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Metody odhadu budoucích klimatických podmínek II Globální cirkulační modely, jejich výsledky a odhad budoucích klimatických podmínek pro střední Evropu.

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Presentation on theme: "Metody odhadu budoucích klimatických podmínek II Globální cirkulační modely, jejich výsledky a odhad budoucích klimatických podmínek pro střední Evropu."— Presentation transcript:

1 Metody odhadu budoucích klimatických podmínek II Globální cirkulační modely, jejich výsledky a odhad budoucích klimatických podmínek pro střední Evropu Martin Dubrovský

2 Úvod 1.1.

3 dopady změny klimatu na výnosy - metodologie notes:- multi-year simulations are made to assess mean and variability - other models may be used instead of the crop model - crop model is assumed to be calibrated and set to deal with variable climate conditions (e.g. “automatic” sowing day is used )

4 problém: Jak získat lokální meteorologické řady reprezentující budoucí klima? - GCM? + v současnosti nejlepší dostupný nástroj k simulaci budoucího klimatu + množství GCM simulací současného i budoucího (i minulého) klimatu je volně dostupné na webu (např. databáze IPCC) ale: ? Jaká je kvalita výstupů z GCM ? ? Lze výstup z GCM použít jako vstup do impaktového modelu?

5 přímý výstup z GCM? Ne! low resolution & smoothed relief implies: -shores are smoothed → no Sardinia, no Italy! -lower mountains sub-grid phenomena (e.g. convection) are not adequately simulated and must be parameterised statistical structure of site-specific surface weather is misreproduced: -strong biases -different annual cycle -different shape of PDFs (e.g. PREC) -different persistence (~ day-to-day variability) grids of AR3 models … we have (large-resolution GCM output) … we need (site-specific surf.wea.series) downscaling the gap between what…. is bridged by downscaling (in space and/or in time)

6 konstrukce lokálních meteo řad (reprezentujících současné a budoucí klima) pomocí “downscalingu” GCM výstupů 1.dynamical downscaling (GCM → RCM): Regional Climate Models (RCMs) are more detailed than GCMs, but direct use of RCM output is still unsatisfactory (RCM-based series should be post-processed ( I have some slides to document the RCM-OBS misfit…) 2.statistical downscaling (= estimating surface weather with empirical- statistical relationships between –larger-scale ”free-air” circulation characteristics –surface weather 3.our favourite approach: using the weather generator, -which is calibrated using observed weather series, and -whose parameters are modified according to the GCM-based climate change scenario [ CCS = changes in selected surface climatic characteristics (AVG, STD, …), typically for individual months]

7 stochastický meteo generátor + scénáře změny klimatu 2 + 3

8 stochastické meteo generátory: úvod now, the same task: produce site specific weather series representing future climate… WGs are often regarded as one of the SDS techniques… similarities: –it relies on statistics (rather than physics-based equations used in GCMs and RCMs) –it produces site specific (or area-specific) surface weather series differences: –to calibrate WG, you need only observed variables required by the impact model, so that –it does not need circulation characteristics (it rather relies on a fact that the circulation regime is inherently reflected in a structure of surface weather series) –stress on the stochastic structure of the surface weather series

9 meteo generátor observed weather ::: time Y(3) Y(n) f(Y prev, e; P=?) Y(2) Y(1) Climate Change Scenario synthetic weather NOW ::: Y(3) Y(n) f(Y prev, e; P) Y(2) Y(1) synthetic weather FUTURE ::: Y(3) Y(n) f(Y prev, e; P*) Y(2) Y(1) P P* Y = vector of surf. wea. variables Y prev = Y on previous day(s) e = random vector P = vector of WG parameters

10 použití sWG při konstrukci meteo řad reprezentujících budoucí klima Weather generator = mathematical model, which generates synthetic weather series, which is statistically similar to the observed series. statistical similarity = major statistical features are the same: AVG, VAR, MAX/MIN, annual cycle, correlations between variables, lag-correlations,... pros: -arbitrary long wea. series -may be interpolated!!! -needs less GCM data -various characteristics may be modified -may be used even for climates not simulated by GCM (using pattern scaling method) con: no WG model is perfect… Climate change scenario = changes in major climatic statistics (monthly, seasonal, annual); may include changes in Variability Ex = present “climate” d(t) = clim.change scenario Ex’ = future climate Scheme: v.1999 scenario

11 stochastické meteo generátory part 2

12 stoch. meteo generátory: jak fungují příklad: M&Rfi (parametrický meteo generátor) M&Rfi weather generator (~ Richardson’s WGEN) –generates daily time series –4 surface weather characteristics (though it can do much more…), which are mostly required as an input to the crop growth models (including DSSAT crop models): PREC, TMAX, TMIN, SRAD each term is generated in three steps: 1. generation of precipitation occurrence ~ 1 st order Markov chain 2. (if the day is wet) generation of precipitation amount: PREC ~ Gamma dist. 3. generation of (SRAD, TMAX, TMIN) ~ AR(1) model

13 stoch. meteo generátory: jak fungují krok 1: generování výskytu srážek: –let P(wet) = 0.35 is a probability of wet day occurrence –generate random number X with uniform distribution Prob(X if X  P(wet) → wet day if X > P(wet) → dry day wet daydry day –to account for the persistence, P(wet) may depend on the previous days’ wet/dry status: P(wet) = P 01 if yesterday was dry P 11 if yesterday was wet n-order Markov chain account for n previous days Markov chain of 1st order) }

14 stoch. meteo generátory: jak fungují krok 1: generování výskytu srážek: Markov chain generator of wet/dry days: a)Pwet = 0.5 ( = P 01 = P 11 ) >>> no persistence!!! b)Pwet = 0.5 ; P 01 = 0.1, P 11 = 0.9 (strong persistence; 2 realizations are shown):

15 stoch. meteo generátory: jak fungují krok 2: generování množství srážek –assumption: PREC ~ Gamma(a,b) PREC R random number with uniform distribution on

16 stoch. meteo generátory: jak fungují krok 3: generování dalších prvků X = (TMAX, TMIN, SRAD) –assumption: X i * ~ N(0,1) where X i * is standardized: X i - avg(X i ) X i * = std(X i ) –to account for the dependence of X on PREC: avg(X) and std(X) are determined separately for wet and dry days –to account for the lag-0 and lag-1 correlations, AR model is used: X* = TMAX*, TMIN*, SRAD* R ( random number with uniform distribution X*(t) = AX*(t-1) + Be A and B are 3x3 matrices e is a 3D white noise corelations between SRAD, TMAX, TMINpersistence

17 stoch. meteo generátory – hlavní vlastnosti spatial resolution: –single-site (OK for crop growth model; example: M&Rfi) –multi-site or spatially continuous (required in hydrological modelling) temporal resolution (~time step) –hourly –daily –monthly –M&Rfi: optional time step = 1,2,3,5 days, 1w, 10d, 2w, ½mo, 1mo) number of variables –single-variate –multi-variate (CERES: 4 vars; WOFOST: 6 vars) –M&Rfi: optional (up to 8) conditioning of WG on circulation –stand-alone surface weather generator (M&Rfi, WGEN, LARS-WG) –conditioned on circulation parametric vs. non-parametric –parametric: WGEN, SIMMETEO, M&Rfi –semi-parametric (Semenov: LARS-WG) –non-parametric (nearest neighbours resampling)

18 parametrické meteo generátory structure of the weather series is represented by a model defined by parameters, which are derived from observed series: –distributions of variables: Gamma, Gauss, exponential, … –persistence and correlations between variables : Markov chains (1 st or higher order) autoregressive models annual cycle: Fourier series examples: WGEN, Met&Roll, M&Rfi, …

19 algorithm: generátor založený na resamplingu learning SRAD TMAX TMIN RAIN... xx xx xx xx xx xx xx xx xx synthetic SRAD TMAX TMIN RAIN choose the first term: randomly from all terms close to 1 st January  10 days) ,3,…. choose the new term: a) choose term(s), which are close (distance ~ Mahalanobis) to the previous term, b) the new term is a follower of the selected term PROs: no assumption on distribution of variables CONs:- much slower (than the parametric model) - problem: how to implement climate change scenario? - non-interpolable

20 example: LARS-WG (by Semenov) main differences from parametric WG: –wet and dry periods modelled by empirical distribution –PDFs of variables are not described by parametric distributions, but rather by empirical distributions, which represented by a set of percentiles (23 used in LARS-WG; compare with 2 parameters for Normal or Gauss) PRO (wrt papametric WG): better treatment of non-normal variables CON: size of the learning sample is more critical ← more parameters are determined from the learning data semi-parametrický meteo generátor X ~ empirical PDF R ~ U(0,1) references: Semenov + his web page, …

21 generátor podmíněný na cirkulaci WT(1), X(1) WT(2), X(2) WT(3), X(3) WT(n), X(n) Y(1) Y(2) Y(3) Y(n) downscaled surface “weather” GCM(1) GCM(2) GCM(3) GCM(n) ::: f(X|WT) time GCM “raw” output predictors …. the Statistial Downscaling scheme (shown earlier):

22 generátor podmíněný na cirkulaci Y(1) Y(2) Y(3) Y(n) downscaled surface “weather” ::: f(X|WT) time predictors 1.learning time series: {WT(i), Y(i)} i=1..n 2.calibration of WG from the L.series: f(X|WT) parameters of WT time series transitional probabilities (if we use set of weather types to characterize circulation) parameters of AR model (if the circulation pattern is characterised by PC scores) 3.applying WG: a) generation of WT series b) generation of surface wea. series PRO: circulation is explicitely involved CON: we need future GCM simulation to calibrate the WG’s circulation component > limited possibilities for uncertainty analysis WT(1) WT(2) WT(3) WT(n) references: Bardossy, …

23 meteo generátor M&Rfi … volně k dispozici na webu:

24 M&Rfi – historie (M&Rfi = Met&Roll flexible and improved) * 1995: first version of Met&Roll (based on WGEN [Richardson, 1981]) to be used with CERES crop models since 1995: improvements of the model –Markov(1) > Markov(3) –conditioning on monthly WG : interpolation 2007: M&Rfi developed (thanks to Juergen Grieser’s initiative for FAO!) –many new features with respect to Met&Roll (on a separate slide) Met&Roll / M&Rfi applications: -crop growth modelling (together with MUAF) -climate change impact studies -probabilistic seasonal crop yield forecasting -> PERUN system /2001/) -climate change impacts on soil climate, pests & diseases, … -hydrological modelling

25 Met&Roll = 4-variate stochastic daily weather generator: step 1: PREC occurrence ~ Markov chain (order: 1-3; parameters: trans.prob.) Prob(PREC(t)>0) = P01 if PREC(t-1) = 0 P11 if PREC(t=1) = 1 step 1b (only if PREC(t)>0) : PERC. amount ~ Gamma distribution (parameters: α, β /~ shape, scale/) step 2: X = (X 1, X 2,... ) ~ AR(1) model (parameters: A, B, avg(X i ), std(X i ),) X*(t) = AX*(t) + Be where X i = [SRAD, TMAX, TMIN] X* i = [X i – avg(X i )] / std(Xi) avg(Xi)] and std(Xi): differ for sry / wet days e = white noise A, B = [3x3] matrices - all parameters are assumed to vary during the year - daily WG is linked to AR(1)-based monthly WG (to improve low-frequency variability) Met&Roll / M&Rfi : model

26 M&Rfi – hlavní vlastnosti optional number of variables (<=8) [typically 3 or 4: (PREC, SRAD, (TMAX + TMIN) or (TAVG + DTR) or TAVG) optional time step (1d, 3d, 5d, 1w, 10d*, 2w, ½m, 1m) 1 variable (PREC) is optionally “the conditioning variable” transformation of variables  may better treat non-normal variables (allows parametric & non-parametric transformations)  VAPO and WIND are first candidates for inclusion) estimation of solar radiation from cloudiness or sunshine estimation of evapotranspiration using Penman-Monteith equation more user-friendly (guide available) run via command line [~M&R] all WG parameters stored in a single file, more stations may be stored in a single file the synthetic weather series may be “forced” to fit [~M&R] –weather forecast for a forthcoming period (following days, month or whole season) –climate change scenario (including changes in both high-frequency and low- frequency variability) through modifying WG parameters through direct modification of input weather series

27 4-variate  6-variate (nearest neighbours resampling) 4-variate SRAD TMAX TMIN RAIN learning SRAD TMAX TMIN RAIN VAPO WIND... xx xx xx xx xx xx xx xx xx xx xx xx xx xx xx variate SRAD TMAX TMIN RAIN VAPO WIND

28 test kvality meteo generátoru motivace: sWG neumí “perfektně” reprodukovat skutečné klima. Jak se chyby projeví v navazujících aplikacích? direct validation –comparison of observed vs. synthetic weather series in terms of derived climatic characteristics (synthetic wea.series should resemble observed series) indirect validation –comparison of characteristics derived from model output (e.g. crop growth model) fed by OBS and SYNT weather series (outputs from impact model fed by OBS and SYNT wea.series should resemble each other)

29 validace sWG - schéma obs.wea.series (~15-30 years…) model “yields” (obs.wea.) CROP GROWTH MODEL WG (present climate) model “yields” (synt.wea: “presence”) series (present climate) B C B:direct validation of WG C:indirect validation of WG info about - plant genetics - soil properties - growing site - management calculation of selected climatic characteristics climatic chars. (obs.wea.) climatic chars. (synt.wea.)

30 length of dry periods: variability of monthly means: normality of SRAD, TMAX, TMIN: B. Met&Roll - přímá validace

31 C. Met&Roll – nepřímá validace Motivation: How the WG imperfections (to fit the structure of real- world weather series) affect output from impact models fed by synthetic series?

32 nepřímá validace Met&Rollu prostřednictvím růstového modelu AVGs & STDs výnosů pšenice (17 stanic x 3 verze sWG) crop model: CERES-Wheat; 30-y simulations for 17 Czech stations weather generator: - WG-BAS: “basic” WG: no annual cycle of AR matrices; 1 st order Markov chain - WG-A3: improved WG: annual cycle of AR matrices; 3rd order Markov chain - WG-A3M: “best” WG: WG-A3 + conditioned on monthly WG (Dubrovsky et al, 2004, Climatic Change)

33 interpolace meteo generátoru Met&Roll (projekt calimaro) Czech institutes (9 people) participated Main aim: interpolation of Met&Roll parameters –motivation: applicability of Met&Roll for sites without observations sub-aims: 1.choice of the interpolation methods 2.validation in terms of the climatic characteristics 3.validation in terms of outputs from models fed by synthetic series produced by the interpolated generator crop model hydrological rainfall-runoff models

34 interpolace sWG: DATA + REGION -Station weather data: 125 stations from Czechia a) circles: “learning” set, b) squares: “validation” set - Topography is derived from the global digital elevation model GTOPO30 (0.5x0.5’) - altitude varies from 115 to 1602 m a.s.l.

35 Schéma: validace interpolovaného sWG climatic characteristics climatic characteristics climatic characteristics Crop yields, river streamflows Crop yields, river streamflows Crop yields, river streamflows impact model (crop-growth model, hydrological model,...) Parameters of interpolated WG interpolation Met&Roll Parameters of site-calibrated WG synthetic series Met&Roll synthetic series Met&Roll A(int) accuracy of interpolation A(int) C(wg) effects of WG inaccuracies on impact models output C(wg) C(int) effect of interpolation of WG on impact models output C(int) B(wg) ability of WG to reproduce climatic characteristics B(wg) WG parameters: Daily weather series (125 st.): Climatic characteristics: Output from impact models: B(int) effect of interpolation of WG on climatic characteristics in synt. series B(int) observed series

36 1) výběr interpolační metody 1) co-kriging (used via ArcGIS) 2) neural networks [Multilayer Perceptron network type = 3-5-1, 29 degrees of freedom, Back Error Propagation and Conjugate Gradient Descent training algorithms used] 3) weighted nearest neighbours y(x,y,z) = weighted average from the surrounding stations (d<100km; bell- shaped weight function) corrected for the zonal + meridional + altitudinal trends + WG parameters mapped using GTOPO30 digital elevation map (0.5x0.5’)

37 3) nepřímá validace interpolovaného generátoru Met&Roll Motivation: We have found imperfections in reproducing climatic characteristics by interpolated WG. Q: How these imperfections affect output from crop model (or any other model) fed by weather series produced by the interpolated WG? !!!

38 interpolovaný sWG: nepřímá validace (via STICS) AVG(modelové výnosy pšenice) [půda = černozem (CZ_01)] interpolated yields yields simulated with interpolated WGyields simulated with site-calibrated WG yields simulated with observed weather !!!

39 Scénáře změny klimatu part 3 … s důrazem na nejistoty

40 konstrukce Scénáře změny klimatu: DELTA metoda climate change scenario defines changes in climatic characteristics It is mostly derived as a difference (or ratio) for the climatic characteristics: commonly: CCscenario =  MEANS but, changes in other characteristics may be also included: –variability –Prob(wet day occurrence) –other parameters, e.g. Gamma dist. pars. present climate future climate scenario climate change scenario:

41 kaskáda nejistot při vývoji regionálního scénáře změny klimatu 1. emission scenario carbon cycle & chemistry model 2. concentration of GHG and aerosols >> radiation forcing GCM 3. large-scale patterns of climatic characteristics downscaling site-specific climate scenario

42 emisní scénáře SRES IPCC - AR3(2001)

43 emise  koncentrace  source: IPCC-TAR-WG1-TS

44  radiační působení   změna teploty & zvýšení hladiny moří source: IPCC-TAR-WG1-TS

45 naším cílem při studiu dopadů změny klimatu: pravděpodobnostní vyhodnocení impaktů zohledňující známé nejistoty For this, we need scenarios from Several emission scenarios X several GCM simulations (GCMs: various models, various settings, various realisations) … but: GCM simulations need huge computer resources ->> only limited number of GCM simulations available ->> GCM simulations do not cover existing uncertainties in emissions, climate sensitivity) so, to account for the uncertainties, we may use: – –pattern scaling, which separates global and regional uncertainties

46 zohlednění nejistot: - distributed modelling - anybody can participate - based on Hadley Centre models

47 ΔT G,2xCO2 (=klimatická citlivost) !!!

48 K= nárůst globální teploty při SRES-A2: range of ΔT glob simulated by a set of GCMs is not representative for the uncertainty in climate sensitivity  “pattern scaling” method helps 11 GCMs (colour time series) vs MAGICC model run at various climate sensitivities; yellow bar on the right)

49 metoda “pattern scaling” allows to separate uncertainties in: - the pattern of change  GCM - “global magnitude of change” (ΔT glob being a result of emission scenario and clim.sensitivity)  MAGICC

50 metoda “pattern scaling” where ΔX S = standardised scenario ( = scenario related to ΔT G = 1 °C ) a) ΔX S = ΔX [tA-tB] / ΔT G [tA-tB] b) linear regression [x = ΔT G ; y = ΔX] ΔT G = change in global mean temperature assumption: pattern (spatial and temporal /annual cycle/) is constant, only magnitude changes proportionally to the change in global mean temperature: !! ΔT G may be estimated by other means than GCMs !! (e.g. simple climate models /~ MAGICC/) ΔX(t) = ΔX S x ΔT G (t)

51 metoda “pattern scaling” standardised change of clim.characteristic X determined by linear regression and coincides with the slope parameter in reg.eq.: ΔX = a*ΔT globe + b : the present example: ΔTEMP S = 1.17 ; ΔPREC S = – (– 0.3%) the low correlation with T globe (R 2 ) may indicate large role of natural variability TEMP (CZ; HadCM2/SRES-A1b)PREC (CZ; HadCM2/SRES-A1b)

52 correlation of site-specific TEMP and PREC with T GLOB Variance of grid-specific TEMP and PREC changes explained by the pattern scaling technique (averaged over 12 monthly values) “pattern scaling” - validace TEMP : well correlated with T GLOB PREC, DTR, SRAD, VAPO, WIND: low correlation with T GLOB  natural variability dominates?  ….. this may be simulated by WG be careful with extrapolation ! (smoothing annual cycles may help) TEMPPREC

53 Nejistoty ve standardizovaném scénáři 7 AOGCMs ( , series of monthly means) from IPCC-DDC: emission scenario: IS92a / bau / 1%-per-year increase of compound CO2 4 weather elements: TAVG - daily average temperature DTR - daily temperature range PREC - daily precipitation sum SRAD - daily sum of radiation 4 exposure units – in Czechia Uncertainties in the scenario pattern: 1. inter-model uncertainty (7 GCMs) 2. internal GCM uncertainty (4 runs of HadCM2) (~ natural climatic variability) 3. choice of the site (4 sites in Czechia) 4. uncertainty in the standardised changes (~ std. error in regress. coefficients)

54 IPCC-AR2: gridová struktura GCM modelů ΔX = º ; ΔY = º; Nz = (hladin)

55 4 zdroje nejistot ve standardizovaném scénáři změny klimatu: TAVG (avg ± std) - compare the 4 uncertainties!

56 4 zdroje nejistot ve standardizovaném scénáři změny klimatu: PREC (avg ± std) - compare the 4 uncertainties!

57 nejistoty ve standardizovaném scénáři ZK pro ČR: 3 generace vyhodnocovacích zpráv IPCC IPCC-AR4 PRECTEMP IPCC-AR3IPCC-AR2

58 nejistoty v modelech z IPCC-AR4 - - Evropa - - [presented at EGU 2009 ]

59 nejistoty v AR4 modelech [presented at EGU 2009 ] list of 18 / 14 GCMs used in the analysis:

60 modely z IPCC-AR4: validace & scénáře Annual cycle of the GCM-based means (regridded into the CRU’s 0.5  0.5º grid) vs CRU gridded climatological means (TS2.1 dataset) temperature is validated in terms of:BIAS, RV, RMSE precipitation is validated in terms of:%BIAS, RV, %RMSE where: BIAS= avg(GCM) – avg(CRU) (*) %BIAS= 100  BIAS / avg(GCM) (*) RV= Reduction in Variance (indep. variable = CRU monthly means; dep. variable = debiased GCM monthly means) RMSE= root mean square error (debiased GCM monthly means vs CRU monthly means) %RMSE= 100  RMSE / avg(GCM) (*) (*) avg(X) is an average of 12 monthly values (in validation of the annual cycle)

61 GCMs vs CRU ( měsíční průměry) RMSE(roční chod TAVG)

62 GCMs vs CRU ( měsíční průměry) RV(roční chod of TAVG)

63 GCMs vs CRU ( měsíční průměry) RMSE(roční chod of PREC)

64 GCMs vs CRU ( měsíční průměry) RV(roční chod of PREC)

65 zobrazení informce z více (18/14) GCMs motivation: to show the multi-model mean/median + uncertainty in a single map step1: results obtained with each of 7 GCMs are re-gridded into 0.5x0.5º grid (~CRU data) step2: median [med(X ) ] and std [std(X)] from the 18/14 values in each grid box are derived step3 (map): the median is represented by a colour, the shape of the symbol represents value of uncertainty factor Q: Q = interpreting the uncertainty: - squares and circles [ std(X)  0.5 * median(X) ] indicate that medX) differs from 0 at significance level higher than 95% (roughly) - 4-point stars indicate high uncertainty [ std(X) > med(X) ] or: the greater is the proportion of grey (over sea) or black (over land) colour, the lower is the significance, with which the median value differs from 0 std(X) med(X )

66 Multi-GCM validace: roční chod (TAVG) (median [~colour] and STD [~symbol] of 18 single-GCM values) BIAS = GCM - CRU - notice the bias in the mountains! 

67 Multi-GCM validace: roční chod (TAVG) (median [~colour] and STD [~symbol] of 18 single-GCM values) RMSE* = sqrt [ avg ( GCM i – CRU i – bias) 2 ] 

68 Multi-GCM validace: roční chod (TAVG) (median [~colour] and STD [~symbol] of 18 single-GCM values) RV* = 1 – RMSE 2 / RMSE 0 2 ; where RMSE 0 = sqrt { avg [ GCM i – avg ( GCM i ) ] 2 } 

69 Multi-GCM validace: roční chod (PREC) (median [~colour] and STD [~symbol] of 18 single-GCM values) %BIAS = (GCM – CRU) / CRU - notice the bias in the mountains and close the shores! 

70 Multi-GCM validace: roční chod (PREC) (median [~colour] and STD [~symbol] of 18 single-GCM values) %RMSE* = 100 * sqrt [ avg ( GCM i – CRU i – bias) 2 ] / avg(CRU) 

71 Multi-GCM validace: roční chod (PREC) (median [~colour] and STD [~symbol] of 18 single-GCM values) RV* = 1 – RMSE 2 / RMSE 0 2 ; where RMSE 0 = sqrt { avg [ GCM i – avg ( GCM i ) ] 2 } 

72 Shrnutí: Multi-GCM validace ročního cyklu: (median [~colour] and STD [~symbol] of 18 GCMs) TAVG: BIAS TAVG: RMSE TAVG: RV PREC: %BIAS PREC: %RMSE PREC: RV

73 multiGCM scénáře (standardizované) :: TAVG (14 GCM, SRES-A2) nearly whole Europe: STD(ΔT) < 0.4 * median(ΔT)

74 multiGCM scénáře (standardizované) :: PREC (14 GCM, SRES-A2) !!! STD > 2*median !!!

75 multiGCM scénáře (standardiz.): TAVG (top ) and PREC (bottom ) (14 GCMs, SRES-A2)

76 Konstrukce sady scénářů změny klimatu pro impaktové studie !!!

77 zohlednění uvedených nejistot: v impatových studiích použijeme více scénářů (např. 3 ΔT G x 3 GCMs: uncertainty in ΔT G ( modelled by MAGICC) : emissionsclim.sensitivity high scenario:SRES-A24.5 K low scenario:SRES-B11.5 K middle scen.:middle2.5 K X IPCC-AR3 set uncertainty in pattern: set of GCMs + natural variability (day-to-day, year-to-year) is modelled by WG preferred GCMs: HadCM3 NCAR-PCM ECHAM5

78 závěry

79 SDS (not talking about WG) + quick (= unexpensive) + provides local information (can be tailored for specific use) + relatively easy to fit the desired statistical properties of model variables – some variables not satisfactorily explained (e.g. WIND, HUMID) – large uncertainties in climate change scenarios RCM: + physical consistency among output variables – “expensive” (large demands on computational resources) – RCM output stll needs postprocessing RCM resolution still not satisfactory distribution of variables (e.g. PREC) not realistic

80 závěry  RCM vs. SDS: not competing, but complementary techniques problem of RCM + SDS: its use limited to existing GCM simulation  they cannot account for uncertainties not represented in available GCM simulations (e.g. climate sensitivity)  my recommendation: use Weather Generator + Climate Change Scenarios determined by the pattern scaling method

81 závěry: WG + pattern scaling –weather series(future) = WG [ PAR(OBS) x CCS(GCM) ] where WG = M&Rfi CCS (climate change scenario) –includes changes in means and variability (daily and monthly) –is determined by the pattern scaling technique: CCS = CCS*(GCM) x ΔT G (MAGICC(clim.sens.,emis.scen) –the methodology accounts for several uncertainties: between-GCM differences  using several GCMs uncertainties due to clim. sensitivity and emission scenario (by using several ΔT G values modelled by MAGICC) natural variability  stochasticity of WG –other advantages of using WG: may generate arbitrarily long weather series easy to modify selected parameters > good for sensitivity studies may be interpolated (to generate series for sites without observed data)

82 k o n e c více informací (+ prezentace na konferencích, články):

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