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Time Structure of Observed, GCM-Simulated, Downscaled, and Stochastically Generated Daily Temperature Series

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Time Structure of Observed, GCM-Simulated, Downscaled, and Stochastically Generated Daily Temperature Series 000 2001 HUTH ET AL. 4047 Time Structure of Observed, GCM-Simulated, Downscaled, and Stochastically Generated Daily Temperature Series RADAN HUTH Institute of Atmospheric Physics, Prague, Czech Republic JAN KYSELY Institute of Atmospheric Physics, and Department of Meteorology and Environment Protection, Charles University, Prague, Czech Republic MARTIN DUBROVSKY Institute of Atmospheric Physics, Prague, Czech Republic (Manuscript received 7 September 2000, in ®nal form 20 February 2001) ABSTRACT The time structure of simulated daily maximum and minimum temperature series, produced by several different methods, is compared with observations at six stations in central Europe. The methods are statistical downscaling, stochastic weather generator, and general circulation models (GCMs). Outputs from control runs of two GCMs are examined: ECHAM3 and CCCM2. Four time series are constructed by statistical downscaling using multiple linear regression of 500-hPa heights and 1000-/500-hPa thickness: (i) from observations with variance reproduced by the in¯ation technique, (ii) from observations with variance reproduced by adding a white noise process, and (iii) from the two GCMs. Two runs of the weather generator were performed, one considering and one neglecting the annual cycle of lag-0 and lag-1 correlations among daily weather characteristics. Standard deviation and skewness of day-to-day temperature changes and lag-1 autocorrelations are examined. For heat and cold waves, the occurrence frequency, mean duration, peak temperature, and mean position within the year are studied. Possible causes of discrepancies between the simulated and observed time series are discussed and identi®ed. They are shown to stem, among others, from (i) the absence of physics in downscaled and stochastically generated series, (ii) inadequacies of treatment of physical processes in GCMs, (iii) assumptions of linearity in downscaling equations, and (iv) properties of the underlying statistical model of the weather generator. In downscaling, variance in¯ation is preferable to the white noise addition in most aspects as the latter results in highly over- estimated day-to-day variability. The inclusion of the annual cycle of correlations into the weather generator does not lead to an overall improvement of the temperature series produced. None of the methods appears to be able to reproduce all the characteristics of time structure correctly. 1. Introduction we must verify that the particular method is capable of simulating present climate conditions reliably. In this Studies of climate change impacts frequently require paper, we concentrate on the evaluation of simulations daily time series of climate variables such as tempera- of present climate by several methods of producing site- ture and precipitation for a future climate state at a speci®c daily temperature series; we do not deal with speci®c site. There are several ways of obtaining site- applications of the methods to future climate conditions. speci®c daily time series, which are to a different extent The latter subject is discussed, for example, in Hulme based on general circulation model (GCM) outputs et al. (1994) for the use of a direct GCM output; in Hay (Giorgi and Mearns 1991; Kattenberg et al. 1996; Dub- et al. (2000) for a direct GCM output and downscaling; rovsky 1997). Those frequently used include taking the in Winkler et al. (1997) and Huth and Kysely (2000) GCM output itself, regional climate models (RCMs), for downscaling; and in Semenov and Barrow (1997) statistical downscaling, and weather generators. In order and Dubrovsky et al. (2000) for weather generators. to have con®dence in simulations of a future climate, Many studies evaluate the performance of the above- mentioned approaches against observed data, but the Corresponding author address: Dr. Radan Huth, Institute of At- vast majority concerns a single method only. A simul- mospheric Physics, BocÏnõ II 1401, 141 31 Prague, Czech Republic. taneous use of several methods and a subsequent com- E-mail: [email protected] parison would, nevertheless, aid in selecting the most q 2001 American Meteorological Society Unauthenticated | Downloaded 10/02/21 11:58 AM UTC 4048 8 VOLUME 6 appropriate technique for speci®c applications in future burg, GermanyÐhence HAM. A detailed description of studies. There are only few studies that attempted to its version 3, used here, is given in Deutsches Klima- make such a comparison for daily temperature. Kidson rechenzentrum (DKRZ 1993). It has a T42 resolution, and Thompson (1998) compared statistical downscaling corresponding approximately to a 2.88 grid spacing both with a limited-area model over New Zealand, arriving in longitude and latitude. Here we examine years 11± at the conclusion that both methods perform with a sim- 40 of the control run, in which climatological SSTs and ilar accuracy. Hay et al. (2000) found that downscaling sea ice were employed. The validation of daily extreme from observations approximates daily temperatures in temperatures produced by ECHAM was performed for selected North American basins better than a direct selected areas of the Czech Republic by NemesÏova and GCM output and downscaling from GCM. Kalvova (1997) and NemesÏova et al. (1998). In most studies, simulated time series are veri®ed The Canadian Climate Centre Model (CCCM) of the against observations in terms of distance measures such second generation is described in McFarlane et al. as root-mean-square error and correlation coef®cient, (1992) where also its basic validation is presented. The and in terms of the ®rst two statistical moments, that CCCM model has a T32 resolution, roughly correspond- is, the mean and standard deviation. These are, however, ing to a 3.75833.758 grid. Its interactive lower bound- only a few of the possible criteria. In many applications, ary consists of a mixed layer ocean model and a ther- for example, in crop growth modeling, the reliability of modynamic ice model. Twenty years of its control in- the time structure of a series is of crucial importance. tegration have been available. The validation of its sur- In spite of this, little has been done in examining the face temperature characteristics over central Europe was time structure of temperature series produced by various performed by Kalvova et al. (2000). methods. Several studies investigated autocorrelations There is a continuing debate as to whether GCM out- of temperature series in GCM and RCM outputs (Buis- puts should be treated as gridbox or gridpoint values hand and Beersma 1993; Mearns et al. 1995; Kalvova (Skelly and Henderson-Sellers 1996), that is, if the grid- and NemesÏova 1998) and the occurrence of prolonged ded output of a model should be compared with indi- extreme events, such as heat waves (Huth et al. 2000). vidual station values or areal aggregates (e.g., spatial No study has so far analyzed the time structure of down- means). Huth et al. (2000) have shown that for daily scaled temperature series, except for Trigo and Palutikof maximum temperatures and heat waves, there is virtu- (1999) who examined the numbers of heat and cold ally no difference between the two approaches, and a waves. Hayhoe (2000) validated the weather generator grid point versus station comparison is thus justi®ed. in terms of the distribution of the length of frost-free Therefore, we compare temperature characteristics at periods. individual stations with GCM outputs at the grid points The aim of this study is to analyze time series of daily closest to them. extreme temperatures produced by two GCMs, a statis- tical downscaling procedure, and a weather generator, b. Downscaling and verify them against observations at six central Eu- ropean sites. The analysis is focused on empirical dis- Downscaled temperatures were calculated by multiple tributions of day-to-day temperature changes and, pri- linear regression with stepwise screening from gridded marily, on prolonged periods of high and low temper- 500-hPa heights and 1000-/500-hPa thickness over the atures, that is, heat and cold waves. Although extreme domain covering most of Europe and extending over events have recently begun to play a more important the adjacent Atlantic Ocean (bounded by 32.18, 65.68N, role in analyses of climate change simulations (Meehl 16.98W, and 28.18E). The stepwise regression of gridded et al. 2000), little attention has so far been paid to heat values was selected because in the intercomparison and cold waves. We also examine the effect on time study by Huth (1999), it turned out to perform best series of (i) the way the variance is reproduced in sta- among other linear methods, including regression of tistical downscaling and (ii) whether the annual cycle principal components and canonical correlation analy- of correlations and lagged correlations among simulated sis. In that study, a detailed description of the procedure variables is implemented in the weather generator or can be found, including the selection of the most in- not. formative predictors. The downscaling procedure is designed so that it re- tains the mean of the original time series. It is important 2. Datasets and methods of their construction to retain also variance; there are two possible ways of a. GCMs doing that, which are both applied and compared in this study. The commonly used way, variance in¯ation, con- The simulations of present climate (control runs) of sists in enhancing each day's anomaly by the same fac- two GCMs have been used in this study. The ECHAM tor, de®ned by the share of variance explained by down- GCM originates from the European Centre for Medium- scaling (Karl et al. 1990). However, the variance in¯a- Range Weather Forecasts modelÐhence EC, modi®ed tion implicitly assumes that all local variability origi- by the Max Planck Institute for Meteorology in Ham- nates in large-scale variability, which is not true (von Unauthenticated | Downloaded 10/02/21 11:58 AM UTC 000 2001 HUTH ET AL.
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