m_mhw

The m_mhw toolbox is an matlab - based tool designed to detect and analyse spatial marine heatwaves (MHWs). Previously, approaches to detecting and analysing MHW time series have been applied in python (https://github.com/ecjoliver/marineHeatWaves, written by Eric C. J. Oliver) and R (Schlegel and Smit, 2018).

The m_mhw toolbox is designed 1) to determine spatial MHWs according to the definition provided in Hobday et al. (2016) and marine cold spells (MCSs) introduced in Schlegel et al. (2017); 2) to visualize MHW/MCS event in a particular location during a period; 3) to explore the mean states and trends of MHW metrics, such as what have done in Oliver et al. (2018).

Installation

The installation of this toolbox could be directly achieved by downloading this repositories and add its path in your MATLAB.

Requirements

The MATLAB Statistics and Machine Learning Toolbox. m_map is recommended for running example.

Functions

Additionally, this toolbox also provides sea surface temperature off eastern Tasmania [147-155E, 45-37S] during 1982-2015, extracted from NOAA OI SST V2 (Reynolds et al., 2007).

Inputs and outputs

The core function detect need some inputs:

The core function detect would return four outputs, which are MHW, mclim, m90 and mhw_ts. Their descriptions are summarized in following table.

The major output MHW contains all detected MHW/MCS events, characterized by 9 different properties, including:

For information of other functions, please see help text via MATLAB. For practical tutorial and example, please see following contents.

Example

We provide examples about how to use functions in m_mhw and how to apply them to real-world data.

Current examples include:

An example about how to apply m_mhw to real-world data (Codes)

Analysing seasonality and monthly variability of MHWs (Codes)

EOF analysis on annual MHW days (Codes)

MHW Category Analysis (Codes)

EOF analysis on annual MHW cumulative intensity (Codes)

Comparison for the efficiency biases between detect and detectc (Codes)

Issues

The results from this toolbox would be slightly different from outputs from Python and R modules. This is due to the fact that MATLAB follows different rules to calculate percentile thresholds. The number of detected events from this toolbox would be slightly less than that from Python and R. Please see a comparison. If you would like to get the same outputs as python, please set the optional input 'percentile' as 'python' (default is 'matlab').

Contributing to m_mhw

To contribute to the package please follow the guidelines here.

Please use this link to report any bugs found.

Citation

If you use this toolbox, please cite the paper:

Zhao, Z., & Marin, M. (2019). A MATLAB toolbox to detect and analyze marine heatwaves. Journal of Open Source Software, 4(33), 1124.

References

Hobday, A.J. et al. (2016). A hierarchical approach to defining marine heatwaves, Progress in Oceanography, 141, pp. 227-238.

Schlegel, R. W., Oliver, E. C. J., Wernberg, T. W., Smit, A. J., 2017. Nearshore and offshore co-occurrences of marine heatwaves and cold-spells. Progress in Oceanography, 151, pp. 189-205.

Schlegel, R. W. and Smit, A. J, 2018. heatwaveR: A central algorithm for the detection of heatwaves and cold-spells. The Journal of Open Source Software, 3, p.821.

Oliver, E.C., Lago, V., Hobday, A.J., Holbrook, N.J., Ling, S.D. and Mundy, C.N., 2018. Marine heatwaves off eastern Tasmania: Trends, interannual variability, and predictability. Progress in Oceanography, 161, pp.116-130.

Reynolds, Richard W., Thomas M. Smith, Chunying Liu, Dudley B. Chelton, Kenneth S. Casey, Michael G. Schlax, 2007: Daily High-Resolution-Blended Analyses for Sea Surface Temperature. J. Climate, 20, 5473-5496.

Contact

Zijie Zhao

School of Earth Science, The University of Melbourne

Parkville VIC 3010, Melbourne, Australia

E-mail: [email protected]

Maxime Marin

CSIRO Oceans & Atmosphere, Indian Ocean Marine Research Centre

Crawley 6009, Western Australia, Australia

E-mail: [email protected]