Vol.32, No.02. 2021
Table of Contents
ARTICLE | Oceanography/Sea Ice
Evaluation of ArcIOPS sea ice forecasting products during the ninth CHINARE-Arctic in summer 2018
Correspondence: email@example.com ORCID:
Numerical sea ice forecasting products during the ninth Chinese National Arctic Research Expedition (CHINARE- Arctic) from Arctic Ice Ocean Prediction System (ArcIOPS) of National Marine Environmental Forecasting Center are evaluated against satellite-retrieved sea ice concentration data, in-situ sea ice thickness observations, and sea ice products from Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). The results show that ArcIOPS forecasts reliable sea ice concentration and thickness evolution. Deviations of the 168 h sea ice concentration and thickness forecasts with respect to the observations are less than 0.2 and 0.36 m. Comparison between outputs of the latest version of ArcIOPS and that of its previous version shows that the latest version has a substantial improvement on sea ice concentration forecasts due to data assimilation of new observational component, the sea surface temperature. Meanwhile, the sea ice volume product of the latest version is more close to the PIOMAS product. In the future, with more and more kinds of observations to be assimilated, the high-resolution version of ArcIOPS will be put into operational running and benefit Chinese scientific and commercial activities in the Arctic Ocean.
1 Key Laboratory of Research on Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Beijing 100081, China; 2 Polar Research Institute of China, Shanghai 200136, China
Comiso J C, Cavalieri D J, Parkinson C L, et al. 1997. Passive microwave algorithms for sea ice concentration: a comparison of two techniques. Remote Sens Environ, 60(3): 357-384.
Crane R G. 1983. Atmosphere-sea ice interactions in the Beaufort/Chukchi Sea and in the European sector of the Arctic. J Geophys Res Oceans, 88(C7): 4505-4523, doi: 10.1029/JC088iC07p04505.
Day J J, Hawkins E, Tietsche S. 2014. Will Arctic sea ice thickness initialization improve seasonal forecast skill? Geophys Res Lett, 41: 7566-7575.
Eastwood S, Larsen K R, Lavergne T, et al. 2011. Global sea ice concentration reprocessing. Product User Manual, EUMETSAT OSISAF, Document version 2.
Evensen G. 1994. Sequential data assimilation with a nonlinear quasi-geostrophic model using Monte Carlo methods to forecast error statistics. J Geophys Res, 99: 10143-10162.
Guo J, Sun B, Tian G, et al. 2008. Research on electromagnetic inductive measurement of sea-ice thickness in Antarctic Prydz Bay. Chinese J Geophys, 51(2): 596-602 (in Chinese with English abstract).
Kaleschke L, Maaβ N, Haas C, et al. 2010. A sea-ice thickness retrieval model for 1.4 GHz radiometry and application to airborne measurements over low salinity sea-ice. Cryosphere, 4: 583-592.
Kaleschke L, Tian-Kunze X, Maaβ N, et al. 2012. Sea ice thickness retrieval from SMOS brightness temperatures during the Arctic freeze-up period. Geophys Res Lett, 39: L05501.
Kwok R, Cunningham G F, Wensnahan M, et al. 2009. Thinning and volume loss of the Arctic Ocean sea ice cover: 2003-2008. J Geophys Res, 114(7), doi: 10.1029/2009JC005312.
Laxon S W, Giles K A, Ridout A L, et al. 2013. Cryosat-2 estimates of Arctic sea ice thickness and volume. J Geophys Res, 40(4): 732-737.
Liang X, Losch M. 2018. On the effects of increased vertical mixing on the Arctic Ocean and sea ice. J Geophys Res Oceans, 123: 9266-9282.
Liang X, Losch M, Nerger L, et al. 2019. Using sea surface temperature observations to constrain upper ocean properties in an Arctic sea ice-ocean data assimilation system. J Geophys Res Oceans, 124: 4727-4743.
Liang X, Yang Q, Nerger L, et al. 2017. Assimilating Copernicus SST Data into a Pan-Arctic Ice-Ocean Coupled Model with a Local SEIK Filter. J Atmos Ocean Tech, 34(9): 1985-1999.
Lisæter K A, Rosanova J, Evensen G. 2003. Assimilation of ice concentration in a coupled ice-ocean model using the ensemble Kalman filter. Ocean Dyn, 53: 368-388.
Losch M, Menemenlis D, Campin J M, et al. 2010. On the formulation of sea-ice models. Part 1: Effects of different solver implementations and parameterizations. Ocean Model, 33: 129-144.
Marshall J, Adcroft A, Hill C, et al. 1997. A finite-volume, incompressible Navier Stokes model for studies of the ocean on parallel computers. J Geophys Res, 102(C3): 5753-5766.
Mu L, Yang Q, Losch M, et al. 2018a. Improving sea ice thickness estimates by assimilating cryosat-2 and smos sea ice thickness data simultaneously. Q J R Meteorol Soc, 144(711): 529-538.
Mu L, Losch M, Yang Q, et al. 2018b. 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. J Geophys Res Oceans, 123: 7763-7780.
Mu L, Liang X, Yang Q, et al. 2019. Arctic ice ocean prediction system: evaluating sea ice forecasts during Xuelong’s first trans-Arctic passage in summer 2017. J Glaciol, doi: 10.1017/jog.2019.55.
Nerger L, Hiller W. 2013. Software for ensemble-based data assimilation systems-implementation strategies and scalability. Comput Geosci, 55: 110-118.
Nerger L, Janji T, Schröter J, et al. 2012. A unification of ensemble square root Kalman filters. Mon Weather Rev, 140(7): 2335-2345.
Nguyen A T, Menemenlis D, Kwok R. 2011. Arctic ice-ocean simulation with optimized model parameters: Approach and assessment. J Geophys Res, 116: C04025.
Ricker R, Hendricks S, Helm V, et al. 2014. Sensitivity of CryoSat-2 Arctic sea ice freeboard and thickness on radar-waveform interpretation. Cryosphere, 8(4): 1607-1622.
Ricker R, Hendricks S, Kaleschke L, et al. 2017. A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data. Cryosphere, 11: 1607-1623.
Sakov P, Counillon F, Bertino L, et al. 2012. TOPAZ4: An ocean-sea ice data assimilation system for the North Atlantic and Arctic. Ocean Sci, 8: 633-656.
Semtner A J J. 1976. A model for the thermodynamic growth of sea ice in numerical investigations of climate. J Phys Oceanogr, 6(3): 379-389.
Smith W H F, Sandwell D T. 1997. Global sea floor topography from satellite altimetry and ship depth soundings. Science, 277(5334): 1956-1962.
Spreen G, Kaleschke L, Heygster G. 2008. Sea ice remote sensing using AMSR-E 89 GHz channels. J Geophys Res, 113: C02S03.
Tian-Kunze X, Kaleschke L, Maaβ N, et al. 2014. SMOS-derived thin sea ice thickness: Algorithm baseline, product specifications and initial verification. Cryosphere, 8: 997-1018.
Thorndike A S, Colony R. 1982. Sea ice motion in response to geostrophic winds. J Geophys Res Oceans, 87: C8.
Wingham D J, Francis C R, Baker S, et al. 2006. Cryosat: A mission to determine the fluctuations in Earths land and marine ice fields. Adv Sp Res, 37(4): 841-871.
Yang J, Comiso J, Walsh D, et al. 2004. Storm-driven mixing and potential impact on the Arctic Ocean. J Geophys Res, 109: C04008.
Yang Q, Losa S N, Losch M, et al. 2014. Assimilating SMOS sea ice thickness into a coupled ice-ocean model using a local SEIK filter. J Geophys Res Oceans, 119: 6680-6692.
Yang Q, Losa S N, Losch M, et al. 2015. Assimilating summer sea-ice concentration into a coupled ice-ocean model using a LSEIK filter. Ann Glaciol, 56(69): 38-44.
Zhang J, Rothrock D A. 2003. Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates. Mon Weather Rev, 131(5): 845-861.