Assessment of contemporary satellite sea ice thickness products for Arctic sea ice

Sallila, Heidi; Farrell, S. L.; McCurry, Joshua; Rinne, Eero

doi:10.5194/tc-13-1187-2019

Cited by 60 publications

(52 citation statements)

References 55 publications

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“…Systematic uncertainties in ice thickness/volume of this magnitude could have severe implications for calculating the seasonal and interannual sea ice thickness/volume budget, including ice growth and melt rates (e.g., Kwok, 2018;Petty et al, 2018;Stroeve et al, 2018), fluxes of sea ice exported from the Arctic via Fram Strait (Ricker et al, 2018), for initializing dynamical sea ice forecasting systems (Day et al, 2014;Schröder et al, 2019), and for constraining freshwater fluxes into the Arctic Ocean (Bacon et al, 2015). Sallila et al (2019) discovered a similar pattern of pan-Arctic offsets between the CPOM and AWI sea ice thickness products to our emulated freeboards, with the MYI consistently thicker and FYI consistently thinner in the AWI product. They also found the GSFC ice thickness product to be universally thicker than the other products, which contradicts our finding that minimum radar freeboard was derived from the Gaussian model retracker.…”

Section: 1029/2019jc015820mentioning

confidence: 99%

Sea Ice Roughness Overlooked as a Key Source of Uncertainty in CryoSat‐2 Ice Freeboard Retrievals

et al. 2020

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ESA's CryoSat-2 has transformed the way we monitor Arctic sea ice, providing routine measurements of the ice thickness with near basin-wide coverage. Past studies have shown that uncertainties in the sea ice thickness retrievals can be introduced at several steps of the processing chain, for instance, in the estimation of snow depth, and snow and sea ice densities. Here, we apply a new physical model to CryoSat-2, which further reveals sea ice surface roughness as a key overlooked feature of the conventional retrieval process. High-resolution airborne observations demonstrate that snow and sea ice surface topography can be better characterized by a lognormal distribution, which varies based on the ice age and surface roughness within a CryoSat-2 footprint, than a Gaussian distribution. Based on these observations, we perform a set of simulations for the CryoSat-2 echo waveform over "virtual" sea ice surfaces with a range of roughness and radar backscattering configurations. By accounting for the variable roughness, our new lognormal retracker produces sea ice freeboards that compare well with those derived from NASA's Operation IceBridge airborne data and extends the capability of CryoSat-2 to profile the thinnest/smoothest sea ice and thickest/roughest ice. Our results indicate that the variable ice surface roughness contributes a systematic uncertainty in sea ice thickness of up to 20% over first-year ice and 30% over multiyear ice, representing one of the principal sources of pan-Arctic sea ice thickness uncertainty. Plain Language Summary We have developed a new way of measuring sea ice thickness in the Arctic, by comparing real and simulated data from the European Space Agency satellite: CryoSat-2. Our simulations are guided by aircraft observations that demonstrate sea ice has distinct patterns of surface roughness. Traditional methods ignore or misrepresent the surface roughness, which reduces our confidence in the measured ice thickness by around one third. If we account for the roughness, however, we can extend the capability of Cryosat-2 for measuring both the thinnest smoothest sea ice and thickest roughest ice. These improvements will boost our confidence in derived estimates of the Arctic Ocean's sea ice volume budget and freshwater fluxes, while enhancing the accuracy of sea ice forecasts primed with satellite data.

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Section: 1029/2019jc015820mentioning

confidence: 99%

Sea Ice Roughness Overlooked as a Key Source of Uncertainty in CryoSat‐2 Ice Freeboard Retrievals

et al. 2020

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“…Loss of Arctic sea ice is exacerbating planetary warming owing to the ice albedo feedback (e.g. Budyko, 1969;Serreze and Francis, 2006;Screen and Simmonds 2010), and loss of land ice is the principal source of global sea level rise (see Intergovernmental Panel on Climate Change, IPCC; SROCC, 2019). The rates and magnitudes of depletion of Earth's marine and terrestrial ice fields are among the most significant elements of future climate projections (Meredith et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) high-priority candidate mission

et al. 2020

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Abstract. The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) mission is one of six high-priority candidate missions (HPCMs) under consideration by the European Commission to enlarge the Copernicus Space Component. Together, the high-priority candidate missions fill gaps in the measurement capability of the existing Copernicus Space Component to address emerging and urgent user requirements in relation to monitoring anthropogenic CO2 emissions, polar environments, and land surfaces. The ambition is to enlarge the Copernicus Space Component with the high-priority candidate missions in the mid-2020s to provide enhanced continuity of services in synergy with the next generation of the existing Copernicus Sentinel missions. CRISTAL will carry a dual-frequency synthetic-aperture radar altimeter as its primary payload for measuring surface height and a passive microwave radiometer to support atmospheric corrections and surface-type classification. The altimeter will have interferometric capabilities at Ku-band for improved ground resolution and a second (non-interferometric) Ka-band frequency to provide information on snow layer properties. This paper outlines the user consultations that have supported expansion of the Copernicus Space Component to include the high-priority candidate missions, describes the primary and secondary objectives of the CRISTAL mission, identifies the key contributions the CRISTAL mission will make, and presents a concept – as far as it is already defined – for the mission payload.

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“…The CryoSat‐2‐derived sea ice thickness is more accurate for the central Arctic ice pack. It is also possible for thin ice measurement but with less robustness (Sallila et al, 2019). The second product is the daily sea ice thickness derived from the SMOS brightness temperature measured by the Microwave Imaging Radiometer using Aperture Synthesis using a single‐layer emissivity model (Kaleschke et al, 2012; Tian‐Kunze et al, 2014; https://icdc.cen.uni-hamburg.de/thredds/catalog/ftpthredds/smos_sea_ice_thickness/catalog.html).…”

Section: Datamentioning

confidence: 99%

Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations

Yang

Liu

2020

J Adv Model Earth Syst

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To increase our capability to predict Arctic sea ice and climate, we have developed a coupled atmosphere-sea ice-ocean model configured for the pan-Arctic with sufficient flexibility. The Los Alamos Sea Ice Model is coupled with the Weather Research and Forecasting Model and the Regional Ocean Modeling System in the Coupled Ocean-Atmosphere-Wave-Sediment Transport modeling system. It is well known that dynamic models used to predict Arctic sea ice at short-term periods strongly depend on model initial conditions. Parallel Data Assimilation Framework is implemented into the new modeling system to assimilate sea ice observations and generate skillful model initialization, which aid in the prediction procedures. The Special Sensor Microwave Imager/Sounder sea ice concentration, the CyroSat-2, and Soil Moisture and Ocean Salinity sea ice thickness are assimilated with the localized error subspace transform ensemble Kalman filter. We conduct Arctic sea ice prediction for the melting seasons of 2017 and 2018. Predictions with improved initial sea ice conditions show reasonable sea ice evolution and small biases in the minimum sea ice extent, although the ice refreezing is delayed. Our prediction experiments suggest that the use of appropriate uncertainty for the observed sea ice thickness can lead to improved spatial distribution of the initial ice thickness and thus the predicted sea ice distribution. Our new modeling system initialized by the output of the National Centers for Environmental Prediction Climate Forecast System seasonal forecasts with data assimilation can significantly increase the sea ice prediction skills in sea ice extent for the entire Arctic as well as in the Northern Sea Route compared with the predictions by the National Centers for Environmental Prediction Climate Forecast System. Plain Language SummaryWe have developed a coupled atmosphere-sea ice-ocean model configured for the Arctic to enhance our capability to predict Arctic sea ice and climate. It is well known that the accuracy of model initial condition strongly influences Arctic sea ice predictions with dynamic models at short-term periods. A data assimilation system is combined with the new coupled model to assimilate satellite sea ice observations to improve initial sea ice conditions. We perform Arctic sea ice predictions using the new modeling system for the summers of 2017 and 2018. Predictions show good predictive skills compared with the observations. Our prediction experiments also suggest that the use of appropriate uncertainty in observed sea ice thickness can improve the predicted sea ice spatial pattern. Our new modeling system initialized by the seasonal forecasts of the National Centers for Environmental Prediction Climate Forecast System and our data assimilation procedures perform much better in predicting Arctic sea ice cover as well as sea ice conditions in the Arctic shipping routes than the National Centers for Environmental Prediction Climate Forecast System. Key Points:• A new atmosphere-sea ice-ocean regional coup...

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Assessment of contemporary satellite sea ice thickness products for Arctic sea ice

Cited by 60 publications

References 55 publications

Sea Ice Roughness Overlooked as a Key Source of Uncertainty in CryoSat‐2 Ice Freeboard Retrievals

Sea Ice Roughness Overlooked as a Key Source of Uncertainty in CryoSat‐2 Ice Freeboard Retrievals

The Copernicus Polar Ice and Snow Topography Altimeter (CRISTAL) high-priority candidate mission

Seasonal Arctic Sea Ice Prediction Using a Newly Developed Fully Coupled Regional Model With the Assimilation of Satellite Sea Ice Observations

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