Evaluation and Enhancement of Permafrost Modeling With the <scp>NASA</scp> Catchment Land Surface Model

Tao, Jing; Reichle, Rolf H.; Koster, Randal D.; Forman, B. A.; Xue, Yuan

doi:10.1002/2017ms001019

Cited by 11 publications

(25 citation statements)

References 59 publications

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“…All the climate data were procured from Global Land Data Assimilation System (GLDAS) Catchment Land Surface Model L4 V2.0 at daily, 0.25 • × 0.25 • resolution, which belong to the Goddard Earth Sciences Data and Information Services Center (GES DISC) [35]. The CLM (Catchment Land Surface Model) data set has been used in a variety of application studies such as soil moisture [36], permafrost [37], and groundwater storage changes [38]. In addition, we collected extra data from 70 meteorological stations in Mongolia (Figure 1d) to evaluate the data quality of the GLDAS CLM.…”

Section: Climatic Datamentioning

confidence: 99%

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

et al. 2020

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As a result of the unique geographical characteristics, pastoral lifestyle, and economic conditions in Mongolia, its fragile natural ecosystems are highly sensitive to climate change and human activities. The normalized difference vegetation index (NDVI) was employed in this study as an indicator of the growth status of vegetation. The Sen's slope, Mann-Kendall test, and geographical detector modelling methods were used to assess the spatial and temporal changes of the NDVI in response to variations in natural conditions and human activities in Mongolia from 1982 to 2015. The corresponding individual and interactive driving forces, and the optimal range for the maximum NDVI value of vegetation distribution were also quantified. The area in which vegetation was degraded was roughly equal to the area of increase, but different vegetation types behaved differently. The desert steppe and the Gobi Desert both in arid regions have degraded significantly, whereas the meadow steppe and alpine steppe showed a significant upward trend. Precipitation can satisfactorily account for vegetation distribution. Changes of livestock quantity was the dominant factor influencing the changes of most vegetation types. The interactions of topographic factors and climate factors have significant effects on vegetation growth. In the region of annual precipitation between 331 mm and 596 mm, forest vegetation type and pine sandy soil type were found to be most suitable for the growth of vegetation in Mongolia. The findings of this study can help us to understand the appropriate range or type of environmental factors affecting vegetation growth in Mongolia, based on which we can apply appropriate interventions to effectively mitigate the impact of environmental changes on vegetation.With the continuous development of earth observation system technology and the continuous enrichment of statistical models, the research content and methods of the relationships between vegetation, climate change, and human activities have become increasingly diverse. The normalized density vegetation index (NDVI), derived from infrared channel and near-infrared channel remote sensing data, is a good indicator of vegetation growth status and spatial distribution density of vegetation, and is linearly related to vegetation distribution density [6,7]. A large number of ecological studies have been carried out related to vegetation change and its influencing factors using NDVI and statistical models. Temperature and precipitation, as important factors in the natural environment, are often used as research priorities to assess their impact on vegetation. However, the dominant factors contributing to NDVI in different regions vary considerably. The variety in vegetation and its connection to climate change have been investigated by Du et al. [5] and Zheng et al. [8] in China. These studies identified precipitation as the key climatic factor governing variation in NDVI. However, Guo et al. [9] found that temperature was the dominant factor on vegetation (NDVI) growth ...

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Section: Climatic Datamentioning

confidence: 99%

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

et al. 2020

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“…We first filled missing gaps vertically by fitting a polynomial to the soil temperature profile (Kurylyk and Hayashi, 2016) on a daily 110 scale, then screened out outliers by examining the daily time series. Further, we aggregated both the ABoVE and the GIPL-UAF soil temperature measurements to ELMv1-ECA soil layer node depths using the inverse distance weighting method (Tao et al, 2017), and then averaged the two sets of aggregated observations. We used the assembled subsurface temperature observation datasets to evaluate the ELMv1-ECA simulated soil temperature profiles and the zero-curtain periods.…”

Section: Study Sites and Datamentioning

confidence: 99%

Improved ELMv1-ECA Simulations of Zero-Curtain Periods and Cold-season CH<sub>4</sub> and CO<sub>2</sub> Emissions at Alaskan Arctic Tundra Sites

Tao¹,

Zhu²,

Riley³

et al. 2020

Preprint

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Abstract. Field measurements have shown that cold-season methane (CH4) and carbon dioxide (CO2) emissions contribute a substantial portion to the annual net carbon emissions in permafrost regions. However, most earth system land models do not accurately reproduce cold-season CH4 and CO2 emissions, especially over the shoulder (i.e., thawing and freezing) seasons. Here we use the Energy Exascale Earth System Model (E3SM) land model version 1 (ELMv1-ECA) to tackle this challenge and fill the knowledge gap of how cold-season CH4 and CO2 emissions contribute to the annual totals at Alaska Arctic tundra sites. Specifically, we improved the ELMv1-ECA soil water phase-change scheme, environmental controls on microbial activity, and cold-season methane transport module. Results demonstrate that both soil temperature and the duration of zero-curtain periods (i.e., the fall period when soil temperatures linger around 0 °C) simulated by the updated ELMv1-ECA were greatly improved, e.g., the Mean Absolute Error in zero-curtain durations at 12 cm depth was reduced by 62 % on average. Furthermore, the simulated cold-season emissions at three tundra sites were improved by 84 % and 81 % on average for CH4 and CO2, respectively. Overall, CH4 and CO2 emitted during the early cold season (Sep. and Oct.), which often includes most of the zero-curtain period in Arctic tundra, accounted for more than 50 % of the total emissions throughout the entire cold season (Sep. to May). From 1950 to 2017, both CO2 emissions during the 12 cm depth zero-curtain period and during the entire cold season showed increasing trends, for example, of 0.26 g C m−2 year−1 and 0.38 g C m−2 year−1 at Atqasuk. This study highlights the importance of zero-curtain periods in facilitating CH4 and CO2 emissions from tundra ecosystems.

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“…Permafrost variations, including pronounced permafrost degradation due to a warming climate, have been reported for many regions, including Alaska (Nicholas and Hinkel, 1996;Osterkamp and Romanovsky, 1996;Jorgenson et al, 2001;Hinkel and Nelson, 2003;Jafarov et al, 2012;Liu et al, 2012;Jones et al, 2016;Batir et al, 2017), Canada (Chen et al, 2003;James et al, 2013), Norway (Gisnas et al, 2013), Sweden (Pannetier and Frampton, 2016), Russia (Romanovsky et al, 2007(Romanovsky et al, , 2010, Mongolia (Sharkhuu and Sharkhuu, 2012), and the Qinghai-Tibet Plateau (Zhou et al, 2013;Wang et al, 2016a;Lu et al, 2017;Ran et al, 2018). For the entire Northern Hemisphere, rapidly accelerated permafrost degradation in recent years has been reported by Luo et al (2016) based on in situ measurements at a point scale or a spatially aggregated scale (up to 1000 m × 1000 m) from the Circumpolar Active Layer Monitoring (CALM) network.…”

Section: Introductionmentioning

confidence: 99%

“…Most of these land models were run at coarse spatial resolutions, e.g. ranging from 0.5 • × 0.5 • to 1.8 • × 3.6 • for LSMs participating in the Permafrost Carbon Network (PCN) (Wang et al, 2016a) and from 0.188 • × 0.188 • to 4.10 • × 5 • for the models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) (Koven et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Differences in the permafrost behaviour simulated with these models reflect model-specific process representations as well as biases associated with different meteorological forcing datasets (Barman and Jain, 2016;Wang et al, 2016a, b;Guimberteau et al, 2018). Such forcing biases are difficult to avoid given the sparsity of direct observations of meteorological variables in most parts of the high latitudes.…”

Section: Introductionmentioning

confidence: 99%

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Permafrost variability over the Northern Hemisphere based on the MERRA-2 reanalysis

et al. 2019

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Abstract. This study introduces and evaluates a comprehensive, model-generated dataset of Northern Hemisphere permafrost conditions at 81 km2 resolution. Surface meteorological forcing fields from the Modern-Era Retrospective Analysis for Research and Applications 2 (MERRA-2) reanalysis were used to drive an improved version of the land component of MERRA-2 in middle-to-high northern latitudes from 1980 to 2017. The resulting simulated permafrost distribution across the Northern Hemisphere mostly captures the observed extent of continuous and discontinuous permafrost but misses the ecosystem-protected permafrost zones in western Siberia. Noticeable discrepancies also appear along the southern edge of the permafrost regions where sporadic and isolated permafrost types dominate. The evaluation of the simulated active layer thickness (ALT) against remote sensing retrievals and in situ measurements demonstrates reasonable skill except in Mongolia. The RMSE (bias) of climatological ALT is 1.22 m (−0.48 m) across all sites and 0.33 m (−0.04 m) without the Mongolia sites. In northern Alaska, both ALT retrievals from airborne remote sensing for 2015 and the corresponding simulated ALT exhibit limited skill versus in situ measurements at the model scale. In addition, the simulated ALT has larger spatial variability than the remotely sensed ALT, although it agrees well with the retrievals when considering measurement uncertainty. Controls on the spatial variability of ALT are examined with idealized numerical experiments focusing on northern Alaska; meteorological forcing and soil types are found to have dominant impacts on the spatial variability of ALT, with vegetation also playing a role through its modulation of snow accumulation. A correlation analysis further reveals that accumulated above-freezing air temperature and maximum snow water equivalent explain most of the year-to-year variability of ALT nearly everywhere over the model-simulated permafrost regions.

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Evaluation and Enhancement of Permafrost Modeling With the NASA Catchment Land Surface Model

Cited by 11 publications

References 59 publications

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

Improved ELMv1-ECA Simulations of Zero-Curtain Periods and Cold-season CH<sub>4</sub> and CO<sub>2</sub> Emissions at Alaskan Arctic Tundra Sites

Permafrost variability over the Northern Hemisphere based on the MERRA-2 reanalysis

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Evaluation and Enhancement of Permafrost Modeling With the NASA Catchment Land Surface Model

Cited by 11 publications

References 59 publications

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

Spatial and Temporal Characteristics of Vegetation NDVI Changes and the Driving Forces in Mongolia during 1982–2015

Improved ELMv1-ECA Simulations of Zero-Curtain Periods and Cold-season CH&lt;sub&gt;4&lt;/sub&gt; and CO&lt;sub&gt;2&lt;/sub&gt; Emissions at Alaskan Arctic Tundra Sites

Permafrost variability over the Northern Hemisphere based on the MERRA-2 reanalysis

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Improved ELMv1-ECA Simulations of Zero-Curtain Periods and Cold-season CH<sub>4</sub> and CO<sub>2</sub> Emissions at Alaskan Arctic Tundra Sites