ª Œª Œ 29ƒ 5B Á 2009 9œ pp. 441 ~ 452 ª x w GCM» w Downscaling Technique of Monthly GCM Using Daily Precipitation Generator Á»Á½x Kyoung, Min SooÁLee, Jung KiÁKim Hung Soo Abstract This paper describes the evaluation technique for climate change effect on daily precipitation frequency using daily precipitation generator that can use outputs of the climate model offered by IPCC DDC. Seoul station of KMA was selected as a study site. This study developed daily precipitation generation model based on two-state markov chain model which have transition probability, scale parameter, and shape parameter of Gamma-2 distribution. Each parameters were estimated from regression analysis between mentioned parameters and monthly total precipitation. Then the regression equations were applied for computing 4 parameters equal to monthly total precipitation downscaled by K-NN to generate daily precipitation considering climate change. A2 scenario of the BCM2 model was projected based on 20c3m(20th Century climate) scenario and difference of daily rainfall frequency was added to the observed rainfall frequency. Gumbel distribution function was used as a probability density function and parameters were estimated using probability weighted moments method for frequency analysis. As a result, there is a small decrease in 2020s and rainfall frequencies of 2050s, 2080s are little bit increased. Keywords : climate change, downscaling, K-NN, daily precipitation generator, frequency analysis IPCC DDC mw œ»z x k w x wš, w»z yƒ e wsƒ»» w wš w. w x 2 state g x», j v w y Gamma-2 s ³ x z w mw. z»z x l K-NN w w»z yƒ š g.»z x BCM2 x w, 20c3m ù» ù w A2 ù w d w. w w sx Gumbel s w, w y ƒ p w. 2020s, 2050s, 2080s ƒw. w :»z y,», K-NN, x, w 1. ùy w»z y y ù ƒ w ƒ ƒ j w,, k, w,,, w eš. p ù y ƒ w vw l j» w ƒ»z y w wš, w ewš»z y w ƒ y w w.»z y ƒ e w sƒw» w w ƒ w w.»z yƒ e wsƒ w GCM ù RCM»z x š. ù w GCM, RCM œ, wù j»ƒ 300~500 km w.»z z Á Á w w z» lœw (E-mail : gigatg@inha.ac.kr) z Á w w z» lœw (E-mail : jungki5425@hanmail.net) z Á w w z» lœw Áœw (E-mail : sookim@inha.ac.kr) 29ƒ 5B 2009 9œ 441
x y w» w œ» w w. w» j w» (dynamical downscaling) m w» (statistical downscaling) ù. w»»z x(rcms, Regional Climate Models) ù LAMs(Limied Area Models), GCMs(General Circulation Models) w y šw»z ù š w. w», x ù w w ƒ wù w.»z y RCMs w»z yƒ w e w w ƒ Flower et al.(2005a), Ekstrom et al.(2005), Frei et al.(2006) w. ù w RCMs, 30 ƒ control run 1961-1990, projection run 2070-2100. w w w transient RCMs š (Flower et al. 2007a). m w» t z x, weather typing,»»(weather generator). z x, Giorgi and Hewitson(2001) w w (Transition function) w., (Predictors) d (Predictand) yw w, w y w w w. ƒ»»» t ù w Hanssen-Bauer and Førland(1998), Hellström et al.(2001) w, œ (ANN, artificial neural network)» w ù (CCA, canonical correlation analysis) w w. Weather typing t p w» x w»» s w w» Goodess and Palutikof (1998), Conway et al.(1996), Flower et al.(2000, 2005b), Bardossy et al.(2002, 2005)., ƒ w,»» w y de w w, x w (EOF, empirical othogonal function)ù r,» w.»» w» ƒ ƒ p w w ƒ y y š m w» wù, w»» j y y w w j ƒ w w»k,, w ù.»z yƒ š» j» w x w»z yƒ e w sƒw» w wš w. w IPCC (Intergovernmental Panel on Climate Change) DDC(Data Distribution Centre) œw»z x Fig. 1 Spatial and temporal downscaling scheme BCM2 x (2009) w KNN w w z, w x w w ww. w œ» w 1. 2. BCM2 x xy 2.1 BCM2 x BCCR(Bjerknes Centre for Climate Research) BCM2(Bergen Climate Model Version) x» x ARPEGE w x MICOM ww»/w w x 2005. -90 o 90, 0-360 o sww x IPCC DDC mw GRIB, netcdfxk œ. IPCC»z y x œw» w DDC wš, IPCC 4 š wì AR4 ù» x sww 2, 3 š ù PCMDI (Program for Climate Model Diagnosis and Intercomparison) l œwš. AR4 ù A1, A2, B1 ù SRES(Special Report on Emissions Scenarios) commit, PIcntrl, 20c3m, 1%2, 1%4 ù ƒ sw ( ù w http://www.ipcc-data.org/sres/ddc_sres_emissions. html#pictl). 20c3m» ù w A2 ù š w 2010~2039(2020s), 2040~2069 (2050s), 2070~2099(2080s) y dw. 20c3m, A2 ù sww AR4 ù» w 2, BCM2 x w Fig. 2 Experiment period of BCM2 442 ª Œª Œ
Fig. 3 Anormaly in o C from mean of 1951-1980 from 20c3m and A2 Table 1. IPCC DDC AR4 data Symbol variable Unit huss surface specific humidity kg/kg orog surface altitude m pr convective precipitation flux kg/m 2 /sec psl sea level pressure Pa rsds surface downwelling shortwave radiation W/m 2 sftlf land area fraction % tas surface monthly average Tmean K tasmax surface monthly average Tmax K tasmin surface monthly average Tmin K uas vas zonal surface wind speed (eastward wind speed) meridional surface wind speed (northward wind speed) m/s m/s Fig. 4 Coordination of NCEP grid around the Korea y (20c3m and A2) 3. AR4 ù t 1» œw. 2.2 NCEP w» w (NOAA ESRL-PSD, Physical Sciences Division) ú»z l ww»»z, ú, Á» w w wš.»,, «y»z ú w, ú j w ÿ,»z x d wš. w»»z y ql wš wš,»z y w w w v w»z» š. w wùƒ d l š w. PSD yr mw NCEP reanalysis data sww 29 d l œwš.» w NCEP/NCAR Reanalysis NOAA National Center for Environment Prediction l œ PSD wš.»z x d w»z x» d. ù»z x, w w w d d»ƒ ƒ w. œ NCEP/NCAR Reanalysis ƒ v w., NCEP/NCAR Reanalysis d w»z x» w» z x d ƒ ƒ w. w NCEP/NCAR Reanalysis,»z x w» w» de œw. x ¾ w» š, w» w»z x mw» NCEP/NCAR Reanalysis mw». 4 w NCEP Reanalysis w. 2.3»» 77 d 1961 l x ¾» œwš ( d d w), v w w., 1 d wš 3 d œwš.» mw œ» 9 ( s³»,»,», 29ƒ 5B 2009 9œ 443
, s³t,,,, ú ). w» x mw» d 1961 d w x š, p ew š.» d d m p t 3 w. 3. K-NN w œ» 3.1 K-NN w» (2009) w K-NN w BCM2 x w., x w e,, t, ƒ w e š ƒ w,,, t, ƒ ƒ ƒ w š ƒ w w. K-NN w» w» w, d NCEP œw» wš w» w w w. w NCEP BCM2 x 5 w. w p wš. ù»z x BCM2, 20c3m, A2 ù ƒ (Precipitable water) sƒ Fig. 5 Atmospheric variable of NCEP and 20c3m and A2 in BCM2 444 ª Œª Œ
Seoul Station Monthly total prec.(mm) Table 2. Correlation coefficient of NCEP and Seoul station data Tas ( o C) Tmax ( o C) Tmin ( o C) NCEP huss (kg/kg) uas (m/s) vas (m/s) Prec. water (kg/m 2 /sec) 0.6021 0.5709 0.6365 0.7164-0.4324 0.5935 0.7471 õncep data (monthly average Tmean, monthly average Tmax, monthly average Tmin, specific humidity, eastward wind speed, northward wind speed, precipitable water) Fig. 6 Calibration Result Fig. 7 Validation results. w» mw» w NCEP s³,, š,, uas, vas w (t 2)., NCEP» ü ƒ. ù» w» w NCEP»z x p š w w w w. w,, s³ w, ƒ û 11 ƒ w ƒ w ƒ w w. w ƒ w sw j, A2 ù 20c3m, ƒ w w ƒ w p xw w y w.,, uas, vas K-NN w» w» w 20c3m A2 ù w w BCM2 x l w. 3.2 K-NN w» K-NN w BCM2 x œw A2 ù 20c3m ù ƒƒ w. w ƒƒ». : 1979~2008 NCEP : 1979~2008 20c3m ù : 1950~1999 A2 ù : 2000~2099 K-NN w Nearest Neighbor 9 6, 7. K-NN, Nearest Neighbor š w s³w w». w ù. ù ƒ ƒ š w y w. w r w» w 29ƒ 5B 2009 9œ 445
Fig. 8 Concept of quantile mapping(½, 2008) Quantile Mapping» y w. 3.3 Quantile mapping»z y x l d r. w r w» w ƒ Panofsy and Brire(1963) w Quantile mapping. Quantile mapping w (Wood et al., 2004; Hashino et al., 2007),»z y w Durman et al.(2001), Palmer et al.(2004), Fowler et al.(2007b) GCM w. ü ½ (2008), (2009) r w» w. w Quantile mapping 8. Quantile mapping w K-NN w 20c3m(1961~1999) ù» w d de»z x w w r w z, w BCM2 x 20c3m ù A2 ù l r CASE w w. Fig. 9 Box plot(obs. vs Case1) CASE 1(1980s) : 1970~1999(20c3m, Reference period) CASE 2(2020s) : 2010~2039(A2, Projection period) CASE 3(2050s) : 2040~2069(A2, Projection period) CASE 4(2080s) : 2070~2099(A2, Projection period) CASE m p s (t 3. 9~12). Fig. 10 Box plot(case1 vs Case2) BCM2 l ù ƒ de m e w ƒ š y w,, 2020s, 2050s 150 mm ƒwš Table 3. Statistical characteristic of observed and quantile mapped data (mm) Observation CASE1 CASE2 CASE3 CASE4 Annual precipitation 1,418.3 1,436.6 1,568.8 1,575.6 1,701.1 Monthly precipitation 118.2 119.7 130.7 131.3 141.8 Variance 25,135 26,878 27,711 30,673 28,925 Maximum 1237.8 1237.8 1154.0 1212.3 1153.7 Minimum 0 0 2.666667 2.116667 1.733333 Skewness 2.904837 2.989338 2.61406 2.824107 2.464253 446 ª Œª Œ
Fig. 11 Box plot(case1 vs Case3) Fig. 12 Box plot(case1 vs Case4) 2080s 300 mm ƒw y w. w 7 wš 8 ƒ ƒw y w. 4. x»» x d m w p w w j. w»» j y y w w j ƒ w w»k,, w ù. w Richardson(1981) Richardson-type š,,, y y w š.»z y w Wilks (1992). ù w»» ¼»»» j w ƒ š» w w w» w š Markov chain(mason, 2004; Dubrovsky et al. 2004) w» w. NSRPM(Neyman Scott rectangular pulses model) Watts et al.(2004) w» w j ww x Kilsby et al.(2007),» Markov chain x w ù w x ù. x»z y w ƒ y w š x Racsko et al.(1991) l»» x LAR-WG w. Semenov et al.(1998) Richardson-type»» LAR-WGƒ s³ ù w y w. ù ¾ x s³ ù y xw w ƒ š.» w x w» d p» xw š w. x d w x w z, BCM2 x l w» z yƒ e w sƒw wš w. 4.1 x» w» w w w w j» w x v w. w x» x 2-state markov chain. j 2 w w ù. w y». M M M P ij = Pr X k + 1 = j X k = i ( ) M» P ij w w y ùkü 2 2xk p xk, w w y w» w y ùkü. Markov chain x j, 2 Gamma-2 s w. Gamma-2 s w g. w Gamma-2 s w «xw ½ (2009) markov chain x w» w. f x ( ) 1 = -----------------x β 1 α β Γ( β) e ( x α)», x, α, β 0 j, α ³ (Scale Parameter) x (Shape parameter) ùkü. w v w α, β, NR (Transition probability of Rain after No rain), RR(Transition probability of Rain after Rain) mw, w d l z mw ³ w. ù z w α, β, NR, RR w x w, ƒ w ƒ w. w w w» w ƒƒ w w» yw (1) (2) 29ƒ 5B 2009 9œ 447
Fig. 13 Regression analysis between monthly total precipitation vs parameters ƒƒ w w 4 w w z w w w. ƒ ƒ z 13. mw x 13 z k»z x l w w w z, w w w j. 4.2 x x w w w z, w» w w d w w. w x, j. w s³ œwš j 500 w z, 500 w w s³ ü w. s³ d s³ w. 14 x d ù ƒ. w ƒ ù» d ƒ ƒ š w Fig. 15 Monthly total precipitation of Obs. and Model Fig. 14 Averaged monthly total precipitation of Obs. and Model. s³ d w 15 mw y ƒ w. w z w ƒƒ y w ƒ x w y w w d x y kœ (State space) w d ƒ ƒ š w x x w y w ( 16). 448 ª Œª Œ
Fig. 16 State space between transition probabilities of Obs. and Model x y w w ww mw» w d w w ww x w w ww 30 30 ù 2z w. w w s x Gumbel sx w š y ƒ p w w. Part I : 1961~1990( 30 ) Part II : 1978~2007( 30 ) Fig. 18 Frequency analysis results(part II) Table 5. Modeling error(part II) Fig. 17 Frequency analysis results(part I) Table 4. Modeling error(part I) yr Obs1 Model1 (%) 2 138.2 126.87 8.19 3 164.2 153.68 6.40 5 193.2 183.54 4.99 10 229.7 221.07 3.75 20 264.7 257.06 2.88 30 284.8 277.76 2.46 50 310 303.65 2.04 70 326.5 320.61 1.80 80 333 327.34 1.69 100 343.9 338.56 1.55 150 363.7 358.91 1.31 200 377.7 373.34 1.15 yr Obs2 Model2 (%) 2 150.8 135.0216 10.46314 3 180.7 165.0692 8.650162 5 214.1 198.5355 7.269724 10 255.9 240.5873 5.983873 20 296.1 280.9243 5.125189 30 319.2 304.1298 4.721243 50 348.1 333.1365 4.298625 70 367.1 352.1527 4.071723 80 374.6 359.6871 3.981009 100 387.1 372.2622 3.833077 150 409.8 395.0745 3.593345 200 425.9 411.2451 3.440932» 5%. w d w w w w w w j ƒ.» x w ƒ w ƒw.»z y w w sƒw» w» z x l d w 29ƒ 5B 2009 9œ 449
Perturbation» wš. mw x»z y w» w x w» (Part I) x» (Part II)» (Part I) d w x» (Part II) w w d w x w v w. t 6 w w. x»z y w w m ww 2-3% yw, ƒ ƒw w» z yƒ ü w e w sƒw» w w x j ƒ ƒw. 4.3»z y š w w»z yƒ e w sƒw» w x w K-NN w BCM2 x l w w ƒƒ Case 500z w. w l e 500set w w ww z, s³ w. ƒƒ Case (Case2, Case3, Case4)» ù (Case1) w w x d w 2020s, 2050s, 2080s w. ƒ (2020s) ƒ (2050s) Fig. 21 Comparison of climate scenarios (CASE1,3) Fig. 22 Comparison between present and climate change (case3) ƒ (2080s) Fig. 19 Comparison of climate scenarios (CASE1,2) Fig. 23 Comparison of climate scenarios (CASE1,4) Fig. 20 Comparison between present and climate change (case2) Fig. 24 Comparison between present and climate change (case4) 450 ª Œª Œ
Freq. Model (past) (1) Table 6. Applicability of daily precipitation generator to climate change study Model (present) (2) Model P (3)=(2)-(1) Observation (past)(4) Estimated present (5)=(4)+(3) Observation (present) (6) Error(%) ((6)-(5))/(6) 2 126.88 135.02 8.15 138.20 146.35 150.80 3.04 3 153.69 165.07 11.38 164.20 175.58 180.70 2.91 5 183.55 198.54 14.99 193.20 208.19 214.10 2.84 10 221.07 240.59 19.52 229.70 249.22 255.90 2.68 20 257.06 280.92 23.86 264.70 288.56 296.10 2.61 30 277.77 304.13 26.36 284.80 311.16 319.20 2.58 50 303.65 333.14 29.49 310.00 339.49 348.10 2.54 70 320.62 352.15 31.53 326.50 358.03 367.10 2.53 80 327.34 359.69 32.35 333.00 365.35 374.60 2.53 100 338.56 372.26 33.70 343.90 377.60 387.10 2.52 150 358.92 395.07 36.16 363.70 399.86 409.80 2.49 200 373.35 411.25 37.90 377.70 415.60 425.90 2.48 Table 7. Increasing and Decreasing percentage of rainfall frequency by climate change 2020s 2050s 2080s 2 2.98 3.45 7.25 3 1.04 3.19 6.19 5 0.39 3.18 5.50 10-0.18 2.71 4.01 20-0.76 2.84 3.25 30-1.22 2.75 2.94 50-1.20 2.68 2.92 70-1.04 2.69 2.81 80-1.20 2.73 2.86 100-1.33 2.77 2.87 150-1.39 2.97 2.74 200-1.56 3.07 2.74 2020s ƒ ƒw x w w w. 2050s ƒw, 200 3.07% ƒw y w. 2080s 2050s w ƒw y w. 5.»z yƒ e w sƒw» w»z x BCM2 x» w» w w. w d NCEP BCM2 x K-NN w w z, x w y š w. y ƒ p w w z, GUMBEL s w w ww»z yƒ e w sƒw. mw BCM2 A2 ù 20c3m ù ƒ wš K-NN x y w š»w.»z x w», w»z x» ÿ ƒ ùkú». wz BCM2 x w x š w»z x y yw» w ƒ v w ƒw. m w m» sƒ»z» y w w 2005»» (05-» -D03-01) w. š x,, ½x, ½ (2009)»z yƒ w» e w sƒ; AR4 SRES A2 ù», wm wz, wm wz, 29«2By, pp. 267-276. «xw, ½ (2009) Markov Chain Model w m w Downscaling», w wz, w wz, 42«3y, pp. 213-225. ½, ½,, ½x (2008)»z yƒ w I- D-F e w sƒ. w wz, w wz, 41«4y, pp. 379-394. Bárdossy, A., Bogardi, I., and Matyasovszky, I. (2005) Fuzzy rulebased downscaling of precipitation. Theoretical and Applied Climatology, Vol. 82, No. 1-2, pp. 119-129. Bárdossy, A., Stehlík, J., and Caspary, H.J. (2002) Automated objective classification of daily circulation patterns for precipitation and temperature downscaling based on optimized fuzzy rules. Climate Research, Vol. 23, No. 1, pp. 11-2. Conway, D., Wilby, R.L., and Jones, P.D. (1996) Precipitation and air flow indices over the british isles. Climate Research, Vol. 7, No. 2, pp. 169-183. 29ƒ 5B 2009 9œ 451
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