RmarineHeatWaves (version 0.14.1)

block_average: Calculate Yearly Means for Event Metrics.

Description

Calculate Yearly Means for Event Metrics.

Usage

block_average(data, report = "full")

Arguments

data

Accepts the data returned by the detect function.

report

Specify either full or partial. Selecting full causes the report to contain NAs for any years in which no events were detected (except for count, which will be zero in those years), while partial reports only the years wherein events were detected. The default is full.

Value

The function will return a data frame of the averaged (or aggregate) metrics. It includes the following:int_max_rel_thresh, int_mean_rel_thresh, int_var_rel_thresh, and int_cum_rel_thresh are as above except relative to the threshold (e.g., 90th percentile) rather than the seasonal climatology.int_max_abs, int_mean_abs, int_var_abs, and int_cum_abs are as above except as absolute magnitudes rather than relative to the seasonal climatology or threshold.int_max_norm and int_mean_norm are as above except units are in multiples of threshold exceedances, i.e., a value of 1.5 indicates the event intensity (relative to the climatology) was 1.5 times the value of the threshold (relative to climatology, i.e., threshold - climatology.)

Details

This function needs to be provided with the full output from the detect function. Note that the yearly averages are calculted only for complete years (i.e. years that start/end part-way through the year at the beginning or end of the original time series are removed from the calculations).

This function differs from the python implementation of the function of the same name (i.e., blockAverage, see https://github.com/ecjoliver/marineHeatWaves) in that we only provide the ability to calculate the average (or aggregate) event metrics in 'blocks' of one year, while the python version allows arbitrary (integer) block sizes.

References

Hobday, A.J. et al. (2016), A hierarchical approach to defining marine heatwaves, Progress in Oceanography, 141, pp. 227-238, doi: 10.1016/j.pocean.2015.12.014

Examples

t_dat <- make_whole(sst_Med)
res <- detect(t_dat, climatology_start = 1983, climatology_end = 2012) # using default values
out <- block_average(res)
summary(glm(count ~ year, out, family = "poisson"))

## Not run: 
# plot(out$year, out$count, col = "salmon", pch = 16,
#      xlab = "Year", ylab = "Number of events")
# lines(out$year, out$count)
# ## End(Not run)