1. Kuala Lumpur, Malaysia, 8th-11th November 2012
Climate Extremes
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2. Contents What is ‘Extreme’ and why use indices?
Calculating Extremes using CDO
Cautionary Note: Comparing Extremes in Model and Observed data
This presentation is the combination of three different parts: the introduction to RClimdex and the RClimdex tutorial (done as separate presentation from previous
Workshops) and a short discussion on issues related to the comparision of station indices with indices estimated from model data.
© Crown copyright 2007

3. What is ‘Extreme’? Wide range of space and time scales
From very small scale (precip) to large scale (droughts)
Definitions?
High impact events
Unprecedented events (in the available record)
Rare events (long return periods)
Exceedance of a relatively low threshold (indices, such as 10th percentile of daily temperature or 95th percentile of daily precipitation amounts)
Persistence of weather conditions (droughts)
Climatic extremes (e.g. extreme seasons)
The most precise definition is “high impact events”, i.e. rather than defining extremes from their spatial or temporal features, it is probably better to have an “impact-based” definition. However, mostly for statistical purposes (i.e. to increase the number of events in the sample), also exceedance of low threshold are included.

4. Definition of 28 core extreme indices
CCl/CLIVAR/JCOMM Expert Team on Climate Change Detection and Indices (ETCCDI)
from ETCCDI:
Definition of 28 core extreme indices
Organization of regional workshop
WMO-guide on extremes, 2009, targeted at NMHSs around the world
The first part of the presentation advertises the activities of ETCCDI, the aim is to invite the participants to contribute their data to this project. These slides have been taken from some available ETCCDI presentations. Additional information about the project, extra documentation and software, including RClimdex, are available at the ETCCDI website

5. ETCCDI indices
Internationally coordinated core set of 28 descriptive indices describe frequency, amplitude, and persistence of moderate extremes
More details on the indices and the way they are estimated

6. Extremes Indices – temperature basedID
Indicator name
Definitions
UNITS
FD0
Frost days
Annual count when TN(daily minimum)<0ºC
Days
SU25
Summer days
Annual count when TX(daily maximum)>25ºC
ID0
Ice days
Annual count when TX(daily maximum)<0ºC
TR20
Tropical nights
Annual count when TN(daily minimum)>20ºC
GSL
Growing season Length
Annual (1st Jan to 31st Dec in NH, 1st July to 30th June in SH) count between first span of at least 6 days with TG>5ºC and first span after July 1 (January 1 in SH) of 6 days with TG<5ºC
TXx
Max Tmax
Monthly maximum value of daily maximum temp ºC
TNx
Max Tmin
Monthly maximum value of daily minimum temp
TXn
Min Tmax
Monthly minimum value of daily maximum temp
TNn
Min Tmin
Monthly minimum value of daily minimum temp
TN10p
Cool nights
Percentage of days when TN<10th percentile
TX10p
Cool days
Percentage of days when TX<10th percentile
TN90p
Warm nights
Percentage of days when TN>90th percentile
TX90p
Warm days
Percentage of days when TX>90th percentile
WSDI
Warm spell duration indicator
Annual count of days with at least 6 consecutive days when TX>90th percentile
CSDI
Cold spell duration indicator
Annual count of days with at least 6 consecutive days when TN<10th percentile
DTR
Diurnal temperature range
Monthly mean difference between TX and TN
APPENDIX A: List of ETCCDMI core Climate Indices

7. Extremes Indices – precip based
APPENDIX A: List of ETCCDMI core Climate Indices
Extremes Indices – precip based ID
Indicator name
Definitions
UNITS
RX1day
Max 1-day precipitation amount
Monthly maximum 1-day precipitation Mm
Rx5day
Max 5-day precipitation amount
Monthly maximum consecutive 5-day precipitation
SDII
Simple daily intensity index
Annual total precipitation divided by the number of wet days (defined as PRCP>=1.0mm) in the year
Mm/day
R10
Number of heavy precipitation days
Annual count of days when PRCP>=10mm
Days
R20
Number of very heavy precipitation days
Annual count of days when PRCP>=20mm
Rnn
Number of days above nn mm
Annual count of days when PRCP>=nn mm, nn is user defined threshold
CDD
Consecutive dry days
Maximum number of consecutive days with RR<1mm
CWD
Consecutive wet days
Maximum number of consecutive days with RR>=1mm
R95p
Very wet days
Annual total PRCP when RR>95th percentile
R99p
Extremely wet days
Annual total PRCP when RR>99th percentile mm
PRCPTOT
Annual total wet-day precipitation
Annual total PRCP in wet days (RR>=1mm)

8. Alexander et al., JGR, 2006; also in IPCC, 2007
With extreme temperatures, instead, trends are larger scale for increasing temperatures. These two indices are based on the frequency of the extremes

9. Example: Calculating TX90p (warm days)
Calculate threshold exceeded by the 10% hottest days (Tmax) in baseline period (i.e )
On average, in the baseline period, 10% of days (36/37 days will exceed this threshold)
10% days exceed 23.2º (av. 36 days per year)
23.2º
1961
1990

10. Example: Calculating TX90p (warm days)
Calculate the average number of times that same threshold is exceeded in a future period
58% days exceed 23.2º (av. 212 days per year)
23.2º
2070
2100
(these are synthetic data, not from real projections!)

11. R95PTOT- Total annual rainfall on heavy rain days
Similarly, calculate the 95th percentile of wet days only (5% wettest ‘wet days’, i.e. days >1mm) in baseline
These are‘ heavy rainfall days’
Calculate the average amount of rain per year that occurs in ‘heavy’ events.
12.7mm

12. R95PTOT- Total annual rainfall on heavy rain days
Identify the ‘heavy’ rainfall days in the future
Sum the rainfall that falls on those days to give average per year.
12.7mm
2070
2100

13. Calculating Indices with CDO
To calculate some of these indices with CDO for whole model fields
we can either use CDO defined extremes operators, or our own.
For the percentage of warm days:
cdo eca_tg90p ifile1 ifile2 ofile
cdo timsum –gt ifile1 ifile2 ofile
cdo mulc,100 –divc,[days] ofile ofile.percent
Some of the CDO extremes operators are not always robust with PRECIS data, but we can calculate them by using other CDO operators together.

14. From global to local climate ….
A word of warning on Validating Extremes
The comparison with models data: model grid-box output as areal average of different local conditions.
… from a GCM grid to the point of interest.

15. Individual station vs. area averages
26 stations in a 25km×25km area (black bars) and their area averages, (red bars).
The area average (c.f. model grid box output) is considerably and inconsistently different to most individual stations
inconsistently means that it is impossible to reconstruct the actual station time series from their relationship with the areal average time series, since there are a component due to noise (or small scale variability).

16. Model grid box vs. point observations
Average(Extreme) ≠ Extreme(Average)
Rules of thumb:
Usually model output has reduced range of values and reduced variability, but it depends on the physiography of the grid box
Trends should be the same if dependent on large scale phenomena (e.g. major mode of variability, climate change) included in the model and observational world
Average(Extreme) /= Extreme(Average) is a result surely valid for precipitation

17. Acknowledgements: John Caesar (Met Office), ETCCDI
Questions
Acknowledgements: John Caesar (Met Office), ETCCDI
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