cc: Ilona.Liesner@gkss.de date: Sun, 26 Sep 1999 18:58:02 +0200 from: Hans von Storch subject: TAR10.6 to: tar10@egs.uct.ac.za Dear Tar10ers, attached please find as WORD documents the revised version of section 10.6, the appendix with the list of applications of empirical downscaling studies, and the list of references for the whole section 10, as it exists presently. In 10.6 the example for a transfer function design is still missing: Bruce was supposed to provide this example. He is presently not responding -I hope that this no reason for concern - and I could provide an example with short term notice on Tuesday. Also one figure caption is missing; I have asked Rick Katz, from whom the example is taken, for a proper formulation. The reference list is the basis for further compilations by my secretayry, Mrs Ilona Liesner. Please send her your input for additional references, updates and deletions. For those of you, who don't like reading WORD, I am adding the texts as ascii as well. But, we should not edit the ascii files but only the WORD files, as otherwise the spelling of nn-English authors and, in case of the reference list, titles of papers would be damaged. I want to take the opportunity to thank Linda and Filippo for their cooperation during the last few days. Can somebody tell me where to send the figures? I have eps-files, but other formats could also be produced. Cheers Hans ----------------------------------------------------- 10.6 Empirical/statistical and statistical/dynamical methods 10.6.1 Introduction Formally, the concept of regional climate being conditioned by the large-scale state may be written as a stochastic and/or deterministic mapping of a predictor (a set of large-scale variables) on a predictand (a set of regional climate variables In general, the mapping is unknown is unknown and is modeled dynamically (i.e., through regional climate models) or empirically from observational (or modeled) data sets. In some cases the predictor and predictand are the same variables but on different spatial scales (for example the disaggregation schemes of Bürger, 1997; Wilks, 1999; and Widmann and Bretherton, 1999), but in most cases they are different. The mapping commonly employed is, in general, not designed to fully model all ranges of temporal scales. When using downscaling for assessing regional climate change, three implicit assumptions are made: - The predictors are variables of relevance and are realistically modeled by the GCM. Since different variables have different characteristic spatial scales, some variables are considered more realistically simulated by GCMs than others. For instance, derived variables (not fundamental to the GCM physics, but derived from the physics) such as precipitation are usually not considered as robust information at the regional and grid scale (e.g., Osborn and Hulme, 1997; Trigo and Palutikof, 1999). Conversely, tropospheric quantities like temperature or geopotential height are intrinsic parameters of the GCM physics and are more skillfully represented by GCMs. However, there is no consensus in the community about what level of spatial aggregation (in terms of number of grid cells) is required for the GCM to be considered skillful. For example Widmann and Bretherton (1999) find monthly precipitation on spatial scales of three grid lengths (in their case: 500 km) reliably simulated. - The transfer function is valid also under altered climatic conditions. This is an assumption that in principle can not be proven in advance. In the case of empirical functions, the observational record should cover a wide range of variations in the past; ideally, all expected future realizations of the predictors should be contained in the observational record. - Critical is the assumption that the predictors employed fully represent the climate change signal. Too little attention has in the past been paid to this assumption, but Hewitson (1999) and Charles et. al. (1999) have made progress in this respect. A diverse range of downscaling methods has been developed, but in principle fall into three categories, which are based upon the application of - weather generators, which are random number generators of realistically looking sequences conditioned upon the large-scale state (10.6.2.1). - transfer functions, where a direct quantitative relationship is derived through, for example, regression (10.6.2.2). - weather typing schemes based on the more traditional synoptic climatology concept (including analogs and phase space partitioning) and which relate a particular atmospheric state to a set of local climate variables (10.6.2.3). Each of these approaches has relative strength and weaknesses in representing the range of temporal variance of the local climate predictand. Consequently, the above approaches are often to some degree merged in order to compensate for the relative deficiencies in one method. Most downscaling applications have dealt with temperature and precipitation. However, a wide array of studies exists in which other variables have been investigated. Appendix XX provides a non-exhaustive list of past studies indicating predictands, geographic domain, and technique category. We are concentrating on references to applications since to 1995, since studies prior to that date made use of now outdated global climate change scenarios. [Appendix XX = Table 10.6.1] 10.6.2 Methodological options 10.6.2.1 Weather generators Weather generators are statistical models of observed sequences of weather variables. They can also be regarded as complex random number generators (Katz and Parlange, 1996), the outputs of which resemble daily weather data at a particular location (Wilks and Wilby, 1999). There are various types of daily weather generators, based on the approach to modeling daily precipitation occurrence: but the types usually fundamentally rely on stochastic processes. Two of these include the Markov chain approach (e.g., Richardson, 1981; Hughes et al., 1993, Lettenmaier, 1995; Hughes et al., 1999, Bellone et al., 1999) and the spell length approach (Racksko et al., 1991; Wilks, 1999a). In the spell length approach, which can be viewed as a natural way of extending the Markov chain approach, the length x of the spell lengths are simulated based on a probability distribution of the lengths. In the Markov chain approach precipitation occurrence is simulated day by day. Wilks (1999a) and Semenov et al. (1998) compare these methods. An additional approach is the so-called ``conceptual model'' approach, which involves chance mechanisms (e.g., clustering) by which storms arise and which is often used by hydrologists (O'Connell et al. 1999). Weather generators have been used for generating climate change scenarios that incorporate changes in climate variability (e.g., Katz, 1996; see Chapter 13, this volume) and for statistical downscaling, or for both simultaneously (Semenov and Barrow, 1997, Wilks 1999b). In the context of statistical downscaling the parameters of the weather generator are conditioned upon a large-scale state (see Katz and Parlange, 1996; Wilby et al., 1998; Charles et al., 1999), or relationships can be developed between large scale parameters sets of the weather generators and local scale parameters (Wilks, 1999b). Conditioning on large-scale states alleviates to some degree one of the chronic flaws of many weather generators, which is the underestimation of interannual variations of the weather variables (Wilks, 1989), and, to a degree, induces spatial correlation (Hughes and Guttorp, 1994). As is the case with other downscaling methods the success of this method is dependent upon the strength of the relationship between the stochastic generator parameters and the large-scale circulation index, and the stability of this relationship over time. As an illustration, the analysis of Katz and Parlange (1993, 1996) is discussed in some detail. They conditioned daily precipitation amount for a location in California on a circulation index, based on sea level pressure off the coast of California. They modeled the daily time series of precipitation as a chain dependent process, modeling occurrence as a first order Markov chain, and a power transform of intensity as normally distributed. The circulation index was allowed only two states, above and below normal pressure over a 78-year record. Using the Akaike and Bayesian Information Criteria they determined that model parameters such as mean intensity, standard deviation of intensity, and the probability of precipitation varied significantly with the circulation index state (high versus low pressure). They found that the conditioned model reproduced the precipitation variance statistics of the observations better than the unconditioned model, for example, interannual variance of monthly total precipitation. They went on to describe the use of their model for climate change scenario formation, i.e., conditions where the probability of obtaining a particular circulation index state is shifted. The mean precipitation changes linearly with the probability of the circulation state, but the standard deviation of the precipitation amount changes nonlinearly (Figure 10.6.1). These relationships indicate that the model allows for changes in the coefficient of variation of monthly total precipitation, which increases under mean drier conditions and decreases under mean wetter conditions. This method thus also allows for change in variability of precipitation along with the mean. Figure 10.6.1- Figure 2 from K and P 96 10.6.2.2 Transfer functions The more common approaches found in the literature are regression-like techniques or piecewise interpolations using a linear or nonlinear formulation. The simplest approach is to build multiple regression models relating free atmosphere grid point values to surface variables. For example Sailor and Li (1999) have in this manner modeled local temperature at a series of US stations. Other regression models use field of spatially distributed variables to specify local temperatures in Sweden (e.g.: Chen et al., 1999), or principal components of regional geopotential height fields (e.g.: Hewitson, 1992). Canonical Correlation Analysis (e.g., von Storch and Zwiers, 1999) has found wide application. A variant of CCA is redundancy analysis, which is theoretically attractive as it maximizes the predictands variance; however, in practical terms it seems similar to CCA (WASA, 1998). Also Singular Value Decomposition has been used (Huth, 1999). Most applications have dealt with precipitation; for instance Busuioc and von Storch (1996) with Rumanian monthly precipitation amounts, or Dehn and Buma (1999) with a French Alpine site. Kaas et al (1996) have successfully specified local pressure tendencies, as a proxy for local storminess, from large-scale monthly mean air pressure fields. Oceanic climate and climate impact variables have also been dealt with: salinity in the German Bight (Heyen and Dippner, 1998); and salinity and oxygen in the Baltic (Zorita and Laine, 1999); sea level (e.g., Cui at al., 1996); and a number of ecological variables such as abundances of species (e.g., Kroencke et al., 1998). In addition statistics of extreme events, expressed as percentiles within a month or season, have been modeled: storm surge levels (e.g., von Storch and Reichardt, 1997) and ocean wave heights (WASA, 1998). An alternative to linear regression is to use piecewise linear or nonlinear interpolation; geostatistics offers elegant "kriging" tools to this end (e.g., Wackernagel, 1995). The potential of this approach has been demonstrated by Biau et al. (1999), who related local precipitation to large-scale pressure distributions. Another approach is to use cubic splines, as was done by Buishand and Klein Tank (1996) for specifying precipitation in Switzerland. Also Hantel et al. (1998) adopt a nonlinear design for modelling snow cover duration in Austria with European mean temperature and altitude. Another non-linear approach is based on artificial neural networks (ANN; Hewitson and Crane,1996), which are generally more powerful than other techniques, although the interpretation of the dynamical character of the relationships is less easy. For example, Trigo and Palutikof (1999) map with an ANN SLP and 500 hPa height values on daily temperature at a station in Portugal and find significantly improved specification as compared to a linear ANNs. Figure 10.6.2 10.6.2.3 Weather typing The synoptic downscaling approach empirically defines weather classes related to local and regional climate variations. These weather classes may be defined synoptically or fitted specifically for downscaling purposes by constructing indices of airflow (Conway et al., 1996). The frequency distributions of local or regional climate are then derived by weighting the local climate states with the relative frequencies of the weather classes. Climate change is then estimated by determining the change of the frequency of weather classes. In many cases, the local and regional climate states are derived from the observational record. Wanner et al. (1997) used changing global to continental scale synoptic structures for understanding and reconstructing Alpine climate variations, while Widmann and Schaer (1997) could not relate changing Swiss precipitation to changing statistics of weather classes. Kidson and Watterson (1995) made a similar analysis for New Zealand. Jones and Davies (1999) apply the technique for studying changing air pollution mechanisms. The analog method was introduced into the downscaling context by Zorita et al (1995). Conceptually similar, but mathematically more demanding are techniques which partition the large-scale state phase space, for instance with Classification Tree Analysis, and use a randomized design for picking regional distributions. This technique was pioneered by Hughes et al (1993). Lettenmaier (1995) gives a general overview of these techniques. Both analog and CART approaches return the right level of variance and correct spatial correlation structures. In the following, we discuss in some more detail a case of statistical-dynamical downscaling as suggested first by (Frey-Buness et al., 1995): Statistical-dynamical downscaling (SDD) is a hybrid approach with statistical and dynamical elements. In a first step GCM results of a multi-year climate period are disaggregated into non-overlapping multi-day episodes of quasi-stationary large-scale flow patterns. Once defined, similar episodes are grouped in classes of different weather types. Typical members of these classes, i.e. episodes which in total comprise only a small fraction of the complete period, are simulated with a regional climate model (RCM). It is driven at its boundaries by the GCM results of the respective episodes. Eventually, the RCM results are statistically evaluated where the frequency of occurrence of the respective classes determines their statistical weight. An advantage over the SSD technique over other empirical downscaling techniques is that in this way spatially distributed local climates are specified. Its feasibility has been demonstrated by a series of studies on climate and climate change in the European Alps (see Appendix XX). As compared with conventional continous RCM simulations (Section 10.5), the computational effort of SDD is small and almost independent of the length of the climate period. That this reduction of computational demands is not combined with a reduction is accuracy, at least in terms of time-mean distributions, is demonstrated by a comparison of mean precipitation distributions as simulated by a continuous RCM simulation and by the SSD technique. Figure 10.6.3 displays correlation coefficients and mean absolute differences, conditional upon the degree of disaggregation. When the computational oad is reduced to 20%, the mean absolute error amounts to about 0.4 mm/day, whereas the correlation coefficient is about 0.96. Thus, in practical applications the intrinsic error of SDD is acceptable if the overall error is largely determined by the error of the used models (GCM and RCM). Figure 10.6.3. Similarity of time mean precipitation distributions obtained in a continuous RCM simulation and through SSD for different levels of disaggregation. Top: mean absolute difference [mm/day], bottom: spatial correlation coefficient. Horizontal axis: computational load of SSD. Ñ is the number of days simulated in SSD, N the number of days simulated win the continuous RCM simulation. 10.6.3 Issues in Statistical Downscaling 10.6.3.1 Temporal variance Transfer function approaches and some of the weather typing approaches suffer to varying degrees from an under-prediction of temporal climate variability, since only part of the regional and local temporal variability of a climate variable is related to large scale climate variations, while another part is generated regionally. (For the case of regression the mathematics of this principle are worked out by Katz and Parlange (1996).) Two approaches for bringing the downscaled climate variables to the right level of variability are in use: inflation and randomization. In the inflation approach, originally suggested by Karl et al. (1990), the variation is increased by the multiplication of a suitable factor; a more sophisticated approach, named "expanded downscaling", was developed by Bürger (1996). It is a variant of Canonical Correlation Analysis that ensures the right level of variability. This approach is utilized by Huth (1999) and Dehn et al. (1999). In the randomization approach the unrepresented variability is added as unconditional noise; that is, in the simplest case, the "missing" variance is added in the form of white noise (Hewitson, 1998). The concept is worked out in von Storch (2000), and applications are offered by Dehn and Buma (1999) and Buma and Dehn (1998). Conversely, weather generators suffer from the inverse of the above, and have difficulty in representing low frequency variance. However, conditioning the generator parameters on the large-scale state may alleviate this to some degree state (see Katz and Parlange, 1996; Wilks, 1999a; Wilby et al., 1998; Charles et al., 1999). 10.6.3.2 Validation The validation of downscaling techniques is an essential but difficult requirement. It requires demonstrating the robustness of the downscaling under future climates, and that the predictors used represent the climate change signal. Both assumptions are not possible to rigorously test, as no empirical knowledge is available so far. The analysis of historical developments as well as simulations with GCMs can provide support for these assumptions. However, the success of a statistical downscaling technique for representing present day conditions does not imply legitimacy for changed climate conditions (Charles et al., 1999). The classical validation approach is to specify the downscaling technique from a segment of available observational evidence and then assess the performance of the empirical model by comparing its predictions with independent observed values. This approach is particularly valuable when the observational record is long and documents significant changes in the course of time. An example is the analysis of absolute pressure tendencies in the North Atlantic by Kaas et al. (1996), who fitted a regression model which related spatial air pressure patterns to pressure tendency statistics. Similarly Wilks (1999) developed a downscaling function on dry years and found it functioning well in wet years. Hanssen-Bauer and Førland (1998) and Hanssen-Bauer (1999) found in their analysis that data series of 50 year length may not be sufficient to derive a valid model. An alternative approach is to use a series of comparisons between models and transfer functions, as demonstrated by Busuioc et al (1999), Charles et al. (1999) and González-Ruoco et al. (199a,b). In the former study, it was first demonstrated that the GCM incorporated the empirical link; in the latter, a regional climate model was used. From these findings it was concluded that the dynamical models would correctly "know" about the empirical downscaling link; then the climatic change, associated with a doubling of carbon dioxide, was estimated through the empirical link and compared with the result of the dynamical model. In both cases, the dynamical response was found to be consistent with the empirical link, indicating the validity of the empirical approach and its legitimate approach in downscaling other global climate change information. 10.6.3.3: Choice of predictors The list of predictands in the literature is very broad and comprise direct climate variables (e.g.: precipitation, temperature, salinity, snow pack), monthly or yearly statistics of climate variables (distributions in wind speeds, wave heights, water levels, frequency of thunderstorm statistics), as well as impacted variables (e.g.: frequency of land slides). The Appendix XX lists a wide range of predictors, predictands, and techniques. Useful summaries of downscaling techniques and the predictors used are also presented in Rummukainen (1997), Wilby et al. (1998) and Wilby and Wigley (1999). However, outside of passing references in many studies to the effect that a range of predictors were evaluated, there is little systematic work that has explicitly evaluated the relevant skill of different atmospheric predictors (Winkler et al., 1997). The one commonality between most studies is, not surprisingly, the use of some indicator of the large-scale circulation. The choice of the predictor variables is of utmost importance. For example, Hewitson (1997, 1998) has demonstrated how the downscaled scenario of future change in precipitation may alter significantly depending on whether or not humidity is included as a predictor. The implication here is that while a predictor may or may not appear as the most significant when developing the downscaling function under present climates, the changes in that predictor under a future climate may be critical for determining the climate change. Some estimation procedures, for example stepwise regression, are not able to recognize this and exclude variables that may be vital for climate change. Such exclusion may lead to misleading scenarios of change. A similar issue exists with respect to downscaling temperature. Werner and von Storch (1993), Hanssen-Bauer (1999) and Mietus (1999) noted that low frequency changes in local temperature during the 20th temperature could not be related to changes in circulation. Schubert (1998) makes a vital point in noting that changes of local temperature under doubled atmospheric CO2 may not be driven by circulation changes alone, but may be dominated by changes in the radiative properties of the atmosphere. This is a particular vulnerability of any downscaling procedure in light of the propensity to use circulation predictors alone that do not necessarily reflect the changed radiative properties of the atmosphere. One possible solution is to incorporate the large-scale temperature field from the GCM as a surrogate indicator of the changed radiative properties of the atmosphere. This approach has been adopted by Dehn and Buma (1999) in their scenario of future Alpine land slides. Another solution is to use several large-scale predictors, such as gridded temperature and circulation fields (e.g., Gyalistras et al., 1998; Huth, 1999). After the availability of homogeneous re-analyses (Kalnay et al., 1996), the number of candidate predictor fields has been greatly enhanced (Solman and Nuñez, 1999); earlier, the empirical evidence about the co-variability of regional/local predictands and large-scale predictors was very limited and made many studies choose either gridded near surface temperature or air pressure, or both (Gyalistras et al., 1994). These "new" data sets will allow significant improvements in the design of empirical downscaling techniques. 10.6.4 Inter-comparison of downscaling methodologies An increasing number of studies comparing different downscaling studies have emerged since SAR. However, there is a paucity of systematic studies that use common data sets applied to different procedures over the same geographic region. A number of articles discussing different empirical and dynamical downscaling approaches (Giorgi and Mearns, 1991; Hewitson & Crane, 1996; Wilby and Wigley, 1997; Buishand and Brandsma, 1997; Rummukainen, 1997; Zorita and von Storch, 1997; Gyalistras et al., 1998; Kidson and Thompson, 1998, Murphy, 1999a,b, von Storch, 1999b, Biau et al., 1999) do present summaries of the relative merits and shortcomings of different procedures. These intercomparisons vary widely with respect to predictors, predictands and measures of skill. A systematic, internationally coordinated intercomparison project would be useful. The most systematic and comprehensive study so far is that one by Wilby et al. (1998) and Wilby and Wigley (1997). They compared empirical transfer functions, weather generators, and circulation classification schemes over the same geographical region using climate change simulations and observational data. The study considered a demanding task to downscale daily precipitation for six locations over North America, spanning arid, moist tropical, maritime, mid-latitude, and continental climate regimes. A suite of 14 measures of skill was used, strongly emphasizing daily statistics. These included such measures as wet spell length, dry spell length, 95th percentile values, wet-wet day probabilities, and several measures of standard deviation. Downscaling procedures in the study included two different weather generators, two variants of an ANN-based technique, and two stochastic/circulation classification schemes based on vorticity classes. The results prove to be illuminating, but require careful evaluation as they are more indicative of the relative merits and shortcoming of the different procedures, rather than a recommendation of one procedure over another. In the validation phase of the study the downscaling results were compared against the observational data, and indicated that the weather generator techniques were superior to the stochastic/circulation classification procedures, which in turn were superior to the ANNs. However, the superiority of the weather generator when validated against the observed data is misleading as the weather generators are constrained to match the original data (Wilby and Wigley, 1997). Similarly, the improved performance of the circulation classification techniques with regard to the ANNs is largely a reflection of the measures of skill used and indicates the tendency of ANNs to over-predict the frequency of trace rainfall days. In contrast, when the inter-annual attributes of monthly totals are examined the performance ranking of the techniques is approximately reversed with the weather generators performing especially poorly. The results indicate strength by weather generators to capture the wet-day occurrence and the amount distributions in the data, but less success at capturing the inter-annual variability (the low frequency component). The important question with this procedure is thus how to perturb the weather generator parameters under future climate conditions. At the other end of the spectrum the ANN procedures performed well at capturing the low frequency characteristics of the data, and showed less ability at representing the range of magnitudes of daily events. The stochastic/circulation typing schemes, being somewhat a combination of the principles underlying weather generators and ANNs, appear to be a better all-round performer. In application to GCM simulations of future climate, the procedures showed some consistency with the ANN indicating the largest changes in precipitation. However, assessing the relative significance of the changes is non-trivial, and at this level of inter-comparison the results of the climate change application are perhaps more useful in a diagnostic capacity of the GCM which appeared to show differences in the strength of the precipitation-circulation relationship. What is not evaluated in this study to any great degree is the range of variance spanned by each technique. Addressing this issue Wilby et al. (1998) and Conway et al. (1996) apply transfer functions to determine wet/dry probabilities and then use a stochastic procedure for the magnitude of precipitation, and in doing so capture some degree of the low frequency and high frequency variance. Zorita et al (1995) and later Cubasch et al. (1996) demonstrated that a suitably designed analog technique reproduces storm interarrival terms well. Similarly, Hewitson (1998) span the range of variance using an ANN transfer function to predict precipitation magnitude, and then stochastically model the residual variance as a function of atmospheric state. An additional factor not yet fully evaluated in any comparative is that of the temporal evolution of daily events. In this respect the manner in which daily events develop may be critical in some areas of impacts analysis, for example hydrological modeling. While a downscaling procedure may correctly represent, for example, the number of rain days, the temporal sequencing of these may be as important. A number of analyses have dealt with the relative merits of non-linear and linear approaches. For example, Conway et al. (1996) and Brandsma and Buishand (1997) use circulation indicators as predictors and note that the relationships with precipitation on daily time scales are often non-linear. Similarly Corte-Real et al. (1995) effectively applied multivariate adaptive regression splines (MARS) to approximate non-linearity in the relationships between large-scale circulation and monthly mean precipitation. However, the application of MARS to large volume daily data may be more problematic (Corte-Real et al., 1995). Other non-linear techniques are kriging and analogs, whose performance were compared by Biau et al., (1999) and von Storch (1999). Kriging resulted in better specifications of averaged quantities but too low variance, whereas analogs returned the right variance but lower correlations. Also analogs can be usefully constructed only on the basis of a large data set. It appears that downscaling of the short-term climate variance benefits significantly from the use of non-linear models. In particular, downscaling of daily precipitation benefits appreciably from the ability to better capture convective events. Most of the comparative studies mentioned above come to the conclusion that techniques differ in their success of specifying regional climate, and the relative merits and shortcomings emerge differently in different studies. This is not surprising, as there is considerable flexibility in setting up a downscaling procedure, and the suitability of a technique and the adaptation to the problem at hand varies. 10.6.5 Summary and Recommendations A broad range of statistical downscaling techniques has been developed in the past few years. Users of GCM based climate and climate change information may choose from a large variety of methods conditional upon their needs. Weather generators provide realistic sequences of events. With transfer functions statistics, like conditional means or quantiles, of regional and local climate may consistently be derived from GCM generated data. Techniques based on weather typing serve both purposes. Downscaling means postprocessing GCM data; it can not account for insufficiencies in the driving GCM data. As statistical techniques are combining the existing empirical knowledge, statistical downscaling can describe only links which have been observed in the past. Thus, it is based on the assumption that presently found links will prevail under different climate conditions. It may be, in particular, that under present conditions some predictors appear less relevant, but become significant in describing climate change. It is recommended to test statistical downscaling methods by comparing their estimates with simulations with high-resolution dynamical models. The advent of decades-long homogeneous atmospheric re-analyses have offered the community many more atmospheric large-scale variables as possible predictors. Statistical downscaling requires the availability of long and homogeneous data series, while the computational resources needed are small. Therefore, statistical downscaling techniques are suitable tools for scientific communities without access to supercomputers and with little competence in process-based climate modeling. Often dynamical downscaling methods are providing much more information than may be needed in a specific application, so that resorting to the much simpler statistical techniques may often be advisable. Furthermore, statistical techniques may relate directly GCM derived data to impact relevant variables, like ecological variables or ocean wave heights, which are not simulated by contemporary climate models. It is concluded that statistical downscaling techniques is many cases a viable alternative to process-based dynamical modeling, and will remain so in the future. -------------------------------------------------------------- echnique category: WG (weather generator), TF (transfer function), and WT (weather typing). Methods utilized in these categories: (1) For WG: M = Markov, SM = semi-Markov, DTM = discrete-time Markov, NHMM = nonhomogeneous hidden Markov, LARS-WG, MED = mixed exponential distribution, CP = conditional probability. (2) For TF: PCA = principal component analysis, CCA = canonical correlation analysis, CPMS = Climatological Projection by Model Statistics, LR = linear/multiple regression, IR = inflated regression, SWR = step-wise regression, MARS = multivariate adaptive regression splines, CS = cubic splines, PR = polynomial regression, ANN = artificial neural networks. (3) For WT: CA = cluster analysis, SOM = self-organizing map, EVD = extreme value distribution. Predictor variables: SLP (sea level pressure); Z1, Z7, Z5 (1000-, 700-, 500-hPa geopotential heights); TH1 TH8 (1000-500, 850-500 hPa thickness); VOR (vorticity); W (wind related); Q1, Q8, Q7, Q5 (1000-, 850-, 700- and 500-hPa specific humidity); RH8, RH7, RH5 (850-, 700-, 500-hPa relative humidity); CC (cloud cover); ZG, MG (zonal and meridional gradients of the predictors). Predictands: T (temperature); Tmax (maximum temperature); Tmin (minimum temperature); P (precipitation). Region is the geographic domain. Time is the timscale of the predictor and predictand: D (daily), M (monthly), S (seasonal), and A (annual). New abbreviations: NST: near surface temperature,TDS = temporal downscaling based on Richardson-type WGSSD = statistical/dynamical downscaling Technique Method Predictor Predictand Region Time Author (s) TF PCA, CCA, IR: CPMS SLP, TH8, Z5, RH8, RH5, W, K-index Tmax, Tmin, P 5 USA stations D Karl et al., 1990 TF LR SLP, Z7 and their ZG & MG T, P 32 stations in Oregon, USA M Wigley et al., 1990 WG, WT SM, CP SLP P Stations in Germany D Bárdossy and Plate, 1991 WG, WT DTM W, CC P Delaware River Basin, USA D Hay et al., 1992 TF PCA, ANN SLP, Z7, Z5 P 1 grid in Southeastern Mexico D Hewitson and Crane, 1992 TF PCA, CCA SLP P Iberian Peninsula S (DJF) Zorita et al., 1992 TF PCA, LR, IR Z5, TH1 P, T 10 Nordic stations M Kaas, 1993a,b TF CCA SLP T Central Europe M(DJF) Werner and von Storch, 1993 TF CCA, LR SLP P Iberian Peninsula S (DJF) von Storch et al., 1993 TF PCA, PR, SWR SLP T Continental USA D Hewitson ,1994 WG NHMM, PCA SLP P 4 stations in Wash. St., USA D Hughes and Guttorp, 1994a WG NHMM, PCA SLP, Z5 P 24 stations in Wash. St., USA D Hughes and Guttorp, 1994b TF PCA, LR, IR Z5, TH1 P, T 10 Nordic stations M Jónsson et al., 1994 TF LR, PCA, ANN Snow pack Colorado River Basin, USA D McGinnis, 1994 WT EVD, analog Tmax, Tmin Tmax, Tmin Several sites in the USA D Brown and Katz, 1995 TF PCA, regression SLP, H500 P Mediterranean stations S Jacobeit, 1994a,b TF PCA, CCA, LR SLP P Iberian Peninsula M Noguer, 1994 TF MARS, PCA SLP P 8 sites in Portugal M (DJF) Corte-Real et al., 1995 TF PCA, LR, IR Z5, TH1 P, T 10 Nordic stations M Jóhannesson et al., 1995 W PCA, CA Z1 Tmax, Tmin, P 82 stations in New Zealand D Kidson and Watterson, 1995 TF LR SLP, P T Mediterranean Pautikof and Wigley, 1995 WG PCA, analog, CART SLP P Two regions in the USA D Zorita et al., 1995 TF CCA, PCA SLP P Stations in Romania M (DJF) Busuioc and von Storch, 1996 WT SSD Frey-Buness et al., 1995 WG LR VOR, W Europe Conway et al., 1996 WG PCA, analog SLP, Z7 P Iberian Peninsula D Cubasch et al., 1996 TF PCA, ANN SLP, Z5 P South Africa D (DJF) Hewitson and Crane, 1996 WT SSD Alps Fuentes and Heimann, 1996 WT Z7, Z5 Matyasovszky and Bogardi, 1996 WG, TF LARS, MED, LR SLP, P, Tmax, Tmin, solar radiation T, P 5 sites in Europe D Semenov and Barrow, 1996 TF CCA SLP, NST T,P 4 sites in the European Alps M Fischlin and Gyalistras, 1997 TF PCA, regression SLP, H500 P Mediterranean stations S Jacobeit, 1996 TF PCA, CCA SLP Sea level Baltic Sea M (DJF) Heyen et al., 1996 TF PCA, CCA SLP Sea level Japanese coast M (DJF) Cui et al., 1995, 1996 Cubic splines P Switzerland Buishand and Klein Tank, 1996 WT SLP, Z7, Z5, VOR, W T, P The Netherlands D, M Buishand and Brandsma, 1997 TF PCA, CCA SLP Pressure tendencies North Atlantic sites M Kaas et al. , 1996 TF CS P Switzerland Brandsma and Buishand, 1997 TF CCA T Phenological event Northern Germany Maak and von Storch, 1997 TF PCA, ANN SLP, TH1, Z5, PNA P 20 grids in NE Mexico and USA D Cavazos, 1997 WT analog Upper air fields Snow French Alps Martin et al., 1997 TF CPMS SLP, Z5 Tmax, Tmin 2 sites: Spain and USA D Palutikof et al., 1997 TF PCA, LR SLP Tmax, Tmin Australia D Schubert & Henderson-Sellers, 1997 PCA Switzerland D Widmann and Schär, 1997 WG, TF ANN Z, T, VOR P 6 sites in the USA D Wilby and Wigley, 1997 TF CPMS SLP, Z5 Tmax, Tmin 2 sites: Spain and USA D Winkler et al., 1997 TF Ecological variables Dippner, 1997a,b Slope stability Buma nd Dehn, 1998 WT Enke and Spekat, 1997 TF PCA, CCA SLP Storm surge quantiles German Bight M Von Storch and Reichardt, 1997 WG NHMM Atmospheric variables P West Australia D Bates et a., 1998 TF ANN Z1, Z7, Q1, Q7, Q5 P Northeast USA D Crane and Hewitson, 1998 TF Sea-level variability Chinese coast M Cui and Zorita, 1998 WT SSD Alps Fuentes et al., 1998 TF Salinity South of Germany Heyen and Dippner, 1998 TF PCA, SR Z1, Z5, TH1, VOR, W T, P Stations in New Zealand D Kidson and Thompson, 1998 TF LR SLP, VOR, W Kilsby et al., 1998 WT Southeast Spain D Goodess and Palutikof, 1998 TF LR SLP, W, VOR, T8, Q8, K-index T, P 976 European stations M Murphy, 1998a, b TF PCA, LR SLP Tmax, Tmin 40 sites in southeastern Australia D Schubert, 1998 TF ANN, LR Z1, Z5 Tmax, Tmin Iberian Peninsula D Trigo and Palutikof, 1998 TF PCA, LR, ANN: RBF Z8, Z5 T, P, vapor pressure A station in Central Europe D Weichert and Bürger, 1998 WG, TF ANN SLP, Z5, T, VOR T, P 6 sites in the USA D Wilby et al., 1998a, b TF Ecol.ogical variables Kroencke at al., 1998 TF Ecol.ogical variables Heyen et al., 1998 TF CCA SLP, NST T,P 40 sites in the European Alps Gyalistras et al., 1998 TF PCA, kriging, analog P Biau et al., 1999 TF PCA, redundancy an. SLP Wave hieghts quant. Siite in North Atlantic M WASA, 1998 TF Nonlinear fit Regional temperature, hieght Snow coverage European Alps Hantel et al., 1998 P => Landslide Southeast France Buma and Dehn, 1999 WT SSD Thunderstorms Southern Germany D Sept, V., 1998 SLP P => Landslide French Alps Dehn and Buma, 1999 TF PCA, CCA SLP P 14 stations in Romania M Busuioc et al., 1999 TF, WT ANN, SOM SLP, TH1, Q0, Q7 P 20 grids in NE Mexico and USA D (DJF) Cavazos, 1999 WT Analog SLP, NST Landslide activity Italian Alps Dehn, 1999a,b Hewitson and Crane, 1999 WT SSD Alps Heinmann and Sept, 1999 WG, WT Z7 T, P 12 sites in eastern Nebraska D Mearns et al., 1999 TF LR Z8, Z5, T8, T5, RH8, RH5, W T 8 sites in the USA D Sailor and Li, 1999 WG P P D Wilks, 1999 TF PCA, CCA SLP Sea level quantiles North Sea coast M Langenberg et al., 1999 Cubic splines P Switzerland Buishand and Brandsma WT PCA, analog P D Zorita and von Storch, 1999 WT PCA, CART Schnur and Lettenmaier, 1999 TF CCA, redundancy analysis SLP T, seaa level, wave hieght, salinity, wind, run-off Polish coast D/M Mietus, 1999 TF,WG CCA, TDS SLP, NST 22 monthly weather statistics 2 sites in the European Alps M Riedo et al., 1999 TF Multiple regression NST,SLP,u700,u200,v700,v200 T, max, min Central Argentina M Solman and Nuñez, 1999 TF CCA Z500 P 16 stations in the European Alps M (DJF) Burkhardt, 1999 TF CCA, SVD NST,SLP, z500 and others T, P and others Norway M(J) Benestad , 1999a,b TF Multiple regression Various tropospheric variables Local weather Norwegian glaciers D Reichert et al., 1999 WT SSD P European Alps D Fuentes and Heimann, 1999 WT SSD T, P European Alps D Heimann and Sept, 1999 WT analog SLP, T P Australia Timbal and McAvaney, 1999 WG NHMM P Stations in the USA D Bellone et al., 1999 TF Analog/resgression P,NST Iberian Peninsula Boren et al., 1999, Ribalaygua et al., 1999 ---------------------------------------------- REFERENCES Appenzeller, C., T.F. 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Attachment Converted: "c:\eudora\attach\tar10_infrev_refs3.doc" Attachment Converted: "c:\eudora\attach\tar10.61.doc" Attachment Converted: "c:\eudora\attach\table 10.6.doc" Hans von Storch Institute of Hydrophysics, GKSS, Geesthacht, Germany