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  • Copernicus GmbH  (7)
  • Kimball, John S.  (7)
  • 1
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    Sensitivity of active-layer freezing process to snow cover in Arctic Alaska
    Copernicus GmbH ; 2019
    In:  The Cryosphere Vol. 13, No. 1 ( 2019-01-23), p. 197-218
    Details
    In: The Cryosphere, Copernicus GmbH, Vol. 13, No. 1 ( 2019-01-23), p. 197-218
    Abstract: Abstract. The contribution of cold-season soil respiration to the Arctic–boreal carbon cycle and its potential feedback to the global climate remain poorly quantified, partly due to a poor understanding of changes in the soil thermal regime and liquid water content during the soil-freezing process. Here, we characterized the processes controlling active-layer freezing in Arctic Alaska using an integrated approach combining in situ soil measurements, local-scale (∼50 m) longwave radar retrievals from NASA airborne P-band polarimetric SAR (PolSAR) and a remote-sensing-driven permafrost model. To better capture landscape variability in snow cover and its influence on the soil thermal regime, we downscaled global coarse-resolution (∼0.5∘) MERRA-2 reanalysis snow depth data using finer-scale (500 m) MODIS snow cover extent (SCE) observations. The downscaled 1 km snow depth data were used as key inputs to the permafrost model, capturing finer-scale variability associated with local topography and with favorable accuracy relative to the SNOTEL site measurements in Arctic Alaska (mean RMSE=0.16 m, bias=-0.01 m). In situ tundra soil dielectric constant (ε) profile measurements were used for model parameterization of the soil organic layer and unfrozen-water content curve. The resulting model-simulated mean zero-curtain period was generally consistent with in situ observations spanning a 2∘ latitudinal transect along the Alaska North Slope (R: 0.6±0.2; RMSE: 19±6 days), with an estimated mean zero-curtain period ranging from 61±11 to 73±15 days at 0.25 to 0.45 m depths. Along the same transect, both the observed and model-simulated zero-curtain periods were positively correlated (R〉0.55, p〈0.01) with a MODIS-derived snow cover fraction (SCF) from September to October. We also examined the airborne P-band radar-retrieved ε profile along this transect in 2014 and 2015, which is sensitive to near-surface soil liquid water content and freeze–thaw status. The ε difference in radar retrievals for the surface (∼〈0.1 m) soil between late August and early October was negatively correlated with SCF in September (R=-0.77, p〈0.01); areas with lower SCF generally showed larger ε reductions, indicating earlier surface soil freezing. On regional scales, the simulated zero curtain in the upper (〈0.4 m) soils showed large variability and was closely associated with variations in early cold-season snow cover. Areas with earlier snow onset generally showed a longer zero-curtain period; however, the soil freeze onset and zero-curtain period in deeper (〉0.5 m) soils were more closely linked to maximum thaw depth. Our findings indicate that a deepening active layer associated with climate warming will lead to persistent unfrozen conditions in deeper soils, promoting greater cold-season soil carbon loss.
    Type of Medium: Online Resource
    ISSN: 1994-0424
    URL: Article
    URL: Article
    DOI: 10.5194/tc-13-197-2019
    DOI: 10.5194/tc-13-197-2019-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2019
    detail.hit.zdb_id: 2393169-3
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  • 2
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    A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations
    Copernicus GmbH ; 2017
    In:  Earth System Science Data Vol. 9, No. 2 ( 2017-11-01), p. 791-808
    Details
    In: Earth System Science Data, Copernicus GmbH, Vol. 9, No. 2 ( 2017-11-01), p. 791-808
    Abstract: Abstract. Spaceborne microwave remote sensing is widely used to monitor global environmental changes for understanding hydrological, ecological, and climate processes. A new global land parameter data record (LPDR) was generated using similar calibrated, multifrequency brightness temperature (Tb) retrievals from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2). The resulting LPDR provides a long-term (June 2002–December 2015) global record of key environmental observations at a 25 km grid cell resolution, including surface fractional open water (FW) cover, atmosphere precipitable water vapor (PWV), daily maximum and minimum surface air temperatures (Tmx and Tmn), vegetation optical depth (VOD), and surface volumetric soil moisture (VSM). Global mapping of the land parameter climatology means and seasonal variability over the full-year records from AMSR-E (2003–2010) and AMSR2 (2013–2015) observation periods is consistent with characteristic global climate and vegetation patterns. Quantitative comparisons with independent observations indicated favorable LPDR performance for FW (R ≥ 0.75; RMSE  ≤  0.06), PWV (R ≥ 0.91; RMSE  ≤  4.94 mm), Tmx and Tmn (R ≥ 0.90; RMSE  ≤  3.48 °C), and VSM (0.63 ≤ R ≤ 0.84; bias-corrected RMSE  ≤  0.06 cm3 cm−3). The LPDR-derived global VOD record is also proportional to satellite-observed NDVI (GIMMS3g) seasonality (R ≥ 0.88) due to the synergy between canopy biomass structure and photosynthetic greenness. Statistical analysis shows overall LPDR consistency but with small biases between AMSR-E and AMSR2 retrievals that should be considered when evaluating long-term environmental trends. The resulting LPDR and potential updates from continuing AMSR2 operations provide for effective global monitoring of environmental parameters related to vegetation activity, terrestrial water storage, and mobility and are suitable for climate and ecosystem studies. The LPDR dataset is publicly available at http://files.ntsg.umt.edu/data/LPDR_v2/.
    Type of Medium: Online Resource
    ISSN: 1866-3516
    URL: Article
    URL: Article
    DOI: 10.5194/essd-9-791-2017
    DOI: 10.5194/essd-9-791-2017-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2017
    detail.hit.zdb_id: 2475469-9
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  • 3
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    Satellite microwave assessment of Northern Hemisphere lake ice phenology from 2002 to 2015
    Copernicus GmbH ; 2017
    In:  The Cryosphere Vol. 11, No. 1 ( 2017-01-12), p. 47-63
    Details
    In: The Cryosphere, Copernicus GmbH, Vol. 11, No. 1 ( 2017-01-12), p. 47-63
    Abstract: Abstract. A new automated method enabling consistent satellite assessment of seasonal lake ice phenology at 5 km resolution was developed for all lake pixels (water coverage  ≥  90 %) in the Northern Hemisphere using 36.5 GHz H-polarized brightness temperature (Tb) observations from the Advanced Microwave Scanning Radiometer for EOS and Advanced Microwave Scanning Radiometer 2 (AMSR-E/2) sensors. The lake phenology metrics include seasonal timing and duration of annual ice cover. A moving t test (MTT) algorithm allows for automated lake ice retrievals with daily temporal fidelity and 5 km resolution gridding. The resulting ice phenology record shows strong agreement with available ground-based observations from the Global Lake and River Ice Phenology Database (95.4 % temporal agreement) and favorable correlations (R) with alternative ice phenology records from the Interactive Multisensor Snow and Ice Mapping System (R = 0.84 for water clear of ice (WCI) dates; R = 0.41 for complete freeze over (CFO) dates) and Canadian Ice Service (R = 0.86 for WCI dates; R = 0.69 for CFO dates). Analysis of the resulting 12-year (2002–2015) AMSR-E/2 ice record indicates increasingly shorter ice cover duration for 43 out of 71 (60.6 %) Northern Hemisphere lakes examined, with significant (p  〈  0.05) regional trends toward earlier ice melting for only five lakes. Higher-latitude lakes reveal more widespread and larger trends toward shorter ice cover duration than lower-latitude lakes, consistent with enhanced polar warming. This study documents a new satellite-based approach for rapid assessment and regional monitoring of seasonal ice cover changes over large lakes, with resulting accuracy suitable for global change studies.
    Type of Medium: Online Resource
    ISSN: 1994-0424
    URL: Article
    DOI: 10.5194/tc-11-47-2017
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2017
    detail.hit.zdb_id: 2393169-3
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  • 4
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    Characterizing permafrost active layer dynamics and sensitivity to landscape spatial heterogeneity in Alaska
    Copernicus GmbH ; 2018
    In:  The Cryosphere Vol. 12, No. 1 ( 2018-01-16), p. 145-161
    Details
    In: The Cryosphere, Copernicus GmbH, Vol. 12, No. 1 ( 2018-01-16), p. 145-161
    Abstract: Abstract. An important feature of the Arctic is large spatial heterogeneity in active layer conditions, which is generally poorly represented by global models and can lead to large uncertainties in predicting regional ecosystem responses and climate feedbacks. In this study, we developed a spatially integrated modeling and analysis framework combining field observations, local-scale ( ∼  50 m resolution) active layer thickness (ALT) and soil moisture maps derived from low-frequency (L + P-band) airborne radar measurements, and global satellite environmental observations to investigate the ALT sensitivity to recent climate trends and landscape heterogeneity in Alaska. Modeled ALT results show good correspondence with in situ measurements in higher-permafrost-probability (PP  ≥  70 %) areas (n = 33; R = 0.60; mean bias  =  1.58 cm; RMSE  =  20.32 cm), but with larger uncertainty in sporadic and discontinuous permafrost areas. The model results also reveal widespread ALT deepening since 2001, with smaller ALT increases in northern Alaska (mean trend  = 0.32±1.18 cm yr−1) and much larger increases (〉  3 cm yr−1) across interior and southern Alaska. The positive ALT trend coincides with regional warming and a longer snow-free season (R =  0.60 ± 0.32). A spatially integrated analysis of the radar retrievals and model sensitivity simulations demonstrated that uncertainty in the spatial and vertical distribution of soil organic carbon (SOC) was the largest factor affecting modeled ALT accuracy, while soil moisture played a secondary role. Potential improvements in characterizing SOC heterogeneity, including better spatial sampling of soil conditions and advances in remote sensing of SOC and soil moisture, will enable more accurate predictions of active layer conditions and refinement of the modeling framework across a larger domain.
    Type of Medium: Online Resource
    ISSN: 1994-0424
    URL: Article
    URL: Article
    DOI: 10.5194/tc-12-145-2018
    DOI: 10.5194/tc-12-145-2018-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2018
    detail.hit.zdb_id: 2393169-3
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  • 5
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    Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors
    Copernicus GmbH ; 2021
    In:  Hydrology and Earth System Sciences Vol. 25, No. 1 ( 2021-01-04), p. 17-40
    Details
    In: Hydrology and Earth System Sciences, Copernicus GmbH, Vol. 25, No. 1 ( 2021-01-04), p. 17-40
    Abstract: Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.
    Type of Medium: Online Resource
    ISSN: 1607-7938
    URL: Article
    URL: Article
    DOI: 10.5194/hess-25-17-2021
    DOI: 10.5194/hess-25-17-2021-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2021
    detail.hit.zdb_id: 2100610-6
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  • 6
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    MEaSUREs Global Record of Daily Landscape Freeze/Thaw Status
    Copernicus GmbH ; 2017
    In:  Earth System Science Data Vol. 9, No. 1 ( 2017-02-16), p. 133-147
    Details
    In: Earth System Science Data, Copernicus GmbH, Vol. 9, No. 1 ( 2017-02-16), p. 133-147
    Abstract: Abstract. The landscape freeze–thaw (FT) signal determined from satellite microwave brightness temperature (Tb) observations has been widely used to define frozen temperature controls on land surface water mobility and ecological processes. Calibrated 37 GHz Tb retrievals from the Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and SSM/I Sounder (SSMIS) were used to produce a consistent and continuous global daily data record of landscape FT status at 25 km grid cell resolution. The resulting FT Earth system data record (FT-ESDR) is derived from a refined classification algorithm and extends over a larger domain and longer period (1979–2014) than prior FT-ESDR releases. The global domain encompasses all land areas affected by seasonal frozen temperatures, including urban, snow- and ice-dominant and barren land, which were not represented by prior FT-ESDR versions. The FT retrieval is obtained using a modified seasonal threshold algorithm (MSTA) that classifies daily Tb variations in relation to grid-cell-wise FT thresholds calibrated using surface air temperature data from model reanalysis. The resulting FT record shows respective mean annual spatial classification accuracies of 90.3 and 84.3 % for evening (PM) and morning (AM) overpass retrievals relative to global weather station measurements. Detailed data quality metrics are derived characterizing the effects of sub-grid-scale open water and terrain heterogeneity, as well as algorithm uncertainties on FT classification accuracy. The FT-ESDR results are also verified against other independent cryospheric data, including in situ lake and river ice phenology, and satellite observations of Greenland surface melt. The expanded FT-ESDR enables new investigations encompassing snow- and ice-dominant land areas, while the longer record and favorable accuracy allow for refined global change assessments that can better distinguish transient weather extremes, landscape phenological shifts, and climate anomalies from longer-term trends extending over multiple decades. The dataset is freely available online (doi:10.5067/MEASURES/CRYOSPHERE/nsidc-0477.003).
    Type of Medium: Online Resource
    ISSN: 1866-3516
    URL: Article
    DOI: 10.5194/essd-9-133-2017
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2017
    detail.hit.zdb_id: 2475469-9
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  • 7
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    Investigating the sensitivity of soil heterotrophic respiration to recent snow cover changes in Alaska using a satellite-based permafrost carbon model
    Copernicus GmbH ; 2020
    In:  Biogeosciences Vol. 17, No. 22 ( 2020-11-27), p. 5861-5882
    Details
    In: Biogeosciences, Copernicus GmbH, Vol. 17, No. 22 ( 2020-11-27), p. 5861-5882
    Abstract: Abstract. The contribution of soil heterotrophic respiration to the boreal–Arctic carbon (CO2) cycle and its potential feedback to climate change remains poorly quantified. We developed a remote-sensing-driven permafrost carbon model at intermediate scale (∼1 km) to investigate how environmental factors affect the magnitude and seasonality of soil heterotrophic respiration in Alaska. The permafrost carbon model simulates snow and soil thermal dynamics and accounts for vertical soil carbon transport and decomposition at depths up to 3 m below the surface. Model outputs include soil temperature profiles and carbon fluxes at 1 km resolution spanning the recent satellite era (2001–2017) across Alaska. Comparisons with eddy covariance tower measurements show that the model captures the seasonality of carbon fluxes, with favorable accuracy in simulating net ecosystem CO2 exchange (NEE) for both tundra (R〉0.8, root mean square error (RMSE – 0.34 g C m−2 d−1), and boreal forest (R〉0.73; RMSE – 0.51 g C m−2 d−1). Benchmark assessments using two regional in situ data sets indicate that the model captures the complex influence of snow insulation on soil temperature and the temperature sensitivity of cold-season soil heterotrophic respiration. Across Alaska, we find that seasonal snow cover imposes strong controls on the contribution from different soil depths to total soil heterotrophic respiration. Earlier snowmelt in spring promotes deeper soil warming and enhances the contribution of deeper soils to total soil heterotrophic respiration during the later growing season, thereby reducing net ecosystem carbon uptake. Early cold-season soil heterotrophic respiration is closely linked to the number of snow-free days after the land surface freezes (R=-0.48, p〈0.1), i.e., the delay in snow onset relative to surface freeze onset. Recent trends toward earlier autumn snow onset in northern Alaska promote a longer zero-curtain period and enhanced cold-season respiration. In contrast, southwestern Alaska shows a strong reduction in the number of snow-free days after land surface freeze onset, leading to earlier soil freezing and a large reduction in cold-season soil heterotrophic respiration. Our results also show nonnegligible influences of subgrid variability in surface conditions on the model-simulated CO2 seasonal cycle, especially during the early cold season at 10 km scale. Our results demonstrate the critical role of snow cover affecting the seasonality of soil temperature and respiration and highlight the challenges of incorporating these complex processes into future projections of the boreal–Arctic carbon cycle.
    Type of Medium: Online Resource
    ISSN: 1726-4189
    URL: Article
    URL: Article
    DOI: 10.5194/bg-17-5861-2020
    DOI: 10.5194/bg-17-5861-2020-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2020
    detail.hit.zdb_id: 2158181-2
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