Downscaling global climate model outputs to fine scales sanjaya ratnayake

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Even though standard precipitation index (SPI) is not a drought predicting tool using downscaled results it was possible to forecast SPI. Forecasted SPI can use for two purposes. Firstly, in order to implement better drought relief payment policy. Secondly, to better water resource planning in order to reduce agriculture risk. Statistical downscaling procedure was developed over Sri Lanka for drought risk. The system was based on Climate Information tool Kit 2.0 (CLIK) by Asia-Pacific Economic Corporation (APEC) Climate Center (APCC). As the predictor region El Niño Southern Oscillation (ENSO) region was considered because recent droughts are more influenced by ENSO compared to traditional South West monsoons (SWM) and North East monsoon (NEM) for the island. Seasonal drought study was carried out considering 3 months intervals for 4 seasons January to March (JFM), April to June (AMJ), July to September (JAS) and October to November (OND). In order to identify optimal SPI scales, models, variables and NINO regions for each season a preliminary study was carried out using a sample stations and based on the results detailed study was carried out for 34 stations covering central and southern parts of the island. Heidke skill score was considered as the categorical verification measure. In order to improve the magnitude of forecasted SPI 2 variance inflation techniques were employed. In CLIK multi model ensemble (MME) predictions were tested considering 3 deterministic models: simple composition method (SCM), multiple regression method (MRG) and synthetic multi model super ensemble method (SSE). However due to low resolution regression based downscaling was considered. Interestingly central high hills showed a very high correlation with ENSO during OND. For highly skilled stations drought characteristics such as trends, onset, duration, frequency and severity can be calculate. Our results highlight that CLIK is skillful over Sri Lanka. Specially in identifying best downscaling characteristics over a station.

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  • Downscaling global climate model outputs to fine scales sanjaya ratnayake

    1. 1. Downscaling Global Climate ModelOutputs to Fine Scales over Sri Lanka for Assessing Drought Impacts Sanjaya Ratnayake Foundation for Environment Climate and Technology Sri Lanka
    2. 2. Overview• Introduction• Problem Definition• Aims and Objectives• My approach• Data and Study Area• Methodology• Results• My Prediction• Conclusion• Acknowledgement
    3. 3. Introduction• Due to the geographical distribution the island is affected with different kinds of natural disasters.• The most frequent natural hazards that affect the island are drought, floods, landslides, cyclones and coastal erosion.• Among all natural disasters, droughts occur most frequently, have the longest duration, and cover the largest area.
    4. 4. Problem Definition• So far there is no proper drought index over Sri Lanka.• So far there is no proper drought characteristics predicting mechanism.• So far drought relief payments made in districts across the country by the government over a 40 years period used as a proxy for drought risk.
    5. 5. Aims and Objectives• Asses the APCC CLIK tool skill over Sri Lanka• Observer station correlations with NINO regions• Identify most appropriate downscale parameters for individual station for different seasons• Develop drought index over Sri Lanka• Predict Seasonal Droughts
    6. 6. My Approach• Calculate SPI over Sri Lanka• Obtain downscale results using SPI• Observe skill of forecasted downscale SPI results• Amplitude the results using variance inflation methods• Predict SPI using most appropriate method.
    7. 7. Data and Study Area• 132 station monthly precipitation data covering entire island.• 26 stations with missing and corrupted data.• SPI 30 years (24 years here)• CLIK most models support : 1984 to 2008• Finally, 34 stations covering southern and central parts of the Island.
    8. 8. (a) (c)(b)
    9. 9. Methodology• Standard Precipitation Index (SPI)• Climate Information tool Kit 2.0• Preliminary Study• Detailed Downscaling• Variance Inflation• Heidke Skill Score
    10. 10. Meteorological drought index• Standard Precipitation Index requires only precipitation as input (McKee, et al., 1993)• SPI scale 1, 3, 6 and 12 – Appropriate for short term drought studies – Appropriate for seasonal drought studies – 3 months droughts have a drastic impact on the agriculture – Highly successful with drought data
    11. 11. SPI Classification SPI Drought classification >2.0 Extreme wet1.5 to 1.99 Very wet1.0 to 1.49 Moderately wet-0.99 to 0.99 Near Normal-1.0 to -1.49 Moderately Dry-1.5 to -1.99 Severe Dry <-2.0 Extremely Dry SPI Drought category 0 to -0.99 Mild drought -1.0 to -1.49 Moderately drought -1.5 to -1.99 Severe drought <-2.0 Extremely drought
    12. 12. Climate Information tool Kit 2.0 (CLIK)• By APEC Climate Center (APCC)• Statistical Downscaling• Linear Regression• 16 GCM models• 10 predictor variables• 3 months seasonal predictions• Screening Process – First Screening Process (FSC) : Hopeful stations – Second Screening Process (SCP) : Good stations
    13. 13. Preliminary StudyPredictor• Training Period: 1984-2008• Variables: SLP, SST, U850• Models: NCEP, NASA, MSC_CANCM4, HMC, CWB, IRIF, GDAPS_F, COLA, MSC_CANCM3, MGO, POAMA, PNU and BCC• Region : NINO1+2, NINO3, NONO4, NINO3.4
    14. 14. Preliminary StudyPredictand• Season: JFM, AMJ, JAS, OND• Variables: Precipitation• Statistical Downscaling: Linear Regression• Significant Level: 5%• Minimum pattern score: 0.3
    15. 15. Detailed Downscaling StudySeason JFM AMJ JAS ONDPredictor Model NCEP NCEP NCEP MGOPredictor Region NINO3 NINO3 NINO3.4 NINO3Predictor Variable SLP SLP SST U850No of Stations 34Predictand Variable SPITraining Period 1984-2008Statistical Downscaling Linear RegressionSignificant Level 5%Mini. Pattern score 0.3
    16. 16. Variance Inflation
    17. 17. Heidke Skill Score Forecast HSS 1: Perfect forecast Observed Yes No HSS 0: No skill Yes A B HSS < 0: Chance No C D forecast is better• For Mild, Moderate, Severe and Extreme droughts HSS was calculated separately.• JFM and OND seasons were considered separately.
    18. 18. Results• Preliminary Study Results• Downscale results• HSS Results comparison for – Downscale results – VIF1 Results – VIF2 Results
    19. 19. Preliminary Study Results
    20. 20. Preliminary Study Results• OND showed the best correlations fallowed by JFM• NINO3 showed higher correlation• JFM : NCEP model with SLP variable• OND : MGO models with U850 variable• Low results for AMJ and JAS
    21. 21. Detailed Downscaling resultsSeason JFM AMJ JAS ONDOnly FSP 3 - 2 8SCP 15 1 7 18Highest Correlation 0.52 0.26 0.59 0.7Corr > 0.6 - - - 3Corr > 0.3 9 - 5 13
    22. 22. JFM AMJ JAS OND- - - - - - -
    23. 23. Variance Inflation• Mild drought – Almost Downscale and VIF2 similar results – VIF2 slight improvement in OND• Moderate drought : Good improvement for VIF (specially in OND) 2• Severe Droughts : Good improvement from VIF1• Extreme Droughts : Longer period data are required
    24. 24. Mild Drought Downscale VIF1 VIF2JFM Downscale VIF1 VIF2OND
    25. 25. Moderate DroughtJFM Downscale VIF1 VIF2OND Downscale VIF1 VIF2
    26. 26. My Prediction for 2012 OND
    27. 27. Conclusion• Overall CLIK is skillful over Sri Lanka• Variance Inflation methods are important when drought magnitude increases• OND was high skill season• Central High hills showed higher correlation• NINO3 showed good correlation over all seasons except AMJ
    28. 28. How to use my study results• Even though SPI is not a drought predicting tool using downscaled SPI it was possible to forecast drought in Sri Lanka.• Predicted drought characteristics for better water resource planning in order to reduce agriculture risk• Use my drought index to proper drought payment relief payment
    29. 29. Acknowledgement• Funders – Asia-Pacific Economic Corporation (APEC) Climate Center (APCC)• Supervisors – Dr. Soo-Jin Sohn, Team Leader(Climate Prediction Team), APCC – Dr. Lareef Zubair, Principal Scientist, Foundation for Environment Climate and Technology, Sri Lanka• Data Providers – Precipitation data : Ministry of Irrigation and Water Resources Management, Sri Lanka – GCM model data providers• Individuals – Mr. Wimal Ratnayake (Irri Mini) : Station details – Ms. Hye-Jin Park (APCC) : CLIK – Dr. Prasanna Venkatraman (APCC) : ENSO – Ms. Ruvindee Rupasinghe (Uni Peradeniya) : English and Report corrections – Ms. Sooyang Joo (APCC) : Operation support – Mr. Dede Tarmana (The Indonesian Agency for Meteorology Climatology and Geophysics, Indonesia) : Arc GIS• Free Software – SPI_tool : The National Drought Mitigation Center – Arc Portable : ESRI – Goole earth : Google Inc – Goole Fusion : : Google Inc – CLIK : APCC – Eviews : HIS Inc.
    30. 30. Flashback• Introduction• Problem Definition• Aims and Objectives• My approach• Data and Study Area• Methodology• Results• Forecast• Conclusion• Acknowledgement
    31. 31. Thank you

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