Climate change and variability -

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1 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

2 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)

3 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

4 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)

5 Uncertain: Regional climate change (IPCC, 2007, Fig. SPM-7) 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

6 Uncertainty in a simple climate model Cox & Stephenson, Science 2007

1y averaging Total, Scenario, Model, Internal variability

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

8 Decadal forecasts of global temperature Doug Smith et al, Science 2006

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

9 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 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),

10 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 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 Cold and wet

11 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 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

12 Asia future climate confidence Strengths Consensus on warming IPCC AR4 models 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

South America current climate skill Strengths IPCC AR4 models: precipitation Small temperature biases: South South American Monsoon AGCMs RCMs improve on GCM precipitation Weaknesses Temperature

13 South America current climate skill Strengths IPCC AR4 models: precipitation 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

South America future climate confidence IPCC AR4 models Strengths Agreement on warming, especially South Precipitation: Tierra del Fuego (JJA), SE South (DJF), parts of North (Ecuador, Peru, N SE

14 South America future climate confidence IPCC AR4 models 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

North America current climate skill Strengths IPCC AR4 models: temperature Temperature: North, Caribbean, North Pacific Precipitation: North, extremes (West USA) RCMs improve on GCMs: North, Central,

15 North America current climate skill Strengths IPCC AR4 models: temperature Temperature: North, Caribbean, North Pacific Precipitation: North, extremes (West USA) RCMs improve on GCMs: North, Central, Caribbean Average error 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 Typical error

North America future climate confidence Strengths IPCC AR4 models Confidence in warming, extremes (W USA, Central, Caribbean, North Pacific) Precipitation: North, Central, Caribbean (G.

16 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 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

SW Pacific current climate skill Strengths Climate/variability: Australia, South Pacific IPCC AR4 models: precipitation Broad ENSO patterns: New Zealand region RCMs better temperature for Australia

17 SW Pacific current climate skill Strengths Climate/variability: Australia, South Pacific IPCC AR4 models: precipitation Broad ENSO patterns: New Zealand region RCMs better temperature for Australia Precipitation extremes: Australia Average error 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) Typical error Precipitation biases: wet (Australia)

SW Pacific future climate confidence IPCC AR4 models Strengths General agreement on annual warming Precipitation: S Australia (JJA/SON), SW Australia (JJA), S New Zealand Extremes: temperature,

18 SW Pacific future climate confidence IPCC AR4 models 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

Europe current climate skill Strengths IPCC AR4 models: pressure C20th temperature changes Area average precipitation RCMs improve on GCM precipitation and temperature Weaknesses Large temperature

19 Europe current climate skill Strengths IPCC AR4 models: pressure 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

Europe future climate confidence IPCC AR4 models Strengths Temperature: annual, winter (North), summer (South) Precipitation: North (DJF), South/Central (JJA) Extremes: temperature most regions,

20 Europe future climate confidence IPCC AR4 models 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

21 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

22 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

23 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%)

24 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

25 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

26 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

27 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)

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

28 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?)

Climate change and variability -

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