Basic Detection and Visualisation of Marine Heatwaves

Marine heatwaves and cold spells as per Hobday et al (2016) and Schlegel et al (2017).

R
analysis
MHW
This vignette demonstrates the basic use of the heatwaveR package for the detection and visualisation of marine heatwaves.
Author
Affiliation

Robert Schlegel

Laboratoire d’Océanographie, Institut de la Mer de Villefranche

Published

November 11, 2023

Modified

November 23, 2023

This material also appears as a heatwaveR vignette.

In the previous post, we demonstrated how to use the heatwaveR package to detect and visualise marine heatwaves (MHWs) and cold spells (MCSs). In this post, we will demonstrate how to use the package to detect and visualise these extreme events. We will also demonstrate how to use the package to calculate the duration, intensity, and cumulative intensity of MHWs and MCSs.

Data

The detect_event() function is the core of this package, and it expects to be fed the output of the second core function, ts2clm(). By default, ts2clm() wants to receive a two-column dataframe with one column labelled t containing all of the date values, and a second column temp containing all of the temperature values. Please note that the date format it expects is “YYYY-MM-DD”. For example, please see the top five rows of one of the datasets included with the heatwaveR package:

head(heatwaveR::sst_WA)
# A tibble: 6 × 2
  t           temp
  <date>     <dbl>
1 1982-01-01  20.9
2 1982-01-02  21.2
3 1982-01-03  21.4
4 1982-01-04  21.2
5 1982-01-05  21.3
6 1982-01-06  21.6

It is possible to use different column names other than t and temp with which to calculate events. Please see the help files for ts2clm() or detect_event() for a thorough explanation of how to do so.

Loading ones data from a .csv file or other text based format is the easiest approach for the calculation of events, assuming one is not working with gridded data (e.g. NetCDF). Please see this vignette for a detailed walkthrough on using the functions in this package with gridded data.

Calculating marine heatwaves (MHWs)

Here are the ts2clm() and detect_event() function applied to the Western Australia test data included with this package (sst_WA), which are also discussed by Hobday et al. (2016):

library(dplyr)
library(ggplot2)
library(heatwaveR)

# Detect the events in a time series
ts <- ts2clm(sst_WA, climatologyPeriod = c("1982-01-01", "2011-12-31"))
mhw <- detect_event(ts)

# View just a few metrics
mhw$event %>% 
  dplyr::ungroup() %>%
  dplyr::select(event_no, duration, date_start, date_peak, intensity_max, intensity_cumulative) %>% 
  dplyr::arrange(-intensity_max) %>% 
  head(5)
# A tibble: 5 × 6
  event_no duration date_start date_peak  intensity_max intensity_cumulative
     <int>    <dbl> <date>     <date>             <dbl>                <dbl>
1       52      105 2010-12-24 2011-02-28          6.58                293. 
2       41       35 2008-03-25 2008-04-14          3.83                 79.3
3       29       95 1999-05-13 1999-05-22          3.64                240. 
4       60       14 2012-12-27 2012-12-31          3.42                 32.3
5       59      101 2012-01-10 2012-01-27          3.38                214.

Visualising marine heatwaves (MHWs)

Default MHW visuals

One may use event_line() and lolli_plot() directly on the output of detect_event() in order to visualise MHWs. Here are the functions being used to visualise the massive Western Australian heatwave of 2011:

event_line(mhw, spread = 180, metric = "intensity_max", 
           start_date = "1982-01-01", end_date = "2014-12-31")

lolli_plot(mhw, metric = "intensity_max")
Figure 1
Figure 2

Custom MHW visuals

The event_line() and lolli_plot() functions were designed to work directly on the list returned by detect_event(). If more control over the figures is required, it may be useful to create them in ggplot2 by stacking geoms. We specifically created two new ggplot2 geoms to reproduce the functionality of event_line() and lolli_plot(). These functions are more general in their functionality and can be used outside of the heatwaveR package, too. To apply them to MHWs and MCSs first requires that we access the climatology or event dataframes within the list that is produced by detect_event(). Here is how:

# Select the region of the time series of interest
mhw2 <- mhw$climatology %>% 
  slice(10580:10720)

ggplot(mhw2, aes(x = t, y = temp, y2 = thresh)) +
  geom_flame() +
  geom_text(aes(x = as.Date("2011-02-25"), y = 25.8, label = "the Destroyer\nof Kelps"))

ggplot(mhw$event, aes(x = date_start, y = intensity_max)) +
  geom_lolli(colour = "salmon", colour_n = "red", n = 3) +
  geom_text(colour = "black", aes(x = as.Date("2006-08-01"), y = 5,
                label = "The marine heatwaves\nTend to be left skewed in a\nGiven time series")) +
  labs(y = expression(paste("Max. intensity [", degree, "C]")), x = NULL)
Figure 3
Figure 4

Spicy MHW visuals

The default output of these function may not be to your liking. If so, not to worry. As ggplot2 geoms, they are highly malleable. For example, if we were to choose to reproduce the format of the MHWs as seen in Hobday et al. (2016), the code would look something like this:

# It is necessary to give geom_flame() at least one row on either side of 
# the event in order to calculate the polygon corners smoothly
mhw_top <- mhw2 %>% 
  slice(5:111)

ggplot(data = mhw2, aes(x = t)) +
  geom_flame(aes(y = temp, y2 = thresh, fill = "all"), show.legend = T) +
  geom_flame(data = mhw_top, aes(y = temp, y2 = thresh, fill = "top"),  show.legend = T) +
  geom_line(aes(y = temp, colour = "temp")) +
  geom_line(aes(y = thresh, colour = "thresh"), size = 1.0) +
  geom_line(aes(y = seas, colour = "seas"), size = 1.2) +
  scale_colour_manual(name = "Line Colour",
                      values = c("temp" = "black", 
                                 "thresh" =  "forestgreen", 
                                 "seas" = "grey80")) +
  scale_fill_manual(name = "Event Colour", 
                    values = c("all" = "salmon", 
                               "top" = "red")) +
  scale_x_date(date_labels = "%b %Y") +
  guides(colour = guide_legend(override.aes = list(fill = NA))) +
  labs(y = expression(paste("Temperature [", degree, "C]")), x = NULL)
Figure 5

It is also worth pointing out that when we use geom_flame() directly like this, but we don’t want to highlight events greater less than our standard five day length, allowing for a two day gap, we want to use the arguments n and n_gap respectively.

mhw3 <- mhw$climatology %>% 
  slice(850:950)

ggplot(mhw3, aes(x = t, y = temp, y2 = thresh)) +
  geom_flame(fill = "black", alpha = 0.5) +
  # Note the use of n = 5 and n_gap = 2 below
  geom_flame(n = 5, n_gap = 2, fill = "red", alpha = 0.5) +
  ylim(c(22, 25)) +
    geom_text(colour = "black", aes(x = as.Date("1984-05-16"), y = 24.5,
                label = "heat\n\n\n\n\nspike"))

Should we not wish to highlight any events with geom_lolli(), plot them with a colour other than the default, and use a different theme, it would look like this:

ggplot(mhw$event, aes(x = date_peak, y = intensity_max)) +
  geom_lolli(colour = "firebrick") +
  labs(x = "Peak Date", 
       y = expression(paste("Max. intensity [", degree, "C]")), x = NULL) +
  theme_linedraw()
Figure 6

Because these are simple ggplot2 geoms possibilities are nearly infinite.

Calculating marine cold-spells (MCSs)

The calculation and visualisation of cold-spells is also provided for within this package. The data to be fed into the functions is the same as for MHWs. The main difference is that one is now calculating the 10th percentile threshold, rather than the 90th percentile threshold. Here are the top five cold-spells (cumulative intensity) detected in the OISST data for Western Australia:

# First calculate the cold-spells
ts_10th <- ts2clm(sst_WA, climatologyPeriod = c("1982-01-01", "2011-12-31"), pctile = 10)
mcs <- detect_event(ts_10th, coldSpells = TRUE)

# Then look at the top few events
mcs$event %>% 
  dplyr::ungroup() %>%
  dplyr::select(event_no, duration, date_start,
                date_peak, intensity_mean, intensity_max, intensity_cumulative) %>%
  dplyr::arrange(intensity_cumulative) %>% 
  head(5)
# A tibble: 5 × 7
  event_no duration date_start date_peak  intensity_mean intensity_max
     <int>    <dbl> <date>     <date>              <dbl>         <dbl>
1       15       76 1990-04-13 1990-05-11          -2.50         -3.19
2       49       58 2003-12-19 2004-01-23          -1.73         -2.59
3       83       41 2020-04-26 2020-05-25          -2.34         -3.14
4       64       52 2014-04-14 2014-05-05          -1.78         -2.54
5       77       46 2018-07-24 2018-08-02          -1.81         -2.43
# ℹ 1 more variable: intensity_cumulative <dbl>

Visualising marine cold-spells (MCSs)

Default MCS visuals

The default plots showing cold-spells look like this:

event_line(mcs, spread = 200, metric = "intensity_cumulative",
           start_date = "1982-01-01", end_date = "2014-12-31")

lolli_plot(mcs, metric = "intensity_cumulative", xaxis = "event_no")
Figure 7
Figure 8

Note that one does not need to specify that MCSs are to be visualised, the functions are able to understand this on their own.

Custom MCS visuals

Cold spell figures may be created as geoms in ggplot2, too:

# Select the region of the time series of interest
mcs2 <- mcs$climatology %>% 
  slice(2900:3190)

# Note that one must specify a colour other than the default 'salmon'
ggplot(mcs2, aes(x = t, y = thresh, y2 = temp)) +
  geom_flame(fill = "steelblue3")

ggplot(mcs$event, aes(x = date_start, y = intensity_max)) +
  geom_lolli(colour = "steelblue3", colour_n = "navy", n = 3) +
  labs(x = "Start Date",
       y = expression(paste("Max. intensity [", degree, "C]")))
Figure 9
Figure 10

Minty MCS visuals

Again, because geom_flame() and geom_lolli() are simple ggplot2 geoms, one can go completely bananas with them:

mcs_top <- mcs2 %>% 
  slice(125:202)

ggplot(data = mcs2, aes(x = t)) +
  geom_flame(aes(y = thresh, y2 = temp, fill = "all"), show.legend = T) +
  geom_flame(data = mcs_top, aes(y = thresh, y2 = temp, fill = "top"), show.legend = T) +
  geom_line(aes(y = temp, colour = "temp")) +
  geom_line(aes(y = thresh, colour = "thresh"), size = 1.0) +
  geom_line(aes(y = seas, colour = "seas"), size = 1.2) +
  scale_colour_manual(name = "Line Colour",
                      values = c("temp" = "black", "thresh" =  "forestgreen", "seas" = "grey80")) +
  scale_fill_manual(name = "Event Colour", values = c("all" = "steelblue3", "top" = "navy")) +
  scale_x_date(date_labels = "%b %Y") +
  guides(colour = guide_legend(override.aes = list(fill = NA))) +
  labs(y = expression(paste("Temperature [", degree, "C]")), x = NULL)

ggplot(mcs$event, aes(x = date_start, y = intensity_cumulative)) +
  geom_lolli(colour = "steelblue3", colour_n = "navy", n = 7) +
  labs( x = "Start Date", y = expression(paste("Cumulative intensity [days x ", degree, "C]")))
Figure 11
Figure 12

Interactive visuals

As of heatwaveR v0.3.6.9002, geom_flame() was also able to be used with plotly to allow for interactive MHW visuals. Unfortunately around December of 2020 the plotly packaged was orphaned and CRAN decided it didn’t want packages to include it as an imported package. Therefore as of v0.4.4.9005 heatwaveR no longer has built in support for using geom_flame() with plotly. It is however still possible with a bit of work and a simple working example is given below. It is not currently possible to use geom_lolli() with plotly. Rather one is advised to just create the dots and segments separately with geom_point() and geom_segment() respectively as these are already recognised by plotly.

Note that the following code chunk is not run as it makes this vignette a bit too large.

# Must load plotly library first
library(plotly)

# Function needed for making geom_flame() work with plotly
geom2trace.GeomFlame <- function (data,
                                  params,
                                  p) {
  
  x <- y <- y2 <- NULL
  
  # Create data.frame for ease of use
  data1 <- data.frame(x = data[["x"]],
                      y = data[["y"]],
                      y2 = data[["y2"]])
  
  # Grab parameters
  n <- params[["n"]]
  n_gap <- params[["n_gap"]]
  
  # Find events that meet minimum length requirement
  data_event <- heatwaveR::detect_event(data1, x = x, y = y,
                                        seasClim = y,
                                        threshClim = y2,
                                        minDuration = n,
                                        maxGap = n_gap,
                                        protoEvents = T)
  
  # Detect spikes
  data_event$screen <- base::ifelse(data_event$threshCriterion == FALSE, FALSE,
                                    ifelse(data_event$event == FALSE, TRUE, FALSE))
  
  # Screen out spikes
  data1 <- data1[data_event$screen != TRUE,]
  
  # Prepare to find the polygon corners
  x1 <- data1$y
  x2 <- data1$y2
  
  # # Find points where x1 is above x2.
  above <- x1 > x2
  above[above == TRUE] <- 1
  above[is.na(above)] <- 0
  
  # Points always intersect when above=TRUE, then FALSE or reverse
  intersect.points <- which(diff(above) != 0)
  
  # Find the slopes for each line segment.
  x1.slopes <- x1[intersect.points + 1] - x1[intersect.points]
  x2.slopes <- x2[intersect.points + 1] - x2[intersect.points]
  
  # # Find the intersection for each segment.
  x.points <- intersect.points + ((x2[intersect.points] - x1[intersect.points]) / (x1.slopes - x2.slopes))
  y.points <- x1[intersect.points] + (x1.slopes * (x.points - intersect.points))
  
  # Coerce x.points to the same scale as x
  x_gap <- data1$x[2] - data1$x[1]
  x.points <- data1$x[intersect.points] + (x_gap*(x.points - intersect.points))
  
  # Create new data frame and merge to introduce new rows of data
  data2 <- data.frame(y = c(data1$y, y.points), x = c(data1$x, x.points))
  data2 <- data2[order(data2$x),]
  data3 <- base::merge(data1, data2, by = c("x","y"), all.y = T)
  data3$y2[is.na(data3$y2)] <- data3$y[is.na(data3$y2)]
  
  # Remove missing values for better plotting
  data3$y[data3$y < data3$y2] <- NA
  missing_pos <- !stats::complete.cases(data3[c("x", "y", "y2")])
  ids <- cumsum(missing_pos) + 1
  ids[missing_pos] <- NA
  
  # Get the correct positions
  positions <- data.frame(x = c(data3$x, rev(data3$x)),
                          y = c(data3$y, rev(data3$y2)),
                          ids = c(ids, rev(ids)))
  
  # Convert to a format geom2trace is happy with
  positions <- plotly::group2NA(positions, groupNames = "ids")
  positions <- positions[stats::complete.cases(positions$ids),]
  positions <- dplyr::left_join(positions, data[,-c(2,3)], by = "x")
  if(length(stats::complete.cases(positions$PANEL)) > 1) 
    positions$PANEL <- positions$PANEL[stats::complete.cases(positions$PANEL)][1]
  if(length(stats::complete.cases(positions$group)) > 1) 
    positions$group <- positions$group[stats::complete.cases(positions$group)][1]
  
  # Run the plotly polygon code
  if(length(unique(positions$PANEL)) == 1){
    getFromNamespace("geom2trace.GeomPolygon", asNamespace("plotly"))(positions)
  } else{
    return()
  }
}

# Time series
ts_res <- heatwaveR::ts2clm(data = heatwaveR::sst_WA,
                            climatologyPeriod = c("1982-01-01", "2011-12-31"))
ts_res_sub <- ts_res[10500:10800,]

# Flame Figure
p <- ggplot(data = ts_res_sub, aes(x = t, y = temp)) +
  heatwaveR::geom_flame(aes(y2 = thresh), n = 5, n_gap = 2) +
  geom_line(aes(y = temp)) +
  geom_line(aes(y = seas), colour = "green") +
  geom_line(aes(y = thresh), colour = "red") +
  labs(x = "", y = "Temperature (°C)")

# Create interactive visuals
ggplotly(p)

Reuse

Citation

BibTeX citation:
@online{smit,_a._j.2023,
  author = {Smit, A. J., and Schlegel, Robert},
  title = {Basic {Detection} and {Visualisation} of {Marine}
    {Heatwaves}},
  date = {2023-11-11},
  url = {http://tangledbank.netlify.app/blog/2023-11-13-basic-mhw-detect/},
  langid = {en}
}
For attribution, please cite this work as:
Smit, A. J., Schlegel R (2023) Basic Detection and Visualisation of Marine Heatwaves. http://tangledbank.netlify.app/blog/2023-11-13-basic-mhw-detect/.