The Impact of Geophysical Corrections on Sea-Ice Freeboard Retrieved from Satellite Altimetry

Ricker, Robert; Hendricks, Stefan; Beckers, Justin

doi:10.3390/rs8040317

Cited by 29 publications

(42 citation statements)

References 23 publications

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“…Overall the AI of the MSS/GGM is large, and affects a substantial portion of grid cells across the Arctic Ocean. These results are even more significant when placed in the context of the AI of the geophysical correction terms reported in (Ricker et al, ). Ricker et al () found that the contributions from geophysical range corrections impacted much fewer grid cells across the Arctic Ocean.…”

Section: Resultsmentioning

confidence: 99%

“…These results are even more significant when placed in the context of the AI of the geophysical correction terms reported in (Ricker et al, ). Ricker et al () found that the contributions from geophysical range corrections impacted much fewer grid cells across the Arctic Ocean. They found that the AI contributions due to the ocean tide and inverse barometer corrections were 7% and 3%, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…When freeboard errors are compared with other sources of uncertainty (e.g., due to remaining uncertainties in geophysical range corrections), the area impacted by MSS uncertainties is more significant. Depending on the choice of MSS/GGM, approximately 60–80% of the CryoSat‐2 grid cells have freeboard differences greater than ±0.01 m. This compares to an area impact of only 3–7% for errors due to uncertainties in the ocean tide or inverse barometer corrections (Ricker et al, ).…”

Section: Discussionmentioning

confidence: 99%

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An Assessment of State‐of‐the‐Art Mean Sea Surface and Geoid Models of the Arctic Ocean: Implications for Sea Ice Freeboard Retrieval

et al. 2017

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State‐of‐the‐art Arctic Ocean mean sea surface (MSS) models and global geoid models (GGMs) are used to support sea ice freeboard estimation from satellite altimeters, as well as in oceanographic studies such as mapping sea level anomalies and mean dynamic ocean topography. However, errors in a given model in the high‐frequency domain, primarily due to unresolved gravity features, can result in errors in the estimated along‐track freeboard. These errors are exacerbated in areas with a sparse lead distribution in consolidated ice pack conditions. Additionally model errors can impact ocean geostrophic currents, derived from satellite altimeter data, while remaining biases in these models may impact longer‐term, multisensor oceanographic time series of sea level change in the Arctic. This study focuses on an assessment of five state‐of‐the‐art Arctic MSS models (UCL13/04 and DTU15/13/10) and a commonly used GGM (EGM2008). We describe errors due to unresolved gravity features, intersatellite biases, and remaining satellite orbit errors, and their impact on the derivation of sea ice freeboard. The latest MSS models, incorporating CryoSat‐2 sea surface height measurements, show improved definition of gravity features, such as the Gakkel Ridge. The standard deviation between models ranges 0.03–0.25 m. The impact of remaining MSS/GGM errors on freeboard retrieval can reach several decimeters in parts of the Arctic. While the maximum observed freeboard difference found in the central Arctic was 0.59 m (UCL13 MSS minus EGM2008 GGM), the standard deviation in freeboard differences is 0.03–0.06 m.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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An Assessment of State‐of‐the‐Art Mean Sea Surface and Geoid Models of the Arctic Ocean: Implications for Sea Ice Freeboard Retrieval

et al. 2017

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“…The Norwegian and Barents Sea are only seasonally covered by sea-ice while the central part up to the Canadian Archipelago and the North coast of Greenland are permanently ice covered (see Figure 1 for an Arctic Ocean overview). The older ice is pushed against these parts, and additionally, the Canadian Archipelago and the land-fast ice areas are also the part with the fewest leads and consequently the most inaccurate sea level determination [9].…”

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confidence: 99%

Arctic Ocean Sea Level Record from the Complete Radar Altimetry Era: 1991–2018

Rose

Andersen

Passaro³

et al. 2019

Remote Sensing

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In recent years, there has been a large focus on the Arctic due to the rapid changes of the region. Arctic sea level determination is challenging due to the seasonal to permanent sea-ice cover, lack of regional coverage of satellites, satellite instruments ability to measure ice, insufficient geophysical models, residual orbit errors, challenging retracking of satellite altimeter data. We present the European Space Agency (ESA) Climate Change Initiative (CCI) Technical University of Denmark (DTU)/Technischen Universität München (TUM) sea level anomaly (SLA) record based on radar satellite altimetry data in the Arctic Ocean from the European Remote Sensing satellite number 1 (ERS-1) (1991) to CryoSat-2 (2018). We use updated geophysical corrections and a combination of altimeter data: Reprocessing of Altimeter Product for ERS (REAPER) (ERS-1), ALES+ retracker (ERS-2, Envisat), combination of Radar Altimetry Database System (RADS) and DTUs in-house retracker LARS (CryoSat-2). Furthermore, this study focuses on the transition between conventional and Synthetic Aperture Radar (SAR) altimeter data to make a smooth time series regarding the measurement method. We find a sea level rise of 1.54 mm/year from September 1991 to September 2018 with a 95% confidence interval from 1.16 to 1.81 mm/year. ERS-1 data is troublesome and when ignoring this satellite the SLA trend becomes 2.22 mm/year with a 95% confidence interval within 1.67-2.54 mm/year. Evaluating the SLA trends in 5 year intervals show a clear steepening of the SLA trend around 2004. The sea level anomaly record is validated against tide gauges and show good results. Additionally, the time series is split and evaluated in space and time.sheet mass losses. Outlet glaciers are losing mass more rapidly [6,7], contributing to the sea level rise, changing the oceans freshwater flux, and influencing the ocean thermohaline circulation [8].The polar oceans are often not included in the global sea level estimations and can be seen as white spots on the global sea level maps. This is because of the challenging polar sea level determination due to; the seasonal to permanent sea-ice cover, the lack of regional coverage of satellites, satellite instruments ability to measure ice, insufficient geophysical models, residual orbit errors and retracking of satellite altimeter data.The sea-ice cover is in constant change. The sea-ice extent is the largest in March and the smallest in September. The Norwegian and Barents Sea are only seasonally covered by sea-ice while the central part up to the Canadian Archipelago and the North coast of Greenland are permanently ice covered (see Figure 1 for an Arctic Ocean overview). The older ice is pushed against these parts, and additionally, the Canadian Archipelago and the land-fast ice areas are also the part with the fewest leads and consequently the most inaccurate sea level determination [9].

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“…In addition, the continued use of research icebreakers to obtain high-quality, full-depth profiles of temperature and salinity across the Arctic Ocean basins is paramount to measure variability in the deep ocean and to ensure adequate calibration/quality-control of the autonomous systems. Estimating sea-ice volume, representing the solid part of liquid FWC, has been facilitated since about 2011 by various satellite missions (e.g., Cryosat-2), that allow the determination of seasonal near-surface changes (largely icemelt/freeze) and interannual variability (Armitage et al, 2016;Ricker et al, 2016). The differences between SSH from altimetry and ocean bottom pressure (OBP) from gravimetry, i.e., depthintegrated steric height that is dominated by FWC changes in the Arctic Ocean, have provided the capability to monitor broadscale FWC changes in the Arctic Ocean (e.g., Morison et al, 2012).…”

Section: Case Study: Arctic Oceanmentioning

confidence: 99%

Adequacy of the Ocean Observation System for Quantifying Regional Heat and Freshwater Storage and Change

et al. 2019

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Considerable advances in the global ocean observing system over the last two decades offers an opportunity to provide more quantitative information on changes in heat and freshwater storage. Variations in these storage terms can arise through internal variability and also the response of the ocean to anthropogenic climate change. Disentangling these competing influences on the regional patterns of change and elucidating their governing processes remains an outstanding scientific challenge. This challenge is compounded by instrumental and sampling uncertainties. The combined use of ocean observations and model simulations is the most viable method to assess the forced signal from noise and ascertain the primary drivers of variability and change. Moreover, this approach offers the potential for improved seasonal-to-decadal predictions and the possibility to develop powerful multi-variate constraints on climate model future projections. Regional heat storage changes dominate the steric contribution to sea level rise over most of the ocean and are vital to understanding both global and regional heat budgets. Variations in regional freshwater storage are particularly relevant to our understanding of changes in the hydrological cycle and can potentially be used to verify local ocean mass addition from terrestrial and cryospheric systems associated with contemporary sea level rise. This White Paper will examine the ability of the current ocean observing system to quantify changes in regional heat and freshwater storage. In particular we will seek to answer the question: What time and space scales are currently Frontiers in Marine Science | www.frontiersin.org 1 August 2019 | Volume 6 | Article 416Palmer et al.Regional Heat and Freshwater Observations resolved in different regions of the global oceans? In light of some of the key scientific questions, we will discuss the requirements for measurement accuracy, sampling, and coverage as well as the synergies that can be leveraged by more comprehensively analyzing the multi-variable arrays provided by the integrated observing system.

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The Impact of Geophysical Corrections on Sea-Ice Freeboard Retrieved from Satellite Altimetry

Cited by 29 publications

References 23 publications

An Assessment of State‐of‐the‐Art Mean Sea Surface and Geoid Models of the Arctic Ocean: Implications for Sea Ice Freeboard Retrieval

An Assessment of State‐of‐the‐Art Mean Sea Surface and Geoid Models of the Arctic Ocean: Implications for Sea Ice Freeboard Retrieval

Arctic Ocean Sea Level Record from the Complete Radar Altimetry Era: 1991–2018

Adequacy of the Ocean Observation System for Quantifying Regional Heat and Freshwater Storage and Change

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