Taking into Account Atmospheric Uncertainty Improves Sequential Assimilation of SMOS Sea Ice Thickness Data in an Ice–Ocean Model

Yang, Qinghua; Lösch, Martin; Losa, Svetlana N.; Jung, Thomas; Nerger, Lars

doi:10.1175/jtech-d-15-0176.1

Cited by 19 publications

(20 citation statements)

References 41 publications

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“…However, further work needs to be done to better understand the uncertainty of the assimilated SIT from the SMOS-Ice. Recently, Yang et al (2016) tested the sensitivity of assimilating the SMOS-Ice data with the LSEIK during the winter of 2011-2012, and they found that perturbations of the atmospheric forcing is important for improving the performance of assimilation, in agreement with Lisaeter et al (2007).…”

Section: Summary and Discussionmentioning

confidence: 99%

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: 99%

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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“…The spatial resolution of the sea ice concentration data is 12.5 km × 12.5 km in a polar stereographic projection. Following Yang, Losch, Losa, Jung, Nerger, and Lavergne () and Yang, Losch, Losa, Jung, and Nerger (), a uniform constant value of 0.25 fractional sea ice area is assumed as observational uncertainties accounting for measurement and representation errors (Janjić et al, ) in the study.…”

Section: The Model Sea Ice Thickness Estimatesmentioning

confidence: 99%

“…For example, Lisæter et al () showed in idealized experiments with synthetic CryoSat data that sea ice and ocean state variables improve with sea ice thickness data assimilation. A series of studies also showed that the assimilation of SMOS ice thickness significantly improves the first‐year ice estimates (Yang et al, ; Yang, Losch, Losa, Jung, & Nerger, ; Xie et al, ). Assimilating CryoSat‐2 ice thickness data in addition to SMOS ice thickness into an ice‐ocean model in the cold season leads to a reliable pan‐Arctic sea ice thickness estimate that is consistent with in situ observations (Mu et al, ) .…”

Section: Introductionmentioning

confidence: 99%

Arctic‐Wide Sea Ice Thickness Estimates From Combining Satellite Remote Sensing Data and a Dynamic Ice‐Ocean Model with Data Assimilation During the CryoSat‐2 Period

Lösch

Yang

et al. 2018

JGR Oceans

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Exploiting the complementary character of CryoSat‐2 and Soil Moisture and Ocean Salinity satellite sea ice thickness products, daily Arctic sea ice thickness estimates from October 2010 to December 2016 are generated by an Arctic regional ice‐ocean model with satellite thickness assimilated. The assimilation is performed by a Local Error Subspace Transform Kalman filter coded in the Parallel Data Assimilation Framework. The new estimates can be generally thought of as combined model and satellite thickness (CMST). It combines the skill of satellite thickness assimilation in the freezing season with the model skill in the melting season, when neither CryoSat‐2 nor Soil Moisture and Ocean Salinity sea ice thickness is available. Comparisons with in situ observations from the Beaufort Gyre Exploration Project, Ice Mass Balance Buoys, and the NASA Operation IceBridge demonstrate that CMST reproduces most of the observed temporal and spatial variations. Results also show that CMST compares favorably to the Pan‐Arctic Ice‐Ocean Modeling and Assimilation System product and even appears to correct known thickness biases in the Pan‐Arctic Ice‐Ocean Modeling and Assimilation System. Due to imperfect parameterizations in the sea ice model and satellite thickness retrievals, CMST does not reproduce the heavily deformed and ridged sea ice along the northern coast of the Canadian Arctic Archipelago and Greenland. With the new Arctic sea ice thickness estimates sea ice volume changes in recent years can be further assessed.

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“…() with the aim to improve 24 h‐forecasts of perennial sea ice thickness. To achieve this, the weekly averaged CryoSat‐2 ice thickness is assimilated into our forecasting system in addition to the Special Sensor Microwave Imager Sounder (SSMIS) sea ice concentration and SMOS sea ice thickness data (Yang et al., ). To account for atmospheric uncertainties, we follow Yang et al .…”

Section: Introductionmentioning

confidence: 99%

“…To account for atmospheric uncertainties, we follow Yang et al . (, ) and use the same ensemble atmospheric forcing from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) dataset (Park et al., ; Bougeault et al., ). The same autumn–winter seasonal transition period from 1 November 2011 to 30 January 2012 is chosen to simplify comparisons with Yang et al .…”

Section: Introductionmentioning

confidence: 99%

Improving sea ice thickness estimates by assimilating CryoSat‐2 and SMOS sea ice thickness data simultaneously

Yang

Lösch

et al. 2018

Quart J Royal Meteoro Soc

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The impact of assimilating weekly CryoSat-2 sea ice thickness data together with daily SMOS sea ice thickness and daily SSMIS sea ice concentration data on the sea ice fields of a coupled sea ice-ocean model of the Arctic Ocean is investigated. The sea-ice model is based on the Massachusetts Institute of Technology general circulation model (MITgcm) and the assimilation is performed by a localized Singular Evolutive Interpolated Kalman (LSEIK) filter coded in the Parallel Data Assimilation Framework (PDAF). A period of three months from 1 November 2011 to 30 January 2012 is selected to assess the skill of the assimilation system in the cold season. Compared to the unassimilated solution and a solution where only sea ice concentration is assimilated, the model-data misfits are substantially reduced in areas of both thick and thin ice. The sea ice thickness estimates agree significantly better with in situ observations in the central Arctic Ocean than the sea ice thickness obtained from assimilating SMOS data alone, while the sea ice concentration shows very small improvements. The sea ice fields obtained by the joint assimilation of SMOS and CryoSat-2 data also have lower errors in thickness and concentration than those obtained from directly assimilating a statistically merged SMOS and CryoSat-2 sea ice thickness product. These lower errors suggest that model dynamics play a significant role in data blending.

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Taking into Account Atmospheric Uncertainty Improves Sequential Assimilation of SMOS Sea Ice Thickness Data in an Ice–Ocean Model

Cited by 19 publications

References 41 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

Arctic‐Wide Sea Ice Thickness Estimates From Combining Satellite Remote Sensing Data and a Dynamic Ice‐Ocean Model with Data Assimilation During the CryoSat‐2 Period

Improving sea ice thickness estimates by assimilating CryoSat‐2 and SMOS sea ice thickness data simultaneously

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