Transcript Climatic Change

Climate Indices
Serhat Sensoy
Chief of Climate & Climate Change Division
Vice-President of WMO CCl
Turkish State Meteorological Service (TSMS)
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How can we detect
Climate Change?
-Climate Indices
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The background
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WMO Commission for Climatology / CLIVAR
Working Group on Climate Change Detection
meets and try to find answer:
“What could a small group of volunteers do to
further global climate change detection?”
The answers are:
 Internationally coordinate a suite of indices
• Mainly highlighting changes in extremes
• Derived from daily data
 Hold regional climate change workshops
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In 2001 two workshops were held
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In Kingston, Jamaica for the Caribbean
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Produced a workshop report
Produced a multi-authored JGR paper
Released all daily data used in the analysis
Released suite of indices
In Casablanca, Morocco for various
countries in Africa
• Produced a workshop report
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In 2003, CCl/CLIVAR Expert Team on Climate Change
Detection Monitoring and Indices.
The ET has met in Norwich UK,
in November, 2003 and has
coordinated improved indices
and additional workshops
ET was renewed again in 2010.
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CCL-XV OPACE-2 Joint CCl/Clivar/JCOMM Expert
Team On Climate Change Detection And Indices
Members
1. Albert Klein-Tank ( The Netherlands) (Co-Lead)
2. Clivar member (Co-Lead)
3. Blair Trewin (Australia)
4. Matilde Rusticucci (Argenatina)
5. Zhai PanMao (China)
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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; and
6.Submit reports in accordance with timetables established by the OPACE 2 cochairs
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Global analyses of changes in extremes used in the IPCC TAR
Did 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
seminars and hands-on data analysis
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Workshop Agenda was modeled as
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Introductions to the issues
Data Quality Control
Calculating indices
Testing data homogeneity
Making sense out of the results
• Country reports
• Regional evaluation
Post workshop planning
• Peer-reviewed articles, etc.
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Indices software
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Workshop suitable software (RClimDex) produced
on behalf of the ET by Xuebin Zhang from
Environment Canada
• http://cccma.seos.uvic.ca/ETCCDMI/
• RClimDex uses the free “R” statistical package
Workshop results
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6 regional workshop peer-review papers
submitted – after careful post workshop analyses
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One global peer-review paper was prepared
newly by Alexander L. et al
These papers have been input for IPCC AR4
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2002
Less Coverage
2005
Improved
Coverage
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What is the characteristic of extremes?
Trends in extreme events
Can't be characterized by the size
of their societal or economic
impacts
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Trends in “very rare” extreme events can’t be analyzed
by the parameters of extreme value distributions
Trends in observational series of phenomena is the
indicators of extremes
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Careful post-Workshop Analysis Addressed
Data Problems
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Many stations’ digital record were too short to use in
this analysis (at least 30 years daily data is needed for
extreme analyses)
QC: a wide variety of checks, including looking for:
• Extreme values due to digitizing errors
• Incorrect English/metric units
• Runs of the same value
• Tmax < Tmin
• Missing precipitation set to 0
Homogeneity
• Evaluation of time series of the indices to weed out
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inhomogeneous data
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Climate Data Homogenization
By Enric Aguilar
A homogeneous climate time series is defined as one where variations
are caused only by variations in climate
(WMO-TD No. 1186)
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Difference between Quebec City and a reference series
2
°C
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Station is located on the roof of
the main building 1942-1960
Adjustment
0
-1
-2
-3
1890
1931
1900
1910
1920
1930
1942
1940
1950
1960
1960
1970
1980
1990
2000
Year
1931: station relocated to
the college with change in
exposure
1942: station relocated
from college to airport
Station is located on the
ground after 1960
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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 Data
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Indices are information derived from data
It represents the data
More readily released than data
Are not reproducible without the data
Useful in a wide variety of climate change
analyses
Useful for Model – observations comparisons
Useful for analyses of extremes
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prec.
p.
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Percentage based Indices
After: Jones et al. (Climatic Change, 1999) Yan et al. (…, 2002, IMPROVE- issue)
“warm
nights”
upper 10-ptile
1961-1990
the year 1996
lower 10-ptile
1961-1990
“cold
nights”21
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Quality Control
1.
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3.
If precipitation value is (–), it is assumed as missing value(-99.9)
If Tmax < Tmin both are assumed as missing value(-99.9)
If the data outside of threshold (mean ±4*STD) it is problematic value.
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Indices Plots
Locally weighted regression
Linear (least square) fit
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 This24
indices show that frost days will be decreasing 26.8 days in 100 years.
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Climate 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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Global
Indices
Analyses
From Alexander, L. et all
Locations of
(a) temperature and
(b) precipitation stations
available for this study.
The colours represent
the different data
sources that are used.
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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 nonlinear 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
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country affecting both maximum and minimum temperatures but there is a much
more mixed pattern of change in precipitation.
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Thank you for your attention
Serhat Sensoy
Chief of Climate & Climate Change Division
Vice-President of WMO CCl
Turkish State Meteorological Service
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