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The response of evapotranspiration (ET) to warming is of critical importance to the water and carbon cycle of the boreal biome, a mosaic of land cover types dominated by forests and peatlands. The effect of warming-induced vapour pressure deficit (VPD) increases on boreal ET remains poorly understood because peatlands are not specifically represented as plant functional types in Earth system models. Here we show that peatland ET increases more than forest ET with increasing VPD using observations from 95 eddy covariance tower sites. At high VPD of more than 2 kPa, peatland ET exceeds forest ET by up to 30%. Future (2091–2100) mid-growing season peatland ET is estimated to exceed forest ET by over 20% in about one-third of the boreal biome for RCP4.5 and about two-thirds for RCP8.5. Peatland-specific ET responses to VPD should therefore be included in Earth system models to avoid biases in water and carbon cycle projections.Climate warming increases evapotranspiration (ET) more in boreal peatlands than in forests. Observations show that peatland ET can exceed forest ET by up to 30%, indicating a stronger warming response in peatlands. Earth system models do not fully account for peatlands and hence may underestimate future boreal ET.

The Arctic–Boreal Zone is rapidly warming, impacting its large soil carbon stocks. Here we use a new compilation of terrestrial ecosystem CO 2 fluxes, geospatial datasets and random forest models to show that although the Arctic–Boreal Zone was overall an increasing terrestrial CO 2 sink from 2001 to 2020 (mean ± standard deviation in net ecosystem exchange, −548 ± 140 Tg C yr −1 ; trend, −14 Tg C yr −1 ; P  < 0.001), more than 30% of the region was a net CO 2 source. Tundra regions may have already started to function on average as CO 2 sources, demonstrating a shift in carbon dynamics. When fire emissions are factored in, the increasing Arctic–Boreal Zone sink is no longer statistically significant (budget, −319 ± 140 Tg C yr −1 ; trend, −9 Tg C yr −1 ), and the permafrost region becomes CO 2 neutral (budget, −24 ± 123 Tg C yr −1 ; trend, −3 Tg C yr −1 ), underscoring the importance of fire in this region. How the carbon stocks of the Arctic–Boreal Zone change with warming is not well understood. Here the authors show that wildfires and large regional differences in net carbon fluxes offset the overall increasing CO 2 uptake.

Wetland methane (CH 4 ) emissions ( F C H 4 ) are important in global carbon budgets and climate change assessments. Currently, F C H 4 projections rely on prescribed static temperature sensitivity that varies among biogeochemical models. Meta-analyses have proposed a consistent F C H 4 temperature dependence across spatial scales for use in models; however, site-level studies demonstrate that F C H 4 are often controlled by factors beyond temperature. Here, we evaluate the relationship between F C H 4 and temperature using observations from the FLUXNET-CH 4 database. Measurements collected across the globe show substantial seasonal hysteresis between F C H 4 and temperature, suggesting larger F C H 4 sensitivity to temperature later in the frost-free season (about 77% of site-years). Results derived from a machine-learning model and several regression models highlight the importance of representing the large spatial and temporal variability within site-years and ecosystem types. Mechanistic advancements in biogeochemical model parameterization and detailed measurements in factors modulating CH 4 production are thus needed to improve global CH 4 budget assessments. Wetland methane emissions contribute to global warming, and are oversimplified in climate models. Here the authors use eddy covariance measurements from 48 global sites to demonstrate seasonal hysteresis in methane-temperature relationships and suggest the importance of microbial processes.

Soils are warming as air temperatures rise across the Arctic and Boreal region concurrent with the expansion of tall-statured shrubs and trees in the tundra. Changes in vegetation structure and function are expected to alter soil thermal regimes, thereby modifying climate feedbacks related to permafrost thaw and carbon cycling. However, current understanding of vegetation impacts on soil temperature is limited to local or regional scales and lacks the generality necessary to predict soil warming and permafrost stability on a pan-Arctic scale. Here we synthesize shallow soil and air temperature observations with broad spatial and temporal coverage collected across 106 sites representing nine different vegetation types in the permafrost region. We showed ecosystems with tall-statured shrubs and trees (>40 cm) have warmer shallow soils than those with short-statured tundra vegetation when normalized to a constant air temperature. In tree and tall shrub vegetation types, cooler temperatures in the warm season do not lead to cooler mean annual soil temperature indicating that ground thermal regimes in the cold-season rather than the warm-season are most critical for predicting soil warming in ecosystems underlain by permafrost. Our results suggest that the expansion of tall shrubs and trees into tundra regions can amplify shallow soil warming, and could increase the potential for increased seasonal thaw depth and increase soil carbon cycling rates and lead to increased carbon dioxide loss and further permafrost thaw.

Spring snowmelt occurs for a short duration on an annual time scale, but their timings considerably affect the carbon and hydrological cycle in high‐latitude ecosystems. Here, we developed a simple snowmelt model, treating the ecosystem surface as a bulk‐surface layer. Energy fluxes across this bulk surface and the snow‐soil boundary determine snow temperature and the energy utilized for snowmelt. Parameterizing the bulk surface using decade‐long eddy covariance site data from two Alaskan open black spruce forests offered an opportunity to quantitatively evaluate meteorological drivers affecting snowmelt timings without the needs for detailed canopy information. The sensitivity analysis suggested that the total snowfall on the forest floor, ranging from 0.35 m in 2016 to about 1 m in 2018 and 2020, was the most crucial driver for snowmelt timing. This factor accounted for a 10‐day difference in the interannual variations in snow disappearance dates. The importance of the snowfall varied from year to year, and in 2013, the late snowmelt was characterized by low air temperatures, which increased sensible heat loss from the snowpack. The importance of atmospheric radiation was revealed in relatively warm years, such as 2016 and 2019. Our modeling approach necessitates adjusting one empirical parameter that reflects the heat conductivity from the bulk surface to the snow, based on observations. Nevertheless, despite this need for adjustment, the bulk‐surface approach helps identify important meteorological drivers underlying observed snowmelt within a simple theoretical framework. Plain Language Summary It is essential to understand why and how spring snowmelt occurs in boreal forests because of its impact on both the biological and hydrological processes in the ecosystem. However, meteorological drivers related to snowmelt often change simultaneously, which makes it challenging to determine which single driver is vital for snowmelt. To address this challenge, we constructed a simple snowmelt model that treats the ecosystem surface as a single bulk layer. The simple model bypasses the needs for detailed canopy information and is easily constrained by observed data. Applying the model to decadal observation data from Alaskan boreal forests, we found that snowfall, air temperature, and atmospheric radiation were important meteorological drivers for explaining interannual variations in snowmelt timing. Each of them explained about 1–2 weeks of snowmelt disappearance dates. We also demonstrated that the model helped explain why the snowmelt was late in 2013 and early in 2016 and 2019 and why snow disappearance dates differed between study sites. While a simple model has its limitations, it can be beneficial for understanding snowmelt characteristics within a simple theoretical framework. Key Points A model‐based approach was introduced to delineate the roles of meteorological drivers on interannual variations in snowmelt timing Snowfall was the most important driver of the interannual variation in snowmelt timing followed by air temperature and atmospheric radiation Late snowmelt in 2013 was attributed to low air temperature, increasing sensible heat loss from the snowpack

To better understand the spatial and temporal dynamics of CO 2 exchange between Arctic ecosystems and the atmosphere, we synthesized CO 2 flux data, measured in eight Arctic tundra and five boreal ecosystems across Alaska (USA) and identified growing season and spatial variations of the fluxes and environmental controlling factors. For the period examined, all of the boreal and seven of the eight Arctic tundra ecosystems acted as CO 2 sinks during the growing season. Seasonal patterns of the CO 2 fluxes were mostly determined by air temperature, except ecosystem respiration (RE) of tundra. For the tundra ecosystems, the spatial variation of gross primary productivity (GPP) and net CO 2 sink strength were explained by growing season length, whereas RE increased with growing degree days. For boreal ecosystems, the spatial variation of net CO 2 sink strength was mostly determined by recovery of GPP from fire disturbance. Satellite-derived leaf area index (LAI) was a better index to explain the spatial variations of GPP and NEE of the ecosystems in Alaska than were the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Multiple regression models using growing degree days, growing season length, and satellite-derived LAI explained much of the spatial variation in GPP and net CO 2 exchange among the tundra and boreal ecosystems. The high sensitivity of the sink strength to growing season length indicated that the tundra ecosystem could increase CO 2 sink strength under expected future warming, whereas ecosystem compositions associated with fire disturbance could play a major role in carbon release from boreal ecosystems.

Dense and widespread tropical rainforests across Southeast Asia are crucial for the global carbon cycle. A new generation geostationary satellite, Himawari-8/9, and onboard sensor, the Advanced Himawari Imager (AHI), enables hyper-temporal vegetation monitoring in this cloud-prone region. However, AHI’s fixed viewing geometry varies spatially, making consistent vegetation monitoring challenging across a wide area at a seasonal scale. This study evaluated four different sun-target-sensor geometry conditions using two-band enhanced vegetation index (EVI2) to achieve near-uniform geometry and enhance the ability to monitor vegetation activities: (i) nadir condition, which used nadir viewing and solar geometry at local solar noon; (ii) local solar noon (LSN) condition, which used pixel-by-pixel fixed viewing and solar geometry at local solar noon; (iii) temporally-constant scattering angle (T-CSA) condition, which used pixel-by-pixel fixed viewing and solar geometry corresponding to the scattering angle which meets the criterion (Gao et al 2024 Remote Sens. Environ. 315 114407); and (iv) spatially-constant scattering angle (S-CSA) condition, which used pixel-by-pixel fixed viewing and solar geometry corresponding to a uniform scattering angle of 140°. Among these, the S-CSA condition most effectively mitigated angular artifacts. The derived EVI2 showed higher correlations with tower-based gross primary productivity (GPP), indicating it better captured seasonal variations in vegetation activity. It also demonstrated spatially consistent temporal variations, least affected by AHI’s unique observation geometry. Applying a near-uniform sun-target-sensor geometry based on the S-CSA condition improves monitoring capability in Southeast Asia and enhances our understanding of vegetation dynamics.

Soil respiration (i.e. from soils and roots) provides one of the largest global fluxes of carbon dioxide (CO 2 ) 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 CO 2 flux database for permafrost-affected tundra and boreal ecosystems in Alaska and Northwest Canada. We then used the CO 2 database, 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 CO 2 –C m −2 d −1 ) relative to tundra (0.94 ± 0.4 g CO 2 –C m −2 d −1 ). We also observed considerable emissions (boreal: 0.24 ± 0.2 g CO 2 –C m −2 d −1 ; tundra: 0.18 ± 0.16 g CO 2 –C m −2 d −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 CO 2 –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 CO 2 to the atmosphere. As this region continues to warm, soil respiration may increasingly offset GPP, further amplifying global climate change.

Wetlands are responsible for 20%–31% of global methane (CH 4 ) emissions and account for a large source of uncertainty in the global CH 4 budget. Data‐driven upscaling of CH 4 fluxes from eddy covariance measurements can provide new and independent bottom‐up estimates of wetland CH 4 emissions. Here, we develop a six‐predictor random forest upscaling model (UpCH4), trained on 119 site‐years of eddy covariance CH 4 flux data from 43 freshwater wetland sites in the FLUXNET‐CH4 Community Product. Network patterns in site‐level annual means and mean seasonal cycles of CH 4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash‐Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH 4 emissions of 146 ± 43 TgCH 4  y −1 for 2001–2018 which agrees closely with current bottom‐up land surface models (102–181 TgCH 4  y −1 ) and overlaps with top‐down atmospheric inversion models (155–200 TgCH 4  y −1 ). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH 4 fluxes has the potential to produce realistic extra‐tropical wetland CH 4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid‐to‐arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC ( https://doi.org/10.3334/ORNLDAAC/2253 ). Wetlands account for a large share of global methane emissions to the atmosphere, but current estimates vary widely in magnitude (∼30% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for tropical rice growing (e.g., Bengal basin), rainforest (e.g., Amazon basin), and floodplain savannah (e.g., Sudd) regions. Wetland methane model estimates could be improved by increased use of land surface methane flux data. Upscaling approaches use flux data collected across globally distributed measurement networks in a machine learning framework to extrapolate fluxes in space and time. Here, we train and evaluate a methane upscaling model (UpCH4) and use it to generate monthly, globally gridded wetland methane emissions estimates for 2001–2018. The UpCH4 model uses only six predictor variables among which temperature is dominant. Global annual methane emissions estimates and associated uncertainty ranges from upscaling fall within state‐of‐the‐art model ensemble estimates from the Global Carbon Project (GCP) methane budget. In some tropical regions, the spatial pattern of UpCH4 emissions diverged from GCP predictions, however, inclusion of flux measurements from additional ground‐based sites, together with refined maps of tropical wetlands extent, could reduce these prediction uncertainties. Random forest models trained on FLUXNET‐CH4 methane fluxes reproduced spatiotemporal patterns in extra‐tropical wetlands ( R 2 : 0.59–0.64) Globally upscaled annual wetland methane emissions (146 TgCH 4  y −1 ) overlapped with land surface and inversion model ensemble estimates Humid/monsoon tropics dominate upscaled wetland methane emissions (∼68%) and uncertainties (∼78%) due to limited FLUXNET‐CH4 site coverage

Peatlands and forests cover large areas of the boreal biome and are critical for global climate regulation. They also regulate regional climate through heat and water vapour exchange with the atmosphere. Understanding how land-atmosphere interactions in peatlands differ from forests may therefore be crucial for modelling boreal climate system dynamics and for assessing climate benefits of peatland conservation and restoration. To assess the biophysical impacts of peatlands and forests on peak growing season air temperature and humidity, we analysed surface energy fluxes and albedo from 35 peatlands and 37 evergreen needleleaf forests-the dominant boreal forest type-and simulated air temperature and vapour pressure deficit (VPD) over hypothetical homogeneous peatland and forest landscapes. We ran an evapotranspiration model using land surface parameters derived from energy flux observations and coupled an analytical solution for the surface energy balance to an atmospheric boundary layer (ABL) model. We found that peatlands, compared to forests, are characterized by higher growing season albedo, lower aerodynamic conductance, and higher surface conductance for an equivalent VPD. This combination of peatland surface properties results in a ∼20% decrease in afternoon ABL height, a cooling (from 1.7 to 2.5 °C) in afternoon air temperatures, and a decrease in afternoon VPD (from 0.4 to 0.7 kPa) for peatland landscapes compared to forest landscapes. These biophysical climate impacts of peatlands are most pronounced at lower latitudes (∼45°N) and decrease toward the northern limit of the boreal biome (∼70°N). Thus, boreal peatlands have the potential to mitigate the effect of regional climate warming during the growing season. The biophysical climate mitigation potential of peatlands needs to be accounted for when projecting the future climate of the boreal biome, when assessing the climate benefits of conserving pristine boreal peatlands, and when restoring peatlands that have experienced peatland drainage and mining.