Consistent CryoSat-2 and Envisat Freeboard Retrieval of Arctic and
Antarctic Sea Ice

Paul, Stephan; Hendricks, Stefan; Ricker, Robert; Kern, Stefan; Rinne, Eero

doi:10.5194/tc-2018-34

Cited by 3 publications

(6 citation statements)

References 16 publications

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“…CryoSat-2 measures sea ice mostly in SAR (Synthetic Aperture Radar) mode utilising delay Doppler processing, which leads to major improvements over ENVISAT -most importantly the smaller along-track footprint of only 300 m. CryoSat-2 has been shown to agree well with in-situ observations and provides a realistic seasonal cycle of the growing season of sea ice in the Arctic (Laxon et al, 2013). ENVISAT has suffered from large biases related to the mentioned footprint characteristics, but the overlapping period with CryoSat-2 has enabled improvements of the data set based on their differences as well as advanced retracking algorithms (Paul et al, 2018).…”

Section: Envisatmentioning

confidence: 96%

“…Nevertheless, also the new product has to be used with caution. Paul et al (2018) Coverage difference and better original resolution as well as more advanced instrumentation lead to better RMSE between ORAS5 and CryoSat-2 than between ORAS5 and ENVISAT. The difference maps from three autumns (Figure 2) suggest that in the beginning of the growing season the agreement is generally good in the Central Arctic, where February and March 2012 maps (Figure 3) reveal the much thicker ice in the CryoSat-2 measurements compared to ORAS5.…”

Section: Agreement With Previous Validationmentioning

confidence: 97%

“…Better resolution and coverage for the old ENVISAT mission have been achieved by developing the freeboard retrieval using the overlapping period of ENVISAT and CryoSat-2 satellite missions (Paul et al, 2018). Nevertheless, also the new product has to be used with caution.…”

Section: Agreement With Previous Validationmentioning

confidence: 99%

“…The product covers time series of 2002-2017 and became available in summer 2018 as a result of the ESA (European Space Agency) Climate Change Initiative. The processing methods used for the freeboard retrieval from ENVISAT was developed by Paul et al (2018). Previously, Guerreiro et al (2017) and Schwegmann et al (2015) have done analysis of the overlapping period between Cryosat-2 and ENVISAT both in the Arctic and Antarctic showing that the overlapping period can help to improve the sea-ice thickness data set by…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Siponen

Uotila

Rinne

et al. 2019

Preprint

Self Cite

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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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Section: Envisatmentioning

confidence: 96%

Section: Agreement With Previous Validationmentioning

confidence: 97%

Section: Agreement With Previous Validationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Siponen

Uotila

Rinne

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The strategy was originally developed for airborne radar altimeter waveforms. This model was also adopted by the Sea Ice CCI project Phase 1 for Envisat RA-2 (Ivanova et al, 2014), but was later replaced by a TFMRA scheme for both lead and sea ice surfaces in Phase 2 (Paul et al, 2018).…”

Section: Lrm Model For Leadsmentioning

confidence: 99%

Review of Radar Altimetry Techniques over the Arctic Ocean: Recent Progress and Future Opportunities for Sea Level and Sea Ice Research

Quartly

Rinne

Passaro

et al. 2018

Preprint

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Abstract. There are numerous needs for monitoring sea level and sea ice in the Arctic, ranging from concern about changes in ice cover being both an indicator and a driver of long-term climate change to shipping interest in alternative routes and the associated risks to the safety of vessels and crew. Furthermore, sea level relative to the geoid allows us to quantify the geostrophic circulation, including any changes in the flow. Radar altimeters provide an important means of quantifying changes in sea level and sea-ice thickness, although there are increased complexities in the interpretation of their data over such a variable surface. This paper reviews the techniques for deriving useful geophysical information over a mix of leads and ice floes, covering the approaches for both conventional (low rate mode) altimetry and the newer delay-Doppler (Synthetic Aperture Radar) instruments. It discusses the challenges in discriminating the returns from different surfaces, the retracking approaches and the corrections required. The review finishes with a look ahead to how new technologies, analyses and understanding may be expected to improve the monitoring in this critical environment.

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Investigation of the Arctic Sea ice volume from 2002 to 2018 using multi‐source data

Shen

et al. 2020

Intl Journal of Climatology

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The Arctic sea ice volume (SIV) was investigated by applying sea ice concentration (SIC) and multi‐source sea ice thickness (SIT) products from the Pan‐Arctic Ice‐Ocean Modelling and Assimilation System (PIOMAS), Envisat and CryoSat‐2 (CS‐2) products. The SIV was estimated during the sea ice growth season (October–April) from October 2002 to December 2018. During the Envisat period (October 2002–April 2010), negative SIV trends were estimated by a hybrid Envisat and PIOMAS SIT dataset (defined as Envi‐PIO); the declining trends for both the maximum/minimum SIV were 360 and 177 km3⋅year−1, respectively; similar SIV trends were obtained by applying only the PIOMAS SIT data. During the CS‐2 period (October 2010–December 2018), no clear trends in the SIV were estimated by either CS‐2 or PIOMAS, except for clear increases in the SIV in northern Greenland and the Canadian Arctic Archipelago (CAA) using the CS‐2 SIT data. The age of sea ice plays an important role in SIV variability. For example, the SIV trend was found to be similar to the multi‐year ice trend between 2003 and 2007. The correlation coefficients between the monthly mean SIV and surface air temperature (SAT) and sea surface temperature (SST) were −0.60 and −0.82, respectively. The decreasing trend in the SIV during the Envisat period was influenced by the increase in the annual maximum SST and minimum SAT. The significant increase in the SIV in northern Greenland and the CAA during the CS‐2 period was related to ice deformation.

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Consistent CryoSat-2 and Envisat Freeboard Retrieval of Arctic and Antarctic Sea Ice

Cited by 3 publications

References 16 publications

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

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

Review of Radar Altimetry Techniques over the Arctic Ocean: Recent Progress and Future Opportunities for Sea Level and Sea Ice Research

Investigation of the Arctic Sea ice volume from 2002 to 2018 using multi‐source data

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