Climate Modeling – A Guide to Investment
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Climate Modeling – A Guide to Investment

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presented by Vicky Pope in Asia Water Week 2013, Manila 11-13 March 2013

presented by Vicky Pope in Asia Water Week 2013, Manila 11-13 March 2013

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Climate Modeling – A Guide to Investment   Climate Modeling – A Guide to Investment Presentation Transcript

  • Cli d lli idClimate modelling: A guide to investment decisionsVicky PopeMet Office Hadley Centre
  • The impact of a global temperature rise of 4 ºCof 4  CChange in temperature from pre‐industrial climate1    2    3    4    5    6    7    8    9    10   11  12  13  14  15  16MeltingiceMethane releaseMOceanReduced cropsForestIncreased droughtMore heatwavesOceanAcidificationRainforestlossForestfire Stronger tropical stormsCurrent City population • 3‐10 million • 10‐20 million© Crown copyright   Met Office
  • The benefits of integrated services:for policy and long term planning
  • Bridging the Gaps and Bridging the ScalesGlobalGlobalWeather/ClimateModel: 25 -100kmRegionalWeather/Climate Regional ImpactsM d lRegional ImpactsModels: Hydrology,model: 25 - 12km Model:Vegetation,TopographyLocalLocal Decision-Making: Landuse, Water use,Localdownscalingmodel:4 1km use, a e use,AdaptiveResponses4 - 1km© Crown copyright   Met Office
  • Future Nile flow: A Climate Change Risk Management Programmeg g g• Impact on water resources by 2050s• Possible adaptation• Regional climate models d Nil f tiand Nile forecasting system• Risk based approachRisk based approach• Rainfall and river runoff –wide range of possible outcomesImage NASA © Crown copyright   Met Office
  • Changes in rain and snowfall in the high mountains of South Asiamountains of South AsiaHimachal Pradesh East Nepal &W ESummer precipitation (Indian monsoon)Winter precipitation (Western disturbances)NWJK000 2050 2100HP1950 2000 2050 2100-20246NB1950 2000 2050 2100-20246JK000 2050 2100HP1950 2000 2050 2100-10010203040NB1950 2000 2050 2100-10010203040JK000 2050 2100HP1950 2000 2050 2100-0.50.00.51.01.5NB1950 2000 2050 2100-0.50.00.51.01.5JK000 2050 2100HP1950 2000 2050 2100-1.5-1.0-0.50.00.51.0NB1950 2000 2050 2100-1.5-1.0-0.50.00.51.0Karakoram Hindu Kush Jammu‐KashmirHimachal Pradesh, Uttarakhand & West NepalEast Nepal &Bhutanion mm/dayJK000 2050 2100HP1950 2000 2050 2100-20246NB1950 2000 2050 2100-20246JK000 2050 2100HP1950 2000 2050 2100-10010203040NB1950 2000 2050 2100-10010203040JK000 2050 2100HP1950 2000 2050 2100-0.50.00.51.01.5NB1950 2000 2050 2100-0.50.00.51.01.5JK000 2050 2100HP1950 2000 2050 2100-1.5-1.0-0.50.00.51.0NB1950 2000 2050 2100-1.5-1.0-0.50.00.51.0nge in precipitatJK000 2050 2100HP1950 2000 2050 2100-20246NB1950 2000 2050 2100-20246JK000 2050 2100HP1950 2000 2050 2100-10010203040NB1950 2000 2050 2100-10010203040JK000 2050 2100HP1950 2000 2050 2100-0.50.00.51.01.5NB1950 2000 2050 2100-0.50.00.51.01.5JK000 2050 2100HP1950 2000 2050 2100-1.5-1.0-0.50.00.51.0NB1950 2000 2050 2100-1.5-1.0-0.50.00.51.0ChandayKarakoramJK000 2050 2100HP1950 2000 2050 2100-20246NB1950 2000 2050 2100-20246JK000 2050 2100HP1950 2000 2050 2100-10010203040NB1950 2000 2050 2100-10010203040JK000 2050 2100HP1950 2000 2050 2100-0.50.00.51.01.5NB1950 2000 2050 2100-0.50.00.51.01.5JK000 2050 2100HP1950 2000 2050 2100-1.5-1.0-0.50.00.51.0NB1950 2000 2050 2100-1.5-1.0-0.50.00.51.0n snowfall mm/dJK000 2050 2100HP1950 2000 2050 2100-20246NB1950 2000 2050 2100-20246JK000 2050 2100HP1950 2000 2050 2100-10010203040NB1950 2000 2050 2100-10010203040JK000 2050 2100HP1950 2000 2050 2100-0.50.00.51.01.5NB1950 2000 2050 2100-0.50.00.51.01.5JK000 2050 2100HP1950 2000 2050 2100-1.5-1.0-0.50.00.51.0NB1950 2000 2050 2100-1.5-1.0-0.50.00.51.0Change inTime ‐ Met Office model   ‐ MPI model
  • Probability forecasts of coastal flood risk:Balanced approach to decision makingCentral estimate Upper limit ofpp gCentral estimate from UKCP modelsUpper limit of "high++“ modelChange in extreme sea level of 50 year ystorm© Crown copyright   Met Office
  • Energy project HeadlineHeadline results© Crown copyright   Met Office© Crown copyright   Met Office
  • The benefits of integrated services:for planning and civil contingencies
  • Seamless predictionSupporting decision makingNowoursDaysweekonthonalcadalmatematence ryNHD1‐w1‐moSeasDecClimPast climConfidenboundaAnalysis of past weather observations to manageobservations to manage climate risksEg. Agriculture: this informs crop choice and planting date to optimise yields and di i i dMonthly to decadal predictions  informs probability of drought, cold, heat.to optimise yields and minimise crop failure risk.Predicting routine and hazardous weather conditions and disseminating tailored and timely warnings. P bliheat.Contingency planners, national and international humanitarian response, government and private Global and regional climate predictions.Informs mitigation policy and adaptation choices Impacts© Crown copyright   Met OfficeForecast lead‐timePublic, emergency response, international disaster risk reduction g pinfrastructure investmentadaptation choices. Impacts on water resurces, heat stress, crops, infrastructure.
  • Example of seasonal prediction of reservoir management  Lake Volta dam AkosombogGhanaLake Volta dam, Akosombo• 1000MWatt facility: provides ~50% of Ghana’s electricity Lake Volta• Rainfall has strong seasonal dependency: peak months June‐September• Inflow prediction needed to assess likely requirement for oil‐fired generation• …thus lake‐level is monitored closely!…thus lake level is monitored closely!© Crown copyright   Met Office
  • Summary• Useful risk based climate information forUseful risk based climate information for adaptation ‐ e.g. River Nile, Hamalayas• Long range forecasts for decision tools MoreLong range forecasts for decision tools. More skilful in some regions and aggregated over regions such as large water catchments – Voltaregions such as large water catchments  Volta • ‘Seamless prediction’ addresses climate variability and changevariability and change• Integrated approach provides environmental information for decision makinginformation for decision making© Crown copyright   Met Office