The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Future Arctic Ocean primary productivity from CMIP5 simulations: Uncertain outcome, but consistent mechanisms
Open accessAbstract
Net Arctic Ocean primary production (PP) is expected to increase over this century, due to less perennial sea ice and more available light, but could decrease depending on changes in nitrate (NO 3) supply. Here Coupled Model Intercomparison Project Phase 5 simulations performed with 11 Earth System Models are analyzed in terms of PP, surface NO 3, and sea ice coverage over 1900–2100. Whereas the mean model simulates reasonably well Arctic‐integrated PP (511 TgC/yr, 1998–2005) and projects a mild 58 TgC/yr increase by 2080–2099 for the strongest climate change scenario, models do not agree on the sign of future PP change. However, similar mechanisms operate in all models. The perennial ice loss‐driven increase in PP is in most models NO 3‐limited. The Arctic surface NO 3 is decreasing over the 21st century (−2.3 ± 1 mmol/m 3), associated with shoaling mixed layer and with decreasing NO 3 in the nearby North Atlantic and Pacific waters. However, the intermodel spread in the degree of NO 3 limitation is initially high, resulting from >1000 year spin‐up simulations. This initial NO 3 spread, combined with the trend, causes a large variation in the timing of oligotrophy onset—which directly controls the sign of future PP change. Virtually all models agree in the open ocean zones on more spatially integrated PP and less PP per unit area. The source of model uncertainty is located in the sea ice zone, where a subtle balance between light and nutrient limitations determines the PP change. Hence, it is argued that reducing uncertainty on present Arctic NO 3 in the sea ice zone would render Arctic PP projections much more consistent.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
An Overview of CMIP5 and the Experiment Design
Taylor, K. E., Stouffer, R. J., & Meehl, G. A. (2012) · Bulletin of the American Meteorological Society · doi:10.1175/bams-d-11-00094.1 · Cited by 14,052
The next generation of scenarios for climate change research and assessment
Moss, R. H., Edmonds, J. A., Hibbard, K. A., Manning, M. R., Rose, S. K., van Vuuren, D. P., Carter, T. R., Emori, S., Kainuma, M., Kram, T., et al. (2010) · Nature · doi:10.1038/nature08823 · Cited by 6,467
Cited by
Borealization of the Arctic Ocean in Response to Anomalous Advection From Sub-Arctic Seas
Polyakov, I. V., Alkire, M. B., Bluhm, B. A., Brown, K. A., Carmack, E. C., Chierici, M., Danielson, S. L., Ellingsen, I., Ershova, E. A., Gårdfeldt, K., et al. (2020) · Frontiers in Marine Science · doi:10.3389/fmars.2020.00491 · Cited by 394
Drivers and uncertainties of future global marine primary production in marine ecosystem models
Laufkötter, C., Vogt, M., Gruber, N., Aita-Noguchi, M., Aumont, O., Bopp, L., Buitenhuis, E., Doney, S. C., Dunne, J., Hashioka, T., et al. (2015) · Biogeosciences · doi:10.5194/bg-12-6955-2015 · Cited by 369
Self-citationRapid evolution of metabolic traits explains thermal adaptation in phytoplankton
Padfield, D., Yvon‐Durocher, G., Buckling, A., Jennings, S., & Yvon‐Durocher, G. (2016) · Ecology Letters · doi:10.1111/ele.12545 · Cited by 346
Leads in Arctic pack ice enable early phytoplankton blooms below snow-covered sea ice
Assmy, P., Fernández-Méndez, M., Duarte, P., Meyer, A., Randelhoff, A., Mundy, C. J., Olsen, L. M., Kauko, H. M., Bailey, A., Chierici, M., et al. (2017) · Scientific Reports · doi:10.1038/srep40850 · Cited by 339
Fu, W., Randerson, J. T., & Moore, J. K. (2016) · Biogeosciences · doi:10.5194/bg-13-5151-2016 · Cited by 279
Metadata
- Authors
- Vancoppenolle, M., Bopp, L., Madec, G., Dunne, J., Ilyina, T., Halloran, P. R., & Steiner, N.
- Venue
- Global Biogeochemical Cycles
- Publisher
- American Geophysical Union (AGU)
- Published
- 2013-07
- Volume
- 27
- Issue
- 3
- Pages
- 605-619
- ISSN
- 0886-6236 (ISSN-L)
- 1944-9224
- OpenAlex
- W1855007063
- Cited by
- 228
- References
- 2
- FWCI
- 9.38
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.