Comparison of effects of cold‐region soil/snow processes and the uncertainties from model forcing data on permafrost physical characteristics

Abstract

We used a land surface model to (1) evaluate the influence of recent improvements in modeling cold-region soil/snow physics on near-surface permafrost physical characteristics (within 0-3 m soil column) in the northern high latitudes (NHL) and (2) compare them with uncertainties from climate and landcover data sets. Specifically, four soil/snow processes are investigated: deep soil energetics, soil organic carbon (SOC) effects on soil properties, wind compaction of snow, and depth hoar formation. In the model, together they increased the contemporary NHL permafrost area by 9.2 3 10 6 km 2 (from 2.9 to 12.3-without and with these processes, respectively) and reduced historical degradation rates. In comparison, permafrost area using different climate data sets (with annual air temperature difference of 0.58C) differed by up to 2.3 3 10 6 km 2 , with minimal contribution of up to 0.7 3 10 6 km 2 from substantial land-cover differences.Individually, the strongest role in permafrost increase was from deep soil energetics, followed by contributions from SOC and wind compaction, while depth hoar decreased permafrost. The respective contribution on 0-3 m permafrost stability also followed a similar pattern. However, soil temperature and moisture within vegetation root zone (0-1 m), which strongly influence soil biogeochemistry, were only affected by the latter three processes. The ecosystem energy and water fluxes were impacted the least due to these soil/snow processes. While it is evident that simulated permafrost physical characteristics benefit from detailed treatment of cold-region biogeophysical processes, we argue that these should also lead to integrated improvements in modeling of biogeochemistry.

Effects of environmental and management factors on worldwide maize and soybean yields over the 20<sup>th</sup> and 21<sup>st</sup> centuries

“…(3) the curvature to the light response curve for the CO2 fertilization effect (Text S7), (4) nutrient (e.g., N) stress while allocating the assimilated carbon to leaf, root, stem, and grain pools (Text S8), and extreme heat stress effect during crop productivity stage of the penology (Text S9) ISAM has been extensively calibrated, validated, and evaluated for agricultural applications (Niyogi et al, 2015;Song et al, 2013Song et al, , 2015Song et al, , 2016 and in other different studies (Barman et al, 2014a(Barman et al, , 2014b(Barman et al, , 2016El-Masri et al, 2013Gahlot et al, 2017;Jain et al, 2006Jain et al, , 2013. In this study the modeled yields for two crops are further evaluated for elevated [CO2], climate, N input and irrigation effects using free-air concentration enrichment (FACE) experiments and other site-specific data sets, and published model results at specific sites, regional and global scales over historical time and under future scenarios.…”

Estimation of Permafrost SOC Stock and Turnover Time Using a Land Surface Model With Vertical Heterogeneity of Permafrost Soils

“…Although a few attempts have been made to improve TEMs' performance by incorporating a limited number of dynamic thaw processes, including better environmental conditions (e.g., soil temperature, moisture content, nutrient, and oxygen availability) and snowpack development (Barman & Jain, 2016; Schaefer et al, 2009), thermal insulation effect of moss (Chadburn et al, 2015), soil organic matter (SOM) (Lawrence et al, 2008), the zero‐curtain effect (Romanovsky & Osterkamp, 2000), anoxic respiration and the organo‐mineral interaction (Riley et al, 2011; Wania et al, 2010). A limited effort has also been made to implement a vertical dimension to the soil biogeochemistry model (e.g., National Center for Atmospheric Research's [NCAR's] Community Earth System Model, UVic ESCM, etc.)…”

Matrix‐Based Sensitivity Assessment of Soil Organic Carbon Storage: A Case Study from the ORCHIDEE‐MICT Model

“…The complexity and computational constraints on models limit our capacity in conducting thorough sensitivity analyses and tracking model behaviors. Improvements in modeling efficiency are becoming urgent as the slow, computationally expensive high-latitude (permafrost) processes are critical in land carbon studies and have been incorporated in a growing number of TBMs (Barman & Jain, 2016;Burke et al, 2017;Chaudhary et al, 2017;Ekici et al, 2014;Guimberteau et al, 2018;Koven et al, 2013;Mcguire et al, 2016;Wania et al, 2009).…”

“…Carbon stored in the permafrost region is highly vulnerable and sensitive to warming as thawing permafrost exposes a large amount of SOC to decomposition and plays an important role in feeding back to future climate change with the release of greenhouse gases such as carbon dioxide and methane (Elberling et al, 2013;Schuur et al, 2015). As a result, a growing number of TBMs started to explicitly incorporate processes that are unique to permafrost regions (Barman & Jain, 2016;Burke et al, 2017;Chaudhary et al, 2017;Ekici et al, 2014;Guimberteau et al, 2018;Koven et al, 2013;Mcguire et al, 2016;Wania et al, 2009). Permafrost regions have distinctive vertical soil carbon dynamics and contain a large amount of soil carbon below the 1-m depth routinely studied for middle and low latitudes (Hugelius et al, 2013).…”