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DOE OSTI2022

Wildfire exacerbates high-latitude soil carbon losses from climate warming

Abstract Arctic and boreal permafrost soil organic carbon (SOC) decomposition has been slower than carbon inputs from plant growth since the last glaciation. Anthropogenic climate warming has threatened this historical trend by accelerating SOC decomposition and altering wildfire regimes. We accurately modeled observed plant biomass and carbon emissions from wildfires in Alaskan ecosystems under current climate conditions. In projections to 2300 under the RCP8.5 climate scenario, we found that warming and increased atmospheric CO 2 will result in plant biomass gains and higher litterfall. However, increased carbon losses from (a) wildfire combustion and (b) rapid SOC decomposition driven by increased deciduous litter production, root exudation, and active layer depth will lead to about 4.4 PgC of soil carbon losses from Alaska by 2300 and most (88%) of these loses will be from the top 1 m of soil. These SOC losses offset plant carbon gains, causing the ecosystem to transition to a net carbon source after 2200. Simulations excluding wildfire increases yielded about a factor of four lower SOC losses by 2300. Our results show that projected wildfire and its direct and indirect effects on plant and soil carbon may accelerate high-latitude soil carbon losses, resulting in a positive feedback to climate change.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1994

Ozone and aerosol distributions in the summertime troposphere over Canada

Measurements of ozone (O3) and aerosol distributions were made with an airborne lidar system in the lowland and boreal forest regions of eastern Canada during July - August 1990 as part of the NASA Global Tropospheric Experiment/Arctic Boundary Layer Expedition (ABLE) 3B. Aerosol and O3 profiles were measured simultaneously above and below the Electra aircraft from near the surface to above the tropopause on long-range flights over these important ecosystems. A broad range of atmospheric conditions were encountered during repeated flights over intensive study sites in the Hudson Bay lowlands near Moosonee, Ontario, and over the boreal forest near Schefferville, Quebec. The tropospheric composition in this high-latitude region was found to be strongly influenced by stratospheric intrusions. Regions of low aerosol scattering and enhanced O3 mixing ratios were correlated with descending air from the lower stratosphere. Over 33% of the troposphere (0-12 km) along our flight track at latitudes from about 45 deg to 55 deg N had significantly enhanced O3 due to stratospheric intrusions, and in the middle to upper troposphere the extent of the enhanced O3 gnerally exceeded 40%. Ozone mixing ratios of 80 parts per billion by volume (ppbv) near 6 km were common in strong intrusions. In the boundary layer over the lowlands, O3 was in the 20-30 ppbv range with a vertical O3 gradient of 6.7 ppbv/km to about 45 ppbv at 3 km. Above 6 km the background tropospheric O3 profile was nearly constant with an average value of 53 ppbv. Due to forest fires in Canada and Alaska, plumes from biomass-burning sources were observed on many flights. Biomass-burning plumes influenced about 25% of the free troposphere below 4 km, and in some of the plumes, O3 was enhanced by 10-20 ppbv over ambient levels of 30-45 ppbv. Several air masses transported from the tropical Pacific were observed over Canada in the middle to upper troposphere with O3 levels 10-20 ppbv below background values of 50-55 ppbv.

Browell, E. V.↗

DOE OSTI2023

Machine learning models inaccurately predict current and future high-latitude C balances

The high-latitude carbon (C) cycle is a key feedback to the global climate system, yet because of system complexity and data limitations, there is currently disagreement over whether the region is a source or sink of C. Recent advances in big data analytics and computing power have popularized the use of machine learning (ML) algorithms to upscale site measurements of ecosystem processes, and in some cases forecast the response of these processes to climate change. Due to data limitations, however, ML model predictions of these processes are almost never validated with independent datasets. To better understand and characterize the limitations of these methods, we develop an approach to independently evaluate ML upscaling and forecasting. We mimic data-driven upscaling and forecasting efforts by applying ML algorithms to different subsets of regional process-model simulation gridcells, and then test ML performance using the remaining gridcells. In this study, we simulate C fluxes and environmental data across Alaska using ecosys, a process-rich terrestrial ecosystem model, and then apply boosted regression tree ML algorithms to training data configurations that mirror and expand upon existing AmeriFLUX eddy-covariance data availability. We first show that a ML model trained using ecosys outputs from currently-available Alaska AmeriFLUX sites incorrectly predicts that Alaska is presently a modeled net C source. Increased spatial coverage of the training dataset improves ML predictions, halving the bias when 240 modeled sites are used instead of 15. However, even this more accurate ML model incorrectly predicts Alaska C fluxes under 21st century climate change because of changes in atmospheric CO 2 , litter inputs, and vegetation composition that have impacts on C fluxes which cannot be inferred from the training data. Our results provide key insights to future C flux upscaling efforts and expose the potential for inaccurate ML upscaling and forecasting of high-latitude C cycle dynamics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Expansion of high-latitude deciduous forests driven by interactions between climate warming and fire: Modeling Archive

This Modeling Archive is in support of an NGEE Arctic publication. More information in the related references. The dataset is a model (ecosys) output used in a study that examined the potential future shifts in high-latitude vegetation types (PFTs) driven by changes in 21st century climate and fire. The study highlights the importance of mechanistically representing differences in plant functional traits on current and possible 21st century trajectories of Alaskan PFTs. This dataset package includes 13 files in pdf, csv, and nc formats.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2000

Atmospheric Effects of Biomass Burning

Biomass fires are both natural and anthropogenic in origin. The natural trigger is lightning, which leads to mid- and high-latitude fires and episodes of smoke and pollution associated with them. Lightning is also prominent in tropical regions when the dry season gives way to the wet season and lightning in convective systems ignites dry vegetation. Atmospheric consequences of biomass fires are complex. When considering the impacts of fires for a given ecosystem, inputs of fires must be compared to other process that emit trace gases and particles into the atmosphere. Other processes include industrial activity, fires for household purposes and biogenic sources which may themselves interact with fires. That is, fires may promote or restrict biogenic processes. Several books have presented various aspects of fire interactions with atmospheric chemistry and a cross-disciplinary review of a 1992 fire-oriented experiment appears in SAFARI: The Role of southern African Fires in Atmospheric and Ecological Environments. The IGAC/BIBEX core activity (see acronyms at end of Chapter) has sponsored field campaigns that integrate multiple aspects of fires ground-based measurements with an ecological perspective, atmospheric measurements with chemical and meteorological components, and remote sensing. This Chapter presents two aspects of biomass fires and the environment. Namely, the relationship between biomass burning and ozone is described, starting with a brief description of the chemical reactions involved and illustrative measurements and interpretation. Second, because of the need to observe biomass burning and its consequences globally, a summary of remote sensing approaches to the study of fires and trace gases is given. Examples in this Chapter are restricted to tropical burning for matters of brevity and because most burning activity globally is within this zone.

Thompson, Anne M.↗

NASA NTRS2012

Net Primary Production of Terrestrial Ecosystems from 2000 to 2009

The CASA (Carnegie-Ames-Stanford) ecosystem model has been used to estimate monthly carbon fluxes in terrestrial ecosystems from 2000 to 2009, with global data inputs from NASA's Terra Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation cover mapping. Net primary production (NPP) flux for atmospheric carbon dioxide has varied slightly from year-to-year, but was predicted to have increased over short multi-year periods in the regions of the high-latitude Northern Hemisphere, South Asia, Central Africa, and the western Amazon since the year 2000. These CASA results for global NPP were found to be in contrast to other recently published modeling trends for terrestrial NPP with high sensitivity to regional drying patterns. Nonetheless, periodic declines in regional NPP were predicted by CASA for the southern and western Untied States, the southern Amazon, and southern and eastern Africa. NPP in tropical forest zones was examined in greater detail to discover lower annual production values than previously reported in many global models across the tropical rainforest zones, likely due to the enhanced detection of lower production ecosystems replacing primary rainforest.

Terrestiral ecosystems↗

DOE OSTI2023

Urbanization effects on the spatial patterns of spring vegetation phenology depend on the climatic background

Cities have been considered ideal surrogates for evaluating ecological responses to climate warming. Although research has revealed that the urban heat island effect is not the only determinant that drives the rural-urban difference in spring phenology, limited attempts further explore the effect of complex interactions between urbanization and climates on the start of the season (SOS). Here, we employed the percentage of impervious surface area as an indicator of urbanization levels, incorporating the urban heat island (UHI) effect and climates to decipher the multiple effects on the temporal shift in SOS. Our results suggest that urbanization, the UHI effect, and climates jointly drive the phenological timing in cities. Further, the SOS is highly linearly correlated with urbanization levels and UHI effects (p<0.001) in the zones with warm and humid climates, whereas relatively warmer, cooler, or arid climates break down the linearity. Geographically, the SOS is north-south symmetrical for the rural-urban ecosystem at the national scale, which initiates at the mid-latitudes (around 30 °N, 67.58 DOY ± 2.23 days) first and then towards the low (around 20 °N, 105.25 DOY ± 1.31 days) and high-latitudes (45 °N, 123.01 DOY ± 1.45 days). Chilling accumulation and spring temperature jointly contribute to this phenomenon. As the UHI effect is similar to the projected global warming in the future, understanding the phenological responses to urbanization in various climates is insightful for evaluating the impact of future warming on plants' phenological behavior in the global ecosystem.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2012

Satellite-Based Evidence for Shrub and Graminoid Tundra Expansion in Northern Quebec from 1986-2010

Global vegetation models predict rapid poleward migration of tundra and boreal forest vegetation in response to climate warming. Local plot and air-photo studies have documented recent changes in high-latitude vegetation composition and structure, consistent with warming trends. To bridge these two scales of inference, we analyzed a 24-year (1986-2010) Landsat time series in a latitudinal transect across the boreal forest-tundra biome boundary in northern Quebec province, Canada. This region has experienced rapid warming during both winter and summer months during the last forty years. Using a per-pixel (30 m) trend analysis, 30% of the observable (cloud-free) land area experienced a significant (p < 0.05) positive trend in the Normalized Difference Vegetation Index (NDVI). However, greening trends were not evenly split among cover types. Low shrub and graminoid tundra contributed preferentially to the greening trend, while forested areas were less likely to show significant trends in NDVI. These trends reflect increasing leaf area, rather than an increase in growing season length, because Landsat data were restricted to peak-summer conditions. The average NDVI trend (0.007/yr) corresponds to a leaf-area index (LAI) increase of ~0.6 based on the regional relationship between LAI and NDVI from the Moderate Resolution Spectroradiometer (MODIS). Across the entire transect, the area-averaged LAI increase was ~0.2 during 1986-2010. A higher area-averaged LAI change (~0.3) within the shrub-tundra portion of the transect represents a 20-60% relative increase in LAI during the last two decades. Our Landsat-based analysis subdivides the overall high-latitude greening trend into changes in peak-summer greenness by cover type. Different responses within and among shrub, graminoid, and tree-dominated cover types in this study indicate important fine-scale heterogeneity in vegetation growth. Although our findings are consistent with community shifts in low-biomass vegetation types over multi-decadal time scales, the response in tundra and forest ecosystems to recent warming was not uniform.

McManus, K. M.↗

NASA NTRS2004

Quantifying Grassland-to-Woodland Transitions and the Implications for Carbon and Nitrogen Dynamics in the Southwest United States

Replacement of grasslands and savannas by shrublands and woodlands has been widely reported in tropical, temperate and high-latitude rangelands worldwide (Archer 1994). These changes in vegetation structure may reflect historical shifts in climate and land use; and are likely to influence biodiversity, productivity, above- and below ground carbon and nitrogen sequestration and biophysical aspects of land surface-atmosphere interactions. The goal of our proposed research is to investigate how changes in the relative abundance of herbaceous and woody vegetation affect carbon and nitrogen dynamics across heterogeneous savannas and shrub/woodlands. By linking actual land-cover composition (derived through spectral mixture analysis of AVIRIS, TM, and AVHRR imagery) with a process-based ecosystem model, we will generate explicit predictions of the C and N storage in plants and soils resulting from changes in vegetation structure. Our specific objectives will be to (1) continue development and test applications of spectral mixture analysis across grassland-to-woodland transitions; (2) quantify temporal changes in plant and soil C and N storage and turnover for remote sensing and process model parameterization and verification; and (3) couple landscape fraction maps to an ecosystem simulation model to observe biogeochemical dynamics under changing landscape structure and climatological forcings.

Wessman, Carol A.↗

DOE OSTI2025

Deep Learning Advances Arctic River Water Temperature Predictions

The accelerated warming in the Arctic poses serious risks to freshwater ecosystems by altering streamflow and river thermal regimes. However, limited research on Arctic River water temperatures exists due to data scarcity and the absence of robust methodologies, which often focus on large, major river basins. To address this, we leveraged the newly released, extensive AKTEMP data set and advanced machine learning techniques to develop a Long Short-Term Memory (LSTM) model. By incorporating ERA5-Land reanalysis data and integrating physical understanding into data-driven processes, our model advanced river water temperature predictions in ungauged, snow- and permafrost-affected basins in Alaska. Our model outperformed existing approaches in high-latitude regions, achieving a median Nash-Sutcliffe Efficiency of 0.95 and root mean squared error of 1.0°C. The LSTM model learned air temperature, soil temperature, solar radiation, and thermal radiation—factors associated with energy balance—were the most important drivers of river temperature dynamics. Soil moisture and snow water equivalent were highlighted as critical factors representing key processes such as thawing, melting, and groundwater contributions. Glaciers and permafrost were also identified as important covariates, particularly in seasonal river water temperature predictions. Our LSTM model successfully captured the complex relationships between hydrometeorological factors and river water temperatures across varying timescales and hydrological conditions. This scalable and transferable approach can be potentially applied across the Arctic, offering valuable insights for future conservation and management efforts.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Key Environmental and Ecological Variables of Wetland CH 4 and CO 2 Fluxes Change With Warming

Wetlands are important ecosystems for the global carbon cycle, impacting regional and global methane (CH 4 ) and carbon dioxide (CO 2 ) budgets. This study examines how environmental and ecological variables impact wetland CH 4 flux and net ecosystem exchange of CO 2 (NEE) across 17 sites globally. We also quantified the importance of variables for each wetland type and site at monthly scale under normal and warm temperatures using dominance analysis. We identified soil and air temperature (TS, TA, respectively) as key variables influencing wetland CH4, and latent heat (LE) and shortwave radiation (SW) for NEE under normal and warm conditions. However, the importance of some variables shifted with warming. For predicting the variability of wetland CH4 flux under warming, gross primary productivity (GPP) and LE, replacing wind direction (WD), were dominant variables for tropical swamps, while NEE was important for high-latitude fens and bogs under warm temperatures. For wetland NEE, the role of TA and TS decreased across all wetland types with warming, while vapor pressure deficit (VPD) became more important for mid and high-latitude wetlands. Our results reveal the complex responses of wetland carbon flux to environmental and ecological variables with warming and provide new insights into improving wetland models by incorporating additional variables and accounting for the changing roles of variables in carbon flux under warming.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

Impacts of abiotic and biotic factors on tundra productivity near Utqiaġvik, Alaska

Abstract Earlier snowmelt, warmer temperatures and herbivory are among the factors that influence high-latitude tundra productivity near the town of Utqiaġvik in northern Alaska. However, our understanding of the potential interactions between these factors is limited. MODIS observations provide cover fractions of vegetation, snow, standing water, and soil, and fractional absorption of photosynthetically active radiation by canopy chlorophyll (fAPAR chl ) per pixel. Here, we evaluated a recent time-period (2001–2014) that the tundra experienced large interannual variability in vegetation productivity metrics (i.e. fAPAR chl and APAR chl ), which was explainable by both abiotic and biotic factors. We found earlier snowmelt to increase soil and vegetation cover, and productivity in June, while warmer temperatures significantly increased monthly productivity. However, abiotic factors failed to explain stark decreases in productivity during August of 2008, which coincided with a severe lemming outbreak. MODIS observations found this tundra ecosystem to completely recover two years later, resulting in elevated productivity. This study highlights the potential roles of both climate and herbivory in modulating the interannual variability of remotely retrieved plant productivity metrics in Arctic coastal tundra ecosystems.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

NASA’s Geospace Dynamics Constellation—Providing the first Systematic Measurements of Global Magnetospheric Energy Inputs and Ionosphere-Thermosphere Responses

The Geospace Dynamics Constellation (GDC) is NASA's next strategic Living With a Star mission. GDC's goals are: 1) Understand how the high-latitude ionosphere-thermosphere system responds to variable solar wind/magnetosphere forcing; and 2) Understand how internal processes in the global ionosphere-thermosphere system redistribute mass, momentum, and energy. Planned for launch by the end of the decade, GDC will use six identical observatories, each identically instrumented to fully characterize the magnetospheric drivers of the I-T system as well as the global response of the ionized and neutral gases. GDC will do this with a series of orbital configurations that will enable it to study the widest range of spatial and temporal scales to date, ranging from hundreds of kilometers and several seconds to tens of minutes, and extending through the regional to the global scale. This talk presents GDC's current status, measurement capabilities, sampling scheme, and model development efforts and show how GDC will fit into the larger Heliophysics ecosystem, by 1) obtaining critically needed scientific observations; 2) providing a source for real-time space weather and situational awareness, as well as retrospective studies to further the science of space weather; 3) serving as a "strategic hub" for other space-based and ground-based efforts that want to leverage GDC to perform complementary science. To get the most benefit from GDC’s observations, it will be critical to identify partnerships with other research efforts in the ITM and Geospace arenas, including those utilizing space-based, ground-based, or theoretical investigations. We particularly would like to discuss with groups who are planning or considering observational campaigns during the GDC era, to find ways to leverage GDC observations to do synergistic science that could not be done otherwise.

Douglas E Rowland↗

NASA NTRS2022

Disturbances in North American Boreal Forest and Arctic Tundra: Impacts, Interactions, and Responses

Ecosystems in the North American Arctic-Boreal Zone (ABZ) experience a diverse set of disturbances associated with wildfire, permafrost dynamics, geomorphic processes, insect outbreaks and pathogens, extreme weather events, and human activity. Climate warming in the ABZ is occurring at over twice the rate of the global average, and as a result the extent, frequency, and severity of these disturbances are increasing rapidly. Disturbances in the ABZ span a wide gradient of spatiotemporal scales and have varying impacts on ecosystem properties and function. However, many ABZ disturbances are relatively understudied and have different sensitivities to climate and trajectories of recovery, resulting in considerable uncertainty in the impacts of climate warming and human land use on ABZ vegetation dynamics and in the interactions between disturbance types. Here we review the current knowledge of ABZ disturbances and their precursors, ecosystem impacts, temporal frequencies, spatial extents, and severity. We also summarize current knowledge of interactions and feedbacks among ABZ disturbances and characterize typical trajectories of vegetation loss and recovery in response to ecosystem disturbance using satellite time-series. We conclude with a summary of critical data and knowledge gaps and identify priorities for future study.

high-latitude↗

DOE OSTI2026

Benchmarking soil moisture and its relationship to ecohydrologic variables in Earth System Models

Soil moisture (SM) is a key regulator of ecosystem biogeophysics, influencing plant water relations and land-atmosphere energy exchanges. We evaluate the representation of SM in 16 Earth System Models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) using the International Land Model Benchmarking (ILAMB) framework, focusing on surface (0–5, 0–10 cm) and rootzone (0–100 cm) depths, as well as key ecohydrological variables like gross primary productivity (GPP), leaf area index (LAI), and evapotranspiration (ET), and their coupling. Models are benchmarked against multiple observational and assimilated datasets to assess both state variables and cross-variable relationships. Surface SM is generally well represented (r > 0.87), while rootzone SM variability is systematically overestimated (normalized standard deviation > 1). ET shows strong agreement with observations (r > 0.9), whereas GPP and LAI exhibit larger inter-model spread. Skill in individual variables does not guarantee realistic SM–ecohydrology coupling, which varies strongly across models and depends on the reference dataset. Köppen-based regional analyses reveal strong regime dependence, with several models performing well in Tropical and Temperate regions but degrading in Continental (high-latitude) zones. Across both global and regional benchmarks, models cluster by land surface framework, indicating that structural choices in soil hydrology and soil–plant coupling exert a first-order control on performance. These results provide process-relevant benchmarks and suggest that improving the representation of vertical soil structure, rooting depth distributions, and soil–plant hydraulic coupling will be central to advancing soil moisture realism in next-generation Earth system models.

CMIP6↗

DOE OSTI2023

How deep should we go to understand roots at the top of the world?

Harsh environmental conditions and the short summers of northern, high-latitude biomes impose unique constraints on the plants that live in the arctic tundra and the boreal forest. To escape the harsh aboveground environment, plants in these habitats often allocate a large portion of their biomass belowground to facilitate nutrient acquisition. In turn, the proximity of living plant roots to vast stores of sequestered soil carbon in these biomes means that shifts in rooting depth distribution and the size of the root–soil interface could significantly contribute to ongoing climate change. Indeed, plant ‘priming’ of rhizosphere decomposition via root exudation, particularly from shallowly distributed roots, can lead to losses of carbon from tundra soils. While we have a hard-won understanding of the distribution of plant communities across the arctic tundra and the boreal forest from direct field observations scaled to the landscape level using climate-informed mapping techniques (i.e. the Circumpolar Arctic Vegetation Map (CAVM); Walker et al. , 2005), these vegetation maps are only the tip of the iceberg (Iversen et al. , 2015). Root form and function remain hidden beneath the land surface. In an article recently published in New Phytologist , Blume-Werry et al. (2023, 10.1111/nph.18998) asked whether rooting depth distribution, and ensuing carbon emissions, could be inferred from commonly used vegetation mapping classifications across the pan-Arctic. An important question to guide our understanding, mapping, and prediction of belowground characteristics and ecosystem feedbacks at the top of the world. Unfortunately, they found that the answer was ‘not quite’. While rooting depth distribution varied demonstrably, in turn causing substantial changes in modeled carbon emissions via rhizosphere ‘priming’, variation across rooting depth profiles did not correspond with vegetation mapping classes. If we are unable to predict belowground rooting depth distributions across large spatial scales by leveraging aboveground vegetation community distributions, how then should belowground researchers proceed?

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2018

Modeling the Distribution and Type of High-Latitude Natural Wetlands for Methane Studies

High latitude (>50N) natural wetlands emit a substantial amount of methane to the atmosphere, and are located in a region of amplified warming. Northern hemisphere high latitudes are characterized by cold climates, extensive permafrost, poor drainage, short growing seasons, and slow decay rates. Under these conditions, organic carbon accumulates in the soil, sequestering CO2 from the atmosphere. Methanogens produce methane from this carbon reservoir, converting stored carbon into a powerful greenhouse gas. Methane emission from wetland ecosystems depends on vegetation type, climate characteristics (e.g, precipitation amount and seasonality, temperature, snow cover, etc.), and geophysical variables (e.g., permafrost, soil type, and landscape slope). To understand how wetland methane dynamics in this critical region will respond to climate change, we have to first understand how wetlands themselves will change and therefore, what the primary controllers of wetland distribution and type are. Understanding these relationships permits data-anchored, physically-based modeling of wetland distribution and type in other climate scenarios, such as paleoclimates or future climates, a necessary first step toward modeling wetland methane emissions in these scenarios. We investigate techniques and datasets for predicting the distribution and type of high latitude (>50N) natural wetlands from a suite of geophysical and climate predictors. Hierarchical clustering is used to derive an empirical methane-centric wetland model. The model is applied in a multistep process first to predict the distribution of wetlands from relevant geophysical parameters, and then, given the predicted wetland distribution, to classify the wetlands into methane-relevant types using an expanded suite of climate and biogeophysical variables. As the optimum set of predictor variables is not known a priori, the model is applied iteratively, and each simulation is evaluated with respect to observed high-latitude wetlands.

Romanski, Joy↗

DOE OSTI2022

Vegetation type is an important predictor of the arctic summer land surface energy budget

Despite the importance of high-latitude surface energy budgets (SEBs) for land-climate interactions in the rapidly changing Arctic, uncertainties in their prediction persist. Here, we harmonize SEB observations across a network of vegetated and glaciated sites at circumpolar scale (1994–2021). Our variance-partitioning analysis identifies vegetation type as an important predictor for SEB-components during Arctic summer (June-August), compared to other SEB-drivers including climate, latitude and permafrost characteristics. Differences among vegetation types can be of similar magnitude as between vegetation and glacier surfaces and are especially high for summer sensible and latent heat fluxes. The timing of SEB-flux summer-regimes (when daily mean values exceed 0 Wm –2 ) relative to snow-free and -onset dates varies substantially depending on vegetation type, implying vegetation controls on snow-cover and SEB-flux seasonality. Our results indicate complex shifts in surface energy fluxes with land-cover transitions and a lengthening summer season, and highlight the potential for improving future Earth system models via a refined representation of Arctic vegetation types.

54 ENVIRONMENTAL SCIENCES↗