Transcript Historical trends and multi-model ensemble

Historical trends and
multi-model ensemble forecasting
of extreme events
Dr. Caio A. S. Coelho
University of Reading, U.K.
E-mail: [email protected]
Thanks to: David Stephenson, Mark New, Bruce Hewitson + Africa extremes workshop participants
Talk plan
• What are extremes?
• Historical trend analysis of extremes in Africa
• What is going to happen to extremes in the
future? - Extreme event forecasting
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What are extremes?
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Examples of wet and windy extremes
Convective severe storm
Hurricane
Extra-tropical cyclone
Polar low
Extra-tropical cyclone
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Examples of dry and hot extremes
Drought
Dust storm
Wild fire
Dust storm
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IPCC 2001 definitions
Simple extremes:
“individual local weather variables
exceeding critical levels on a continuous
scale”
Complex extremes:
“severe weather associated with particular
climatic phenomena, often requiring
a critical combination of variables”
Extreme weather event:
“an event that would normally be
as rare or rarer than the
10th or 90th percentile.”
Extreme climate event:
“an average of a number of weather events
over a certain period of time which is itself
extreme (e.g. rainfall over a season)”
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Some properties of extreme events
Severity
large impacts (extreme losses):
– Injury and loss of life
– Damage to the environment
– Damage to ecosystems
90th percentile
Extremeness
large values of meteorological variables:
– maxima or minima
– exceedance above a high threshold
– exceedance above all previous
recorded values (record breaker)
Rarity/frequency
small probability of occurrence
Longevity
– Acute: Having a rapid onset and following a short but severe course
– Chronic: Lasting for a long period of time (> 3 months) or marked by
frequent recurrence
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Historical trend analysis of
extremes in Africa
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Southern and West Africa workshop on
weather and climate extremes
Cape Town, South Africa, 31May - 4 June 2004
Organization:
• Expert Team on Climate Change Detection Monitoring and Indices (ETCCDMI)
• WMO Commission of Climatology (CCI)
• Climate Variability and Predictability (CLIVAR) project
Aim: Derive indices from daily data to measure changes in extremes
Participants: 14 countries
Data: 63 stations (1961-2000)
daily (minimun and maximum) temperature
and precipitation
New, M., B. Hewitson, D. B. Stephenson, A. Tsiga, A. Kruger, A. Manhique, B. Gomez,
C. A. S. Coelho, D. N. Masisi, E. Kululanga, E. Mbambalala, F. Adesina, H. Saleh,
J. Kanyanga, J. Adosi, L. Bulane, L. Fortunata, M. L. Mdoka and R. Lajoie, 2005:
Evidence of trends in daily climate extremes over Southern and West Africa,
Submitted to J. Geophys. Res. (Atmospheres).
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Workshop methodology
Software: RClimDex ( http://cccma.seos.uvic.ca/ETCCDMI/ )
Data quality control
• negative precipitatoin
• max. temp. < min. temp.
• search for outliers based on threshold defined in terms of
standard deviation from the long-term (1961-2000) daily
mean
• visual inspection of time series plots
Computation of climate indices using RClimDex
• 15 temperature indices
• 10 precipitation indices
Trend estimation and interpretation of results
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Trends in temperature extreme indices
Source: New et al. 2005 (submitted to J. Geophys. Res. (Atmospheres).)
Minimum
Cold night frequency
Maximum
Cold day frequency
Hot night frequency
Hot day frequency
Cold
T< 10th
percentile
Hot
T> 90th
percentile
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Summary of findings for
temperature extremes in Africa
10th percentile
90th percentile
Shift in the frequency distribution towards larger values
• Frequency of extremely cold days and nights has decreased
• Frequency of extremely hot days and nights has increased
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Trends in precipitation indices
Source: New et al. 2005 (submitted to J. Geophys. Res. (Atmospheres).)
Annual total precipitation Annual total precip. > 95th perc.
no of days with prec. > 20 mm Max. no of consec. dry days
Very heavy precipitation day
Longest dry spell
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Summary of findings for
precipitation indices in Africa
No trends found in many stations
Only a few stations show statistically significant trends
• Some stations are getting drier
• Longest dry spells are getting longer for a few stations
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Suggestion for collaboration work
Perform similar extreme indices analysis for Cuban stations
Required tools:
• RClimDex ( http://cccma.seos.uvic.ca/ETCCDMI/ )
• R ( http://www.r-project.org/ )
(both are freely available)
Such study will allow us:
• To identify how extremes behaved in the past in Cuba
• To diagnose observed changes in extremes in Cuba
• Compare results with findings of Caribbean climate and
weather extremes workshop held in Jamaica 2001
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What is going to happen
to extremes in the future?
Extreme event forecasting
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ENSEMBLES: ENSEMBLE-based Predictions of Climate
Changes and their Impacts
WP4.3: Understanding Extreme Weather and Climate
Events
Provision of statistical methods for identifying and forecasting extreme events
and the climate regimes with which they are associated. More robust
assessments of the effects of climate change on the probability of extreme
events and on the characteristics of natural modes of climate variability.
How best to make probability forecasts of
extremes?
us!
multi-model ensemble  tail probabilities
Need to develop:
Multi-model calibration and combination approach for extremes
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Calibration and combination of
multi-model ensemble
seasonal forecasts:
South American rainfall example
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Conceptual framework
Data Assimilation
“Forecast Assimilation”
p( y i | x i ) p( x i )
p( x i | y i ) 
p( y i )
p( x f | y f ) p( y f )
p( y f | x f ) 
p( x f )
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DJF rainfall anomalies for 1975/76 and 1982/83
Obs
Multi-model
Forecast Assimilation
La Nina
1975/76
ACC=-0.09
ACC=0.59
ACC=0.32
ACC=0.56
El Nino
1982/83
(mm/day)
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Summary of multi-model ensemble forecast
calibration and combination
• Forecast assimilation: Unified framework for
calibration and combination
• Useful approach for improving skill of South
American rainfall seasonal forecasts
• Similar approach will be developed for
extreme event forecasts in ENSEMBLES
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The EUROBRISA Project
Lead Investigator: Dr Caio Coelho
Key Idea:
To improve seasonal forecasts in S. America:
a region where there is seasonal forecast skill
and useful value.
http://www.met.rdg.ac.uk/~swr01cac/EUROBRISA
Aims
Institutions
Country
Partners
CPTEC
Brazil
Coelho, Cavalcanti,
Silva Dias, Pezzi
ECMWF
EU
Anderson, Balmaseda,
Doblas-Reyes, Stockdale
• Produce improved well-calibrated real-time
probabilistic seasonal forecasts for South
America
INMET
Brazil
Moura, Silveira
Met Office
UK
Graham, Davey, Colman
Météo France
France
Déqué
• Develop real-time forecast products for
non-profitable governmental use (e.g.
reservoir management, hydropower
production, and agriculture)
SIMEPAR
Brazil
Guetter
Uni. of Reading
UK
Stephenson
Uni. of Sao Paulo
Brazil
Ambrizzi, Silva Dias
CIIFEN
Ecuador
Camacho, Santos
• Strengthen collaboration and promote
exchange of expertise and information
between European and S. American
seasonal forecasters
EUROBRISA was approved by ECMWF council in June 2005
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Climate Analysis Group
http://www.met.reading.ac.uk/cag/
Aim: develop and apply statistical analysis
techniques to improve both understanding and
predictive capability of weather and climate
variations
Main areas of interest:
• climate modes and regimes e.g. NAO and Asian
Monsson
• weather and climate extremes
• Forecast verification, combination and calibration
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The End
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