#### Transcript Antarctica on the scales

Linking probabilistic climate scenarios with downscaling methods for impact studies Dr Hayley Fowler School of Civil Engineering and Geosciences University of Newcastle, UK With Contributions from: Claudia Tebaldi (NCAR) Stephen Blenkinsop, Andy Smith (Newcastle University) Aim Develop a framework for the construction of probabilistic climate change scenarios to assess climate change impacts at the: regional (~100,000 to 250,000 km2) river basin (~10,000 to ~100,000 km2) catchment (~1000 to ~5000 km2) scales Motivation Different GCMs produce different climate change projections, especially on a regional scale Therefore no one model provides a true representation Most probabilistic scenarios to date have been produced for large regions or globally Regional scale studies more relevant for impacts How can we combine probabilistic climate scenarios with downscaling methods to study impacts at the catchment scale? How can we combine probabilistic climate scenarios with downscaling methods to study impacts at the catchment scale? Examining how well different RCMs simulate different statistical properties of current climate in their control climates Do different RCM-GCM combinations produce different future projections? How can we combine the estimates of different models to produce probabilistic scenarios? Case-study Locations 1 British Isles 2 Eden 3 Ebro 4 Gallego 5 Meuse 6 Dommel 7 Brenta 8 Scandinavia 9 Eastern Europe Method: RCMs + WG PRUDENCE RCMs Extract CFs (Catchment) Tebaldi Bayesian UK Regions EARWIG Weather Generator Calibrated Eden R-R model λ Monte-Carlo resampling of flow sections based on λs Data available for UK RCM data – 50km x 50km Control 1961-90 Future SRES A2 2070-2100 Interpolated observations – 5km x 5km Data – Observations & Models Observed series - Aggregated 5km interpolated precipitation dataset Regional Climate Models – PRUDENCE (http://prudence.dmi.dk/) RCM Driving Data Danish Meteorological Institute (DMI) HIRHAM Swedish Meteorological and Hydrological Institute (SMHI) Hadley Centre – UK Met Office Météo-France, France RCAO HadRM3P HadAM3H A2 ECHAM4/OPYC (OGCM SSTs) HadAM3H A2 ECHAM4/OPYC A2 HadAM3P Arpège Observed SST PRUDENCE Acronym HC1 ecctrl AquaTerra Acronym HIRHAM-H HIRHAM-E HCCTL MPICTL RCAO-H RCAO-E adeha HAD-P DA9 ARP-A How well do RCMs represent the seasonal cycle? Mean Rainfall Comparison Mean Daily Rainfall (mm) 6 5 HIRHAM_E HIRHAM_H 4 RCAO_E 3 RCAO_H 2 HAD_P ARPEGE_C 1 OBSERVED 0 jan feb mar apr may jun jul Month aug sep oct nov dec How well do RCMs represent the seasonal cycle? Mean Temperature Comparison Mean Temperature (DegC) 16 14 HIRHAM_E 12 HIRHAM_H RCAO_E 10 8 RCAO_H 6 HAD_P 4 ARPEGE_C OBSERVED 2 0 jan feb mar apr may jun jul Month aug sep oct nov dec How well do RCMs represent the seasonal cycle? Daily Rainfall Variance Comparison 35 HIRHAM_E 30 (mm2) Variance of Daily Rainfall 40 HIRHAM_H 25 20 RCAO_E RCAO_H 15 HAD_P 10 ARPEGE_C OBSERVED 5 0 jan feb mar apr may jun jul Month aug sep oct nov dec Summer Skewness Coefficient UK Regions Method: RCMs + WG PRUDENCE RCMs Extract CFs (Catchment) Tebaldi Bayesian UK Regions EARWIG Weather Generator Calibrated Eden R-R model λ Monte-Carlo resampling of flow sections based on λs Model weighting (a la Tebaldi) Bayesian statistical model delivers a fully probabilistic assessment of the uncertainty of climate change projections at regional scales Based on: Reliability Ensemble Average method (Giorgi and Mearns, 2002) Summary measures of regional climate change, based on a WEIGHTED AVERAGE of different climate model responses Model weighting (a la Tebaldi) Weights account for: BIAS - the performance of GCMs when compared to present day climate ( i.e. results from model validation) CONVERGENCE - the degree of consensus among the various GCMs’ responses/ Model weighting (a la Tebaldi) pdf of change in temperature and precipitation fitted using area-averages of the model output Prior pdfs are assumed to be uninformative Data from regional models/observation incorporated through Bayes’ theorem, to derive posterior pdfs Model-specific “reliabilities parameters” estimated as a function of model performance in reproducing current climate (1961-1990) and agreement with the ensemble consensus for future projections These are standardised and applied as weights in the downscaling step NWE Seasonal Mean λ Precipitation ARP_C HAD_P HIRH_E HIRH_H RCAO_E RCAO_H DJF 0.07 0.19 0.25 0.26 0.08 0.15 MAM 0.08 0.05 0.11 0.23 0.26 0.27 JJA 0.15 0.06 0.16 0.23 0.18 0.22 SON 0.11 0.11 0.21 0.20 0.14 0.23 Temperature ARP_C HAD_P HIRH_E HIRH_H RCAO_E RCAO_H DJF 0.23 0.22 0.12 0.19 0.11 0.13 MAM 0.17 0.22 0.15 0.26 0.09 0.1 JJA 0.08 0.18 0.09 0.25 0.16 0.25 SON 0.12 0.23 0.16 0.24 0.13 0.12 Method: RCMs + WG PRUDENCE RCMs Extract CFs (Catchment) Tebaldi Bayesian UK Regions EARWIG Weather Generator Calibrated Eden R-R model λ Monte-Carlo resampling of flow sections based on λs EArWiG EA Weather Generator Developed for EA for catchment scale Decision Support Tool models Generates series of daily rainfall, T, RH, wind, sunshine and PET on 5km UK grid Observed and climate change based on UKCIP02 scenarios Collaborative with CRU, UEA EArWiG Map viewer interface developed Can select catchments, time periods and different UKCIP02 scenarios Toolbar Catchments tab Model tab Catchment finder OSGB locator OSGB pointer coords Map window Neyman-Scott Rectangular Pulses Rainfall Model • Storm origins arrive in a Poisson time process with arrival rate λ • Raincell duration is exponentially distributed with parameter η time intensity • Each storm origin generates C raincells separated from the storm origin by time intervals exponentially distributed with parameter β time • Rainfall intensity is equal to the sum of the intensities of all the active cells at that instant total intensity • Raincell intensity is exponentially distributed with parameter ξ time Weather Generator Depending on whether the day is wet or dry, other meteorological variables are determined by regression relationships with precipitation and values of the variables on the previous day Regression relationships maintain both the cross- and autocorrelations between and within each of the variables Change factor fields Change factor fields are applied to the fitted rainfall model statistics: Mean Variance PD Skewness Coefficient Lag 1 Autocorrelation Change factor fields are applied to the weather generator statistics: Mean temperature Temperature SD CF Summer mean temperature CF Winter mean precipitation CF Spring PD Method: RCMs + WG PRUDENCE RCMs Extract CFs (Catchment) Tebaldi Bayesian UK Regions EARWIG Weather Generator Calibrated Eden R-R model λ Monte-Carlo resampling of flow sections based on λs Rainfall-runoff model ADM model, simplified version of Arno Calibrated for Eden catchment on observed data R2=0.73, 0.78 Each simulated climate used to produce simulated flow series (30 years) for each climate model using P and PET EARWIG run for each RCM 1 2071-2100 2 Had_P RCAO_E 1961-1990 Control Each series is 30 years in length 3 4 … 1000 NWE Seasonal Mean λ Precipitation ARP_C HAD_P HIRH_E HIRH_H RCAO_E RCAO_H DJF 0.07 0.19 0.25 0.26 0.08 0.15 MAM 0.08 0.05 0.11 0.23 0.26 0.27 JJA 0.15 0.06 0.16 0.23 0.18 0.22 SON 0.11 0.11 0.21 0.20 0.14 0.23 Temperature ARP_C HAD_P HIRH_E HIRH_H RCAO_E RCAO_H DJF 0.23 0.22 0.12 0.19 0.11 0.13 MAM 0.17 0.22 0.15 0.26 0.09 0.1 JJA 0.08 0.18 0.09 0.25 0.16 0.25 SON 0.12 0.23 0.16 0.24 0.13 0.12 Re-sampling Monte-Carlo re-sampling technique used to weight models according to λ values from Bayesian weighting Random numbers used to choose a control and future run for a particular RCM, then seasonal statistics of change in mean flow, SD flow, 5th and 95th percentiles calculated. If seasonal λ=0.14 then random number generator produces 140 resamples from a particular RCM Generates total of 1000 change statistics for each season – pdf fitted used kernel density 2080s 2020s Questions for the audience Should we weight models (CG)? Should we be weighting on statistics other than mean? If so, what? Should we be looking at weighting by some spatial bias measure rather than a simple regional average? Makes the statistics harder… Models may produce reasonable mean statistics and get higher order statistics important for impact studies wrong