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

Tietsche, Steffen; Balmaseda, Magdalena; Rosnay, Patricia de; Zuo, Hao; Tian-Kunze, Xiangshan; Kaleschke, Lars

doi:10.5194/tc-12-2051-2018

Cited by 25 publications

(39 citation statements)

References 41 publications

(67 reference statements)

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“…This can be improved upon e.g., by introducing multiple sea-ice categories that allow treatment of sub-grid-scale variability in the ice thickness, but large-scale observations of sea-ice thickness are urgently needed to validate these new model developments. At present, there are large discrepancies in the representation of thickness of thin sea ice between observational products derived from L-band and ocean analyses with a prognostic ice model, assimilating sea-ice concentration but not sea-ice thickness [292]. This is not entirely due to limitations in the sea-ice model or data assimilation methods, but systematic biases in the observational data set also play a role.…”

Section: Ocean Cryosphere: Snow and Icementioning

confidence: 99%

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

et al. 2018

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In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.

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Section: Ocean Cryosphere: Snow and Icementioning

confidence: 99%

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

et al. 2018

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“…ORAS5 sea-ice uncertainty has been tested by Richter et al (2018) in two radiative transfer models to generate atmosphere brightness temperatures. In addition, an evaluation of ORAS5 sea-ice thickness in the Arctic has been carried out by Tietsche et al (2018) with a focus on thin sea ice with respect to a data set derived from L-band radiances from the SMOS satellite. The interested reader is also referred to Zuo et al (2018) for a case study about extreme sea-ice conditions derived from ORAS5 in 2016 and possible causes for both Arctic and Antarctic.…”

Section: Sea-ice Concentrationmentioning

confidence: 99%

“…It also allows for uncertainty estimation of climate signals, which however is beyond the scope of this document and will be investigated elsewhere (Zuo et al, in preparation). Evaluations of ORAS5 have also been carried out within the framework of ESA SL_cci (Legeais et al, 2018), ESA-SMOS (Tietsche et al, 2018) and CMEMS projects (Zuo et al, 2018).…”

Section: Sea-ice Concentrationmentioning

confidence: 99%

The ECMWF operational ensemble reanalysis-analysis system for ocean and sea-ice: a description of the system and assessment

Zuo¹,

Balmaseda²,

Tietsche³

et al. 2019

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Abstract. The ECMWF OCEAN5 system is a global ocean and sea-ice ensemble of reanalysis and real-time analysis. This manuscript gives a full description of the OCEAN5 system, with the focus on upgrades of system components with respect to its predecessors ORAS4 and ORAP5. An important novelty in OCEAN5 is the ensemble generation strategy that includes perturbation of initial conditions, and a generic perturbation scheme for observations and forcing fields. Other upgrades include revisions to the a-priori bias correction scheme, observation quality control and assimilation method for sea-level anomaly. The OCEAN5 historical reconstruction of the ocean and sea-ice state is the ORAS5 reanalysis, which includes 5 ensemble members and covers the period from 1979 onwards, and with a backward extension until 1958. Updated version of observation data sets are used in ORAS5 production, with special attention devoted to the consistency of sea surface temperature (SST) and sea-ice observations. Assessment of ORAS5 in the observation space suggests that assimilation of observations contribute to reducing the analysis error, with the most prominent contribution from direct assimilation of ocean in-situ observations. Results of observing system experiment further suggest that Argo float is the most influential observation type in our data assimilation system. Assessment of ORAS5 has also been carried out with several key ocean state variables and verified against independent observation data sets from ESA CCI project. With respect to ORAS4, ORAS5 has improved ocean climate state and variability in terms of SST and sea-level, mostly due to increased model resolution and updates in assimilated observation data sets. In spite of the improvements, ORAS5 still underestimates the temporal variance of sea level, and continue exhibiting large SST biases in the Gulf Stream and extension regions which is possibly associated with misrepresentation of front positions. Overall, the SST and sea-ice uncertainties estimated using five ORAS5 ensemble members have spatial patterns consistent with those of analysis error. The ensemble spread of sea-ice is commensurable with the sea-ice analysis error. On the contrary, the ensemble is under-dispersive for SST.

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“…In addition, polynyas often form at the edge of the land-fast ice when winds blow off-shore. These re-freeze quickly, resulting in large areas of thin ice (Tietsche et al, 2018). The satellite observations can be expected to perform moderately well in the region of less variable thickness distribution, provided that there are some leads available for the reference sea surface height measurements.…”

Section: Potential Sources Of Biases In Reanalysis and Observationsmentioning

confidence: 99%

Consistent variability but different spatial patterns between observed and reanalysed sea-ice thickness

Siponen

Uotila

Rinne

et al. 2019

Preprint

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Abstract. Changes in sea-ice thickness are one of the most visible signs of climate change. However, to gain a comprehensive understanding of mechanisms involved, long time series are needed. Importantly, the development of more accurate predictions of sea ice in the Arctic requires good observational products. To assist this, a new sea-ice thickness product by ESA Climate Change Initiative (CCI) is here compared to the ocean reanalysis ORAS5 by ECMWF for the first time. The CCI product is based on two satellite altimetry missions, CryoSat-2 and ENVISAT, which are combined to the longest continuous satellite altimetry time series of Arctic-wide sea-ice thickness, 2002–2017 and continuing. Time series of sea-ice volume for the CCI coverage reveal years of extremely low volume as well as recovery during the winter season. The 15-year trends in sea-ice volume are clearly negative over the time series and despite large variability between years statistically significant. The 15-year ORAS5 trends have larger interannual variability than the CCI trends and are therefore not statistically significant despite of a good match in terms of year-to-year variability. The observed negative trends result from changes in both atmospheric and oceanic forcing. The CCI product performs well in the validation of the ORAS5 reanalysis: overall root-mean-square difference (RMSD) between sea-ice thickness from CCI and ORAS5 is below 1 m. However, seasonal and interannual RMSD variations during the time series are large, from 0.5 m to 1.3 m. The differences are a sum of reanalysis biases, such as incorrect physics or forcing, as well as uncertainties in satellite altimetry, such as the snow climatology used in the thickness retrieval.

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

Cited by 25 publications

References 41 publications

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

Satellite and In Situ Observations for Advancing Global Earth Surface Modelling: A Review

The ECMWF operational ensemble reanalysis-analysis system for ocean and sea-ice: a description of the system and assessment

Consistent variability but different spatial patterns between observed and reanalysed sea-ice thickness

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