Articles | Volume 19, issue 4
https://doi.org/10.5194/os-19-1203-2023
© 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-19-1203-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
10 Aug 2023
Research article |
| 10 Aug 2023
| 10 Aug 2023
Assessment of Indonesian Throughflow transports from ocean reanalyses with mooring-based observations
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
b.geos GmbH, Industriestraße 1, 2100 Korneuburg, Austria
Michael Mayer
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
b.geos GmbH, Industriestraße 1, 2100 Korneuburg, Austria
Research Department, European Centre for Medium-Range Weather Forecasts, Reading, UK
Leopold Haimberger
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
Susanna Winkelbauer
Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
b.geos GmbH, Industriestraße 1, 2100 Korneuburg, Austria
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Susanna Winkelbauer, Isabella Winterer, Michael Mayer, Yao Fu, and Leopold Haimberger
EGUsphere, https://doi.org/10.5194/egusphere-2025-4093, https://doi.org/10.5194/egusphere-2025-4093, 2025
This preprint is open for discussion and under review for Ocean Science (OS).
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Ocean reanalyses combine models and observations to reconstruct past ocean conditions. We evaluate their performance against detailed measurements from the subpolar North Atlantic at the OSNAP section. While reanalyses capture long-term averages and broad circulation patterns, they miss some more regional features and variability. This highlights both their value and their limitations, stressing the need for improved observations and higher-resolution models.
Lucie Bakels, Michael Blaschek, Marina Dütsch, Andreas Plach, Vincent Lechner, Georg Brack, Leopold Haimberger, and Andreas Stohl
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-26, https://doi.org/10.5194/essd-2025-26, 2025
Revised manuscript accepted for ESSD
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Meteorological reanalyses are crucial datasets. Most reanalyses are Eulerian, providing data at specific, fixed points in space and time. When studying how air moves, it is more convenient to follow air masses through space and time, requiring a Lagrangian reanalysis (LARA). We explain how the LARA dataset is organized, and provide four examples of applications. These include studying the evolution of wind patterns, understanding weather systems, and measuring air mass travel time over land.
Mona Zolghadrshojaee, Susann Tegtmeier, Sean M. Davis, Robin Pilch Kedzierski, and Leopold Haimberger
EGUsphere, https://doi.org/10.5194/egusphere-2025-82, https://doi.org/10.5194/egusphere-2025-82, 2025
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The tropical tropopause layer (TTL) is a crucial region where the troposphere transitions into the stratosphere, influencing air mass transport. This study examines temperature trends in the TTL and lower stratosphere using data from weather balloons, satellites, and reanalysis datasets. We found cooling trends in the TTL from 1980–2001, followed by warming from 2002–2023. These shifts are linked to changes in atmospheric circulation and impact water vapor transport into the stratosphere.
Susanna Winkelbauer, Michael Mayer, and Leopold Haimberger
Geosci. Model Dev., 17, 4603–4620, https://doi.org/10.5194/gmd-17-4603-2024, https://doi.org/10.5194/gmd-17-4603-2024, 2024
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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 are complicated by the distortion of the used modelling grids and the large number of different grid types. We present two new methods that allow the calculation of oceanic fluxes of volume, heat, salinity, and ice through almost arbitrary sections for various models and reanalyses that are independent of the used modelling grids.
Ulrich Voggenberger, Leopold Haimberger, Federico Ambrogi, and Paul Poli
Geosci. Model Dev., 17, 3783–3799, https://doi.org/10.5194/gmd-17-3783-2024, https://doi.org/10.5194/gmd-17-3783-2024, 2024
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This paper presents a method for calculating balloon drift from historical radiosonde ascent data. The drift can reach distances of several hundred kilometres and is often neglected. Verification shows the beneficial impact of the more accurate balloon position on model assimilation. The method is not limited to radiosondes but would also work for dropsondes, ozonesondes, or any other in situ sonde carried by the wind in the pre-GNSS era, provided the necessary information is available.
Johannes Mayer, Leopold Haimberger, and Michael Mayer
Earth Syst. Dynam., 14, 1085–1105, https://doi.org/10.5194/esd-14-1085-2023, https://doi.org/10.5194/esd-14-1085-2023, 2023
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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.
Michael Mayer, Takamasa Tsubouchi, Susanna Winkelbauer, Karin Margretha H. Larsen, Barbara Berx, Andreas Macrander, Doroteaciro Iovino, Steingrímur Jónsson, and Richard Renshaw
State Planet, 1-osr7, 14, https://doi.org/10.5194/sp-1-osr7-14-2023, https://doi.org/10.5194/sp-1-osr7-14-2023, 2023
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This paper compares oceanic fluxes across the Greenland–Scotland Ridge (GSR) from ocean reanalyses to largely independent observational data. Reanalyses tend to underestimate the inflow of warm waters of subtropical Atlantic origin and hence oceanic heat transport across the GSR. Investigation of a strong negative heat transport anomaly around 2018 highlights the interplay of variability on different timescales and the need for long-term monitoring of the GSR to detect forced climate signals.
Jonathan Andrew Baker, Richard Renshaw, Laura Claire Jackson, Clotilde Dubois, Doroteaciro Iovino, Hao Zuo, Renellys C. Perez, Shenfu Dong, Marion Kersalé, Michael Mayer, Johannes Mayer, Sabrina Speich, and Tarron Lamont
State Planet, 1-osr7, 4, https://doi.org/10.5194/sp-1-osr7-4-2023, https://doi.org/10.5194/sp-1-osr7-4-2023, 2023
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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.
Karina von Schuckmann, Audrey Minière, Flora Gues, Francisco José Cuesta-Valero, Gottfried Kirchengast, Susheel Adusumilli, Fiammetta Straneo, Michaël Ablain, Richard P. Allan, Paul M. Barker, Hugo Beltrami, Alejandro Blazquez, Tim Boyer, Lijing Cheng, John Church, Damien Desbruyeres, Han Dolman, Catia M. Domingues, Almudena García-García, Donata Giglio, John E. Gilson, Maximilian Gorfer, Leopold Haimberger, Maria Z. Hakuba, Stefan Hendricks, Shigeki Hosoda, Gregory C. Johnson, Rachel Killick, Brian King, Nicolas Kolodziejczyk, Anton Korosov, Gerhard Krinner, Mikael Kuusela, Felix W. Landerer, Moritz Langer, Thomas Lavergne, Isobel Lawrence, Yuehua Li, John Lyman, Florence Marti, Ben Marzeion, Michael Mayer, Andrew H. MacDougall, Trevor McDougall, Didier Paolo Monselesan, Jan Nitzbon, Inès Otosaka, Jian Peng, Sarah Purkey, Dean Roemmich, Kanako Sato, Katsunari Sato, Abhishek Savita, Axel Schweiger, Andrew Shepherd, Sonia I. Seneviratne, Leon Simons, Donald A. Slater, Thomas Slater, Andrea K. Steiner, Toshio Suga, Tanguy Szekely, Wim Thiery, Mary-Louise Timmermans, Inne Vanderkelen, Susan E. Wjiffels, Tonghua Wu, and Michael Zemp
Earth Syst. Sci. Data, 15, 1675–1709, https://doi.org/10.5194/essd-15-1675-2023, https://doi.org/10.5194/essd-15-1675-2023, 2023
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Earth's climate is out of energy balance, and this study quantifies how much heat has consequently accumulated over the past decades (ocean: 89 %, land: 6 %, cryosphere: 4 %, atmosphere: 1 %). Since 1971, this accumulated heat reached record values at an increasing pace. The Earth heat inventory provides a comprehensive view on the status and expectation of global warming, and we call for an implementation of this global climate indicator into the Paris Agreement’s Global Stocktake.
Susanna Winkelbauer, Michael Mayer, Vanessa Seitner, Ervin Zsoter, Hao Zuo, and Leopold Haimberger
Hydrol. Earth Syst. Sci., 26, 279–304, https://doi.org/10.5194/hess-26-279-2022, https://doi.org/10.5194/hess-26-279-2022, 2022
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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.
Noemi Imfeld, Leopold Haimberger, Alexander Sterin, Yuri Brugnara, and Stefan Brönnimann
Earth Syst. Sci. Data, 13, 2471–2485, https://doi.org/10.5194/essd-13-2471-2021, https://doi.org/10.5194/essd-13-2471-2021, 2021
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Upper-air data form the backbone of reanalysis products, particularly in the pre-satellite era. However, historical upper-air data are error-prone because measurements at high altitude were especially challenging. Here, we present a collection of data from historical intercomparisons of radiosondes and error assessments reaching back to the 1930s that may allow us to better characterize such errors. The full database, including digitized data, images, and metadata, is made publicly available.
Beena Balan-Sarojini, Steffen Tietsche, Michael Mayer, Magdalena Balmaseda, Hao Zuo, Patricia de Rosnay, Tim Stockdale, and Frederic Vitart
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.
Anne Tipka, Leopold Haimberger, and Petra Seibert
Geosci. Model Dev., 13, 5277–5310, https://doi.org/10.5194/gmd-13-5277-2020, https://doi.org/10.5194/gmd-13-5277-2020, 2020
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Flex_extract v7.1 is an open-source software to retrieve and prepare meteorological fields from the European Centre for Medium-Range Weather Forecasts (ECMWF) MARS archive to serve as input for the FLEXTRA–FLEXPART atmospheric transport modelling system. It can be used by public as well as member-state users and enables the retrieval of a variety of different data sets, including the new reanalysis ERA5. Instructions are given for installation along with typical usage scenarios.
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
Earth Syst. Sci. Data, 12, 2013–2041, https://doi.org/10.5194/essd-12-2013-2020, https://doi.org/10.5194/essd-12-2013-2020, 2020
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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.
Cited articles
Arakawa, A. and Lamb, V.: Computational Design of the Basic Dynamical
Processes of the UCLA General Circulation Model, Method. Comput.
Phys., 17, 173–265, https://doi.org/10.1016/B978-0-12-460817-7.50009-4, 1977. a
Argo: Argo float data and metadata from Global Data Assembly Centre (Argo
GDAC), SEANOE [data set], https://doi.org/10.17882/42182, 2000. a
Asbjørnsen, H., Arthun, M., Skagseth, Ø., and Eldevik, T.: Mechanisms of
Ocean Heat Anomalies in the Norwegian Sea, Clim. Dynam., 124,
2908–2923, https://doi.org/10.1029/2018JC014649, 2019. 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, 2013. a
Balmaseda, M. A., Hernandez, F., Storto, A., Palmer, M. D., Alves, O., Shi, L.,
Smith, G. C., Toyoda, T., Valdivieso, M., Barnier, B., Behringer, D., Boyer,
T., Chang, Y.-S., Chepurin, G. A., Ferry, N., Forget, G., Fujii, Y., Good,
S., Guinehut, S., Haines, K., Ishikawa, Y., Keeley, S., Köhl, A., Lee, T.,
Martin, M. J., Masina, S., Masuda, S., Meyssignac, B., Mogensen, K., Parent,
L., Peterson, K. A., Tang, Y. M., 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), The
Ocean Reanalyses Intercomparison Project (ORA-IP), 8, 80–97, https://doi.org/10.1080/1755876X.2015.1022329, 2015. a
Boy, R.: Lombok Strait: An Indonesian Throughflow Passage, Oseana, 2, 33–40,
1995. a
Cabanes, C., Grouazel, A., von Schuckmann, K., Hamon, M., Turpin, V., Coatanoan, C., Paris, F., Guinehut, S., Boone, C., Ferry, N., de Boyer Montégut, C., Carval, T., Reverdin, G., Pouliquen, S., and Le Traon, P.-Y.: The CORA dataset: validation and diagnostics of in-situ ocean temperature and salinity measurements, Ocean Sci., 9, 1–18, https://doi.org/10.5194/os-9-1-2013, 2013. a
Clarke, A. J. and Liu, X.: Observations and dynamics of semiannual and annual
sea levels near the eastern equatorial Indian Ocean boundary, J.
Phys. Oceanogr., 23, 386–399, https://doi.org/10.1175/1520-0485(1993)023<0386:OADOSA>2.0.CO;2, 1993. a
Clarke, A. J. and Liu, X.: Interannual sea level in the northern and eastern
Indian Ocean, J. Phys. Oceanogr., 24, 1224–1235, https://doi.org/10.1175/1520-0485(1994)024<1224:ISLITN>2.0.CO;2, 1994. a
Copernicus Marine Service (CMEMS): Global Ocean Physics Reanalysis, CMEMS [data set], https://data.marine.copernicus.eu/products (last access: 7 August 2023), 2019. a
Cowley, R., Heaney, B., Wijffels, S., Pender, L., Sprintall, J., Kawamoto, S.,
and Molcard, R.: INSTANT Sunda Data Report Description and Quality Control,
CSIRO Marine and Atmospheric Research, http://www.marine.csiro.au/~cow074/INSTANTdataQC_v4.pdf (last access: 8 August 2023), 2009. a
Cresswell, G.: Coastal currents of northern Papua New Guinea, and the Sepik
River outflow, Mar. Freshwater Res., 51, 553–564, https://doi.org/10.1071/MF99135, 2000. a
Cowley, R.: INSTANT, CSIRO Marine Research [data set], http://www.marine.csiro.au/~cow074/instantdata.htm (last access: 7 August 2023), 2011. 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., 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
Desportes, C., Drévillon, M., Drillet, Y., Garric, G., Parent, L.,
Régnier, C., Masina, S., Storto, A., Petterson, D., Wood, R., Balmaseda,
M., and Zuo, H.: GREP: Evaluation of the Copernicus Marine Service Global
Reanalysis Ensemble Product: deriving uncertainty estimates for 3D T and S
variability in the ocean, Geophys. Res. Abstr.,
EGU2017-16232, EGU General Assembly 2017, Vienna, Austria, 2017. a
Dobricic, S. and Pinardi, N.: An oceanographic three-dimensional variational
data assimilation scheme, Ocean Model., 22, 89–105, https://doi.org/10.1016/j.ocemod.2008.01.004, 2008. a
Drushka, K., Sprintall, J., Gille, S. T., and Brodjonegoro, I.: Vertical
structure of Kelvin waves in the Indonesian Throughflow exit passages,
J. Phys. Oceanogr., 40, 1965–1987, https://doi.org/10.1175/2010JPO4380.1, 2010. a
Ekman, V. W.: On the Influence of the Earth's Rotation on Ocean-Currents,
Arkiv For Matematik, Astronimi och Fysik, 2, no. 11, 1905. a
England, M. H. and Huang, F.: On the Interannual Variability of the Indonesian
Throughflow and Its Linkage with ENSO, J. Clim., 18, 1435–1444,
https://doi.org/10.1175/JCLI3322.1, 2005. a, b
Garric, G. and Parent, L.: Quality Information Document For Global Ocean
Reanalysis Products,
https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-GLO-QUID-001-025.pdf
(last access: 28 May 2022), 2017. a
Godfrey, J. S.: The effect of the Indonesian Throughflow on ocean circulation
and heat exchange with the atmosphere: A review, J. Geophys.
Res., 101, 12217–12237, https://doi.org/10.1029/95JC03860, 1996. 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
Gordon, A. L.: Oceanography of the Indonesian Seas and Their Throughflow,
Oceanography, 18, 14–27, https://doi.org/10.5670/oceanog.2005.01, 2005. a
Gordon, A. L., Huber, B. A., Metzger, J. E., Susanto, D. R., Hurlburt, H. E.,
and Adi, R. T.: South China Sea throughflow impact on the Indonesian
throughflow, Geophys. Res. Lette., 39, L11602, https://doi.org/10.1029/2012GL052021, 2012. a
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., and Thépaut, J. N.: The ERA5 global
reanalysis, Q. J. Roy. Meteor. Soc., 146,
1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Large, W. G. and Yeager, S.: Diurnal to decadal global forcing for ocean and
sea-ice models: the data sets and flux climatologies, NCAR Technical Note,
NCAR/TN-460+STR, CGD Division of the National Center for Atmospheric
Research, https://doi.org/10.5065/D6KK98Q6, 2004. a
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
MacLachlan, C., Arribas, A., Peterson, K. A., Maidens, A., Fereday, D., Scaife,
A. A., Gordon, M., Vellinga, M., Williams, A., Comer, R. E., Camp, J.,
Xavier, P., and Madec, G.: Global Seasonal forecast system version 5
(GloSea5): a high-resolution seasonal forecast system, Q. J. Roy. Meteor. Soc., 141, 1072–1084, https://doi.org/10.1002/qj.2396, 2015. a
Madec, G. and Imbard, M.: A global ocean mesh to overcome the North Pole
singularity, Clim. Dynam., 12, 381–388, https://doi.org/10.1007/BF00211684, 1996. a
Madec, G., Bourdallé-Badie, R., Chanut, J., Clementi, E., Coward, A., Ethé,
C., Iovino, D., Lea, D., Lévy, C., Lovato, T., Martin, N., Masson, S.,
Mocavero, S., Rousset, C., Storkey, D., Müeller, S., Nurser, G., Bell, M.,
Samson, G., and Moulin, A.: NEMO ocean engine, Note du Pole de
modélisation, Institut Pierre-Simon Laplace (IPSL), France, 27, Zenodo, https://doi.org/10.5281/zenodo.8167700, 2008. a
Mayer, M. and Alonso Balmaseda, M.: Indian Ocean impact on ENSO evolution
2014–2016 in a set of seasonal forecasting experiments, Clim. Dynam.,
56, 2631–2649, https://doi.org/10.1007/s00382-020-05607-6, 2021. a
Mayer, M., Balmaseda, M. A., and Haimberger, L.: Unprecedented 2015/2016
Indo-Pacific heat transfer speeds up tropical Pacific heat recharge,
Geophys. Res. Lett., 45, 3274–3284, https://doi.org/10.1002/2018GL077106, 2018. a, b, c, d
Mayer, M., Tietsche, S., Haimberger, L., Tsubouchi, T., Mayer, J., and Zuo, H.:
An Improved Estimate of the Coupled Arctic Energy Budget, J.
Clim., 32, 7915–7934, https://doi.org/10.1175/JCLI-D-19-0233.1, 2019. a
Mayer, M., Tsubouchi, T., von Schuckmann, K., Seitner, V., Winkelbauer, S., and
Haimberger, L.: Atmospheric and oceanic contributions to observed Nordic
Seas and Arctic Ocean heat content variations 1993–2020, in: Copernicus
Ocean State Report, Issue 6, J. Oper. Oceanogr., 15,
20–28, https://doi.org/10.1080/1755876X.2022.2095169, 2022. a
Mogensen, K., Balmaseda, M., and Weaver, A.: The NEMOVAR ocean data
assimilation system as implemented in the ECMWF ocean analysis for System 4,
https://www.ecmwf.int/file/23284/download?token=L2fInOlQ (last access: 28
May 2022), 2012. a
Molcard, R., Fieux, M., and Ilahude, A.: The Indo-Pacific Throughflow in the
Timor Passage, J. Geophys. Res., 1011, 12411–12420, https://doi.org/10.1029/95JC03565, 1996. a
Molcard, R., Fieux, M., and Syamsudin, F.: The throughflow within Ombai
Strait, Deep-Sea Res. Pt. I, 48,
1237–1253, https://doi.org/10.1016/S0967-0637(00)00084-4, 2001. a
Nieva Tamasiunas, M., Shinoda, T., Susanto, R. D., Zamudio, L., and Metzger,
J.: Intraseasonal variability of the Indonesian throughflow associated with
the Madden-Julian Oscillation, Deep-Sea Res. Pt. II, 193, 104985, https://doi.org/10.1016/j.dsr2.2021.104985, 2021. a
Nieves, V., Willis, J. K., and Patzert, W. C.: Recent hiatus caused by decadal
shift in Indo-Pacific heating, Science, 349, 532–535, https://doi.org/10.1126/science.aaa4521, 2015. a
NOAA Physical Sciences Laboratory: Nino 3.4 SST Index, NOAA [data set], https://psl.noaa.gov/gcos_wgsp/Timeseries/Data/nino34.long.data (last access: 7 August 2023), 2003. a
Oort, A. and Yienger, J.: Observed Interannual Variability in the Hadley
Circulation and Its Connection to ENSO, J. Clim., 9, 2751–2767,
https://doi.org/10.1175/1520-0442(1996)009<2751:OIVITH>2.0.CO;2, 1996. a
Palmer, M. D., Roberts, C. D., Balmaseda, M., Chang, Y.-S., Chepurin, G.,
Ferry, N., Fujii, Y., Good, S. A., Guinehut, S., Haines, K., Hernandez, F.,
Köhl, A., Lee, T., Martin, M. J., Masina, S., Masuda, S., Peterson, K.,
Storto, A., Toyoda, T., Valdivieso, M., Vernieres, G., Wang, O., and Xue, Y.:
Ocean heat content variability and change in an ensemble of ocean
reanalyses, Clim. Dynam., 49, 909–930, https://doi.org/10.1007/s00382-015-2801-0, 2017. a
Pietschnig, M., Mayer, M., Tsubouchi, T., Storto, A., Stichelberger, S., and Haimberger, L.: Volume and temperature transports through the main Arctic Gateways: A comparative study between an ocean reanalysis and mooring-derived data, Ocean Sci. Discuss. [preprint], https://doi.org/10.5194/os-2017-98, 2017. a
Piola, A. and Gordon, A. L.: Pacific and Indian Ocean Upper-Layer Salinity
Budget, J. Phys. Oceanogr., 14, 747–753, https://doi.org/10.1175/1520-0485(1984)014<0747:PAIOUL>2.0.CO;2, 1984. a
Potemra, J., Hautala, S., and Sprintall, J.: Vertical structure of Indonesian
throughflow in a large-scale model, Deep-Sea Res. Pt. II, 50,
2143–2161, https://doi.org/10.1016/S0967-0645(03)00050-X, 2003. a
Potemra, J. T. and Schneider, N.: Interannual variations of the Indonesian
Throughflow, J. Geophys. Res., 112, C05035, https://doi.org/10.1029/2006JC003808, 2007. a
Schönau, M. C., Rudnick, D. L., Cerovecki, I., Gopalakrishnan, G., Cornuelle,
B. D., McClean, J. L., and Qiu, B.: The Mindanao Current: Mean structure and
connectivity, Oceanography, 28, 34–45, https://doi.org/10.5670/oceanog.2015.79, 2015. a
Sprintall, J., Gordon, A. L., Murtugudde, R., and Susanto, R. D.: A semiannual
Indian Ocean forced Kelvin wave observed in the Indonesian seas in May 1997,
J. Geophys. Res., 105, 17217–17230, https://doi.org/10.1029/2000JC900065, 2000. a
Sprintall, J., Wijffels, S., Gordon, A. L., Ffield, A., Molcard, R., Susanto,
R. D., Soesilo, I., Sopaheluwakan, J., Surachman, Y., and Aken, H. M.:
INSTANT: A new international array to measure the Indonesian Throughflow,
EOS, 85, 369–376, https://doi.org/10.1029/2004EO390001, 2004. a
Sprintall, J., Gordon, A., Koch-Larrouy, A., Lee, T., Potemra, J. T., Pujiana,
K., and Wijffels, S. E.: The Indonesian seas and their role in the coupled
ocean-climate system, Nat. Geosci., 7, 487–492, https://doi.org/10.1038/ngeo2188, 2014. a
Storto, A. and Masina, S.: C-GLORSv5: an improved multipurpose global ocean
eddy-permitting physical reanalysis, Earth Syst. Sci. Data, 8,
679–696, https://doi.org/10.5194/essd-8-679-2016, 2016. a
Trenberth, K. E.: The Definition of El Niño, Bull. Am.
Meteorol. Soc., 78, 2771–2778, https://doi.org/10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2, 1997. a
Ummenhofer, C. C., Biastoch, A., and Böning, C. W.: Multidecadal Indian Ocean
Variability Linked to the Pacific and Implications for Preconditioning Indian
Ocean Dipole Events, J. Clim., 30, 1739–1751, https://doi.org/10.1175/JCLI-D-16-0200.1, 2017. 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., 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
Van Aken, H. M., Brodjonegoro, I. S., and Jaya, I.: The deep water motion
through the Lifamatola Passage and its contribution to the Indonesian
Throughflow, Deep-Sea Res., 56, 1203–1216, https://doi.org/10.1016/j.dsr.2009.02.001, 2009.
a
Van Riel, P. M.: The Snellius Expedition in the Eastern Part of the East Indian Archipelago 1929–1930, Vol. II, 5, Leiden, 1956. a
Vranes, K., Gordon, A. L., and Ffield, A.: The heat transport of the
Indonesian throughflow and implications for the Indian Ocean Heat Budget,
Deep-Sea Res. Pt. II, 49, 1391–1410, https://doi.org/10.1016/S0967-0645(01)00150-3, 2002. a
Wyrkti, K.: An equatorial jet in the Indian Ocean, Science, 181, 262–264,
https://doi.org/10.1126/science.181.4096.262, 1973. a
Wyrtki, K.: Physical Oceanography of the Southeast Asian waters, UC San
Diego: Library – Scripps Digital Collection,
https://escholarship.org/uc/item/49n9x3t4 (last access: 6 July 2022),
1961. a
Wyrtki, K.: Indonesian Throughflow and the associated pressure gradient,
J. Geophys. Res., 92, 12941–12946, https://doi.org/10.1029/JC092iC12p12941, 1987. a, b
Wyrtki, K. and Kendall, R.: Transports of Pacific equatorial countercurrent,
J. Geophys. Res., 72, 2073–2076, https://doi.org/10.1029/JZ072i008p02073, 1967. a
Zuo, H., Balmaseda, M., 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, 2015. a
Zuo, H., Balmaseda, M. A., de Boisseson, E., Tietsche, S., Mayer, M., and de Rosnay, P.: The ORAP6 ocean and sea-ice reanalysis: description and evaluation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9997, https://doi.org/10.5194/egusphere-egu21-9997, 2021. a
Short summary
The interaction between the Indonesian Throughflow (ITF) and regional climate phenomena indicates the high relevance for monitoring the ITF. Observations remain temporally and spatially limited; hence near-real-time monitoring is only possible with reanalyses. We assess how well ocean reanalyses depict the intensity of the ITF via comparison to observations. The results show that reanalyses agree reasonably well with in situ observations; however, some aspects require higher-resolution products.
The interaction between the Indonesian Throughflow (ITF) and regional climate phenomena...
