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Record W4400833731 · doi:10.5194/egusphere-2024-2087

Impacts of North American forest cover changes on the North Atlantic ocean circulation

2024· preprint· en· W4400833731 on OpenAlexaboutno aff
Victoria Bauer, Sebastian Schemm, Raphael Portmann, Jingzhi Zhang, Gesa K. Eirund, Steven De Hertog, Jan Zibell

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsnot available
FundersH2020 European Research CouncilSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsAtlantic multidecadal oscillationNorth Atlantic Deep WaterThermohaline circulationNorth Atlantic oscillationEnvironmental scienceDeforestation (computer science)Shutdown of thermohaline circulationClimatologyOceanographyGulf StreamOcean currentAtlantic Equatorial modeClimate changeSea surface temperatureGeology

Abstract

fetched live from OpenAlex

Abstract. Atmosphere-ocean heat fluxes in the North Atlantic Labrador Sea region are a key driver of deep water formation and the Atlantic Meridional Overturning Circulation (AMOC). Previous research has shown that anthropogenic warming leads to reduced ocean heat loss and thereby reduced deep mixing in the North Atlantic. This results in AMOC decline and causes regional cooling of sea surface temperatures (SSTs) which has been referred to as the North Atlantic warming hole (NAWH). Similar responses of the AMOC and the formation of a NAWH have been found for changes in wind stress and fresh water forcing in the North Atlantic. Moreover, recent research has also revealed such an AMOC and North Atlantic SST response in global-scale forestation experiments and a reversed response in deforestation experiments. Here, we test the hypothesis that forest cover changes in particular over North America are an important driver of this response in the downstream North Atlantic ocean. To this end, we perform simulations using the fully coupled Earth system model CESM2 where pre-industrial vegetation-sustaining areas over North America are either completely forested (forestNA) or turned into grasslands (grassNA), and compare it to the control scenario without any forest cover changes. Our results show that North American forestation and deforestation induce a North Atlantic warming and cooling hole, respectively. Furthermore, the response is qualitatively similar to previously published results based on global extreme land cover change scenarios. Forest cover changes mainly impact the ocean through modulating land surface albedo and, subsequently, air temperatures. Around 80 % of the ocean heat loss in the Labrador Sea occurs within comparably short-lived cold air outbreaks (CAOs) during which the atmosphere is colder than the underlying ocean. A warmer atmosphere in forestNA compared to the control scenario results in fewer CAOs over the ocean and thereby reduced ocean heat loss, with the opposite being true for grassNA. The induced SST responses further decrease CAO frequency in forestNA and increase it in grassNA. Lagrangian backward trajectories starting from CAOs over the Labrador Sea confirm that their source regions include (de-)forested areas. A closer inspection of the ocean circulation reveals that the subpolar gyre circulation is more sensitive to ocean density changes driven by heat fluxes than to changes in wind forcing modulated by land surface roughness. In forestNA, sea ice growth and the corresponding further reduction of ocean-to-atmosphere heat fluxes forms an additional positive feedback loop. Conversely, a buoyancy flux decomposition shows that freshwater forcing only plays a minor role for the ocean density response in both scenarios. Overall, this study shows that forest cover changes over North America alter the frequency of CAOs over the North Atlantic and, as a consequence, the circulation of the North Atlantic. This highlights the relevance of CAOs for the formation of North Atlantic SST anomalies.

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)0.000
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 score0.089
Threshold uncertainty score0.176

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.026
GPT teacher head0.244
Teacher spread0.218 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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