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Record W2126168892 · doi:10.5194/tc-10-179-2016

Simulated high-latitude soil thermal dynamics during the past 4 decades

2016· article· en· W2126168892 on OpenAlexaff
Shushi Peng, Philippe Ciais, Gerhard Krinner, T. Wang, Isabelle Gouttevin, A. David McGuire, David M. Lawrence, Eleanor Burke, Xiaodong Chen, Bertrand Decharme, Charles D. Koven, Andrew H. MacDougall, Annette Rinke, Kazuyuki Saitô, Wenxin Zhang, Ramdane Alkama, T. J. Bohn, Christine Delire, T. Hajima, Duoying Ji, Dennis P. Lettenmaier, Paul Miller, John C. Moore, Benjamin Smith, Tetsuo Sueyoshi

Bibliographic record

Venue˜The œcryosphere · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Victoria
FundersEuropean CommissionAgence Nationale de la RechercheNational Science Foundation
KeywordsPermafrostEnvironmental scienceForcing (mathematics)ClimatologyBiogeochemical cycleAtmospheric sciencesRadiative forcingClimate changeActive layerSoil carbonEcosystemPhysical geographySoil waterSoil scienceGeologyEcologyLayer (electronics)OceanographyGeography

Abstract

fetched live from OpenAlex

Soil temperature ( T s ) change is a key indicator of the dynamics of permafrost. On seasonal and interannual timescales, the variability of T s determines the active-layer depth, which regulates hydrological soil properties and biogeochemical processes. On the multi-decadal scale, increasing T s not only drives permafrost thaw/retreat but can also trigger and accelerate the decomposition of soil organic carbon. The magnitude of permafrost carbon feedbacks is thus closely linked to the rate of change of soil thermal regimes. In this study, we used nine process-based ecosystem models with permafrost processes, all forced by different observation-based climate forcing during the period 1960–2000, to characterize the warming rate of T s in permafrost regions. There is a large spread of T s trends at 20 cm depth across the models, with trend values ranging from 0.010 ± 0.003 to 0.031 ± 0.005 °C yr −1 . Most models show smaller increase in T s with increasing depth. Air temperature ( T sub>a) and longwave downward radiation (LWDR) are the main drivers of T s trends, but their relative contributions differ amongst the models. Different trends of LWDR used in the forcing of models can explain 61 % of their differences in T s trends, while trends of T a only explain 5 % of the differences in T s trends. Uncertain climate forcing contributes a larger uncertainty in T s trends (0.021 ± 0.008 °C yr −1 , mean ± standard deviation) than the uncertainty of model structure (0.012 ± 0.001 °C yr −1 ), diagnosed from the range of response between different models, normalized to the same forcing. In addition, the loss rate of near-surface permafrost area, defined as total area where the maximum seasonal active-layer thickness (ALT) is less than 3 m loss rate, is found to be significantly correlated with the magnitude of the trends of T s at 1 m depth across the models ( R = −0.85, P = 0.003), but not with the initial total near-surface permafrost area ( R = −0.30, P = 0.438). The sensitivity of the total boreal near-surface permafrost area to T s at 1 m is estimated to be of −2.80 ± 0.67 million km 2 °C −1 . Finally, by using two long-term LWDR data sets and relationships between trends of LWDR and T s across models, we infer an observation-constrained total boreal near-surface permafrost area decrease comprising between 39 ± 14 × 10 3 and 75 ± 14 × 10 3 km 2 yr −1 from 1960 to 2000. This corresponds to 9–18 % degradation of the current permafrost area.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.205
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations42
Published2016
Admission routes1
Has abstractyes

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