Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems

Posey, Pamela G.; Metzger, E. Joseph; Wallcraft, Alan J.; Hébert, David; Allard, Richard A; Smedstad, Ole Martin; Phelps, Michael W; Fetterer, F.; Stewart, J.E.; Meier, Walter N.; Helfrich, S.

doi:10.5194/tc-9-1735-2015

Cited by 50 publications

(67 citation statements)

References 13 publications

(11 reference statements)

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“…As discussed in Posey et al . [], the effective resolutions of observed sea ice concentration from SSMI/S and AMSR2 are 25 km and 10 km, respectively. These observed values must be interpolated to the higher resolution model grid (Figure ).…”

Section: The Arctic Cap Nowcast/forecast System (Acnfs)mentioning

confidence: 99%

“…Such a method was developed and described in Posey et al . [], where the IMS mask is applied to the NCODA analysis ice concentration,

A

, as follows:

A = ({|, \begin{matrix} \begin{matrix} left 0 %, \\ left 70 %, \\ left A, \end{matrix} \end{matrix}) \begin{matrix} IMS = 0 \\ I M S = 1 a n d A < 70 % \\ I M S = 1 a n d A \geq 70 % \end{matrix}

…”

Section: The Arctic Cap Nowcast/forecast System (Acnfs)mentioning

confidence: 99%

“…If IMS = 1 and the NCODA analysis ice concentration is greater or equal to 70%, the NCODA analysis ice concentration value is not changed. The threshold of 70% was chosen as the midpoint of the range of valid IMS ice concentration values (40%–100%) [ Posey et al ., ]. This blending algorithm in (1) was created in conjunction with the NSIDC [ Fetterer et al ., ].…”

Section: The Arctic Cap Nowcast/forecast System (Acnfs)mentioning

confidence: 99%

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Short‐term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

et al. 2015

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In this study the forecast skill of the U.S. Navy operational Arctic sea ice forecast system, the Arctic Cap Nowcast/Forecast System (ACNFS), is presented for the period February 2014 to June 2015. ACNFS is designed to provide short term, 1–7 day forecasts of Arctic sea ice and ocean conditions. Many quantities are forecast by ACNFS; the most commonly used include ice concentration, ice thickness, ice velocity, sea surface temperature, sea surface salinity, and sea surface velocities. Ice concentration forecast skill is compared to a persistent ice state and historical sea ice climatology. Skill scores are focused on areas where ice concentration changes by ±5% or more, and are therefore limited to primarily the marginal ice zone. We demonstrate that ACNFS forecasts are skilful compared to assuming a persistent ice state, especially beyond 24 h. ACNFS is also shown to be particularly skilful compared to a climatologic state for forecasts up to 102 h. Modeled ice drift velocity is compared to observed buoy data from the International Arctic Buoy Programme. A seasonal bias is shown where ACNFS is slower than IABP velocity in the summer months and faster in the winter months. In February 2015, ACNFS began to assimilate a blended ice concentration derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) and the Interactive Multisensor Snow and Ice Mapping System (IMS). Preliminary results show that assimilating AMSR2 blended with IMS improves the short‐term forecast skill and ice edge location compared to the independently derived National Ice Center Ice Edge product.

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Section: The Arctic Cap Nowcast/forecast System (Acnfs)mentioning

confidence: 99%

“…Such a method was developed and described in Posey et al . [], where the IMS mask is applied to the NCODA analysis ice concentration,

A

, as follows:

A = ({|, \begin{matrix} \begin{matrix} left 0 %, \\ left 70 %, \\ left A, \end{matrix} \end{matrix}) \begin{matrix} IMS = 0 \\ I M S = 1 a n d A < 70 % \\ I M S = 1 a n d A \geq 70 % \end{matrix}

…”

Section: The Arctic Cap Nowcast/forecast System (Acnfs)mentioning

confidence: 99%

Section: The Arctic Cap Nowcast/forecast System (Acnfs)mentioning

confidence: 99%

See 1 more Smart Citation

Short‐term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

et al. 2015

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“…The verification domain in this evaluation included ice in concentrations >15%. Selecting only the marginal ice zone (<80%), following Posey et al (2015), would offer more fitting metrics in line with the new NWS forecast product that focuses on changes in the marginal ice zone.…”

Section: Discussion Of Acnfs Skillmentioning

confidence: 99%

“…The ACNFS has been validated in the context of addressing improvements from its predecessor on the pan-Arctic scale (Posey et al 2010) and at the time of this study was still undergoing forecast verification and skill testing Posey et al 2015). Targeted verification studies for the Bering Sea have not yet been presented and are explored further in this paper.…”

Section: The Arctic Cap Nowcast Forecast Systemmentioning

confidence: 99%

Broadening the sea-ice forecaster toolbox with community observations: A case study from the northern Bering Sea

et al. 2017

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Impacts of a warming climate are amplified in the Arctic. One notorious impact is recent and record-breaking summertime sea-ice loss. Expanding areas of open water and a prolonged ice-free season create opportunity for some industries but challenge indigenous peoples relying on sea ice for transportation and access to food. The observed and projected increase of Arctic maritime activity requires accurate sea-ice forecasts to protect life, environment, and property. Motivated by emerging prediction needs on the operational timescale (≤10 days), this study explores where local indigenous knowledge (LIK) fits into the forecaster toolbox and how it can be woven into useful sea-ice information products. The 2011 spring ice retreat season in the Bering Sea is presented as a forecasting case study. LIK, housed in a database of community-based ice and weather logs, and an ice-ocean forecast model developed by the US Navy are analyzed for their ability to provide information relevant to stakeholder needs. Additionally, metrics for verifying numerical sea-ice forecasts on multiple scales are derived. The model exhibits skill relative to persistence and climatology on the regional scale. At the community scale, we discuss how LIK and new model guidance can enhance public sea-ice information resources.Key words: sea ice, forecasting, Bering Sea, indigenous knowledge, community observations, operations.Résumé : Les impacts du réchauffement climatique sont amplifiés en Arctique. Un impact certain est la récente perte record de glace de mer en été. L'expansion des zones d'eau libre et une saison sans glace prolongée créent des occasions pour quelques industries, mais constituent un défi pour les peuples autochtones qui comptent sur la glace de mer pour le transport et l'accès à la nourriture. L'augmentation observée et projetée de l'activité maritime arctique nécessite des prévisions de glace de mer précises afin de protéger la vie, l'environnement et la propriété. Cette étude, motivée par de nouveaux besoins de prédiction sur une échelle de temps opérationnelle (≤10 jours), explore à savoir à quel niveau la connaissance indigène locale (CIL) peut s'insérer dans la boîte à outils des prévisionnistes et comment cette connaissance peut être intégrée dans des produits servant d'information sur la glace de mer. La saison de recul des glaces au printemps

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Predictability of the Arctic sea ice edge

Goessling

Tietsche

Day

et al. 2016

Geophysical Research Letters

131

124

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Skillful sea ice forecasts from days to years ahead are becoming increasingly important for the operation and planning of human activities in the Arctic. Here we analyze the potential predictability of the Arctic sea ice edge in six climate models. We introduce the integrated ice‐edge error (IIEE), a user‐relevant verification metric defined as the area where the forecast and the “truth” disagree on the ice concentration being above or below 15%. The IIEE lends itself to decomposition into an absolute extent error, corresponding to the common sea ice extent error, and a misplacement error. We find that the often‐neglected misplacement error makes up more than half of the climatological IIEE. In idealized forecast ensembles initialized on 1 July, the IIEE grows faster than the absolute extent error. This means that the Arctic sea ice edge is less predictable than sea ice extent, particularly in September, with implications for the potential skill of end‐user relevant forecasts.

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Improving Arctic sea ice edge forecasts by assimilating high horizontal resolution sea ice concentration data into the US Navy's ice forecast systems

Cited by 50 publications

References 13 publications

Short‐term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

Short‐term sea ice forecasting: An assessment of ice concentration and ice drift forecasts using the U.S. Navy's Arctic Cap Nowcast/Forecast System

Broadening the sea-ice forecaster toolbox with community observations: A case study from the northern Bering Sea

Predictability of the Arctic sea ice edge

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