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

Xie, Jiping; Counillon, François; Bertino, Laurent; Tian-Kunze, Xiangshan; Kaleschke, Lars

doi:10.5194/tc-10-2745-2016

Cited by 43 publications

(60 citation statements)

References 59 publications

(72 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 89%

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

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

“…Results from the other three EnKF runs (not shown) showed the exact same behavior. Thus, the initial ensemble is set as the first 99 members of the reanalysis ensemble of Xie et al (2016) on 31 December 2013. The initial ensemble is then spun up from January, 2014 until the start of the assimilation experiment (i.e., November 11) with perturbed forcing to increase the variability.…”

Section: Free-runmentioning

confidence: 99%

“…Assimilating only thin ice observations, as in Xie et al (2016, Figure 5 and 6), induces a low bias, which is caused by the partial nature of the observation of thin ice. With a new method intended for semi-qualitative data as the EnKF-SQ, the question arise whether this bias can be mitigated or not?…”

Section: Introductionmentioning

confidence: 99%

Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment

Shah¹,

Bertino²,

Counillon³

et al. 2020

Tellus A: Dynamic Meteorology and Oceanography

Self Cite

View full text Add to dashboard Cite

A newly introduced stochastic data assimilation method, the Ensemble Kalman Filter Semi-Qualitative (EnKF-SQ) is applied to a realistic coupled ice-ocean model of the Arctic, the TOPAZ4 configuration, in a twin experiment framework. The method is shown to add value to range-limited thin ice thickness measurements, as obtained from passive microwave remote sensing, with respect to more trivial solutions like neglecting the out-of-range values or assimilating climatology instead.Some known properties inherent to the EnKF-SQ are evaluated: the tendency to draw the solution closer to the thickness threshold, the skewness of the resulting analysis ensemble and the potential appearance of outliers. The experiments show that none of these properties prove deleterious in light of the other sub-optimal characters of the sea ice data assimilation system used here (non-linearities, non-Gaussian variables, lack of strong coupling). The EnKF-SQ has a single tuning parameter that is adjusted for best performance of the system at hand. The sensitivity tests reveal that the results do not depend critically on the choice of this tuning parameter. The EnKF-SQ makes overall a valid approach for assimilating semi-qualitative observations into high-dimensional nonlinear systems.

show abstract

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

Cited by 43 publications

References 59 publications

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

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

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

Assimilation of semi-qualitative sea ice thickness data with the EnKF-SQ: a twin experiment

Contact Info

Product

Resources

About