The Arctic Ocean in CMIP6 models: Biases and projected changes in temperature and salinity

Khosravi, Narges; Wang, Qiang; Koldunov, Nikolay; Hinrichs, Claudia; Semmler, Tido; Danilov, Sergey; Jung, Thomas

doi:10.1002/essoar.10507417.1

Cited by 3 publications

(2 citation statements)

References 23 publications

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“…First and foremost, we used only one realisation per model, which is known to introduce a sampling error as each different realisation simulates a different possible outcome of the chaotic climate system (Wang et al, 2022). However, past studies suggested intramodel biases to be quite small compared to inter-model biases (e.g., Zanowski et al, 2021;Khosravi et al, 2022;Wang et al, 2022). We used a bootstraping approach to estimate those sampling errors and found this to be true for for most variables in our study.…”

Section: Summary and Discussionmentioning

confidence: 87%

“…However, due to the harsh environmental conditions and sheer remoteness, measurements in the polar regions are relatively sparse (Khosravi et al, 2022), complicating especially ocean and sea ice diagnostics. Satellite observations help in the quantification of surface properties, however in-situ data to assess subsurface properties, like vertically resolved temperatures in the ocean, are limited.…”

Section: Introductionmentioning

confidence: 99%

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Validation of key Arctic energy and water budget components in CMIP6

Winkelbauer,

Mayer,

Haimberger

2023

Preprint

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We investigate historical simulations of relevant components of the Arctic energy and water budgets for 39 Coupled Model Intercomparison Project Phase 6 (CMIP6) models and validate them against observation-based estimates. We look at simulated seasonal cycles, long-term averages and trends of lateral transports and storage rates in atmosphere and ocean as well as vertical fluxes attop-of-atmosphere and the surface. We find large inter-model spreads and systematic biases in the representation of annual cycles andlong-term averages. Surface freshwater fluxes associated with precipitation and evaporation as well as runoff from Arctic lands tend to be overestimated by most CMIP6 models and about two thirds of the analysed models feature an early timing bias of one month in the runoff cycle phase, related to an early snow melt bias and the lack of realistic river routing schemes. Further, large biases are found for oceanic volume transports, partly because data required for accurate oceanic transport computations has not been archived. Biases are also present in the simulated energy budget components. The net vertical energy flux at the Arctic surface as well as poleward oceanic heat transports are systematically underestimated by all models. We find strong anti-correlation between average oceanic heat transports and mean sea ice cover, atmospheric heat transports, and also the long-term ocean warming rate. The latter strongly suggests that accurate depiction of the mean state is a prerequisite for realistic projections of future warming of the Arctic. Our diagnostics also provide useful process-based metrics for model selection to constrain projections.

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Section: Summary and Discussionmentioning

confidence: 87%

Section: Introductionmentioning

confidence: 99%

Validation of key Arctic energy and water budget components in CMIP6

Winkelbauer,

Mayer,

Haimberger

2023

Preprint

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A Decomposition of Feedback Contributions to the Arctic Surface Temperature Biases in the CMIP5 Climate Models

Park

Hong

2022

Asia-Pac J Atmos Sci

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Atmospheric Wind Biases: A Challenge for Simulating the Arctic Ocean in Coupled Models?

et al. 2021

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The Arctic is one of the fastest changing regions in the world (Serreze & Barry, 2011;Serreze et al., 2009) and hence it has attracted an increasing amount of scientists' attention. Global climate models are used to predict changes in the climate system, including the Arctic, in a warming world, and to understand climate dynamics and associated feedback. Efforts are ongoing to improve the representation of key processes in contemporary climate models, both in general and specifically in the Arctic. The ultimate aim is to increase predictive capacity (Jung et al., 2016). Of particular interest is the process of oceanic heat transport from the North Atlantic into the Arctic Ocean in the form of Atlantic Water (AW) inflow and subsequent AW circulation and heat distribution within the Arctic Basin and how it will change in the future.

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The Arctic Ocean in CMIP6 models: Biases and projected changes in temperature and salinity

Cited by 3 publications

References 23 publications

Validation of key Arctic energy and water budget components in CMIP6

Validation of key Arctic energy and water budget components in CMIP6

A Decomposition of Feedback Contributions to the Arctic Surface Temperature Biases in the CMIP5 Climate Models

Atmospheric Wind Biases: A Challenge for Simulating the Arctic Ocean in Coupled Models?

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