Buenos días. Perdón - Hablo un poco de español!

Transcription

1 Buenos días Perdón - Hablo un poco de español!

2 Introduction to different downscaling tools Rob Wilby Climate Change Science Manager Source:

3 Downscaling - a growth industry "downscaling and climate" "dynamical downscaling" "statistical downscaling" "downscaling and impact" Journal publications Year Peer reviewed journal publications listed on the Web of Science

4 What the climate model 300km centres provide downscaling 1m 50km Point 10km what the climate impacts community needs.

5 Downscaling approaches

6 High level decision: dynamical or statistical downscaling (or both)? Changes in summer precipitation by the 2080s under SRES A2 emissions projected by four RCMs Source: Frei (pers. comm.)

7 Comparable skill DJF MAM Downscaling model skill for selected STARDEX indices over SE England JJA SON Source: Haylock et al. (2006)

8 Weather classification - schemes to condition daily surface variables

9 Weather classification - LWT scheme to condition daily rainfall Rainfall occurrence Probability A W C N NW S E Rainfall intensity 6 5 Amount (mm) A W C N NW S E Conditional probabilities of rainfall and mean intensity in the Cotswolds associated with the main Lamb Weather Types (LWT),

10 Downscaling techniques Method Strengths Weaknesses Weather typing Yields physically interpretable Requires classification (e.g. analogue method, linkages to surface climate scheme hybrid approaches, Versatile (e.g., can be applied Circulation can be fuzzy classification, self to surface climate, air quality, insensitive to future climate organizing maps, flooding, erosion, etc.) forcing Monte Carlo methods). Compositing of extreme May not capture intra-type events variations in surface climate

11 Weather generators Observed Modelled Probability Daily precipitation total (m m) Daily precipitation totals over the Berkshire Downs modelled using a stretched exponential distribution with shape parameter, c = 0.75

12 Tools for regional climate change impact assessments Example screen for the Environment Agency Rainfall and Weather Impacts Generator (EARWIG)

13 Downscaling techniques Method Strengths Weaknesses Weather typing Yields physically interpretable Requires classification (e.g. analogue method, linkages to surface climate scheme hybrid approaches, Versatile (e.g., can be applied Circulation can be fuzzy classification, self to surface climate, air quality, insensitive to future climate organizing maps, flooding, erosion, etc.) forcing Monte Carlo methods). Compositing of extreme May not capture intra-type Weather generators (e.g. Markov chains, stochastic models, spell length methods, storm arrival times, mixture modelling). events Simultaneous weather generation at multiple sites Multivariate outputs Spatial interpolation of model parameters using landscape Captures variability across a range of space-time scales variations in surface climate Arbitrary adjustment of parameters for future climate Unanticipated effects to secondary variables of changing precipitation parameters

14 Transfer function approaches Regional temperature effects on maximum ozone concentrations at Russell Square, London

15 Downscaling techniques Method Strengths Weaknesses Weather typing Yields physically interpretable Requires classification (e.g. analogue method, linkages to surface climate scheme hybrid approaches, Versatile (e.g., can be applied Circulation can be fuzzy classification, self to surface climate, air quality, insensitive to future climate organizing maps, flooding, erosion, etc.) forcing Monte Carlo methods). Compositing of extreme May not capture intra-type Weather generators (e.g. Markov chains, stochastic models, spell length methods, storm arrival times, mixture modelling). Regression methods (e.g. linear regression, neural networks, canonical correlation analysis, kriging). events Simultaneous weather generation at multiple sites Multivariate outputs Spatial interpolation of model parameters using landscape Captures variability across a range of space-time scales Relatively straightforward to apply Employs full range of available predictor variables Transparency of relationships variations in surface climate Arbitrary adjustment of parameters for future climate Unanticipated effects to secondary variables of changing precipitation parameters Poor representation of observed variance May assume linearity and/or normality of data Poor representation of extreme events

16 Different methods give different results! Catchment UKCIP02 Resampling UKCIP SDSM RCM Resampling RCM Low Med Low Med High High Min Max B2 A2 A2 Min Max % changes in 20-year flood volume by the 2080s for selected UK basins

17 When might statistical downscaling be applicable? g Sub-grid scales (small islands, point processes) g Complex/heterogeneous environments g Extreme events g Exotic predictands g Transient change/ensembles

18 Downscaling daily rainfall at 3000m+ Niwot C1 PRCP Jan 1983 to May Precipitation (mm) Observed SDSM 0 01/01/ /02/ /03/ /04/ /05/ /06/1983 Downscaled and observed daily precipitation for a high elevation site in the Rockies

19 Uncertainty in the future intensity of London s heat island CGCM2 CSIRO ECHA M4 HA DCM3 OBS NCEP CGCM2 CSIRO ECHA M4 HA DCM3 OBS NCEP 26 6 Mean (tenths degc) Episodes (days>4 degc) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec CGCM2 CSIRO ECHA M4 HA DCM3 CGCM2 CSIRO ECHA M4 HA DCM Change in mean (tenths degc) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Change in episodes (days>4 degc) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Downscaled nocturnal temperature gradient between central and outer London for the present and 2050s

20 Uncertainty in future ozone concentrations in central London Summer 10-yr mean maximum 15 min ozone 60 CGCM2 CSIRO ECHAM4 HADCM3 Concentration (ppb) Summer 10-yr 95th percentile maximum 15 min ozone CGCM2 CSIRO ECHAM4 HADCM3 Concentration (ppb) Running means of maximum 15 min ozone concentrations at Russell Square downscaled from four GCMs under A2 emissions

21 When is statistical downscaling not applicable? gdata-poor regions gnon-stationary predictor-predictand relationships (e.g., land-surface feedbacks) gsimpler approaches are sufficient gsensitivity analyses ( bottom-up methods) gspatial interpolation gspatial/temporal analogues gchange factors

22 Local forcing factor - late snow cover? Niwot C1 winter mean TAVG (r=0.83) Niwot C1 spring mean TAVG (r=0.82) Temperature (degc) Observed SDSM Temperature (degc) Observed SDSM Niwot C1 summer mean TAVG (r=0.57) Niwot C1 fall mean TAVG (r=0.87) Temperature (degc) Observed SDSM 1982/83 El Nino Temperature (degc) Observed SDSM Observed and downscaled seasonal mean TAVG at Niwot Ridge, CO

23 A few words of caution

24 Extrapolation to conditions beyond the training data set Percentage of days that specific humidity (upper panel) and 850 geopotential heights (lower panel) for the climate of the 2080s under A2 emissions lie outside the range of the climatology of the ECHAM4 GCM. Source: Hewitson (2004).

25 Variations in predictor strength 0.6 MSLP QSUR Correlation coefficient Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Variations in the strength of the correlation between daily wet day amounts at Eskdalemuir (55º 19 N, 3º 12 W) and mean sea level pressure (MSLP), and near surface specific humidity (QSUR) over the Scottish Borders region,

26 Predictor variable domain 45.0 N W E Frequency and location of predictors selected for downscaling daily precipitation occurrence (top) and amounts (bottom)

27 Low-frequency variability (in semi-arid regions) Correlation pav pint pq90 px5d pxcdd pf l90 pnl90 Indices/sea son Correlation pav pint pq90 px5d pxcdd pfl90 pnl90 Indices/season Comparison of four downscaling models skill at reproducing the STARDEX indices in west Iberia (top panel) and southeast Iberia (lower panel)

28 Statistical DownScaling Model (SDSM)

29 Statistical DownScaling Model (SDSM) and predictor variable archive

30 Key functions of SDSM (v3.1) Quality control Evaluation of predictor-predictand relationships Model building Weather generation (present climate) Climate change scenarios Summary statistics/diagnostics Visualization and intercomparison

31 Examples of climate change impacts and adaptation research using SDSM Applications Evaluation of Hadley Centre s RCM for extreme events, UK Land-use and climate change impacts on floods, Luxembourg Projections of glacier melt and retreat, Swiss Alps Climate change and and incidence of dengue fever, Caribbean Climate change and water resources, Sweden STAtistical and Regional Dynamical downscaling of EXtremes for European Regions (STARDEX)

32 Global access to SDSM predictor variables

33 Example application: London s nocturnal heat island intensity Intensity (tenths degc) Apr May Jun Jul Aug Sep Oct-95 Validation of downscaled nocturnal UHI intensity in London for the summer of 1995: Grey lines denote observations, red the modelled UHI

34 Example application: Peak ozone concentrations in central London Maximum 15-minute ozone concentration Mean concentration (ppb) OBS SDSM Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Probability ppb Downscaled maximum daily ozone concentrations for Russell Square, London

35 Concluding remarks

36 When applying downscaling Consider the objectives of the climate change impact study - is downscaling really needed? Appreciate the generic strengths/weaknesses of different downscaling methods Recognise key sources of uncertainty Test the model using independent data Apply multiple GCMs (and RCMs) in parallel Review added-value/insights gained

37 Gracias!