An optimal transformation method applied to diagnose the ocean carbon budget Article Swipe
Neill Mackay
,
Taimoor Sohail
,
Jan D. Zika
,
Richard G. Williams
,
Oliver Andrews
,
Andrew Watson
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/gmd-17-5987-2024
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.5194/gmd-17-5987-2024
The ocean carbon sink plays a critical role in climate, absorbing anthropogenic carbon from the atmosphere and mitigating climate change. The sink shows significant variability on decadal timescales, but estimates from models and observations disagree with one another, raising uncertainty over the magnitude of the sink, its variability, and its driving mechanisms. There is a need to reconcile observation-based estimates of air–sea CO2 fluxes with those of the changing ocean carbon inventory in order to improve our understanding of the sink, and doing so requires knowledge of how carbon is transported within the interior by the ocean circulation. Here we employ a recently developed optimal transformation method (OTM) that uses water-mass theory to relate interior changes in tracer distributions to transports and mixing and boundary forcings, and we extend its application to include carbon using synthetic data. We validate the method using model outputs from a biogeochemical state estimate, and we test its ability to recover boundary carbon fluxes and interior transports consistent with changes in heat, salt, and carbon. Our results show that the OTM effectively reconciles boundary carbon fluxes with interior carbon distributions when given a range of prior fluxes. The OTM shows considerable skill in its reconstructions, reducing root-mean-squared errors from biased priors between model “truth” and reconstructed boundary carbon fluxes by up to 71 %, with the bias of the reconstructions consistently ≤0.06 molCm-2yr-1 globally. Inter-basin transports of carbon also compare well with the model truth, with residuals <0.25 Pg C yr−1 for reconstructions produced using a range of priors. The OTM has significant potential for application to reconcile observational estimates of air–sea CO2 fluxes with the interior accumulation of anthropogenic carbon.
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- https://doi.org/10.5194/gmd-17-5987-2024
- OA Status
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- References
- 53
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https://doi.org/10.5194/gmd-17-5987-2024Digital Object Identifier
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An optimal transformation method applied to diagnose the ocean carbon budgetWork title
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articleOpenAlex work type
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2024Year of publication
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2024-08-13Full publication date if available
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Neill Mackay, Taimoor Sohail, Jan D. Zika, Richard G. Williams, Oliver Andrews, Andrew WatsonList of authors in order
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https://doi.org/10.5194/gmd-17-5987-2024Publisher landing page
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goldOpen access status per OpenAlex
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https://doi.org/10.5194/gmd-17-5987-2024Direct OA link when available
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Transformation (genetics), Environmental science, Carbon fibers, Climatology, Computer science, Chemistry, Geology, Algorithm, Composite number, Biochemistry, GeneTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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| abstract_inverted_index.fluxes | 64, 159, 181, 214, 269 |
| abstract_inverted_index.method | 107, 141 |
| abstract_inverted_index.mixing | 123 |
| abstract_inverted_index.models | 32 |
| abstract_inverted_index.priors | 206 |
| abstract_inverted_index.relate | 114 |
| abstract_inverted_index.theory | 112 |
| abstract_inverted_index.tracer | 118 |
| abstract_inverted_index.truth, | 240 |
| abstract_inverted_index.within | 92 |
| abstract_inverted_index.yr−1 | 246 |
| abstract_inverted_index.ability | 154 |
| abstract_inverted_index.between | 207 |
| abstract_inverted_index.carbon. | 170, 276 |
| abstract_inverted_index.change. | 20 |
| abstract_inverted_index.changes | 116, 165 |
| abstract_inverted_index.climate | 19 |
| abstract_inverted_index.compare | 235 |
| abstract_inverted_index.decadal | 27 |
| abstract_inverted_index.driving | 51 |
| abstract_inverted_index.fluxes. | 192 |
| abstract_inverted_index.improve | 76 |
| abstract_inverted_index.include | 133 |
| abstract_inverted_index.optimal | 105 |
| abstract_inverted_index.outputs | 144 |
| abstract_inverted_index.priors. | 254 |
| abstract_inverted_index.raising | 39 |
| abstract_inverted_index.recover | 156 |
| abstract_inverted_index.results | 172 |
| abstract_inverted_index.≤0.06 | 227 |
| abstract_inverted_index.<0.25 | 243 |
| abstract_inverted_index.another, | 38 |
| abstract_inverted_index.boundary | 125, 157, 179, 212 |
| abstract_inverted_index.changing | 69 |
| abstract_inverted_index.climate, | 10 |
| abstract_inverted_index.critical | 7 |
| abstract_inverted_index.disagree | 35 |
| abstract_inverted_index.interior | 94, 115, 161, 183, 272 |
| abstract_inverted_index.produced | 249 |
| abstract_inverted_index.recently | 103 |
| abstract_inverted_index.reducing | 201 |
| abstract_inverted_index.requires | 85 |
| abstract_inverted_index.validate | 139 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.absorbing | 11 |
| abstract_inverted_index.air–sea | 62, 267 |
| abstract_inverted_index.developed | 104 |
| abstract_inverted_index.estimate, | 149 |
| abstract_inverted_index.estimates | 30, 60, 265 |
| abstract_inverted_index.forcings, | 126 |
| abstract_inverted_index.globally. | 229 |
| abstract_inverted_index.inventory | 72 |
| abstract_inverted_index.knowledge | 86 |
| abstract_inverted_index.magnitude | 43 |
| abstract_inverted_index.potential | 259 |
| abstract_inverted_index.reconcile | 58, 263 |
| abstract_inverted_index.residuals | 242 |
| abstract_inverted_index.synthetic | 136 |
| abstract_inverted_index.atmosphere | 16 |
| abstract_inverted_index.consistent | 163 |
| abstract_inverted_index.mitigating | 18 |
| abstract_inverted_index.reconciles | 178 |
| abstract_inverted_index.transports | 121, 162, 231 |
| abstract_inverted_index.water-mass | 111 |
| abstract_inverted_index.Inter-basin | 230 |
| abstract_inverted_index.application | 131, 261 |
| abstract_inverted_index.effectively | 177 |
| abstract_inverted_index.mechanisms. | 52 |
| abstract_inverted_index.molCm-2yr-1 | 228 |
| abstract_inverted_index.significant | 24, 258 |
| abstract_inverted_index.timescales, | 28 |
| abstract_inverted_index.transported | 91 |
| abstract_inverted_index.uncertainty | 40 |
| abstract_inverted_index.variability | 25 |
| abstract_inverted_index.“truth” | 209 |
| abstract_inverted_index.accumulation | 273 |
| abstract_inverted_index.circulation. | 98 |
| abstract_inverted_index.considerable | 196 |
| abstract_inverted_index.consistently | 226 |
| abstract_inverted_index.observations | 34 |
| abstract_inverted_index.variability, | 48 |
| abstract_inverted_index.anthropogenic | 12, 275 |
| abstract_inverted_index.distributions | 119, 185 |
| abstract_inverted_index.observational | 264 |
| abstract_inverted_index.reconstructed | 211 |
| abstract_inverted_index.understanding | 78 |
| abstract_inverted_index.biogeochemical | 147 |
| abstract_inverted_index.transformation | 106 |
| abstract_inverted_index.reconstructions | 225, 248 |
| abstract_inverted_index.reconstructions, | 200 |
| abstract_inverted_index.observation-based | 59 |
| abstract_inverted_index.root-mean-squared | 202 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/14 |
| sustainable_development_goals[0].score | 0.6800000071525574 |
| sustainable_development_goals[0].display_name | Life below water |
| citation_normalized_percentile.value | 0.18489188 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |