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Arctic and boreal ecosystems of western North America as components of the climate system

2000· article· en· W2129214137 on OpenAlexaff
F. Stuart Chapin, A. D. McGuire, J. T. Randerson, R. A. Pielke, Dennis Baldocchi, Sarah E. Hobbie, Nigel T. Roulet, Werner Eugster, Eric S. Kasischke, Edward B. Rastetter, S. A. Zimov, Steven W. Running

Bibliographic record

VenueGlobal Change Biology · 2000
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMcGill University
Fundersnot available
KeywordsTundraTaigaEnvironmental scienceBorealBiomeAlbedo (alchemy)Boreal ecosystemArcticEvapotranspirationClimatologyClimate changeEcosystemSensible heatThermokarstGlobal warmingEcologyPhysical geographyAtmospheric sciencesGeographyGeology

Abstract

fetched live from OpenAlex

Summary Synthesis of results from several Arctic and boreal research programmes provides evidence for the strong role of high‐latitude ecosystems in the climate system. Average surface air temperature has increased 0.3 °C per decade during the twentieth century in the western North American Arctic and boreal forest zones. Precipitation has also increased, but changes in soil moisture are uncertain. Disturbance rates have increased in the boreal forest; for example, there has been a doubling of the area burned in North America in the past 20 years. The disturbance regime in tundra may not have changed. Tundra has a 3–6‐fold higher winter albedo than boreal forest, but summer albedo and energy partitioning differ more strongly among ecosystems within either tundra or boreal forest than between these two biomes. This indicates a need to improve our understanding of vegetation dynamics within, as well as between, biomes. If regional surface warming were to continue, changes in albedo and energy absorption would likely act as a positive feedback to regional warming due to earlier melting of snow and, over the long term, the northward movement of treeline. Surface drying and a change in dominance from mosses to vascular plants would also enhance sensible heat flux and regional warming in tundra. In the boreal forest of western North America, deciduous forests have twice the albedo of conifer forests in both winter and summer, 50–80% higher evapotranspiration, and therefore only 30–50% of the sensible heat flux of conifers in summer. Therefore, a warming‐induced increase in fire frequency that increased the proportion of deciduous forests in the landscape, would act as a negative feedback to regional warming. Changes in thermokarst and the aerial extent of wetlands, lakes, and ponds would alter high‐latitude methane flux. There is currently a wide discrepancy among estimates of the size and direction of CO2flux between high‐latitude ecosystems and the atmosphere. These discrepancies relate more strongly to the approach and assumptions for extrapolation than to inconsistencies in the underlying data. Inverse modelling from atmospheric CO2concentrations suggests that high latitudes are neutral or net sinks for atmospheric CO2, whereas field measurements suggest that high latitudes are neutral or a net CO2source. Both approaches rely on assumptions that are difficult to verify. The most parsimonious explanation of the available data is that drying in tundra and disturbance in boreal forest enhance CO2efflux. Nevertheless, many areas of both tundra and boreal forests remain net sinks due to regional variation in climate and local variation in topographically determined soil moisture. Improved understanding of the role of high‐latitude ecosystems in the climate system requires a concerted research effort that focuses on geographical variation in the processes controlling land–atmosphere exchange, species composition, and ecosystem structure. Future studies must be conducted over a long enough time‐period to detect and quantify ecosystem feedbacks.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.908
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.038
GPT teacher head0.244
Teacher spread0.206 · 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 designObservational
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

Citations635
Published2000
Admission routes1
Has abstractyes

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