An introduction to homogenisation

Transcription

1 An introduction to homogenisation WMO-ETSCI Workshop, Pune 3-7 October 2016 Acacia Pepler Australian Bureau of Meteorology

2 What do we want?

3 A perfect station A well-situated and maintained station with appropriate instruments No non-climatic changes in e.g. observing practices, location, instruments All site details & any changes well-documented metadata!

4 Why homogenise? Most stations are NOT perfect! Changes in observation time, maintenance, environment, instruments, location etc can change the data for non-climatic reasons We call these "inhomogeneities"

5 Why homogenise? Most stations are NOT perfect! Changes in observation time, maintenance, environment, instruments, location etc can change the data for non-climatic reasons We call these "inhomogeneities" 22" Minimum temperatures - Port Hedland, Australia! 21" 20" 19" 18" Pre-1948 site at Post Office near ocean Post-1948 site at airport several km inland 17" 1910" 1920" 1930" 1940" 1950" 1960" 1970" 1980" 1990" 2000" 2010"

6 Why homogenise? If we use the raw data from these stations to assess trends, we will get meaningless results. 22" Minimum temperatures - Port Hedland, Australia! 21" 20" 19" 18" 17" 1910" 1920" 1930" 1940" 1950" 1960" 1970" 1980" 1990" 2000" 2010"

7 Why homogenise? If we use the raw data from these stations to assess trends, we will get meaningless results. By homogenising data, we can distinguish the true climate variability from the non-climate influences 22" Minimum temperatures - Port Hedland, Australia! 21" 20" 19" 18" 17" 1910" 1920" 1930" 1940" 1950" 1960" 1970" 1980" 1990" 2000" 2010"

8 Why homogenise? Can have a particularly big effect on extremes Nights below 10 C - Port Hedland, Australia! 40" 35" 30" 25" 20" 15" 10" 5" 0" 1910" 1920" 1930" 1940" 1950" 1960" 1970" 1980" 1990" 2000" 2010"

9 Where do we start? Best option: run parallel observations for a common period (years!) Calculate exact differences

10 Where do we start? Option 2: Compare data to nearby stations "reference series" Look for & quantify changes in the relationship Relies on having a good/homogenous reference! Best when using multiple reference stations C Year Vincent, L.A., 1998: A technique for the identification of inhomogeneities in Canadian temperature series. Journal of. Climate, 11,

11 Where do we start? Option 3: Statistical testing for "breakpoints" Used when there is no reference series Dangerous, must be supported by metadata Best used in identifying sites/periods to discard

12 How do we do it? 1. Identify the breakpoints Use metadata to identify the dates accurately

13 How do we do it? 1. Identify the breakpoints Use metadata to identify the dates accurately 2. Calculate the differences between the series This can be a single annual value, or vary by month/temperature More complex methods can update daily data, e.g. quantile matching

14 How do we do it? 1. Identify the breakpoints Use metadata to identify the dates accurately 2. Calculate the differences between the series This can be a single annual value, or vary by month/temperature More complex methods can update daily data, e.g. quantile matching 3. Apply the changes to the old data

15 Complications Adjustments can vary with temperature & season Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Monthly mean maximum temperature differences, Port Macquarie Winter (JJA) minimum temperature differences, Port Macquarie

16 Complications Adjustments can vary with temperature & season Some changes can affect the whole network Changes in observation time/daylight savings Shift from imperial to metric Changes in automation or instruments

17 Complications Adjustments can vary with temperature & season Some changes can affect the whole network Changes in observation time/daylight savings Shift from imperial to metric Changes in automation or instruments Strength of effect changes over time Urbanised stations often excluded when calculating trends

18 Complications Adjustments can be sensitive to other observations e.g. rainfall, wind speed/direction Mean maximum temperature (deg C) Town Airport Rainfall Rainfall (mm) 24 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Monthly mean maximum temperature differences, Burketown

19 How to homogenise? Many different approaches We will use RHtests Note: Monthly/annual is easier than daily Temperature is easier than rainfall

20 Introduction to RHtests

21 Opening the software Open R Change directory to where RHtests is stored Available from: etccdi.pacificclimate.org/ software.shtml

22 Opening the software "Source" the Rhtests code StartGUI()

23 Data format Data in tab-delimited format Missing value code Convert from ClimPACT format if necessary

24 Settings

25 Step 1: Identify changepoints Using a reference station is best!

26 Step 2: Look at results Type? 0 means needs metadata Is it significant? When is the break? output/<name>_u.pdf output/<name>_1cs.pdf

27 Step 3: Metadata Can run "FindUD" to identify less-significant breaks Remove nonsignificant or undocumented breaks Manually add details of known changes, or change dates of breaks to documented dates output/<name>_mcs.pdf

28 Step 4: Update breakpoints

29 Step 5: Repeat Repeat the process until happy with the remaining breakpoints Only remove one breakpoint at a time output/<name>_f.pdf output/<name>_mcs.pdf

30 Step 6: Apply to daily data It s much easier to identify changepoints in monthly data We can then apply these big adjustments to the daily data

31 Adjusted data Adjusted data in output/<name>_f.dat Stats & trends in output/<name>_fstat.txt Adjusted DAILY data in output/<name>_qmadjdly.dat Original data (col3) Data adjusted by means (col 5) Data adjusted by quantiles (col 11)

32 Warning!!! Do not keep change points unless very large or supported by metadata it is far worse to over- than under- adjust Big internal variability like El Niño can look like a break! If in doubt, use RHtest to identify where station data is unreliable When using neighbouring sites, make sure you're adjusting the right site! Using 2-3 neighbours is best Homogenisation takes time do not expect to be able to use homogenised data this week!

33 Summary Station data series can have many non-climate related changes To create good data series, these need to be identified and corrected for we call this "homogenisation" One way to do this is using RHtests, which we will work with for the rest of today Read the manual for more detailed instructions