Downscaling for impact studies

Downscaling for impact studies Fredrik Wetterhall FoUh, SMHI Kings College London Uppsala university Stockholm University

Outline Introduction Statistical downscaling Model output statistics (bias correction) Outlook 2

Outline Introduction Statistical downscaling Model output statistics (bias correction) Outlook 3

How is the local weather affected by a global climate change? Flooding Droughts Eutrophication Extreme events? Global models struggle to give quantifiable estimations of these potential problems Simulation of regional climates has improved in AOGCMs and, as a consequence, in nested regional climate models and in empirical downscaling techniques. FAR 2007 4

Flooding in UK and Wales 2000 5

Buildwas 2 November 2000 6

Arvika, Sweden 2000 7

GCM RCM Dynamical downscaling Pungwe Nhazonia Frontiera Pungwe Falls Katiyo Vunduzi Honde Mavonde Pungue Sul Tacuraminga Statistical downscaling? Bue Maria Bias-correction of RCM Runoff modelling 8

Outline Introduction Statistical downscaling Model output statistics (bias correction) Conclusions 9

Statistical downscaling 1. Find a relationship between predictor (global) and predictand (local) 2. Fit a statistical method to that relationship 3. Use that relationship to model future precipitation 4. Methods Analogue methods Perfect prognosis Weather patterns Regression Weather states Generalised linear models Model output statistics Statistical emulators 10

Example: Weather states 11

Statistical vs dynamical downscaling Statistical downscaling takes into account local conditions Local observations represent the climate at one point RCM-scale of 25-50 km evens out the horisontal variability 12

RCM versus SDS Strength Weakness RCM Responds in a physically consistent way to different forcings. Includes many of the feedbacks that exist in the climate system. Very dependent on the boundary conditions imposed by the parent GCM. Sensitive to applied parameterizations. Requires quite advanced computing resources. SDS Accounts for the local scale of climate variability. Effective in describing extreme events Computationally cheap. Therefore, easy to create an ensemble of climate scenarios. Requires long time- series of high quality observations Performance depends on the choice of predictor variables. Scenarios only include future changes in statistical relationships between climate variables that are given as input to the method 13

Statistical downscaling Sweden Three Methods: 1. Analog method 2. Weather pattern combined with weather generator 3. Weather generator conditioned on large-scale variation Moisture flux was to used to model precipitation amount 14

Classified weather patterns for Sweden 15

Qmax simulated with SD output (control) Catchment area: 293 km 2 Predictors: MSLP and humidity flux at H850 100 model parameterisations of the HBV model driven by 100 downscaled data series with three different models Acknowledgement: Jan Seibert and Claudia Teutschbein 16

Qmax simulated with SD output (future) Future change in modelled runoff during spring. Mainly due to increased winter temperature Acknowledgement: Jan Seibert and Claudia Teutschbein 17

Outline Introduction Statistical downscaling Model output statistics (bias correction) Outlook 18

Example from the UK Small black dots: Stations in the observational database Large black dots: Centres of RCM grid boxes Watershed area: 4000 km 2 19

Maximum 5-day precipitation Severn 20

Monthly precipitation 21

HBV model vs ENSEMBLES RCMs 22

Bias correction of RCM output Define cut-off value for precipitation values Fit a gamma distribution to observed and modelled precipitation for values below and above the 95 percentile Adjust the RCM output P P Scaled Scaled = = F F 1 1 ( α ( α Obs, β Obs,95 Obs, β, F 1 Obs,95 ( P, α, F 1 CTL ( P, α, β CTL CTL,95 )), β CTL,95 )) if if P p 95 P 95 th th percentile value percentile value 23

Bias-corrected RCM 24

Bias-corrected, NS-weighted RCM 25

Multimodel ensemble max 5-day prec 26

Multimodel ensemble max 5-day prec bias-corrected 27

Distribution-Based-Scaling (DBS) method Current status of method development: Meteo. Variables to be scaled [unit] Temp. Probablility distribution Remark Precipitation [mm] Daily Gamma Distribution α 1 ( x / β ) exp( x / β ) f ( xα, β ) =, βγ( α) x, α, β f 0. Identify a threshold value to remove drizzle days normal rainfall and extrem rainfall events are adjusted differently per season Temperature [º C] Daily Normal Distribution f ( x ( x µ ) 1 2 2σ µ σ, ) = e 2 2πσ 2, β f 0. Dependent on weather state of being rainy or non-rainy Fourier series used to describe annual cycle Wind velocity [m/s] Daily Weibull Distribution f ( x a, b) = b a x a b 1 e x b a, xf 0. wind events are adjusted per season Relative humidity [%] Daily Beta Distribution Γ( a + b) f ( x a, b) = x ( a) ( b) Γ Γ a 1 (1 x) b 1, 0 x 1 a, bf 0 relative humidity is adjusted per season and dependent on weather state 28 06

Rengen 20 Change of 100-yr flood frequency 0-20 20 Munkedal 0-20 2000 2100 29 16

Outline Introduction Statistical downscaling Model output statistics (bias correction) Outlook 30

Future outlook for statistical downscaling Still need for local adjustments (orography etc) Still need for higher resolution Precipitation (convective vs stratiform) Extreme events Sub-daily precipitation Full field interpolation Bias correction Hybrid models (RCM-Weather generators) Need for statistical downscaling will depend on the future ability of climate models to describe important processes 31

Higher resolution improves the result Intensity (mm/h) 50 45 40 35 30 25 20 15 10 5 0 10-year IDF 0 3 6 9 12 15 18 21 24 Duration (h) OBS ERA40-50 ERA40-25 ERA40-12 ERA40-6 32

Circulation patterns for climate change, seasonal forecast, extreme rainfall etc. Torpshammar 8 7 Monthly precipitation per rainy day [mm/d] 6 5 4 3 2 CP09 CP09_ERA40 CP08 CP08_ERA40 1 0 1 51 101 151 201 251 301 351 Day No. Adjust more meteorological variables (Solar radiation -- evapotranspiration) 33 19

Thank you for you attention! 34