Towards reliable Arctic sea ice prediction using multivariate data assimilation

Liu, Jiping; Chen, Zhiqiang; Hu, Yongyun; Zhang, Yuanyuan; Ding, Yifan; Cheng, Xiao; Yang, Qinghua; Nerger, Lars; Spreen, Gunnar; Horton, Radley M.; Inoue, Jun; Yang, Chao-Yuan; Li, Ming; Song, Mirong

doi:10.1016/j.scib.2018.11.018

Cited by 33 publications

(29 citation statements)

References 153 publications

(183 reference statements)

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“…In combination with a weakly coupled assimilation of ocean hydrography, this system is the first reported climate prediction system that applies joint updates of the ocean and sea ice with a fully coupled Earth system model. It is capable to provide skillful seasonal retrospective predictions in the Arctic, particularly for the challenging July‐initialized prediction of SIE up to September (Liu et al, ). In the North Atlantic, the retrospective prediction shows skill up to 10‐12 lead months in the winter season, while the skill is lower in the North Pacific.…”

Section: Summary and Discussionmentioning

confidence: 99%

Impact of Ocean and Sea Ice Initialisation On Seasonal Prediction Skill in the Arctic

Kimmritz

Counillon

Smedsrud

et al. 2019

J Adv Model Earth Syst

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There is a growing demand for skillful prediction systems in the Arctic. Using the Norwegian Climate Prediction Model that combines the fully coupled Norwegian Earth System Model and the Ensemble Kalman filter, we present a system that performs both, weakly coupled data assimilation (wCDA) when assimilating ocean hydrography (by updating the ocean alone) and strongly coupled data assimilation when assimilating sea ice concentration (SIC) (by jointly updating the sea ice and ocean). We assess the seasonal prediction skill of this version of the Norwegian Climate Prediction Model, the first climate prediction system using strongly coupled data assimilation, by performing retrospective predictions (hindcasts) for the period 1985 to 2010. To better understand the origins of the prediction skill of Arctic sea ice, we compare this version with a version that solely performs wCDA of ocean hydrography. The reanalysis that assimilates just ocean data exhibits skillful hydrography in the upper Arctic Ocean and features an improved sea ice state, such as improved summer SIC in the Barents Sea, or reduced biases in sea ice thickness. Skillful prediction of SIE up to 10-12 lead months are only found during winter in regions of a relatively deep ocean mixed layer outside the Arctic basin. Additional DA of SIC data notably further corrects the initial seaice state, confirming the applicability of the results of Kimmritz et al. (2018) in a historical setting. The resulting prediction skill of SIE is widely enhanced compared to predictions initialized through wCDA of only ocean data. Particularly high skill is found for July-initialized autumn SIE predictions. Plain Language SummaryThe declining Arctic sea ice entails both risks and opportunities for the Arctic ecosystem, communities, and economic activities. Reliable seasonal predictions of the Arctic sea ice could help to guide decisionmakers to benefit from arising opportunities and to mitigate increased risks in the Arctic. However, despite some success, seasonal prediction systems in the Arctic have not exploited their full potential yet. For instance, so far only a single model component, for example, the ocean, has been updated in isolation to derive a skillful initial state, though joint updates across model components, for example, the ocean and the sea ice, are expected to perform better. Here, we introduce a system that, for the first time, deploys joint updates of the ocean and the sea ice state, using data of the ocean hydrography and sea ice concentration, for seasonal prediction in the Arctic. By comparing this setup with a system that updates only the ocean in isolation, we assess the added skill of facilitating sea ice concentration data to jointly update the ocean and the sea ice. While the update of the ocean alone leads to skillful winter predictions only in the North Atlantic, the joint update strongly enhances the overall skill.

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Section: Summary and Discussionmentioning

confidence: 99%

Impact of Ocean and Sea Ice Initialisation On Seasonal Prediction Skill in the Arctic

Kimmritz

Counillon

Smedsrud

et al. 2019

J Adv Model Earth Syst

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“…Low spatial (∼3-25 km) and high temporal resolution PMW satellites provide synoptic coverage of both hemispheres and can yield sea-ice information, such as SIC, ice age, ice motion, and timing of ice retreat and advance (Inoue, 2008;Liu et al, 2019;Shokr & Sinha, 2015;Waseda et al, 2018). The data do not accurately resolve the MIZ, ice edge, and coastal locations due to coarse spatial resolutions and also underestimate the true ice-fraction once melt begins.…”

Section: Availability Of Sea-ice Information From Service Providersmentioning

confidence: 99%

“…Drift measurements, both in situ from buoys and derived from satellites (Löptien & Axell, 2014;Schweiger & Zhang, 2015) especially high-resolution SAR (Karvonen, 2012;Korosov & Rampal, 2017) and optical, are another underutilized resource that could improve drift forecasts. The modeling community relies on PMW and deems it essential for forecast model initialization (Liu et al, 2019). However, its usage may also result in a forecast of the future PMW product.…”

Section: Next Steps and New Technologies For Derived Productsmentioning

confidence: 99%

Sea-ice information and forecast needs for industry maritime stakeholders

et al. 2020

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Profound changes in Arctic sea-ice, a growing desire to utilize the Arctic's abundant natural resources, and the potential competitiveness of Arctic shipping routes, all provide for increased industry marine activity throughout the Arctic Ocean. This is anticipated to result in further challenges for maritime safety. Those operating in ice-infested waters require various types of information for sea-ice and iceberg hazards. Ice information requirements depend on regional needs and whether the stakeholder wants to avoid ice all together, operate near or in the Marginal Ice Zone, or areas within the ice pack. An insight into user needs demonstrates how multiple spatial and temporal resolutions for sea-ice information and forecasts are necessary to provide information to the marine operating community for safety, planning, and situational awareness. Although ship-operators depend on sea-ice information for tactical navigation, stakeholders working in route and capacity planning can benefit from climatological and long-range forecast information at lower spatial and temporal resolutions where the interest is focused on open-water season. The advent of the Polar Code has brought with it additional information requirements, and exposed gaps in capacity and knowledge. Thus, future satellite data sources should be at resolutions that support both tactical and planning activities.

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“…Arctic sea ice extent/area has been decreasing since the satellite era. The decreasing trend in areal extent is found for every month (e.g., Cavalieri & Parkinson, 2012; Liu et al, 2019). Meanwhile, the thickness of the ice pack has been thinning (e.g., Kwok & Cunningham, 2015; Kwok & Rothrock, 2009; Kwok & Untersteiner, 2011), largely due to the thicker multiyear sea ice being replaced by the thinner first year sea ice (e.g., Maslanik et al, 2007, 2011; Tschudi et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…It is well known that the performance of dynamic models to predict Arctic sea ice at short‐term periods strongly depends on model initial conditions (e.g., Blanchard‐Wrigglesworth et al, 2011; Msadek et al, 2014; Liu et al, 2019; Yang et al, 2016). The accurate sea ice initialization requires not only sea ice concentration but also variables (e.g., sea ice thickness) that influence surface energy fluxes and ocean‐atmosphere interactions (Yang et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations

Yang

Liu

2020

J Adv Model Earth Syst

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To increase our capability to predict Arctic sea ice and climate, we have developed a coupled atmosphere-sea ice-ocean model configured for the pan-Arctic with sufficient flexibility. The Los Alamos Sea Ice Model is coupled with the Weather Research and Forecasting Model and the Regional Ocean Modeling System in the Coupled Ocean-Atmosphere-Wave-Sediment Transport modeling system. It is well known that dynamic models used to predict Arctic sea ice at short-term periods strongly depend on model initial conditions. Parallel Data Assimilation Framework is implemented into the new modeling system to assimilate sea ice observations and generate skillful model initialization, which aid in the prediction procedures. The Special Sensor Microwave Imager/Sounder sea ice concentration, the CyroSat-2, and Soil Moisture and Ocean Salinity sea ice thickness are assimilated with the localized error subspace transform ensemble Kalman filter. We conduct Arctic sea ice prediction for the melting seasons of 2017 and 2018. Predictions with improved initial sea ice conditions show reasonable sea ice evolution and small biases in the minimum sea ice extent, although the ice refreezing is delayed. Our prediction experiments suggest that the use of appropriate uncertainty for the observed sea ice thickness can lead to improved spatial distribution of the initial ice thickness and thus the predicted sea ice distribution. Our new modeling system initialized by the output of the National Centers for Environmental Prediction Climate Forecast System seasonal forecasts with data assimilation can significantly increase the sea ice prediction skills in sea ice extent for the entire Arctic as well as in the Northern Sea Route compared with the predictions by the National Centers for Environmental Prediction Climate Forecast System. Plain Language SummaryWe have developed a coupled atmosphere-sea ice-ocean model configured for the Arctic to enhance our capability to predict Arctic sea ice and climate. It is well known that the accuracy of model initial condition strongly influences Arctic sea ice predictions with dynamic models at short-term periods. A data assimilation system is combined with the new coupled model to assimilate satellite sea ice observations to improve initial sea ice conditions. We perform Arctic sea ice predictions using the new modeling system for the summers of 2017 and 2018. Predictions show good predictive skills compared with the observations. Our prediction experiments also suggest that the use of appropriate uncertainty in observed sea ice thickness can improve the predicted sea ice spatial pattern. Our new modeling system initialized by the seasonal forecasts of the National Centers for Environmental Prediction Climate Forecast System and our data assimilation procedures perform much better in predicting Arctic sea ice cover as well as sea ice conditions in the Arctic shipping routes than the National Centers for Environmental Prediction Climate Forecast System. Key Points:• A new atmosphere-sea ice-ocean regional coup...

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Towards reliable Arctic sea ice prediction using multivariate data assimilation

Cited by 33 publications

References 153 publications

Impact of Ocean and Sea Ice Initialisation On Seasonal Prediction Skill in the Arctic

Impact of Ocean and Sea Ice Initialisation On Seasonal Prediction Skill in the Arctic

Sea-ice information and forecast needs for industry maritime stakeholders

Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations

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