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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Distinct roles of land cover in regulating spatial variabilities of temperature responses to radiative effects of aerosols and clouds

Surface temperature responses to radiative perturbations due to aerosols and clouds are complicated by the land surface properties. To disentangle these complexities, this study, from a terrestrial surface energy budget perspective, isolates the underlying biophysical processes from the instantaneous radiative effects of aerosols and clouds on surface temperature using the National Center for Atmospheric Research Community Earth System Model version 1.2.1. It is found that in comparison with the global heterogeneous distributions of instantaneous radiative perturbations at the surface induced by aerosols and clouds, the spatial variations of the corresponding surface temperature responses to aerosol direct radiative effects (DRE) during the daytime and cloud radiative effects (CRE) during the nighttime are amplified. It is because of the consistent global distribution of the local surface climate sensitivity (a function of land cover properties such as surface roughness and Bowen ratio) with those of daytime DRE and nighttime CRE. By applying identical anthropogenic aerosol and precursor emissions over eight major past, present and projected future anthropogenic aerosol emitting regions (i.e. Brazil, China, East Africa, India, Indonesia, South Africa, United States and Western Europe), surface temperature responses to aerosol radiative cooling in the daytime and cloud radiative warming in the nighttime over these regions positively regulated by local surface climate sensitivities are prominent.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Probability of committed warming exceeding 1.5 ∘ C and 2.0 ∘ C Paris targets

Abstract The feasibility of achieving the Paris 1.5 ∘ C target continues to be a complex and hotly debated question. To help resolve this question we calculate probability distributions of the committed warming that would ensue if all anthropogenic emissions were stopped immediately, or at successive future times. We use a simple Earth system model together with a Bayesian approach that incorporates multiple lines of evidence and accounts for known model biases. This analysis reveals a wide range of possible outcomes, including no further warming, but also a 15% chance of overshooting the 1.5 ∘ C target, and 1%–2% chance for 2 ∘ C, even if all emissions had stopped in 2020. If emissions merely stabilize in 2020 and stop in 2040, these probabilities increase to 90% and 17%. The uncertainty arises mainly from that of present forcing by aerosols. Rather than there being a fixed date by which emissions must stop, the probability of reaching either target—which is already below 100%—gradually diminishes with delays in eliminating emissions, by 3%–4% per year for 1.5 ∘ C.

Sherwood, Steven C. (ORCID:0000000174208216)↗
DOE OSTI2022

Characterizing and quantifying uncertainty in projections of climate change impacts on air quality

Climate change can aggravate air pollution, with important public health and environmental consequences. While major sources of uncertainty in climate change projections - greenhouse gas (GHG) emissions scenario, model response, and internal variability - have been investigated extensively, their propagation to estimates of air quality impacts has not been systematically assessed. Here, we compare these uncertainties using a coupled modeling framework that includes a human activity model, an Earth system model of intermediate complexity, and a global atmospheric chemistry model. Uncertainties in projections of U.S. air quality under 21st century climate change are quantified based on a climate-chemistry ensemble that includes multiple initializations, representations of climate sensitivity, and climate policy scenarios, under constant air pollution emissions. We find that climate-related uncertainties are comparable at mid-century, making it difficult to distinguish the impact of variations in GHG emissions on ozone and particulate matter pollution. While GHG emissions scenario eventually becomes the dominant uncertainty based on the scenarios considered, all sources of uncertainty are significant through the end of the century. The results provide insights into intrinsically different uncertainties in projections of air pollution impacts and the potential for large ensembles to better capture them.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Long-term trends of impacts of global gasoline and diesel emissions on ambient PM 2.5 and O 3 pollution and the related health burden for 2000–2015

Global economic development and urbanization during the past two decades have driven the increases in demand of personal and commercial vehicle fleets, especially in developing countries, which has likely resulted in changes in year-to-year vehicle tailpipe emissions associated with aerosols and trace gases. However, long-term trends of impacts of global gasoline and diesel emissions on air quality and human health are not clear. In this study, we employ the Community Earth System Model in conjunction with the newly developed Community Emissions Data System as anthropogenic emission inventory to quantify the long-term trends of impacts of global gasoline and diesel emissions on ambient air quality and human health for the period of 2000–2015. Global gasoline and diesel emissions contributed to regional increases in annual mean surface PM 2.5 (particulate matter with aerodynamic diameters ≤2.5 μm) concentrations by up to 17.5 and 13.7 µg m –3 , and surface ozone (O 3 ) concentrations by up to 7.1 and 7.2 ppbv, respectively, for 2000–2015. However, we also found substantial declines of surface PM 2.5 and O 3 concentrations over Europe, the US, Canada, and China for the same period, which suggested the co-benefits of air quality and human health from improving gasoline and diesel fuel quality and tightening vehicle emissions standards. Globally, we estimate the mean annual total PM 2.5- and O 3 -induced premature deaths are 139 700–170 700 for gasoline and 205 200–309 300 for diesel, with the corresponding years of life lost of 2.74–3.47 and 4.56–6.52 million years, respectively. Diesel and gasoline emissions create health-effect disparities between the developed and developing countries, which are likely to aggravate afterwards.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Carbon flux estimates are sensitive to data source: a comparison of field and lab temperature sensitivity data

Abstract A large literature exists on mechanisms driving soil production of the greenhouse gases CO 2 and CH 4 . Although it is common knowledge that measurements obtained through field studies vs. laboratory incubations can diverge because of the vastly different conditions of these environments, few studies have systematically examined these patterns. These data are used to parameterize and benchmark ecosystem- to global-scale models, which are then susceptible to the biases of the source data. Here, we examine how greenhouse gas measurements may be influenced by whether the measurement/incubation was conducted in the field vs. laboratory, focusing on CO 2 and CH 4 measurements. We use Q 10 of greenhouse gas flux (temperature sensitivity) for our analyses because this metric is commonly used in biological and Earth system sciences and is an important parameter in many modeling frameworks. We predicted that laboratory measurements would be less variable, but also less representative of true field conditions. However, there was greater variability in the Q 10 values calculated from lab-based measurements of CO 2 fluxes, because lab experiments explore extremes rarely seen in situ , and reflect the physical and chemical disturbances occurring during sampling, transport, and incubation. Overall, respiration Q 10 values were significantly greater in laboratory incubations (mean = 4.19) than field measurements (mean = 3.05), with strong influences of incubation temperature and climate region/biome. However, this was in part because field measurements typically represent total respiration (Rs), whereas lab incubations typically represent heterotrophic respiration (Rh), making direct comparisons difficult to interpret. Focusing only on Rh-derived Q 10 , these values showed almost identical distributions across laboratory ( n = 1110) and field ( n = 581) experiments, providing strong support for using the former as an experimental proxy for the latter, although we caution that geographic biases in the extant data make this conclusion tentative. Due to a smaller sample size of CH 4 Q 10 data, we were unable to perform a comparable robust analysis, but we expect similar interactions with soil temperature, moisture, and environmental/climatic variables. Our results here suggest the need for more concerted efforts to document and standardize these data, including sample and site metadata.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Boosts in leaf-level photosynthetic capacity aid Pinus ponderosa recovery from wildfire

Forests mitigate climate change by sequestering massive amounts of carbon, but recent increases in wildfire activity are threatening carbon storage. Currently, our understanding of wildfire impacts on forest resilience and the mechanisms controlling post-fire recovery remains unresolved due to a lack of empirical data on mature trees in natural settings. Here, we quantify the physiological mechanisms controlling carbon uptake immediately following wildfire in mature individuals of ponderosa pine (Pinus ponderosa), a wide-spread and canopy-dominant tree species in fire-prone forests. While photosynthetic capacity was lower in burned than unburned trees due to an overall depletion of resources, we show that within the burned trees, photosynthetic capacity increases with the severity of damage. Our data reveal that boosts in the efficiency of carbon uptake at the leaf-level may compensate for whole-tree damage, including the loss of leaf area and roots. We further show that heightened photosynthetic capacity in remaining needles on burned trees may be linked with reduced water stress and leaf nitrogen content, providing pivotal information about post-fire physiological processes. Our results have implications for Earth system modeling efforts because measurements of species-level physiological parameters are used in models to predict ecosystem and landscape-level carbon trajectories. Finally, current land management practices do not account for physiological resilience and recovery of severely burned trees. Our results suggest premature harvest may remove individuals that may otherwise survive, irrevocably altering forest carbon balance.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Investigating permafrost carbon dynamics in Alaska with artificial intelligence

Abstract Positive feedbacks between permafrost degradation and the release of soil carbon into the atmosphere impact land–atmosphere interactions, disrupt the global carbon cycle, and accelerate climate change. The widespread distribution of thawing permafrost is causing a cascade of geophysical and biochemical disturbances with global impacts. Currently, few earth system models account for permafrost carbon feedback (PCF) mechanisms. This research study integrates artificial intelligence (AI) tools and information derived from field-scale surveys across the tundra and boreal landscapes in Alaska. We identify and interpret the permafrost carbon cycling links and feedback sensitivities with GeoCryoAI, a hybridized multimodal deep learning (DL) architecture of stacked convolutionally layered, memory-encoded recurrent neural networks (NN). This framework integratesin-situmeasurements and flux tower observations for teacher forcing and model training. Preliminary experiments to quantify, validate, and forecast permafrost degradation and carbon efflux across Alaska demonstrate the fidelity of this data-driven architecture. More specifically, GeoCryoAI logs the ecological memory and effectively learns covariate dynamics while demonstrating an aptitude to simulate and forecast PCF dynamics—active layer thickness (ALT), carbon dioxide flux (CO 2 ), and methane flux (CH 4 )—with high precision and minimal loss (i.e. ALT RMSE : 1.327 cm [1969–2022]; CO 2 RMSE : 0.697µmolCO 2 m −2 s −1 [2003–2021]; CH 4 RMSE : 0.715 nmolCH 4 m −2 s −1 [2011–2022]). ALT variability is a sensitive harbinger of change, a unique signal characterizing the PCF, and our model is the first characterization of these dynamics across space and time.

Environmental Sciences & Ecology↗
DOE OSTI2024

Evaluating the impact of peat soils and snow schemes on simulated active layer thickness at pan-Arctic permafrost sites

Abstract Permafrost stability is significantly influenced by the thermal buffering effects of snow and active-layer peat soils. In the warm season, peat soils act as a barrier to downward heat transfer mainly due to their low thermal conductivity. In the cold season, the snowpack serves as a thermal insulator, retarding the release of heat from the soil to the atmosphere. Currently, many global land models overestimate permafrost soil temperature and active layer thickness (ALT), partially due to inaccurate representations of soil organic matter (SOM) density profiles and snow thermal insulation. In this study, we evaluated the impacts of SOM and snow schemes on ALT simulations at pan-Arctic permafrost sites using the Energy Exascale Earth System Model (E3SM) land model (ELM). We conducted simulations at the Circumpolar Active Layer Monitoring (CALM) sites across the pan-Arctic domain. We improved ELM-simulated site-level ALT using a knowledge-based hierarchical optimization procedure and examined the effects of precipitation-phase partitioning methods (PPMs), snow compaction schemes, and snow thermal conductivity schemes on simulated snow depth, soil temperature, ALT, and CO 2 fluxes. Results showed that the optimized ELM significantly improved agreement with observed ALT (e.g. RMSE decreased from 0.83 m to 0.15 m). Our sensitivity analysis revealed that snow-related schemes significantly impact simulated snow thermal insulation levels, soil temperature, and ALT. For example, one of the commonly used snow thermal conductivity schemes (quadratic Sturm or SturmQua) generally produced warmer soil temperatures and larger ALT compared to the other two tested schemes. The SturmQua scheme also amplified the model’s sensitivity to PPMs and predicted deeper ALTs than the other two snow schemes under both current and future climates. The study highlights the importance of accurately representing snow-related processes and peat soils in land models to enhance permafrost dynamics simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Overlooked cooling effects of albedo in terrestrial ecosystems

Radiative forcing (RF) resulting from changes in surface albedo is increasingly recognized as a significant driver of global climate change but has not been adequately estimated, including by Intergovernmental Panel on Climate Change (IPCC) assessment reports, compared with other warming agents. Here, we first present the physical foundation for modeling albedo-induced RF and the consequent global warming impact (GWI Δα ). We then highlight the shortcomings of available current databases and methodologies for calculating GWI Δα at multiple temporal scales. There is a clear lack of comprehensive in situ measurements of albedo due to sparse geographic coverage of ground-based stations, whereas estimates from satellites suffer from biases due to the limited frequency of image collection, and estimates from earth system models (ESMs) suffer from very coarse spatial resolution land cover maps and associated albedo values in pre-determined lookup tables. Field measurements of albedo show large differences by ecosystem type and large diurnal and seasonal changes. As indicated from our findings in southwest Michigan, GWI Δα is substantial, exceeding the RF Δα values of IPCC reports. Inclusion of GWI Δα to landowners and carbon credit markets for specific management practices are needed in future policies. We further identify four pressing research priorities: developing a comprehensive albedo database, pinpointing accurate reference sites within managed landscapes, refining algorithms for remote sensing of albedo by integrating geostationary and other orbital satellites, and integrating the GWI Δα component into future ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Changing windstorm characteristics over the US Northeast in a single model large ensemble

Abstract Extreme windstorms pose a significant hazard to infrastructure and public safety, particularly in the highly populated US Northeast (NE). However, the influence climate change and changing land use will have on these events remains unclear. A large ensemble generated using the Max-Planck Institute (MPI) Earth system model is used to generate projections of NE windstorms under different shared socioeconomic pathways (SSPs) and to attribute changes to projected land use land cover (LULC) change, externally forced changes and internal climate variability. To reduce the influence of coarse grid cell resolution and uncertainties in surface roughness lengths, windstorms are identified using simultaneous widespread exceedance of local 99th percentile 10 m wind speeds (U 99 ). Projected declines in forest cover in the NE and the resulting reductions in surface roughness length under SSP3-7.0 lead to projections of large increases in U 99 and derived windstorm intensity and scale. However, these projected changes in regional LULC under SSP3-7.0 are unprecedented in a historical context and may not be realistic. After corrections are applied to remove the influence of LULC on wind speeds, regionally averaged U 99 exhibit declines for most of the single model initial-condition large ensemble (SMILE) members which are broadly proportional to the radiative forcing and global air temperature increase in the SSPs, with a median value of −0.15 ms −1 °C −1 . While weak cyclones are projected to decline in frequency in the NE, intense cyclones and the resulting windstorms and indices of socioeconomic loss do not. Where present, significant trends in these loss indices are positive, and some MPI SMILE members generate future windstorms that are unprecedented in the historical period.

Coburn, Jacob (ORCID:0000000309538117)↗
DOE OSTI2025

Long-term decarbonization impacts on residential energy security across income groups and US states

Abstract The impact of a transition to a net-zero economy on the residential energy sector across diverse income groups in the US remains uncertain. Here, we employ an integrated human-Earth system model, incorporating an expanded set of ten income groups in the residential energy sector, to examine the distributional impacts of long-term decarbonization scenarios on residential energy security at the state level through 2050. We use multiple metrics of energy security, including energy burden, energy satiation gap, and the distribution of energy service across income groups. Our findings show that the net-zero decarbonization scenarios affect residential energy security differently across income groups, with low-to-mid-income groups experiencing larger negative impacts on the dimensions studied here. Comparatively, climate change impact on residential energy security is minor through 2050 based on our model outcomes. Specifically, the net-zero decarbonization scenarios lead to increased energy burden across all income groups and states in 2050, where the lowest (highest) income group in each state shows an average of 0.6 (0.2) percentage point increase in energy burden, relative to the business-as-usual in 2050. The distribution of energy service consumption across income groups is also slightly more skewed under these scenarios. As incomes grow across all deciles in the future, residential energy security generally improves through 2050. Targeted interventions could mitigate the disproportionate impacts that some groups could incur under a transition to a net-zero economy.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Downscaled CMIP5 projections of physical fire risk understate historical trends

Reliable projections of wildfire risk are important for multi-sector impacts analysis. Statistically downscaled and bias-corrected Earth system model ensemble products are routinely used to analyze regional physical wildfire risk, but evaluations of historical observed trends and variability are lacking. Here, we evaluate physical fire risk over the western United States using the Canadian Forest Fire Weather Index (FWI) by comparing model outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), statistically downscaled via the Multivariate Adaptive Constructed Analogs (MACA) approach, against the observational target dataset gridMET, a gridded high-resolution surface meteorological product. We analyze multidecadal trends and interannual variability in seasonal average FWI for the historical period and future projections under two emissions scenarios, and we compare MACA-CMIP5 ensemble results with a simple time series model that generates historical and future projections of seasonal FWI based on bootstrapping observed historical trends and variability. Our findings indicate that MACA-CMIP5 accurately captures the magnitude and spatial patterns of seasonally averaged FWI but tends to underestimate historical decadal trends. We show that future increases in fire risk may be underestimated relative to the simple time series model that projects historical variability into the future. We also highlight that model biases in relative humidity contribute significantly to model-data differences. Our results underscore the importance of historical hindcasting exercises for informing broader multi-sector applications.

FWI↗
DOE OSTI2022

Severe declines in hydraulic capacity and associated carbon starvation drive mortality in seawater exposed Sitka-spruce (Picea sitchensis) trees

Abstract Sea-level rise is causing widespread tree mortality of coastal forests, with large consequences on the Earth system as a result of these forests’ importance in carbon and nutrient export. The mechanisms of mortality under these conditions are, however, poorly tested. We used wood anatomy traits, wood δ 13 C, and tree radial growth to retrospectively assess the physiological process of seawater effects on whole tree xylem hydraulic capacity, gas exchange, and radial growth. During the latter stages of mortality (2018–2019), we directly measured metrics of water use and carbon metabolism across trees having crowns ranging from fully foliated to completely defoliated to investigate mortality processes at the sub-annual scale. Upon seawater exposure, soil salinity increased and allocation to hydraulic function declined, resulting in a dramatic reduction in water supply to the crown, increased crown-level water stress, and subsequent crown foliage loss. Simultaneously, leaf-level photosynthetic capacity declined steeply with increasing salinity. The combined loss of crown foliage area and photosynthetic rates per unit leaf area promoted carbon starvation, while no evidence of hydraulic failure was observed. These results elucidate mechanisms of coastal forest death under seawater exposure, enabling more accurate modeling in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Projections of wildfire risk and activities under 1.5 °C and 2.0 °C global warming scenarios

Wildfires are important ecosystem processes that have a significant impact on terrestrial vegetation, environment, and climate. This study investigates how future wildfire risk and activities could change under 1.5 °C and 2.0 °C warming scenarios relative to pre-industrial levels using a modified McArthur Forest Fire Danger Index (FFDIn) and the CLM4.5-BGC land surface model. Sixteen Earth System Models (ESMs) from CMIP5 and CMIP6 were employed to supply the variables of climate change under low, middle, and high greenhouse emission scenarios in the 1.5 °C and 2.0 °C scenarios. The ensemble means from the FFDIn and results from the CLM4.5-BGC with multiple forcings show that the dry areas in the southwestern US, Brazilian Highlands, and Arabian islands are projected to face higher wildfire risk with larger burned areas and more carbon emissions under a warmer climate. The Congo Basin and part of the Amazon could have a lower wildfire risk with smaller burned areas and less carbon emissions. The absolute changes in the projected FFDIn are small, although large increases are observed in boreal areas, particularly in the winter and spring. Burned area and carbon emissions are projected to increase in general in the boreal area but decrease in northeastern Asia. Compared to the 1.5 °C scenario, the wildfire risk and burned area levels are projected to increase under the 2.0 °C scenario except in the western Amazon. However, fire carbon emissions are projected to decrease more in tropical areas under the 2.0 °C scenario. The different change directions in eastern North America and eastern China produced by the FFDIn and CLM4.5-BGC suggest the potential effect of non-meteorological elements on fire activities.

1.5 °C and 2.0 °C warming↗
DOE OSTI2023

Thermal acclimation of plant photosynthesis and autotrophic respiration in a northern peatland

Peatlands contain one-third of global soil carbon (C), but the responses of peatland ecosystems to long-term warming are not well understood. Here, we pursue an emergent understanding of warming effects on ecosystem C fluxes at peatlands by constraining a process-oriented model, the terrestrial ECOsystem model, with observational data from a long-term warming experiment at the Spruce and Peatland Responses Under Changing Environments site. Model-based assessments show that ecosystem-level photosynthesis and autotrophic respiration exhibited significant thermal acclimation, with temperature sensitivities being linearly decreased with warming. Using the thermal-acclimated parameter values, simulated gross primary production, net primary production, and plant autotrophic respiration (R a ), were all lower than those simulated with non-thermal acclimated parameter values. In contrast, ecosystem respiration simulated with thermal acclimated parameter values was higher than that simulated with non-thermal acclimated parameter values. Net ecosystem CO 2 exchange was much higher after constraining model parameters with observational data from the warming treatments, releasing C at a rate of 28.3 g C m -2 yr -1 °C -1 . Our data-model integration study suggests that peatlands are likely to release more C than previously estimated. Earth system models may overestimate C uptake by peatlands under warming if physiological thermal acclimation of plants is not incorporated. Thus, it is critical to consider the long-term physiological thermal acclimation of plants in the models to better predict global C dynamics under future climate and their feedback to climate change.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluation of historical precipitation interannual variability in CMIP6 over the United States

Interannual precipitation variability profoundly influences society via its effects on agriculture, water resources, infrastructure, and disaster risks. In this study, we use daily in situ precipitation observations from the global historical climatology network-daily (GHCN-D) to assess the ability of 21 Coupled Model Intercomparison Project Phase 6 (CMIP6) models, including the 50-member fifth-generation Canadian Earth System Model single model initial-condition large ensemble (CanESM5_SMILE), to realistically simulate historical interannual precipitation variability trends within 17 regions of the contiguous United States (CONUS). We assess how accurately the CMIP6 simulations align with observational data across annual, summer, and winter periods, focusing on four key hydrometeorological metrics, including interannual precipitation variability, relative interannual precipitation variability (coefficient of variation), annual mean precipitation, and annual wet day frequency. Our findings reveal that CMIP6 ensemble members generally reproduce the spatial patterns of observed trends in annual mean precipitation. In most regions, models agree well with the signs of observed changes in annual mean precipitation, though discrepancies in trend magnitude are evident. Further, observed trends in winter mean precipitation broadly exhibit a spatial pattern similar to that of the observed annual mean. However, analysis of the CanESM5_SMILE shows that trends in precipitation variability may primarily be the result of model-simulated internal variability, suggesting caution in interpreting multi-model single-realization ensemble results. Challenges in accurately simulating interannual precipitation variability underscore the need for ongoing model refinement and validation to enhance climate projections, especially in regions vulnerable to extreme precipitation events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Characterizing Uncertainty in Synthetic Tropical Cyclone Hazard Models for U.S. Energy Infrastructure Resilience

Tropical Cyclones (TCs) are intense storms that pose a persistent and considerable risk to coastal communities and infrastructure in the global tropics and subtropics, including the United States (US). With known limitations associated with observations and high-resolution earth system models, synthetic TC models that capture a wide spectrum of storm possibilities have been developed to robustly quantify TC risk. Here we examine the simulation of various TC features in the North Atlantic relevant for US coastal risk in three synthetic TC models forced with ERA5 reanalysis: MIT, CHAZ and RAFT. While there is a broad agreement among these models in terms of their representation of salient TC characteristics, certain differences do exist. To connect these modeling uncertainties with energy infrastructure resilience, we apply fragility curves that link simulated TC intensities to damage probabilities, demonstrating how uncertainty in storm states may translate into that in coastal impacts. Our study indicates that acknowledging and accounting for inter-model uncertainty leads to more reliable risk assessments, strengthening science-to-action pathways for managing risks associated with TCs.

Baby John, Effy↗
DOE OSTI2026

Vegetation heterogeneity reflects soil thermal state and surface soil displacement in a thawing permafrost landscape

Thawing permafrost has the potential to dramatically alter the physical and ecological structure of northern landscapes. Warming of the Arctic and subsequent degradation of permafrost have created a need to assess the stability and movement of soils on hillslopes and the potential impacts on ecosystem structure. In this work, we explore the relationships among vegetation heterogeneity, soil temperature, and soil surface displacements observed from 2019 to 2022 in a watershed in the discontinuous permafrost region on the Seward Peninsula of Alaska. Vegetation heterogeneity was measured as the standard deviation (SD) of the normalized difference vegetation index (NDVI) from 3 m PlanetScope satellite imagery around each soil temperature and active layer thickness observation. Locations of observations were clustered into three soil thermal groups, warm, intermediate, and cold, based on soil temperature and active layer thickness. Average annual horizontal surface displacements were significantly lower for soils within the warm thermal group (median = 0.033 m yr −1 ) compared to soils within the cold thermal group (median = 0.090 m yr −1 ; p < 0.001). Conversely, vegetation heterogeneity was significantly higher in the warm (median = 0.014 SD NDVI; p = 0.002) and intermediate (median = 0.015 SD NDVI; p = 0.002) groups compared with the cold thermal group (median = 0.012 SD NDVI), suggesting a warming-induced shift in vegetation community complexity. Because of the observed associations of ground surface displacement rates and vegetation heterogeneity with soil thermal state, we hypothesize that warming soil conditions induce changes in the rates and patterns of hillslope erosion due to an increase in surface movement as near-surface permafrost thaws, followed by a decrease as the permafrost table deepens and excess ice content diminishes. The transition to warm soils promotes surface ecosystem transformation, shifting the dominant vegetation at the site, given the warming climatic conditions of the region. We integrated our observations of soil temperature, vegetation heterogeneity, and soil surface displacements into a conceptual model that describes the co-evolution of hillslopes and vegetation in warming permafrost environments, which is currently unrepresented in earth system models.

54 ENVIRONMENTAL SCIENCES↗