XMHW identifies marine heatwaves from a timeseries of sea surface temperature data and calculates statistics associated to the detected heatwaves. This python module is based on the marineHeatWaves code by Eric Olivier. Main difference with the original code are: We have two separate functions: one to calculate the climatologies and one to detect the heatwaves. By using xarray sst on a multidimensional grid can be passed as argument We added event severity to the MHW characteristics that are calculated Intermediate results of the detect function can be also returned Results are returned as xarray datasets, with the original spatial dimensions but 'event' instead of 'time' . Each event representing the starting day of an event. It is possible to calculate climatologies and detect events at different time frequencies, not only for daily data Currently available functions: threshold() - to calculate the percentile and mean climatologies detect() - to detect the MHW events and their main characteristics block_average() - to calculate statistics on blocks of events, still missing "total mhw days per year" In particular in this version: Updated coordinates handling following recent updates of xarray Added a separate workflow if only a single point time-series is passed as input, the functions are called in the same way, the code will check number of dimensions and if when only one dimension I present it will skip the operations needed to work with multidimensional grids This code uses python3 and the following modules: xarray, numpy, pandas, dask. To install clone the repository from GitHub, and from the main directory conda install -c coecms -c conda-forge xmhw This is a work in progress so any feedback/collaboration is appreciated, please create an issue on GitHub.

For questions, comments and if you find a bug please open an issue on https://github.com/coecms/xmhw. Author email available on orchid account.