Abstract Emerging responses of northern tree cover and composition under climate change have consequences for ecosystem functioning. Satellite-based global tree and land cover datasets have improved our understanding of northern tree cover dynamics. Yet, definitions of forests, design of retrieval algorithms, different spatial resolutions and satellite sensor quality can introduce uncertainties in these data. Here, our objective was to identify consistent patterns of recent change directions of tree cover and tree cover composition over northern lands using multiple datasets. We found large differences in area of tree cover changes in these datasets, ranging from 9% to 82% for increasing trends over North America and Europe. Consequently, we generated a synthesis map that captures consistent trends in tree cover in the four global datasets and found that the synthesis map showed higher agreement with visually interpreted tree cover changes from very high-resolution imagery . This result reflects improved consistency in areas of multi-dataset agreement, rather than improved absolute accuracy. The new synthesis map revealed a substantially larger area of increasing tree cover trends as compared to decreases. This pattern of net increases in tree cover was also relatively consistent among the major northern biomes, whereby large areas of increasing tree cover were particularly evident in boreal regions. Drier boreal and temperate biomes showed more areas with decreasing tree cover in comparison to wetter biomes. Our comparison of changes in the cover of evergreen and deciduous trees based on two land cover products uncovered large disagreements, making it nearly impossible at this point to attribute the identified tree cover changes in regards to forest type. These results may serve as a reference for model simulations of vegetation dynamics. Our findings also call for concerted efforts to produce more consistent tree cover and land cover datasets especially tree cover composition.

The North American arctic and boreal regions (ABRs) are rapidly warming and experiencing intensifying disturbances. Accurately quantifying aboveground biomass (AGB) is critical for understanding the impacts of these changes on the carbon cycle and for designing climate change mitigation strategies. Several AGB maps have been developed for the North American ABRs, including recent contributions from National Aeronautics and Space Administration's Arctic-Boreal Vulnerability Experiment (ABoVE) campaign. However, these maps differ widely in training data, methodology, and resulting AGB density estimates. Presently, a comprehensive comparative evaluation is lacking, making it difficult for users to select datasets suited to their research or management needs. Here, we conducted a comparative analysis of nine AGB density datasets across North American ABRs, specifically for Alaska and Canada. We (1) summarized AGB by ecoregion and Canadian provinces, (2) evaluated their accuracy against field-based measurements, (3) analyzed spatial and temporal similarities among datasets, and (4) assessed their ability to capture disturbance (fire and harvest) impacts on AGB. We found substantial variation in regional and local AGB estimates across datasets, with overall accuracy ranging from R-2 = 0.25-0.62 and Bias% from -47.8% to 69.9% when validated against field plots. Despite these differences, most datasets have comparatively consistent spatial patterns in AGB (r > 0.8 for most cases). In contrast, agreement on the temporal patterns of AGB change is generally low. We found datasets with spatial resolutions <= 300 m are capable of capturing disturbance impacts on AGB dynamics, though sensitivity varies across products. Our findings and dataset summary provide guidance for selecting appropriate AGB datasets for different applications within our study area. Our analysis also highlights the need to decrease map bias and increase capability to detect temporal change to decrease uncertainty of AGB datasets potentially by using training data which is representative of major plant functional types within the mapped area.

Abstract The integrity of forest-based climate solutions and carbon credits requires persistent carbon storage, but climate change is increasing the risk of natural disturbances that release carbon back into the atmosphere. Using global satellite data, disturbance modeling, and machine learning, we provide the first spatially explicit and scenario-based maps of long-term probability of carbon loss in global forests under different disturbance severities and climate scenarios. We find that North American conifer forests, tropical rainforests, and Asian (sub)tropical dry forests face the greatest risks, and that Eurasian temperate forests, African (sub)tropical dry forests face the lowest. Globally, the likelihood of reversals over 100 years is 31%−42% across all scenarios. Our work helps to maximize the benefits of forest-based climate solutions by informing more strategic project placement and more robust reversal-risk compensation mechanisms, such as buffer pools, and highlights critical additional science to better understand and manage risks of these essential climate solutions. Plain Language Summary Forests can help slow and lessen climate impacts. However, in places this benefit is becoming less reliable as climate change increases natural disturbances such as wildfires, drought, storms, and insect outbreaks, which can release stored carbon back into the atmosphere. In this study, we created the first scenario-based global maps of risks and found that the risk of carbon loss is widespread and highly variable across regions, with especially high vulnerability in North American conifer forests, tropical rainforests, and Asian tropical and subtropical dry forests. Our study highlights the importance of considering disturbance risks when siting forest projects for climate mitigation, and developing protocols for carbon markets, such as in voluntary programs and under the UNFCCC Paris Agreement. Key Points A demographic model framework estimates the reversal risk from natural disturbances over 100 years in global forests Spatially explicit maps under different severity scenarios show variation in the integrated 100-year risk of carbon reversal Spatially explicit maps estimate the required buffer pool needed to compensate for disturbance-driven reversals in global forests

Changes in Arctic tundra vegetation, driven by climate change, may be inducing major shifts in ecosystem services and the Arctic carbon budget, and altering high latitude feedbacks to the climate system. Field-based studies have documented warming-induced shrub expansion, and remote sensing has revealed heterogeneous, but primarily positive, trends in peak summer greenness across the Arctic. However, efforts to move beyond remotely sensed measures of spectral greening to quantify the spatial extent and rate of shrub expansion have been constrained by spectral similarities among tundra vegetation types, limited ground truth data, low revisit frequency of satellite observations, and sub-pixel heterogeneity of land cover at medium spatial resolution (30 m). To address these challenges, we developed a methodology that integrates high spatial resolution (2 m) commercial satellite imagery with Harmonized Landsat and Sentinel-2 observations in a machine learning framework, and used it to produce annual maps for 2016 to 2023 of sub-pixel land cover fractions at 30-m spatial resolution across three Arctic tundra ecoregions spanning 3.35 × 105 km2 between the Seward and Tuktoyaktuk Peninsulas. Uncertainty was quantified at each pixel via Monte Carlo resampling. Independent accuracy assessments yielded good accuracies (mean squared errors of 15.98% and 11.89% for low-stature vegetation and erect shrub cover, respectively), that were comparable to or exceeded previous mapping efforts. Further, repeat commercial satellite image pairs enabled the first assessment of mapped fractional cover change in Arctic tundra (R2 of 0.46 and 0.55, change direction accuracies of 77% and 78% for low-stature vegetation and erect shrub cover, respectively). This novel, scalable, multi-sensor approach to fractional land cover mapping produced the first annual maps of land cover fractions in the Arctic tundra, which support more accurate representation of vegetation dynamics and their linkages to climate change and disturbance processes.

Although the reduction of fossil fuel emissions remains of the utmost importance to mitigate climate change, maintaining and enhancing carbon sinks in forests have been widely promoted as nature-based climate solutions1-4. However, disturbances that could result in losses of forest carbon stocks are poorly accounted for when estimating the potential role of forests in climate mitigation5-7. This makes it difficult to appropriately size 'buffer pools': a mechanism designed to compensate for unintended carbon losses in carbon crediting projects8,9. Here we use forest inventory, satellite data, disturbance modelling and machine learning to map reversal (carbon loss) risk in the contiguous United States (CONUS) from natural disturbance. Across CONUS forests, we show that climate change increases the 100-year risk of carbon losses from natural disturbance, particularly in California and the Intermountain West. The current buffer pool of the largest CONUS forest climate mitigation programme is likely too small by an average factor of 6.3, and this could range from 2.2- to 8.0-fold too small when considering uncertainties around future climate scenarios, disturbance severity and other carbon pools. We provide spatially explicit maps of the long-term risks to forest carbon losses from natural disturbances, which highlight that current methodologies used for constructing carbon offset buffer pools require revisions to succeed under climate change.

Arctic and boreal regions (ABRs) are experiencing rapid warming and increasingly severe wildfires, threatening their roles as global carbon sinks. High quality time series maps of aboveground biomass (AGB) are key for characterizing and attributing spatiotemporal dynamics of carbon stocks in these regions. However, existing maps at regional to global scales often lack the spatial resolution or temporal coverage needed to capture the heterogeneous and dynamic nature of Arctic-boreal AGB change. To address these limitations, we developed annual (1984-2022) 30-m resolution AGB density maps for Alaska and Canada (11.2 x 10(6) km(2)). The maps were produced by using extensive training datasets, including 45,002 unique ground plots and 100,000 km(2) of airborne lidar data, and time-series spectral features derived from the Continuous Change Detection and Classification (CCDC) algorithm fitted on Landsat Collection 2 Surface Reflectance. Using the eXtreme Gradient Boosting model, we generated annual wall-to-wall maps of AGB along with associated uncertainties. Our maps suggest a similar to 41 Pg stock of AGB at 2022, representing a 12% increase from 1984. Our maps achieve high accuracy and low bias on holdout testing data (R-2 = 0.72, Bias= -5.03%, RMSE% = 62.7%), representing an average of 16.0 percentage points increase in R-2 values and 22.7 percentage points decrease in relative bias compared to six existing AGB products. We show using repeat ground measurements that these maps effectively capture AGB loss and recovery due to fire and harvest, gradual AGB changes, and both live and dead tree AGB components in boreal regions. By integrating extensive calibration data with multi-decadal satellite observations and advanced machine learning techniques, these map products provide a robust tool for advancing the understanding of carbon dynamics under global change in Arctic-boreal North America.

Wildfires influence the distribution of biomass across the Earth’s surface and drive losses of carbon from the land surface to the atmosphere. Although the global budget between terrestrial and atmospheric carbon pools is comparatively well understood ([Jones et al., 2024][1]; [MacCarthy et al., 2024][2]), the growing size and severity of wildfires present an increasing challenge to regional carbon accounting. To quantify vegetation biomass across arid systems in the western US, we used remote sensing data to estimate the aboveground live biomass density (Mg ha-1) at 30-meter resolution across the states of Utah and Nevada annually from 2000 to 2022. Time series biomass maps showed accelerated loss of terrestrial carbon to the atmosphere as a result of increasing wildfire, with annual biomass burnt increasing at 0.105 ± 0.024 Mt yr-1 (mean ± standard deviation) after 2015. Recent shifts in the wildfire regime show a transition from predominantly early-season fires in low-biomass grasses and shrubs to late-season fires in higher-biomass forestlands. The proportion of total biomass loss attributed to wildfires in forestland areas increased from an average of 76% per year before 2015 to 94% in the years that followed. Furthermore, we found that recent droughts contributed to increased biomass loss in forestland areas, whereas biomass in non-forestland areas appeared unaffected by drought conditions. These findings underscore the escalating impact of climate change on fire regimes, highlighting the urgent need for adaptive land management strategies to mitigate carbon loss and preserve ecosystem resilience in the face of ongoing aridification. ### Competing Interest Statement The authors have declared no competing interest. [1]: #ref-34 [2]: #ref-48

Over the past three decades, assessments of the contemporary global carbon budget consistently report a strong net land carbon sink. Here, we review evidence supporting this paradigm and quantify the differences in global and Northern Hemisphere estimates of the net land sink derived from atmospheric inversion and satellite-derived vegetation biomass time series. Our analysis, combined with additional synthesis, supports a hypothesis that the net land sink is substantially weaker than commonly reported. At a global scale, our estimate of the net land carbon sink is 0.8 ± 0.7 petagrams of carbon per year from 2000 through 2019, nearly a factor of two lower than the Global Carbon Project estimate. With concurrent adjustments to ocean (+8%) and fossil fuel (-6%) fluxes, we develop a budget that partially reconciles key constraints provided by vegetation carbon, the north-south CO2 gradient, and O2 trends. We further outline potential modifications to models to improve agreement with a weaker land sink and describe several approaches for testing the hypothesis.

Terrestrial ecosystems could contribute to climate mitigation through nature-based climate solutions (NbCS), which aim to reduce ecosystem greenhouse gas emissions and/or increase ecosystem carbon storage. Forests have the largest potential for NbCS, aligned with broader sustainability benefits, but-unfortunately-a broad body of literature has revealed widespread problems in forest NbCS projects and protocols that undermine the climate mitigation of forest carbon credits and hamper efforts to reach global net zero. Therefore, there is a need to bring better science and policy to improve NbCS climate mitigation outcomes going forward. Here we synthesize challenges to crediting forest NbCS and offer guidance and key next steps to make improvements in the implementation of these strategies immediately and in the near-term. We structure our Perspective around four key components of rigorous forest NbCS, illuminating key science and policy considerations and providing solutions to improve rigour. Finally, we outline a 'contribution approach' to support rigorous forest NbCS that is an alternative funding mechanism that disallows compensation or offsetting claims.

Establishing protected areas (PAs) in Amazon forests is crucial for safeguarding tropical forest ecosystem from human land use and mitigating forest degradation. However, PAs across the Amazon basin have increasingly suffered from intensified fires. Understanding post-fire recovery trajectories in these protected forests is essential for assessing the resilience and effectiveness of PAs. However, recovery trajectories under natural conditions remain unclear, as human settlements often disrupt or influence the recovery process, potentially diminishing recovery rates and forest potential. To address this challenge, we investigated 4,036 fire events that occurred from 2001 to 2020 within PAs in the eastern Amazon detected by Moderate Resolution Imaging Spectroradiometer (MODIS) satellite. Furthermore, we explored the effectiveness of multi-source earth observation data and eXtreme Gradient Boost machine learning model in distinguishing fire areas where recovery of local forests undergoes natural conditions (N-recovery) from those impacted by human activities (H-recovery). We then analyzed temporal trends in fire burn severity (based on the relationship between fire year and Landsat-derived burn severity metrics) and post-fire canopy structure recovery (based on the relationship between GEDI lidar-derived canopy structure metrics and fire age using a space-for-time substitution approach) for both recovery types. Our model accurately differentiated N-recovery (n=2019) from H-recovery (n=2017) with an overall classification accuracy of 87.61%. Our analysis further reveals a clear increasing trend in fire burn severity for N-recovery from 2001 to 2020, while the trend for H-recovery was relatively stable with no significant change. Moreover, the recovery rates of relative heights (RH), canopy ratio (CR), and plant area index (PAI) in N-recovery areas were significantly higher than those in H-recovery areas over 20 years, highlighting the importance of separating these two recovery types. By focusing on N-recovery areas, we found that forest structural traits related to understory recovery and plant vertical space use (i.e., PAI values across the entire vertical strata) exhibited stronger recovery rates than traits related to height metrics (i.e., RHs), revealing their utility for characterizing more complex ecosystem recovery processes. These findings demonstrate the potential and necessity of using multi-source earth observation data to distinguish between the two types of post-fire forest recovery. This distinction contributes to an improved understanding of ecological recovery rates and processes of post-fire forest successional dynamics under natural conditions, offering new opportunities to further study their biogeographical distribution, recovery rate variabilities, and impacts on carbon sequestration and ecosystem resilience under climate change.

Protected areas (PAs) in Amazon forests are vital in preserving tropical forest ecosystems and mitigating forest degradation. However, the increasing frequency and severity of fires in these regions necessitate a comprehensive understanding of post-fire vegetation recovery trajectories, which is essential to evaluate the effectiveness and resilience of PAs in the face of ongoing climate change. Recovery trajectories under natural conditions remain uncertain, as unregulated human settlements often interfere with or influence the recovery process, skewing the actual recovery rates detected by satellite remote sensing. To tackle this issue, we examined 2990 MODIS-derived fire events in eastern Amazon PAs from 2001 to 2020. We assessed the effectiveness of multi-source Earth observation data and the eXtreme Gradient Boost machine learning model to distinguish burned areas undergoing natural recovery (natural recovery areas) from areas that are permanently converted to other uses (permanently converted areas). We then analyzed greenness recovery rates and canopy structure recovery trajectories across all burned areas, natural recovery areas, and permanently converted areas. Greenness recovery rates were derived from Landsat data, while canopy structure recovery was assessed using GEDI lidarderived metrics and the space-for-time substitution approach. Our model achieved an overall classification accuracy of 87.90 %, accurately differentiating natural recovery areas (n = 1944) from permanently converted areas (n = 1046). The differing patterns of post-fire greenness recovery rates and structure recovery trajectories highlight the importance of this distinction. In natural recovery areas, significant recovery of structural traits such as relative heights (RHs), canopy cover (CC), and plant area index (PAIs), was observed, returning to their pre-disturbance levels over a 20-year period. Notably, metrics related to understory recovery and plant vertical space use, such as PAI values across the entire vertical strata, exhibited stronger recovery rates than height-related metrics like RHs, highlighting their utility in characterizing complex ecosystem recovery processes. These findings demonstrate the potential and necessity of using multi-source Earth observation data to distinguish different post-fire vegetation recovery processes. This distinction improves our understanding of ecological recovery rates and the successional dynamics of post-fire forests under natural conditions, offering new opportunities to explore their biogeographical distribution, recovery rate variabilities, and impacts on carbon sequestration and ecosystem resilience.

Accurate accounting of greenhouse‐gas (GHG) emissions and removals is central to tracking progress toward climate mitigation and for monitoring potential climate‐change feedbacks. GHG budgeting and reporting can follow either the Intergovernmental Panel on Climate Change methodologies for National Greenhouse Gas Inventory (NGHGI) reporting or use atmospheric‐based “top‐down” (TD) inversions or process‐based “bottom‐up” (BU) approaches. To help understand and reconcile these approaches, the Second REgional Carbon Cycle Assessment and Processes study (RECCAP2) was established to quantify GHG emissions and removals for carbon dioxide (CO 2 ), methane (CH 4 ) and nitrous oxide (N 2 O), for ten‐land and five‐ocean regions for 2010–2019. Here, we present the results for the North American land region (Canada, the United States, Mexico, Central America and the Caribbean). For 2010–2019, the NGHGI reported total net‐GHG emissions of 7,270 TgCO 2 ‐eq yr −1 compared to TD estimates of 6,132 ± 1,846 TgCO 2 ‐eq yr −1 and BU estimates of 9,060 ± 898 TgCO 2 ‐eq yr −1 . Reconciling differences between the NGHGI, TD and BU approaches depended on (a) accounting for lateral fluxes of CO 2 along the land‐ocean‐aquatic continuum (LOAC) and trade, (b) correcting land‐use CO 2 emissions for the loss‐of‐additional‐sink capacity (LASC), (c) avoiding double counting of inland water CH 4 emissions, and (d) adjusting area estimates to match the NGHGI definition of the managed‐land proxy. Uncertainties remain from inland‐water CO 2 evasion, the conversion of nitrogen fertilizers to N 2 O, and from less‐frequent NGHGI reporting from non‐Annex‐1 countries. The RECCAP2 framework plays a key role in reconciling independent GHG‐reporting methodologies to support policy commitments while providing insights into biogeochemical processes and responses to climate change.

Plant functional trait-based approaches are powerful tools to assess the consequences of global environmental changes for plant ecophysiology, population and community ecology, ecosystem functioning, and landscape ecology. Here, we present data capturing these ecological dimensions from grazing, nitrogen addition, and warming experiments conducted along a 821 m a.s.l. elevation gradient and from a climate warming experiment conducted across a 3,200 mm precipitation gradient in boreal and alpine grasslands in Vestland County, western Norway. From these systems we collected 28,762 plant and leaf functional trait measurements from 76 vascular plant species, 88 leaf assimilation-temperature responses, 577 leaf handheld hyperspectral readings, 2.26 billion leaf temperature measurements, 3,696 ecosystem CO2 flux measurements, and 10.69 ha of multispectral (10-band) and RGB cm-resolution imagery from 4,648 individual images obtained from airborne sensors. These data augment existing longer-term data on local climate, soils, plant populations, plant community composition, and ecosystem functioning from within the same experiments and study systems and from similar systems in other mountain regions globally.

In the face of climate change, understanding the dynamic responses of vegetation is crucial for predicting shifts in biosphere functioning. Plant functional traits, particularly leaf mass per area (LMA), are critical links between plant metabolism, vegetation responses to climate change, and the broader exchanges of energy and matter within the biosphere. Despite their importance, a comprehensive, predictive understanding of traits and biosphere changes is hampered by spatial and temporal gaps in trait observations. Here, we introduce a novel remote sensing method for the global, continuous mapping of LMA and its historical shifts. Consistent with ecological theory predicting a widespread decrease in LMA with global warming, our findings reveal a global LMA reduction of 6.5-7.6 % between 1985 and 2019, primarily due to increasing temperatures. This decrease varies among biomes, with evergreen conifer and tropical forests showing the most significant declines. Due to LMA connections with carbon metabolism in ecosystems, a global decrease in LMA points to a quickening of the carbon cycle, including largely unexplored contributions to increased global photosynthesis in recent decades. Collectively, these results signal an ongoing widespread and profound transformation in the functioning of the biosphere resulting from climate-related changes in vegetation and its traits.

Nature-based climate solutions (NbCSs) could play an important role in meeting the goals of the Paris Agreement. The contribution approach offers an alternative model to carbon offsetting for funding NbCSs. This paper presents three crucial design principles to help ensure the contribution approach results in high-quality climate and other benefits and avoids harms.

Application of the best available science to improve quantification of greenhouse gas (GHG) emissions at regional and national scales is key to climate action. Here, we present a two-decade (2000-2019) GHG (CO2, CH4, and N2O) budget for Mexico derived from multiple products. Data from the National GHG Inventory, global observations, and the scientific literature were compared to identify knowledge gaps on GHG flux dynamics and discrepancies among estimates. Total mean annual GHG emissions were estimated at 695-910 TgCO2-eq year-1 over these two decades, with 70% of the emissions attributable to CO2, 23% to CH4, and 5% to N2O (2% to other gases). When divided by sectors, we found agreement across emission estimates from various sources for fossil fuels, cattle, agriculture, and waste for all GHGs. However, considerable discrepancies were identified in the fluxes from terrestrial ecosystems. The disagreement was particularly large for the land CO2 sink, where net biome production estimations from the national inventory were double those from any other observational product. Extensive knowledge gaps exist, mainly related to aquatic systems (e.g., outgassing in rivers) and the lateral fluxes (e.g., wood trade). In addition, limited information is available on CH4 emissions from wetlands and soil CH4 consumption. We expect these results to guide future research to reduce estimation uncertainties and fill the information gaps across Mexico. Mexico represents the 13th global emitter of greenhouse gases (GHGs) and the second among Latin American countries, releasing roughly 485 million tons of CO2, six million tons of CH4 (equivalent to 160 million tons of CO2), and 0.1 million tons of N2O (equivalent to 35 million tons of CO2). However, large uncertainties prevail in the estimation of several components of each flux; thus, the application of the best available science to account for and improve these estimations is essential for sound mitigation policies. Here, we used multiple available products (national inventories, satellites, flux towers, dynamic global vegetation models, inversion models, and other data sources) to advance our understanding of the GHG budget of Mexico over the last 20 years. We found that fluxes from fossil fuel combustion (including energy production, transportation, industry, etc.), agriculture, livestock, and waste management agree remarkably well across products. In contrast, we documented large differences among products for land-based fluxes (e.g., CO2 capture) and scant available information on aquatic ecosystems (e.g., CH4 emission from wetlands) and on lateral fluxes (e.g., emissions from trade). We expect these results to help in guiding future measuring efforts and policies to mitigate GHG emissions from Mexico. A greenhouse gas (GHG) budget for Mexico was calculated based on multiple products. We estimated fluxes for CO2, CH4, and N2O over the 2000-2019 period Total GHG emissions were 695 TgCO2-eq year-1 in the national inventory and as much as a mean 910 TgCO2-eq year-1 across products GHG fluxes agreed well across products for emissions, but large discrepancies exist for the land CO2 and CH4 sinks

Observations of the annual cycle of atmospheric CO2 in high northern latitudes provide evidence for an increase in terrestrial metabolism in Arctic tundra and boreal forest ecosystems. However, the mechanisms driving these changes are not yet fully understood. One proposed hypothesis is that ecological change from disturbance, such as wildfire, could increase the magnitude and change the phase of net ecosystem exchange through shifts in plant community composition. Yet, little quantitative work has evaluated this potential mechanism at a regional scale. Here we investigate how fire disturbance influences landscape-level patterns of photosynthesis across western boreal North America. We use Alaska and Canadian large fire databases to identify the perimeters of wildfires, a Landsat-derived land cover time series to characterize plant functional types (PFTs), and solar-induced fluorescence (SIF) from the Orbiting Carbon Observatory-2 (OCO-2) as a proxy for photosynthesis. We analyze these datasets to characterize post-fire changes in plant succession and photosynthetic activity using a space-for-time approach. We find that increases in herbaceous and sparse vegetation, shrub, and deciduous broadleaf forest PFTs during mid-succession yield enhancements in SIF by 8-40% during June and July for 2- to 59-year stands relative to pre-fire controls. From the analysis of post-fire land cover changes within individual ecoregions and modeling, we identify two mechanisms by which fires contribute to long-term trends in SIF. First, increases in annual burning are shifting the stand age distribution, leading to increases in the abundance of shrubs and deciduous broadleaf forests that have considerably higher SIF during early- and mid-summer. Second, fire appears to facilitate a long-term shift from evergreen conifer to broadleaf deciduous forest in the Boreal Plain ecoregion. These findings suggest that increasing fire can contribute substantially to positive trends in seasonal CO2 exchange without a close coupling to long-term increases in carbon storage.

Warmer temperatures and severe drought are driving increases in wildfire activity in the western United States, threatening forest ecosystems. However, identifying the influence of fire severity on tree cover loss (TCL) is challenging using commonly used categorical metrics. In this study, we quantify regional trends in wildfire-driven TCL as the product of annual burned area, average forest exposure (pre-fire tree cover), and average fire severity (relative loss of tree cover). We quantified these trends with Landsat-based 30 m resolution fire and tree cover datasets for California wildfires from 1986-2021. Rates of TCL rose faster than trends in burned area, with the magnitude of tree cover area loss per unit of area burned increasing by 70% from 0.20 +/- 0.05 during 1986-1996 to 0.34 +/- 0.10 during 2011-2021. Forest exposure (pre-fire tree cover) within fires increased by 41% from a decadal mean of 23.4% +/- 5.5% (1986-1996) to 33.1% +/- 7.8% (2011-2021). Increasing forest exposure is associated with a recent expansion of fires in dense northern forests. Concurrently, fire severity (relative TCL) rose by 30% from a decadal mean of 50.4% +/- 7.2% during 1986-1996 to 65.6% +/- 6.5% during 2011-2021. We developed and applied a simple conceptual framework to quantify the combined effect of wildfires affecting denser forests and burning more severely. The combined effect of these two processes contributed to nearly half (47%) of the TCL since 1986, highlighting that recent changes in burned areas alone cannot explain observed tree cover trends. Linear regression analysis revealed that warmer summers and drier winters were significant drivers of increasing forest exposure, fire severity, and burned area (R2 from 0.54 to 0.80, p <= 0.001), particularly in the northern forests. Climate extremes had a disproportionate impact on dense forests that were once more resistant to wildfire but now face risks from a shifting wildfire regime.

Climate change is amplifying both wildfire burned area and severity, as well as incidents of drought-induced tree mortality (dieback). Direct effects from climate change amplify wildfires and episodes of drought-induced dieback have well-known impacts on forest's ability to regulate climate, provide water, and store carbon. Less understood are how past disturbances produce interaction effects that can change subsequent disturbance occurrence and intensity, with implications for management decisions that can promote forest resistance and resilience. We constructed two parallel forest chrono-sequences by combining a geospatial database of historical fire with satellite and airborne observations of forests in the Sierra Nevada of California to assess the impact of fire history on vegetation recovery, water use (evapotranspiration), and drought-induced forest dieback. We used these data sets to assess two research questions: (1.) Does fire history amplify or reduce drought-dieback intensity? (2.) What mechanisms explain how fire-induced changes to forest structure and ET alter subsequent forest dieback intensity? We show that recent fire history decreased drought-induced forest dieback intensity, compared to unburned controls. These fire-affected forests were characterized by reduced tree cover and decreased evapotranspiration, which combined to increase drought resistance more than would be expected by either effect individually. Two decades post-fire, evapotranspiration returned to pre-fire conditions. Tree and shrub cover started to approach pre-fire conditions, except for high severity fires where decreased tree cover and increased shrub cover persisted. Field based research on fuels treatments suggests that fire history may also increase longer term forest resilience. In fire-prone conifer forests, interaction effects from recent low and moderate severity fires will increase drought resistance and perhaps longer-term forest stability.

Global climate change is influencing the seasonal cycle amplitude of atmospheric CO2 (SCA), with the strongest increases at northern high latitudes (NHL; >45° N). In this Review, we explore the changes and underlying mechanisms influencing the NHL SCA, focusing on Arctic and boreal terrestrial ecosystems. Latitudinal gradients in the SCA are largely governed by seasonality in temperature and primary production, and their influence on ecosystem carbon dynamics. In the NHL, the SCA has increased by 50% since the 1960s, mostly due to enhanced seasonality in net carbon dioxide (CO2) exchange in NHL terrestrial ecosystems. Temperature most strongly influences this trend, owing to warming impacts on growing season length and plant productivity; CO2 fertilization effects have a secondary role. Eurasian boreal ecosystems exert the strongest influence on the SCA, and spring and summer are the most influential seasons. Enhanced ecosystem respiration during the non-growing season exhibits most uncertainty in the SCA response to global and landscape drivers. Observed changes in the seasonal amplitude are projected to continue. Key priorities include extending carbon flux and ecosystem observation networks, particularly in tundra ecosystems, and including drivers such as vegetation cover and permafrost in process models to better simulate seasonal dynamics of net CO2 exchange in the NHL. Changes in the seasonal cycle amplitude of atmospheric CO2 (SCA) reflect large-scale changes in the global carbon cycle. This Review summarizes the positive SCA trend in the northern high latitudes, where the signal is strongest, and explores the underlying mechanisms driving the trend and their relative importance.