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
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)
Africa current climate skill IPCC AR4 models: precipitation RCMs improve on GCM skill (tropics, West & South Africa) AGCMs good skill for C20th precipitation and temperature 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 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) 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 IPCC AR4 models: SE Asia annual cycles Precipitation: South East (DJF/JJA), South, Central Small temperature biases (South, Indian Ocean) 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 IPCC AR4 models 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 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 IPCC AR4 models: precipitation Small temperature biases: South South American Monsoon AGCMs RCMs improve on GCM precipitation 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
South America future climate confidence 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) 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 IPCC AR4 models: temperature Temperature: North, Caribbean, North Pacific Precipitation: North, extremes (West USA) RCMs improve on GCMs: North, Central, Caribbean Average error 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 Typical error
IPCC AR4 models North America future climate confidence Confidence in warming, extremes (W USA, Central, Caribbean, North Pacific) Precipitation: North, Central, Caribbean (G. Antilles summer) Snow depth (California, Rockies) Systematic errors: complex terrain, ENSO, NAO, AO, MOC Precipitation: South, 30-40 o N, Caribbean RCM skill, lack of studies (Caribbean, North Pacific) Sea level rise, cyclones, few studies of extremes
SW Pacific current climate skill IPCC AR4 models: precipitation Climate/variability: Australia, South Pacific Broad ENSO patterns: New Zealand region RCMs better temperature for Australia Precipitation extremes: Australia Average error 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) Typical error Precipitation biases: wet (Australia)
SW Pacific future climate confidence IPCC AR4 models General agreement on annual warming Precipitation: S Australia (JJA/SON), SW Australia (JJA), S New Zealand Extremes: temperature, precipitation & drought (Australia) 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 IPCC AR4 models: pressure C20th temperature changes Area average precipitation RCMs improve on GCM precipitation and temperature 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 Temperature: annual, winter (North), summer (South) IPCC AR4 models Precipitation: North (DJF), South/Central (JJA) Extremes: temperature most regions, precipitation North (DJF), Central/South (JJA) Snow Uncertainties: circulation, MOC, variability, water/energy cycles Large seasonal temperature spread Large precipitation spread: annual, summer, complex topography Extremes: temperature Central (JJA), precipitation, winds
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
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)
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