© Crown copyright Met Office 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

© Crown copyright Met Office 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)

© Crown copyright Met Office 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

© Crown copyright Met Office Africa – future climate confidence Strengths 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

© Crown copyright Met Office 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

© Crown copyright Met Office 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 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

© Crown copyright Met Office South America – current climate skill Strengths Small temperature biases: South South American Monsoon – AGCMs RCMs improve on GCM precipitation Weaknesses Temperature biases – cold: Amazon; warm: 30 o S, 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

© Crown copyright Met Office 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

© Crown copyright Met Office North America – current climate skill Strengths 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

© Crown copyright Met Office 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, o N, Caribbean RCM skill, lack of studies (Caribbean, North Pacific) Sea level rise, cyclones, few studies of extremes IPCC AR4 models

© Crown copyright Met Office SW Pacific – current climate skill Strengths 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: 50 o S 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

© Crown copyright Met Office 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

© Crown copyright Met Office 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

© Crown copyright Met Office Europe – future climate confidence Strengths 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

© Crown copyright Met Office 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

© Crown copyright Met Office 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)

© Crown copyright Met Office Uncertain: Regional climate change Projected precipitation changes 2090s (% relative to ) White: <2/3 of models agree on sign of change (+ or -) Stippled: >90% of models agree on sign of change