Questions about Empirical Downscaling Bruce Hewitson 1, Rob Wilby 2, Rob Crane 3 1, South Africa 2 Environment Agency, United Kingdom 3 Penn State University & AESEDA, USA "downscaling and climate" "dynamical downscaling" "statistical downscaling" "downscaling and impact" Journal publications 7 6 5 4 3 2 1 1991 1992 1993 1994 1995 1996 1997 1998 1999 2 21 22 23 24 25 Year Peer reviewed journal publications listed on the Web of Science [accessed March 26]
Wish I could be in Beijing!! (Rob Wilby)
Downscaling: idealism and pragmatism 1 cm 127km WWF
A: The context for downscaling a) A complex environment; ranging from pessimism around uncertainty and model limitations, to blind faith in perfect results b) A user community misunderstanding projections versus forecasts c) Yet, a pressing demand from stakeholders for any information that is better than tossing a coin d) A tension between scientific idealism and stakeholder pragmatism e) Downscaling is only one of a number of possible sources of information; e.g. AR4 CH 11 attributes for developing robust statements - past trends, GCM envelope, downscaling studies, process understanding f) Downscaling drivers come from GCMs that are not the real world, but a reduced dimensionality representation that is responsive to the same forcing parameters as the real world g) Downscaling (as does all climate change science) operates in a realm of incomplete knowledge, imperfect tools, and a society whose policy and development measures will continue despite this
Future society Emissions pathway Climate model Regional scenario Downscaling is at the heart of the uncertainty cascade Impact model Impact
B: Questions about empirical downscaling: a) What are we actually trying to achieve? - The target lies somewhere between point-scale high temporal resolution and simple, broad, regional indications of the direction of change. - The future under focus is dominantly the time horizon of policy and development plans b) Just how robust are the tools? To what degree are the solutions sensitive to methodological choice and predictor suite (subject to minimum criteria), and to what degree do the differences matter? c) As empirical downscaling bypasses model grid-cell parameterization and works from the skill-scale of the model, can one achieve better convergence? d) Can downscaling tools be geographically transferable without custom caseby-case tuning? e) What about stationarity and feedbacks f) Do the limitations preclude usability? (e.g. there are pressing philosophical issues to be answered first & downscaling cannot credibly succeed )
C: Objectives of downscaling a) To provide an additional information resource targeted at assessing regional climate responses that is: - Reflective of the first order response to large scale forcing - Consistent with physical process changes - At spatial and temporal scales of stakeholder relevance - Provides defensible information on projections (multiple information) - Contextualized in term of uncertainty - Without trying to capture local feedback modulation b) Serves to facilitate model diagnosis c) Allow for rapid evaluation of regional attributes from many GCMs d) Derive regional response for exotic variables (e.g. Storm surge) even RCMs cannot directly achieve some of these e) Aid understanding of process coupling across spatial scales
Examination: assess 2 techniques in 3 tough situations Two downscaling methods in broad current usage - The two methods evolve from different starting points and are implemented in different ways a. Begins with a weather generator and conditions this with the atmosphere predictors b. Starts with a cross-scale transfer function with the atmosphere predictors and adds the local high frequency variance Test against challenging situations - Target the downscaling of precipitation in different climate regimes of Africa, using dirty training data in complex local climates Apply the tools in a non-proprietary manner - i.e. apply without tuning the algorithm separately for each case
Case #1: Addis Ababa, Ethiopia
Case #2: Casablanca, Morocco
Case #3: Steenbras dam, South Africa
Downscaling conceptualization and goals A procedure that derives a normative regional response to the large scale forcing. Draws on the empirical information present in the observed data record, and in so doing: - Reflects the deterministic component of the large scale forcing - Includes the local (sub-gcm-grid scale) variance Downscaling does not seek to reproduce the real world in observation for observation, but rather a realistic time evolution that: - at seasonal and inter-annual scales should match relative magnitude of the temporal evolution of the forcing - at daily time scales should match the statistics of the daily events (frequency of events, etc) Downscaling should not seek to correct errors in the predictors; but predictor errors (such as too many low pressure systems) should be propagated The methods lend themselves to ensemble downscaling and allows for an assessment of the envelope of response
What do we know already? GCM boundary conditions are the main source of uncertainty affecting all downscaling methods Statistical and dynamical downscaling have similar skill Different downscaling methods yield different scenarios There are no universally optimum predictor(s)/domains, but there are guidelines to the baseline criteria Downscaling extreme events can be problematic Traditional skill measures for current climate may not be the best guide to the value of future scenarios of change
Case #1: NCEP predictors, daily precipitation, Addis Ababa Continental environment, convective rainfall systems, tropical location. Method A: three downscalings with different predictor sets Method B: one downscaling, different predictor set to Method A Predictors include parameters reflecting lower and mid troposphere circulation and humidity Monthly totals (mm) 3 25 2 15 1 Observed Series2 Series3 Series4 Series5 5 1 2 3 4 5 6 7 8 9 1 11 12
Case #1: NCEP predictors, daily precipitation, Addis Ababa 365-day climatology of 3-day total rain 3 25 2 15 1 Observed Series2 Series3 Series4 Series5 5 365-day climatology of raindays per 3-day window 3 25 2 15 1 Observed Series2 Series3 Series4 Series5 5
Case #1: NCEP predictors, daily precipitation, Addis Ababa 1 1 1 1 Histogram of magnitude of rainfall events Observed Series2 Series3 Series4 Series5 1 5 1 15 2 25 3 35 4 45 5 55 6 65 7 75 8 85 9 95 1 Raindays > 1mm 1 9 8 7 6 5 4 3 2 1 1 2 3 4 5 6 7 8 9 1 11 12 Observed Series2 Series3 Series4 Series5
Case #1: NCEP predictors, daily precipitation, Addis Ababa 4 35 3 25 2 15 1 5-5 Monthly time series (1979-1988) Observed Series2 Series3 Series4 Series5 Overall a credible representation of local climate in terms of both low frequency and high frequency response to the daily atmospheric predictors One method over-predicts the frequency of low magnitude rain events not a major impact on totals, but relevant to, for example, soil moisture and landscape hydrology Other method over predicts frequency of high magnitude events marginally gives too high totals in some months No systematic evidence of one set of predictors outperforming another
Case #2: Casablanca Coastal environment without strong topographical forcing. Very dirty data with missing days and some very suspect large values. Meta-data for the station is poor. Monthly totals (mm) 8 7 6 5 4 3 Observed Series2 Series3 Series4 Series5 2 1 1 2 3 4 5 6 7 8 9 1 11 12
Case #2: Casablanca Raindays > 1mm 2.5 2 1.5 1 Series1 Series2 Series3 Series4 Series5.5 1 2 3 4 5 6 7 8 9 1 11 12 Dry spell duration 9 8 7 6 5 4 3 Observed Series2 Series3 Series4 Series5 2 1 1 2 3 4 5 6 7 8 9 1 11 12
Case #2: Casablanca Monthly time series of rainfall totals (1979-1988) 35 3 Observed Series2 Series3 Series4 Series5 25 2 15 1 5-5 Problems of over-prediction in the summer but how good is the station data? Possible explanations: The downscaling is infilling the missing days, hence increasing totals The suspicious high values in the observed time series are possibly skewing the downscaling function Phase errors in the low quality data could be ascribing high precipitation to an incorrect atmospheric state, which might have a high frequency of occurrence
Case #3: Steenbras Dam Coastal environment with strong topographical forcing, very sensitive to occurrence of orographic cloud, subject to winter rainfall from the mid-latitude westerly flow On the face of it, high quality data, but some suspicion of a mid-series phase shift Monthly totals (mm) 18 16 14 12 1 8 6 Observed Series2 Series3 Series4 Series5 4 2 1 2 3 4 5 6 7 8 9 1 11 12
Case #3: Steenbras Dam How much of a role could NCEP quality be playing? Especially as the location is very sensitive to boundary layer moisture and boundary layer wind direction! 365-day climatology of 3-day total rain 2 18 16 14 12 1 8 6 Observed Series2 Series3 Series4 Series5 4 2
Case #3: Steenbras Dam 35 3 25 2 15 1 5 Monthly precipitation (1979-199) Observed Series2 Series3 Series4 Series5-5 1 8 15 22 29 36 43 5 57 64 71 78 85 92 99 16 113 12 127 134 141 Generally good, realistic, and captures the low and high frequency variance well Erroneously high precipitation in late winter, most apparent in one method which has greater sensitivity to the day-to-day phase matching with the predictors
Interim Conclusions a) Both methods perform comparably; - Conditioned weather generator has a tendency to over do frequency of low rainfall events - Transfer function with sampling of CDF has a tendency to over estimate high magnitude events b) Variation in predictor suite does not have a major influence; subject to minimum criteria of incorporating some representation of the base circulation and humidity attributes. c) The results can be very credible; but have a vulnerability to data quality d) Difficulty in separating out sources of error; station data, phase errors, NCEP realism.
What about Climate change and the delta-issue? On the assumption that the GCMs are simplified representations of reality, and proportionally sensitive to the real world anthropogenic forcing; Given empirical downscaling propagates signal and error of the large scale atmospheric response; And evidence that circulation-delta is largely consistent across GCMs Annual precipitation scenarios 6 4 UCT-CSIRO UCT-ECHAM4 UCT-HadAM3 SDSM-HadCM3 % change 2-2 -4-6 Tanger M ekness Casablanca Beni M ellal M arrakech Oujda M idelt Agadir Ouarzazate Downscaled annual precipitation scenarios for sites in Morocco by the 28s under SRES A2 emissions. Source: World Bank (27)
What about Climate change and the delta-issue? For many locations, a strong multi-model agreement on the direction of change (better than using GCM grid cell data), but still a large intermodel range in magnitude. Monthly anomaly for two locations Casablanca rainfall totals (A2 emissions, 28s) Midelt rainfall totals (A2 emissions, 28s) % change 4 2-2 -4-6 -8-1 UCT-CSIRO UCT-ECHAM4 UCT-HadAM3 SDSM-HadCM3 Coastal % change 15 1 5-5 UCT-CSIRO UCT-ECHAM4 UCT-HadAM3 SDSM-HadCM3 High mountains Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec DJF MAM JJA Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec DJF MAM JJA SON ANN SON ANN Change = drying; consistent with process understanding Change = very uncertain! No confidence.
# # 1 th percentile 9 th percentile West Coast West Coast Central Karoo Central Karoo Cape Winelands Cape Winelands City of Cape Town Overberg Eden City of Cape Town Overberg Eden AR4 multi-model downscaling: multi-site example Median Downscaled mean JJA rainfall (mm/month) response anomaly West Coast Central Karoo Cape Winelands City of Cape Town # Eden X Overberg
So what? Given: - The demand for regional information - The limitations and uncertainty of GCM grid cell data - The value of using multiple sources of information to assess change Then, empirical downscaling is arguably: - Informative about the regional response to large scale forcing - Relatively insensitive to method subject to some baseline criteria - A fruitful avenue to support the impacts and adaptation community But: - Whether traditional skill measures under the current climate are the best guide to the skill of future scenarios of change needs more assessment And: - There are fruitful avenues to be explored in using downscaling for model diagnosis and understanding regional process
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