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Climate change and variability Current capabilities - a synthesis of IPCC AR4 (WG1)
Pete Falloon, Manager – Impacts Model Development, Met Office Hadley Centre
WMO CaGM/SECC Workshop, Orlando, 18 November 2008
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Current capabilities – climate
modelling (IPCC, 2007)
• Global
• Atmosphere Ocean GCMs (~100km, centennial)
• [Earth System Models]
• [Seasonal and decadal forecast models]
• Regional
• RCMs (~25km, centennial)
• statistical downscaling
• Uncertainty?
• Multi-model ensembles (e.g. AR4 models)
• Emissions scenarios (e.g. IPCC SRES)
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• Perturbed physics ensembles (~300 members)
Africa – current climate skill
Strengths
IPCC AR4 models: precipitation
•
RCMs improve on GCM skill (tropics, West &
South Africa)
•
AGCMs – good skill for C20th precipitation
and temperature
Weaknesses
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•
Significant systematic errors (e.g. Sahel
variability & droughts, MJO)
•
Missing feedbacks (dust, vegetation, LUC)
•
Precipitation spread and warm bias in Indian
Ocean
•
Few studies of extremes
Africa – future climate confidence
Strengths
IPCC AR4 models
•
Consensus on annual warming
•
Agreement in annual precipitation: Mediterranean, N
Sahara (DJF/MAM), W Coast, S Africa, E Africa (DJF/MAM/SON),
Seychelles (DJF), Mauritius (JJA)
•
Confidence in extremes: temperature, precipitation
(East, West, South)
Weaknesses
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•
Precipitation uncertain – Sahel, Guinea coast, S Sahara,
West & East (JJA), South (DJF)
•
Few downscaling studies (esp. Indian Ocean)
•
Sea level rise, storm surges, cyclones uncertain
Asia – current climate skill
Strengths
IPCC AR4 models:
SE Asia annual cycles
•
Precipitation: South East (DJF/JJA), South, Central
•
Small temperature biases (South, Indian Ocean)
Weaknesses
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•
Cold and wet bias in all regions/seasons,
particularly North, Tibet (DJF/MAM), East
•
Lack of observations (Tibet)
•
Precipitation variability: South East
•
Precipitation spread, warm/dry bias, systematic
errors (ENSO, MJO): Indian Ocean
Asia – future climate confidence
Strengths
•
Consensus on warming
•
Precipitation: North/East/South East/W Central(JJA), Tibet,
Central(DJF), Indian Ocean – Seychelles/Maldives(DJF)
•
Some extremes: Temperature – East, Indian Ocean;
Precipitation – South, East, South East
IPCC AR4 models
Weaknesses
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•
Lack of regional analysis; climate-mode RCM studies,
extremes
•
Precipitation spread: South, South East, Tibet(JJA), East(DJF)
•
Systematic errors: ENSO, monsoon, cyclones, extremes,
complex topography
•
Indian Ocean downscaling & sea level rise
South America – current climate skill
Strengths
IPCC AR4 models: precipitation •
Small temperature biases: South
•
South American Monsoon – AGCMs
•
RCMs improve on GCM precipitation
Weaknesses
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•
Temperature biases – cold: Amazon; warm: 30oS,
Central (SON)
•
Precipitation biases – wet: North, Uruguay,
Patagonia; dry: Amazon, South
•
Systematic errors: weak ITCZ
•
Few, short, RCM studies, poor if AGCM driven
South America – future climate
confidence
Strengths
IPCC AR4 models
•
Agreement on warming, especially South
•
Precipitation: Tierra del Fuego(JJA), SE South(DJF), parts of
North (Ecuador, Peru, N SE Brazil)
•
Temperature extremes (all regions/seasons)
•
Precipitation extremes: dry - Central, wet – Amazon(DJF/MAM)
Weaknesses
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•
Significant systematic errors: variability, ENSO, carbon
cycle, land use change, Andes orography
•
Small precipitation signal:noise – Amazon, North, South
(seasons)
•
Little research on extremes
North America – current climate skill
Strengths
IPCC AR4 models: temperature
Average error
Typical error
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•
Temperature: North, Caribbean, North Pacific
•
Precipitation: North, extremes (West USA)
•
RCMs improve on GCMs: North, Central,
Caribbean
Weaknesses
•
Temperature: cold (Central), warm (North Pacific)
•
Precipitation and spread: Central, Caribbean,
North Pacific, North in some seasons (W, N)
•
RCMs: formulation, few (Central), short runs
(North), GCM biases
North America – future climate
confidence
Strengths
IPCC AR4 models
•
Confidence in warming, extremes (W USA, Central,
Caribbean, North Pacific)
•
Precipitation: North, Central, Caribbean (G. Antilles summer)
•
Snow depth (California, Rockies)
Weaknesses
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•
Systematic errors: complex terrain, ENSO, NAO, AO, MOC
•
Precipitation: South, 30-40oN, Caribbean
•
RCM skill, lack of studies (Caribbean, North Pacific)
•
Sea level rise, cyclones, few studies of extremes
SW Pacific – current climate skill
Strengths
IPCC AR4 models: precipitation
Average error
•
Climate/variability: Australia, South Pacific
•
Broad ENSO patterns: New Zealand region
•
RCMs – better temperature for Australia
•
Precipitation extremes: Australia
Weaknesses
•
Lack of detailed validation
•
Systematic errors: 50oS pressure bias,
monsoon, SPCZ, ENSO
•
Temperature biases: warm (oceans, South
Pacific, SE/SW Australia); cold (Australia)
•
Precipitation biases: wet (Australia)
Typical error
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SW Pacific – future climate confidence
Strengths
IPCC AR4 models
•
General agreement on annual warming
•
Precipitation: S Australia(JJA/SON), SW Australia(JJA), S New
Zealand
•
Extremes: temperature, precipitation & drought (Australia)
Weaknesses
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•
Systematic errors: ENSO, monsoon
•
Large warming spread: Australia(DJF)
•
Large precipitation spread – most of the region
•
Extremes, cyclones, winds: few studies
•
Sea level rise/downscaling – small islands
Europe – current climate skill
Strengths
IPCC AR4 models: pressure •
C20th temperature changes
•
Area average precipitation
•
RCMs – improve on GCM precipitation and temperature
Weaknesses
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•
Large temperature bias/range: cold - North(DJF), warm –
South(JJA), excessive variability
•
Precipitation biases: wet – North(SON/MAM), dry – East,
South
•
Observational uncertainty: precipitation – North
•
Range in extreme temperature biases
Europe – future climate confidence
Strengths
IPCC AR4 models
•
Temperature: annual, winter (North), summer (South)
•
Precipitation: North(DJF), South/Central(JJA)
•
Extremes: temperature – most regions, precipitation –
North(DJF), Central/South(JJA)
•
Snow
Weaknesses
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•
Uncertainties: circulation, MOC, variability, water/energy
cycles
•
Large seasonal temperature spread
•
Large precipitation spread: annual, summer, complex
topography
(JJA)
Conclusions
• Confidence in annual warming, uncertainty in regional (seasonal) precipitation
• Remaining issues with variability
•
NAO, AO, MJO, ENSO, Sahel, MOC, monsoons, ITCZ, SPCZ
• Incomplete/missing processes and feedbacks
•
Dust, vegetation, carbon cycle, complex topography, water/energy cycles
• Observations
•
Lacking: Tibet, Northern Europe
•
Signal/noise, uncertainty not considered
• Lack of studies of extremes, (time) downscaling in some regions
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Conclusions & further work
• Largest present-day median climate biases:
•
~2K temperature – Sahel, N Europe, Tibet, E Asia
•
Precipitation – Tibet (+110%), W North America (+65%), S Africa (+35%)
• Lowest future annual precipitation confidence (<2/3 models agree on sign):
•
Central Europe, Central USA, Sahel, Amazon, Tibet/E Asia, Central/E Australia
• Lowest future temperature confidence (30y lead, 10y average – signal:noise < 2)*:
•
Northern North America, Northern Europe
• What do these uncertainties mean for impacts & adaptation (hedging/confidence)?
• Future tasks:
•
Review IPCC AR4 working group 2 (Impacts) capabilities
•
Review post-IPCC science
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*Hawkins & Sutton, BAMS, submitted (2008)
Uncertain:
Regional climate change
Projected precipitation changes 2090s (% relative to 1980-99)
White: <2/3 of models agree on sign of change (+ or -)
Stippled: >90% of models agree on sign of change
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