A Multimodel Approach for Improving Seasonal Probabilistic Forecasts of Regional Arctic Sea Ice

Dirkson, Arlan; Denis, B.; Merryfield, William J.

doi:10.1029/2019gl083831

Cited by 22 publications

(24 citation statements)

References 45 publications

(61 reference statements)

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“…Other statistical prediction systems report skillful detrended SIE predictions for forecasts initialized after 1 May, but not prior to this date, consistent with a spring predictability barrier (Kapsch et al., 2014; Lindsay et al., 2008; Liu et al., 2015; Petty et al., 2017; Schröder et al., 2014; Walsh et al., 2019; Williams et al., 2016; Yuan et al., 2016). Similarly, while not necessarily mentioning a spring barrier, other studies documenting the detrended Arctic SIE prediction skill of dynamical prediction systems display a barrier‐like skill structure corresponding to initialization month May (Dirkson et al., 2019; Merryfield et al., 2013; Msadek et al., 2014; Sigmond et al., 2013; Wang et al., 2013).…”

Section: Introductionmentioning

confidence: 86%

A Mechanism for the Arctic Sea Ice Spring Predictability Barrier

Bushuk

Winton

Bonan

et al. 2020

Geophysical Research Letters

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The decline of Arctic sea ice extent has created a pressing need for accurate seasonal predictions of regional summer sea ice. Recent work has shown evidence for an Arctic sea ice spring predictability barrier, which may impose a sharp limit on regional forecasts initialized prior to spring. However, the physical mechanism for this barrier has remained elusive. In this work, we perform a daily sea ice mass (SIM) budget analysis in large ensemble experiments from two global climate models to investigate the mechanisms that underpin the spring predictability barrier. We find that predictability is limited in winter months by synoptically driven SIM export and negative feedbacks from sea ice growth. The spring barrier results from a sharp increase in predictability at melt onset, when ice‐albedo feedbacks act to enhance and persist the preexisting export‐generated mass anomaly. These results imply that ice thickness observations collected after melt onset are particularly critical for summer Arctic sea ice predictions.

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Section: Introductionmentioning

confidence: 86%

A Mechanism for the Arctic Sea Ice Spring Predictability Barrier

Bushuk

Winton

Bonan

et al. 2020

Geophysical Research Letters

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“…Merryfield et al (2013) showed that the combination of CanSIPS and CFSv2 seasonal forecast systems led in most cases to improved sea ice concentration forecast skill over the Arctic. Dirkson et al (2019a) recently provided new evidence of the additional skill of multi-model combinations over single models for September sea ice concentration using six different state-of-the-art seasonal forecasting systems. In the framework of the H2020-APPLICATE project, which aims to broaden the understanding of linkages between the Arctic region and the Northern Hemisphere mid-latitudes and improve models over these regions, several seasonal re-forecasts were run using state-of-the-art coupled climate models initialized in May and November, over a period covering at least 22 years.…”

Section: Introductionmentioning

confidence: 99%

Summer predictions of Arctic sea ice edge in multi-model seasonal re-forecasts

et al. 2020

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In this study, the forecast quality of 1993-2014 summer seasonal predictions of five global coupled models, of which three are operational seasonal forecasting systems contributing to the Copernicus Climate Change Service (C3S), is assessed for Arctic sea ice. Beyond the Pan-Arctic sea ice concentration and extent deterministic re-forecast assessments, we use sea ice edge error metrics such as the Integrated Ice Edge Error (IIEE) and Spatial Probability Score (SPS) to evaluate the advantages of a multi-model approach.Skill in forecasting the September sea ice minimum from late April to early May start dates is very limited, and only one model shows significant correlation skill over the period when removing the linear trend in total sea ice extent. After bias and trend-adjusting the sea ice concentration data, we find quite similar results between the different systems in terms of ice edge forecast errors. The highest values of September ice edge error in the 1993-2014 period are found for the sea ice minima years (2007 and 2012), mainly due to a clear overestimation of the total extent. Further analyses of deterministic and probabilistic skill over the Barents-Kara, Laptev-East Siberian and Beaufort-Chukchi regions provide insight on differences in model performance.For all skill metrics considered, the multi-model ensemble, whether grouping all five systems or only the three operational C3S systems, performs among the best models for each forecast time, therefore confirming the interest of multi-system initiatives building on model diversity for providing the best forecasts.

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“…First, the SAR imagery is segmented using the Markov random field paradigm. The sizes of the resulting segments exhibited high variation: the segment sizes varied from 20 km 2 to over 11,000 km 2 , the mean of them was about 1000 km 2 . The goal is for the resulting segments to be mainly composed of one DIR category.…”

Section: Degree Of Sea-ice Ridging Using Synthetic Aperture Radar (Sar)mentioning

confidence: 99%

“…The charts are produced by national ice services and have typically regional coverage, e.g., surrounding Greenland. (2) Global products showing sea-ice concentration (SIC), ice edge, SIT, ice types (e.g., first-year ice (FYI) versus MYI), snow thickness on sea ice, melt pond fraction (MPF), or ice drift. The resolution of the products is typically 5 to 30 km.…”

Section: Introductionmentioning

confidence: 99%

“…These include ice type charts, ice drift, degree of ice ridging, iceberg charts, MPF, and landfast ice extent. (4) Sea-ice forecast products for SIT, SIC, ice drift, etc., with some of the sea-ice models assimilating satellite data [2][3][4]. The large-scale products in the categories (1), (3) and (4) can be used for strategic planning of shipping and offshore activities, whereas for tactical planning (e.g., ship routing) in the local scale (~100-1000 m) category (3) and (4) products are needed.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme

et al. 2020

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The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services.

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A Multimodel Approach for Improving Seasonal Probabilistic Forecasts of Regional Arctic Sea Ice

Cited by 22 publications

References 45 publications

A Mechanism for the Arctic Sea Ice Spring Predictability Barrier

A Mechanism for the Arctic Sea Ice Spring Predictability Barrier

Summer predictions of Arctic sea ice edge in multi-model seasonal re-forecasts

Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme

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