Atmospheric Response to Kilometer‐Scale Changes in Sea Ice Concentration Within the Marginal Ice Zone

Batrak, Yurii; Müller, Malte

doi:10.1029/2018gl078295

Cited by 25 publications

(19 citation statements)

References 26 publications

(55 reference statements)

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“…ice edge) the background error correlation length scale may be in this range. This data fusion result could be relevant for the generation of merged sea ice products, where sharp features are desired (see for example the ice concentration used at the lower boundary in (Batrak and M€ uller, 2018)). Fig.…”

Section: Discussionmentioning

confidence: 99%

“…The data points were averaged over segments of 1 km in length. This was done to obtain a state at a spatial resolution closer to that of interest in operational sea ice forecasting (Carrieres et al, 2017;Batrak and M€ uller, 2018;Mohammadi-Aragh et al, 2018), in addition to one where the continuum assumption used in the ice model should be valid Flato (1998). When this true state was used to initialise the sea ice model it was found the ice did not move very significantly over the 72 h period.…”

Section: Data Assimilation Experimentsmentioning

confidence: 99%

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Data fusion and data assimilation of ice thickness observations using a regularisation framework

Asadi¹,

Scott²,

Clausi

2019

Tellus A: Dynamic Meteorology and Oceanography

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Accurate estimates of sharp features in the sea ice cover, such as leads and ridges, are critical for shipping activities, ice operations and weather forecasting. These sharp features can be difficult to preserve in data fusion and data assimilation due to the spatial correlations in the background error covariance matrices. In this article, a set of data fusion and data assimilation experiments are carried out comparing two objective functions, one with a conventional l2-norm and one that imposes an additional l1-norm on the derivative of the ice thickness state estimate. The latter is motivated by analysis of high resolution ice thickness observations derived from an airborne electromagnetic sensor demonstrating the sparsity of the ice thickness in the derivative domain. Data fusion and data assimilation experiments (using a 1 D toy sea-ice model) are carried out over a wide range of background and observation error correlation length scales. Results show the superiority of using an l1-l2 regularisation framework. For the data fusion experiments it was found when both background and observation error correlation length scales are zero, the ice thickness root mean squared error for the l1-l2 method was 0.16 m as compared to 0.20 m for the l2 method. The differences between the methods were greater when the background error correlation length scale was relatively short (approximately five times the analysis grid spacing), and were not significant for larger background error correlation length scales (e.g. 10 times the analysis grid spacing). For data assimilation experiments it was found that openings in the ice cover were captured better with the l1-l2 regularisation, with reduced errors in ice thickness, concentration and velocity. In addition, the ice thickness derivatives in the analyses were found to be more sparse when the l1-l2 method was used and are closer to the those from the true model run.

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Section: Discussionmentioning

confidence: 99%

Section: Data Assimilation Experimentsmentioning

confidence: 99%

Data fusion and data assimilation of ice thickness observations using a regularisation framework

Asadi¹,

Scott²,

Clausi

2019

Tellus A: Dynamic Meteorology and Oceanography

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“…Other studies point out that not only the average lead width is important, but also their spatial arrangement and the shape of their size distribution [16]. According to modeling results [17,18], even very small open water areas might result in the development of small convective plumes which penetrate the ABL and modify its circulation and properties. The areas of updraft air motion can be recognized in the winter Arctic from the satellites due to the presence of steam fog.…”

Section: Introductionmentioning

confidence: 99%

“…This fog type can be recognized in satellite images as linear features, organized into bands of different width, interspersed with clear air areas [20], or as plume-like streaks, with a typical spacing of a few kilometers [21]. Numerical studies indicate that such features may have significant impact on atmospheric processes not only locally, but also regionally [5,18,22].…”

Section: Introductionmentioning

confidence: 99%

Area-Averaged Surface Moisture Flux over Fragmented Sea Ice: Floe Size Distribution Effects and the Associated Convection Structure within the Atmospheric Boundary Layer

Wenta

Herman

2019

Atmosphere

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Sea ice fragmentation results in the transformation of the surface from relatively homogeneous to highly heterogeneous. Atmospheric boundary layer (ABL) rapidly responds to those changes through a range of processes which are poorly understood and not parametrized in numerical weather prediction (NWP) models. The aim of this work is to increase our understanding and develop parametrization of the ABL response to different floe size distributions (FSD). The analysis is based on the results of simulations with the Weather Research and Forecasting model. Results show that FSD determines the distribution and intensity of convection within the ABL through its influence on the atmospheric circulation. Substantial differences between various FSDs are found in the analysis of spatial arrangement and strength of ABL convection. To incorporate those sub-grid effects in the NWP models, a correction factor for the calculation of surface moisture heat flux is developed. It is expressed as a function of floe size, sea ice concentration and wind speed, and enables a correction of the flux computed from area-averaged quantities, as is typically done in NWP models. In general, the presented study sheds some more light on the sea ice–atmosphere interactions and provides the first attempt to parametrize the influence of FSD on the ABL.

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“…The errors in SAT remain stubborn despite numerous attempts to approach the problem from different perspectives over a few decades (Sandu et al 2013; hereafter referred to as S13). These errors have resisted model improvement efforts, especially in cold climate regions over the northern continents and in the Arctic (Vogelezang and Holtslag 1996, Atlaskin and Vihma 2012, Holtslag et al 2013, Kleczek et al 2014, Groisman et al 2016, Batrak and Müller 2018. The challenge for SAT modeling in cold regions was highlighted, among other calls for coherent studies and new observations, during the Year of Polar Prediction (YOPP) as stated by, for example, Jung et al (2016).…”

Section: Introductionmentioning

confidence: 99%

Systematic errors in northern Eurasian short-term weather forecasts induced by atmospheric boundary layer thickness

Esau

Tolstykh

Fadeev

et al. 2018

Environ. Res. Lett.

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Systematic errors in forecast near-surface air temperature (SAT) still constitute a considerable problem for numerical weather prediction (NWP) at high latitudes. Numerous studies in the past have attempted to reduce this problem through recalibration of physical parameterization schemes and better approximation of the surface energy budget. The errors, however, remain despite notable improvements in the overall weather forecast performance. This study looks at the problem from a different perspective. It analyzes asymmetries in the SAT forecast errors. The study reveals a statistical pattern of warm SAT biases under cold weather conditions and cold SAT biases under warm weather conditions. The largest errors were found in shallow atmospheric boundary layers (ABLs). The study attributes the problem to the modeled excessive ABL thickness in northern Eurasia (the NEFI region). The ABL thickness is considered as a scaling factor controlling the efficacy of the applied surface heating. Too thick an ABL damps the magnitude and agility of the SAT response. The study utilized the operational model SL-AV of the Russian Hydrometeorological Centre. Two turbulence schemes were evaluated in the northern European and western Siberian regions of Russia against observations from 73 meteorological stations. The pTKE (old) scheme is based on the local balance of the turbulence characteristics. The TOUCANS (new) scheme incorporated the total turbulence energy equations in an energy-flux balance approach. Neither scheme uses the ABL thickness as a prognostic parameter. The study reveals that the SAT errors are consistent with the damped response of temperature and reduced agility of temperature fluctuations in too thick ABLs. The TOUCANS scheme did not improve those features, probably because it links the turbulent fluxes and the ABL thickness. The SAT errors in shallow ABLs persist in the new scheme. This study emphasizes the need for a closer look at the ABL thickness in the NWP models.

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Atmospheric Response to Kilometer‐Scale Changes in Sea Ice Concentration Within the Marginal Ice Zone

Cited by 25 publications

References 26 publications

Data fusion and data assimilation of ice thickness observations using a regularisation framework

Data fusion and data assimilation of ice thickness observations using a regularisation framework

Area-Averaged Surface Moisture Flux over Fragmented Sea Ice: Floe Size Distribution Effects and the Associated Convection Structure within the Atmospheric Boundary Layer

Systematic errors in northern Eurasian short-term weather forecasts induced by atmospheric boundary layer thickness

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