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Record W4362554841 · doi:10.5194/bg-2023-60

Spatiotemporal heterogeneity in the increase of ocean acidity extremes in the Northeast Pacific

2023· preprint· en· W4362554841 on OpenAlexaboutno aff
Flora Desmet, Matthias Münnich, Nicolas Gruber

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
FundersEidgenössische Technische Hochschule ZürichEuropean CommissionSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungNational Science Foundation
KeywordsHindcastBiogeochemical cycleEnvironmental scienceSpatial distributionAtmosphere (unit)ClimatologyBaseline (sea)PercentileEcosystemAtmospheric sciencesOceanographyGeographyMeteorologyGeologyChemistryEnvironmental chemistryMathematicsEcologyBiology

Abstract

fetched live from OpenAlex

Abstract. The acidification of the ocean (OA) increases the frequency and intensity of ocean acidity extreme events (OAXs), but this increase is not occurring homogeneously in time and space. Here we use daily output from a hindcast simulation with a high-resolution regional ocean model coupled to a biogeochemical-ecosystem model (ROMS-BEC) to investigate this heterogeneity in the progression of OAX in the upper 250 m of the Northeast Pacific from 1984 to 2019. We focus on the temporal and spatial changes in OAX using a relative threshold approach and using a fixed baseline reflecting the initial conditions. Concretely, conditions are considered extreme when the local hydrogen ion concentration ([H+]) exceeds the 99th percentile of the [H+] distribution of the baseline simulation where atmospheric CO2 was held at its 1979 level. Within the 36 years of our hindcast simulation, the increase in atmospheric CO2 causes a strong increase in OAX throughout the upper 250 m, but most accentuated near the surface. On average across the entire Northeast Pacific, for every additional 10 μatm of CO2 in the atmosphere, OAXs occupy an additional 6.3 % of the upper 250 m depth, last 7.6 days longer, and are 0.18 nmol L−1 (~ −0.006 pH units) more intense. This causes the OAXs to occupy at the end of the simulation a more than 10-times larger volume. The more than 11-fold increase in length, and the strong increase in the number of extreme days per year causes 88 % of the surface area in 2019 to experience near permanent extreme conditions. Finally, the model simulates a more than 6-fold intensification of the OAXs, causing also the intensity of the events with return periods of 10 years or more to increase by more than 80 %. Superimposed on these overall trends are very substantial spatial and temporal differences in these changes. The fraction of the volume identified as extreme across the top 250 m increases in the Central Northeast Pacific up to 160-times, while the deeper layers of the nearshore regions experience "only" a 4-fold increase. Throughout the upper 50 m of the Northeast Pacific, OAXs increase relatively linearly with time, but sudden rapid increases in yearly extreme days and OAX duration are simulated to occur in the thermocline of the Central Northeast Pacific. These differences largely emerge from the large spatial differences in the magnitude and nature of variability in [H+], with the transition between the rather variable thermocline waters of the Offshore Northeast Pacific and the very stable waters of the Central Northeast Pacific causing a very sharp transition in the occurrence of OAX. This transition is caused by the limited offshore reach of offshore propagating eddies that are the dominant driver of OAX in the Northeast Pacific. As the OAXs become more extreme, more of them also become undersaturated with respect to aragonite (ΩA < 1), i.e., become corrosive. In the final year of our hindcast, we find that below 100 m OAXs are characterized by corrosive conditions across a wide stretch of the region offshore of the U.S. and Canadian Coasts. The spatially and temporal heterogeneous increases in OAX, including the abrupt appearance of extremes, likely have negative effects on the ability of marine organisms to adapt to the progression of OA and its associated extremes.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.018
Threshold uncertainty score0.990

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.059
GPT teacher head0.268
Teacher spread0.209 · 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 teacher head, 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

Citations4
Published2023
Admission routes1
Has abstractyes

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