Subsurface intensification of marine heatwaves off southeastern Australia: The role of stratification and local winds

Schaeffer, Amandine; Roughan, Moninya

doi:10.1002/2017gl073714

Cited by 109 publications

(117 citation statements)

References 49 publications

Supporting

Mentioning

112

Contrasting

Order By: Relevance

“…For example, they have triggered widespread mortalities and abrupt redistribution of marine species (Garrabou et al, ; Wernberg et al, ), toxic algal blooms (Cavole et al, ), and mass coral bleaching (Hughes et al, ) in a matter of weeks or months, with economic losses in fisheries and aquaculture industry (Mills et al, ; Oliver et al, ). Superimposed on the underlying warming trend of the ocean, MHWs occur regionally from coastal to open ocean, can extend up to thousands of kilometers (Scannell et al, ), and may also propagate deeper to the water column (Schaeffer & Roughan, ).…”

Section: Introductionmentioning

confidence: 99%

“…Yet it remains unknown how frequently and deep a MHW may penetrate in the open ocean or what are the characteristics of deep events relative to surface MHWs. The only systematic analysis to date is based on coastal in situ data in Australia for 1953–2016 and shows regular warming throughout the water column and a maximum MHW intensity just below the thermocline (Schaeffer & Roughan, ). In the case of the Mediterranean Sea, a quantitative assessment using a standarized framework is currently missing for both past surface and subsurface MHWs characteristics.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Past Variability of Mediterranean Sea Marine Heatwaves

Darmaraki

Somot

Sevault

et al. 2019

Geophysical Research Letters

101

View full text Add to dashboard Cite

Marine heatwaves (MHWs) are episodes of anomalous warming in the ocean, responsible for widespread impacts on marine ecosystems. For the first time summer MHW variability at surface and three ecosystem‐relevant depths of the upper Mediterranean Sea are assessed here for 1982–2017. We apply a MHW detection algorithm on a hindcast simulation, performed with a high‐resolution, fully coupled regional climate system model. Identified surface events last, on average, 15 days with a mean intensity of 0.6 °C above threshold and a maximum sea surface coverage of around 39%. Subsurface events are seasonally shifted and appear, on average, longer and more intense but less frequent and less extended in space than surface MHWs. We also find significant trends of increase in most MHW properties throughout the period, with severe surface MHWs detected for the first time in 2012, 2015, and 2017. However, MHW spatial ≪ hot spots ≫ are inhomogeneously distributed in surface and deeper layers.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Past Variability of Mediterranean Sea Marine Heatwaves

Darmaraki

Somot

Sevault

et al. 2019

Geophysical Research Letters

101

View full text Add to dashboard Cite

show abstract

“…The ranges of uncertainty that sub-optimal data introduce into MHW results could be determined and users may now decide their acceptable level of uncertainty. It must be noted that having used only SST data for these investigations the results may not accurately represent the properties of sub-surface MHWs, which may last longer and be more intense than those at the surface (Schaeffer and Roughan, 2017;Darmaraki et al, 2019). The MHW results from time series with 10 years of data are not appreciably different from the MHWs detected with 30 years of data.…”

Section: Discussionmentioning

confidence: 83%

Detecting Marine Heatwaves With Sub-Optimal Data

et al. 2019

View full text Add to dashboard Cite

Marine heatwaves (MHWs), or prolonged periods of anomalously warm sea water temperature, have been increasing in duration and intensity globally for decades. However, there are many coastal, oceanic, polar, and sub-surface regions where our ability to detect MHWs is uncertain due to limited high quality data. Here, we investigate the effect that short time series length, missing data, or linear long-term temperature trends may have on the detection of MHWs. We show that MHWs detected in time series as short as 10 years did not have durations or intensities appreciably different from events detected in a standard 30 year long time series. We also show that the output of our MHW algorithm for time series missing less than 25% data did not differ appreciably from a complete time series, and that the level of allowable missing data could cautiously be increased to 50% when gaps were filled by linear interpolation. Finally, linear long-term trends of 0.10 • C/decade or greater added to a time series caused larger changes (increases) to the count and duration of detected MHWs than shortening a time series to 10 years or missing more than 25% of the data. The longterm trend in a time series has the largest effect on the detection of MHWs and has the largest range in added uncertainty in the results. Time series length has less of an effect on MHW detection than missing data, but adds a larger range of uncertainty to the results. We provide suggestions for best practices to improve the accuracy of MHW detection with sub-optimal time series and show how the accuracy of these corrections may change regionally.

show abstract

“…This sentiment reflects that of previous research, whereby Smale and Wernberg (2009) concluded that satellite SST data do not "adequately detect ecologically important small-scale variability or provide reliable information on temperature extremes" and Stobart et al (2016), who highlighted that SST was only reliable for broad temperature patterns. These, together with increasing frequency of extreme temperature events (e.g., marine heatwaves; Schaeffer and Roughan, 2017), makes long-term monitoring programs critical especially those that provide high resolution temperature over a range of spatial scales (including depth).…”

Section: Implications For Future Researchmentioning

confidence: 99%

“…Therefore, long-term in situ monitoring is needed for water depths >16 m, as this is information that is not available from satellite data. It has been shown that extreme marine temperature events (e.g., marine heatwaves: Schaeffer and Roughan, 2017) can occur throughout the water column, and their intensity and duration may be either not detected or underestimated by satellite SST. Such events can have devastating consequences on marine biota (Smale and Wernberg, 2012;Wernberg et al, 2013).…”

Section: Satellite-derived Temperaturesmentioning

confidence: 99%

Assessing the Use of Area- and Time-Averaging Based on Known De-correlation Scales to Provide Satellite Derived Sea Surface Temperatures in Coastal Areas

et al. 2018

View full text Add to dashboard Cite

Satellite derived sea surface temperatures (SSTs) are often used as a proxy for in situ water temperatures, as they are readily available over large spatial and temporal scales. However, contamination of satellite images can prohibit their use in coastal areas. We compared in situ temperatures to SST foundation (∼10 m depth) at 31 sites inshore of the East Australian Current (EAC), the dynamic western boundary current of the south Pacific gyre, using an area averaging approach to overcome coastal contamination. Varying across-and along-shelf distances were used to area average SST measurements and de-correlation time scales were used to gap fill data. As the EAC is typically anisotropic (dominant along-shore flow) the choice of across-shelf distances influenced the correlation with in situ temperatures more than along-shelf distances. However, the "optimal" distances for both measurements were within known de-correlation length scales. Incorporating both SST area and time averaging (based on de-correlation time scales) produced data for an average of 96% of days that in situ loggers were deployed, compared to 27% (52%) without (with) area averaging. Temperature differences between the in situ data and SSTs varied depending on time of year, with higher differences in the austral summer when daily in situ temperatures can range by up to 4.20 • C. The differences between the in situ and SST measurements were, however, significant with or without area averaging (t-test: p-values < 0.05). Nevertheless, when using the area averaging approaches SSTs were only an average of ∼1.05 • C different from in situ temperatures and less than in situ temperature fluctuations. Linear mixed models revealed that latitude, distance to the coast and nearest estuary did not influence the difference between the in situ and satellite data as much as the water depth. This study shows that using de-correlation length and time scales to inform how to process satellite data can overcome contamination and missing data thereby greatly increasing the coverage and utility of SST data, particularly in coastal areas.

show abstract

Subsurface intensification of marine heatwaves off southeastern Australia: The role of stratification and local winds

Cited by 109 publications

References 49 publications

Past Variability of Mediterranean Sea Marine Heatwaves

Past Variability of Mediterranean Sea Marine Heatwaves

Detecting Marine Heatwaves With Sub-Optimal Data

Assessing the Use of Area- and Time-Averaging Based on Known De-correlation Scales to Provide Satellite Derived Sea Surface Temperatures in Coastal Areas

Contact Info

Product

Resources

About