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DOE OSTI2020

Divergent responses of spring phenology to daytime and nighttime warming

Spring phenology (i.e., start of season, SOS) has shifted earlier in response to warmer temperatures. However, the respective influences of daytime and nighttime temperatures on SOS changes remain poorly understood. Here we characterized the responses of satellite-derived SOS to minimum temperature (Tmin) and maximum temperature (Tmax) across Appalachian Trail regions in the Eastern United States during 2000-2013. We found SOS responded differently to Tmin and Tmax at 81.5% of the study area. Furthermore, the SOS responses to Tmin and Tmax both changed across space, and specifically, these two responses showed significant divergent trends from cold to warm regions (P<0.001). We propose a new framework utilizing both Tmin and Tmax, instead of daily average temperature, in modeling phenology. This study, for the first time, disentangled phenological responses to daytime temperature from nighttime temperature across a wide range of temperatures. The findings suggest that model projections of future phenological changes should incorporate the divergent phenology responses to daytime and nighttime temperatures, in order to improve the representation of land-atmosphere interactions in Earth system models in light of asymmetric diurnal warming.

asymmetric warming, temperature sensitivity, Appal↗

DOE OSTI2022

Technology Pathways Could Help Drive the U.S. West Coast Grid's Exposure to Hydrometeorological Uncertainty

Abstract Previous studies investigating deep decarbonization of bulk electric power systems and wholesale electricity markets have not sufficiently explored how future grid pathways could affect the grid's vulnerability to hydrometeorological uncertainty on multiple timescales. Here, we employ a grid operations model and a large synthetic weather ensemble to “stress test” a range of future grid pathways for the U.S. West Coast developed by ReEDS, a well‐known capacity planning model. Our results show that gradual changes in the underlying capacity mix from 2020 to 2050 can cause significant “re‐ranking” of weather years in terms of annual wholesale electricity prices (with “good” years becoming bad, and vice versa). Nonetheless, we find the highest and lowest ranking price years in terms of average electricity price remain mostly tied to extremes in hydropower availability (streamflow) and load (summer temperatures), with the strongest sensitivities related to drought. Seasonal dynamics seen today involving spring snowmelt and hot, dry summers remain well‐defined out to 2050. In California, future supply shortfalls in our model are concentrated in the evening and occur mostly during periods of high temperature anomalies in late summer months and in late winter; in the Pacific Northwest, supply shortfalls are much more strongly tied to negative streamflow anomalies. Under our more robust sampling of stationary hydrometeorological uncertainty, we also find that the ratio of dis‐patchable thermal (i.e., natural gas) capacity to wind and solar required to ensure grid reliability can differ significantly from values reported by ReEDS.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Characterizing How Meteorological Forcing Selection and Parameter Uncertainty Influence Community Land Model Version 5 Hydrological Applications in the United States

Despite the increasing use of large-scale Land Surface Models (LSMs) in predicting hydrological responses in extreme conditions, there's a critical gap in understanding the uncertainties in these predictions. This study addresses this gap through a detailed diagnostic evaluation of the uncertainties arising from meteorological forcing selection and model parametrization in hydrological simulations of the Community Land Model version 5 (CLM5). CLM5 is configured at a spatial scale of about 12-km to simulate runoff processes for 464 headwater watersheds, selected from the Catchment Attributes for Large-Sample Studies (CAMELS) dataset to be representative of physiographic and climatic gradients across the conterminous United States. For each watershed, CLM5 is driven by five commonly used gridded forcing datasets in combination with a large ensemble (> 1200) of key CLM5 hydrologic parameters. Our results suggest that uncertainty in CLM5 runoff simulations resulting from both forcing and parametric sources is markedly higher in arid regions, e.g., Great Plains and Midwest regions. Uncertainty in low flow is dominated by parametric uncertainty, while the selection of meteorological forcing contributes more dominantly to high flow and seasonal flows during fall and spring. Our analysis also demonstrates that the selection of forcing datasets and the metrics used to calibrate CLM5 significantly impact the model’s predictive accuracy in extreme event severity for both floods and droughts. Overall, the results from this study highlight the need to understand and account for forcing and parametric uncertainties in CLM5 simulations, particularly for hazard and risk assessments addressing hydrologic extremes.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

HyPerPy (Hydrogen Extraction and Parabolic Trough Plant Performance Models using Python) [SWR-21-53]

The HyPerPy package consists of three scripts: 1) Model for Hydrogen Tracking in Parabolic Trough Power Plants, 2) Model for Receiver Performance, and 3) Model for Hydrogen Extraction Process. The power plant model (1) tracks hydrogen generation and transport within the circulating heat transfer fluid (HTF) of the power plant. This script is a transient, initial value simulation, in which the hydrogen concentration in the circulating HTF is 0 moles per cubic meter everywhere at time 0 seconds. Hydrogen concentration is calculated at discrete locations within the circulating HTF with 4-second time resolution. During each time step, the change in hydrogen concentration due to hydrogen generation and permeation is calculated for each location according to the local HTF temperature, vessel or piping properties, hydrogen concentration and partial pressure. This model predicts hourly hydrogen concentrations for typical operating days in the spring, summer, fall, and winter seasons. Data is used to create hourly mappings of hydrogen concentrations for a typical operating year. The receiver model (2) uses a 1-hour time step to estimate getter loading and annulus pressure. For each time step, the model uses HTF and ambient temperature data to estimate absorber tube, bellows, and getter temperatures for the time step. In addition, the model uses hourly HTF hydrogen concentrations that are generated by the power plant model (1), and annulus hydrogen pressure from the previous time step. With these data, the model calculates the moles of hydrogen permeating across the absorber tube and bellows during the time step. The net change in moles hydrogen is added or subtracted from the getter loading for the previous time step, and the hydrogen pressure is re-calculated based on getter loading and temperature. The model uses this algorithm to simulate hydrogen permeation and loading 24 hours per day, 365 days per year using seasonal temperature data. The model repeats these calculations for 25 years to create a mapping of receiver getter loading and annulus hydrogen pressure for four seasons of each year. The model for hydrogen extraction (3) estimates hydrogen extraction rates for a specific separation module configuration. The rate depends primarily on membrane area, vacuum pump performance, headspace gas flowrate to the membrane, and headspace gas hydrogen partial pressure. This model has two versions. The steady-state version predicts hydrogen extraction rates when the module is operating in separation mode. The dynamic version predicts hydrogen transfer through the membrane when the module is operating in sensor mode. The steady-state version is used with the plant model (1) to predict hydrogen partial pressures in the power plant when the extraction process is operating.

Glatzmaier, Gregory↗

DOE OSTI2023

Photosynthesis and rhizome carbohydrate concentrations of switchgrass grown from reserve-depleted rhizomes

A long-standing question in perennial grass breeding and physiology is whether yield improvement strategies could compromise winter survival. Perennial grasses rely on the pool of carbohydrates accumulated in storage organs from the previous growing season for winter maintenance and spring regrowth. Yield improvement strategies could reduce winter survival if they increase biomass and grain yields at the expense of carbon allocation to storage. Therefore, it is crucial to better understand the dependence of regrowth on storage reserves. We experimentally depleted switchgrass ( Panicum virgatum L.) rhizome reserves by storing rhizomes for two weeks at 5 °C (control treatment) and 25 °C (reserve-depleted treatment).

bioenergy↗

NASA NTRS1985

Wave-mean flow interaction during the winter to summer transition in the Southern Hemisphere stratosphere

The evolution of the flow and wave-mean flow interactions in the Southern Hemisphere during the period when the final stratospheric warming and the associated reversal of the flow in the upper stratosphere occurred is described. The spring is the most interesting season as far as the wave mean flow interaction in the Southern Hemisphere is concerned. The data for this study are NMC analysis of temperature and geopotential fields between 1000 and 0.45 mb for the period August 31 to November 10, 1979.

Yamazaki, K.↗

NASA NTRS1987

Dynamical factors affecting ozone mixing ratios in the Antarctic lower stratosphere

An account is given of the climatology and interannual variability of dynamical quantities and ozone mixing ratios during the Southern Hemisphere spring for 1979-1984. The seasonal variation in temperature in the lower stratosphere is repeatable; a steep decrease in zonal mean ozone mixing ratios is observed around 60 deg S toward the South Pole in September which, with time, becomes shallower in association with minor warmings and a final warming. Climatological synoptic charts in the lower stratosphere show the circumpolar circulation in the geopotential height field and the prominence of planetary wave 1 in the temperature and ozone fields. When wave activity is strong, there are weaker westeries, higher temperatures, and higher ozone mixing ratios at high latitudes.

Shiotani, Masato↗

NASA NTRS1995

Titan's stratospheric temperature asymmetry: a radiative origin?

During the 1981 Voyager encounter, Titan's stratosphere exhibited a large thermal asymmetry, with high northern latitudes being colder than comparable southern latitudes. Given the short radiative time constant, this asymmetry would not be expected at the season of the Voyager observations (spring equinox), if the infrared and solar opacity sources were distributed symmetrically. We have investigated the radiative budget of Titan's stratosphere, using two selections of Voyager IRIS spectra recorded at symmetric northern and southern latitudes. In the region 0.1-1 mbar, temperatures are 7 K colder at 50 degrees N than at 53 degrees S and the difference reaches approximately 13 K at 5 mbar. On the other hand, the northern region is strongly enriched in nitriles and hydrocarbons, and the haze optical depth derived from the continuum emission between 8 and 15 micrometers is twice as large as in the south. Cooling rate profiles have been computed at the two locations, using the gas and haze abundances derived from the IRIS measurements. We find that, despite lower temperatures, the cooling rate profiles in the pressure range 0.15-5 mbar are 20 to 40% larger in the north than in the south, because of the enhanced concentrations of infrared radiators. Because the northern hemisphere appears darker than the southern one in the Voyager images, enhanced solar heating is also expected to take place at 50 degrees N. Solar heating rate profiles have been calculated, with two different assumptions on the origin of the hemispheric asymmetry. In the most likely case where it results from a variation in the absorbance of the haze material, the heating rates are found to be 12-15% larger at the northern location than at the southern one, a smaller increase than that in the cooling rates. If the lower albedo in the north results from an increase in the particle number density, a 55 to 75% difference is found for the pressure range 0.15-5 mbar, thus larger than that calculated for the cooling rates. Considering the uncertainties in the haze model, dynamical heat transport may significantly contribute to the meridional temperature gradients observed in the stratosphere. On the other hand, the latitudinal variation in gas and haze composition may be sufficient to explain the entire temperature asymmetry observed, without invoking a lag in the thermal response of the atmosphere due to dynamical inertia.

NASA Program Exobiology↗

NASA NTRS2011

Global Assimilation of Multi-Sensor Snow Observations for Improved Characterization of Snow Processes

Snow conditions on the land surface are recognized to be key components of the global hydrological cycle as they play a critical role in the determination of local and regional climate. In many mid-latitude and high-latitude regions, the seasonal water storage and associated spring snowmelt dominate the local hydrology. The contribution to the runoff and moisture conditions from snow is vital in supporting agriculture and in determining water resources management practices. Consequently, accurate characterization of snow properties becomes important for both end-use applications and weather and climate research. Recently a joint effort between the u.S. Air Force and NASA has enabled a blended, multi-sensor snow product known as the AFWA NASA Snow Algorithm (ANSA). This global snow dataset has been generated by utilizing the Earth Observation System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) datasets. ANSA product includes estimates of snow cover extent, snow water equivalent (SWE) and SWE-derived snow depth fields. The MODIS-based products enable snow cover mappings under cloud-free conditions whereas the passive microwave data from AMSR-E provides measurements under cloudy conditions. These remotely-sensed snow observations are further augmented with the information from ground-based snow measurements through data fusion techniques. The resulting ANSA products are employed in the NASA Land Information System (LIS) data assimilation framework, which provides a comprehensive environment for integrating community land surface models, ground and satellite-based observations, and ensemble-based data assimilation tools. LIS incorporates the multisensor ANSA snow retrievals with the land surface model estimates to generate spatially and temporally continuous estimates of snow states, through data assimilation. A suite of experiments to assimilate ANSA snow cover, SWE and snow depth estimates with different land surface models in LIS are conducted and the resulting estimates of snow conditions are evaluated against a number of in-situ observational datasets, over several regions of the world. These evaluations are used to compare and contrast the advantages and disadvantages of these multi-sensor snow observations.

Kumar, Sujay↗

NASA NTRS2017

A Deep Search for the Release of Volcanic Gases on Mars Using Ground-Based High-Resolution Infrared and Submillimeter Spectroscopy: Sensitive Upper Limits for OCS and SO2

Recent volcanic activity has long been considered a distinct possibility that would place major constraints on the evolution of Marsâ€™ interior. Volcanic activity would result in the outgassing of sulfur-bearing species. As part of our multi-band search for active release of volcanic gases on Mars, we looked for carbonyl sulfide (OCS) at its combination band (v1 + v3) at 3.42 micrometers (2924 cm(exp -1), and sulfur dioxide (SO2) at 346.652 GHz, in two successive Mars years during its late Northern spring and mid Northern summer seasons (L(sub)s= 43 deg - 44 deg). The targeted volcanic districts, Tharsis and Syrtis Major, were observed during the two intervals, 15 Dec. 2011 to 6 Jan. 2012 in the first year, and 23 May 2014 to 12 June 2014 in the second year using the high resolution infrared spectrometer CSHELL on the NASA Infrared Telescope Facility, and the high resolution heterodyne receiver HARP at the James Clerk Maxwell Telescope atop Maunakea, Hawaii. No active release of such gases was detected, and we report 2 sigma upper limits of 1.8 ppbv and 3.1 ppbv for OCS and SO2, respectively, compared to 0.3 ppbv for SO2 (Encrenaz, T. et al. [2011] Astron. & Astrophys. 530, A37; Krasnopolsky, V.A. [2012] Icarus 217, 144-152) over the disk of Mars. Our retrieved upper limit on the SO2 outgassing rate of 156 tons/day (1.8 kg/s), corresponds to a mass rate of magma that is able to degas the SO2 of 104 kilotons/day (1200 kg/s), or 40,000 cu m/day (0.46 cu m/s). Our campaign places stringent limits on the concentration of sulfur-bearing species into the atmosphere of Mars.

Khayat, A. S. J.↗

NASA NTRS2021

Inter-comparison of snow depth over Arctic sea ice from reanalysis reconstructions and satellite retrieval

In this study, we compare eight recently developed snow depth products over Arctic sea ice, which use satellite observations, modeling, or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance observations and airborne measurements. Large mean snow depth discrepancies are observed over the Atlantic and Canadian Arctic sectors. The differences between climatology and the snow products early in winter could be in part a result of the delaying in Arctic ice formation that reduces early snow accumulation, leading to shallower snowpacks at the start of the freeze-up season. These differences persist through spring despite overall more winter snow accumulation in the reanalysis-based products than in the climatologies. Among the products evaluated, the University of Washington (UW) snow depth product produces the deepest spring (March–April) snowpacks, while the snow product from the Danish Meteorological Institute (DMI) provides the shallowest spring snow depths. Most snow products show significant correlation with snow depths retrieved from Operational IceBridge (OIB) while correlations are quite low against buoy measurements, with no correlation and very low variability from University of Bremen and DMI products. Inconsistencies in reconstructed snow depth among the products, as well as differences between these products and in situ and airborne observations, can be partially attributed to differences in effective footprint and spatial–temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in situ and remote sensing observations.

Lu Zhou↗

NASA NTRS2020

Mapping Temperate Forest Phenology Using Tower, UAV, and Ground-Based Sensors

Phenology is a distinct marker of the impacts of climate change on ecosystems. Accordingly, monitoring the spatiotemporal patterns of vegetation phenology is important to understand the changing Earth system. A wide range of sensors have been used to monitor vegetation phenology, including digital cameras with different viewing geometries mounted on various types of platforms. Sensor perspective, view-angle, and resolution can potentially impact estimates of phenology. We compared three different methods of remotely sensing vegetation phenology—an unoccupied aerial vehicle (UAV)-based, downward-facing RGB camera, a below-canopy, upward-facing hemispherical camera with blue (B), green (G), and near-infrared (NIR) bands, and a tower-based RGB PhenoCam, positioned at an oblique angle to the canopy—to estimate spring phenological transition towards canopy closure in a mixed-species temperate forest in central Virginia, USA. Our study had two objectives: (1) to compare the above- and below-canopy inference of canopy greenness (using green chromatic coordinate and normalized difference vegetation index) and canopy structural attributes (leaf area and gap fraction) by matching below-canopy hemispherical photos with high spatial resolution (0.03 m) UAV imagery, to find the appropriate spatial coverage and resolution for comparison; (2) to compare how UAV, ground-based, and tower-based imagery performed in estimating the timing of the spring phenological transition. We found that a spatial buffer of 20 m radius for UAV imagery is most closely comparable to below-canopy imagery in this system. Sensors and platforms agree within +/− 5 days of when canopy greenness stabilizes from the spring phenophase into the growing season. We show that pairing UAV imagery with tower-based observation platforms and plot-based observations for phenological studies (e.g., long-term monitoring, existing research networks, and permanent plots) has the potential to scale plot-based forest structural measures via UAV imagery, constrain uncertainty estimates around phenophases, and more robustly assess site heterogeneity.

Jeff W. Atkins↗

NASA NTRSDate not supplied

Hunga Tonga-Hunga Ha’apai Water Vapour and Composition of the Stratospheric Polar Vortices

We investigate the impacts of water vapor from the Hunga Tonga-Hunga Ha'apai (HTHH) eruption on the Antarctic stratospheric vortices in 2022 and 2023 and the Arctic stratospheric vortex in 2022/2023. Trace gas abundances from the Aura Microwave Limb Sounder (MLS) and the Modern Era Retrospective-analysis for Research and Applications (MERRA-2) Stratospheric Composition REanalysis with Aura MLS (M2-SCREAM), along with meteorological fields from the longer MERRA-2 record, are used to detail the evolution of the stratospheric polar vortex and its composition during these winter/spring seasons. Through austral winter and spring 2022, HTHH water vapor was effectively excluded from the Antarctic vortex until it broke up. The 2022 Antarctic vortex itself was large, strong, and long-lived, but not exceptionally so. By austral fall 2023, HTHH stratospheric water vapor had spread globally, and Antarctic vortex values in the lower through middle stratosphere were over 2 ppmv higher than usual when the vortex formed; these record-shattering water vapor concentrations led to earlier polar stratospheric cloud formation and chlorine activation, especially at levels that are typically the highest altitudes for polar processing. In the 2022/2023 Arctic winter/spring, HTHH water vapor reached the Arctic vortex region after the vortex had formed and was largely excluded until a major sudden stratospheric warming in mid-February 2023 eroded the vortex and mixed midlatitude and vortex air throughout the hemisphere.

Gloria L Manney↗

NASA NTRSDate not supplied

Hunga Tonga-Hunga Ha’apai Water Vapour and Composition of the Stratospheric Polar Vortices

Gloria L Manney↗

NASA NTRS2023

ENSO and MJO Modulation of U.S. Cloud-to-Ground Lightning Activity

Cloud-to-ground (CG) lightning substantially impacts human health and property. However, the relations between U.S. lightning activity and the Madden-Julian Oscillation (MJO) and El Niño-Southern Oscillation (ENSO), two predictable drivers of global climate variability, remain uncertain, in part because most lightning datasets have short records that cannot robustly reveal MJO- and ENSO-related patterns. To overcome this limitation, we developed an empirical model of 6-hourly lightning flash count over the contiguous U.S. (CONUS) using environmental variables (convective available potential energy and precipitation) andNational Lightning Detection Network data for 2003–2016. This model is shown to reproduce the observed daily and seasonal variability of lightning over most of CONUS. Then, the empirical model was applied to construct a proxy lightning dataset for the period 1979–2021, which was used to investigate the summer MJO-lightning relationship at daily resolution and the winter-spring ENSO-lightning relationship at seasonal resolution. Overall, no robust relationship between MJO phase and lightning patterns was found when seasonality was taken into consideration. El Niño is associated with increased lightning activity over the Coastal Southeast U.S. during early winter, the Southwest during winter through spring, and the Northwest during late spring, whereas La Niña is associated with increased lightning activity over the Tennessee River Valley during winter.

Lightning↗

DOE OSTI2020

Much stronger tundra methane emissions during autumn-freeze than spring-thaw

Warming in the Arctic has been more apparent in the non-growing season than in the typical growing season. In this context, methane (CH 4 ) emissions in the non-growing season, particularly in the shoulder seasons, account for a substantial proportion of the annual budget. However, CH 4 emissions in spring and autumn shoulders are often underestimated by land models and measurements due to limited data availability and unknown mechanisms. This study investigates CH 4 emissions during spring thaw and autumn freeze using eddy covariance CH 4 measurements from three Arctic sites with multi-year observations. We find that the shoulder seasons contribute to about a quarter (25.6±2.3%, mean ± standard deviation) of annual total CH 4 emissions. Our study highlights the three to four times higher contribution of autumn freeze CH 4 emission to total annual emission than that of spring thaw. Autumn freeze exhibits significantly higher CH 4 flux (0.88±0.03 mg m -2 h -1 ) than spring thaw (0.48±0.04 mg m -2 h -1 ). The mean duration of autumn freeze (58.94±26.39 days) is significantly longer than that of spring thaw (20.94±7.79 days), which predominates the much higher cumulative CH 4 emission during autumn freeze (1212.31±280.39 mg m -2 yr -1 ) than that during spring thaw (307.39±46.11 mg m -2 yr -1 ). Near-surface soil temperatures cannot completely reflect the freeze-thaw processes in deeper soil layers and appears to have a hysteresis effect on CH 4 emissions from early spring thaw to late autumn freeze. Therefore, it is necessary to consider commonalities and differences in CH 4 emissions during spring thaw versus autumn freeze to accurately estimate CH 4 source from tundra ecosystems for evaluating carbon-climate feedback in Arctic.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Employing Machine Learning for New Particle Formation Identification and Mechanistic Analysis: Insights From a Six‐Year Observational Study in the Southern Great Plains

We present a supervised machine learning (ML) framework to automatically identify new particle formation (NPF) events and analyze key atmospheric factors associated with their occurrence and growth. We applied ML to detect NPF events using start time and particle concentrations across size ranges, while identifying atmospheric variables including ambient temperature, relative humidity, solar radiation intensity (SRI), wind speed, wind direction, boundary layer height, total organics, sulfate, nitrate, total surface area concentration, sulfur dioxide, and turbulent kinetic energy (TKE). We analyzed a 6-year data set from the Atmospheric Radiation Measurement at the Southern Great Plains (SGP) site in Oklahoma, USA. Using long-term ground-based measurements, we identified NPF events and applied Random Forest Classifiers, which achieved 90%–95% prediction accuracy. Feature importance analysis highlighted SRI, relative humidity, and ambient temperature as the most influential variables, contributing normalized importances of 28%, 17%, and 10%. Partial Dependence Plots (PDPs) indicated that higher SRI and lower relative humidity were critical in promoting NPF formation at SGP. Seasonally, NPF events were more frequent in winter (42.1%) and spring (35.5%), and least in summer (4.0%). Particle growth rates also exhibited a seasonal variation, with the lowest in winter (below 2 nm hr −1 ) and highest in late spring and early summer (exceeding 5 nm hr −1 ). Temperature, turbulent kinetic energy, and aerosol properties were the primary factors of growth rate variability. This study advances predictive modeling of NPF, offers insights for future campaign deployments, and demonstrates the effectiveness of ML in understanding the formation and growth of atmospheric aerosols.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2011

Fire Return Interval Within the Northern Boundary of the Larch Forest

Larch (Larix spp.) dominant forests compose a large proportion of the forests of Russia (i.e., about 40% of forested areas). These forests range from the Yenisei ridge on the west to the Pacific Ocean on the east, and from Lake Baikal on the south to the 73rd parallel in the north. Larch stands comprise the world s northern most forest at Ary-Mas (72 deg 28' N, 102 deg 15' E). Larch dominated forests occupy about 70% of the permafrost areas in Siberia. Larch forms high closure stands as well as open forests, and is found mainly over permafrost, where other tree species barely survive. Wildfires are typical for this territory with the majority occurring as ground fires due to low crown closure. Due to the thin active layer in permafrost soils and a dense lichen-moss cover, ground fires may cause stand mortality. The vast areas of larch-dominant forests is generally considered as a "carbon sink"; however, positive long-term temperature trends at higher latitudes are expected to result in an increase of fire frequency, and thus may convert this area to a source for greenhouse gases. There are recent observations regarding the increase of fire frequency within non-protected territories. Surprisingly, there are few publications on fire chronoseqences for the huge forested territory between the Ural Mountains and the Pacific Ocean. Also there is a general understanding that bimodal (late spring -- early summer and late summer-beginning of fall) fire seasonal distribution in the south becomes uni-modal (late spring -- early summer) in the north. The purpose of this study is to investigate the wildfire history at the northern edge of the zone of larch dominance.

Kharuk, V. I.↗