a rcm bias correction method for climatic indices
DESCRIPTION
A RCM bias correction method for climatic indices expressed in a daily based frequency applied to temperature projections of the North Iberian Peninsula. Iratxe González Dr. Julia Hidalgo. TECNALIA – LABEIN. Environment Unit. Spain. - PowerPoint PPT PresentationTRANSCRIPT
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A RCM bias correction method for climatic indices expressed in a daily based frequency applied to
temperature projections of the North Iberian Peninsula
Iratxe González Dr. Julia Hidalgo
TECNALIA – LABEIN. Environment Unit. Spain.
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BIAS models vs. observations
Adjustment datato a probability density
function
- Piani et al, 2009- Collins 2007- Planton et al, 2008- Kjellstrom et al, 2005- Alexander et al, 2005-Chauvin and Devil 2005
Adjustment data distribution to a delta change temperature
- Roossmalen et al 2009- Kjellstrom et al 200 7- Christensen and Christensen 2007
The goodness of fit, or bias, summarize the discrepancy between the observations and the model outputs. It should be evaluated before analysing future climatic scenarios from such models.
• Seasonally frequency based indices• Daily frequency based indices
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OUTLINE
USED DATA REGIONAL SERIES
STATISTICAL INDICESCORRECTION BIAS
RCM vs. OBSERVATIONS
ANALYSIS RESULTS CONCLUSIONS
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METHODOLOGY.
Summer:- 90th percentile Tmax- days with T > 90th p Tmax- Human Comfort Index (THI)
Winter:- 10th percentile Tmin-Days with T < 10th p Tmin- Number of frost days
Summer:Heat waveDuration
WMO
Winter:Cold waveDuration
WMOBasque Country
North Iberian Peninsula.
Climate Scenarios:ENSEMBLES project
Observed data:Spanish Meteorological Agency (AEMET)
Time period : 1978-2000 ; 2000-2100
PRUDENCE resultsAbanades et al. 2007
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RCM Models 10th percentile (ºC) Tmin Figure
90th percentile (ºC) Tmax Figure
HIRHAM-ARPEGE 4 a2 4 a1
ALADIN-ARPEGE 4 a2 4 a1
PROMES-HadCM3 1 a2 4 a1
CLM-HadCM3 1 a2 4 a1
REMO-ECHAM5 10 b2 - 2 b1
RACMO-ECHAM5 10 b2 - 2 b1
METHODOLOGY. Evaluation of models
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METHODOLOGY. Bias correction
Correction Seasonally indices
∆T (ºC)
.. ObskModk TTT
(T10thp model corrected)i = (T10thp model not corrected)i – (∆T)associated
to this temperature
in the calibration curve
1th 99th… Percentiles(21 in total)
*
* *
* * **
* * * *
*
*
1
.
.
.
10
K- pecentile 1:99th
; i- daily datan
TT
TTT
n
ji
jim
iimimcorr
1
))((
)()(
Correction daily indices
Christensen et al 2008 Jacob et al, 2007 Kjellstrom et al 2007
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CORRECTION OF MODELS: Mean Maximum TemperaturesFor Summer
Obs.
20-27ºC
M. REFERENCE
22-32ºC
19-27ºC
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RESULTS. Bias correction90
th
per
cen
tile
max
imu
m t
emp
erat
ure
wit
ho
ut
corr
ecti
on
co
rrec
ted
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RESULTS. Summer time 90th percentile Tmax
EnsembleAv. 35.6ºC
Spread:3.5ºCStdv:1.41ºC
EnsembleAv. 39.7ºC
Spread: 4.6ºCStdv:1.87ºC
Future projection: 2000-2100trend: 3ºCStdv . 1.41 ºCSpread: 4.2ºC
EnsembleAv. 37.8ºC
Spread:6.52ºCStdv:2.54ºC
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RESULTS. Summer time Heat Waves duration
EnsemblesFreq. 1-2 heat wavesAv. 9-11 days [12%]
EnsemblesFreq. 1-2 heat wavesAv. 13-16 days [16%]
EnsemblesFreq. 1-3 heat wavesAv. 18-22 days [22%]
Temp involved 34.7 ºC Temp involved 34.3 ºC Temp involved 34.2 ºC
Obs.Av. 1.3 heat wavesFreq: 13.7 daysTemp: 29 ºC
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RESULTS. Summer time Thermal Comfort Index (THI)
THI % population uncomfortable70 1075 5080 90
(Gates, 1972)
EnsembleAv. 67.91
Stdv:1 .60
EnsembleAv. 67.99
Stdv:1 .64
EnsembleAv. 69.34
Stdv: 3.97
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RESULTS. Winter time 10th percentile Tmin
EnsembleAv. - 0.72ºC
Spread:1.5ºCStdv: 0.92ºC
Future scenario: 2000-2100trend: 3ºCStdv . 0.83 ºCSpread: 1.83ºC
EnsembleAv. -3.04ºC
Spread: 1.8ºCStdv: 0.71ºC
EnsembleAv. -1.70ºC
Spread: 1.72ºCStdv:0.68ºC
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RESULTS. Winter time
Future scenario (2000-2100)Decrease 50% of daysRespect with Ref. Period
EnsembleAv. 7.5 d
Stdv: 3.57 d
EnsembleAv. 20.6 dStdv: 3.5 d
EnsembleAv. 11.5 dStdv: 3.9 d
Number of frost days
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RESULTS. Winter time
EnsemblesFreq. 1-3 cold wavesAv. 7-19 days [14%]
EnsemblesFreq. 1-2 cold wavesAv. 6-10 days [9%]
RACMO/REMO/ALADINFreq. 1 cold waveAv. 6- 9 days [8%]HIRHAM/CLM/PROMES: 0
Temp involved 2.7 ºC Temp involved 3.3 ºC Temp involved 1.3ºC
Cold Waves duration
Obs.Av. 1.25 cold wavesFreq: 11.1 days [12%]Temp: 3.3 ºC
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Conclusions & Discussions
Methodology to correct the bias based on the percentiles approach
Proposed method to correct indices when absolute values are required (daily based frequency)
Correction methodology suitable for interdisciplinary groups
Applicability for the Basque Country case study: indices indicate that for summer and winter periods the maximum and minimum temperature tend to increase. The duration of the heat episodes tend to increase and for cold episodes tend to decrease ; as well for the number of the frost days in winter.