Articles | Volume 19, issue 2
https://doi.org/10.5194/os-19-305-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/os-19-305-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Effects of including the adjoint sea ice rheology on estimating Arctic Ocean–sea ice state
Shanghai Key Laboratory of Polar Life and Environment Sciences, School
of Oceanography, Shanghai Jiao Tong University, Shanghai, China
Armin Koehl
Center for Earth System Research and Sustainability (CEN), University
of Hamburg, Hamburg, Germany
Xinrong Wu
Key Laboratory of Marine Environmental Information Technology,
National Marine Data and Information Service, Tianjin, China
Meng Zhou
Shanghai Key Laboratory of Polar Life and Environment Sciences, School
of Oceanography, Shanghai Jiao Tong University, Shanghai, China
MNR Key Laboratory for Polar Science, Polar Research Institute of
China, Shanghai, China
Detlef Stammer
Center for Earth System Research and Sustainability (CEN), University
of Hamburg, Hamburg, Germany
Related authors
Guokun Lyu, Nuno Serra, Meng Zhou, and Detlef Stammer
Ocean Sci., 18, 51–66, https://doi.org/10.5194/os-18-51-2022, https://doi.org/10.5194/os-18-51-2022, 2022
Short summary
Short summary
This study explores the Arctic sea level variability depending on different timescales and the relation to temperature, salinity and mass changes, identifying key parameters and regions that need to be observed coordinately. The decadal sea level variability reflects salinity changes. But it can only reflect salinity change at periods of greater than 1 year, highlighting the requirement for enhancing in situ hydrographic observations and complicated interpolation methods.
Bjorn Stevens, Stefan Adami, Tariq Ali, Hartwig Anzt, Zafer Aslan, Sabine Attinger, Jaana Bäck, Johanna Baehr, Peter Bauer, Natacha Bernier, Bob Bishop, Hendryk Bockelmann, Sandrine Bony, Guy Brasseur, David N. Bresch, Sean Breyer, Gilbert Brunet, Pier Luigi Buttigieg, Junji Cao, Christelle Castet, Yafang Cheng, Ayantika Dey Choudhury, Deborah Coen, Susanne Crewell, Atish Dabholkar, Qing Dai, Francisco Doblas-Reyes, Dale Durran, Ayoub El Gaidi, Charlie Ewen, Eleftheria Exarchou, Veronika Eyring, Florencia Falkinhoff, David Farrell, Piers M. Forster, Ariane Frassoni, Claudia Frauen, Oliver Fuhrer, Shahzad Gani, Edwin Gerber, Debra Goldfarb, Jens Grieger, Nicolas Gruber, Wilco Hazeleger, Rolf Herken, Chris Hewitt, Torsten Hoefler, Huang-Hsiung Hsu, Daniela Jacob, Alexandra Jahn, Christian Jakob, Thomas Jung, Christopher Kadow, In-Sik Kang, Sarah Kang, Karthik Kashinath, Katharina Kleinen-von Königslöw, Daniel Klocke, Uta Kloenne, Milan Klöwer, Chihiro Kodama, Stefan Kollet, Tobias Kölling, Jenni Kontkanen, Steve Kopp, Michal Koran, Markku Kulmala, Hanna Lappalainen, Fakhria Latifi, Bryan Lawrence, June Yi Lee, Quentin Lejeun, Christian Lessig, Chao Li, Thomas Lippert, Jürg Luterbacher, Pekka Manninen, Jochem Marotzke, Satoshi Matsouoka, Charlotte Merchant, Peter Messmer, Gero Michel, Kristel Michielsen, Tomoki Miyakawa, Jens Müller, Ramsha Munir, Sandeep Narayanasetti, Ousmane Ndiaye, Carlos Nobre, Achim Oberg, Riko Oki, Tuba Özkan-Haller, Tim Palmer, Stan Posey, Andreas Prein, Odessa Primus, Mike Pritchard, Julie Pullen, Dian Putrasahan, Johannes Quaas, Krishnan Raghavan, Venkatachalam Ramaswamy, Markus Rapp, Florian Rauser, Markus Reichstein, Aromar Revi, Sonakshi Saluja, Masaki Satoh, Vera Schemann, Sebastian Schemm, Christina Schnadt Poberaj, Thomas Schulthess, Cath Senior, Jagadish Shukla, Manmeet Singh, Julia Slingo, Adam Sobel, Silvina Solman, Jenna Spitzer, Philip Stier, Thomas Stocker, Sarah Strock, Hang Su, Petteri Taalas, John Taylor, Susann Tegtmeier, Georg Teutsch, Adrian Tompkins, Uwe Ulbrich, Pier-Luigi Vidale, Chien-Ming Wu, Hao Xu, Najibullah Zaki, Laure Zanna, Tianjun Zhou, and Florian Ziemen
Earth Syst. Sci. Data, 16, 2113–2122, https://doi.org/10.5194/essd-16-2113-2024, https://doi.org/10.5194/essd-16-2113-2024, 2024
Short summary
Short summary
To manage Earth in the Anthropocene, new tools, new institutions, and new forms of international cooperation will be required. Earth Virtualization Engines is proposed as an international federation of centers of excellence to empower all people to respond to the immense and urgent challenges posed by climate change.
Yoshihiro Nakayama, Alena Malyarenko, Hong Zhang, Ou Wang, Matthis Auger, Ian Fenty, Matthew Mazloff, Köhl Armin, and Dimitris Menemenlis
EGUsphere, https://doi.org/10.5194/egusphere-2024-727, https://doi.org/10.5194/egusphere-2024-727, 2024
Short summary
Short summary
Global and basin-scale ocean reanalyses are becoming easily accessible. Yet, such ocean reanalyses are optimized for their entire model domains and their ability to simulate the Southern Ocean requires evaluations. We conduct intercomparison analyses of Massachusetts Institute of Technology general circulation model (MITgcm)-based ocean reanalyses. They generally perform well for the open ocean, but open ocean temporal variability and Antarctic continental shelves require improvements.
Xiaoqiao Wang, Zhaoru Zhang, Michael S. Dinniman, Petteri Uotila, Xichen Li, and Meng Zhou
The Cryosphere, 17, 1107–1126, https://doi.org/10.5194/tc-17-1107-2023, https://doi.org/10.5194/tc-17-1107-2023, 2023
Short summary
Short summary
The bottom water of the global ocean originates from high-salinity water formed in polynyas in the Southern Ocean where sea ice coverage is low. This study reveals the impacts of cyclones on sea ice and water mass formation in the Ross Ice Shelf Polynya using numerical simulations. Sea ice production is rapidly increased caused by enhancement in offshore wind, promoting high-salinity water formation in the polynya. Cyclones also modulate the transport of this water mass by wind-driven currents.
Guokun Lyu, Nuno Serra, Meng Zhou, and Detlef Stammer
Ocean Sci., 18, 51–66, https://doi.org/10.5194/os-18-51-2022, https://doi.org/10.5194/os-18-51-2022, 2022
Short summary
Short summary
This study explores the Arctic sea level variability depending on different timescales and the relation to temperature, salinity and mass changes, identifying key parameters and regions that need to be observed coordinately. The decadal sea level variability reflects salinity changes. But it can only reflect salinity change at periods of greater than 1 year, highlighting the requirement for enhancing in situ hydrographic observations and complicated interpolation methods.
Nikolay V. Koldunov, Armin Köhl, Nuno Serra, and Detlef Stammer
The Cryosphere, 11, 2265–2281, https://doi.org/10.5194/tc-11-2265-2017, https://doi.org/10.5194/tc-11-2265-2017, 2017
Short summary
Short summary
The paper describes one of the first attempts to use the so-called adjoint data assimilation method to bring Arctic Ocean model simulations closer to observation, especially in terms of the sea ice. It is shown that after assimilation the model bias in simulating the Arctic sea ice is considerably reduced. There is also additional improvement in the sea ice thickens representation that is not assimilated directly.
Jonathan M. Gregory, Nathaelle Bouttes, Stephen M. Griffies, Helmuth Haak, William J. Hurlin, Johann Jungclaus, Maxwell Kelley, Warren G. Lee, John Marshall, Anastasia Romanou, Oleg A. Saenko, Detlef Stammer, and Michael Winton
Geosci. Model Dev., 9, 3993–4017, https://doi.org/10.5194/gmd-9-3993-2016, https://doi.org/10.5194/gmd-9-3993-2016, 2016
Short summary
Short summary
As a consequence of greenhouse gas emissions, changes in ocean temperature, salinity, circulation and sea level are expected in coming decades. Among the models used for climate projections for the 21st century, there is a large spread in projections of these effects. The Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) aims to investigate and explain this spread by prescribing a common set of changes in the input of heat, water and wind stress to the ocean in the participating models.
Cited articles
AMAP (Arctic Climate Change Update 2021): Key Trends and Impacts. Summary for
Policy-makers. Arctic Monitoring and Assessment Programme (AMAP), Tromsø, Norway, 16 pp., 2021.
Chevallier, M., Smith, G. C., Dupont, F., Lemieux, J.-F., Forget, G.,
Fujii, Y., Hernandez, F., Msadek, R., Peterson, K. A., Storto, A., Toyoda,
T., Valdivieso, M., Vernieres, G., Zuo, H., Balmaseda, M., Chang, Y.-S.,
Ferry, N., Garric, G., Haines, K., Keeley, S., Kovach, R. M., Kuragano, T.,
Masina, S., Tang, Y., Tsujino, H., and Wang, X.: Intercomparison of the
Arctic sea ice cover in global ocean–sea ice reanalyses from the ORA-IP
project, Clim. Dynam., 49, 1107–1136, https://doi.org/10.1007/s00382-016-2985-y, 2017.
Comiso, J. C., Parkinson, C. L., Gersten, R., and Stock, L.: Accelerated
decline in the Arctic sea ice cover, Geophys. Res. Lett., 35, L01703,
https://doi.org/10.1029/2007gl031972, 2008.
Fekete, B. M., Vörösmarty, C. J., and Grabs, W.: High-resolution
fields of global runoff combining observed river discharge and simulated
water balances, Global Biogeochem. Cy., 16, 15-1–15-10, https://doi.org/10.1029/1999GB001254, 2002.
Fenty, I. and Heimbach, P.: Coupled Sea Ice–Ocean-State Estimation in the
Labrador Sea and Baffin Bay, J. Phys. Oceanogr., 43, 884–904,
https://doi.org/10.1175/jpo-d-12-065.1, 2013.
Fenty, I., Menemenlis, D., and Zhang, H.: Global coupled sea ice-ocean state
estimation, Clim. Dynam., 49, 931–956, https://doi.org/10.1007/s00382-015-2796-6, 2017.
Giering, R. and Kaminski, T.: Recipes for adjoint code construction, ACM T.
Math. Software, 24, 437–474, 1998.
Gilbert, J. C. and Lemaréchal, C.: Some numerical experiments with variable-storage quasi-Newton algorithms, Math Program., 45, 407–435, https://doi.org/10.1007/BF01589113, 1989.
Good, S. A., Martin, M. J., and Rayner, N. A.: EN4: Quality controlled ocean
temperature and salinity profiles and monthly objective analyses with
uncertainty estimates, J. Geophys. Res.-Oceans, 118, 6704–6716,
https://doi.org/10.1002/2013jc009067, 2013.
Heimbach, P., Menemenlis, D., Losch, M., Campin, J.-M., and Hill, C.: On the
formulation of sea-ice models. Part 2: Lessons from multi-year adjoint
sea-ice export sensitivities through the Canadian Arctic Archipelago, Ocean
Model., 33, 145–158, https://doi.org/10.1016/j.ocemod.2010.02.002, 2010.
Heimbach, P., Fukumori, I., Hill, C. N., Ponte, R. M., Stammer, D., Wunsch,
C., Campin, J.-M., Cornuelle, B., Fenty, I., Forget, G., Köhl, A.,
Mazloff, M., Menemenlis, D., Nguyen, A. T., Piecuch, C., Trossman, D.,
Verdy, A., Wang, O., and Zhang, H.: Putting It All Together: Adding Value to
the Global Ocean and Climate Observing Systems With Complete Self-Consistent
Ocean State and Parameter Estimates, Front. Mar. Sci., 6, 55,
https://doi.org/10.3389/fmars.2019.00055, 2019.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D.,
Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer,
A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková,
M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay,
P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.-N.: The ERA5
global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020.
Hibler, W.: A Dynamic Thermodynamic Sea Ice Model, J. Phys. Oceanogr., 9,
815–846, https://doi.org/10.1175/1520-0485(1979)009<0815:adtsim>2.0.co;2, 1979.
Kaleschke, L., Lüpkes, C., Vihma, T., Haarpaintner, J., Bochert, A.,
Hartmann, J., and Heygster, G.: SSM/I Sea Ice Remote Sensing for Mesoscale
Ocean-Atmosphere Interaction Analysis, Can. J. Remote Sens., 27, 526–537,
https://doi.org/10.1080/07038992.2001.10854892, 2001.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L.,
Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M.,
Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang,
J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR
40-Year Reanalysis Project, B. Am. Meteorol. Soc., 77, 437–472,
https://doi.org/10.1175/1520-0477(1996)077<0437:tnyrp>2.0.co;2, 1996.
Kauker, F., Kaminski, T., Karcher, M., Giering, R., Gerdes, R., and
Voßbeck, M.: Adjoint analysis of the 2007 all time Arctic sea-ice
minimum, Geophys. Res. Lett., 36, L03707, https://doi.org/10.1029/2008gl036323, 2009.
Koldunov, N. V., Köhl, A., and Stammer, D.: Properties of adjoint sea
ice sensitivities to atmospheric forcing and implications for the causes of
the long term trend of Arctic sea ice, Clim. Dynam., 41, 227–241, 2013.
Koldunov, N. V., Köhl, A., Serra, N., and Stammer, D.: Sea ice assimilation into a coupled ocean–sea ice model using its adjoint, The Cryosphere, 11, 2265–2281, https://doi.org/10.5194/tc-11-2265-2017, 2017.
Kwok, R.: Arctic sea ice thickness, volume, and multiyear ice coverage:
losses and coupled variability (1958–2018), Environ. Res. Lett., 13, 105005,
https://doi.org/10.1088/1748-9326/aae3ec, 2018.
Large, W. G. and Yeager, S.: Diurnal to decadal global forcing for ocean and sea-ice models: The datasets and flux climatologies, NCAR Tech. Note NCAR/TN-4601STR, 105 pp., 2004.
Large, W. G., McWilliams, J. C., and Doney, S. C.: Oceanic vertical mixing:
A review and a model with a nonlocal boundary layer parameterization, Rev.
Geophys., 32, 363–403, 1994.
Lavergne, T., Sørensen, A. M., Kern, S., Tonboe, R., Notz, D., Aaboe, S., Bell, L., Dybkjær, G., Eastwood, S., Gabarro, C., Heygster, G., Killie, M. A., Brandt Kreiner, M., Lavelle, J., Saldo, R., Sandven, S., and Pedersen, L. T.: Version 2 of the EUMETSAT OSI SAF and ESA CCI sea-ice concentration climate data records, The Cryosphere, 13, 49–78, https://doi.org/10.5194/tc-13-49-2019, 2019.
Lindsay, R. W. and Zhang, J.: Assimilation of Ice Concentration in an
Ice–Ocean Model, J. Atmos. Ocean. Tech., 23, 742–749, https://doi.org/10.1175/jtech1871.1,
2006.
Liu, C., Köhl, A., and Stammer, D.: Adjoint-Based Estimation of
Eddy-Induced Tracer Mixing Parameters in the Global Ocean, J. Phys. Oceanogr.,
42, 1186–1206, https://doi.org/10.1175/jpo-d-11-0162.1, 2012.
Losch, M., Menemenlis, D., Campin, J.-M., Heimbach, P., and Hill, C.: On the
formulation of sea-ice models. Part 1: Effects of different solver
implementations and parameterizations, Ocean Model., 33, 129–144, 2010.
Lu, Y., Wang, X., and Dong, J.: Melt pond scheme parameter estimation using an adjoint model, Adv. Atmos. Sci., 38, 1525−-1536, https://doi.org/10.1007/s00376-021-0305-x, 2021.
Lyu, G., Koehl, A., Serra, N., and Stammer, D.: Assessing the current and
future Arctic Ocean observing system with observing system simulating
experiments, Q. J. Roy. Meteor. Soc., 147, 2670–2690, https://doi.org/10.1002/qj.4044, 2021a.
Lyu, G., Koehl, A., Serra, N., Stammer, D., and Xie, J.: Arctic ocean–sea
ice reanalysis for the period 2007–2016 using the adjoint method, Q. J. Roy.
Meteor. Soc., 147, 1908–1929, https://doi.org/10.1002/qj.4002,
2021b.
Ma, X., Mu, M., Dai, G., Han, Z., Li, C., and Jiang, Z.: Influence of Arctic
Sea Ice Concentration on Extended-Range Prediction of Strong and
Long-Lasting Ural Blocking Events in Winter, J. Geophys. Res.-Atmos., 127,
e2021JD036282, https://doi.org/10.1029/2021JD036282, 2022.
Marshall, J., Adcroft, A., Hill, C., Perelman, L., and Heisey, C.: A
finite-volume, incompressible Navier Stokes model for studies of the ocean
on parallel computers, J. Geophys. Res.-Oceans., 102, 5753–5766, 1997.
Massonnet, F., Goosse, H., Fichefet, T., and Counillon, F.: Calibration of sea ice dynamic parameters in an ocean-sea ice model using an ensemble Kalman filter, J. Geophys. Res.-Oceans, 119, 4168–4184, https://doi.org/10.1002/2013JC009705, 2014.
Maykut, G. A. and McPhee, M. G.: Solar heating of the Arctic mixed layer, J.
Geophys. Res.-Oceans, 100, 24691–24703, https://doi.org/10.1029/95jc02554, 1995.
Morison, J., Wahr, J., Kwok, R., and Peralta-Ferriz, C.: Recent trends in
Arctic Ocean mass distribution revealed by GRACE, Geophys. Res. Lett., 34, L07602, https://doi.org/10.1029/2006GL029016,
2007.
Mu, L., Losch, M., Yang, Q., Ricker, R., Losa, S. N., and Nerger, L.:
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 Period, J. Geophys. Res.-Oceans, 123, 7763–7780, https://doi.org/10.1029/2018JC014316, 2018.
Nguyen, A. T., Pillar, H., Ocaña, V., Bigdeli, A., Smith, T. A., and
Heimbach, P.: The Arctic Subpolar Gyre sTate Estimate: Description and
Assessment of a Data-Constrained, Dynamically Consistent Ocean-Sea Ice
Estimate for 2002–2017, J. Adv. Model Earth Sy., 13, e2020MS002398, https://doi.org/10.1029/2020MS002398, 2021.
Overland, J. E., Ballinger, T. J., Cohen, J., Francis, J. A., Hanna, E.,
Jaiser, R., Kim, B. M., Kim, S. J., Ukita, J., Vihma, T., Wang, M., and
Zhang, X.: How do intermittency and simultaneous processes obfuscate the
Arctic influence on midlatitude winter extreme weather events?, Environ. Res.
Lett., 16, 043002, https://doi.org/10.1088/1748-9326/abdb5d, 2021.
Polyakov, I. V., Pnyushkov, A. V., Alkire, M. B., Ashik, I. M., Baumann, T.
M., Carmack, E. C., Goszczko, I., Guthrie, J., Ivanov, V. V., Kanzow, T.,
Krishfield, R., Kwok, R., Sundfjord, A., Morison, J., Rember, R., and Yulin,
A.: Greater role for Atlantic inflows on sea-ice loss in the Eurasian Basin
of the Arctic Ocean, Science, 356, 285–291, https://doi.org/10.1126/science.aai8204, 2017.
Proshutinsky, A., Krishfield, R., Timmermans, M.-L., Toole, J., Carmack, E.,
McLaughlin, F., Williams, W. J., Zimmermann, S., Itoh, M., and Shimada, K.:
Beaufort Gyre freshwater reservoir: State and variability from observations,
J. Geophys. Res.-Oceans, 114, C00A10, https://doi.org/10.1029/2008jc005104, 2009.
Proshutinsky, A., Krishfield, R., Toole, J. M., Timmermans, M.-L., Williams,
W., Zimmermann, S., Yamamoto-Kawai, M., Armitage, T. W. K., Dukhovskoy, D.,
Golubeva, E., Manucharyan, G. E., Platov, G., Watanabe, E., Kikuchi, T.,
Nishino, S., Itoh, M., Kang, S.-H., Cho, K.-H., Tateyama, K., and Zhao, J.:
Analysis of the Beaufort Gyre Freshwater Content in 2003–2018, J. Geophys. Res.-Oceans, 124, 9658–9689, https://doi.org/10.1029/2019jc015281, 2019 (data available at: https://www2.whoi.edu/site/beaufortgyre/, last access: 12 March 2023).
Quadfasel, D., Sy, A., Wells, D., and Tunik, A.: Warming in the Arctic,
Nature, 350, 385, https://doi.org/10.1038/350385a0, 1991.
Ricker, R., Hendricks, S., Kaleschke, L., Tian-Kunze, X., King, J., and Haas, C.: A weekly Arctic sea-ice thickness data record from merged CryoSat-2 and SMOS satellite data, The Cryosphere, 11, 1607–1623, https://doi.org/10.5194/tc-11-1607-2017, 2017.
Sakov, P., Counillon, F., Bertino, L., Lisæter, K. A., Oke, P. R., and Korablev, A.: TOPAZ4: an ocean-sea ice data assimilation system for the North Atlantic and Arctic, Ocean Sci., 8, 633–656, https://doi.org/10.5194/os-8-633-2012, 2012.
Schauer, U., Beszczynska-Möller, A., Walczowski, W., Fahrbach, E.,
Piechura, J., and Hansen, E.: Variation of measured heat flow through the
Fram Strait between 1997 and 2006, in: Arctic–Subarctic Ocean Fluxes, edited by: Dickson, R. R., Meincke, J., and Rhines, P.,
Springer, Dordrecht, 65–85, 2008.
Serra, N., Käse, R. H., Köhl, A., Stammer, D., and Quadfasel, D.: On
the low-frequency phase relation between the Denmark Strait and the
Faroe-Bank Channel overflows, Tellus A, 62, 530–550,
https://doi.org/10.1111/j.1600-0870.2010.00445.x, 2010.
Smith, W. H. F. and Sandwell, D. T.: Global Sea Floor Topography from
Satellite Altimetry and Ship Depth Soundings, Science, 277, 1956–1962,
https://doi.org/10.1126/science.277.5334.1956, 1997.
Spreen, G., Kaleschke, L., and Heygster, G.: Sea ice remote sensing using
AMSR-E 89-GHz channels, J. Geophys. Res.-Oceans, 113, C02S03, https://doi.org/10.1029/2005jc003384,
2008.
Stammer, D., Wunsch, C., Giering, R., Eckert, C., Heimbach, P., Marotzke,
J., Adcroft, A., Hill, C. N., and Marshall, J.: Global ocean circulation
during 1992–1997, estimated from ocean observations and a general
circulation model, J. Geophys. Res.-Oceans, 107, 1-1–1-27, https://doi.org/10.1029/2001JC000888, 2002.
Sumata, H., Kauker, F., Karcher, M., and Gerdes, R.: Simultaneous Parameter Optimization of an Arctic Sea IceOcean Model by a Genetic Algorithm, Mon. Weather Rev., 147, 1899–1926, https://doi.org/10.1175/MWR-D-18-0360.1, 2019.
Tilling, R. L., Ridout, A., and Shepherd, A.: Estimating Arctic sea ice
thickness and volume using CryoSat-2 radar altimeter data, Adv. Space Res.,
62, 1203–1225, https://doi.org/10.1016/j.asr.2017.10.051, 2018.
Toole, J. M. and Krishfield, R.: Woods Hole Oceanographic Institution Ice-Tethered Profiler Program, Ice-Tethered Profiler observations: Vertical profiles of temperature, salinity, oxygen, and ocean velocity from an Ice-Tethered Profiler buoy system, NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/v5mw2f7x, 2016.
Toyoda, T., Hirose, N., Urakawa, L. S., Tsujino, H., Nakano, H., Usui, N.,
Fujii, Y., Sakamoto, K., and Yamanaka, G.: Effects of Inclusion of Adjoint
Sea Ice Rheology on Backward Sensitivity Evolution Examined Using an Adjoint
Ocean–Sea Ice Model, Mon. Weather Rev., 147, 2145–2162,
https://doi.org/10.1175/mwr-d-18-0198.1, 2019.
Uotila, P., Goosse, H., Haines, K., Chevallier, M., Barthélemy, A.,
Bricaud, C., Carton, J., Fučkar, N., Garric, G., Iovino, D., Kauker, F.,
Korhonen, M., Lien, V. S., Marnela, M., Massonnet, F., Mignac, D., Peterson,
K. A., Sadikni, R., Shi, L., Tietsche, S., Toyoda, T., Xie, J., and Zhang,
Z.: An assessment of ten ocean reanalyses in the polar regions, Clim. Dynam.,
52, 1613–1650, https://doi.org/10.1007/s00382-018-4242-z, 2019.
Woodgate, R. A., Weingartner, T. J., and Lindsay, R.: Observed increases in
Bering Strait oceanic fluxes from the Pacific to the Arctic from 2001 to
2011 and their impacts on the Arctic Ocean water column, Geophys. Res. Lett.,
39, L24603, https://doi.org/10.1029/2012GL054092, 2012.
Wunsch, C. and Heimbach, P.: Practical global oceanic state estimation,
Physica D, 230, 197–208, https://doi.org/10.1016/j.physd.2006.09.040, 2007.
Yang, C.-Y., Liu, J., and Xu, S.: Seasonal Arctic Sea Ice Prediction Using a
Newly Developed Fully Coupled Regional Model With the Assimilation of
Satellite Sea Ice Observations, J. Adv. Model Earth Sy., 12, e2019MS001938,
https://doi.org/10.1029/2019MS001938, 2020.
Zhang, J. and Hibler, W.: On an efficient numerical method for modeling sea
ice dynamics, J. Geophys. Res.-Oceans, 102, 8691–8702, 1997.
Zhang, J. and Rothrock, D. A.: Modeling Arctic sea ice with an efficient
plastic solution, J. Geophys. Res.-Oceans, 105, 3325–3338, 2000.
Zweng, M. M., Reagan, J. R., Seidov, D., Boyer, T. P., Locarnini, R. A., Garcia, H. E., Mishonov, A. V., Baranova, O. K., Weathers, K., Paver, C. R., and Smolyar I.: World Ocean Atlas, Volume 2: Salinity, A. Mishonov Technical Ed., NOAA Atlas NESDIS 82, 50 pp., 2018.
Short summary
Data assimilation techniques are important for combining observations with numerical models. Here, we approximate the adjoint of viscous-plastic dynamics (adjoint-VP) to replace the adjoint of free-drift dynamics (adjoint-FD) for developing an advanced Arctic Ocean and sea ice modeling and adjoint-based assimilation system. We find that adjoint-VP provides a better ocean and sea ice estimation than adjoint-FD, considering the residual errors and adjustments of the atmospheric states.
Data assimilation techniques are important for combining observations with numerical models....