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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)

• Perturbed physics ensembles (~300 members)

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Africa – current climate skill

IPCC AR4 models: precipitation

Strengths

• RCMs improve on GCM skill (tropics, West & South Africa)

• AGCMs – good skill for C20th precipitation and temperature

Weaknesses

• 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

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Africa – future climate confidenceStrengths

• 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

• 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

IPCC AR4 models

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Asia – current climate skill

Strengths

• Precipitation: South East (DJF/JJA), South, Central

• Small temperature biases (South, Indian Ocean)

Weaknesses

• 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

IPCC AR4 models:

SE Asia annual cycles

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Asia – future climate confidenceStrengths

• 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

Weaknesses

• 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

IPCC AR4 models

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South America – current climate skillStrengths

• Small temperature biases: South

• South American Monsoon – AGCMs

• RCMs improve on GCM precipitation

Weaknesses

• 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

IPCC AR4 models: precipitation

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South America – future climate confidence

Strengths

• 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

• Significant systematic errors: variability, ENSO, carbon cycle, land use change, Andes orography

• Small precipitation signal:noise – Amazon, North, South (seasons)

• Little research on extremes

IPCC AR4 models

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North America – current climate skillStrengths

• 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

IPCC AR4 models: temperature

Average error

Typical error

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North America – future climate confidence

Strengths

• Confidence in warming, extremes (W USA, Central, Caribbean, North Pacific)

• Precipitation: North, Central, Caribbean (G. Antilles summer)

• Snow depth (California, Rockies)

Weaknesses

• 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

IPCC AR4 models

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SW Pacific – current climate skillStrengths

• 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)

IPCC AR4 models: precipitation

Average error

Typical error

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SW Pacific – future climate confidence

Strengths

• General agreement on annual warming

• Precipitation: S Australia(JJA/SON), SW Australia(JJA), S New Zealand

• Extremes: temperature, precipitation & drought (Australia)

Weaknesses

• 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

IPCC AR4 models

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Europe – current climate skill

Strengths

• C20th temperature changes

• Area average precipitation

• RCMs – improve on GCM precipitation and temperature

Weaknesses

• 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

IPCC AR4 models: pressure

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Europe – future climate confidenceStrengths

• Temperature: annual, winter (North), summer (South)

• Precipitation: North(DJF), South/Central(JJA)

• Extremes: temperature – most regions, precipitation – North(DJF), Central/South(JJA)

• Snow

Weaknesses

• Uncertainties: circulation, MOC, variability, water/energy cycles

• Large seasonal temperature spread

• Large precipitation spread: annual, summer, complex topography

• Extremes: temperature – Central(JJA), precipitation, winds

IPCC AR4 models

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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

*Hawkins & Sutton, BAMS, submitted (2008)

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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