CTO efficiently removes common petroleum source rock constituents\u003csup\u003e29\u003c/sup\u003e, geologically preserved macerals (e.g., pyrofusinites) may survive thermal oxidation and contribute fossil \u003csup\u003e14\u003c/sup\u003eC signatures to the measured ConAC pool\u003csup\u003e42\u003c/sup\u003e. In agreement with these observations, polycyclic aromatic hydrocarbons (PAHs) in Mackenzie River suspended and bank sediments exhibit source-diagnostic ratios indicative of petrogenic rather than pyrogenic inputs\u003csup\u003e42,78\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eExtensive Devonian-Carboniferous coal and shale deposits of the Interior Platform and Cordilleran foreland serve as large reservoirs for petrogenic carbon\u003csup\u003e79,80\u003c/sup\u003e. Particularly in the Liard and Peel catchments, erosive, barren, and mountainous terrain facilitates the delivery of fossil carbon into the river system\u003csup\u003e72,73,81\u003c/sup\u003e. Although reworked Pleistocene soils and fossil fuel-derived soot\u003csup\u003e42,82,83\u003c/sup\u003e cannot be excluded as secondary contributors, bedrock weathering as the dominant source of aged pConAC.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe combined \u003csup\u003e14\u003c/sup\u003eC, molecular, and geomorphic evidence reveals a systematic decoupling between dConAC and pConAC pools during fluvial transport. The dConAC fraction retains a distinctly contemporary pyrogenic signature, mobilized rapidly through the river network without significant storage. In contrast, pConAC is dominated by \u003csup\u003e14\u003c/sup\u003eC-depleted, non-pyrogenic material sourced primarily from sedimentary lithologies. This divergence arises from erosion efficiency, sediment trapping, and the distribution of carbon-rich substrates within the basin. Geomorphic and lithological controls overprint wildfire signals in the particulate phase, rendering exported pConAC a tracer of watershed structure rather than a record of basin-wide fire activity.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFrom plume to permanence: fate of ConAC across the shelf system\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe estimated dConAC stocks within the Mackenzie plume region (~60,000 km\u003csup\u003e2\u003c/sup\u003e, 0\u0026ndash;1,800 m depth, salinity \u0026lt;27)\u003csup\u003e55\u003c/sup\u003e by applying the reported range of dConAC content (1.5\u0026ndash;8.6%)\u003csup\u003e21,84\u003c/sup\u003e from adjacent Arctic shelves to kriged DOC concentrations\u003csup\u003e85,86\u003c/sup\u003e. This yielded a stock estimate of 1.2 \u0026plusmn; 0.5 Tg ConAC, embedded within a DOC pool of 23.6 \u0026plusmn; 0.6 Tg C. Dividing the computed stock by the annual riverine dConAC flux translates to a shelf residence time of approximately six years. This residence time implies that the Mackenzie Shelf is not a passive transition zone but an intermediate reservoir. The shelf operates either as a net accumulator, with fluvial inputs exceeding degradation and offshore export, or as a quasi-steady state system with balanced inputs and losses.\u003c/p\u003e\n\u003cp\u003eHowever, the limited on-shelf aging of dConAC is insufficient to explain the ~1,500 yr discrepancy between Western Beaufort Sea (~3,740 m; F\u003csup\u003e14\u003c/sup\u003eC: 0.72 \u0026plusmn; 0.28\u003csup\u003e21\u003c/sup\u003e) and the Arctic Red River dConAC (\u003cstrong\u003eFig. 3b\u003c/strong\u003e). This divergence is likely shaped by several controls on transport mechanisms, carbon sourcing, and shelf-basin exchange. Seasonal bias may partly reflect this age difference, as the Arctic Red River sample was collected during the spring freshet. Shallow subsurface flow and minimal infiltration through deeper soil horizons favor the delivery of more modern dConAC. The Beaufort Sea offshore samples closely match corresponding DOC-F\u003csup\u003e14\u003c/sup\u003eC values, indicating a shared, aged terrestrial origin\u003csup\u003e84,87\u003c/sup\u003e. Although the Canadian and Alaskan ecosystems are broadly similar, rivers on the Alaskan North Slope may export older permafrost- and yedoma-derived ConAC to the coastal margin, contributing to the aged offshore signal. The shift toward more depleted offshore \u003csup\u003e14\u003c/sup\u003eC signatures can further be attributed to advection of aged water masses from the Chukchi and East Siberian Seas, selective preservation of refractory dConAC, and dissolution of severely aged pConAC and fossil fuel residues. A sample collected near Barrow Canyon (~150 m) exhibits an extreme \u003csup\u003e14\u003c/sup\u003eC age of ~14,000 years, likely reflecting localized upwelling of legacy carbon\u003csup\u003e20,21\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe removal of dConAC on the Mackenzie Shelf encompasses multiple pathways, including photochemical and microbial degradation\u003csup\u003e20,40,65,67,88\u003c/sup\u003e, sedimentation\u003csup\u003e16,20,37\u003c/sup\u003e, and export to the interior ocean\u003csup\u003e19,21\u003c/sup\u003e. To assess oxidative loss, we modeled CO\u003csub\u003e2\u003c/sub\u003e outgassing from dConAC using a regional ECCO-Darwin configuration\u003csup\u003e55\u003c/sup\u003e. The resulting flux (0.007 \u0026plusmn; 0.001 Tg ConAC yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) is negligible relative to modeled DOC emission fluxes of up to 0.13 Tg C yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (Extended Data Fig. 3)\u003csup\u003e55\u003c/sup\u003e, indicating that outgassing represents a minor removal pathway for dConAC on the Mackenzie Shelf. The model, however, excludes photochemical degradation. As condensed aromatic structures are highly susceptible to photooxidation\u003csup\u003e66,89,90\u003c/sup\u003e, its exclusion likely leads to an underestimation of total oxidative losses.\u003c/p\u003e\n\u003cp\u003eDissolved ConAC can be further removed from the water column by aggregation, flocculation, and particle interactions\u003csup\u003e16,37,40\u003c/sup\u003e, including complexation with iron\u003csup\u003e91\u003c/sup\u003e. In the Mackenzie\u0026ndash;Beaufort system, the particle-mediated removal of DOC, ranging from 45 to 87%, is driven by strong salinity gradients that develop along the estuarine transition\u003csup\u003e92\u0026ndash;94\u003c/sup\u003e. Whether dConAC experiences losses of comparable magnitude remains unknown, as direct observations on the Mackenzie Shelf are scarce. In the absence of direct observations, the only large-scale constraint is provided by a transect spanning the Bering Sea to the Canada Basin, where linear mixing models suggest that 47\u0026ndash;97% of terrestrial dConAC is lost during shelf transfer, with less than 23% reaching the interior ocean\u003csup\u003e84\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eDespite the potentially significant role of biological and physicochemical removal processes, the six-year shelf residence time of dConAC suggests that riverine inputs accumulate on the Mackenzie Shelf. This accumulation may result from a combination of molecularly resistant compounds and sustained fluxes that outpace decomposition and export\u003csup\u003e65\u003c/sup\u003e. Physical retention is limited, as surface stratification and wind-driven advection promote the offshore transport of plume waters rather than prolonged residence\u003csup\u003e95\u003c/sup\u003e. The sparse and spatially variable dConAC concentrations and \u003csup\u003e14\u003c/sup\u003eC signatures highlight the need for expanded sampling across Arctic river and shelf systems to resolve the sources, dispersal, and fate of dConAC in coastal environments.\u003c/p\u003e\n\u003cp\u003eA substantial portion of pConAC is retained within Mackenzie Shelf sediments, forming a long-term carbon reservoir. Published estimates place POC sedimentation rates for the Mackenzie plume region between 0.22 and 0.52 Tg C yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e (refs.\u0026nbsp;\u003csup\u003e72,96\u0026ndash;99\u003c/sup\u003e), while pConAC sediment accumulation rates, constrained by \u003csup\u003e137\u003c/sup\u003eCs and \u003csup\u003e210\u003c/sup\u003ePb profiles\u003csup\u003e100\u003c/sup\u003e, amount to 0.14 \u0026plusmn; 0.01 Tg C yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e. The upper 100 cm of these sediments contains 500 \u0026plusmn; 73 Tg C\u003csup\u003e101\u003c/sup\u003e, representing 800\u0026ndash;1,300 years of accumulation\u003csup\u003e84\u003c/sup\u003e. Given an observed pConAC content of 17.8 \u0026plusmn; 4.3% (n = 3)\u003csup\u003e100\u003c/sup\u003e, this yields a total ConAC stock of 89 \u0026plusmn; 22 Tg. Mass balance calculations imply that ~64% of the riverine pConAC export is deposited within the plume area. Yet, the retention efficiency likely overestimates true pConAC burial as dConAC flocculation during estuarine mixing contributes additional ConAC to sediments beyond the original fluvial particulate fraction\u003csup\u003e16,102,103\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe deposited pConAC is strongly \u003csup\u003e14\u003c/sup\u003eC-depleted (F\u003csup\u003e14\u003c/sup\u003eC = 0.14), consistent with its predominantly petrogenic origin. Comparable F\u003csup\u003e14\u003c/sup\u003eC values in Chukchi Sea sediments (F\u003csup\u003e14\u003c/sup\u003eC = 0.11\u0026ndash;0.30)\u003csup\u003e102\u003c/sup\u003e point to a regional pattern of aged pConAC accumulation across Arctic margin depocenters, underscoring the preservation of refractory pConAC in marine sediments. The Mackenzie\u0026ndash;Beaufort Sea system efficiently transfers rock-derived pConAC to long-term marine storage, limiting its exposure to oxidation during land\u0026ndash;ocean transport and reducing the potential return of petrogenic CO\u003csub\u003e2\u003c/sub\u003e to the atmosphere.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFrom source to signal: implications and uncertainties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study presents a quantitative, source-to-sink budget of ConAC fluxes across the Mackenzie River\u0026ndash;Beaufort Sea continuum, linking wildfire-driven production, fluvial mobilization, and marine sequestration within a single integrated framework (\u003cstrong\u003eFig. 4\u003c/strong\u003e). Three findings emerge from this synthesis. First, the Mackenzie River Basin functions as an active regulator of ConAC dynamics rather than a passive conduit, with geomorphic routing and sediment trapping governing the efficiency with which fire-derived carbon reaches coastal margins. Second, the dissolved and particulate fractions export carbon from fundamentally different landscape reservoirs. dConAC is derived from modern, pyrogenic sources and mobilized rapidly through the fluvial network, while pConAC is dominated by ancient, non-pyrogenic carbon sourced from shales and coal-bearing siliciclastics. In watersheds underlain by carbon-rich sedimentary rocks, lithology is a first-order control that reduces the application of pConAC as a proxy for fire activity. Third, the Mackenzie Shelf operates not as a passive transition zone but as an active intermediate reservoir, retaining riverine inputs over multiyear timescales, efficiently transferring radiocarbon-depleted pConAC to long-term marine sediment storage, and limiting the re-oxidation of ancient petrogenic carbon to atmospheric CO\u003csub\u003e2\u003c/sub\u003e. These findings position the Mackenzie\u0026ndash;Beaufort system as a reference point for land\u0026ndash;ocean ConAC coupling across the Arctic-Boreal domain.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBeyond documenting stocks and fluxes, this budget provides a quantitative framework for constraining the dominant fluxes in the ConAC cycle. The difference between wildfire production and riverine export brackets the magnitude of terrestrial ConAC retention and loss, while shelf residence time and sediment accumulation rates define the marine sink. As a diagnostic tool, the budget identifies the flux pathways where targeted observations and process-level understanding are most required. The largest unquantified flux is the export of ConAC from soils to fluvial networks, particularly in permafrost terrain where thermal erosion amplifies lateral carbon delivery to streams\u003csup\u003e103,104\u003c/sup\u003e. During fluvial transport, losses through sedimentation in lakes, floodplains, and deltas\u003csup\u003e105,106\u003c/sup\u003e, and through oxidative decomposition\u003csup\u003e107\u003c/sup\u003e, remain largely unconstrained (\u003cstrong\u003eFig. 4\u003c/strong\u003e). Wildfire production estimates incorporate uncertainty from burn severity and fuel consumption assumptions, but are expected to improve as combustion models and emission databases are refined. The processing of ConAC during estuarine mixing persists as the key unresolved marine pathway, with limited data on the balance between degradation, aggregation, and offshore export.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe budget provides a roadmap informing future sampling design and targeted field observations to characterize ConAC mobilization, transformation, and sequestration. It further establishes a baseline for detecting and attributing changes driven by amplifying fire activity\u003csup\u003e9,10\u003c/sup\u003e, permafrost degradation\u003csup\u003e53,104\u003c/sup\u003e, precipitation\u003csup\u003e108\u0026ndash;110\u003c/sup\u003e and hydrological regime shift\u003csup\u003e111\u003c/sup\u003e. With Arctic-Boreal systems undergoing the most rapid environmental change on Earth\u003csup\u003e112,113\u003c/sup\u003e, the fate of ConAC represents an unaccounted but consequential term in the global carbon budget.\u003c/p\u003e"},{"header":"Online Methods","content":"\u003cp\u003e\u003cstrong\u003eSample collection.\u0026nbsp;\u003c/strong\u003eSediment and water samples were collected in 2009, 2010, 2011, 2017, and 2018 from the Mackenzie River and Delta and three tributaries, the Liard, Arctic Red, and the Peel Rivers\u003csup\u003e72,73,114\u003c/sup\u003e. Freshly deposited bank sediments were collected with a metal spoon, while riverbed sediments were obtained using a metal bucket. Surface water was collected using a pre-rinsed bucket and transferred to cubitainers or sterilized plastic bags. Surface water was subsequently filtered through 0.2 \u0026micro;m 90-142 mm polyethersulfone (PES) filters using pressure filtration towers. Bank, bed, and suspended sediments were immediately frozen post-collection and stored frozen until freeze-drying.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor each dConAC sample, one liter of filtered water was acidified to pH 2 with hydrochloric acid and slowly passed through four columns (each 250 mL) of styrene divinyl benzene copolymer resin (Sigma Aldrich Diaion 13605, HP-20, pore size 200\u0026Aring;) to concentrate dConAC\u003csup\u003e115,116\u003c/sup\u003e. Cartridges were wrapped in combusted aluminum and preserved at -18\u0026ordm;C.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003epConAC and dConAC analysis and radiocarbon measurements.\u003c/strong\u003e Suspended sediments, along with the \u0026lt;63 \u0026micro;m fraction of channel sediments\u0026mdash;considered equivalent to suspended sediments\u0026mdash;were used to quantify pConAC. Clay and silica matrices can interfere with \u003csup\u003e14\u003c/sup\u003eC analysis of pConAC by adsorbing extraneous organic carbon, thus obscuring the \u003csup\u003e14\u003c/sup\u003eC signal of pConAC, particularly in low-organic-carbon samples\u003csup\u003e117\u003c/sup\u003e. To isolate pConAC, the \u0026lt;63 \u0026micro;m fraction was treated sequentially: first with 1 N hydrochloric acid for 24 hours to remove carbonates and salts, followed by 10% hydrofluoric acid to remove silicate minerals. The treatment was repeated five times, with the sample rinsed ten times with Milli-Q water to remove residual hydrofluoric acid and salts.\u003c/p\u003e\n\u003cp\u003eBefore eluting the DOC fraction containing dConAC, salts were removed by rinsing all columns with 0.01 mol L\u003csup\u003e\u0026ndash;1\u003c/sup\u003e hydrochloric acid. The DOC was then eluted with 30 mL of methanol.\u003c/p\u003e\n\u003cp\u003eSamples of pConAC and dConAC were isolated using the benzene polycarboxylic acid (BPCA) method\u003csup\u003e115,116,118\u003c/sup\u003e, which oxidizes condensed aromatic structures to yield distinct BPCA compounds: benzene tricarboxylic acids (B3CA, specifically 1,2,3-B3CA, and 1,2,4-B3CA), benzene tetracarboxylic acid (B4CA, specifically 1,2,4,5-B4CA), benzene pentacarboxylic acid (B5CA), and benzene hexacarboxylic acid (B6CA)\u003csup\u003e119\u003c/sup\u003e. For BPCA production, concentrated nitric acid was added to each sample within a pressurized digestion chamber, maintained at 170\u0026deg;C for 8 hours. Following oxidation, samples were filtered under vacuum using glass syringes equipped with glass fiber filters (Chromabond\u0026reg;, 0.7 \u0026micro;m pore size). The filtered solution was then pipetted onto a cation exchange resin (Dowex\u0026reg; 50WX8) for initial purification and subsequently processed by solid phase extraction (Discovery\u0026reg; DSC-18, 500 mg columns) for further refinement.\u003c/p\u003e\n\u003cp\u003eBPCAs were separated and collected using preparative high-performance liquid chromatography (HPLC; Agilent 1290 Infinity LC system). Chromatographic separation was performed on a reverse-phase 2.7 \u0026micro;m Poroshell 120 C-18 column with two mobile phases: pH 2 Milli-Q water (adjusted with 1.7% H\u003csub\u003e3\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e) and high-purity acetonitrile (\u0026gt;99.98%, Scharlau, F\u003csup\u003e14\u003c/sup\u003eC \u0026lt;0.004). BPCA quantification was performed via seven-point calibration curves (2\u0026ndash;200 ng \u0026micro;L\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) with commercially available BPCA standards, including pentacarboxylic acid (Aldrich S437107) and hexacarboxylic acid (Aldrich M2705). BPCA concentrations were calculated from peak areas observed in chromatographs generated by the diode array detector (60 mm path length). To convert BPCAs to ConAC estimates and allow comparison with published values, a ConAC recovery factor of 23.2 \u0026plusmn; 0.4% was applied\u003csup\u003e120,121\u003c/sup\u003e. The HPLC fraction collector was used to collect B3CA through B6CA marker compounds, including nitrated B3CAs and B4CAs\u003csup\u003e116\u003c/sup\u003e. Notably, B2CA markers were excluded from the collection, as they can also derive from aromatic compounds of non-combusted origin (e.g., lignin).\u003c/p\u003e\n\u003cp\u003eBPCAs were oxidized to CO\u003csub\u003e2\u003c/sub\u003e using a wet chemical oxidation method\u003csup\u003e116,117,122\u003c/sup\u003e. Samples containing 30 \u0026micro;gC and 4 mL of purified sodium persulfate were transferred into gas-tight 12 mL borosilicate Exetainer vials. The mixture was purged with ultrahigh-purity helium (100 mL min\u003csup\u003e-1\u003c/sup\u003e for 8 min) and oxidized to CO\u003csub\u003e2\u003c/sub\u003e at 95 \u0026deg;C for 1 hr.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRadiocarbon measurements were conducted on a Mini Carbon Dating System (MICADAS) Accelerator Mass Spectrometer at the ETH Zurich Ion Beam Laboratory, using a modified carbonate handling system with a sparging needle. To control for procedural carbon contamination, \u003csup\u003e14\u003c/sup\u003eC-dead ConAC (F\u003csup\u003e14\u003c/sup\u003eC = 0.003 \u0026plusmn; 0.001), modern ConAC (F\u003csup\u003e14\u003c/sup\u003eC = 1.149 \u0026plusmn; 0.004) wood char standards, marine sediment standard NIST 1941b, and procedural blanks were processed alongside samples\u003csup\u003e29,123\u003c/sup\u003e. ConAC samples were corrected for extraneous carbon using these standards\u003csup\u003e117,123\u003c/sup\u003e. \u003csup\u003e14\u003c/sup\u003eC values are reported as fraction modern (F\u003csup\u003e14\u003c/sup\u003eC)\u003csup\u003e124\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimating riverine ConAC export fluxes.\u0026nbsp;\u003c/strong\u003eRiverine export fluxes of dConAC and pConAC were estimated by multiplying measured ConAC contents with corresponding DOC and POC fluxes. We used ConAC content data synthesized from the Mackenzie River system, including published values from Stubbins et al. (2015)\u003csup\u003e45\u003c/sup\u003e and Coppola et al. (2018)\u003csup\u003e17\u003c/sup\u003e, along with 4 new dConAC and 25 new pConAC measurements from this study. Flow-weighted mean ConAC contents were computed for the Mackenzie River and its tributaries using discharge records from the Water Survey of Canada (https://wateroffice.ec.gc.ca/). For the Mackenzie River Delta, discharge was estimated as the sum of the Mackenzie and Arctic Red Rivers at Tsiigehtshik and the Peel River at Teetł\u0026rsquo;it Zheh, assuming negligible losses (e.g., evaporation). ConAC fluxes were calculated by multiplying the flow-weighted dConAC and pConAC content by independent estimates of DOC and POC fluxes, respectively. To propagate uncertainty in both ConAC content and riverine carbon flux, we employed a Monte Carlo simulation (n = 10,000) using a truncated normal distribution for ConAC content (to constrain values to physically realistic, non-negative ranges) and a uniform distribution for DOC and POC fluxes (Extended Data Fig. 2). DOC fluxes were assumed to range from 1.04 to 1.76 Tg C yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, and POC fluxes from 0.3 to 2.6 Tg C yr\u003csup\u003e\u0026ndash;1\u003c/sup\u003e, based on available literature\u003csup\u003e50,51,72,96,125\u0026ndash;132\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFire Proximity to Rivers Across Sub-basins.\u0026nbsp;\u003c/strong\u003eWe used QGIS (version 3.40.4-Bratislava; QGIS Development Team) for all spatial analyses, including stream classification, buffer creation, and burned area calculations. HydroRIVERS (HydroSHEDS) waterways were categorized into streams (Strahler order 1\u0026ndash;3) and rivers (Strahler order 4\u0026ndash;9)\u003csup\u003e133\u003c/sup\u003e. Buffer zones were defined as 0\u0026ndash;1 km, 1\u0026ndash;2 km, 2\u0026ndash;3 km, 3\u0026ndash;4 km, 4\u0026ndash;5 km, and \u0026gt;5 km for streams, and 0\u0026ndash;10 km, 10\u0026ndash;20 km, 20\u0026ndash;30 km, 30\u0026ndash;40 km, 40\u0026ndash;50 km, and \u0026gt;50 km for rivers. These buffers were overlaid with burned area data from the National Burned Area Composite (NBAC), provided by Natural Resources Canada and the Canadian Forest Service through the Fire Monitoring, Accounting, and Reporting System (FireMARS) (\u003ca href=\"https://cwfis.cfs.nrcan.gc.ca/datamart\" target=\"_new\"\u003ehttps://cwfis.cfs.nrcan.gc.ca/datamart\u003c/a\u003e). The burned area within each buffer was extracted using vector intersection and expressed as a percentage of the total burned area within each sub-basin. Analyses were conducted using the equal area projection NAD83(CSRS) / Canada Atlas Lambert (EPSG: 3979).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHigh-resolution estimation of fire-derived ConAC production.\u0026nbsp;\u003c/strong\u003eWe estimated ConAC production from wildfires across seven sub-basins of the Mackenzie River by integrating data from the Arctic Boreal Vulnerability Experiment Fire Emissions Database (ABoVE-FED)\u003csup\u003e54,134\u003c/sup\u003e and the Global Fire Emissions Database version 4s+ConAC (GFED4s+PyC; http://www.globalfiredata.org)\u003csup\u003e33\u003c/sup\u003e. The ABoVE-FED provided carbon emissions and burned area fraction at a high spatial resolution of 500 m, while the GFED4s+PyC supplied ConAC production-to-carbon emission ratios at a 0.25\u0026ordm; resolution. To align the spatial resolution of these datasets, we applied nearest neighbor interpolation to resample GFED4s+PyC data to match the 500 m resolution of the ABoVE-FED. ConAC production was computed by multiplying the interpolated ratios by carbon emissions and the burned fraction of each pixel. Uncertainties in ConAC production were derived by propagating the uncertainties from the carbon emissions data\u003csup\u003e54\u003c/sup\u003e. Finally, we aggregated the ConAC production estimates for each of the seven sub-basins and the entire Mackenzie River basin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEstimating organic carbon and pyrogenic carbon stocks in the Mackenzie River system.\u003c/strong\u003e We estimated organic carbon stocks in plant biomass and soils across the Mackenzie River basin using model outputs and associated uncertainties from Sothe et al.\u003csup\u003e48,49\u003c/sup\u003e For marine sediments in the Mackenzie plume region, we used the gridded carbon stock and uncertainty data from Atwood et al.\u003csup\u003e101\u003c/sup\u003e To quantify marine DOC stocks on the Mackenzie Shelf, we performed ordinary kriging on in situ DOC concentration measurements (n = 573)\u003csup\u003e85,86\u003c/sup\u003e. All spatial datasets were projected to EPSG:3979 to ensure consistency in distance and area calculations.\u003c/p\u003e\n\u003cp\u003eWe first fit an empirical semivariogram to DOC concentrations using a spherical model, with a maximum lag set to 66% of the maximum pairwise distance and 15 lags. To robustly estimate spatially explicit uncertainties, we performed leave-one-out cross-validation. For each iteration, a single point was withheld from kriging, and the prediction error (difference between observed and predicted DOC) was recorded. The absolute cross-validation errors were then modeled with an exponential variogram, which tends to better capture the structure of stochastic residuals. We conducted the final kriging over a regular 300\u0026times;300 grid covering the study domain, generating both predicted DOC concentrations and spatially explicit uncertainty surfaces. Absolute uncertainties were derived by kriging the cross-validation errors, while relative uncertainties (%) were calculated as the ratio of absolute uncertainty to predicted DOC concentration. Model performance was assessed with root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R\u003csup\u003e2\u003c/sup\u003e), based on cross-validation predictions.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDOC stock was estimated by multiplying the kriged mean DOC concentration (\u0026micro;mol L\u003csup\u003e\u0026ndash;1\u003c/sup\u003e) by the total water volume of the plume (13,249.53 km\u003csup\u003e3\u003c/sup\u003e) and converting it to Tg C using a molar mass of 12.0107 g mol\u003csup\u003e\u0026ndash;1\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eTo estimate total carbon stocks, rasters of plant biomass and soil organic carbon, along with their uncertainty layers, were clipped to the Mackenzie River basin extent. Marine sediment carbon and kriged DOC concentrations, with associated uncertainties, were clipped to the Mackenzie Plume extent. Pixel-level values were summed and weighted by pixel area. To propagate uncertainty while accounting for spatial autocorrelation, we applied block bootstrapping to the uncertainty rasters. Block size was set based on the median correlation length estimated from 30 bootstrapped semivariograms of the uncertainty field, using exponential models fit to spatially random subsets (\u0026le;4000 points). During each of the 1000 bootstrap iterations, spatially contiguous blocks of pixels were resampled with replacement. Errors were drawn from the uncertainty surface within each sampled block and added to the original carbon values. The total stock was recalculated for each realization, producing an empirical distribution from which mean, standard deviation, and 95% confidence intervals were derived.\u003c/p\u003e\n\u003cp\u003eConAC stocks in soils, marine sediments, and marine DOC were estimated using published ConAC fractions applied to the corresponding carbon stock components. Uncertainty was propagated using Monte Carlo simulations (n = 10,000). The lower bound of the resulting 95% confidence intervals was truncated at zero to ensure physical plausibility. Soil ConAC in the Mackenzie River basin was estimated using a mean fraction of 6.9% \u0026plusmn; 0.5%, based on Schiedung et al.\u003csup\u003e18\u003c/sup\u003e. Marine sediment ConAC was estimated from two values reported by Yang and Guo\u003csup\u003e100\u003c/sup\u003e, along with one additional value from this study. Marine dConAC content was approximated using a representative range (1.5\u0026ndash;8.6%) observed across the Beaufort and Chukchi Seas and the Canada Basin\u003csup\u003e21,84\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eAll calculations were performed using the skgstat and rasterio libraries in Python.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermining the contribution of dConAC to the air-sea CO\u003csub\u003e2\u003c/sub\u003e flux using ECCO-Darwin\u003c/strong\u003e. We estimated the contribution of dConAC to the air-sea CO\u003csub\u003e2\u003c/sub\u003e flux in the Mackenzie region using the ECCO-Darwin biogeochemistry state estimate for the Southeastern Beaufort Sea (ED-SBS)\u003csup\u003e55\u003c/sup\u003e. The ED-SBS is a regional variant of the global model, which combines a physical ocean circulation and sea ice model (Estimating the Circulation and Climate of the Ocean, ECCO), the Massachusetts Institute of Technology general circulation model (MITgcm)\u003csup\u003e135\u003c/sup\u003e, with an ocean ecology model, Darwin\u003csup\u003e136\u003c/sup\u003e. ECCO-Darwin assimilates \u003cem\u003ein situ\u003c/em\u003e and remotely-sensed observations from 1992 to 2019, allowing for an ocean state estimate for physics and biogeochemistry at global and regional scales\u003csup\u003e136\u0026ndash;138\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eBuilding on the ECCO-LLC270 global-ocean and sea-ice configuration\u003csup\u003e139\u003c/sup\u003e, the ED-SBS regional model integrates the global-ocean biogeochemistry state estimate by Carroll et al.\u003csup\u003e140,141\u003c/sup\u003e. This configuration simulates four Arctic Ocean plankton functional types: diatoms, large eukaryotes, and small and large zooplankton. The biogeochemical component of the model explicitly simulates the cycling of carbon, nitrogen, phosphorus, silica, iron, and oxygen as they transition between inorganic, living, and dead organic pools \u003csup\u003e142\u003c/sup\u003e, and includes a simulation of the carbonate cycle\u003csup\u003e143\u003c/sup\u003e. Daily land-to-ocean fluxes of freshwater and six biogeochemical tracers (DON, DOP, DSi, DOC, DIC, Alk) are prescribed in the model based on estimates relying on the chemodynamic relationship (flux of nutrients as a function of discharge)\u003csup\u003e132,144\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eGiven that this manuscript focuses on ConAC and its export from the terrestrial biosphere to the marine reservoir, we adapted the ED-SBS model to include a third DOC pool specifically for dConAC (DOC\u003csub\u003ebc\u003c/sub\u003e). This adaptation was essential to simulate the fate and reactivity of dConAC within the riverine freshwater plume on the shelf. In addition to this newly introduced DOC\u003csub\u003ebc\u003c/sub\u003e pool, the model simulates two oceanic pools of DOC following Bertin et al.\u003csup\u003e55\u003c/sup\u003e: a semi-labile pool (DOC\u003csub\u003esl\u003c/sub\u003e) with a residence time of t\u0026nbsp;=\u0026nbsp;1 month and a semi-refractory pool (DOC\u003csub\u003esr\u003c/sub\u003e) with a residence time of t = 10 years. For DOC\u003csub\u003ebc\u003c/sub\u003e, a residence time of t\u0026nbsp;=\u0026nbsp;1 year was assigned to represent the behavior of ConAC exported by the Mackenzie River. The Mackenzie River terrestrial DOC flux was allocated among the three pools, with 12% designated for the DOC\u003csub\u003ebc\u003c/sub\u003e and the remaining 88% equally distributed between DOC\u003csub\u003esr\u003c/sub\u003e and DOC\u003csub\u003esl\u003c/sub\u003e\u003csup\u003e55\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eWe conducted two simulations: one considering only the DOC\u003csub\u003esr\u003c/sub\u003e and DOC\u003csub\u003esl\u003c/sub\u003e pools, forced with 88% of the total riverine DOC flux, and another including the DOC\u003csub\u003ebc\u003c/sub\u003e pool. By comparing the results of these simulations, we estimated the portion of CO\u003csub\u003e2\u003c/sub\u003e originating from dConAC. This contribution was calculated within the river plume region, defined by the isohaline threshold of S = 27, which differentiates between fresh and marine water\u003csup\u003e95\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data used in this study are publicly accessible. Discharge records were obtained from the Water Survey of Canada (https://wateroffice.ec.gc.ca/). River networks were derived from HydroRIVERS (https://www.hydrosheds.org/products/hydrorivers). Burned area data for Canada were obtained from the National Burned Area Composite (NBAC), provided by Natural Resources Canada and the Canadian Forest Service through FireMARS (https://cwfis.cfs.nrcan.gc.ca/datamart), and for Alaska from the Alaska Interagency Coordination Center (https://fire.ak.blm.gov/predsvcs/maps.php). Fire emissions and activity were obtained from the Arctic Boreal Vulnerability Experiment Fire Emissions Database (ABoVE-FED; https://doi.org/10.3334/ORNLDAAC/2063) and the Global Fire Emissions Database version 4s+ConAC (GFED4s+PyC; http://www.globalfiredata.org). Forest and soil carbon stock maps and associated uncertainties were obtained from https://doi.org/10.4121/14572929.v1 and https://doi.org/10.4121/16686154.v3, respectively. Marine sediment carbon stocks for the Mackenzie plume region are available at https://figshare.com/articles/Global_marine_sedimentary_carbon_stock/11956356. Model code and instructions for ED-SBS simulations are available at https://doi.org/10.5281/zenodo.7417828, with model forcing files accessible from https://ecco.jpl.nasa.gov/drive/files/ECCO2/LLC270/Mac_Delta. All data generated for this study are submitted to Zenodo and will be publicly available upon publication.\u003cbr clear=\"all\"\u003e \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Mathieu Dellinger, Christina Larkin, and Edwin Amos for their invaluable field support in the collection of samples from the Mackenzie River, conducted under Northwest Territories Research Licenses 14557 and 15288. We also acknowledge B.B., a former master’s student under A.I.C, for his significant contributions to sample processing. A.I.C is grateful for funding from the Swiss National Science Foundation Ambizione Research Grant (PZ00P2_185835). M.S.S. acknowledges support from the NASA Postdoctoral Program at the Jet Propulsion Laboratory, administered by Oak Ridge Associated Universities under contract with NASA. M.vG and S.V.’s contributions were funded by the European Research Council (ERC) through a Consolidator Grant under the European Union’s Horizon 2020 research and innovation program (grant agreement No. 101000987) awarded to S.V. Fieldwork was partially funded by an ERC Starting Grant (ROC-CO2, 678779) awarded to R.G.H., who also received support from an ERC Consolidator Grant (RIV-ESCAPE, 101002563. M.W.J. received support from the UK Natural Environment Research Council (NE\\V01417X\\1). We also appreciate the constructive feedback on the manuscript provided by Carlos Sierra and David Nielsen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eR.G.H., V.G., and M.S.S. collected samples from the Mackenzie River Basin. A.I.C., T.I.E., R.G.H., and V.G. contributed resources. Samples were processed and analyzed by A.I.C. and B.B. M. vG. calculated fire-derived ConAC fluxes, while air-sea ConAC-CO\u003csub\u003e2\u003c/sub\u003e was computed by C.M.B. N.H. performed radiocarbon measurements. Data analysis and interpretation were led by A.I.C. and M.S.S. Figures were produced by A.I.C., M.S.S., and C.B. A.I.C. and M.S.S. wrote the manuscript. All the authors contributed to the refinement of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExtended data \u003c/strong\u003eis available for this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrespondence and requests for materials \u003c/strong\u003eshould be addressed to Melissa S. Schwab.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBond-Lamberty, B., Peckham, S. 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E.) 75\u0026ndash;105 (Wiley, New York, 1991).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9476681/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9476681/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eArctic–Boreal ecosystems hold vast carbon stocks, yet intensifying wildfires threaten to shift them from net sinks to net sources. Fires produce condensed aromatic carbon (ConAC), a chemically stable form of pyrogenic carbon that accumulates in terrestrial and marine reservoirs. However, ConAC mobilization, retention, and export along the riverine continuum remain poorly constrained. Here, we present a comprehensive source-to-sink ConAC budget for the Mackenzie River–Beaufort Sea system, quantifying wildfire production, riverine transport, and shelf accumulation. Between 2001 and 2017, fires burned 9.7% of the basin and generated 36.5 Tg ConAC, while soils stored ~4 Pg ConAC with millennial-scale turnover. Fluvial export averaged 0.42 Tg ConAC yr\u003csup\u003e–1\u003c/sup\u003e. The dissolved fraction reflected modern pyrogenic sources, in contrast to particulate ConAC dominated by radiocarbon-depleted carbon derived from sedimentary rocks. On the Beaufort Shelf, waters retained ~1.2 Tg ConAC with a ~6-year residence time, whereas sediments accumulated ~0.14 Tg ConAC yr\u003csup\u003e–1\u003c/sup\u003e. This budget reveals that basin properties including lithology, geomorphic routing, and sediment trapping control the composition and efficiency of land-to-ocean ConAC transfer. It establishes a quantitative baseline for assessing how ongoing environmental change will reshape ConAC cycling across high-latitude systems.\u003c/p\u003e","manuscriptTitle":"Arctic condensed aromatic carbon budget reveals efficient fluvial transfer and shelf storage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-04 04:50:28","doi":"10.21203/rs.3.rs-9476681/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d27f4b79-a47a-4d04-a385-4e7778bcb370","owner":[],"postedDate":"May 4th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-05-07T01:42:37+00:00","index":5,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-06T21:06:08+00:00","index":5,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-06T18:46:46+00:00","index":4,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-01T19:05:49+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-01T18:24:00+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-05-01T01:38:49+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"7","date":"2026-04-30T18:04:36+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67341024,"name":"Earth and environmental sciences/Biogeochemistry/Carbon cycle"},{"id":67341025,"name":"Earth and environmental sciences/Climate sciences/Biogeochemistry/Carbon cycle"}],"tags":[],"updatedAt":"2026-05-04T04:50:28+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-04 04:50:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9476681","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9476681","identity":"rs-9476681","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}