Climate modelling: A guide to investment decisions Vicky Pope Met Office Hadley Centre This is not an ADB material. The views expressed in this document are the views of the author/s and/or their organizations and do not necessarily reflect the views or policies of the Asian Development Bank, or its Board of Governors, or the governments they represent. ADB does not guarantee the accuracy and/or completeness of the material s contents, and accepts no responsibility for any direct or indirect consequence of their use or reliance, whether wholly or partially. Please feel free to contact the authors directly should you have queries.
The impact of a global temperature rise of 4 ºC of 4 C Change in temperature from pre industrial climate 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 Melting ice Methane release More heatwaves Increased drought Reduced crops Ocean Acidification Rainforest loss Forest fire Stronger tropical storms Current City population 3 10 million 10 20 million
The benefits of integrated services: for policy and long term planning
Bridging the Gaps and Bridging the Scales Global Weather/Climate Model: 25-100km Regional Weather/Climate model: 25-12km Local downscaling model: 4-1km Regional Impacts Models: Regional Hydrology, Impacts Vegetation, Model: Topography Local Decision- Making: Land use, Water use, Adaptive Responses
Future Nile flow: A Climate Change Risk Management Programme g g g Impact on water resources by 2050s Possible adaptation Regional climate models and Nile forecasting system Risk based approach Rainfall and river runoff wide range of possible outcomes Image NASA
JK HP NB 6 6 4 4 2 2 0 0-2 -2 000 2050 2100 JK HP NB 40 40 30 30 20 20 10 10 0 0-10 -10 000 2050 2100 JK HP NB 1.5 1.5 1.0 1.0 0.5 0.5 0.0 0.0-0.5-0.5 000 2050 2100 JK HP NB 1.0 1.0 0.5 0.5 0.0 0.0-0.5-0.5-1.0-1.0-1.5-1.5 000 2050 2100 ion mm/day NW Changes in rain and snowfall in the high mountains of South Asia Winter precipitation (Western disturbances) W Karakoram Hindu Kush Jammu Kashmir Summer precipitation (Indian monsoon) E Himachal Pradesh, East Nepal & Uttarakhand & West Nepal Bhutan Change in snowfall mm/d day Chan nge in precipitat Karakoram Time Met Office model MPI model
Probability forecasts of coastal flood risk: Balanced approach to decision making Central estimate from UKCP models Upper limit of "high++ model Change in extreme sea level of 50 year storm
Energy project Headline results
The benefits of integrated services: for planning and civil contingencies
Seamless prediction Supporting decision making Past clim mate Now Analysis of past weather observations to manage climate risks ours H Days 1 w week Eg. Agriculture: this informs crop choice and planting date to optimise yields and Predicting i routine and minimise crop failure risk. hazardous weather conditions and disseminating tailored and timely warnings. Public, emergency response, international disaster risk reduction 1 mo onth Forecast lead time onal Seas Dec cadal Monthly to decadal predictions informs probability of drought, cold, heat. Contingency planners, national and international humanitarian response, government and private infrastructure investment Clim mate Confiden nce bounda ry Global and regional climate predictions. Informs mitigation policy and adaptation choices. Impacts on water resurces, heat stress, crops, infrastructure.
Example of seasonal prediction of reservoir management Lake Volta dam, Akosombo 1000MWatt facility: provides ~50% of Ghana s electricity Rainfall has strong seasonal dependency: peak months June September Inflow prediction needed to assess likely requirement for oil fired generation thuslake level level is monitored closely! Ghana Lake Volta
Summary Usefulrisk based climate information for adaptation e.g. River Nile, Hamalayas Long range forecasts for decisiontools tools. More skilful in some regions and aggregated over regions such as large water catchments Volta Seamless prediction addresses climate variability and change Integrated approach provides environmental information for decision making