MétaCan
Menu
← Back to cohort
Record W3083704968 · doi:10.5194/essd-12-2013-2020

Heat stored in the Earth system: where does the energy go?

2020· article· en· W3083704968 on OpenAlexafffund
Karina von Schuckmann, Lijing Cheng, Matthew D. Palmer, James E. Hansen, Caterina Tassone, Valentin Aich, Susheel Adusumilli, Hugo Beltrami, Tim Boyer, Francisco José Cuesta‐Valero, Damien Desbruyères, Catia M. Domingues, Almudena García‐García, Pierre Gentine, John Gilson, Maximilian Gorfer, Leopold Haimberger, Masayoshi Ishii, Gregory C. Johnson, Rachel Killick, Brian King, Gottfried Kirchengast, Nicolas Kolodziejczyk, John M. Lyman, Ben Marzeion, Michael Mayer, Maeva Monier, Didier P. Monselesan, Sarah G. Purkey, Dean Roemmich, Axel Schweiger, Sonia I. Seneviratne, Andrew Shepherd, Donald Slater, Andrea K. Steiner, Fiammetta Straneo, Mary‐Louise Timmermans, Susan Wijffels

Bibliographic record

VenueEarth system science data · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsMemorial University of NewfoundlandSt. Francis Xavier University
FundersNatural Environment Research CouncilNatural Sciences and Engineering Research Council of CanadaScheme for Promotion of Academic and Research CollaborationNuclear Safety and Security CommissionMet OfficeSight Research UKAustrian Science FundCanada Research ChairsNational Science FoundationSt. Francis Xavier UniversityNational Aeronautics and Space AdministrationDepartment for Environment, Food and Rural Affairs, UK GovernmentMemorial University of Newfoundland
KeywordsEnvironmental scienceAtmosphere (unit)Global warmingClimatologyClimate changeClimate modelEarth system scienceAtmospheric sciencesCryosphereIce-albedo feedbackMeteorologySea iceGeologyGeographyOceanographyAntarctic sea ice

Abstract

fetched live from OpenAlex

Abstract. Human-induced atmospheric composition changes cause a radiative imbalance at the top-of-atmosphere which is driving global warming. This Earth Energy Imbalance (EEI) is a fundamental metric of climate change. Understanding the heat gain of the Earth system from this accumulated heat – and particularly how much and where the heat is distributed in the Earth system – is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This study is a Global Climate Observing System (GCOS) concerted international effort to update the Earth heat inventory, and presents an updated international assessment of ocean warming estimates, and new and updated estimates of heat gain in the atmosphere, cryosphere and land over the period 1960–2018. The study obtains a consistent long-term Earth system heat gain over the past 58 years, with a total heat gain of 393 ± 40 ZJ, which is equivalent to a heating rate of 0.42 ± 0.04 W m−2. The majority of the heat gain (89 %) takes place in the global ocean (0–700 m: 53 %; 700–2000 m: 28 %; > 2000 m: 8 %), while it amounts to 6 % for the land heat gain, to 4 % available for the melting of grounded and floating ice, and to 1 % for atmospheric warming. These new estimates indicate a larger contribution of land and ice heat gain (10 % in total) compared to previous estimates (7 %). There is a regime shift of the Earth heat inventory over the past 2 decades, which appears to be predominantly driven by heat sequestration into the deeper layers of the global ocean, and a doubling of heat gain in the atmosphere. However, a major challenge is to reduce uncertainties in the Earth heat inventory, which can be best achieved through the maintenance of the current global climate observing system, its extension into areas of gaps in the sampling, as well as to establish an international framework for concerted multi-disciplinary research of the Earth heat inventory. Earth heat inventory is published at DKRZ (https://www.dkrz.de/) under the doi: https://doi.org/10.26050/WDCC/GCOS_EHI_EXP (von Schuckmann et al., 2020).

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.004
Scholarly communication0.0060.009
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.002

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 head0.039
GPT teacher head0.238
Teacher spread0.200 · 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

Citations378
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueEarth system science data→Same topicClimate variability and models→French-language works237,207→