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 km 2 ) river basin (~10,000 to ~100,000 km 2 ) catchment (~1000 to ~5000 km 2 ) 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?

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) EARWIG Weather Generator Tebaldi Bayesian UK Regions 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 Future SRES A Interpolated observations – 5km x 5km

Data – Observations & Models Observed series - Aggregated 5km interpolated precipitation dataset Regional Climate Models – PRUDENCE (

How well do RCMs represent the seasonal cycle?

Summer Skewness Coefficient

UK Regions

Method: RCMs + WG PRUDENCE RCMs Extract CFs (Catchment) EARWIG Weather Generator Tebaldi Bayesian UK Regions 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 ( ) and agreement with the ensemble consensus for future projections These are standardised and applied as weights in the downscaling step

NWE Seasonal Mean λ ARP_CHAD_PHIRH_EHIRH_HRCAO_ERCAO_H DJF MAM JJA SON Precipitation Temperature ARP_CHAD_PHIRH_EHIRH_HRCAO_ERCAO_H DJF MAM JJA SON

Method: RCMs + WG PRUDENCE RCMs Extract CFs (Catchment) EARWIG Weather Generator Tebaldi Bayesian UK Regions 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 Catchments tab Model tab Catchment finder OSGB locator OSGB pointer coords Toolbar Map window

Neyman-Scott Rectangular Pulses Rainfall Model time intensity time total intensity Storm origins arrive in a Poisson process with arrival rate λ Each storm origin generates C raincells separated from the storm origin by time intervals exponentially distributed with parameter β Raincell duration is exponentially distributed with parameter η Raincell intensity is exponentially distributed with parameter ξ Rainfall intensity is equal to the sum of the intensities of all the active cells at that instant

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 auto- correlations 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) EARWIG Weather Generator Tebaldi Bayesian UK Regions 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 R 2 =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 Had_P RCAO_E Control Each series is 30 years in length …

NWE Seasonal Mean λ ARP_CHAD_PHIRH_EHIRH_HRCAO_ERCAO_H DJF MAM JJA SON Precipitation Temperature ARP_CHAD_PHIRH_EHIRH_HRCAO_ERCAO_H DJF MAM JJA SON

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, 5 th and 95 th 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