Abstract
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 long-term 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.
Highlights
The idea of locally warm seawater disrupting species distributions or ecosystem functioning is not a novel concept
Marine Heatwaves (MHWs) was close to linear, meaning that one may be able to say what the change in the count of MHWs may be as a time series is shortened, but it does not allow us to say if this change is positive or negative
The acceptable sub-optimal data limits, their proposed corrections, and the amount of uncertainty they introduce into the results are as follows: (1) Time series length:
Summary
The idea of locally warm seawater disrupting species distributions or ecosystem functioning is not a novel concept. In order to quantify the increased occurrence and severity of these events it was necessary to develop a methodology that would be inter-comparable for any location on the globe This was Detecting Marine Heatwaves accomplished in 2016 after the International Working Group on Marine Heatwaves (MHWs) initiated a series of workshops to address this issue. This definition for anomalously warm seawater events, known as MHWs, has seen wide-spread and rapid adoption due to ease of use and global applicability (Hobday et al, 2016). To avoid contention on the use of the word “quality,” time series that meet the aforementioned standards are referred to here as “optimal,” whereas those that do not meet one or more of the standards are referred to as “sub-optimal.” Another unresolved issue with the Hobday et al (2016) algorithm, which does not fall within the requirements for optimal data, is how much of an effect the long-term (secular) trend in a time series may have on detection of MHWs compared to that same time series when the trend has been removed
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