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Record W4289341919 · doi:10.5194/essd-2022-239

Heat stored in the Earth system 1960–2020: Where does the energy go?

2022· preprint· en· W4289341919 on OpenAlexaff
Karina von Schuckmann, Audrey Minère, Flora Gues, Francisco José Cuesta‐Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Richard P. Allan, Paul M. Barker, Hugo Beltrami, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyères, A. J. Dolman, Catia M. Domingues, Almudena García‐García, Donata Giglio, John Gilson, Maximilian Gorfer, Leopold Haimberger, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nikolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Moritz Langer, Thomas Lavergne, Isobel R. Lawrence, John M. Lyman, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor J. McDougall, Didier P. Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah G. Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald Slater, Thomas Slater, Noah Smith, Andrea K. Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary‐Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, Michael Zemp

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicClimate variability and models
Canadian institutionsSt. Francis Xavier University
FundersNatural Environment Research CouncilSight Research UKDeutsches Klimarechenzentrum
KeywordsCryosphereEarth system scienceClimate changeEnvironmental scienceGlobal warmingAtmosphere (unit)ClimatologyEarth's energy budgetClimate commitmentEnergy balanceEarth scienceAtmospheric sciencesMeteorologyEffects of global warmingGeographyGeologySea iceOceanographyEcology

Abstract

fetched live from OpenAlex

Abstract. The Earth climate system is out of energy balance and heat has accumulated continuously over the past decades, warming the ocean, the land, the cryosphere and the atmosphere. According to the 6th Assessment Report of the Intergovernmental Panel on Climate Change, this planetary warming over multiple decades is human-driven and results in unprecedented and committed changes to the Earth system, with adverse impacts for ecosystems and human systems. The Earth heat inventory provides a measure of the Earth energy imbalance, and allows for quantifying how much heat has accumulated in the Earth system, and where the heat is stored. Here we show that 380 ± 62 ZJ of heat has accumulated in the Earth system from 1971 to 2020, at a rate of 0.48 ± 0.1 W m−2, with 89 ± 17 % of this heat stored in the ocean, 6 ± 0.1 % on land, 4 ± 1 % in the cryosphere and 1 ± 0.2 % in the atmosphere. Over the most recent decade (2006–2020), the Earth heat inventory shows increased warming at rate of 0.48 ± 0.3 W m−2/decade, and the Earth climate system is out of energy balance by 0.76 ± 0.2 Wm−2. The Earth heat inventory is the most fundamental global climate indicator that the scientific community and the public can use as the measure of how well the world is doing in the task of bringing anthropogenic climate change under control. We call for an implementation of the Earth heat inventory into the Paris agreement’s global stocktake based on best available science. The Earth heat inventory in this study, updated from von Schuckmann et al, 2020, is underpinned by worldwide multidisciplinary collaboration and demonstrates the critical importance of concerted international efforts for climate change monitoring and community-based recommendations as coordinated by the Global Climate Observing System (GCOS). We also call for urgently needed actions for enabling continuity, archiving, rescuing and calibrating efforts to assure improved and long-term monitoring capacity of the relevant GCOS Essential Climate Variables (ECV) for the Earth heat inventory.

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)0.003
metaresearch head score (Gemma)0.007
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 score0.048
Threshold uncertainty score0.095

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0010.002
Scholarly communication0.0040.005
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0150.004

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 head0.014
GPT teacher head0.219
Teacher spread0.205 · 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

Citations10
Published2022
Admission routes1
Has abstractyes

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