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Ian R. Young, Emmanuel Fontaine, Qingxiang Liu, and Alexander V. Babanin

Abstract

The wave climate of the Southern Ocean is investigated using a combined dataset from 33 years of altimeter data, in situ buoy measurements at five locations, and numerical wave model hindcasts. The analysis defines the seasonal variation in wind speed and significant wave height, as well as wind speed and significant wave height for a 1-in-100-year return period. The buoy data include an individual wave with a trough to crest height of 26.4 m and suggest that waves in excess of 30 m would occur in the region. The extremely long fetches, persistent westerly winds, and procession of low pressure systems that traverse the region generate wave spectra that are unique. These spectra are unimodal but with peak frequencies that propagate much faster than the local wind. This situation results in a unique energy balance in which waves at the spectra peak grow as a result of nonlinear transfer without any input from the local wind.

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Qingxiang Liu, Alexander V. Babanin, Changlong Guan, Stefan Zieger, Jian Sun, and Yongjun Jia

Abstract

Hai Yang-2 (HY-2) satellite altimeter measurements of significant wave height () are analyzed over the period from 1 October 2011 to 6 December 2014. They are calibrated and validated against in situ buoys and other concurrently operating altimeters: Jason-2, CryoSat-2, and Satellite with Argos and ALtiKa (SARAL). In general, the HY-2 altimeter measurements agree well with buoy measurements, with a bias of −0.22 m and a root-mean-square error (RMSE) of 0.30 m. When the reduced major axis (RMA) regression procedure was applied to the entire period, the RMSE was reduced by 33% to 0.2 m. A further comparison with other satellite altimeters, however, revealed two additional features of HY-2 estimates over this period. First, a noticeable mismatch is present between HY-2 and the other satellite altimeters for high seas ( > 6 m). Second, a jump increase in HY-2 values was detected starting in April 2013, which was associated with the switch to backup status of the HY-2 sensors and the subsequent update of its data processing software. Although reported by previous studies, these two deficiencies had not been accounted for in calibrations. Therefore, the HY-2 wave height records are now subdivided into two phases (time periods pre- and post-April 2013) and a two-branched calibration is proposed for each phase. These revised calibrations, validated throughout the range of significant wave heights of 1–9 m, are expected to improve the practical applicability of HY-2 measurements significantly.

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Qingxiang Liu, W. Erick Rogers, Alexander Babanin, Jingkai Li, and Changlong Guan

Abstract

Three dissipative (two viscoelastic and one viscous) ice models are implemented in the spectral wave model WAVEWATCH III to estimate the ice-induced wave attenuation rate. These models are then explored and intercompared through hindcasts of two field cases: one in the autumn Beaufort Sea in 2015 and the other in the Antarctic marginal ice zone (MIZ) in 2012. The capability of these dissipative models, along with their limitations and applicability to operational forecasts, are analyzed and discussed. The sensitivity of the simulated wave height to different source terms—the ice-induced wave decay S ice and other physical processes S other (e.g., wind input, nonlinear four-wave interactions)—is also investigated. For the Antarctic MIZ experiment, S other is found to be remarkably less than S ice and thus contributes little to the simulated significant wave height H s. The saturation of dH s/dx at large wave heights in this case, as reported by a previous study, is well reproduced by the three dissipative ice models with or without the utilization of S other in the ice-infested seas. A clear downward trend in the peak frequency f p is found as H s increases. As f p decreases, the dominant wave components of a wave spectrum will experience reduced damping by sea ice, and finally result in the flattening of dH s/dx for H s > 3 m in this specific case. Nonetheless, S other should not be disregarded within a more general modeling perspective, as our simulations suggest S other could be comparable to S ice in the Beaufort Sea case where wave and ice conditions are remarkably different.

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Qingxiang Li, Hongzheng Zhang, Xiaoning Liu, Ji Chen, Wei Li, and Phil Jones

Abstract

No Abstract available.

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Qingxiang Liu, Alexander V. Babanin, Stefan Zieger, Ian R. Young, and Changlong Guan

Abstract

Twenty years (1996–2015) of satellite observations were used to study the climatology and trends of oceanic winds and waves in the Arctic Ocean in the summer season (August–September). The Atlantic-side seas, exposed to the open ocean, host more energetic waves than those on the Pacific side. Trend analysis shows a clear spatial (regional) and temporal (interannual) variability in wave height and wind speed. Waves in the Chukchi Sea, Beaufort Sea (near the northern Alaska), and Laptev Sea have been increasing at a rate of 0.1–0.3 m decade−1, found to be statistically significant at the 90% level. The trend of waves in the Greenland and Barents Seas, on the contrary, is weak and not statistically significant. In the Barents and Kara Seas, winds and waves initially increased between 1996 and 2006 and later decreased. Large-scale atmospheric circulations such as the Arctic Oscillation and Arctic dipole anomaly have a clear impact on the variation of winds and waves in the Atlantic sector. Comparison between altimeter observations and ERA-Interim shows that the reanalysis winds are on average 1.6 m s−1 lower in the Arctic Ocean, which translates to a low bias of significant wave height (−0.27 m) in the reanalysis wave data.

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Qingxiang Liu, W. Erick Rogers, Alexander V. Babanin, Ian R. Young, Leonel Romero, Stefan Zieger, Fangli Qiao, and Changlong Guan

Abstract

The observation-based source terms available in the third-generation wave model WAVEWATCH III (i.e., the ST6 package for parameterizations of wind input, wave breaking, and swell dissipation terms) are recalibrated and verified against a series of academic and realistic simulations, including the fetch/duration-limited test, a Lake Michigan hindcast, and a 1-yr global hindcast. The updated ST6 not only performs well in predicting commonly used bulk wave parameters (e.g., significant wave height and wave period) but also yields a clearly improved estimation of high-frequency energy level (in terms of saturation spectrum and mean square slope). In the duration-limited test, we investigate the modeled wave spectrum in a detailed way by introducing spectral metrics for the tail and the peak of the omnidirectional wave spectrum and for the directionality of the two-dimensional frequency–direction spectrum. The omnidirectional frequency spectrum E(f) from the recalibrated ST6 shows a clear transition behavior from a power law of approximately f −4 to a power law of about f −5, comparable to previous field studies. Different solvers for nonlinear wave interactions are applied with ST6, including the Discrete Interaction Approximation (DIA), the more expensive Generalized Multiple DIA (GMD), and the very expensive exact solutions [using the Webb–Resio–Tracy method (WRT)]. The GMD-simulated E(f) is in excellent agreement with that from WRT. Nonetheless, we find the peak of E(f) modeled by the GMD and WRT appears too narrow. It is also shown that in the 1-yr global hindcast, the DIA-based model overestimates the low-frequency wave energy (wave period T > 16 s) by 90%. Such model errors are reduced significantly by the GMD to ~20%.

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Shaobo Qiao, Meng Zou, Ho Nam Cheung, Jieyu Liu, Jinqing Zuo, Qingxiang Li, Guolin Feng, and Wenjie Dong

Abstract

This study investigates the prediction of southern China surface air temperature (SAT) in January and February using hindcast and forecast dataset from the second version of the National Centers for Environmental Prediction Climate Forecast System, version 2 (NCEP CFSv2), for the period of 1983–2017. The observed January and February SAT in southern China is teleconnected with the Euro-Atlantic dipole (EAD) and the North Atlantic Oscillation (NAO), respectively. The February SAT is also teleconnected with El Niño–Southern Oscillation (ENSO) via the bridge with the Philippine Sea anticyclone. The CFSv2 better predicts southern China SAT in February than January, where the temporal correlation coefficients between the observed and predicted regional-mean SAT in February and January are +0.81 and +0.27 (+0.32 and +0.04), respectively, for the one-month (two month) ahead prediction. The better prediction in February coincides with 1) accurate responses of the Eurasian circulation and the Philippine Sea anticyclone to the NAO and the ENSO, respectively, and 2) a strong ENSO–NAO linkage. The poorer prediction in January is related to a stronger linkage of the predicted January SAT with the NAO rather than the EAD, as well as a weak ENSO–EAD linkage. These results advance our understanding of the subseasonal prediction of the winter temperature in southern China.

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Zhiyuan Hu, Haiyan Li, Jiawei Liu, Shaobo Qiao, Dongqian Wang, Nicolas Freychet, Simon F. B. Tett, Buwen Dong, Fraser C. Lott, Qingxiang Li, and Wenjie Dong
Open access
Diana Greenslade, Mark Hemer, Alex Babanin, Ryan Lowe, Ian Turner, Hannah Power, Ian Young, Daniel Ierodiaconou, Greg Hibbert, Greg Williams, Saima Aijaz, João Albuquerque, Stewart Allen, Michael Banner, Paul Branson, Steve Buchan, Andrew Burton, John Bye, Nick Cartwright, Amin Chabchoub, Frank Colberg, Stephanie Contardo, Francois Dufois, Craig Earl-Spurr, David Farr, Ian Goodwin, Jim Gunson, Jeff Hansen, David Hanslow, Mitchell Harley, Yasha Hetzel, Ron Hoeke, Nicole Jones, Michael Kinsela, Qingxiang Liu, Oleg Makarynskyy, Hayden Marcollo, Said Mazaheri, Jason McConochie, Grant Millar, Tim Moltmann, Neal Moodie, Joao Morim, Russel Morison, Jana Orszaghova, Charitha Pattiaratchi, Andrew Pomeroy, Roger Proctor, David Provis, Ruth Reef, Dirk Rijnsdorp, Martin Rutherford, Eric Schulz, Jake Shayer, Kristen Splinter, Craig Steinberg, Darrell Strauss, Greg Stuart, Graham Symonds, Karina Tarbath, Daniel Taylor, James Taylor, Darshani Thotagamuwage, Alessandro Toffoli, Alireza Valizadeh, Jonathan van Hazel, Guilherme Vieira da Silva, Moritz Wandres, Colin Whittaker, David Williams, Gundula Winter, Jiangtao Xu, Aihong Zhong, and Stefan Zieger
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Diana Greenslade, Mark Hemer, Alex Babanin, Ryan Lowe, Ian Turner, Hannah Power, Ian Young, Daniel Ierodiaconou, Greg Hibbert, Greg Williams, Saima Aijaz, João Albuquerque, Stewart Allen, Michael Banner, Paul Branson, Steve Buchan, Andrew Burton, John Bye, Nick Cartwright, Amin Chabchoub, Frank Colberg, Stephanie Contardo, Francois Dufois, Craig Earl-Spurr, David Farr, Ian Goodwin, Jim Gunson, Jeff Hansen, David Hanslow, Mitchell Harley, Yasha Hetzel, Ron Hoeke, Nicole Jones, Michael Kinsela, Qingxiang Liu, Oleg Makarynskyy, Hayden Marcollo, Said Mazaheri, Jason McConochie, Grant Millar, Tim Moltmann, Neal Moodie, Joao Morim, Russel Morison, Jana Orszaghova, Charitha Pattiaratchi, Andrew Pomeroy, Roger Proctor, David Provis, Ruth Reef, Dirk Rijnsdorp, Martin Rutherford, Eric Schulz, Jake Shayer, Kristen Splinter, Craig Steinberg, Darrell Strauss, Greg Stuart, Graham Symonds, Karina Tarbath, Daniel Taylor, James Taylor, Darshani Thotagamuwage, Alessandro Toffoli, Alireza Valizadeh, Jonathan van Hazel, Guilherme Vieira da Silva, Moritz Wandres, Colin Whittaker, David Williams, Gundula Winter, Jiangtao Xu, Aihong Zhong, and Stefan Zieger

Abstract

The Australian marine research, industry, and stakeholder community has recently undertaken an extensive collaborative process to identify the highest national priorities for wind-waves research. This was undertaken under the auspices of the Forum for Operational Oceanography Surface Waves Working Group. The main steps in the process were first, soliciting possible research questions from the community via an online survey; second, reviewing the questions at a face-to-face workshop; and third, online ranking of the research questions by individuals. This process resulted in 15 identified priorities, covering research activities and the development of infrastructure. The top five priorities are 1) enhanced and updated nearshore and coastal bathymetry; 2) improved understanding of extreme sea states; 3) maintain and enhance the in situ buoy network; 4) improved data access and sharing; and 5) ensemble and probabilistic wave modeling and forecasting. In this paper, each of the 15 priorities is discussed in detail, providing insight into why each priority is important, and the current state of the art, both nationally and internationally, where relevant. While this process has been driven by Australian needs, it is likely that the results will be relevant to other marine-focused nations.

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