| 1. |
- Abrahamsen, E. Povl, et al.
(author)
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ANTARCTICA AND THE SOUTHERN OCEAN
- 2020
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In: BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY. - 0003-0007 .- 1520-0477. ; 101:8
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Journal article (peer-reviewed)
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| 2. |
- Döscher, Ralf, et al.
(author)
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The EC-Earth3 Earth system model for the Coupled Model Intercomparison Project 6
- 2022
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In: Geoscientific Model Development. - : Copernicus GmbH. - 1991-959X .- 1991-9603. ; 15:7, s. 2973-3020
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Journal article (peer-reviewed)abstract
- The Earth system model EC-Earth3 for contributions to CMIP6 is documented here, with its flexible coupling framework, major model configurations, a methodology for ensuring the simulations are comparable across different high-performance computing (HPC) systems, and with the physical performance of base configurations over the historical period. The variety of possible configurations and sub-models reflects the broad interests in the EC-Earth community. EC-Earth3 key performance metrics demonstrate physical behavior and biases well within the frame known from recent CMIP models. With improved physical and dynamic features, new Earth system model (ESM) components, community tools, and largely improved physical performance compared to the CMIP5 version, EC-Earth3 represents a clear step forward for the only European community ESM. We demonstrate here that EC-Earth3 is suited for a range of tasks in CMIP6 and beyond.
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| 3. |
- Goosse, Hugues, et al.
(author)
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Quantifying climate feedbacks in polar regions
- 2018
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In: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 9
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Journal article (peer-reviewed)abstract
- The concept of feedback is key in assessing whether a perturbation to a system is amplified or damped by mechanisms internal to the system. In polar regions, climate dynamics are controlled by both radiative and non-radiative interactions between the atmosphere, ocean, sea ice, ice sheets and land surfaces. Precisely quantifying polar feedbacks is required for a process-oriented evaluation of climate models, a clear understanding of the processes responsible for polar climate changes, and a reduction in uncertainty associated with model projections. This quantification can be performed using a simple and consistent approach that is valid for a wide range of feedbacks, offering the opportunity for more systematic feedback analyses and a better understanding of polar climate changes.
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| 4. |
- Notz, Dirk, et al.
(author)
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Arctic Sea Ice in CMIP6
- 2020
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In: Geophysical Research Letters. - 0094-8276 .- 1944-8007. ; 47:10
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Journal article (peer-reviewed)abstract
- We examine CMIP6 simulations of Arctic sea‐ice area and volume. We find that CMIP6 models produce a wide spread of mean Arctic sea‐ice area, capturing the observational estimate within the multimodel ensemble spread. The CMIP6 multimodel ensemble mean provides a more realistic estimate of the sensitivity of September Arctic sea‐ice area to a given amount of anthropogenic CO2 emissions and to a given amount of global warming, compared with earlier CMIP experiments. Still, most CMIP6 models fail to simulate at the same time a plausible evolution of sea‐ice area and of global mean surface temperature. In the vast majority of the available CMIP6 simulations, the Arctic Ocean becomes practically sea‐ice free (sea‐ice area <1 × 106 km2) in September for the first time before the Year 2050 in each of the four emission scenarios SSP1‐1.9, SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5 examined here.
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| 5. |
- Ortega, Pablo, et al.
(author)
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Improving Arctic Weather and Seasonal Climate Prediction : Recommendations for Future Forecast Systems Evolution from the European Project APPLICATE
- 2022
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In: Bulletin of The American Meteorological Society - (BAMS). - 0003-0007 .- 1520-0477. ; 103:10, s. E2203-E2213
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Journal article (peer-reviewed)abstract
- The Arctic environment is changing, increasing the vulnerability of local communities and ecosystems, and impacting its socio-economic landscape. In this context, weather and climate prediction systems can be powerful tools to support strategic planning and decision-making at different time horizons. This article presents several success stories from the H2020 project APPLICATE on how to advance Arctic weather and seasonal climate prediction, synthesizing the key lessons learned throughout the project and providing recommendations for future model and forecast system development.
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| 6. |
- Tian, Tian, et al.
(author)
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Benefits of sea ice initialization for the interannual-to-decadal climate prediction skill in the Arctic in EC-Earth3
- 2021
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In: Geoscientific Model Development. - : Copernicus GmbH. - 1991-959X .- 1991-9603. ; 14:7, s. 4283-4305
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Journal article (peer-reviewed)abstract
- A substantial part of Arctic climate predictability at interannual timescales stems from the knowledge of the initial sea ice conditions. Among all sea ice properties, its volume, which is a product of sea ice concentration (SIC) and thickness (SIT), is the most responsive parameter to climate change. However, the majority of climate prediction systems are only assimilating the observed SIC due to lack of long-term reliable global observation of SIT. In this study, the EC-Earth3 Climate Prediction System with anomaly initialization to ocean, SIC and SIT states is developed. In order to evaluate the regional benefits of specific initialized variables, three sets of retrospective ensemble prediction experiments are performed with different initialization strategies: ocean only; ocean plus SIC; and ocean plus SIC and SIT initialization. In the Atlantic Arctic, the Greenland–Iceland–Norway (GIN) and Barents seas are the two most skilful regions in SIC prediction for up to 5–6 lead years with ocean initialization; there are re-emerging skills for SIC in the Barents and Kara seas in lead years 7–9 coinciding with improved skills of sea surface temperature (SST), reflecting the impact of SIC initialization on ocean–atmosphere interactions for interannual-to-decadal timescales. For the year 2–9 average, the region with significant skill for SIT is confined to the central Arctic Ocean, covered by multi-year sea ice (CAO-MYI). Winter preconditioning with SIT initialization increases the skill for September SIC in the eastern Arctic (e.g. Kara, Laptev and East Siberian seas) and in turn improve the skill of air surface temperature locally and further expanded over land. SIT initialization outperforms the other initialization methods in improving SIT prediction in the Pacific Arctic (e.g. East Siberian and Beaufort seas) in the first few lead years. Our results suggest that as the climate warming continues and the central Arctic Ocean might become seasonal ice free in the future, the controlling mechanism for decadal predictability may thus shift from sea ice volume to ocean-driven processes.
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