Presentation on theme: "Performance evaluation and climate projections over Sub-Saharian Africa with COSMO-CLM Offenbach, 8 March 2012 Paola Mercogliano, CIRA and CMCC Edoardo."— Presentation transcript:
Performance evaluation and climate projections over Sub-Saharian Africa with COSMO-CLM Offenbach, 8 March 2012 Paola Mercogliano, CIRA and CMCC Edoardo Bucchignani, CIRA and CMCC Myriam Montesarchio, CMCC Alessandra Zollo, CMCC COSMO User Seminar 2012
1. Outlook Introduction Short summary of activity shown at CLM Assembly 2011 The CLUVA project The domains simulated List of simulations The West domain: validation and climate projections The Lower East domain: validation and climate proj. Conclusions
Project Co-ordinator: AMRA, Center of Competence in the field of Analysis and Monitoring of Environmental Risk, Italy The project objective is to develop methods and knowledge to be applied to African cities, to manage climate risks, to reduce vulnerabilities and to improve their coping capacity and resilience towards climate changes. The project will explore the issues of climate change vulnerability, resilience, risk management and adaptation in selected African cities with local partners. Task 1.1: Model projection of climate change (Leader: CMCC) The aim is to set up methods and work out probabilistic scenarios of climate change affected hazards having a resolution that fits for regional and urban systems (for the 5 selected cities) and related uncertainties. More detailed aims are: To produce downscaled regional climate scenarios (IPCC scenarios: RCP4.5 and RCP8.5) for selected African areas surrounding the African cities of interest, at high resolution (about 8 km). To produce very high resolution projection (about 1-2 km) for the climate of some African cities using specific and accurate statistical techniques 2. CLUVA – Climate change and urban vulnerability in Africa
3. Areas of interest for CLUVA WEST Domain: (18 W E; 3.3 – 16.8 N) 465 x 190 grid points Spatial Resolution: 8 km EAST Domains: (34.4 – 42.9 E; 6.1N – 12.5N) U (34.4 – 42.9 E; 6.1N – 12.5N) 120 x 90 grid points 120 x 90 grid points (34.5 – 41.3 E; 11.8S – 2.1S) L (34.5 – 41.3 E; 11.8S – 2.1S) 95 x 135 grid points 95 x 135 grid points St.Louis (16.5 W, N) Ougadougou (1.55 W, N) Douala (9.71 E, N) Addis Abeba (38.75 E, 9.02 N) Dar es Salaam (39.27 E, 6.82 S)
4. Orography of the three areas West Upper East Lower East
5. Details of the Numerical simulations 8 km resolution Supercomputer used: Cluster of 30 IBM P575 nodes (32 cores per node) Driving data: CMCC-MED 80 km resolution Model version: cosmo_090213_4.8_clm13 Time step: 40 sec. Numerical scheme: Runge-Kutta 2-time level HE-VI integration Validation: CRU data and observed datasets for the 5 cities.
6. List of simulations Scenario A1B West domain Scenario RCP 4.5 Lower East domain Scenario RCP 4.5 Upper East domain (*) Scenario RCP 4.5 West domain Scenario RCP 8.5 Lower East domain Scenario RCP 8.5 Upper East domain (*) Scenario RCP 8.5 West domain (*): completed, but not yet post processed.
7. Mean temperature bias with CRU (COSMO-CRU) DJF JJA Cold bias between -1 and -2 degrees. In the north part, up to -5 Hot bias between 2 and 3 degrees. Higher values in the west part
8. Mean precipitation (mm/month) bias with CRU DJF JJA Underestimation of about 25% in the south coastal area. Good agreement in the other parts. A strong wet bias is registered in the south coastal area. Underestimation in other parts
St. Louis 9. Seasonal cycle of temperature (COSMO vs Observations) Max Mean : 30-year daily average temperature Applied Bias correction (Sperna et. Al 2010): Maximum bias in April (2 0 C)
DoualaOuagadougou Max Mean 10. Seasonal cycle of temperature (COSMO vs CRU)
DJF JJA 11. T2m variation :future ( ) vs past ( ) A1B General increase of temperature, up to 2.4 o ; it is more evident in winter.
DJF JJA 12. T2m variation :future ( ) vs past ( ) RCP4.5 Less evident increase of temperature, especially in summer. In winter, significant increase in the northern part.
DJF JJA 13. T2m variation:future ( ) vs past ( ) RCP8.5 Larger increase of temperature in summer with respect the other scenarios. In winter, the increase is evident only in the northern part.
14. Precipitation variation : future ( ) vs past ( ) DJF JJA There is a big difference between winter and summer. In winter, there is a slight decrease of precipitation, while in summer there is a general increase with some exceptions. A1B mm/month
15. Precipitation variation : future ( ) vs past ( ) DJF JJA RCP4.5 There are differences between winter and summer. In winter, there is a general increase of precipitation, while in summer there is a behavior similar to A1B
16. Precipitation variation : future ( ) vs past ( ) DJF JJA RCP8.5 In winter, there is a general increase of precipitation, similar to RCP4.5 In summer there is a behavior similar to A1B and RCP4.5
Ouagadougou 17. T2m trend (A1B vs RCP 4.5) St.Louis
Ouagadougou 18. Precipitation trend (A1B vs RCP 4.5) St.Louis
19. Mean temperature bias with CRU (COSMO-CRU) DJF JJA In winter, there is a cold bias between -2 and -3 degrees. In some parts, up to -5 In summer, there is a hot bias between 1 and 2 degrees
Dar es Salaam Max Mean 20. Seasonal cycle of temperature (COSMO vs CRU) In winter, there is a cold bias especially evident in the maximum values of daily temperature.
DJFJJA 21. T2m variation: future ( ) vs past ( ) RCP4.5 In winter, two different areas are visible, but both characterized by an increase of temperature. In summer, a general increase of 1.5 o C is evident.
DJFJJA 22. T2m variation: future ( ) vs past ( ) RCP8.5 With this scenario, the increase of temperature is more uniform and evident with respect to RCP4.5.
DJFJJA 23. Precipitation variation: future ( ) vs past ( ) RCP4.5 In winter, we register reductions in the central part of the domain and increases along the coast. In summer, a general reduction is evident.
DJFJJA 24. Precipitation variation: future ( ) vs past ( ) RCP8.5 In winter, similar behavior as RCP4.5, but less evident increase along the coast. In summer, similar behavior as RCP4.5.
25. Conclusions Numerical results related to the simulation of the climate of the west and the east lower domain at high resolution have been shown. for the west domain: cold bias in winter, and hot bias in summer with respect to CRU. Better agreement registered with observed data provided by project partners. Quite good agreement of precipitations in winter, while in summer there is a strong bias; for the east lower domain: the temperature is underestimated in winter and overestimated in summer with respect to CRU; better agreement with observed data, especially in summer; for the west domain: in the future, the temperature is projected to increase, especially in winter with all the three scenarios; in winter the precipitation is projected to slightly increase by RCP4.5 and 8.5; in summer is projected to increase by all the scenarios. for the east lower domain: in the future, the temperature is projected to increase by both the scenarios, especially in winter.