© 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 TOR C tasks and approach 1.Review and synthesise skill/confidence in climate modelling from IPCC AR4 WG1 (nearly complete Nov 08) Mostly chapters 8 (global validation) and 11 (regional scenarios) 2.Review IPCC AR4 working group 2 (Impacts) capabilities (by end April 2009) 3.Review post-IPCC science (since 2006?) (by end April 2009)

© Crown copyright Met Office Outline of talk General capabilities – climate scenarios Climate capabilities by WMO region Africa Asia South America North And Central America South West Pacific Europe Conclusions Recommendations Further work

© Crown copyright Met Office Current capabilities – climate modelling 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 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 (IPCC, 2007, Fig. SPM-7)

© Crown copyright Met Office Uncertainty in a simple climate model Cox & Stephenson, Science 2007

© Crown copyright Met Office Uncertainty in IPCC AR4 models: temperature Total uncertainty, 1 year averaging: Total, Scenario, Model, Internal variability Hawkins & Sutton, submitted 2008 Signal to noise, 30y lead, 1y averaging Signal to noise, 30y lead, 10y averaging

© Crown copyright Met Office Decadal forecasts of global temperature Doug Smith et al, Science 2006

© 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 (1) 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 (2) 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 disagree on sign): Central Europe, Central USA, Sahel, Amazon, Tibet/E Asia, Central/E Australia Lowest future temperature confidence (30y lead, 10y average – signal:noise < 2.0): Northern North America, Northern Europe

© Crown copyright Met Office Recommendations so far (WG1) 1.Need to ensure systematic climate model errors and limitations are addressed under appropriate research programmes (long-term) 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 2.Need to improve general climate model skill, particularly where biases are large (long-term) ~2K temperature – Sahel, N Europe, Tibet, E Asia Precipitation – Tibet (+110%), W North America (+65%), S Africa (+35%)

© Crown copyright Met Office Recommendations so far (WG1) 3.(long-term) Need to reduce uncertainty in future projections, particularly for: Annual precipitation (<2/3 models agree on sign): Central Europe, Central USA, Sahel, Amazon, Tibet/E Asia, Central/E Australia Annual temperature (30y lead, 10y average – signal:noise < 2.0):Northern North America, Northern Europe 4.2 and 3 above are not exclusive, significant errors/uncertainties exist elsewhere and should also be addressed (long-term) E.g. where consensus on precipitation changes exists, but there is a large spread

© Crown copyright Met Office Recommendations so far (WG1) 5.Since climate extremes and seasonal changes are crucial to the sectors considered by the ICT, more information (further studies) on skill and confidence in these is required (short-mid term) 6.In some regions, observations need strengthening to facilitate climate model validation and development (mid-long term), particularly (not exclusively): Tibet, Northern Europe 7.Climate model validation could be strengthened by considering uncertainties in observations (e.g. sampling/spatial errors) as well as model uncertainties (e.g. ensemble approaches) – short-mid term

© Crown copyright Met Office Recommendations so far (WG1) 8.The lack of downscaling studies for some regions (quality, length, extremes) needs to be addressed – (short-mid term) – particularly: Small islands (SW Pacific, Caribbean, North Pacific, Indian Oceans), South America, Africa 9.Uncertainty in sea level rise, storm surges and tropical cyclones are crucial for many RAs, particularly small islands and should be addressed 10.Further investigation of usefulness of seasonal and decadal forecasts is needed to support adaptation planning (short-mid term): Downscaling seasonal to decadal forecasts Assessing (improving?) skill (decadal forecasts) for precipitation and extremes

© Crown copyright Met Office Recommendations so far (WG1) 11.In the meantime, we need to develop and recommend ways to best use the information we have now (short-term) : Develop and promote clear, robust sources of guidance/advice on ‘reliability’ of climate projections across sectors (skill and confidence) Make information on aspects of climate model validation specific to agriculture, forestry and fisheries available and accessible (i.e. not just temperature and precipitation e.g. agroclimate indices) Also make future projection outputs available (and uncertainties) (Strengthen, consolidate, update and..) Make recommendations on ‘best practice’ for applying climate model data in impact assessments (cf UKCIP) Promote robust adaptation measures based on these, for instance Where uncertainties are large: hedging (e.g. planting 50% drought tolerant crops for a 50% certainty of increased drought risk)

© Crown copyright Met Office Future TOR C tasks This summary has over-simplified some issues in climate model skill and future confidence – we need to ensure these subtleties are considered in impact assessments Seasonal changes Extremes What do these uncertainties mean for impacts & adaptation (hedging/confidence)? Future tasks (end April 2009) Review IPCC AR4 working group 2 (Impacts) capabilities Review post-IPCC science (since 2006?)