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Climate change is driving substantial changes in North American boreal forests, including changes in productivity, mortality, recruitment, and biomass. Despite the importance for carbon budgets and informing management decisions, there is a lack of near‐term (5–30 year) forecasts of expected changes in aboveground biomass (AGB). In this study, we forecast AGB changes across the North American boreal forest using machine learning, repeat measurements from 25,000 forest inventory sites, and gridded geospatial datasets. We find that AGB change can be predicted up to 30 years into the future, and that training on sites across the entire domain allows accurate predictions even in regions with only a small amount of existing field data. While predicting AGB loss is less skillful than gains, using a multi‐model ensemble can improve the accuracy in detecting change direction to >90% for observed increases, and up to 70% for observed losses. Higher stem density, winter temperatures, and the presence of temperate tree species in forest plots were positively associated with AGB change, whereas greater initial biomass, continentality (difference between mean summer and winter temperatures), prevalence of black spruce (Picea mariana), summer precipitation, and early warning metrics from long‐term remote sensing time series were negatively associated with AGB change. Across the domain, we predict nondisturbance‐induced declines in AGB at 23% of sites by 2030. The approach developed here can be used to estimate near‐future forest biomass in boreal North America and inform relevant management decisions. Our study also highlights the power of machine learning multi‐model ensembles when trained on a large volume of forest inventory plots, which could be applied to other regions with adequate plot density and spatial coverage.

 

Abstract. Tundra environments are experiencing elevated levels of wildfire, and thefrequency is expected to keep increasing due to rapid climate change in theArctic. Tundra wildfires can release globally significant amounts ofgreenhouse gasses that influence the Earth's radiative balance. Here wedevelop a novel method for estimating carbon loss and the resultingradiative forcings of gaseous and aerosol emissions from the 2015 tundrawildfires in the Yukon–Kuskokwim Delta (YKD), Alaska. We paired burn depthmeasurements using two vegetative reference points that survived the fireevent – Sphagnum fuscum and Dicranum spp. – with measurements of local organic matter and soil carbonproperties to estimate total ecosystem organic matter and carbon loss. Weused remotely sensed data on fire severity from Landsat 8 to scale ourmeasured losses to the entire fire-affected area, with an estimated totalloss of 2.04 Tg of organic matter and 0.91 Tg of carbon and an average lossof 3.76 kg m−2 of organic matter and 1.68 kg m−2 of carbon in the2015 YKD wildfires. To demonstrate the impact of these fires on the Earth'sradiation budget, we developed a simple but comprehensive framework toestimate the radiative forcing from Arctic wildfires. We synthesizedexisting research on the lifetime and radiative forcings of gaseous andaerosol emissions of CO2, N2O, CH4, O3 and itsprecursors, and fire aerosols. The model shows a net positive cumulativemean radiative forcing of 3.67 W m−2 using representative concentration pathway (RCP) 4.5 and 3.37 W m−2using RCP 8.5 at 80 years post-fire, which was dominated by CO2emissions. Our results highlight the climate impact of tundra wildfires,which positively reinforce climate warming and increased fire frequencythrough the radiative forcings of their gaseous emissions. 

Abstract. Fire is the dominant disturbance agent in Alaskan and Canadianboreal ecosystems and releases large amounts of carbon into the atmosphere.Burned area and carbon emissions have been increasing with climate change,which have the potential to alter the carbon balance and shift the regionfrom a historic sink to a source. It is therefore critically important totrack the spatiotemporal changes in burned area and fire carbon emissionsover time. Here we developed a new burned-area detection algorithm between2001–2019 across Alaska and Canada at 500 m (meters) resolution thatutilizes finer-scale 30 m Landsat imagery to account for land coverunsuitable for burning. This method strictly balances omission andcommission errors at 500 m to derive accurate landscape- and regional-scaleburned-area estimates. Using this new burned-area product, we developedstatistical models to predict burn depth and carbon combustion for the sameperiod within the NASA Arctic–Boreal Vulnerability Experiment (ABoVE) coreand extended domain. Statistical models were constrained using a database offield observations across the domain and were related to a variety ofresponse variables including remotely sensed indicators of fire severity,fire weather indices, local climate, soils, and topographic indicators. Theburn depth and aboveground combustion models performed best, with poorerperformance for belowground combustion. We estimate 2.37×106 ha (2.37 Mha) burned annually between 2001–2019 over the ABoVE domain (2.87 Mhaacross all of Alaska and Canada), emitting 79.3 ± 27.96 Tg (±1standard deviation) of carbon (C) per year, with a mean combustionrate of 3.13 ± 1.17 kg C m−2. Mean combustion and burn depthdisplayed a general gradient of higher severity in the northwestern portionof the domain to lower severity in the south and east. We also found larger-fire years and later-season burning were generally associated with greatermean combustion. Our estimates are generally consistent with previousefforts to quantify burned area, fire carbon emissions, and their drivers inregions within boreal North America; however, we generally estimate higherburned area and carbon emissions due to our use of Landsat imagery, greateravailability of field observations, and improvements in modeling. The burnedarea and combustion datasets described here (the ABoVE Fire EmissionsDatabase, or ABoVE-FED) can be used for local- to continental-scaleapplications of boreal fire science. 

The northern permafrost region has been projected to shift from a net sink to a net source of carbon under global warming. However, estimates of the contemporary net greenhouse gas (GHG) balance and budgets of the permafrost region remain highly uncertain. Here, we construct the first comprehensive bottom‐up budgets of CO₂, CH₄, and N₂O across the terrestrial permafrost region using databases of more than 1000 in situ flux measurements and a land cover‐based ecosystem flux upscaling approach for the period 2000–2020. Estimates indicate that the permafrost region emitted a mean annual flux of 12 (−606, 661) Tg CO₂–C yr⁻¹, 38 (22, 53) Tg CH₄–C yr⁻¹, and 0.67 (0.07, 1.3) Tg N₂O–N yr⁻¹to the atmosphere throughout the period. Thus, the region was a net source of CH₄and N₂O, while the CO₂balance was near neutral within its large uncertainties. Undisturbed terrestrial ecosystems had a CO₂sink of −340 (−836, 156) Tg CO₂–C yr⁻¹. Vertical emissions from fire disturbances and inland waters largely offset the sink in vegetated ecosystems. When including lateral fluxes for a complete GHG budget, the permafrost region was a net source of C and N, releasing 144 (−506, 826) Tg C yr⁻¹and 3 (2, 5) Tg N yr⁻¹. Large uncertainty ranges in these estimates point to a need for further expansion of monitoring networks, continued data synthesis efforts, and better integration of field observations, remote sensing data, and ecosystem models to constrain the contemporary net GHG budgets of the permafrost region and track their future trajectory.

 

Soil respiration (i.e. from soils and roots) provides one of the largest global fluxes of carbon dioxide (CO₂) to the atmosphere and is likely to increase with warming, yet the magnitude of soil respiration from rapidly thawing Arctic-boreal regions is not well understood. To address this knowledge gap, we first compiled a new CO₂flux database for permafrost-affected tundra and boreal ecosystems in Alaska and Northwest Canada. We then used the CO₂database, multi-sensor satellite imagery, and random forest models to assess the regional magnitude of soil respiration. The flux database includes a new Soil Respiration Station network of chamber-based fluxes, and fluxes from eddy covariance towers. Our site-level data, spanning September 2016 to August 2017, revealed that the largest soil respiration emissions occurred during the summer (June–August) and that summer fluxes were higher in boreal sites (1.87 ± 0.67 g CO₂–C m⁻²d⁻¹) relative to tundra (0.94 ± 0.4 g CO₂–C m⁻²d⁻¹). We also observed considerable emissions (boreal: 0.24 ± 0.2 g CO₂–C m⁻²d⁻¹; tundra: 0.18 ± 0.16 g CO₂–C m⁻²d⁻¹) from soils during the winter (November–March) despite frozen surface conditions. Our model estimates indicated an annual region-wide loss from soil respiration of 591 ± 120 Tg CO₂–C during the 2016–2017 period. Summer months contributed to 58% of the regional soil respiration, winter months contributed to 15%, and the shoulder months contributed to 27%. In total, soil respiration offset 54% of annual gross primary productivity (GPP) across the study domain. We also found that in tundra environments, transitional tundra/boreal ecotones, and in landscapes recently affected by fire, soil respiration often exceeded GPP, resulting in a net annual source of CO₂to the atmosphere. As this region continues to warm, soil respiration may increasingly offset GPP, further amplifying global climate change.