Marginal ice zone fraction benchmarks sea ice and climate model skill
Nature Communications, ISSN: 2041-1723, Vol: 12, Issue: 1, Page: 2221
2021
- 24Citations
- 32Captures
- 1Mentions
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations24
- Citation Indexes24
- 24
- CrossRef5
- Captures32
- Readers32
- 32
- Mentions1
- News Mentions1
- 1
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Marginal ice zone fraction benchmarks sea ice and climate model skill
Global climate models (GCMs) consistently underestimate the response of September Arctic sea-ice area (SIA) to warming. Modeled SIA losses are highly correlated to global mean
Article Description
Global climate models (GCMs) consistently underestimate the response of September Arctic sea-ice area (SIA) to warming. Modeled SIA losses are highly correlated to global mean temperature increases, making it challenging to gauge if improvements in modeled sea ice derive from improved sea-ice models or from improvements in forcing driven by other GCM components. I use a set of five large GCM ensembles, and CMIP6 simulations, to quantify GCM internal variability and variability between GCMs from 1979–2014, showing modern GCMs do not plausibly estimate the response of SIA to warming in all months. I identify the marginal ice zone fraction (MIZF) as a metric that is less correlated to warming, has a response plausibly simulated from January–September (but not October–December), and has highly variable future projections across GCMs. These qualities make MIZF useful for evaluating the impact of sea-ice model changes on past, present, and projected sea-ice state.
Bibliographic Details
Springer Science and Business Media LLC
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