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

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.
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2. Table of Content
1) Abstract 3
2) Introduction 3
3) Goal 4
4) Objectives 4
5) Date 5
6) Methodology 5
7) Acknowledgement 6
8) List of References 7
9) List of Figures 8
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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) Introduction
Sri Lanka is an island with tropical monsoon climate located in the Indian Ocean. Due to its
location and topographical features it has high rainfall variation throughout the year [5,6]. Sri
Lanka have produced a wide range of topographic features specially three zones are
distinguishable by elevation: the central highlands, the plains, and the coastal belt. Central
highlands 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 the
temperatures in November-January are a few degrees lower (around 24ºC / 75ºF at the coast). It
has a total area of 65,610 km², with 64,740 km² of land and 870 km² of water. Its coastline is
1,340 km long.
Due to the geographical distribution Sri Lanka is affected with different kinds of natural
disasters. 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, 62
numbers of natural hazards occurred killing 36,982 people and 17,457,668 were affected. The
economic 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 cover
the 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 which
affects 2,200,000 people [10].
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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 useful
information 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 drought
risk) and meteorological drought indicators is examined through a retrospective analysis for Sri
Lanka (1960–2000) based on records of district-level drought relief payments and a dense
network of 284 rainfall stations [1,7]. The study provides an empirical approach to testing which
meteorological drought indicators bear a statistically significant relationship to drought relief
across a wide range of tropical climates in that research a correspondence was established
between the spatial distribution of meteorological drought occurrence and historical drought
relief payments at the district scale. Time series of drought indices averaged roughly over the
four main climatic zones of Sri Lanka showed statistically significant relationships with the
occurrence of drought relief. In Ghosh et al, PP (2007) drought indicator is generated with
downscaled precipitation from available GCMs and scenarios [2].
The overarching strategy is to connect global scale predictions and regional dynamics to generate
regionally specific drought forecasts. Nesting Sri Lankan regional climate model into an existing
GCM is one way to downscale data. Also downscaling climate data through the use of statistical
regression can be use. A third strategy for downscaling data is also statistically driven using
stochastic weather generators.
However a less attention was given to downscaling techniques in modeling droughts in Sri
Lanka. Specially, dynamic downscaling requires significant computational resources because it
is dependent on the use of complex models. Nevertheless, downscaling techniques have
successfully applied in many climate and drought disciplines in the world. Therefore it is
proposed to use downscaling global climate model output to fine scale to access drought impact.
4) Goal
The goal is to resolve how well the global climate models on downscaling capture variability of
climate and drought over Sri Lanka and to assess the impact of climate change on drought in the
near-term and the uncertainty associated with these assessments. An associated goal is to set up
the IT and software resources for continued collaborative research after my return to Sri Lanka.
5) Objectives
The 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.
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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) Data
Sri Lanka Climate:
Observed Data: FECT has daily and monthly climatological variables until mid 2000’s. Rainfall
data 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 some
updating. Monthly gridded precipitation data are available from 1970-2000.
Model Output Data: Archives of simulations for 1970-200 using ECHAM4.5 GCM simulations
with 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]. Downscaled
rainfall 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 scenarios
shall be available from IPCC at APCC and other international centers.
Drought: Geo-referenced proxy data for drought (relief payments) are available from 1961
onwards. Rainfall based drought indices (Standardized Precipitation Index -SPI, Weighted
Anomaly Standardized Precipitation Index -WASP) based on the 20-km gridded rainfall data
(Lyon et al., 2009) are available [1,9].
6) Methodology
Access to Global Climate Model Outputs: IPCC has the CMIP3 and CMIP5 archives for
multiple models. Further APCC archive outputs can be used.
Statistical Downscaling using CLIK: CLIK is a downscaling methodology developed by
APCC that uses the observed and predicted data in the historical record to undertake bias
correction. CPT is a downscaling methodology developed at IRI that can be used for the
historical 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 is
available. RegCM4 model outputs for Sri Lankan region are also available from APCC. My
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6. work shall be on model output analysis and possibly statistical bias correction from these model
outputs.
Assessing Skill and Uncertainty of Simulated (Hindcast) and Predicted Model Outputs
Skill: In order to assess the relative skill of the fitted models, the Heidke skill score (HSS) will
be 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 d
Further ranked probability skilled scores (RPSS) can be used to characterize skill of seasonal
predictions. It is a widely used measure to quantify the skill of ensemble forecasts as it is
sensitive to the shape and the shift of the predicted probability distributions.
Uncertainty : comparison of spread among model ensemble members and among models can be
used 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 uncertainty
spatially.
Drought Measures: Meteorological drought indicators such as WASP, and SPI have to be
shown to capture drought incidence as well as Palmer Drought Severity Index (PDSI). There is a
choice of windows over which drought should be computed – in the case of Sri Lanka, 3-6
month is found to be suitable for the bimodal climatology with drought possible in February to
April 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 for
two seasons of interest.
Acknowledgments
I would like to acknowledge Foundation for Environment, Climate and Technology Climate
Technologies, APEC Climate Center, Irrigation Department and Department of Meteorology of
Sri Lanka and Post Graduate Institute of Science, University of Peradeniya.
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7. References
[1] Branfield Lyon, Lareef Zubair, Vidhura Ralapanawe, Zeenas Yahiya, “Finescale Evaluation
of 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 scenario
uncertainty 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 the
Quality of Ensemble Regional Climate Downscaling over South Asia during the Northeasterly
Monsoon”. 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 soil
moisture initialization in sub seasonal and seasonal stream flow prediction – A case study in Sri
Lanka”.
[5] Lareef Zubair and C. F. Ropelewski, “The Strengthening Relationship between ENSO and
Northeast Monsoon Rainfall over Sri Lanka and Southern India”. Manuscript received 9 August
2004, 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. Razick
and B. Lyon. “Fine Scale Natural Hazard Risk and Vulnerability Identification Informed by
Climate in Sri Lanka”. Project Report: Foundation for Environment, Climate and Technology,
Digana Village, August 2011.
[8] Drought, Sri Lanka:http://www.priu.gov.lk/news_update/Drought/Statistics.htm,12thMarch 2012
[9] Sri Lanka Disaster Statistics:
http://www.preventionweb.net/english/countries/statistics/?cid=162, 12th March 2012
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8. Figures
Fig 1: Drought Hazardous Index
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9. Fig 2: Proxy drought risk map for Sri Lanka based on the frequency (and size) of historical drought relief payments. The figure
was constructed by summing the number of drought relief payments made to each district (1960–2000) after assigning them a
numeric value depending on the category of drought as determined by the Department of Social Services. Major droughts are
assigned a value of 1.5, medium droughts are given 1.0, and minor droughts are assigned 0.5.
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10. Fig 3: Statistical Downscaling for GCM
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