2019
DOI: 10.1029/2019gl083831
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A Multimodel Approach for Improving Seasonal Probabilistic Forecasts of Regional Arctic Sea Ice

Abstract: We formulate seasonal probabilistic forecasts of Arctic sea ice concentration from a multimodel (MM) ensemble constructed from six state‐of‐the‐art climate models. Trend‐adjusted quantile mapping is applied to postprocess individual model forecasts prior to MM combination, and a comparison is made against two benchmark MM ensembles: one uncorrected and another where individual models are adjusted for mean and trend bias. Focusing on September hindcasts over 2000–2015 initialized monthly from April–August, cali… Show more

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Cited by 22 publications
(24 citation statements)
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References 45 publications
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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%
“…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%
“…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%
“…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%
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