Forecast Verification of the Polar Ice Prediction System (PIPS) Sea Ice Concentration Fields*

Woert, Michael L. Van; Zou, Cheng‐Zhi; Meier, Walter N.; Hovey, Philip D.; Preller, Ruth H.; Posey, Pamela G.

doi:10.1175/1520-0426(2004)021<0944:fvotpi>2.0.co;2

Cited by 32 publications

(37 citation statements)

References 24 publications

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“…Starting from a forecast x i f and associated error covariance matrix P i f , the optimal solution x i a and a posteriori error covariance matrix P i a , are obtained by applying the Kalman gain K i to the innovation vector d : The time evolution of the state vector is predicted by a linear equation of the form where η i −1 is the prediction error, with covariance Q i −1 . This equation is applied to predict the evolution of the fields (state vector) and corresponding errors, that is used at time t i as forecast and a priori error matrix: The prediction equation used here is a simple persistence, relaxed to climatology ( x c ): Such formulation was used for ice cover estimation by Van Woert et al [2004]. The ν coefficient represents the autocorrelation between the fields at times t i −1 and t i .…”

Section: Estimation Problemsupporting

confidence: 52%

A synthesis of the POMME physical data set: One year monitoring of the upper layer

2005

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The Programme Océan Multidisciplinaire Méso Echelle (POMME) experiment was designed to describe and quantify the role of mesoscale processes in the subduction of mode waters in the northeast Atlantic. During 1 year (September 2000–October 2001), in situ measurements were conducted over a 8° square area centered on 18°W, 42°N. We present the synthesis of the physical data set collected during this experiment. To improve time and space coverages, these measurements are combined with satellite information from sea surface temperature and altimetry. Daily fields of temperature, salinity, and stream function are produced over a seasonal cycle with a simplified Kalman filter. We analyze the annual cycle of the upper layer. The 1 year mean circulation in the upper 400 m resembles the scheme proposed by Paillet and Mercier (1997). The meridional component of the flow carries cold water southward, thus contributing to cool the POMME area. The annual mean heat budget shows that this advection by the mean current nearly balances the warming by the surface heat fluxes. The mixed layer maximum depth is reached in March. It increases, in zonal averages, from 100 m south of 38°N to more than 270 m north of 45°N and, remarkably, is shaped by the mesoscale. The subpolar mode water formed in the north of the POMME area has a lower density (26.8–27.0) than the mode water formed in the years 1988–1990 that Paillet and Arhan (1996) found in the density range (27.0–27.1). This mode water is continuously advected southward across the 42°N latitude, leading to an annual mean transport of 1.4 106 m3 s−1.

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Section: Estimation Problemsupporting

confidence: 52%

A synthesis of the POMME physical data set: One year monitoring of the upper layer

2005

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“…In assessing the ice concentration forecast skill of ACNFS, a skill score using the mean square error is computed based on the analysis of Murphy and Epstein [] and Van Woert et al . []. Following the notation of Van Woert et al .…”

Section: Forecast Assessmentsmentioning

confidence: 99%

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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“…Six hourly three-dimensional fields of temperature, salinity and ocean velocities for the same time period as the atmospheric forcing are linearly interpolated onto the model grid over a relaxation zone of width 100 km around the lateral boundaries as described in Holt and James (2001). Initial and six hourly boundary conditions for ice concentration and thickness are provided by the Polar Ice Prediction System (Preller and Posey, 1996;Woert et al, 2004) and interpolated onto the same relaxation zone. No restoring was applied to the domain interior.…”

Section: Surface and Boundary Forcingmentioning

confidence: 99%

The effect of tides on dense water formation in Arctic shelf seas

et al. 2011

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Abstract. Ocean tides are not explicitly included in many ocean general circulation models, which will therefore omit any interactions between tides and the cryosphere. We present model simulations of the wind and buoyancy driven circulation and tides of the Barents and Kara Seas, using a 25 km × 25 km 3-D ocean circulation model coupled to a dynamic and thermodynamic sea ice model. The modeled tidal amplitudes are compared with tide gauge data and sea ice extent is compared with satellite data. Including tides in the model is found to have little impact on overall sea ice extent but is found to delay freeze up and hasten the onset of melting in tidally active coastal regions. The impact that including tides in the model has on the salt budget is investigated and found to be regionally dependent. The vertically integrated salt budget is dominated by lateral advection. This increases significantly when tides are included in the model in the Pechora Sea and around Svalbard where tides are strong. Tides increase the salt flux from sea ice by 50% in the Pechora and White Seas but have little impact elsewhere. This study suggests that the interaction between ocean tides and sea ice should not be neglected when modeling the Arctic.

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Forecast Verification of the Polar Ice Prediction System (PIPS) Sea Ice Concentration Fields*

Cited by 32 publications

References 24 publications

A synthesis of the POMME physical data set: One year monitoring of the upper layer

A synthesis of the POMME physical data set: One year monitoring of the upper layer

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

The effect of tides on dense water formation in Arctic shelf seas

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