1. ETCCDI climate extremes indices
Jana Sillmann
January, 23rd 2015

2. Expert Team on Climate Change Detection and Indices
Joint WMO CLIVAR/CCl/JCOMM/GEWEX
Expert Team on Climate Change Detection and Indices
Develop indices relevant to climate change monitoring and detection
Create observational dataset(s) of indices with (ideally) global coverage (
Indices are:
Statistically robust
Easy to understand
Globally valid
Enable comparison of modeled data and observations

3. ETCCDI indices

4. ETCCDI percentile threshold indices
TN10p, TX10p Percentage of days when TN (orTX) < 10th percentile:
Let TNij be the daily minimum temperature on day i in period j and let TNin10 be the calendar day 10th percentile centred on a 5-day window for the base period The percentage of time for the base period is determined where:
TNij < TNin10
TN90p, TX90p Percentage of days when TN (or TX) > 90th percentile:
Let TNij be the daily minimum temperature on day i in period j and let TNin90 be the calendar day 90th percentile centred on a 5-day window for the base period The percentage of time for the base period is determined where:
TNij > TNin90
To avoid possible inhomogeneity across the in-base and out-base periods, the calculation for the base period ( ) requires the use of a bootstrap processure. Details are described in Zhang et al. (2005) .

5. ETCCDI Extremes Indices Calculation
Rclimdex  used at regional workshops for station data
Fclimdex  adapted for climate model data/faster processing
climdex.pcic.R  open source R package for large datasets

6. ETCCDI Extremes Indices Archive (CMIP3/CMIP5)

7. ETCCDI Extremes Indices Calculation Preparation
Anne installed all necessary R packages on sverdrupt
e.g., climdex.pcic_0.7-2.tar.gz, ncdf4_1.8.tar.gz, ncdf4.helpers_0.2-5.tar.gz, PCICt_0.5-4.tar.gz, udunits2_0.6.tar.gz, udunits tar.gz
Have your data ready
i.e., input and output directory

8. ETCCDI Extremes Indices Calculation Preparation
What you need to pay attention to:
Format of Data Files  needs to follow CMIP5 convention (name, attributes, etc.)
variable_timeinterval_model_experiment_ensemblemember_startday_endday.nc
Example: "pr_day_CESM1-CAM4_CO2BB1850_ens1_ nc”
Base Period  make sure it is within data range
(standard base period for observation based indices )

9. compute_index_on_gcm_improved.r
ETCCDI Extremes Indices Calculation
Preparation
What you also need: The magic R script that does everything!
compute_index_on_gcm_improved.r
Or write your own code…

10. ETCCDI Extremes Indices Calculation Preparation
Read R package description:
In R
> getAnywhere(“climdex.cdd”)

11. ETCCDI Extremes Indices Calculation
###calculate indices
library(climdex.pcic.ncdf)
Loading required package: PCICt
> input.files <-
> c("pr_day_CESM1-CAM4_CO2BB1850_ens1_ nc",
> "tasmin_day_CESM1-CAM4_CO2BB1850_ens1_ nc",
> "tasmax_day_CESM1-CAM4_CO2BB1850_ens1_ nc")
> author.data <- list(institution="Center for International Climate and
> Environmental Research - Oslo, Norway", institution_id="CICERO")
> create.indices.from.files(input.files,
> "/div/enso/d12/janasi/CESM1-CAM4/indices/", input.files[1],
> author.data, base.range=c(1850,1879), parallel=8, max.vals.millions=70
> )
Creating cluster of 8 nodes of type SOCK Finished computing indices
###calculate thresholds
> library(climdex.pcic.ncdf)
> c("pr_day_CanESM2_historical_r1i1p1_ nc",
> "tasmax_day_CanESM2_historical_r1i1p1_ nc",
> "tasmin_day_CanESM2_historical_r1i1p1_ nc")
> "/div/ferrel/d2-3/cmip5/janasi/testground/indices/", input.files[1],
> author.data, base.range=c(1961,1990), parallel=16,
> max.vals.millions=70 )
Creating cluster of 16 nodes of type SOCK Finished computing indices
> create.thresholds.from.file(input.files, "threshholds_CanESM2.nc",
> author.data, base.range=c(1961,1990), parallel=FALSE,
Finished computing thresholds
> input.files <- c("pr_day_CanESM2_rcp85_r1i1p1_ nc",
> "tasmax_day_CanESM2_rcp85_r1i1p1_ nc",
> "tasmin_day_CanESM2_rcp85_r1i1p1_ nc")
> author.data, base.range=c(1961, 1990), parallel=16,
> max.vals.millions=70, thresholds.files="threshholds_CanESM2.nc")
ETCCDI Extremes Indices Calculation
###calculate indices
library(climdex.pcic.ncdf)
Loading required package: PCICt
> input.files <-
> c("pr_day_CESM1-CAM4_CO2BB1850_ens1_ nc",
> "tasmin_day_CESM1-CAM4_CO2BB1850_ens1_ nc",
> "tasmax_day_CESM1-CAM4_CO2BB1850_ens1_ nc")
> author.data <- list(institution="Center for International Climate and
> Environmental Research - Oslo, Norway", institution_id="CICERO")
> create.indices.from.files(input.files,
> "/div/enso/d12/janasi/CESM1-CAM4/indices/", input.files[1],
> author.data, base.range=c(1850,1879), parallel=8, max.vals.millions=70
> )
Creating cluster of 8 nodes of type SOCK Finished computing indices

12. ETCCDI Extremes Indices Calculation
###calculate thresholds
> library(climdex.pcic.ncdf)
Loading required package: PCICt
> input.files <-
> c("pr_day_CanESM2_historical_r1i1p1_ nc",
> "tasmax_day_CanESM2_historical_r1i1p1_ nc",
> "tasmin_day_CanESM2_historical_r1i1p1_ nc")
> author.data <- list(institution="Center for International Climate and
> Environmental Research - Oslo, Norway", institution_id="CICERO")
> create.thresholds.from.file(input.files, "threshholds_CanESM2.nc",
> author.data, base.range=c(1961,1990), parallel=FALSE,
> max.vals.millions=70 )
Finished computing thresholds

13. ETCCDI Extremes Indices Calculation
###calculate indices with new thresholds
> input.files <- c("pr_day_CanESM2_rcp85_r1i1p1_ nc",
> "tasmax_day_CanESM2_rcp85_r1i1p1_ nc",
> "tasmin_day_CanESM2_rcp85_r1i1p1_ nc")
> create.indices.from.files(input.files,
> "/div/ferrel/d2-3/cmip5/janasi/testground/indices/", input.files[1],
> author.data, base.range=c(1961, 1990), parallel=16,
> max.vals.millions=70, thresholds.files="threshholds_CanESM2.nc")
Creating cluster of 16 nodes of type SOCK
Finished computing indices

14. ETCCDI Extremes Indices Calculation Output
r1i1p1]$ ll
total
-rw-rw-r-- 1 janasi janasi Nov 3 09:01 altcddETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:01 altcsdiETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:01 altcwdETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:01 altwsdiETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:01 cddETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:01 csdiETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:02 cwdETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:02 dtrETCCDI_mon_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:02 dtrETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:02 fdETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:02 gslETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:02 idETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:02 prcptotETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:02 r10mmETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:02 r1mmETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:02 r20mmETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:02 r95pETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:02 r99pETCCDI_yr_NorESM1-M_historical_r1i1p1_ nc
-rw-rw-r-- 1 janasi janasi Nov 3 09:03 rx1dayETCCDI_mon_NorESM1-M_historical_r1i1p1_ nc

15. ETCCDI Extremes Indices Calculation