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Serhat SensoyChief of Climate & Climate Change Division

Vice-President of WMO CClTurkish State Meteorological Service (TSMS)

Climate IndicesClimate Indices

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How can we detect How can we detect Climate Change?Climate Change?

--Climate IndicesClimate Indices

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The backgroundThe background WMO Commission for Climatology / CLIVAR WMO Commission for Climatology / CLIVAR

Working Group on Climate Change Detection Working Group on Climate Change Detection meets and try to find answer:meets and try to find answer:

“ “What could a small group of volunteers do to What could a small group of volunteers do to further global climate change detection?”further global climate change detection?”

The answerThe answerss are: are: Internationally coordinate a suite of indicesInternationally coordinate a suite of indices

• Mainly highlighting changes in extremesMainly highlighting changes in extremes• Derived from daily dataDerived from daily data

Hold regional climate change workshopsHold regional climate change workshops

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In 2001 two workshops were heldIn 2001 two workshops were held In Kingston, Jamaica for the CaribbeanIn Kingston, Jamaica for the Caribbean

• Produced a workshop reportProduced a workshop report• Produced a multi-authored Produced a multi-authored JGRJGR paper paper• Released all daily data used in the analysisReleased all daily data used in the analysis• Released suite of indicesReleased suite of indices

In Casablanca, Morocco for various In Casablanca, Morocco for various countries in Africacountries in Africa• Produced a workshop reportProduced a workshop report

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The ET has met in Norwich UK, in November, 2003 and has coordinated improved indices coordinated improved indices and additional workshopsand additional workshops

ET ET was renewed again in 2010.

In 2003, CCl/CLIVAR Expert Team on Climate Change Detection Monitoring and Indices.

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Members1. Albert Klein-Tank ( The Netherlands) (Co-Lead)2. Clivar member (Co-Lead)3. Blair Trewin (Australia) 4. Matilde Rusticucci (Argenatina)5. Zhai PanMao (China)

CCL-XV OPACE-2 Joint CCl/Clivar/JCOMM Expert Team On Climate Change Detection And Indices

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Terms of Reference:1. Provide international coordination and help organize collaboration on climate change detection and indices;2.Further develop and publicize indices and indicators of climate variability and change and related methodologies, from the surface and subsurface ocean to the stratosphere, with international consensus;3. Encourage the comparison of modeled data and observations, perhaps via the development of indices appropriate for both sources of information;4.Coordinate these and other relevant activities the ET chooses to engage in with other appropriate working bodies including of those affiliated under OPACE-4, WCRP and JCOMM as well as others such as GCOS, CBS, CIMO, CAgM, CHy, IPCC and START; and regional associations;5.Explore, document and make recommendations for addressing the needs for capacity-building in each region, pertinent to this topic with consideration of the GFCS requirements; and6.Submit reports in accordance with timetables established by the OPACE 2 co-chairs

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Global analyses of changes in extremes used in the IPCC TARDid not represent nearly half of the world. (Frich et al)

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Six regional workshop were held to fill the gap in the global extreme analyses.

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The workshop was composed combination of The workshop was composed combination of seminars and hands-on data analysisseminars and hands-on data analysis

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Workshop Agenda was modeled asWorkshop Agenda was modeled as

Introductions to the issuesIntroductions to the issues Data Quality ControlData Quality Control Calculating indicesCalculating indices Testing data homogeneityTesting data homogeneity Making sense out of the resultsMaking sense out of the results

• Country reportsCountry reports• Regional evaluationRegional evaluation

Post workshop planningPost workshop planning• Peer-reviewed articles, etc.Peer-reviewed articles, etc.

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Indices softwareIndices software Workshop suitable software (RClimDex) produced Workshop suitable software (RClimDex) produced

on behalf of the ET by Xuebin Zhang from on behalf of the ET by Xuebin Zhang from Environment CanadaEnvironment Canada• http://cccma.seos.uvic.ca/ETCCDMI/• RClimDex uses the free “R” statistical packageRClimDex uses the free “R” statistical package

Workshop resultsWorkshop results 6 regional workshop peer-review papers 6 regional workshop peer-review papers

submitted – after careful post workshop analysessubmitted – after careful post workshop analyses

One global peer-review paper was prepared One global peer-review paper was prepared newly by Alexander L. et alnewly by Alexander L. et al

These papers have been input for IPCC AR4These papers have been input for IPCC AR4

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2002

2005

Less Coverage

Improved Coverage

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What is the characteristic of extremes?What is the characteristic of extremes? Trends in extreme events Trends in extreme events

Can't be characterized by the sizeCan't be characterized by the size

of their societal or economic of their societal or economic

impactsimpacts

Trends in “very rare” extreme events can’t be analyzed Trends in “very rare” extreme events can’t be analyzed by the parameters of extreme value distributionsby the parameters of extreme value distributions

Trends in observational series of phenomena is theTrends in observational series of phenomena is the

indicators of extremesindicators of extremes

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Careful post-Workshop Analysis Addressed Careful post-Workshop Analysis Addressed Data ProblemsData Problems

Many stations’ digital record were too short to use in Many stations’ digital record were too short to use in this analysis (at least 30 years daily data is needed for this analysis (at least 30 years daily data is needed for extreme analyses)extreme analyses)

QC: a wide variety of checks, including looking for:QC: a wide variety of checks, including looking for:• Extreme values due to digitizing errorsExtreme values due to digitizing errors• Incorrect English/metric unitsIncorrect English/metric units• Runs of the same valueRuns of the same value• Tmax < TminTmax < Tmin• Missing precipitation set to 0Missing precipitation set to 0

HomogeneityHomogeneity• Evaluation of time series of the indices to weed out Evaluation of time series of the indices to weed out

inhomogeneous datainhomogeneous data

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Climate Data Homogenization

-3

-2

-1

0

1

2

3

1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000

Year

°C

1931 1942 1960

1931: station relocated to the college with change in exposure

1942: station relocated from college to airport

Station is located on the roof of the main building 1942-1960

Station is located on the ground after 1960

By Enric AguilarBy Enric Aguilar

Adjustment

A homogeneous climate time series is defined as one where variations are caused only by variations in climate(WMO-TD No. 1186)

Difference between Quebec City and a reference series

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Figure shows homogeneity test of annual minimum temperature for station Rize, Turkey. The discontinuity in 1995 is reflected in metadata which shows that the station relocated in this year.

Data homogeneity is assessed using R-based program, RHtest, developed at the Meteorological Service of Canada. It is based on two-phase regression model with a linear trend for the entire base series (Wang, 2003)

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Advantages of Indices versus DataAdvantages of Indices versus Data

Indices are information derived from dataIndices are information derived from data It represents the dataIt represents the data More readily released than dataMore readily released than data Are not reproducible without the dataAre not reproducible without the data Useful in a wide variety of climate change Useful in a wide variety of climate change

analysesanalyses Useful forUseful for Model – observations comparisonsModel – observations comparisons Useful forUseful for analyses of extremesanalyses of extremes

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prec.p.

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After: Jones et al. (Climatic Change, 1999) Yan et al. (…, 2002, IMPROVE- issue)

upper 10-ptile 1961-1990

the year 1996

lower 10-ptile1961-1990

“warm nights”

“cold nights”

Percentage based Indices

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Quality Control1. If precipitation value is (–), it is assumed as missing value(-99.9)2. If Tmax < Tmin both are assumed as missing value(-99.9) 3. If the data outside of threshold (mean ±4*STD) it is problematic value.

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Kendall’s tau based slope estimator has been used to compute the trends since this method doesn’t assume a distribution for the residuals and is robust to the effect of outliers in the series. If slope error greater than slope estimate we can’t trust slope estimate.If PValue is less than 0.05 this trend is significant at 95% level of confidence This indices show that frost days will be decreasing 26.8 days in 100 years.

Indices Plots

Linear (least square) fit

Locally weighted regression

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Climate Indices Study in TurkeyClimate Indices Study in Turkey

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Numbers of Frost Days have been increasing mainly in Black Sea and Marmara Region. 53 stations have decreasing trend while 32 are increasing. Average decreasing is 28 days in 100 years. Although Istanbul, Elazığ, Diyarbakır and Hakkari show opposite trend with their located regions, they trends are not linear and have some breakpoint.

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Numbers of Summer Days have been increasing all over Turkey especially northern part stations have greatest trends. Average increasing is 59 days in 100 years. Most of the trends are statistically significant at the 5% level

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Numbers of Ice Days have been decreasing all over Turkey except 6 stations. Inland stations have greatest trends. There is no ice day in the Mediterranean region. Average decreasing is 20 days in 100 years. Although Bilecik, Tekirdağ and Hakkari show opposite trend with their located regions, they trends are not significant and have some breakpoint.

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Numbers of Tropical Nights have been increasing except Euphrates Basin. Elazığ has significant decreasing trend after Keban Dam constructed. Diyarbakır has non significant decreasing. Especially coastal stations have greatest trends. Average increasing is 47 days in 100 years. Most of the trends are statistically significant at the 5% level.

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Locations of (a) temperature and (b) precipitation stations

available for this study. The colours represent the different data sources that are used.

GlobalIndicesAnalysesFrom Alexander, L. et all

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Trends in (a) cold nights (TN10p), (b) warm nights (TN90p), (c) cold days (TX10p) and (d) warm days (TX90p).

Trends were calculated only for the grid boxes with sufficient data (at least 40 years of data. Black lines enclose regions where trends are significant at the 95% confidence of level. The red curves on the plots are non-linear trend estimates obtained by smoothing using a 21-term binomial filter.

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precipitation indices (a)R10 in days, (b)R95pT (i.e.

(R95p/PRCPTOT)*100) in %, (c) CDD(d)SDII

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Conclusion

The results show that numbers of summer days and tropical nights have been increasing all over Turkey while ice days and frost days decreasing. Summer days have increased about 6 days per decade. Most of the trends are statistically significant at the 5% level. Extreme temperature both maximum and minimum have increased at most stations. Warm days and warm nights have been increasing all over Turkey while cool days and cool nights have been decreasing. Warm spells have increased while cold spells have decreased. Diurnal temperature range has increased in most inland stations while it has decreased along coastal areas.

Trends in simple daily intensity index have been increasing in most of the stations even mean annual total precipitation declined in 30 stations located in the Aegean and inland Anatolia. The number of heavy precipitations days have been increasing especially in the Black Sea and Mediterranean regions and usually cause extreme flood events. The maximum one-day and 5 days precipitation have also increased except eastern Marmara and southeast Anatolia region. Unfortunately consecutive dry days have been increasing in Aegean and Black Sea, Diyarbakır, Batman and central Anatolia while decreasing Eastern Aegean, Mediterranean and East Anatolia Region. Average increasing is 25 days in 100 years . Consecutive Wet Days have been increasing especially in Eastern part of the Marmara and around of Burdur, Nigde, Nevşehir, Sinop, Sivas, Rize and Muş but decreasing in Aegean and Konya. Average increasing is 2 and decreasing is 2 days in 100 years.

In summary, in general there are large coherent patterns of warming across in the country affecting both maximum and minimum temperatures but there is a much more mixed pattern of change in precipitation.

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Serhat SensoyChief of Climate & Climate Change Division

Vice-President of WMO CClTurkish State Meteorological Service

Thank you for your attentionThank you for your attention