Origin and effects of resolution–dependent discretization biases in ocean biogeochemical simulations Article Swipe
Khan Muhammad Hassan
,
Paparella, Francesco
,
Vichi, Marcello
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17720865
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17720865
Dataset Contents This dataset contains the complete model inputs at all three resolutions needed to replicate the results for the corresponding paper, as well as a Jupyter Notebook detailing all the analysis done that is included in the aforementioned paper. Simple scripts are also provided to compile and run the model. Overview We investigate biases generated in numerical models of ocean biogeochemistry, which are the discrepancies between the mathematical solution and the numerical approximation that is obtained at a given spatial resolution. We propose separating them into biases stemming from unresolved fluctuations in the velocity field and from unresolved patchiness in the biological fields. Although the first has previously been singled out as a significant source of bias, we find that the second is the most common source. We use a realistic physical model to simulate the same open–ocean dynamics at eddy–permitting, eddy–resolving and submesoscale–resolving resolutions (16 km, 4 km, 1 km). The model is coupled with a nutrient–phytoplankton biogeochemical model which is simple enough to allow for easy interpretation of the results but retains the main nonlinear features of larger models. We show that unresolved patchiness may produce biases of either sign that may confound the interpretation of the model against observations of real–ocean processes. Co–located nutrient and phytoplankton lead to an underestimation of growth on coarser grids; conversely, segregated nutrient and phytoplankton lead to an overestimation of growth. In certain conditions, biases of different sign may concurrently occur in different regions, implying that domain averages may lead to bias compensation and hence to reduce the sensitivity to resolution enhancement. Our results suggest that eddy diffusivities may do more harm than good in the presence of nonlinear biological interactions, unless biological patchiness is adequately resolved. We conclude suggesting that the difficulties highlighted above may require to move away from grid-based simulations, and embrace a Lagrangian approach to discretize ocean biochemical models.
Related Topics
Concepts
Replicate
Discretization
Biogeochemical cycle
Environmental science
Field (mathematics)
Sign (mathematics)
Phytoplankton
Climate model
Lead (geology)
Computer science
Predictability
Nonlinear system
Domain (mathematical analysis)
Interpretation (philosophy)
Biogeochemistry
Simple (philosophy)
Statistical physics
Econometrics
Geophysics
Bootstrapping (finance)
Numerical models
Geology
Ocean dynamics
Autocovariance
Lag
Residual
Meteorology
Sampling (signal processing)
Current (fluid)
Climatology
Metadata
- Type
- other
- Landing Page
- https://doi.org/10.5281/zenodo.17720865
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7106813118
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7106813118Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.17720865Digital Object Identifier
- Title
-
Origin and effects of resolution–dependent discretization biases in ocean biogeochemical simulationsWork title
- Type
-
otherOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
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2025-11-26Full publication date if available
- Authors
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Khan Muhammad Hassan, Paparella, Francesco, Vichi, MarcelloList of authors in order
- Landing page
-
https://doi.org/10.5281/zenodo.17720865Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.5281/zenodo.17720865Direct OA link when available
- Concepts
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Replicate, Discretization, Biogeochemical cycle, Environmental science, Field (mathematics), Sign (mathematics), Phytoplankton, Climate model, Lead (geology), Computer science, Predictability, Nonlinear system, Domain (mathematical analysis), Interpretation (philosophy), Biogeochemistry, Simple (philosophy), Statistical physics, Econometrics, Geophysics, Bootstrapping (finance), Numerical models, Geology, Ocean dynamics, Autocovariance, Lag, Residual, Meteorology, Sampling (signal processing), Current (fluid), ClimatologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.most | 125 |
| abstract_inverted_index.move | 297 |
| abstract_inverted_index.same | 137 |
| abstract_inverted_index.show | 183 |
| abstract_inverted_index.sign | 192, 236 |
| abstract_inverted_index.than | 271 |
| abstract_inverted_index.that | 33, 74, 120, 184, 193, 244, 264, 289 |
| abstract_inverted_index.them | 85 |
| abstract_inverted_index.well | 23 |
| abstract_inverted_index.with | 156 |
| abstract_inverted_index.above | 293 |
| abstract_inverted_index.allow | 166 |
| abstract_inverted_index.bias, | 117 |
| abstract_inverted_index.field | 95 |
| abstract_inverted_index.first | 106 |
| abstract_inverted_index.given | 79 |
| abstract_inverted_index.hence | 253 |
| abstract_inverted_index.model | 7, 133, 153, 160, 200 |
| abstract_inverted_index.occur | 239 |
| abstract_inverted_index.ocean | 60, 309 |
| abstract_inverted_index.three | 11 |
| abstract_inverted_index.which | 62, 161 |
| abstract_inverted_index.Simple | 40 |
| abstract_inverted_index.biases | 54, 87, 189, 233 |
| abstract_inverted_index.common | 126 |
| abstract_inverted_index.domain | 245 |
| abstract_inverted_index.either | 191 |
| abstract_inverted_index.enough | 164 |
| abstract_inverted_index.grids; | 218 |
| abstract_inverted_index.growth | 215 |
| abstract_inverted_index.inputs | 8 |
| abstract_inverted_index.larger | 180 |
| abstract_inverted_index.model. | 50 |
| abstract_inverted_index.models | 58 |
| abstract_inverted_index.needed | 13 |
| abstract_inverted_index.paper, | 21 |
| abstract_inverted_index.paper. | 39 |
| abstract_inverted_index.reduce | 255 |
| abstract_inverted_index.second | 122 |
| abstract_inverted_index.simple | 163 |
| abstract_inverted_index.source | 115 |
| abstract_inverted_index.unless | 280 |
| abstract_inverted_index.Dataset | 0 |
| abstract_inverted_index.Jupyter | 26 |
| abstract_inverted_index.against | 201 |
| abstract_inverted_index.between | 66 |
| abstract_inverted_index.certain | 231 |
| abstract_inverted_index.coarser | 217 |
| abstract_inverted_index.compile | 46 |
| abstract_inverted_index.coupled | 155 |
| abstract_inverted_index.dataset | 3 |
| abstract_inverted_index.embrace | 303 |
| abstract_inverted_index.fields. | 103 |
| abstract_inverted_index.growth. | 229 |
| abstract_inverted_index.models. | 181, 311 |
| abstract_inverted_index.produce | 188 |
| abstract_inverted_index.propose | 83 |
| abstract_inverted_index.require | 295 |
| abstract_inverted_index.results | 17, 172, 262 |
| abstract_inverted_index.retains | 174 |
| abstract_inverted_index.scripts | 41 |
| abstract_inverted_index.singled | 110 |
| abstract_inverted_index.source. | 127 |
| abstract_inverted_index.spatial | 80 |
| abstract_inverted_index.suggest | 263 |
| abstract_inverted_index.Although | 104 |
| abstract_inverted_index.Contents | 1 |
| abstract_inverted_index.Notebook | 27 |
| abstract_inverted_index.Overview | 51 |
| abstract_inverted_index.analysis | 31 |
| abstract_inverted_index.approach | 306 |
| abstract_inverted_index.averages | 246 |
| abstract_inverted_index.complete | 6 |
| abstract_inverted_index.conclude | 287 |
| abstract_inverted_index.confound | 195 |
| abstract_inverted_index.contains | 4 |
| abstract_inverted_index.dynamics | 139 |
| abstract_inverted_index.features | 178 |
| abstract_inverted_index.implying | 243 |
| abstract_inverted_index.included | 35 |
| abstract_inverted_index.nutrient | 207, 221 |
| abstract_inverted_index.obtained | 76 |
| abstract_inverted_index.physical | 132 |
| abstract_inverted_index.presence | 275 |
| abstract_inverted_index.provided | 44 |
| abstract_inverted_index.regions, | 242 |
| abstract_inverted_index.simulate | 135 |
| abstract_inverted_index.solution | 69 |
| abstract_inverted_index.stemming | 88 |
| abstract_inverted_index.velocity | 94 |
| abstract_inverted_index.detailing | 28 |
| abstract_inverted_index.different | 235, 241 |
| abstract_inverted_index.generated | 55 |
| abstract_inverted_index.nonlinear | 177, 277 |
| abstract_inverted_index.numerical | 57, 72 |
| abstract_inverted_index.realistic | 131 |
| abstract_inverted_index.replicate | 15 |
| abstract_inverted_index.resolved. | 285 |
| abstract_inverted_index.Lagrangian | 305 |
| abstract_inverted_index.adequately | 284 |
| abstract_inverted_index.biological | 102, 278, 281 |
| abstract_inverted_index.discretize | 308 |
| abstract_inverted_index.grid-based | 300 |
| abstract_inverted_index.patchiness | 99, 186, 282 |
| abstract_inverted_index.previously | 108 |
| abstract_inverted_index.processes. | 205 |
| abstract_inverted_index.resolution | 259 |
| abstract_inverted_index.segregated | 220 |
| abstract_inverted_index.separating | 84 |
| abstract_inverted_index.suggesting | 288 |
| abstract_inverted_index.unresolved | 90, 98, 185 |
| abstract_inverted_index.biochemical | 310 |
| abstract_inverted_index.conditions, | 232 |
| abstract_inverted_index.conversely, | 219 |
| abstract_inverted_index.highlighted | 292 |
| abstract_inverted_index.investigate | 53 |
| abstract_inverted_index.resolution. | 81 |
| abstract_inverted_index.resolutions | 12, 145 |
| abstract_inverted_index.sensitivity | 257 |
| abstract_inverted_index.significant | 114 |
| abstract_inverted_index.Co–located | 206 |
| abstract_inverted_index.compensation | 251 |
| abstract_inverted_index.concurrently | 238 |
| abstract_inverted_index.difficulties | 291 |
| abstract_inverted_index.enhancement. | 260 |
| abstract_inverted_index.fluctuations | 91 |
| abstract_inverted_index.mathematical | 68 |
| abstract_inverted_index.observations | 202 |
| abstract_inverted_index.open–ocean | 138 |
| abstract_inverted_index.real–ocean | 204 |
| abstract_inverted_index.simulations, | 301 |
| abstract_inverted_index.approximation | 73 |
| abstract_inverted_index.corresponding | 20 |
| abstract_inverted_index.diffusivities | 266 |
| abstract_inverted_index.discrepancies | 65 |
| abstract_inverted_index.interactions, | 279 |
| abstract_inverted_index.phytoplankton | 209, 223 |
| abstract_inverted_index.aforementioned | 38 |
| abstract_inverted_index.biogeochemical | 159 |
| abstract_inverted_index.interpretation | 169, 197 |
| abstract_inverted_index.overestimation | 227 |
| abstract_inverted_index.underestimation | 213 |
| abstract_inverted_index.biogeochemistry, | 61 |
| abstract_inverted_index.eddy–resolving | 142 |
| abstract_inverted_index.eddy–permitting, | 141 |
| abstract_inverted_index.nutrient–phytoplankton | 158 |
| abstract_inverted_index.submesoscale–resolving | 144 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |