Prediction skill of Arctic sea ice in decadal climate simulations of the EC-Earth3 model

2020 ◽  
Author(s):  
Pasha Karami ◽  
Tim Kruschke ◽  
Tian Tian ◽  
Torben Koenigk ◽  
Shuting Yang
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Lower Troposphere ◽  
Climate Prediction ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea ◽  
The Mean ◽  
Decadal Climate ◽  
Decadal Climate Prediction

<p>Arctic sea ice variability and long-term trend play a major role in affecting the climate of polar and lower latitudes via complex coupling with the polar atmospheric circulation and the North Atlantic Ocean circulation. Moreover, sea ice conditions in the Arctic have direct impacts on socio-economy (e.g. the key shipping regions) and on the ecosystem. Understanding and improving predictions of Arctic sea ice on seasonal to decadal time scales is therefore crucial. <span>We investigate the skill of decadal climate prediction simulations of the EC-Earth3 model (T255L91, ORCA1L75) with a focus on Arctic sea ice. In line with the protocol for the CMIP6 Decadal Climate Prediction Project (DCPP), we launched 59 hindcasts/forecasts from 1960 to 2018. Each hindcast/forecast has 15 ensemble members which were initialized on 1 November and integrated for 10 years (+ 2 months). Anomaly initialization approach for the ocean and sea-ice (based on data from the ORA-S5-reanalysis) and full-field initialization for the atmosphere/land surface (based on ERA-Interim/ERA-Land) were applied. We first present a comparison of our hindcasts to observations for global key parameters and provide quantitative estimates of hindcast skill by using </span><span>common deterministic metrics such as</span><span> correlation and the Mean Squared Error Skill Score. We focus particularly on the skill regarding sea ice concentration and area in the Arctic’s sub-basins and its relation to the temperature and circulation of lower troposphere as well as the mean state of the ocean </span><span>outside the Arctic</span><span>. We </span><span>also </span><span>explore relevant processes and how the ocean state and natural climate variability can </span><span>a</span><span>ffect our prediction skills to improve the prediction system.</span></p>

scholarly journals Benefits of sea ice thickness initialization for the Arctic decadal climate prediction skill in EC-Earth3

2020 ◽  
Author(s):  
Tian Tian ◽  
Shuting Yang ◽  
Mehdi Pasha Karami ◽  
François Massonnet ◽  
Tim Kruschke ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Climate Prediction ◽  
Arctic Sea Ice ◽  
Prediction Skill ◽  
The Arctic ◽  
Ensemble Prediction ◽  
Central Arctic Ocean ◽  
Ice Volume ◽  
Arctic Sea

Abstract. A substantial part of Arctic climate predictability at interannual time scales stems from the knowledge of the initial sea ice conditions. Among all the variables characterizing sea ice, sea ice volume, being a product of sea ice area/concentration (SIC) and thickness (SIT), is the most sensitive parameter for 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 benefits of specific initialized variables at regional scales, 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. The increased skill from ocean plus SIC initialization is small in most regions, compared to ocean-only initialization. In the marginal ice zone covered by seasonal ice, skills regarding winter SIC are mainly gained from the initial ocean temperature anomalies. Consistent with previous studies, the Arctic sea ice volume anomalies are found to play a dominant role for the prediction skill of September Arctic sea ice extent. Winter preconditioning of SIT for the perennial ice in the central Arctic Ocean results in increased skill of SIC in the adjacent Arctic coastal waters (e.g. the Laptev/East Siberian/Chukchi Seas) for lead time up to a decade. This highlights the importance of initializing SIT for predictions of decadal time scale in regional Arctic sea ice. 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 being the sea ice volume playing the major role to a more ocean-related processes.

scholarly journals Present-Day Arctic Sea Ice Variability in the Coupled ECHAM5/MPI-OM Model

2010 ◽  
Vol 23 (10) ◽  
pp. 2520-2543 ◽  
Author(s):  
Nikolay V. Koldunov ◽  
Detlef Stammer ◽  
Jochem Marotzke
Keyword(s):  
Sea Ice ◽  
Arctic Ocean ◽  
Ocean Circulation ◽  
Climate Models ◽  
Climate Model ◽  
Heat Content ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Max Planck ◽  
Arctic Sea

Abstract As a contribution to a detailed evaluation of Intergovernmental Panel on Climate Change (IPCC)-type coupled climate models against observations, this study analyzes Arctic sea ice parameters simulated by the Max-Planck-Institute for Meteorology (MPI-M) fully coupled climate model ECHAM5/Max-Planck-Institute for Meteorology Hamburg Primitive Equation Ocean Model (MPI-OM) for the period from 1980 to 1999 and compares them with observations collected during field programs and by satellites. Results of the coupled run forced by twentieth-century CO2 concentrations show significant discrepancies during summer months with respect to observations of the spatial distribution of the ice concentration and ice thickness. Equally important, the coupled run lacks interannual variability in all ice and Arctic Ocean parameters. Causes for such big discrepancies arise from errors in the ECHAM5/MPI-OM atmosphere and associated errors in surface forcing fields (especially wind stress). This includes mean bias pattern caused by an artificial circulation around the geometric North Pole in its atmosphere, as well as insufficient atmospheric variability in the ECHAM5/MPI-OM model, for example, associated with Arctic Oscillation/North Atlantic Oscillation (AO/NAO). In contrast, the identical coupled ocean–ice model, when driven by NCEP–NCAR reanalysis fields, shows much increased skill in its ice and ocean circulation parameters. However, common to both model runs is too strong an ice export through the Fram Strait and a substantially biased heat content in the interior of the Arctic Ocean, both of which may affect sea ice budgets in centennial projections of the Arctic climate system.

scholarly journals Influence of stochastic sea ice parametrization on climate and the role of atmosphere–sea ice–ocean interaction

2014 ◽  
Vol 372 (2018) ◽  
pp. 20130283 ◽  
Author(s):  
Stephan Juricke ◽  
Thomas Jung
Keyword(s):  
Sea Ice ◽  
Coupled Model ◽  
Ocean Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Polar Regions ◽  
Stochastic Perturbations ◽  
Arctic Sea ◽  
The Mean ◽  
Mean Climate

The influence of a stochastic sea ice strength parametrization on the mean climate is investigated in a coupled atmosphere–sea ice–ocean model. The results are compared with an uncoupled simulation with a prescribed atmosphere. It is found that the stochastic sea ice parametrization causes an effective weakening of the sea ice. In the uncoupled model this leads to an Arctic sea ice volume increase of about 10–20% after an accumulation period of approximately 20–30 years. In the coupled model, no such increase is found. Rather, the stochastic perturbations lead to a spatial redistribution of the Arctic sea ice thickness field. A mechanism involving a slightly negative atmospheric feedback is proposed that can explain the different responses in the coupled and uncoupled system. Changes in integrated Antarctic sea ice quantities caused by the stochastic parametrization are generally small, as memory is lost during the melting season because of an almost complete loss of sea ice. However, stochastic sea ice perturbations affect regional sea ice characteristics in the Southern Hemisphere, both in the uncoupled and coupled model. Remote impacts of the stochastic sea ice parametrization on the mean climate of non-polar regions were found to be small.

Evaluation of a multilayer snow scheme in NEMO-LIM3

2020 ◽  
Author(s):  
Gaëlle Gilson ◽  
Thierry Fichefet ◽  
Olivier Lecomte ◽  
Pierre-Yves Barriat ◽  
Jean Sterlin ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Climate Models ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Optimum Number ◽  
Ice Growth ◽  
Arctic Sea ◽  
The Impact

<p>Arctic sea ice is a major component of the Earth’s climate system and has been experiencing a drastic decline over the past decades, with important consequences regionally and globally. With the sustained warming of the Arctic, sea ice loss is expected to continue in the future. However, the estimation of its magnitude is model-dependent. As a result, the representation of sea ice in climate models requires further consideration. A major issue relates to the long-standing misrepresentation of snow properties on sea ice. However, the presence of snow strongly impacts sea ice growth and surface energy balance. Through its high albedo, snow reflects more solar radiation than bare sea ice does. When a snow cover is present, sea ice growth is reduced because snow is an effective insulator, with a thermal conductivity an order of magnitude lower than that of sea ice. Ocean circulation models usually use multiple layers to resolve sea ice thermodynamics but only one single layer for snow. Lecomte et al. (2013) developed a multilayer snow scheme for ocean circulation models and improved the snow depth distribution by considering the macroscopic effects of wind packing and redeposition. Since then, this snow scheme has been revisited and implemented in a more recent and much more robust NEMO-LIM version, using a simpler technical approach. In addition, new instrumental observations of snow thickness, distribution and density are available since these exploratory works. They are used in the current study to: 1) evaluate the performance of the multilayer snow scheme for sea ice in the NEMO-LIM3 model, and 2) investigate the climatic importance of this snow scheme. Here, we present results of simulations with a varying number of snow layers. By comparing these to the latest observational datasets, we recommend an optimum number of snow  layers to be used in ocean circulation models in both hemispheres. Finally, we explore the impact of a few specific parameterizations of snow thermophysical properties on the representation of sea ice in climate models.</p>

Prévoir les variations saisonnières de la glace de mer arctique et leurs impacts sur le climat

2020 ◽  
pp. 024
Author(s):  
Rym Msadek ◽  
Gilles Garric ◽  
Sara Fleury ◽  
Florent Garnier ◽  
Lauriane Batté ◽  
...  
Keyword(s):  
Sea Ice ◽  
Arctic Region ◽  
Arctic Sea Ice ◽  
Climate Impacts ◽  
The Arctic ◽  
Recent Effort ◽  
Sea Ice Thickness ◽  
Arctic Sea ◽  
Ice Conditions ◽  
Upper Level

L'Arctique est la région du globe qui s'est réchauffée le plus vite au cours des trente dernières années, avec une augmentation de la température de surface environ deux fois plus rapide que pour la moyenne globale. Le déclin de la banquise arctique observé depuis le début de l'ère satellitaire et attribué principalement à l'augmentation de la concentration des gaz à effet de serre aurait joué un rôle important dans cette amplification des températures au pôle. Cette fonte importante des glaces arctiques, qui devrait s'accélérer dans les décennies à venir, pourrait modifier les vents en haute altitude et potentiellement avoir un impact sur le climat des moyennes latitudes. L'étendue de la banquise arctique varie considérablement d'une saison à l'autre, d'une année à l'autre, d'une décennie à l'autre. Améliorer notre capacité à prévoir ces variations nécessite de comprendre, observer et modéliser les interactions entre la banquise et les autres composantes du système Terre, telles que l'océan, l'atmosphère ou la biosphère, à différentes échelles de temps. La réalisation de prévisions saisonnières de la banquise arctique est très récente comparée aux prévisions du temps ou aux prévisions saisonnières de paramètres météorologiques (température, précipitation). Les résultats ayant émergé au cours des dix dernières années mettent en évidence l'importance des observations de l'épaisseur de la glace de mer pour prévoir l'évolution de la banquise estivale plusieurs mois à l'avance. Surface temperatures over the Arctic region have been increasing twice as fast as global mean temperatures, a phenomenon known as arctic amplification. One main contributor to this polar warming is the large decline of Arctic sea ice observed since the beginning of satellite observations, which has been attributed to the increase of greenhouse gases. The acceleration of Arctic sea ice loss that is projected for the coming decades could modify the upper level atmospheric circulation yielding climate impacts up to the mid-latitudes. There is considerable variability in the spatial extent of ice cover on seasonal, interannual and decadal time scales. Better understanding, observing and modelling the interactions between sea ice and the other components of the climate system is key for improved predictions of Arctic sea ice in the future. Running operational-like seasonal predictions of Arctic sea ice is a quite recent effort compared to weather predictions or seasonal predictions of atmospheric fields like temperature or precipitation. Recent results stress the importance of sea ice thickness observations to improve seasonal predictions of Arctic sea ice conditions during summer.

scholarly journals Retrieval of Snow Depth over Arctic Sea Ice Using a Deep Neural Network

2019 ◽  
Vol 11 (23) ◽  
pp. 2864 ◽  
Author(s):  
Jiping Liu ◽  
Yuanyuan Zhang ◽  
Xiao Cheng ◽  
Yongyun Hu
Keyword(s):  
Neural Network ◽  
Sea Ice ◽  
Snow Depth ◽  
Deep Neural Network ◽  
Arctic Basin ◽  
Temporal Variations ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Sea Ice Thickness ◽  
Arctic Sea

The accurate knowledge of spatial and temporal variations of snow depth over sea ice in the Arctic basin is important for understanding the Arctic energy budget and retrieving sea ice thickness from satellite altimetry. In this study, we develop and validate a new method for retrieving snow depth over Arctic sea ice from brightness temperatures at different frequencies measured by passive microwave radiometers. We construct an ensemble-based deep neural network and use snow depth measured by sea ice mass balance buoys to train the network. First, the accuracy of the retrieved snow depth is validated with observations. The results show the derived snow depth is in good agreement with the observations, in terms of correlation, bias, root mean square error, and probability distribution. Our ensemble-based deep neural network can be used to extend the snow depth retrieval from first-year sea ice (FYI) to multi-year sea ice (MYI), as well as during the melting period. Second, the consistency and discrepancy of snow depth in the Arctic basin between our retrieval using the ensemble-based deep neural network and two other available retrievals using the empirical regression are examined. The results suggest that our snow depth retrieval outperforms these data sets.

scholarly journals Observation-based selection of climate models projects Arctic ice-free summers around 2035

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
David Docquier ◽  
Torben Koenigk
Keyword(s):  
Model Selection ◽  
Sea Ice ◽  
Climate Models ◽  
Global Climate ◽  
Coupled Model ◽  
Arctic Sea Ice ◽  
Global Climate Models ◽  
The Arctic ◽  
Arctic Sea ◽  
Area And Volume

AbstractArctic sea ice has been retreating at an accelerating pace over the past decades. Model projections show that the Arctic Ocean could be almost ice free in summer by the middle of this century. However, the uncertainties related to these projections are relatively large. Here we use 33 global climate models from the Coupled Model Intercomparison Project 6 (CMIP6) and select models that best capture the observed Arctic sea-ice area and volume and northward ocean heat transport to refine model projections of Arctic sea ice. This model selection leads to lower Arctic sea-ice area and volume relative to the multi-model mean without model selection and summer ice-free conditions could occur as early as around 2035. These results highlight a potential underestimation of future Arctic sea-ice loss when including all CMIP6 models.

scholarly journals Polar cod in jeopardy under the retreating Arctic sea ice

2019 ◽  
Vol 2 (1) ◽  
Author(s):  
Mats Brockstedt Olsen Huserbråten ◽  
Elena Eriksen ◽  
Harald Gjøsæter ◽  
Frode Vikebø
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Keystone Species ◽  
Ice Cover ◽  
Arctic Sea Ice ◽  
Biophysical Model ◽  
The Arctic ◽  
Polar Cod ◽  
Arctic Sea ◽  
Shelf Sea

Abstract The Arctic amplification of global warming is causing the Arctic-Atlantic ice edge to retreat at unprecedented rates. Here we show how variability and change in sea ice cover in the Barents Sea, the largest shelf sea of the Arctic, affect the population dynamics of a keystone species of the ice-associated food web, the polar cod (Boreogadus saida). The data-driven biophysical model of polar cod early life stages assembled here predicts a strong mechanistic link between survival and variation in ice cover and temperature, suggesting imminent recruitment collapse should the observed ice-reduction and heating continue. Backtracking of drifting eggs and larvae from observations also demonstrates a northward retreat of one of two clearly defined spawning assemblages, possibly in response to warming. With annual to decadal ice-predictions under development the mechanistic physical-biological links presented here represent a powerful tool for making long-term predictions for the propagation of polar cod stocks.

scholarly journals On the Link between Arctic Sea Ice Decline and the Freshwater Content of the Beaufort Gyre: Insights from a Simple Process Model

2014 ◽  
Vol 27 (21) ◽  
pp. 8170-8184 ◽  
Author(s):  
Peter E. D. Davis ◽  
Camille Lique ◽  
Helen L. Johnson
Keyword(s):  
Sea Ice ◽  
Momentum Transfer ◽  
Momentum Flux ◽  
Process Model ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Simple Process ◽  
Beaufort Gyre ◽  
Arctic Sea ◽  
The Arctic Ocean

Abstract Recent satellite and hydrographic observations have shown that the rate of freshwater accumulation in the Beaufort Gyre of the Arctic Ocean has accelerated over the past decade. This acceleration has coincided with the dramatic decline observed in Arctic sea ice cover, which is expected to modify the efficiency of momentum transfer into the upper ocean. Here, a simple process model is used to investigate the dynamical response of the Beaufort Gyre to the changing efficiency of momentum transfer, and its link with the enhanced accumulation of freshwater. A linear relationship is found between the annual mean momentum flux and the amount of freshwater accumulated in the Beaufort Gyre. In the model, both the response time scale and the total quantity of freshwater accumulated are determined by a balance between Ekman pumping and an eddy-induced volume flux toward the boundary, highlighting the importance of eddies in the adjustment of the Arctic Ocean to a change in forcing. When the seasonal cycle in the efficiency of momentum transfer is modified (but the annual mean momentum flux is held constant), it has no effect on the accumulation of freshwater, although it does impact the timing and amplitude of the annual cycle in Beaufort Gyre freshwater content. This suggests that the decline in Arctic sea ice cover may have an impact on the magnitude and seasonality of the freshwater export into the North Atlantic.

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