Arctic Ocean in CMIP6 Models: Historical and projected temperature and salinity in the deep basins

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

doi:10.1002/essoar.10505254.1

Cited by 2 publications

(4 citation statements)

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“…These reanalysis capabilities are critical considering the problems which current generation of the general circulation models have with simulating the evolution of Arctic halocline (e.g. Khosravi et al 2021). As a word of caution, we add here that the reanalysis underestimated significantly the rates of salinification in the EB halocline and freshening in the AB halocline compared to available observations; that may lead to underestimating of the effects of thermodynamics on Arctic sea ice predictability in this study.…”

Section: Accepted For Publication Inmentioning

confidence: 76%

“…Sea-ice drift is often overlooked when evaluating sea-ice forecasts, but given our results it probably needs much more attention in developing reliable regional sea ice predictions. Furthermore, with a projected increase of freshening of the upper Arctic Ocean in the 21 st century (Khosravi et al 2021), the enhanced oceanic stratification would lead to reduced ocean heat fluxed from the ocean interior to the bottom of sea ice, thus further strengthening the role of advection in shaping Arctic sea ice and reducing summer sea ice predictability. Finally, we note that this study delivers critical information about sea ice predictability in the "new Arctic" conditions, increasing awareness regarding sea ice state and implementation of sea ice forecasts for the needs of shipping, high-latitude tourism, fishing, industry, etc.…”

Section: Accepted For Publication Inmentioning

confidence: 99%

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Climate Change Fosters Competing Effects of Dynamics and Thermodynamics in Seasonal Predictability of Arctic Sea Ice

Polyakov,

Mayer,

Tietsche

et al. 2022

Journal of Climate

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The fast decline of Arctic sea ice necessitates stronger focus on understanding the Arctic sea ice predictability and developing advanced forecast methods for all seasons and for pan-Arctic and regional scales. In this study, the operational forecasting system combining an advanced eddy-permitting ocean-sea ice ensemble reanalysis ORAS5 and state-of-the-art seasonal model-based forecasting system SEAS5 is used to investigate effects of sea ice dynamics and thermodynamics on seasonal (growth-to-melt) Arctic sea ice predictability in 1993-2020. We demonstrate that thermodynamics (growth/melt) dominates the seasonal evolution of mean sea ice thickness at pan-Arctic and regional scales. The thermodynamics also dominates the seasonal predictability of sea ice thickness at pan-Arctic scale; however, at regional scales, the predictability is dominated by dynamics (advection), while contribution from ice growth/melt remains perceptible. We show competing influences of sea ice dynamics and thermodynamics on the temporal change of ice thickness predictability from 1993-2006 to 2007-2020. Over these decades, there was increasing predictability due to growth/melt, attributed to increased winter ocean heat flux in both Eurasian and Amerasian basins; and decreasing predictability due to advection. Our results demonstrate an increasing impact of advection on seasonal sea-ice predictability as the region of interest becomes smaller implying that correct modelling of sea-ice drift is crucial for developing reliable regional sea-ice predictions. This study delivers important information about sea ice predictability in the “new Arctic” conditions. It increases awareness regarding sea-ice state and implementation of sea-ice forecasts for various scientific and practical needs which depend on accurate seasonal sea ice forecasts.

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Section: Accepted For Publication Inmentioning

confidence: 76%

Section: Accepted For Publication Inmentioning

confidence: 99%

Climate Change Fosters Competing Effects of Dynamics and Thermodynamics in Seasonal Predictability of Arctic Sea Ice

Polyakov,

Mayer,

Tietsche

et al. 2022

Journal of Climate

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“…Using global coupled climate models to study the evolution of Atlantification and any associated feedback in the Arctic climate system requires realistic simulations of AW inflow and circulation through the Arctic Ocean. A skill assessment of the AW layer representations in the CMIP5 (Shu et al., 2019) and CMIP6 (Khosravi et al., 2021) models shows that, in many of the models that do simulate a distinct Arctic AW layer, this layer is too thick, too deep, or does not show the observed warming trend. Biases like this are commonly related to insufficient resolution, too much mixing in the ocean component, and unrealistic Atlantic‐Arctic Ocean exchanges.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, they found that the interannual variability in AW temperature was much weaker than observed, and none of the models simulated the warming trend observed in the recent decades. A follow-up study of 23 CMIP6 models by Khosravi et al (2021) shows that the AW layer is still too deep and too thick in most models and the MMM, suggesting that representation of Arctic Ocean hydrography did not visibly improve between CMIP5 and CMIP6. For AWI-CM1, that study shows a simulated average AW core depth for present-day conditions in range with observations; that is, the AW layer is not too deep in AWI-CM1 as it is in many other models.…”

mentioning

confidence: 99%

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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Arctic Ocean in CMIP6 Models: Historical and projected temperature and salinity in the deep basins

Cited by 2 publications

References 0 publications

Climate Change Fosters Competing Effects of Dynamics and Thermodynamics in Seasonal Predictability of Arctic Sea Ice

Climate Change Fosters Competing Effects of Dynamics and Thermodynamics in Seasonal Predictability of Arctic Sea Ice

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

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