Downscaling global climate model outputs to fine scales over sri lanka for assessing drought impacts


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Downscaling global climate model outputs to fine scales over sri lanka for assessing drought impacts

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Downscaling global climate model outputs to fine scales over sri lanka for assessing drought impacts

  1. 1. Downscaling Global Climate Model Outputs to Fine Scales over Sri Lanka for Assessing Drought Impacts Research Proposal to APCC Climate Center: 2012 Young Scientist Support Program R.M.S.P. Ratnayake Junior Research Scientist Foundation for Environment, Climate and Technology (FECT), c/o Mahaweli Authority –H.A.O. & M Division, Digana Village, Kandy, Sri Lanka. 1
  2. 2. Table of Content1) Abstract 32) Introduction 33) Goal 44) Objectives 45) Date 56) Methodology 57) Acknowledgement 68) List of References 79) List of Figures 8 2
  3. 3. 1) Abstract Multiple statistical/dynamic regional climate downscaling methodologies shall be evaluated over Sri Lanka for drought risk. Among all the natural hazards droughts occur more frequently, have the longer period and affects most part of Sri Lanka. Downscaling global climate model data if skillful shall be highly useful for retrospective and prospective analysis of drought is a strategy to predict Sri Lanka droughts. The project will downscale GCM outputs to Sri Lanka climate using downscaling techniques in the historical period. Drought indices shall be computed from the downscaled data and assessed against gridded archive of drought indices based on observed data that are known to effectively capture drought occurrence. Based on the skill of these relationships, downscaled GCM’s for the near-term future may be used to characterize changes in drought patterns spatial and seasonally over Sri Lanka in the near-term. APCC and IPCC (CMIP3, CMIP5) data archives are available for analysis. RegCM3 model data (archive data 30 year data) which is downscaled for Sri Lanka and surrounding region from 1970 to 2000 produced at IRI is available for the analysis. In addition RegCM4 model archives are available at APCC. APCC CLIK will be used for statistical downscaling. The skill will be assessed using Heidke Skill Score(HSS) and Ranked Probability Skilled Scores (RPSS). Comparison of spread among model ensemble members and among models can be used to characterize uncertainty.2) IntroductionSri Lanka is an island with tropical monsoon climate located in the Indian Ocean. Due to itslocation and topographical features it has high rainfall variation throughout the year [5,6]. SriLanka have produced a wide range of topographic features specially three zones aredistinguishable by elevation: the central highlands, the plains, and the coastal belt. Centralhighlands areas of Sri Lanka are cooler and more temperate, with a yearly average around 16-20ºC (60-68ºF), and coastal areas are warmer with average temperatures around 27ºC (80ºF).The March-June season experiences slightly higher temperatures (up to 33ºC / 92ºF), while thetemperatures in November-January are a few degrees lower (around 24ºC / 75ºF at the coast). Ithas a total area of 65,610 km², with 64,740 km² of land and 870 km² of water. Its coastline is1,340 km long.Due to the geographical distribution Sri Lanka is affected with different kinds of naturaldisasters. The most frequent natural hazard that effect Sri Lanka are drought, floods, landslides,cyclones, vector borne epidemics and coastal erosion. During the period 1980 to 2010, 62numbers of natural hazards occurred killing 36,982 people and 17,457,668 were affected. Theeconomic damage per year is averaging to US$ 54,012,000 on average [10].Among all natural disasters, droughts occur most frequently, have the longest duration, and coverthe largest area. During the last 50 years period Hambantota, Moneragala, Puttalam, Kurunegala,Ratnapura, Badulla and Ampara area affected mostly considering number of persons affected[Fig 1,2] [9]. For the 1980-2000 period biggest disaster report was the drought in 1987 whichaffects 2,200,000 people [10]. 3
  4. 4. There have been many researchers carried out on to identify the drought conditions in Sri Lanka.Peries et al., (2007) have analyzed the annual and weekly climate data to provide usefulinformation to farmers, planners, and scientist to assist the suitability of different types of crops.Lyon et al., (2008) analyze the relationship between drought relief payments (a proxy for droughtrisk) and meteorological drought indicators is examined through a retrospective analysis for SriLanka (1960–2000) based on records of district-level drought relief payments and a densenetwork of 284 rainfall stations [1,7]. The study provides an empirical approach to testing whichmeteorological drought indicators bear a statistically significant relationship to drought reliefacross a wide range of tropical climates in that research a correspondence was establishedbetween the spatial distribution of meteorological drought occurrence and historical droughtrelief payments at the district scale. Time series of drought indices averaged roughly over thefour main climatic zones of Sri Lanka showed statistically significant relationships with theoccurrence of drought relief. In Ghosh et al, PP (2007) drought indicator is generated withdownscaled precipitation from available GCMs and scenarios [2].The overarching strategy is to connect global scale predictions and regional dynamics to generateregionally specific drought forecasts. Nesting Sri Lankan regional climate model into an existingGCM is one way to downscale data. Also downscaling climate data through the use of statisticalregression can be use. A third strategy for downscaling data is also statistically driven usingstochastic weather generators.However a less attention was given to downscaling techniques in modeling droughts in SriLanka. Specially, dynamic downscaling requires significant computational resources because itis dependent on the use of complex models. Nevertheless, downscaling techniques havesuccessfully applied in many climate and drought disciplines in the world. Therefore it isproposed to use downscaling global climate model output to fine scale to access drought impact.4) GoalThe goal is to resolve how well the global climate models on downscaling capture variability ofclimate and drought over Sri Lanka and to assess the impact of climate change on drought in thenear-term and the uncertainty associated with these assessments. An associated goal is to set upthe IT and software resources for continued collaborative research after my return to Sri Lanka.5) ObjectivesThe primary objectives of this research are 1. Downscale Global Climate Model hindcast outputs for quarterly seasons over Sri Lanka at fine scales (20 km) using Statistical and Dynamical downscaling approaches for a 30 year historical period. Asses predictability and uncertainty of downscaled simulations for these selected variables 2. Compute drought indices from downscaled data. Assess ability of the hindcasts to capture drought incidents in the historical past. 4
  5. 5. 3. Using the skilful downscaling methodologies, downscale climate change projections for the near-term future from an ensemble of models in the CMIP5 over Sri Lanka. Characterize uncertainty and confidence in the predictions 4. Assess changes in drought tendency based on the downscaled predictions and estimate confidence in future climate change projections characterize drought tendency in future.5) DataSri Lanka Climate:Observed Data: FECT has daily and monthly climatological variables until mid 2000’s. Rainfalldata is available for over 300 stations and a fuller set of observations (temperature, wind,evaporation, humidity, and pressure) are available for 60 stations. These data require someupdating. Monthly gridded precipitation data are available from 1970-2000.Model Output Data: Archives of simulations for 1970-200 using ECHAM4.5 GCM simulationswith RegCM3 downscaling methods prepared by Qian et al., (2010) is available to me.Moreover, the APCC has RegCM4 outputs for a domain that includes Sri Lanka [3]. Downscaledrainfall and streamflow using the Catchment Land Surface Model (Mahanama et al., 2008,Mahanama et al., 2010) are available through FECT [4].In addition the CIMP3 and CIMP5 ensemble of model outputs for the global change scenariosshall be available from IPCC at APCC and other international centers.Drought: Geo-referenced proxy data for drought (relief payments) are available from 1961onwards. Rainfall based drought indices (Standardized Precipitation Index -SPI, WeightedAnomaly Standardized Precipitation Index -WASP) based on the 20-km gridded rainfall data(Lyon et al., 2009) are available [1,9].6) MethodologyAccess to Global Climate Model Outputs: IPCC has the CMIP3 and CMIP5 archives formultiple models. Further APCC archive outputs can be used.Statistical Downscaling using CLIK: CLIK is a downscaling methodology developed byAPCC that uses the observed and predicted data in the historical record to undertake biascorrection. CPT is a downscaling methodology developed at IRI that can be used for thehistorical and future analysis [Fig 3].Dynamic Downscaling using RegCM3/4: RegCM3 model data (archive data 30 year data)which is downscaled for Sri Lanka and surrounding region from 1970 to 2000 produced at IRI isavailable. RegCM4 model outputs for Sri Lankan region are also available from APCC. My 5
  6. 6. work shall be on model output analysis and possibly statistical bias correction from these modeloutputs.Assessing Skill and Uncertainty of Simulated (Hindcast) and Predicted Model OutputsSkill: In order to assess the relative skill of the fitted models, the Heidke skill score (HSS) willbe employed, HSS is commonly used to summarize square contingency tables (Wilks, 1995).The HSS will calculated as follows,Where, Yes No Yes a b No c dFurther ranked probability skilled scores (RPSS) can be used to characterize skill of seasonalpredictions. It is a widely used measure to quantify the skill of ensemble forecasts as it issensitive to the shape and the shift of the predicted probability distributions.Uncertainty : comparison of spread among model ensemble members and among models can beused to characterize uncertainty. In the case of future climate change scenarios, the model inter-comparisons and model skill for different seasons shall be used to guide estimates of uncertaintyspatially.Drought Measures: Meteorological drought indicators such as WASP, and SPI have to beshown to capture drought incidence as well as Palmer Drought Severity Index (PDSI). There is achoice of windows over which drought should be computed – in the case of Sri Lanka, 3-6month is found to be suitable for the bimodal climatology with drought possible in February toApril and June to August. In the historical period, the skill of the model output based on PDSI,WASP and SPI shall be compared against the observed – results shall be presented spatially fortwo seasons of interest.AcknowledgmentsI would like to acknowledge Foundation for Environment, Climate and Technology ClimateTechnologies, APEC Climate Center, Irrigation Department and Department of Meteorology ofSri Lanka and Post Graduate Institute of Science, University of Peradeniya. 6
  7. 7. References[1] Branfield Lyon, Lareef Zubair, Vidhura Ralapanawe, Zeenas Yahiya, “Finescale Evaluationof Drought in a Tropical Setting: Case Study in Sri Lanka”. Manuscript received 23 April 2007,in final form 5 August 2008).[2] Ghosh, Subimal and Mujumdar, “Nonparametric methods for modeling GCM and scenariouncertainty in drought assessment”. Water Resources Research, 43 (W07405). pp. 1-19.[3] Jian Hua Qian and Lareef Zubair., “The Effect of Grid Spacing and Domain Size on theQuality of Ensemble Regional Climate Downscaling over South Asia during the NortheasterlyMonsoon”. Manuscript received 11 August 2009, in final form 9 February 2010.[4] Sarith P.P. Mahanama., Randal D. Koster , Rolf H. Reichle , Lareef Zubair, “The role of soilmoisture initialization in sub seasonal and seasonal stream flow prediction – A case study in SriLanka”.[5] Lareef Zubair and C. F. Ropelewski, “The Strengthening Relationship between ENSO andNortheast Monsoon Rainfall over Sri Lanka and Southern India”. Manuscript received 9 August2004, in final form 9 August 2005.[6] Lareef Zubair, Manjula Siriwardhana, Janaki Chandimalab and Zeenas Yahiyab,“Predictability of Sri Lankan rainfall based on ENSO”. International Journal Of Climatology Int.J. Climatol. 28: 91–101 (2008).[7] Zubair, L., V. Ralapanawe, Z. Yahiya, R. Perera, U. Tennakoon, J. Chandimala, S. Razickand B. Lyon. “Fine Scale Natural Hazard Risk and Vulnerability Identification Informed byClimate in Sri Lanka”. Project Report: Foundation for Environment, Climate and Technology,Digana Village, August 2011.[8] Drought, Sri Lanka:,12thMarch 2012[9] Sri Lanka Disaster Statistics:, 12th March 2012 7
  8. 8. Figures Fig 1: Drought Hazardous Index 8
  9. 9. Fig 2: Proxy drought risk map for Sri Lanka based on the frequency (and size) of historical drought relief payments. The figurewas constructed by summing the number of drought relief payments made to each district (1960–2000) after assigning them anumeric value depending on the category of drought as determined by the Department of Social Services. Major droughts areassigned a value of 1.5, medium droughts are given 1.0, and minor droughts are assigned 0.5. 9
  10. 10. Fig 3: Statistical Downscaling for GCM 10