Articles | Volume 15, issue 3
https://doi.org/10.5194/os-15-779-2019
© Author(s) 2019. 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-15-779-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
20 Jun 2019
Research article |
| 20 Jun 2019
| 20 Jun 2019
The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment
The European Centre for Medium-Range Weather Forecasts, Shinfield Rd, Reading RG2 9AX, UK
Magdalena Alonso Balmaseda
The European Centre for Medium-Range Weather Forecasts, Shinfield Rd, Reading RG2 9AX, UK
Steffen Tietsche
The European Centre for Medium-Range Weather Forecasts, Shinfield Rd, Reading RG2 9AX, UK
Kristian Mogensen
The European Centre for Medium-Range Weather Forecasts, Shinfield Rd, Reading RG2 9AX, UK
Michael Mayer
The European Centre for Medium-Range Weather Forecasts, Shinfield Rd, Reading RG2 9AX, UK
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We use ocean reanalyses, in which ocean models are combined with observations, to infer past changes in ocean circulation and heat transport in the South Atlantic. Comparing these estimates with other observation-based estimates, we find differences in their trends, variability, and mean heat transport but closer agreement in their mean overturning strength. Ocean reanalyses can help us understand the cause of these differences, which could improve estimates of ocean transports in this region.
Susanna Winkelbauer, Michael Mayer, Vanessa Seitner, Ervin Zsoter, Hao Zuo, and Leopold Haimberger
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We evaluate Arctic river discharge using in situ observations and state-of-the-art reanalyses, inter alia the most recent Global Flood Awareness System (GloFAS) river discharge reanalysis version 3.1. Furthermore, we combine reanalysis data, in situ observations, ocean reanalyses, and satellite data and use a Lagrangian optimization scheme to close the Arctic's volume budget on annual and seasonal scales, resulting in one reliable and up-to-date estimate of every volume budget term.
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The Cryosphere, 15, 325–344, https://doi.org/10.5194/tc-15-325-2021, https://doi.org/10.5194/tc-15-325-2021, 2021
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Our study for the first time shows the impact of measured sea ice thickness (SIT) on seasonal forecasts of all the seasons. We prove that the long-term memory present in the Arctic winter SIT is helpful to improve summer sea ice forecasts. Our findings show that realistic SIT initial conditions to start a forecast are useful in (1) improving seasonal forecasts, (2) understanding errors in the forecast model, and (3) recognizing the need for continuous monitoring of world's ice-covered oceans.
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The Cryosphere, 12, 2051–2072, https://doi.org/10.5194/tc-12-2051-2018, https://doi.org/10.5194/tc-12-2051-2018, 2018
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We compare Arctic sea-ice thickness from L-band microwave satellite observations and an ocean–sea ice reanalysis. There is good agreement for some regions and times but systematic discrepancy in others. Errors in both the reanalysis and observational products contribute to these discrepancies. Thus, we recommend proceeding with caution when using these observations for model validation or data assimilation. At the same time we emphasise their unique value for improving sea-ice forecast models.
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Sea level is one of the best indicators of climate change and has been listed as one of the essential climate variables. Sea level measurements have been provided by satellite altimetry for 25 years, and the Climate Change Initiative (CCI) program of the European Space Agency has given the opportunity to provide a long-term, homogeneous and accurate sea level record. It will help scientists to better understand climate change and its variability.
Thomas Rackow, Xabier Pedruzo-Bagazgoitia, Tobias Becker, Sebastian Milinski, Irina Sandu, Razvan Aguridan, Peter Bechtold, Sebastian Beyer, Jean Bidlot, Souhail Boussetta, Michail Diamantakis, Peter Dueben, Emanuel Dutra, Richard Forbes, Helge F. Goessling, Ioan Hadade, Jan Hegewald, Sarah Keeley, Lukas Kluft, Nikolay Koldunov, Alexei Koldunov, Tobias Kölling, Josh Kousal, Kristian Mogensen, Tiago Quintino, Inna Polichtchouk, Domokos Sármány, Dmitry Sidorenko, Jan Streffing, Birgit Sützl, Daisuke Takasuka, Steffen Tietsche, Mirco Valentini, Benoît Vannière, Nils Wedi, Lorenzo Zampieri, and Florian Ziemen
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Detailed global climate model simulations have been created based on a numerical weather prediction model, offering more accurate spatial detail down to the scale of individual cities ("kilometre-scale"), and a better understanding of climate phenomena such as atmospheric storms, whirls in the ocean, and cracks in sea ice. The new model aims to provide globally consistent information on local climate change with greater precision, benefiting environmental planning and local impact modelling.
Eric de Boisséson and Magdalena Alonso Balmaseda
Ocean Sci., 20, 265–278, https://doi.org/10.5194/os-20-265-2024, https://doi.org/10.5194/os-20-265-2024, 2024
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Marine heatwaves are long periods of extremely warm ocean surface temperatures. Predicting such events a few months in advance would help decision-making to mitigate their impacts on marine ecosystems. This work investigates how well operational seasonal forecasts can predict marine heatwaves. Results show that such events can be predicted a few months in advance in the tropics but that extending the predictability skill to other regions will require additional work on the forecast models.
Susanna Winkelbauer, Michael Mayer, and Leopold Haimberger
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Oceanic transports shape the global climate, but the evaluation and validation of this key quantity based on reanalysis and model data is complicated by the distortion of the used modelling grids and the large number of different grid-types. We present two new methods that allow to calculate oceanic fluxes of volume, heat, salinity and ice through almost arbitrary sections for various models and reanalyses, independent of the used modelling grids.
Johannes Mayer, Leopold Haimberger, and Michael Mayer
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This study investigates the temporal stability and reliability of winter-month trends of air–sea heat fluxes from ERA5 forecasts over the North Atlantic basin for the period 1950–2019. Driving forces of trends and the impact of modes of climate variability and analysis increments on air–sea heat fluxes are investigated. Finally, a new and independent estimate of the Atlantic Meridional Overturning Circulation weakening is provided and associated with a decrease in air–sea heat fluxes.
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Karina von Schuckmann, Lijing Cheng, Matthew D. Palmer, James Hansen, Caterina Tassone, Valentin Aich, Susheel Adusumilli, Hugo Beltrami, Tim Boyer, Francisco José Cuesta-Valero, Damien Desbruyères, Catia Domingues, Almudena García-García, Pierre Gentine, John Gilson, Maximilian Gorfer, Leopold Haimberger, Masayoshi Ishii, Gregory C. Johnson, Rachel Killick, Brian A. King, Gottfried Kirchengast, Nicolas Kolodziejczyk, John Lyman, Ben Marzeion, Michael Mayer, Maeva Monier, Didier Paolo Monselesan, Sarah Purkey, Dean Roemmich, Axel Schweiger, Sonia I. Seneviratne, Andrew Shepherd, Donald A. Slater, Andrea K. Steiner, Fiammetta Straneo, Mary-Louise Timmermans, and Susan E. Wijffels
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Understanding how much and where the heat is distributed in the Earth system is fundamental to understanding how this affects warming oceans, atmosphere and land, rising temperatures and sea level, and loss of grounded and floating ice, which are fundamental concerns for society. This study is a Global Climate Observing System (GCOS) concerted international effort to obtain the Earth heat inventory over the period 1960–2018.
Christine Pohl, Larysa Istomina, Steffen Tietsche, Evelyn Jäkel, Johannes Stapf, Gunnar Spreen, and Georg Heygster
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A spectral to broadband conversion is developed empirically that can be used in combination with the Melt Pond Detector algorithm to derive broadband albedo (300–3000 nm) of Arctic sea ice from MERIS data. It is validated and shows better performance compared to existing conversion methods. A comparison of MERIS broadband albedo with respective values from ERA5 reanalysis suggests a revision of the albedo values used in ERA5. MERIS albedo might be useful for improving albedo representation.
Joula Siponen, Petteri Uotila, Eero Rinne, and Steffen Tietsche
The Cryosphere Discuss., https://doi.org/10.5194/tc-2019-272, https://doi.org/10.5194/tc-2019-272, 2019
Manuscript not accepted for further review
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Long sea-ice thickness time series are needed to better understand the Arctic climate and improve its forecasts. In this study 2002–2017 satellite observations are compared with reanalysis output, which is used as initial conditions for long forecasts. The reanalysis agrees well with satellite observations, with differences typically below 1 m when averaged in time, although seasonally and in certain years the differences are large. This is caused by uncertainties in reanalysis and observations.
Stephanie J. Johnson, Timothy N. Stockdale, Laura Ferranti, Magdalena A. Balmaseda, Franco Molteni, Linus Magnusson, Steffen Tietsche, Damien Decremer, Antje Weisheimer, Gianpaolo Balsamo, Sarah P. E. Keeley, Kristian Mogensen, Hao Zuo, and Beatriz M. Monge-Sanz
Geosci. Model Dev., 12, 1087–1117, https://doi.org/10.5194/gmd-12-1087-2019, https://doi.org/10.5194/gmd-12-1087-2019, 2019
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In this article, we describe the new ECMWF seasonal forecast system, SEAS5, which replaced its predecessor in November 2017. We describe the forecast methodology used in SEAS5 and compare results from SEAS5 to results from the previous seasonal forecast system, highlighting the strengths and weaknesses of SEAS5. SEAS5 data are publicly available through the Copernicus Climate Change Service's multi-system seasonal forecast.
Christopher D. Roberts, Retish Senan, Franco Molteni, Souhail Boussetta, Michael Mayer, and Sarah P. E. Keeley
Geosci. Model Dev., 11, 3681–3712, https://doi.org/10.5194/gmd-11-3681-2018, https://doi.org/10.5194/gmd-11-3681-2018, 2018
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This paper presents climate model configurations of the European Centre for Medium-Range Weather Forecasts Integrated Forecast System (ECMWF-IFS) for different combinations of ocean and atmosphere resolution. These configurations are used to perform multi-decadal experiments following the protocols of the High Resolution Model Intercomparison Project (HighResMIP) and phase 6 of the Coupled Model Intercomparison Project (CMIP6).
Steffen Tietsche, Magdalena Alonso-Balmaseda, Patricia Rosnay, Hao Zuo, Xiangshan Tian-Kunze, and Lars Kaleschke
The Cryosphere, 12, 2051–2072, https://doi.org/10.5194/tc-12-2051-2018, https://doi.org/10.5194/tc-12-2051-2018, 2018
Short summary
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We compare Arctic sea-ice thickness from L-band microwave satellite observations and an ocean–sea ice reanalysis. There is good agreement for some regions and times but systematic discrepancy in others. Errors in both the reanalysis and observational products contribute to these discrepancies. Thus, we recommend proceeding with caution when using these observations for model validation or data assimilation. At the same time we emphasise their unique value for improving sea-ice forecast models.
Jean-François Legeais, Michaël Ablain, Lionel Zawadzki, Hao Zuo, Johnny A. Johannessen, Martin G. Scharffenberg, Luciana Fenoglio-Marc, M. Joana Fernandes, Ole Baltazar Andersen, Sergei Rudenko, Paolo Cipollini, Graham D. Quartly, Marcello Passaro, Anny Cazenave, and Jérôme Benveniste
Earth Syst. Sci. Data, 10, 281–301, https://doi.org/10.5194/essd-10-281-2018, https://doi.org/10.5194/essd-10-281-2018, 2018
Short summary
Short summary
Sea level is one of the best indicators of climate change and has been listed as one of the essential climate variables. Sea level measurements have been provided by satellite altimetry for 25 years, and the Climate Change Initiative (CCI) program of the European Space Agency has given the opportunity to provide a long-term, homogeneous and accurate sea level record. It will help scientists to better understand climate change and its variability.
Marianne Pietschnig, Michael Mayer, Takamasa Tsubouchi, Andrea Storto, Sebastian Stichelberger, and Leopold Haimberger
Ocean Sci. Discuss., https://doi.org/10.5194/os-2017-98, https://doi.org/10.5194/os-2017-98, 2017
Revised manuscript not accepted
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Jonathan J. Day, Steffen Tietsche, Mat Collins, Helge F. Goessling, Virginie Guemas, Anabelle Guillory, William J. Hurlin, Masayoshi Ishii, Sarah P. E. Keeley, Daniela Matei, Rym Msadek, Michael Sigmond, Hiroaki Tatebe, and Ed Hawkins
Geosci. Model Dev., 9, 2255–2270, https://doi.org/10.5194/gmd-9-2255-2016, https://doi.org/10.5194/gmd-9-2255-2016, 2016
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Recent decades have seen significant developments in seasonal-to-interannual timescale climate prediction. However, until recently the potential of such systems to predict Arctic climate had not been assessed. This paper describes a multi-model predictability experiment which was run as part of the Arctic Predictability and Prediction On Seasonal to Interannual Timescales (APPOSITE) project. The main goal of APPOSITE was to quantify the timescales on which Arctic climate is predictable.
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Transports and budgets in a 1/4 ° global ocean reanalysis 1989–2010
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P. Sakov, F. Counillon, L. Bertino, K. A. Lisæter, P. R. Oke, and A. Korablev
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K. Haines, M. Valdivieso, H. Zuo, and V. N. Stepanov
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T. Janjić, J. Schröter, R. Savcenko, W. Bosch, A. Albertella, R. Rummel, and O. Klatt
Ocean Sci., 8, 65–79, https://doi.org/10.5194/os-8-65-2012, https://doi.org/10.5194/os-8-65-2012, 2012
S. Seitz, R. Feistel, D. G. Wright, S. Weinreben, P. Spitzer, and P. De Bièvre
Ocean Sci., 7, 45–62, https://doi.org/10.5194/os-7-45-2011, https://doi.org/10.5194/os-7-45-2011, 2011
Cited articles
Ablain, M., Cazenave, A., Larnicol, G., Balmaseda, M. A., Cipollini, P.,
Faugère, Y., Fernandes, M. J., Henry, O., Johannessen, J. A., Knudsen,
P., Andersen, O., Legeais, J., Meyssignac, B., Picot, N., Roca, M., Rudenko,
S., Scharffenberg, M. G., Stammer, D., Timms, G., and Benveniste, J.:
Improved sea level record over the satellite altimetry era (1993–2010)
from the Climate Change Initiative project, Ocean Sci., 11, 67–82,
https://doi.org/10.5194/os-11-67-2015, 2015. a
Balmaseda, M., Hernandez, F., Storto, A., Palmer, M., Alves, O., Shi, L.,
Smith, G., Toyoda, T., Valdivieso, M., Barnier, B., Behringer, D., Boyer, T.,
Chang, Y.-S., Chepurin, G., Ferry, N., Forget, G., Fujii, Y., Good, S.,
Guinehut, S., Haines, K., Ishikawa, Y., Keeley, S., Köhl, A., Lee, T.,
Martin, M., Masina, S., Masuda, S., Meyssignac, B., Mogensen, K., Parent, L.,
Peterson, K., Tang, Y., Yin, Y., Vernieres, G., Wang, X., Waters, J., Wedd,
R., Wang, O., Xue, Y., Chevallier, M., Lemieux, J.-F., Dupont, F., Kuragano,
T., Kamachi, M., Awaji, T., Caltabiano, A., Wilmer-Becker, K., and Gaillard,
F.: The Ocean Reanalyses Intercomparison Project (ORA-IP), J.
Oper. Oceanogr., 8, s80–s97, https://doi.org/10.1080/1755876X.2015.1022329, 2015. a
Balmaseda, M. A.: Ocean analysis at ECMWF: From real-time ocean initial
conditions to historical ocean reanalysis, ECMWF Newsletter, 105, 24–42,
2005. a
Balmaseda, M. A. and Anderson, D.: Impact of initialization strategies and
observations on seasonal forecast skill, Geophys. Res. Lett.,
36, L01701,
https://doi.org/10.1029/2008GL035561, 2009. a
Balmaseda, M. A., Vidard, A., and Anderson, D. L. T.: The ECMWF Ocean
Analysis
System: ORA-S3, Mon. Weather Rev., 136, 3018–3034,
https://doi.org/10.1175/2008MWR2433.1,
2008. a
Balmaseda, M. A., Trenberth, K. E., and Källén, E.: Distinctive
climate signals in reanalysis of global ocean heat content, Geophys.
Res. Lett., 40, 1754–1759, https://doi.org/10.1002/grl.50382,
2013b. a
Barnier, B., Madec, G., Penduff, T., Molines, J.-M., Treguier, A.-M.,
Le Sommer, J., Beckmann, A., Biastoch, A., Böning, C., Dengg, J.,
Derval, C., Durand, E., Gulev, S., Remy, E., Talandier, C., Theetten, S.,
Maltrud, M., McClean, J., and De Cuevas, B.: Impact of partial steps and
momentum advection schemes in a global ocean circulation model at
eddy-permitting resolution, Ocean Dynam., 56, 543–567,
https://doi.org/10.1007/s10236-006-0082-1, 2006. a
Bloom, S. C., Takacs, L. L., Da Silva, A. M., and Ledvina, D.: Data
assimilation using incremental analysis updates, Mon. Weather Rev.,
124, 1256–1271, 1996. a
Breivik, Ø., Mogensen, K., Bidlot, J.-R., Balmaseda, M. A., and Janssen,
P. A.: Surface wave effects in the NEMO ocean model: Forced and coupled
experiments, J. Geophys. Res.-Ocean., 120, 2973–2992,
https://doi.org/10.1002/2014JC010565, 2015. a, b, c
Browne, P., Rosnay, P. D., Zuo, H., Bennett, A., and Dawson, A.: Weakly coupled ocean–atmosphere data assimilation in the
ECMWF NWP system, ECMWF Technical Memorandum, 836, 1–28, https://doi.org/10.21957/eqe8rx02, 2018. a
Buizza, R., Bidlot, J. R., Janousek, M., Keeley, S., Mogensen, K., and
Richardson, D.: New IFS cycle brings sea-ice coupling and higher ocean
resolution, ECMWF Newsletter, 150, 14–17, 2016. a
Buizza, R., Balsamo, G., and Haiden, T.: IFS upgrade brings more seamless
coupled forecasts, ECMWF Newsletter, 156, 18–22, 2018. a
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. a
Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P.,
Kobayashi,
S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P.,
Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C.,
Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B.,
Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler,
M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J.,
Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N.,
and Vitart, F.: The ERA-Interim reanalysis: configuration and performance of
the data assimilation system, Q. J. Roy. Meteor.
Soc., 137, 553–597, https://doi.org/10.1002/qj.828, 2011. a
Desai, S., Wahr, J., and Beckley, B.: Revisiting the pole tide for and from
satellite altimetry, J. Geodesy, 89, 1233–1243,
https://doi.org/10.1007/s00190-015-0848-7, 2015. a
Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of
observation, background and analysis-error statistics in observation space,
Q. J. Roy. Meteor. Soc., 131, 3385–3396, 2005. a
Dibarboure, G., Pujol, M.-I., Briol, F., Traon, P. Y. L., Larnicol, G.,
Picot,
N., Mertz, F., and Ablain, M.: Jason-2 in DUACS: Updated System Description,
First Tandem Results and Impact on Processing and Products, Mar. Geod.,
34, 214–241, https://doi.org/10.1080/01490419.2011.584826, 2011. a, b
Donlon, C. J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., and
Wimmer, W.: The Operational Sea Surface Temperature and Sea Ice Analysis
(OSTIA) system, Remote Sens. Environ., 116, 140–158,
https://doi.org/10.1016/j.rse.2010.10.017, 2012. a, b
European Commission: Copernicus Marine Environment Monitoring Service, available at: http://marine.copernicus.eu/services-portfolio/access-to-products, last access: 5 June 2019. a
Fichefet, T. and Maqueda, M. A.: Sensitivity of a global sea ice model to
the
treatment of ice thermodynamics and dynamics, J. Geophys.
Res.-Ocean., 102, 12609–12646, https://doi.org/10.1029/97JC00480, 1997. a, b
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.-Ocean., 118,
6704–6716, https://doi.org/10.1002/2013JC009067, 2013. a, b
Gouretski, V. and Reseghetti, F.: On depth and temperature biases in
bathythermograph data: Development of a new correction scheme based on
analysis of a global ocean database, Deep-Sea Res. Pt. I, 57, 812–833, https://doi.org/10.1016/j.dsr.2010.03.011, 2010. a, b
Haney, R. L.: Surface Thermal Boundary Condition for Ocean Circulation
Models, J. Phys. Oceanogr., 1, 241–248,
https://doi.org/10.1175/1520-0485(1971)001<0241:STBCFO>2.0.CO;2, 1971. a
ICDC: ICDC TDSCatalog, available at: http://icdc.cen.uni-hamburg.de/thredds/catalog/ftpthredds/EASYInit/oras5/catalog.html, last access: 5 June 2019. a
Karvonen, J., Vainio, J., Marnela, M., Eriksson, P., and Niskanen, T.: A
Comparison Between High-Resolution EO-Based and Ice Analyst-Assigned Sea Ice
Concentrations, IEEE J. Sel. Top. Appl., 8, 1799–1807,
https://doi.org/10.1109/JSTARS.2015.2426414, 2015. a
Kennedy, J. J., Rayner, N. A., Smith, R. O., Parker, D. E., and Saunby, M.:
Reassessing biases and other uncertainties in sea surface temperature
observations measured in situ since 1850: 2. Biases and homogenization,
J. Geophys. Res., 116, D14104, https://doi.org/10.1029/2010JD015220,
2011a. a
Kennedy, J. J., Rayner, N. A., Smith, R. O., Parker, D. E., and Saunby, M.:
Reassessing biases and other uncertainties in sea surface temperature
observations measured in situ since 1850: 1. Measurement and sampling
uncertainties, J. Geophys. Res.-Atmos., 116, D14103,
https://doi.org/10.1029/2010JD015218, 2011b. a
Kwok, R., Cunningham, G. F., Wensnahan, M., Rigor, I., Zwally, H. J., and Yi,
D.: Thinning and volume loss of the Arctic Ocean sea ice cover:
2003–2008, J. Geophys. Res., 114, C07005,
https://doi.org/10.1029/2009JC005312, 2009. a
Large, W. G. and Yeager, S. G.: The global climatology of an interannually
varying air–sea flux data set, Clim. Dynam., 33, 341–364, 2009. a
Lea, D. J., Drecourt, J.-P., Haines, K., and Martin, M. J.: Ocean altimeter
assimilation with observational- and model-bias correction, Q.
J. Roy. Meteor. Soc., 134, 1761–1774,
https://doi.org/10.1002/qj.320, 2008. a, b
Legeais, J.-F., Ablain, M., Zawadzki, L., Zuo, H., Johannessen, J. A.,
Scharffenberg, M. G., Fenoglio-Marc, L., Fernandes, M. J., Andersen, O. B.,
Rudenko, S., Cipollini, P., Quartly, G. D., Passaro, M., Cazenave, A., and
Benveniste, J.: An improved and homogeneous altimeter sea level record from
the ESA Climate Change Initiative, Earth Syst. Sci. Data, 10, 281–301,
https://doi.org/10.5194/essd-10-281-2018, 2018. a, b, c
Lellouche, J.-M., Greiner, E., Le Galloudec, O., Garric, G., Regnier, C.,
Drevillon, M., Benkiran, M., Testut, C.-E., Bourdalle-Badie, R., Gasparin,
F., Hernandez, O., Levier, B., Drillet, Y., Remy, E., and Le Traon, P.-Y.:
Recent updates to the Copernicus Marine Service global ocean monitoring and
forecasting real-time 1∕12∘ high-resolution system, Ocean Sci., 14,
1093–1126, https://doi.org/10.5194/os-14-1093-2018, 2018. a
Madec, G.: NEMO ocean engine, Note du Pôle de modélisation, Institut
Pierre-Simon Laplace (IPSL), France, No. 27, ISSN No. 1288-1619, 2008. a
Masina, S., Storto, A., Ferry, N., Valdivieso, M., Haines, K., Balmaseda, M.,
Zuo, H., Drevillon, M., and Parent, L.: An ensemble of eddy-permitting
global ocean reanalyses from the MyOcean project, Clim. Dynam., 49,
813–841, https://doi.org/10.1007/s00382-015-2728-5, 2017. a
Merchant, C. J., Embury, O., Roberts-Jones, J., Fiedler, E., Bulgin, C. E.,
Corlett, G. K., Good, S., McLaren, A., Rayner, N., Morak-Bozzo, S., and
Donlon, C.: Sea surface temperature datasets for climate applications from
Phase 1 of the European Space Agency Climate Change Initiative (SST CCI),
Geosci. Data J., 1, 179–191, https://doi.org/10.1002/gdj3.20, 2014. a
Merchant, C. J., Embury, O., Roberts-Jones, J., Fiedler, E. K., Bulgin,
C. E.,
Corlett, G., Good, S., McLaren, A., Rayner, N., and Donlon, C.: ESA Sea
Surface Temperature Climate Change Initiative (ESA SST CCI): Analysis long
term product version 1.1, Tech. Rep., Centre for Environmental Data
Analysis, https://doi.org/10.5285/2262690A-B588-4704-B459-39E05527B59A, 2016. a, b
Penduff, T., Juza, M., Brodeau, L., Smith, G. C., Barnier, B., Molines,
J.-M.,
Treguier, A.-M., and Madec, G.: Impact of global ocean model resolution on
sea-level variability with emphasis on interannual time scales, Ocean
Sci., 6, 269–284, https://doi.org/10.5194/os-6-269-2010, 2010. a
Poli, P., Hersbach, H., Dee, D. P., Berrisford, P., Simmons, A. J., Vitart,
F.,
Laloyaux, P., Tan, D. G. H., Peubey, C., Thépaut, J.-N.,
Trémolet, Y., Hólm, E. V., Bonavita, M., Isaksen, L., Fisher, M.,
Poli, P., Hersbach, H., Dee, D. P., Berrisford, P., Simmons, A. J., Vitart,
F., Laloyaux, P., Tan, D. G. H., Peubey, C., Thépaut, J.-N.,
Trémolet, Y., Hólm, E. V., Bonavita, M., Isaksen, L., and Fisher,
M.: ERA-20C: An Atmospheric Reanalysis of the Twentieth Century, J.
Clim., 29, 4083–4097, https://doi.org/10.1175/JCLI-D-15-0556.1,
2016. a, b
Pujol, M.-I., Faugère, Y., Taburet, G., Dupuy, S., Pelloquin, C.,
Ablain,
M., and Picot, N.: DUACS DT2014: the new multi-mission altimeter data set
reprocessed over 20 years, Ocean Sci., 12, 1067–1090,
https://doi.org/10.5194/os-12-1067-2016, 2016. a, b
Quartly, G. D., Legeais, J.-F., Ablain, M., Zawadzki, L., Fernandes, M. J.,
Rudenko, S., Carrère, L., García, P. N., Cipollini, P., Andersen, O.
B., Poisson, J.-C., Mbajon Njiche, S., Cazenave, A., and Benveniste, J.: A
new phase in the production of quality-controlled sea level data, Earth Syst.
Sci. Data, 9, 557–572, https://doi.org/10.5194/essd-9-557-2017, 2017. a, b
Reynolds, R. W., Smith, T. M., Liu, C., Chelton, D. B., Casey, K. S., and
Schlax, M. G.: Daily high-resolution-blended analyses for sea surface
temperature, J. Clim., 20, 5473–5496, 2007. a
Richter, F., Drusch, M., Kaleschke, L., Maaß, N., Tian-Kunze, X., and
Mecklenburg, S.: Arctic sea ice signatures: L-band brightness temperature
sensitivity comparison using two radiation transfer models, The Cryosphere,
12, 921–933, https://doi.org/10.5194/tc-12-921-2018, 2018. a
Rio, M. H., Mulet, S., and Picot, N.: Beyond GOCE for the ocean circulation
estimate: Synergetic use of altimetry, gravimetry, and in situ data provides
new insight into geostrophic and Ekman currents, Geophys. Res.
Lett., 41, 8918–8925, https://doi.org/10.1002/2014GL061773, 2014. a, b
Servonnat, J., Mignot, J., Guilyardi, E., Swingedouw, D.,
Séférian,
R., and Labetoulle, S.: Reconstructing the subsurface ocean decadal
variability using surface nudging in a perfect model framework, Clim.
Dynam., 44, 315–338, https://doi.org/10.1007/s00382-014-2184-7, 2014. a
Sørensen, A. and Lavergne, T.: Sea Ice Climate Change Initiative: D3.4 Product User Guide (PUG), Version 1.0, Tech. Rep., 1–27, 2017. a
Stockdale, T., Johnson, S., Ferranti, L., Balmaseda, M. A., and Briceag, S.:
ECMWF's new long-range forecasting system SEAS5, ECMWF Newsletter, 154,
15–20, 2017. a
Tietsche, S., Notz, D., Jungclaus, J. H., and Marotzke, J.: Assimilation of
sea-ice concentration in a global climate model – physical and statistical
aspects, Ocean Sci., 9, 19–36, https://doi.org/10.5194/os-9-19-2013, 2013. a
Tietsche, S., Balmaseda, M. A., Zuo, H., and Mogensen, K.: Arctic sea ice in
the global eddy-permitting ocean reanalysis ORAP5, Clim.
Dynam., 49, 775–789,
1–15, https://doi.org/10.1007/s00382-015-2673-3, 2017. a, b
Tietsche, S., Alonso-Balmaseda, M., Rosnay, P., Zuo, H., Tian-Kunze, X., and
Kaleschke, L.: Thin Arctic sea ice in L-band observations and an ocean
reanalysis, The Cryosphere, 12, 2051–2072, https://doi.org/10.5194/tc-12-2051-2018, 2018. a, b
Titchner, H. A. and Rayner, N. A.: The Met Office Hadley Centre sea ice and
sea surface temperature data set, version 2: 1. Sea ice concentrations,
J. Geophys. Res.-Atmos., 119, 2864–2889,
https://doi.org/10.1002/2013JD020316, 2014. a, b
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. a
Uppala, S. M., KÅllberg, P. W., Simmons, A. J., Andrae, U., Bechtold, V.
D. C., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A.,
Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E.,
Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Berg, L. V. D., Bidlot, J.,
Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher,
M., Fuentes, M., Hagemann, S., Hólm, E., Hoskins, B. J., Isaksen, L.,
Janssen, P. a. E. M., Jenne, R., Mcnally, A. P., Mahfouf, J.-F., Morcrette,
J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth,
K. E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40
re-analysis, Q. J. Roy. Meteor. Soc., 131,
2961–3012, https://doi.org/10.1256/qj.04.176, 2005. a
Waters, J., Lea, D. J., Martin, M. J., Mirouze, I., Weaver, A., and While,
J.:
Implementing a variational data assimilation system in an operational 1/4
degree global ocean model, Q. J. Roy. Meteor.
Soc., 141, 333–349, https://doi.org/10.1002/qj.2388, 2015. a, b
Wijffels, S. E., Willis, J., Domingues, C. M., Barker, P., White, N. J.,
Gronell, A., Ridgway, K., and Church, J. A.: Changing expendable
bathythermograph fall rates and their impact on estimates of thermosteric sea
level rise, J. Clim., 21, 5657–5672, 2008. a
Xue, Y., Huang, B., Hu, Z.-Z., Kumar, A., Wen, C., Behringer, D., and Nadiga,
S.: An assessment of oceanic variability in the NCEP climate forecast system
reanalysis, Clim. Dynam., 37, 2511–2539,
https://doi.org/10.1007/s00382-010-0954-4, 2011. a
Xue, Y., Wen, C., Kumar, A., Balmaseda, M., Fujii, Y., Alves, O., Martin, M.,
Yang, X., Vernieres, G., Desportes, C., Lee, T., Ascione, I., Gudgel, R., and
Ishikawa, I.: A real-time ocean reanalyses intercomparison project in the
context of tropical pacific observing system and ENSO monitoring, Clim.
Dynam., 49, 3647–3672, https://doi.org/10.1007/s00382-017-3535-y, 2017. a
Zuo, H., Balmaseda, M. A., Boisseson, E. D., Hirahara, S., Chrust, M., and
Rosnay, P. D.: A generic ensemble generation scheme for data assimilation
and ocean analysis, ECMWF Technical Memorandum, 95, 1–46, https://doi.org/10.21957/cub7mq0i4, 2017a.
a, b, c, d
Zuo, H., Balmaseda, M. A., and Mogensen, K.: The new eddy-permitting ORAP5
ocean reanalysis: description, evaluation and uncertainties in climate
signals, Clim. Dynam., 49, 791–811, https://doi.org/10.1007/s00382-015-2675-1,
2017b. a, b
Zuo, H., Vidar, L., Sandø, A. B., Garric, G., Bricaud, C., Storto, A.,
Peterson, K. A., Tietsche, S., and Mayer, M.: Extreme sea-ice conditions,
in: Copernicus Marine Service Ocean State Report, Issue 2, J.
Oper. Oceanogr., 11, S1–S142, https://doi.org/10.1080/1755876X.2018.1489208,
2018. a, b
Zuo, H., Balmaseda, M. A., Tietsche, S., Mayer, M., Robert, C. D., Mogensen,
K., and de Rosney, P.: Evaluation of the ECMWF ensemble ocean and sea-ice
reanalysis system ORAS5, in preparation, 2019. a
Short summary
OCEAN5 is the fifth generation of the ocean and sea-ice analysis system at ECMWF. It was used for production of historical ocean and sea-ice states from 1979 onwards and is also used for generating real-time ocean and sea-ice states responsible for initializing the operational ECMWF weather forecasting system. This is a valuable data set with broad applications. A description of the OCEAN5 system and an assessment of the historical data set have been documented in this reference paper.
OCEAN5 is the fifth generation of the ocean and sea-ice analysis system at ECMWF. It was used...
