Calculating basic climatic indices with data from easyclimate • easyclimate

First, let’s download daily climatic data for a specific location and store it in a dataframe:


library(easyclimate)
library(tidyr)
library(dplyr)

coords <- matrix(c(-5.36, 37.40), ncol = 2)

daily <- get_daily_climate(coords,
                           period = 2001:2005,
                           climatic_var = c("Prcp", "Tmin", "Tmax"))

Here we calculate the daily mean temperature:


daily <- daily |> 
  mutate(Tmean = (Tmin + Tmax) / 2, # Daily mean temperature
         date = as.Date(date),
         month = format(date, format = "%m"),
         year = format(date, format = "%Y"))

Average climatic values per site or time period

To calculate average temperatures by site or time period we can use group_by and summarise from dplyr, or by and aggregate from base R:


daily |> 
  group_by(ID_coords) |> 
  summarise(Tmin.site = mean(Tmin),
            Tmean.site = mean(Tmean),
            Tmax.site = mean(Tmax))
## # A tibble: 1 × 4
##   ID_coords Tmin.site Tmean.site Tmax.site
##       <dbl>     <dbl>      <dbl>     <dbl>
## 1         1      11.5       18.2      24.9

daily |> 
  group_by(year) |>  
  summarise(Tmin.year = mean(Tmin),
            Tmean.year = mean(Tmean),
            Tmax.year = mean(Tmax)) |> 
  kable(digits = 1)

year	Tmin.year	Tmean.year	Tmax.year
2001	11.9	18.2	24.6
2002	11.5	18.0	24.5
2003	12.2	18.7	25.2
2004	11.1	18.0	25.0
2005	10.6	17.9	25.2

Similarly, you can calculate accumulated precipitation over different time periods:

daily |> 
  group_by(year) |>  
  summarise(sumPrec = sum(Prcp)) |> 
  kable(digits = 0)

year	sumPrec
2001	539
2002	594
2003	605
2004	359
2005	238

Climatic anomalies

Sometimes it can be useful to calculate the deviation of climate values from a reference value. For example, if we consider as the reference the mean value for the period 2001-2005, we can calculate the deviations per site and year as follows:


daily |>    
  group_by(ID_coords, year) |>  
  summarise(avgTm = mean(Tmean)) |>  
  group_by(ID_coords) |>  
  mutate(refTm = mean(avgTm)) |>  # Reference mean temperature
  mutate(devTm = avgTm - refTm) |>  
  kable(digits = 1)

ID_coords	year	avgTm	refTm	devTm
1	2001	18.2	18.2	0.1
1	2002	18.0	18.2	-0.2
1	2003	18.7	18.2	0.5
1	2004	18.0	18.2	-0.1
1	2005	17.9	18.2	-0.3


daily |>    
  group_by(ID_coords, year) |>  
  summarise(sumPrec = sum(Prcp)) |> 
  group_by(ID_coords) |> 
  mutate(refPrec = mean(sumPrec)) |>  # Reference precipitation
  mutate(devPrec = sumPrec - refPrec) |>  
  kable(digits = 0)

ID_coords	year	sumPrec	refPrec	devPrec
1	2001	539	467	72
1	2002	594	467	127
1	2003	605	467	138
1	2004	359	467	-108
1	2005	238	467	-229

Climatic events

Having daily values allows us to calculate climatic indices such as the number of consecutive days above a given temperature (e.g. heat waves or growing degree days), the number of spring frosts or the number of consecutive days without rain.

Let’s start with an example on heat waves, or number of days with maximum temperatures above a given threshold:


threshold <- 32  

daily |>  
  mutate(ID_coords = as.factor(ID_coords),
         year = factor(year)) |>  
  group_by(ID_coords, year) |>  
  mutate(event = ifelse(Tmax >= threshold, 1, 0), # NAs will be 0 
         start = c(1, diff(event) != 0),
         run = cumsum(start)) |>  
  filter(event == 1) |> 
  group_by(run, .add = TRUE) |>  
  mutate(length = n()) |>  
  group_by(ID_coords, year) |> 
  summarise(max_heatwave = max(length), # longest heatwave per year (in days)
            mean_heatwave = mean(length), # average length of heatwaves per year
            n_heatwave = n()) |>  # No. of events per year
  kable(digits = 1)

ID_coords	year	max_heatwave	mean_heatwave	n_heatwave
1	2001	16	9.4	90
1	2002	12	7.2	76
1	2003	32	14.7	100
1	2004	30	15.6	100
1	2005	26	14.7	90

To calculate the number of days with spring frosts (i.e. minimum temperatures below 0 during spring months) we can use the next code:


spring.months <- c("03","04","05") # March to May

daily |>  
  mutate(ID_coords = as.factor(ID_coords),
         year = factor(year)) |>  
  filter(month %in% spring.months) |>  
  mutate(event = ifelse(Tmin < 0, 1, 0)) |>  # NAs will be 0 
  group_by(ID_coords, year) |>  
  mutate(n_frost = sum(event)) |>  # No. of days with minimum temperature < 0 per year
  filter(event == 1) |>  
  group_by(ID_coords, year, n_frost) |>  
  summarise(Tmin_frost_avg = mean(Tmin) / 100) |>  # Mean minimum temperature of frost days
  ungroup() |>  
  complete(ID_coords, year, fill = list(n_frost = 0, Tmin_frost_avg = NA)) |>  
  kable()

ID_coords	year	n_frost	Tmin_frost_avg
1	2001	0	NA
1	2002	0	NA
1	2003	0	NA
1	2004	2	-0.00795
1	2005	0	NA

A dry period can be defined as a specific number of consecutive days with no precipitation:


threshold <- 30 # threshold in days to count extreme long events of no-rain

daily |>  
  mutate(event = ifelse(Prcp < 0.01, 1, 0), # NAs will be 0 
         start = c(1, diff(event) != 0)) |>  
    group_by(ID_coords, year) |>  
    mutate(run = cumsum(start)) |> 
    filter(event == 1) |>  
    group_by(ID_coords, year, run) |>  
    summarise(length = n()) |>  
    ungroup(run) |> 
    summarise(max_days_norain = max(length), # Maximum length of periods of consecutive days without rain per year
              mean_days_norain = mean(length), 
              nextreme_events = sum(length > threshold)) |> # No. of events per year
  ungroup() |>  
  complete(ID_coords, year, 
           fill = list(max_days_norain = NA, mean_days_norain = NA, nevents = 0))  |>  
  kable(digits = 1)

ID_coords	year	max_days_norain	mean_days_norain	nextreme_events
1	2001	103	9.8	2
1	2002	102	8.9	1
1	2003	145	8.9	1
1	2004	112	13.7	2
1	2005	99	13.5	4

Using ClimInd in combination with easyclimate

The package climInd can be used to calculate multiple climatic indices from data obtained through {easyclimate}. For example:

Number of days with maximum temperature > 32ºC in summer (June-August):

library(ClimInd)

Tmax <- as.vector(daily$Tmax)
names(Tmax) <- as.character(format(daily$date, format = "%m/%d/%Y"))

d32(Tmax)
## 2001 2002 2003 2004 2005 
##   72   66   74   75   74

Growing degree days:

Tmean <- as.vector(daily$Tmean)
names(Tmean) <- as.character(format(daily$date, format = "%m/%d/%Y"))

gd4(data = Tmean, time.scale = "year") 
##     2001     2002     2003     2004     2005 
## 5197.805 5112.590 5369.960 5135.705 5078.865

Growing season length:

gsl(data = Tmean, time.scale = "year") 
## 2001 2002 2003 2004 2005 
##  365  365  365  366  365

Heavy precipitation days:

Prec <- as.vector(daily$Prcp)
names(Prec) <- as.character(format(daily$date, format = "%m/%d/%Y"))

d50mm(data = Prec, time.scale = "month")
##      Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
## 2001   0   0   0   0   0   0   0   0   0   0   0   0
## 2002   0   0   0   0   0   0   0   0   1   0   0   0
## 2003   0   0   0   0   0   0   0   0   0   0   0   0
## 2004   0   0   0   0   0   0   0   0   0   0   0   0
## 2005   0   0   0   0   0   0   0   0   0   0   0   0

Learn more

To learn more about downloading daily and monthly climatic data, please consult this vignette for point coordinates, or this other if you need to extract data for several polygons or a region.