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Creators/Authors contains: "Natali, Susan M"

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  1. Abstract

    Accelerated warming of the Arctic can affect the global climate system by thawing permafrost and exposing organic carbon in soils to decompose and release greenhouse gases into the atmosphere. We used a process-based biosphere model (DVM-DOS-TEM) designed to simulate biophysical and biogeochemical interactions between the soil, vegetation, and atmosphere. We varied soil and environmental parameters to assess the impact on cryohydrological and biogeochemical outputs in the model. We analyzed the responses of ecosystem carbon balances to permafrost thaw by running site-level simulations at two long-term tundra ecological monitoring sites in Alaska: Eight Mile Lake (EML) and Imnavait Creek Watershed (IMN), which are characterized by similar tussock tundra vegetation but differing soil drainage conditions and climate. Model outputs showed agreement with field observations at both sites for soil physical properties and ecosystem CO2fluxes. Model simulations of Net Ecosystem Exchange (NEE) showed an overestimation during the frozen season (higher CO2emissions) at EML with a mean NEE of 26.98 ± 4.83 gC/m2/month compared to observational mean of 22.01 ± 5.67 gC/m2/month, and during the fall months at IMN, with a modeled mean of 19.21 ± 7.49 gC/m2/month compared to observation mean of 11.9 ± 4.45 gC/m2/month. Our results underscore the importance of representing the impact of soil drainage conditions on the thawing of permafrost soils, particularly poorly drained soils, which will drive the magnitude of carbon released at sites across the high-latitude tundra. These findings can help improve predictions of net carbon releases from thawing permafrost, ultimately contributing to a better understanding of the impact of Arctic warming on the global climate system.

     
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    Free, publicly-accessible full text available June 11, 2025
  2. Abstract

    Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we presentThe Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m−2) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.

     
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    Free, publicly-accessible full text available December 1, 2025
  3. Abstract

    In the Arctic, winter soil temperatures exert strong control over mean annual soil temperature and winter CO2emissions. In tundra ecosystems there is evidence that plant canopy influences on snow accumulation alter winter soil temperatures. By comparison, there has been relatively little research examining the impacts of heterogeneity in boreal forest cover on soil temperatures. Using seven years of data from six sites in northeastern Siberia that vary in stem density we show that snow-depth and forest canopy cover exert equally strong control on cumulative soil freezing degrees days (FDDsoil). Together snow depth and canopy cover explain approximately 75% of the variance in linear models of FDDsoiland freezingn-factors (nf; calculated as the quotient of FDDsoiland FDDair), across sites and years. Including variables related to air temperature, or antecedent soil temperatures does not substantially improve models. The observed increase in FDDsoilwith canopy cover suggests that canopy interception of snow or thermal conduction through trees may be important for winter soil temperature dynamics in forested ecosystems underlain by continuous permafrost. Our results imply that changes in Siberian larch forest cover that arise from climate warming or fire regime changes may have important impacts on winter soil temperature dynamics.

     
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  4. Abstract Purpose of Review

    While previously thought to be negligible, carbon emissions during the non-growing season (NGS) can be a substantial part of the annual carbon budget in the Arctic boreal zone (ABZ), which can shift the carbon balance of these ecosystems from a long-held annual carbon sink towards a net annual carbon source. The purpose of this review is to summarize NGS carbon dioxide (CO2) flux research in the ABZ that has been published within the past 5 years.

    Recent Findings

    We explore the processes and magnitudes of CO2fluxes, and the status of modeling efforts, and evaluate future directions. With technological advances, direct measurements of NGS fluxes are increasing at sites across the ABZ over the past decade, showing ecosystems in the ABZ are a large source of CO2in the shoulder seasons, with low, consistent, winter emissions.

    Summary

    Ecosystem carbon cycling models are being improved with some challenges, such as modeling below ground and snow processes, which are critical to understanding NGS CO2fluxes. A lack of representative in situ carbon flux data and gridded environmental data are leading limiting factors preventing more accurate predictions of NGS carbon fluxes.

     
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  5. Accurate representation of permafrost carbon emissions is crucial for climate projections, yet current Earth system models inadequately represent permafrost carbon. Sustained funding opportunities are needed from government and private sectors for prioritized model development. 
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    Free, publicly-accessible full text available February 1, 2025
  6. Abstract

    The changing thermal state of permafrost is an important indicator of climate change in northern high latitude ecosystems. The seasonally thawed soil active layer thickness (ALT) overlying permafrost may be deepening as a consequence of enhanced polar warming and widespread permafrost thaw in northern permafrost regions (NPRs). The associated increase in ALT may have cascading effects on ecological and hydrological processes that impact climate feedback. However, past NPR studies have only provided a limited understanding of the spatially continuous patterns and trends of ALT due to a lack of long-term high spatial resolution ALT data across the NPR. Using a suite of observational biophysical variables and machine learning (ML) techniques trained with availablein situALT network measurements (n= 2966 site-years), we produced annual estimates of ALT at 1 km resolution over the NPR from 2003 to 2020. Our ML-derived ALT dataset showed high accuracy (R2= 0.97) and low bias when compared within situALT observations. We found the ALT distribution to be most strongly affected by local soil properties, followed by topographic elevation and land surface temperatures. Pair-wise site-level evaluation between our data-driven ALT with Circumpolar Active Layer Monitoring data indicated that about 80% of sites had a deepening ALT trend from 2003 to 2020. Based on our long-term gridded ALT data, about 65% of the NPR showed a deepening ALT trend, while the entire NPR showed a mean deepening trend of 0.11 ± 0.35 cm yr−1[25%–75% quantile: (−0.035, 0.204) cm yr−1]. The estimated ALT trends were also sensitive to fire disturbance. Our new gridded ALT product provides an observationally constrained, updated understanding of the progression of thawing and the thermal state of permafrost in the NPR, as well as the underlying environmental drivers of these trends.

     
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    Free, publicly-accessible full text available December 5, 2024
  7. Abstract

    In the Arctic waterbodies are abundant and rapid thaw of permafrost is destabilizing the carbon cycle and changing hydrology. It is particularly important to quantify and accurately scale aquatic carbon emissions in arctic ecosystems. Recently available high-resolution remote sensing datasets capture the physical characteristics of arctic landscapes at unprecedented spatial resolution. We demonstrate how machine learning models can capitalize on these spatial datasets to greatly improve accuracy when scaling waterbody CO2and CH4fluxes across the YK Delta of south-west AK. We found that waterbody size and contour were strong predictors for aquatic CO2emissions, attributing greater than two-thirds of the influence to the scaling model. Small ponds (<0.001 km2) were hotspots of emissions, contributing fluxes several times their relative area, but were less than 5% of the total carbon budget. Small to medium lakes (0.001–0.1 km2) contributed the majority of carbon emissions from waterbodies. Waterbody CH4emissions were predicted by a combination of wetland landcover and related drivers, as well as watershed hydrology, and waterbody surface reflectance related to chromophoric dissolved organic matter. When compared to our machine learning approach, traditional scaling methods that did not account for relevant landscape characteristics overestimated waterbody CO2and CH4emissions by 26%–79% and 8%–53% respectively. This study demonstrates the importance of an integrated terrestrial-aquatic approach to improving estimates and uncertainty when scaling C emissions in the arctic.

     
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