Creates a daily climatology from a time series of daily temperatures using a user-specified sliding window for the mean and threshold calculation, followed by an optional moving average smoother as used by Hobday et al. (2016).

ts2clm(data, x = t, y = temp, climatologyPeriod, robust = FALSE,
maxPadLength = FALSE, windowHalfWidth = 5, pctile = 90,
smoothPercentile = TRUE, smoothPercentileWidth = 31,
clmOnly = FALSE, var = FALSE, roundClm = 4)

## Arguments

data A data frame with two columns. In the default setting (i.e. ommitting the arguments x and y; see immediately below), the data set is expected to have the headers t and temp. The t column is a vector of dates of class Date, while temp is the measured variable (by default it is assumed to be temperature). This column is expected to contain a vector of dates. If a column headed t is present in the dataframe, this argument may be ommitted; otherwise, specify the name of the column with dates here. This is a column containing the measurement variable. If the column name differs from the default (i.e. temp), specify the name here. Required. To this argument should be passed two values (see example below). The first value should be the chosen date for the start of the climatology period, and the second value the end date of said period. This chosen period (preferably 30 years in length) is then used to calculate the seasonal cycle and the extreme value threshold. This argument has been deprecated and no longer has affects how the function operates. Specifies the maximum length of days over which to interpolate (pad) missing data (specified as NA) in the input temperature time series; i.e., any consecutive blocks of NAs with length greater than maxPadLength will be left as NA. The default is FALSE. Set as an integer to interpolate. Setting maxPadLength to TRUE will return an error. Width of sliding window about day-of-year (to one side of the center day-of-year) used for the pooling of values and calculation of climatology and threshold percentile. Default is 5 days, which gives a window width of 11 days centered on the 6th day of the series of 11 days. Threshold percentile (%) for detection of events (MHWs). Default is 90th percentile. Should the intent be to use these threshold data for MCSs, set pctile = 10. Or some other low value. Boolean switch selecting whether to smooth the climatology and threshold percentile time series with a moving average of smoothPercentileWidth. Default is TRUE. Full width of moving average window for smoothing climatology and threshold. The default is 31 days. Choose to calculate and return only the climatologies. The default is FALSE. This argument has been introduced to allow the user to choose if the variance of the seasonal signal per doy should be calculated. The default of FALSE will prevent the calculation, potentially increasing speed of calculations on gridded data and reducing the size of the output. The variance was initially introduced as part of the standard output from Hobday et al. (2016), but few researchers use it and so it is generally regarded now as unnecessary. This argument allows the user to choose how many decimal places the seas and thresh outputs will be rounded to. Default is 4. To precent rounding set roundClm = FALSE. This argument may only be given numeric values or FALSE.

## Value

The function will return a tibble (see the tidyverse) with the input time series and the newly calculated climatology. The climatology contains the seasonal climatology and the threshold for calculating MHWs. The software was designed for creating climatologies of daily temperatures, and the units specified below reflect that intended purpose. However, various other kinds of climatologies may be created, and if that is the case, the appropriate units need to be determined by the user.

doy

Julian day (day-of-year). For non-leap years it runs 1...59 and 61...366, while leap years run 1...366.

t

The date vector in the original time series supplied in data. If an alternate column was provided to the x argument, that name will rather be used for this column.

temp

The measurement vector as per the the original data supplied to the function. If a different column was given to the y argument that will be shown here.

seas

Climatological seasonal cycle [deg. C].

thresh

Seasonally varying threshold (e.g., 90th percentile) [deg. C]. This is used in detect_event for the detection/calculation of events (MHWs).

var

Seasonally varying variance (standard deviation) [deg. C]. This column is not returned if var = FALSE (defaut).

Should clmOnly be enabled, only the 365 or 366 day climatology will be returned.

## Details

1. This function assumes that the input time series consists of continuous daily values with few missing values. Time ranges which start and end part-way through the calendar year are supported.

2. It is recommended that a period of at least 30 years is specified in order to produce a climatology that smooths out any decadal thermal periodicities that may be present. It is further advised that full the start and end dates for the climatology period result in full years, e.g. "1982-01-01" to "2011-12-31" or "1982-07-01" to "2012-06-30"; if not, this may result in an unequal weighting of data belonging with certain months within a time series. A daily climatology will be created; that is, the climatology will be comprised of one mean temperature for each day of the year (365 or 366 days, depending on how leap years are dealt with), and the mean will be based on a sample size that is a function of the length of time determined by the start and end values given to climatologyPeriod and the width of the sliding window specified in windowHalfWidth.

3. This function supports leap years. This is done by ignoring Feb 29s for the initial calculation of the climatology and threshold. The values for Feb 29 are then linearly interpolated from the values for Feb 28 and Mar 1.

4. Previous versions of ts2clm() tested to see if some rows are duplicated, or if replicate temperature readings are present per day, but this has now been disabled. Should the user be concerned about such repeated measurements, we suggest that the necessary checks and fixes are implemented prior to feeding the time series to ts2clm().

The original Python algorithm was written by Eric Oliver, Institute for Marine and Antarctic Studies, University of Tasmania, Feb 2015, and is documented by Hobday et al. (2016).

## 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

res <- ts2clm(sst_WA, climatologyPeriod = c("1983-01-01", "2012-12-31"))
res[1:10, ]#>     doy          t  temp    seas  thresh
#>  1:   1 1982-01-01 20.94 21.6080 22.9605
#>  2:   2 1982-01-02 21.25 21.6348 22.9987
#>  3:   3 1982-01-03 21.38 21.6621 23.0376
#>  4:   4 1982-01-04 21.16 21.6895 23.0771
#>  5:   5 1982-01-05 21.26 21.7169 23.1130
#>  6:   6 1982-01-06 21.61 21.7436 23.1460
#>  7:   7 1982-01-07 21.74 21.7699 23.1775
#>  8:   8 1982-01-08 21.50 21.7958 23.2080
#>  9:   9 1982-01-09 21.40 21.8217 23.2366
#> 10:  10 1982-01-10 21.36 21.8478 23.2649
# Or if one only wants the 366 day climatology
res_clim <- ts2clm(sst_WA, climatologyPeriod = c("1983-01-01", "2012-12-31"),
clmOnly = TRUE)
res_clim[1:10, ]#>     doy    seas  thresh
#>  1:   1 21.6080 22.9605
#>  2:   2 21.6348 22.9987
#>  3:   3 21.6621 23.0376
#>  4:   4 21.6895 23.0771
#>  5:   5 21.7169 23.1130
#>  6:   6 21.7436 23.1460
#>  7:   7 21.7699 23.1775
#>  8:   8 21.7958 23.2080
#>  9:   9 21.8217 23.2366
#> 10:  10 21.8478 23.2649
# Or if one wants the variance column included in the results
res_var <- ts2clm(sst_WA, climatologyPeriod = c("1983-01-01", "2012-12-31"),
var = TRUE)
res_var[1:10, ]#>     doy          t  temp    seas  thresh    var
#>  1:   1 1982-01-01 20.94 21.6080 22.9605 0.9585
#>  2:   2 1982-01-02 21.25 21.6348 22.9987 0.9636
#>  3:   3 1982-01-03 21.38 21.6621 23.0376 0.9684
#>  4:   4 1982-01-04 21.16 21.6895 23.0771 0.9728
#>  5:   5 1982-01-05 21.26 21.7169 23.1130 0.9764
#>  6:   6 1982-01-06 21.61 21.7436 23.1460 0.9797
#>  7:   7 1982-01-07 21.74 21.7699 23.1775 0.9828
#>  8:   8 1982-01-08 21.50 21.7958 23.2080 0.9860
#>  9:   9 1982-01-09 21.40 21.8217 23.2366 0.9884
#> 10:  10 1982-01-10 21.36 21.8478 23.2649 0.9906