Assimilating SMOS sea ice thickness into a coupled ice‐ocean model using a local SEIK filter

Yang, Qinghua; Losa, Svetlana N.; Lösch, Martin; Tian-Kunze, Xiangshan; Nerger, Lars; Liu, Jiping; Kaleschke, Lars; Zhang, Zhanghai

doi:10.1002/2014jc009963

Cited by 83 publications

(84 citation statements)

References 43 publications

(87 reference statements)

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“…These studies follow from the first attempt of assimilation of SMOS-Ice with the LSEIK in a regional MITgcm configuration (Yang et al, 2014). Compared to this study, it is found that assimilation of SMOS-Ice has a more moderate impact.…”

Section: Summary and Discussionmentioning

confidence: 44%

“…This may be related to the fact that TOPAZ uses a more complete observation network and that the assimilation has been spun up over a longer period of time (from 1989). We also find that assimilation of SMOS-Ice is comparatively larger in October-November than in February-March the time period when Yang et al (2014) tested assimilation of SMOS-Ice. We also verified that assimilation of SMOS-Ice does not degrade ocean variables (SST and SLA), which could happen with a strongly coupled data assimilation scheme.…”

Section: Summary and Discussionmentioning

confidence: 52%

“…The first demonstration of assimilating SMOS-Ice has been presented by Yang et al (2014) for the period from November 2011 to January 2012. The system assimilates both SIT (thinner than 1 m) from SMOS-Ice and SIC from Special Sensor Microwave Imager/Sounder (SSMIS) in a nested Arctic configuration of the Massachusetts Institute of Technology general circulation model (MITgcm).…”

Section: Introductionmentioning

confidence: 99%

“…A comparison of SIT from three moorings from the Beaufort Gyre Experiment Program (BGEP) and from one autonomous ice mass balance (IMB) buoy shows that the overestimation of SIT is reduced. The present study follows up the work from Yang et al (2014) but it further explores the impact and relative importance of SMOS-Ice in the perspective of an ice-ocean forecasting system: 1. the impact of assimilating SMOS-Ice is tested both during the onsets of the melting and freezing seasons;…”

Section: Introductionmentioning

confidence: 99%

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Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system

Xie¹,

Counillon²,

Bertino³

et al. 2016

The Cryosphere

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Abstract. An observation product for thin sea ice thickness (SMOS-Ice) is derived from the brightness temperature data of the European Space Agency's (ESA) Soil Moisture and Ocean Salinity (SMOS) mission. This product is available in near-real time, at daily frequency, during the cold season. In this study, we investigate the benefit of assimilating SMOS-Ice into the TOPAZ coupled ocean and sea ice forecasting system, which is the Arctic component of the Copernicus marine environment monitoring services. The TOPAZ system assimilates sea surface temperature (SST), altimetry data, temperature and salinity profiles, ice concentration, and ice drift with the ensemble Kalman filter (EnKF). The conditions for assimilation of sea ice thickness thinner than 0.4 m are favorable, as observations are reliable below this threshold and their probability distribution is comparable to that of the model. Two parallel Observing System Experiments (OSE) have been performed in March and November 2014, in which the thicknesses from SMOS-Ice (thinner than 0.4 m) are assimilated in addition to the standard observational data sets. It is found that the root mean square difference (RMSD) of thin sea ice thickness is reduced by 11 % in March and 22 % in November compared to the daily thin ice thicknesses of SMOS-Ice, which suggests that SMOS-Ice has a larger impact during the beginning of the cold season. Validation against independent observations of ice thickness from buoys and ice draft from moorings indicates that there are no degradations in the pack ice but there are some improvements near the ice edge close to where the SMOS-Ice has been assimilated. Assimilation of SMOS-Ice yields a slight improvement for ice concentration and degrades neither SST nor sea level anomaly. Analysis of the degrees of freedom for signal (DFS) indicates that the SMOS-Ice has a comparatively small impact but it has a significant contribution in constraining the system (> 20 % of the impact of all ice and ocean observations) near the ice edge. The areas of largest impact are the Kara Sea, Canadian Archipelago, Baffin Bay, Beaufort Sea and Greenland Sea. This study suggests that the SMOS-Ice is a good complementary data set that can be safely included in the TOPAZ system.

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Section: Summary and Discussionmentioning

confidence: 44%

Section: Summary and Discussionmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system

Xie¹,

Counillon²,

Bertino³

et al. 2016

The Cryosphere

Self Cite

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show abstract

“…This assessment is a first necessary step towards the eventual assimilation of these observational data, because large systematic errors in either the observations or the forecast model will make successful data assimilation difficult. Previous studies report slightly positive results overall when assimilating L-band sea-ice thickness observations (Yang et al, 2014;Xie et al, 2016) but without doubting the validity of the observational data. As we will show here, both reanalysis and observations can contain large and systematic errors.…”

Section: Introductionmentioning

confidence: 89%

Thin Arctic sea ice in L-band observations and an ocean reanalysis

et al. 2018

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Abstract. L-band radiance measurements of the Earth's surface such as those from the SMOS satellite can be used to retrieve the thickness of thin sea ice in the range 0-1 m under cold surface conditions. However, retrieval uncertainties can be large due to assumptions in the forward model, which converts brightness temperatures into ice thickness and due to uncertainties in auxiliary fields which need to be independently modelled or observed. It is therefore advisable to perform a critical assessment with independent observational and model data before using sea-ice thickness products from L-band radiometry for model validation or data assimilation. Here, we discuss version 3.1 of the University of Hamburg SMOS sea-ice thickness data set (SMOS-SIT) from autumn 2011 to autumn 2017 and compare it to the global ocean reanalysis ORAS5, which does not assimilate the SMOS-SIT data. ORAS5 currently provides the ocean and sea-ice initial conditions for all coupled weather, monthly and seasonal forecasts issued by ECMWF. It is concluded that SMOS-SIT provides valuable and unique information on thin sea ice during winter and can under certain conditions be used to expose deficiencies in the reanalysis. Overall, there is a promising match between sea-ice thicknesses from ORAS5 and SMOS-SIT early in the freezing season (October-December), while later in winter, sea ice is consistently modelled thicker than observed. This is mostly attributable to refrozen polynyas and fracture zones, which are poorly represented in ORAS5 but easily detected by SMOS-SIT. However, there are other regions like Baffin Bay, where biases in the observational data seem to be substantial, as comparisons with independent observational data suggest. Despite considerable uncertainties and discrepancies between thin sea ice in SMOS-SIT and ORAS5 on local scales, interannual variability and trends of its large-scale distribution are in good agreement. This gives some confidence in our current ability to monitor climate variability and change in thin sea ice. With further improvements in retrieval methods, forecast models and data assimilation methods, the huge potential of L-band radiometry to derive the thickness of thin sea ice in winter will be realised and will provide an important building block for improved predictions in polar regions.

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Will Arctic sea ice thickness initialization improve seasonal forecast skill?

Day

Hawkins

Tietsche

2014

Geophysical Research Letters

142

126

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Arctic sea ice thickness is thought to be an important predictor of Arctic sea ice extent. However, coupled seasonal forecast systems do not generally use sea ice thickness observations in their initialization and are therefore missing a potentially important source of additional skill. To investigate how large this source is, a set of ensemble potential predictability experiments with a global climate model, initialized with and without knowledge of the sea ice thickness initial state, have been run. These experiments show that accurate knowledge of the sea ice thickness field is crucially important for sea ice concentration and extent forecasts up to 8 months ahead, especially in summer. Perturbing sea ice thickness also has a significant impact on the forecast error in Arctic 2 m temperature a few months ahead. These results suggest that advancing capabilities to observe and assimilate sea ice thickness into coupled forecast systems could significantly increase skill.

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Assimilating SMOS sea ice thickness into a coupled ice‐ocean model using a local SEIK filter

Cited by 83 publications

References 43 publications

Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system

Benefits of assimilating thin sea ice thickness from SMOS into the TOPAZ system

Thin Arctic sea ice in L-band observations and an ocean reanalysis

Will Arctic sea ice thickness initialization improve seasonal forecast skill?

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