university rovira i virgili, tarragona, catalonia extremes...

Title

Climate Change

Extremes analysis: the ETCCDI two-pronged approach

Manola Brunet University Rovira i Virgili, Tarragona, Catalonia

5 December 2017

Climate

Change

The role of the joint WMO-CCl/WCRP-CLIVAR/JCOMM

Expert Team on Climate Change Detection and Indices

(ETCCDI) to assess climate extremes

In 1999 a group of international experts decided to enhance

climate change detection studies by

1. Internationally defining a suite of extreme indices and

coordinating their estimation globally as well as regionally

• Core set consists of 27 descriptive indices for moderate extremes

• Focus on counts of days crossing a threshold; either absolute/fixed thresholds or

percentile/variable thresholds relative to local climate

• Simple, straight forward, reliable, and consistent across different regions

• Easy to calculate, update, understand, having physical meaning and high S/N ratio for

climate change detection

• Derived from daily data of quality proven

• Used in trend analysis, model evaluation, projection… and can be coupled with standard

detection and attribution methods

Fourth Session of the ETCCDI,

Victoria, Feb 2011

Climate

Change

The role of the joint WMO-CCl/WCRP-CLIVAR/JCOMM

Expert Team on Climate Change Detection and Indices

(ETCCDI) to assess climate extremes

In 1999 a group of international experts decided to enhance

climate change detection studies by

2. Holding regional climate change workshops (modelled after

the Asia-Pacific Network Workshops) to fill in knowledge

and data gaps of how moderate extremes are changing

regionally and globally and provide:

• Free software + hands-on training + post workshop follow-ups

• Building capacity to analyse observed changes in extremes

• Publishing peer-reviewed papers from each workshop

• Contributing to worldwide database of derived indices (e.g. HadEX)

• More than 25 regional workshops organised by the ETCCDI over the 1998-

2016 period, with many regions now self-organising them

Observations provide crucial

underpinning but are often not

well-constrained and critical

gaps exist in the amount, quality,

consistency and availability,

especially for extremes analysis

Climate

Change

The ETCCDI core indices: a classification

• Indices based on percentiles

• Absolute indices

• Thresholds Indices

• Duration indices

• Other indices

4

http://etccdi.pacificclimate.org/list_27_indices.shtml



Climate

Change

Indices based on percentiles (global results

from Donat et al., 2013)

• Cold nights and days (TN10p &

TX10p): Number of nights (TN)

and days (TX) below the 10th

percentile

• Warm nights and days (TN90p &

TX90p): Number of nights and

days with TN and Tx above the

90th percentile

• Very wet days (R95p) & extremely

wet days (R99p): number of wet

days exceeding the 95th & 99th

percentiles of the base period

• Easy to compare among the

different stations and find out

common spatial signals

5

Climate

Change

Absolute indices

The highest and the

lowest annual or seasonal

values of TN & TX or RR,

such as:

• The warmest day (TXx)

and night (TNx)

• The coldest day (TXn) an

night (TNn)

• The highest 1 (RX1day) &

5 (RX5day) wet days

6

Climate

Change

Thresholds indices Defined as number of days falling down

below or above a predefined absolute

threshold that have physical meaning,

such as:

• Frost days (FD) where TN < 0ºC & icy

days (ID) where TX < 0ºC

• Summer days (SU): No. of days with TX

> 25°C

• Tropical nights (TR): No. of days with TX

> 20 ºC

• Heavy precipitation days (R10): Annual

count of days when RR ≥ 10 mm

• Very heavy precipitation days (R20):

Annual count of days when RR ≥ 20 mm

7

Climate

Change

Duration indices

They define periods of excessive heat,

cold, rain or drought or the growing

season length:

• Cold Spell Duration Index (CSDI): Annual

count of days with at least 6 consecutive

days when TN < 10th percentile

• Warm Spell Duration Index (WSDI) Annual

count of days with at least 6 consecutive

days when TX > 90th percentile

• Consecutive Dry Days (CDD) Max number

of consecutive dry days RR<1mm)

• Consecutive Wet Days (CWD) Max

number of consecutive dry days RR≥1mm

8

Climate

Change

Other indices

• Total Annual precipitation (PRCPTOT): Annual

total precipitation from wet days

• Diurnal Temperature Range (DTR): Mean of

the difference between TX and TN

• Extreme Temperature Range (ETR): Difference

between highest TX and lowest TN during the

year

• Simple Daily Intensity Index (SDII): Annually

averaged precipitation from wet days

• Very wet day proportion (R95pTOT):

Percentage of annual total precipitation from

days with RR ≥ 95th percentile of the base

period

• Extremely wet day proportion (R99pTOT):

Percentage of annual total precipitation from

days with RR ≥ 95th percentile of the base

period

9

Climate

Change

The RClimDex software for QC and extreme indices

calculation: a general overview

• The ETCCDI developed and provides

(http://etccdi.pacificclimate.org/software.shtml) the RClimDex code to ensure

daily time-series (Tx, Tn, RR) are reasonably free of non-systematic biases

and the values they contain are true observations

• It also calculates the 27 ETCCDI core extreme indices

• The RClimDex is based on ClimDex (an Excel based program developed by

Byron Gleason at NCDC time ago). Moving to R platform to solve a problem

in percentile indices calculation and because limitations associated with excel

environment. Developed, updated and maintained by Environmental Canada

(EC)

• R platform chosen because there is a powerful computing environment for

statistical analysis, it’s freely available, it is portable across all platforms (Unix,

MS-Windows, MacOS), GUI and command line

• User guide available in English and Spanish and technical support provided

by EC

http://etccdi.pacificclimate.org/software.shtml

Climate

Change

Climate

Change

Demonstration: Applying RClimDex to QC daily Tx and Tn

and RR time-series and estimate extremes

The data must be in the format known internationally as "RClimDex” format:

• Data samples provided in correct format to ease the exercise, such as:

1938 9 1 0 28 18.6

1938 9 2 0 27.3 18.1

1938 9 3 1.9 27.2 21.1

1938 9 4 0 26.8 18.6

1938 9 5 0 27.1 19.8

1938 9 6 0 26.6 18.9

1938 9 7 0 27 19.7

• Each row containing one date of daily data following the calendar order & six fields

(columns) with this sequence: Year, Month, Day, RR (mm), Tx and Tn (Cdeg) and missing

values coded as -99.9

It is advisable to install the script (rclimdex.r) and data files in the same folder

Labeling files using a numeric code, such as: VVCCCCCCCC.txt

VV: file version (ra, for raw data; qc for QC’ed data. CCCCCCCC: station code. .txt: for the extension

For example:

ra00000001.txt, qc00000001.txt

Climate

Change

Running the RClimDex software

To have downloaded platform R in your laptop

Only for the first time running the RClimDex, it’s in need to install the

‘tkrplot’ statistical package by setting your CRAM mirror and selecting

the tkrplot package

Climate

Change


Changing directory and upload time-series

Climate

Change


Running QC tests

Climate

Change


Setting parameters

Code or name the station (same code than the data file)

Set number of SD depending on the climate uniformity (4 SD for variable

climates and more restrictive if the annual cycle is more uniform)

Give an upper limit for daily RR in mm

Click OK and after few minutes several messages will appear

Climate

Change


Several folders have been created: indices, log, plots, trends

Look at the folder “log” first

Climate

Change


RClimDex QC files:

graph files: • prcpPLOT

• tmaxPLOT

• tminPLOT

• dtrPLOT

Climate

Change


RClimDex QC text files: • tempQC (Tx ≤ Tn)

• tepstdQC (Tx/Tn ± x SD)

• prcpQC

• nastatistic

Climate

Change

Verifying QC results

Checking suspicious values against original data source, if possible

Using expert judgment if original source is not available. E.g. checking

value consistency comparing it with previous and next days values or with

other observations taken in nearby stations

Decisions to take:

• Validate it

• Reject it and set it to missing

• Reject it, but it is recoverable: correct it

Make corrections in a copied file (never in the original file), document

decisions taken to guarantee QC exercise traceability (e.g. identify station

name, country, WMO code, date: year, month, day, variable, original

value, replacement value, detection test)

Climate

Change

Before estimating extreme indices there is in

need to ensure time series homogeneity

After QC’ing daily data, make sure any time series is

homogeneous: testing homogeneity and homogenising

records, if in need

Why homogeneous time series are required before using

them in any climate assessment-product-service?

• They ensure variations and trends in the time series only respond to the

forcing of weather and climate factors and aren’t the results of artificial

causes

• Provide reliability and robustness to any analysis based on high-quality

and homogeneous data

• Provide the expectable coherent spatial pattern

Climate

Change

RClimDex extreme indices calculation

The RClimDex is also used to compute the ETCCDI extreme indices, once time-series

have been QC’ed and their homogeneity proven

Climate

Change

RClimDex extreme indices calculation

Indices calculation folders: “indices”, “plots”, “trend”

Climate

Change

Extreme indices plots

Title

Climate Change

Thanks for your

attention

Questions/Comments

university rovira i virgili, tarragona, catalonia extremes...

Documents