Articles | Volume 19, issue 4
https://doi.org/10.5194/os-19-1145-2023
© 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-19-1145-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Jul 2023
Research article |
| 26 Jul 2023
| 26 Jul 2023
Observed multi-decadal trends in subsurface temperature adjacent to the East Australian Current
Michael P. Hemming
CORRESPONDING AUTHOR
Coastal and Regional Oceanography Lab, School of Biological, Earth & Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia
Moninya Roughan
Coastal and Regional Oceanography Lab, School of Biological, Earth & Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia
Neil Malan
Coastal and Regional Oceanography Lab, School of Biological, Earth & Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia
Amandine Schaeffer
Coastal and Regional Oceanography Lab, School of Biological, Earth & Environmental Sciences, University of New South Wales, Sydney, New South Wales, Australia
School of Mathematics and Statistics, University of New South Wales, Sydney, New South Wales, Australia
Related authors
Michael Hemming, Moninya Roughan, and Amandine Schaeffer
Earth Syst. Sci. Data, 16, 887–901, https://doi.org/10.5194/essd-16-887-2024, https://doi.org/10.5194/essd-16-887-2024, 2024
Short summary
Short summary
We present new datasets that are useful for exploring extreme ocean temperature events in Australian coastal waters. These datasets span multiple decades, starting from the 1940s and 1950s, and include observations from the surface to the bottom at four coastal sites. The datasets provide valuable insights into the intensity, frequency and timing of extreme warm and cold temperature events and include event characteristics such as duration, onset and decline rates and their categorisation.
Liliane Merlivat, Michael Hemming, Jacqueline Boutin, David Antoine, Vincenzo Vellucci, Melek Golbol, Gareth A. Lee, and Laurence Beaumont
Biogeosciences, 19, 3911–3920, https://doi.org/10.5194/bg-19-3911-2022, https://doi.org/10.5194/bg-19-3911-2022, 2022
Short summary
Short summary
We use in situ high-temporal-resolution measurements of dissolved inorganic carbon and atmospheric parameters at the air–sea interface to analyse phytoplankton bloom initiation identified as the net rate of biological carbon uptake in the Mediterranean Sea. The shift from wind-driven to buoyancy-driven mixing creates conditions for blooms to begin. Active mixing at the air–sea interface leads to the onset of the surface phytoplankton bloom due to the relaxation of wind speed following storms.
Michael P. Hemming, Jan Kaiser, Jacqueline Boutin, Liliane Merlivat, Karen J. Heywood, Dorothee C. E. Bakker, Gareth A. Lee, Marcos Cobas García, David Antoine, and Kiminori Shitashima
Ocean Sci., 18, 1245–1262, https://doi.org/10.5194/os-18-1245-2022, https://doi.org/10.5194/os-18-1245-2022, 2022
Short summary
Short summary
An underwater glider mission was carried out in spring 2016 near a mooring in the northwestern Mediterranean Sea. The glider deployment served as a test of a prototype ion-sensitive field-effect transistor pH sensor. Mean net community production rates were estimated from glider and buoy measurements of dissolved oxygen and inorganic carbon concentrations before and during the spring bloom. Incorporating advection is important for accurate mass budgets. Unexpected metabolic quotients were found.
Manh Cuong Tran, Moninya Roughan, and Amandine Schaeffer
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-480, https://doi.org/10.5194/essd-2024-480, 2024
Preprint under review for ESSD
Short summary
Short summary
The East Australian Current (EAC) plays an important role in the marine ecosystem and climate of the region. To understand the EAC regime and the inner shelf dynamics, we implement the variational approach to produce the first multi-year coastal radar dataset (2012–2023) in this region. The validated data allows for a comprehensive investigation of the EAC dynamics. This dataset will be useful for understanding the complex EAC regime and its far-reaching impacts on the shelf environment.
Clea Baker Welch, Neil Malan, Daneeja Mawren, Tamaryn Morris, Janet Sprintall, and Juliet Clair Hermes
EGUsphere, https://doi.org/10.5194/egusphere-2024-2210, https://doi.org/10.5194/egusphere-2024-2210, 2024
Short summary
Short summary
Marine heatwaves (MHWs) are prolonged periods of extreme ocean temperatures with significant impacts on marine ecosystems. Much research has focused on surface MHWs, but less is known about their subsurface extent. This study uses satellite and in situ data to investigate MHWs in the Southwest Indian Ocean (SWIO). We find that MHWs in the SWIO are typically moderate in severity, closely linked to mesoscale eddies, and that strong temperature anomalies extend below surface-identified MHWs.
Colette Gabrielle Kerry, Moninya Roughan, Shane Keating, David Gwyther, Gary Brassington, Adil Siripatana, and Joao Marcos A. C. Souza
Geosci. Model Dev., 17, 2359–2386, https://doi.org/10.5194/gmd-17-2359-2024, https://doi.org/10.5194/gmd-17-2359-2024, 2024
Short summary
Short summary
Ocean forecasting relies on the combination of numerical models and ocean observations through data assimilation (DA). Here we assess the performance of two DA systems in a dynamic western boundary current, the East Australian Current, across a common modelling and observational framework. We show that the more advanced, time-dependent method outperforms the time-independent method for forecast horizons of 5 d. This advocates the use of advanced methods for highly variable oceanic regions.
Michael Hemming, Moninya Roughan, and Amandine Schaeffer
Earth Syst. Sci. Data, 16, 887–901, https://doi.org/10.5194/essd-16-887-2024, https://doi.org/10.5194/essd-16-887-2024, 2024
Short summary
Short summary
We present new datasets that are useful for exploring extreme ocean temperature events in Australian coastal waters. These datasets span multiple decades, starting from the 1940s and 1950s, and include observations from the surface to the bottom at four coastal sites. The datasets provide valuable insights into the intensity, frequency and timing of extreme warm and cold temperature events and include event characteristics such as duration, onset and decline rates and their categorisation.
Joao Marcos Azevedo Correia de Souza, Sutara H. Suanda, Phellipe P. Couto, Robert O. Smith, Colette Kerry, and Moninya Roughan
Geosci. Model Dev., 16, 211–231, https://doi.org/10.5194/gmd-16-211-2023, https://doi.org/10.5194/gmd-16-211-2023, 2023
Short summary
Short summary
The current paper describes the configuration and evaluation of the Moana Ocean Hindcast, a > 25-year simulation of the ocean state around New Zealand using the Regional Ocean Modeling System v3.9. This is the first open-access, long-term, continuous, realistic ocean simulation for this region and provides information for improving the understanding of the ocean processes that affect the New Zealand exclusive economic zone.
David E. Gwyther, Shane R. Keating, Colette Kerry, and Moninya Roughan
Geosci. Model Dev., 16, 157–178, https://doi.org/10.5194/gmd-16-157-2023, https://doi.org/10.5194/gmd-16-157-2023, 2023
Short summary
Short summary
Ocean eddies are important for weather, climate, biology, navigation, and search and rescue. Since eddies change rapidly, models that incorporate or assimilate observations are required to produce accurate eddy timings and locations, yet the model accuracy is rarely assessed below the surface. We use a unique type of ocean model experiment to assess three-dimensional eddy structure in the East Australian Current and explore two pathways in which this subsurface structure is being degraded.
David E. Gwyther, Colette Kerry, Moninya Roughan, and Shane R. Keating
Geosci. Model Dev., 15, 6541–6565, https://doi.org/10.5194/gmd-15-6541-2022, https://doi.org/10.5194/gmd-15-6541-2022, 2022
Short summary
Short summary
The ocean current flowing along the southeastern coast of Australia is called the East Australian Current (EAC). Using computer simulations, we tested how surface and subsurface observations might improve models of the EAC. Subsurface observations are particularly important for improving simulations, and if made in the correct location and time, can have impact 600 km upstream. The stability of the current affects model estimates could be capitalized upon in future observing strategies.
Liliane Merlivat, Michael Hemming, Jacqueline Boutin, David Antoine, Vincenzo Vellucci, Melek Golbol, Gareth A. Lee, and Laurence Beaumont
Biogeosciences, 19, 3911–3920, https://doi.org/10.5194/bg-19-3911-2022, https://doi.org/10.5194/bg-19-3911-2022, 2022
Short summary
Short summary
We use in situ high-temporal-resolution measurements of dissolved inorganic carbon and atmospheric parameters at the air–sea interface to analyse phytoplankton bloom initiation identified as the net rate of biological carbon uptake in the Mediterranean Sea. The shift from wind-driven to buoyancy-driven mixing creates conditions for blooms to begin. Active mixing at the air–sea interface leads to the onset of the surface phytoplankton bloom due to the relaxation of wind speed following storms.
Michael P. Hemming, Jan Kaiser, Jacqueline Boutin, Liliane Merlivat, Karen J. Heywood, Dorothee C. E. Bakker, Gareth A. Lee, Marcos Cobas García, David Antoine, and Kiminori Shitashima
Ocean Sci., 18, 1245–1262, https://doi.org/10.5194/os-18-1245-2022, https://doi.org/10.5194/os-18-1245-2022, 2022
Short summary
Short summary
An underwater glider mission was carried out in spring 2016 near a mooring in the northwestern Mediterranean Sea. The glider deployment served as a test of a prototype ion-sensitive field-effect transistor pH sensor. Mean net community production rates were estimated from glider and buoy measurements of dissolved oxygen and inorganic carbon concentrations before and during the spring bloom. Incorporating advection is important for accurate mass budgets. Unexpected metabolic quotients were found.
Daniel Lee, Amandine Schaeffer, and Sjoerd Groeskamp
Ocean Sci., 17, 1341–1351, https://doi.org/10.5194/os-17-1341-2021, https://doi.org/10.5194/os-17-1341-2021, 2021
Short summary
Short summary
The bluebottle (Physalia physalis), or Portuguese man o' war, is well known for the painful stings caused by its tentacles. Its drifting dynamics have not been widely explored, with previous studies using simple assumptions to calculate its drift. Considering similarities with a sailboat, we present a new theoretical model for the drifting speed and course of the bluebottle in different wind and ocean conditions, providing new insights into the parameterization of its complex drifting dynamics.
Carlos Rocha, Christopher A. Edwards, Moninya Roughan, Paulina Cetina-Heredia, and Colette Kerry
Geosci. Model Dev., 12, 441–456, https://doi.org/10.5194/gmd-12-441-2019, https://doi.org/10.5194/gmd-12-441-2019, 2019
Short summary
Short summary
Off southeast Australia, the East Australian Current (EAC) moves warm nutrient-poor waters towards the pole. In this region, the EAC and a large number of vortices pinching off it strongly affect phytoplankton’s access to nutrients and light. To study these dynamics, we created a numerical model that is able to solve the ocean conditions and how they modulate the foundation of the region’s ecosystem. We validated model results against available data and this showed that the model performs well.
Colette Kerry, Brian Powell, Moninya Roughan, and Peter Oke
Geosci. Model Dev., 9, 3779–3801, https://doi.org/10.5194/gmd-9-3779-2016, https://doi.org/10.5194/gmd-9-3779-2016, 2016
Short summary
Short summary
Ocean circulation drives weather and climate and supports marine ecosystems, so providing accurate predictions is important. The ocean circulation is complex, 3-D and highly variable, and its prediction requires advanced numerical models combined with real-time measurements. Focusing on the dynamic East Austr. Current, we use novel mathematical techniques to combine an ocean model with measurements to better estimate the circulation. This is an important step towards improving ocean forecasts.
Peter R. Oke, Roger Proctor, Uwe Rosebrock, Richard Brinkman, Madeleine L. Cahill, Ian Coghlan, Prasanth Divakaran, Justin Freeman, Charitha Pattiaratchi, Moninya Roughan, Paul A. Sandery, Amandine Schaeffer, and Sarath Wijeratne
Geosci. Model Dev., 9, 3297–3307, https://doi.org/10.5194/gmd-9-3297-2016, https://doi.org/10.5194/gmd-9-3297-2016, 2016
Short summary
Short summary
The Marine Virtual Laboratory (MARVL) is designed to help ocean modellers hit the ground running. Usually, setting up an ocean model involves a handful of technical steps that time and effort. MARVL provides a user-friendly interface that allows users to choose what options they want for their model, including the region, time period, and input data sets. The user then hits "go", and MARVL does the rest – delivering a "take-away bundle" that contains all the files needed to run the model.
Amandine Schaeffer, Moninya Roughan, Emlyn M. Jones, and Dana White
Biogeosciences, 13, 1967–1975, https://doi.org/10.5194/bg-13-1967-2016, https://doi.org/10.5194/bg-13-1967-2016, 2016
Short summary
Short summary
The water properties of the coastal ocean such as temperature, salt, oxygen, or chlorophyll content vary spatially, and estimates need to be made regarding the scales of variability. Here, we use statistical techniques to determine the spatial variability of ocean properties from high-resolution measurements by gliders. We show that biological activity is patchy compared to the distribution of physical characteristics, and that the size and shape of this is determined by coastal ocean processes.
Paulina Cetina-Heredia, Erik van Sebille, Richard Matear, and Moninya Roughan
Biogeosciences Discuss., https://doi.org/10.5194/bg-2016-53, https://doi.org/10.5194/bg-2016-53, 2016
Revised manuscript not accepted
Short summary
Short summary
Characterizing phytoplankton growth influences fisheries and climate. We use a lagrangian approach to identify phytoplankton blooms in the Great Australian Bight (GAB), and associate them with nitrate sources. We find that 88 % of the nitrate utilized in blooms is originated between the GAB and the SubAntarctic Front. Large nitrate concentrations are supplied at depth but do not reach the euphotic zone often. As a result, 55 % of blooms utilize nitrate supplied in the top 100 m.
A. M. Waite, V. Rossi, M. Roughan, B. Tilbrook, P. A. Thompson, M. Feng, A. S. J. Wyatt, and E. J. Raes
Biogeosciences, 10, 5691–5702, https://doi.org/10.5194/bg-10-5691-2013, https://doi.org/10.5194/bg-10-5691-2013, 2013
Cited articles
AODN: Pre-processing routines,
https://github.com/aodn/imos-toolbox/wiki/PPRoutines (last access: 27 April 2023), 2023. a
Barnes, E. A. and Barnes, R. J.: Estimating linear trends: simple linear
regression versus epoch differences, J. Climate, 28, 9969–9976,
2015. a
Cai, W., Shi, G., Cowan, T., Bi, D., and Ribbe, J.: The response of the
Southern Annular Mode, the East Australian Current, and the southern
mid-latitude ocean circulation to global warming, Geophys. Res.
Lett., 32, L23706, https://doi.org/10.1029/2005GL024701, 2005. a
Atta-ur-Rahman and Dawood, M.: Spatio-statistical analysis of temperature fluctuation
using Mann–Kendall and Sen’s slope approach, Clim. Dynam., 48,
783–797, 2017. a
Douglas, E., Vogel, R., and Kroll, C.: Trends in floods and low flows in the
United States: impact of spatial correlation, J. Hydrol., 240,
90–105, 2000. a
Foster, S. D., Griffin, D. A., and Dunstan, P. K.: Twenty years of
high-resolution sea surface temperature imagery around Australia:
Inter-annual and annual variability, PLoS one, 9, e100762, https://doi.org/10.1371/journal.pone.0100762, 2014. a, b, c
Godfrey, J., Cresswell, G., Golding, T., Pearce, A., and Boyd, R.: The
separation of the East Australian Current, J. Phys.
Oceanogr., 10, 430–440, 1980. a
Hamed, K. H. and Rao, A. R.: A modified Mann-Kendall trend test for
autocorrelated data, J. Hydrol., 204, 182–196, 1998. a
Hartmann, D. L., Tank, A. M. K., Rusticucci, M., Alexander, L. V.,
Brönnimann, S., Charabi, Y. A. R., Dentener, F. J., Dlugokencky, E. J.,
Easterling, D. R., Kaplan, A., Soden, B. J., Thorne, P. W., Wild, M., and Zhai, P.: Observations: atmosphere and surface,
in: Climate change 2013 the physical science basis: Working group I
contribution to the fifth assessment report of the intergovernmental panel on
climate change, edited by: Hurrell, J., Marengo, J., Tangang, F., and Viterbo, P., 159–254, Cambridge University Press, https://doi.org/10.1017/CBO9781107415324.008, 2013. a
Hemming, M.: UNSW-oceanography/NRS-temperature: v1.0.0.0, Zenodo [software],
https://doi.org/10.5281/zenodo.8112175, 2023. a
Hemming, M. P., Roughan, M., and Schaeffer, A.: Daily Subsurface Ocean
Temperature Climatology Using Multiple Data Sources: New Methodology,
Front. Mar. Sci., 7, 485, https://doi.org/10.3389/fmars.2020.00485, 2020. a
Hu, D., Wu, L., Cai, W., Gupta, A. S., Ganachaud, A., Qiu, B., Gordon, A. L.,
Lin, X., Chen, Z., Hu, S., Wang, G., Wang, Q., Sprintall, J., Tangdong, Q., Kashino, Y., Wang, F., and Kessler, W. S.: Pacific western boundary currents and
their roles in climate, Nature, 522, 299–308, 2015. a
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen,
N.-C., Tung, C. C., and Liu, H. H.: The empirical mode decomposition and the
Hilbert spectrum for nonlinear and non-stationary time series analysis,
P. Roy. Soc. Lond. A, 454, 903–995, 1998. a
IMOS: Long Timeseries Velocity Aggregated product: TEMP at BMP120 between
2011-03-29T22:35:00Z and 2021-01-19T23:15:00Z, https://portal.aodn.org.au (last access: 19 February 2021),
2021a. a
IMOS: Long Timeseries Velocity Aggregated product: TEMP at CH100 between
2009-08-15T01:35:04Z and 2020-10-28T23:00:00Z,
https://portal.aodn.org.au (last access: 19 February 2021),
2021b. a
IMOS: Long Timeseries Velocity Aggregated product: TEMP at NRSNSI between
2010-12-13T02:28:34Z and 2020-10-28T00:29:59Z,
https://portal.aodn.org.au (last access: 19 February 2021),
2021c. a
Ingleton, T., Morris, B., Pender, L., Darby, I., Austin, T., and Galibert, G.: Standardised Profiling CTD Data Processing Procedures V2.0, Sydney, NSW: Integrated Marine Observing System, https://s3-ap-southeast-2.amazonaws.com/content.aodn.org.au/Documents/IMOS/Facilities/national_mooring/anmn_ctd_processing_procedures.pdf (last access: 9 November 2022), 2014. a
Katana: Published online 2010, https://doi.org/10.26190/669X-A286, 2010. a
Lara-Lopez, A., Mancini, S.,Moltmann, T., and Proctor, R.: Quality Assurance and Quality Control by
Variable, version 6, https://repository.oceanbestpractices.org/bitstream/handle/11329/568/QAQC%20by%20variable_final.pdf?sequence=1&isAllowed=y (last access: 9 November 2022), 2017. a
Levitus, S., Antonov, J. I., Boyer, T. P., Baranova, O. K., Garcia, H. E.,
Locarnini, R. A., Mishonov, A. V., Reagan, J., Seidov, D., Yarosh, E. S., and Zweng, M. M.: World ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010, Geophys. Res. Lett., 39, L10603, https://doi.org/10.1029/2012GL051106, 2012. a
Li, J., Roughan, M., and Kerry, C.: Dynamics of interannual eddy kinetic
energy modulations in a Western Boundary Current, Geophys. Res.
Lett., e2021GL094115, https://doi.org/10.1029/2021GL094115, 2021. a, b
Lynch, T. P., Morello, E. B., Evans, K., Richardson, A. J., Rochester, W.,
Steinberg, C. R., Roughan, M., Thompson, P., Middleton, J. F., Feng, M.,
Sherrington, R., Brando, V., Tilbrook, B., Ridgway, K., Allen, S., Doherty, P., Hill, K., and Moltmann, T. C.: IMOS National Reference Stations: a continental-wide physical,
chemical and biological coastal observing system, PloS one, 9, e113652, https://doi.org/10.1371/journal.pone.0113652,
2014. a
Mann, H. B.: Nonparametric tests against trend, Econometrica, 13, 245–259, 1945. a
Marin, M., Bindoff, N. L., Feng, M., and Phillips, H. E.: Slower long-term
coastal warming drives dampened trends in coastal marine heatwave exposure,
J. Geophys. Res.-Oceans, 126, e2021JC017930, https://doi.org/10.1029/2021JC017930, 2021. a
Masson-Delmotte, V., Zhai, P., Pirani, A., et al.: Climate change 2021: the physical science basis. Contribution of
Working Group I to the Sixth Assessment Report of the Intergovernmental Panel
on Climate Change, Geneva, Intergovernmental Panel on Climate Change,
2021. a
Mohorji, A. M., Şen, Z., and Almazroui, M.: Trend analyses revision and
global monthly temperature innovative multi-duration analysis, Earth Syst. Environ., 1, 9, https://doi.org/10.1007/s41748-017-0014-x, 2017. a
Molla, M. K. I., Sumi, A., and Rahman, M. S.: Analysis of temperature change
under global warming impact using empirical mode decomposition,
Int. J. Inf. Tech., 3, 131–139, 2006. a
Niella, Y., Smoothey, A. F., Peddemors, V., and Harcourt, R.: Predicting
changes in distribution of a large coastal shark in the face of the
strengthening East Australian Current, Mar. Ecol.-Prog. Ser., 642,
163–177, 2020. a
Nilsson, C. and Cresswell, G.: The formation and evolution of East Australian
Current warm-core eddies, Prog. Oceanogr., 9, 133–183, 1980. a
Oke, P. R., Pilo, G. S., Ridgway, K., Kiss, A., and Rykova, T.: A search for
the Tasman Front, J. Marine Syst., 199, 103217, https://doi.org/10.1016/j.jmarsys.2019.103217, 2019. a
Oliver, E. and Holbrook, N.: Extending our understanding of South Pacific
gyre “spin-up”: Modeling the East Australian Current in a future
climate, J. Geophys. Res.-Oceans, 119, 2788–2805, 2014. a
Oliver, E. C., Benthuysen, J. A., Darmaraki, S., Donat, M. G., Hobday, A. J.,
Holbrook, N. J., Schlegel, R. W., and Sen Gupta, A.: Marine heatwaves, Annu.
Rev. Mar. Sci., 13, 313–342, 2021. a
Praveen, B., Talukdar, S., Mahato, S., Mondal, J., Sharma, P., Islam, A. R. M. T., and Rahman, A.: Analyzing trend and forecasting of rainfall
changes in India using non-parametrical and machine learning approaches,
Sci. Rep.-UK, 10, 1–21, 2020. a
Ridgway, K. R.: Long-term trend and decadal variability of the southward
penetration of the East Australian Current, Geophys. Res. Lett.,
34, L13613, https://doi.org/10.1029/2007GL030393, 2007. a, b, c, d
Rohde, R. A. and Hausfather, Z.: The Berkeley Earth Land/Ocean Temperature Record, Earth Syst. Sci. Data, 12, 3469–3479, https://doi.org/10.5194/essd-12-3469-2020, 2020. a, b
Roughan, M. and Middleton, J. H.: On the East Australian Current:
variability, encroachment, and upwelling, J. Geophys. Res.-Oceans, 109, C07003, https://doi.org/10.1029/2003JC001833, 2004. a
Roughan, M., Schaeffer, A., and Kioroglou, S.: Assessing the design of the
NSW-IMOS Moored Observation Array from 2008–2013: recommendations for the
future, in: 2013 OCEANS-San Diego, 1–7, IEEE, 2013. a
Roughan, M., Hemming, M., Schaeffer, A., Austin, T., Beggs, H., Chen, M., Feng,
M., Galibert, G., Holden, C., Hughes, D., Ingleton, T., Milburn, S., and Ridgway, K.: Multi-decadal ocean
temperature time-series and climatologies from Australia’s long-term
National Reference Stations, Sci. Data, 9, 1–15, 2022a. a, b, c, d, e, f, g, h, i
Roughan, M., Hemming, M., Schaeffer, A., Austin, T., Beggs, H., Chen, M., Feng, M., Galibert, G., Holden, C., Hughes, D., Ingleton, T., Milburn, S., and Ridgeway, K.: Multi-decadal ocean temperature time-series and climatologies from Australia's long-term National Reference Stations, Australian Ocean Data Network [data set], https://doi.org/10.26198/5cd1167734d90, 2022b. a
Sanikhani, H., Kisi, O., Mirabbasi, R., and Meshram, S. G.: Trend analysis of
rainfall pattern over the central India during 1901–2010, Arabian J. Geosci., 11, 437, https://doi.org/10.1007/s12517-018-3800-3, 2018. a
Schaeffer, A., Roughan, M., and Morris, B. D.: Cross-shelf dynamics in a
western boundary current regime: Implications for upwelling, J.
Phys. Oceanogr., 43, 1042–1059, 2013. a
Seidel, D. J. and Lanzante, J. R.: An assessment of three alternatives to
linear trends for characterizing global atmospheric temperature changes,
J. Geophys. Res.-Atmos., 109, D14108, https://doi.org/10.1029/2003JD004414, 2004. a
Smith, K. E., Burrows, M. T., Hobday, A. J., King, N. G., Moore, P. J.,
Sen Gupta, A., Thomsen, M. S., Wernberg, T., and Smale, D. A.: Biological
Impacts of Marine Heatwaves, Annu. Rev. Mar. Sci., 15, 119–145, https://doi.org/10.1146/annurev-marine-032122-121437, 2022. a
Torrence, C. and Compo, G. P.: A practical guide to wavelet analysis,
B. Am. Meteorol. Soc., 79, 61–78, 1998. a
Van-Ruth, P., Redondo Rodriguez, A., Davies, C., and Richardson, A. J.:
Indicators of depth layers important to phytoplankton production, Deep-Sea
Res. Pt. II, 157–158, https://doi.org/10.26198/5e16a98549e7d, 2020. a
Vergés, A., Steinberg, P. D., Hay, M. E., Poore, A. G., Campbell, A. H.,
Ballesteros, E., Heck Jr, K. L., Booth, D. J., Coleman, M. A., Feary, D. A.,
et al.: The tropicalization of temperate marine ecosystems: climate-mediated
changes in herbivory and community phase shifts, P. Roy.
Soc. B, 281, 20140846, https://doi.org/10.1098/rspb.2014.0846, 2014. a, b
Von Storch, H. and Navarra, A.: Analysis of climate variability: Applications
of statistical techniques proceedings of an autumn school organized by the
Commission of the European Community on Elba from October 30 to November 6,
1993, Springer Science & Business Media, ISBN 978-3-540-66315-7, 2013. a
Wdowinski, S., Bray, R., Kirtman, B. P., and Wu, Z.: Increasing flooding
hazard in coastal communities due to rising sea level: Case study of Miami
Beach, Florida, Ocean Coast. Manage., 126, 1–8, 2016. a
Wijffels, S. E., Beggs, H., Griffin, C., Middleton, J. F., Cahill, M., King,
E., Jones, E., Feng, M., Benthuysen, J. A., Steinberg, C. R., and Sutton, P.: A fine
spatial-scale sea surface temperature atlas of the Australian regional seas
(SSTAARS): Seasonal variability and trends around Australasia and New
Zealand revisited, J. Marine Syst., 187, 156–196, 2018 (data available at: https://thredds.aodn.org.au/thredds/catalog/CSIRO/Climatology/SSTAARS/2017/catalog.html, last access: 9 November 2022). a, b, c, d, e, f, g, h
Wood, J. E., Roughan, M., and Tate, P. M.: Annual cycling in wind,
temperature and along shore currents on the Sydney shelf, in: Coasts and
Ports 2013: 21st Australasian Coastal and Ocean Engineering Conference and
the 14th Australasian Port and Harbour Conference, p. 866, Citeseer, ISBN 9781922107053, 2013. a
Wu, Z. and Huang, N. E.: Ensemble empirical mode decomposition: a
noise-assisted data analysis method, Advances in adaptive data analysis, 1,
1–41, 2009. a
Wu, Z., Huang, N. E., Wallace, J. M., Smoliak, B. V., and Chen, X.: On the
time-varying trend in global-mean surface temperature, Clim. Dynam., 37,
759–773, https://doi.org/10.1007/s00382-011-1128-8, 2011. a, b
Yang, H., Lohmann, G., Wei, W., Dima, M., Ionita, M., and Liu, J.:
Intensification and poleward shift of subtropical western boundary currents
in a warming climate, J. Geophys. Res.-Oceans, 121,
4928–4945, 2016. a
Yang, H., Lohmann, G., Krebs-Kanzow, U., Ionita, M., Shi, X., Sidorenko, D.,
Gong, X., Chen, X., and Gowan, E. J.: Poleward shift of the major ocean
gyres detected in a warming climate, Geophys. Res. Lett., 47,
e2019GL085868, https://doi.org/10.1029/2019GL085868, 2020.
a, b
Yue, S. and Wang, C. Y.: Applicability of prewhitening to eliminate the
influence of serial correlation on the Mann-Kendall test, Water Resour.
Res., 38, 4–1, 2002. a
Short summary
We estimate subsurface linear and non-linear temperature trends at five coastal sites adjacent to the East Australian Current (EAC). We see accelerating trends at both 34.1 and 42.6 °S and place our results in the context of previously reported trends, highlighting that magnitudes are depth-dependent and vary across latitude. Our results indicate the important role of regional dynamics and show the necessity of subsurface data for the improved understanding of regional climate change impacts.
We estimate subsurface linear and non-linear temperature trends at five coastal sites adjacent...
