Home

Using SDSM Version 3.1 — A decision support tool for the

1. NCEP HadCM2 Day month oanvanonwow oF NOUN OE oe Jan Feb Timer Apr Imay sun T su Taug sep T oct Tov ec Figure 11 4 Monthly frequency of hot days gt 25 C at Blogsville for the current climate downscaled using observed NCEP predictors 1961 1990 and GCM HadCM2 predictors 1960 1989 The second example of the Generate Scenario operation uses HadCM2 predictors for the period 2080 2099 As before Figure 11 5 shows the Results screen for this scenario obtained from the Analyse Model Output operation File amp Back Print SUMMARY STATISTICS FOR TMAXFCF OUT Analysis Start Date 01 01 1961 Number of Days 7200 Ensemble Member s ALL Month Mean Maximum Minimum Variance POT January 7 171 19 104 6 929 20 132 0 000 February 8 035 18 378 3 668 14 847 0 000 March 9 384 21 471 3 220 17 011 0 000 April 12 543 23 710 1 753 13 601 0 008 May 16 450 27 750 4 219 15 606 1 208 June 19 873 31 425 8 693 13 270 8 150 July 21 701 30 404 12 034 10 120 15 433 August 21 887 31 568 12 641 10 350 16 842 September 19 707 28 783 10 027 9 476 4 042 October 15 883 24 872 6 128 10 497 0 092 November 11 581 22 503 0 553 14 729 0 008 December 8 263 20 099 5 850 17 197 0 000 Winter 7 823 19 194 5 483 17 392 0 000 Spring 12 792 24 310 0 917 15 406 0 406 Summer 21 154 31 132 11 123 11 247 13 475 Autumn 15 724 25 386 5 201 11 567 1 381 Annual 14 373 25 006 2 940 13 9
2. Nothing The fourth root button in Advanced Settings tells the software that this transformation is to be used throughout including calibration weather generation and scenario generation If checked there s no need to apply any further transformations as this is all backed out automatically So when calibrating the model with fourth root checked you should supply the model with untransformed rainfall data making sure that the Conditional process button is checked in the Calibrate Model screen Q Is it OK to model precipitation as an unconditional process As a general rule precipitation should be modelled as a Conditional process It does not make much sense to neglect the occurrence process 1 e sequences of wet or dry days are first modelled then the amounts if it is a wet day If you are being swayed by higher R sq values of an unconditional model during calibration beware the result is probably seriously biased by the large number of zero values entered in the multiple regression Remember daily precipitation amount is the most problematic daily variable to downscale Q When I use the Weather Generator I get unrealistically large maximum daily precipitation values What s going wrong Unrealistically large values generally imply that the variance inflation and or bias correction in Advanced Settings are too high Q Why do I get slightly different results every time I run the Weather Generator with the same inputs
3. 1998 A comparison of statistical and model based downscaling techniques for estimating local climate variations Journal of Climate 11 735 753 Mearns L O Bogardi I Giorgi F Matyasovszky I and Palecki M 1999a Comparison of climate change scenarios generated from regional climate model experiments and statistical downscaling Journal of Geophysical Research 104 6603 6621 Mearns L O Mavromatis T Tsvetsinskaya E Hays C and Easterling W 1999b Comparative responses of EPIC and CERES crop models to high and low spatial resolution climate change scenarios Journal of Geophysical Research 104 6623 6646 Murphy J 1999 An evaluation of statistical and dynamical techniques for downscaling local climate Journal of Climate 12 2256 2284 Murphy J 2000 Predictions of climate change over Europe using statistical and dynamical downscaling methods International Journal of Climatology 20 489 501 Takle E S Gutowski Jr W J Arritt R W Pan Z Anderson C J Silva R Caya D Chen S C Christensen J H Hong S Y Juang H M H Katzfey J J Lapenta W M Laprise R Lopez P McGregor J and Roads J O 1999 Project to Intercompare Regional Climate Simulations PIRCS Description and initial results Journal of Geophysical Research 104 19 443 19 462 Wilby R L Hay L E and Leavesley G H 1999 A comparison of downscaled and raw GCM output implications for climate chan
4. 3 4 NCEP HadcM2 3 3 Jan Feb mar apr Tmay T jun T sut Taug sep oct Tov Toes Figure 11 8 Monthly mean daily precipitation totals at Blogsville for the current climate downscaled using observed NCEP predictors 1961 1990 and GCM HadCM2 predictors 1960 1989 The Generate Scenario operation was implemented for a second time using HadCM2 predictors representing future 2080 2099 climate forcing Figures 11 9 and Figure 11 10 compare selected outputs of this scenario with equivalent results from the current 1960 1989 climate downscaling using HadCM2 predictors xi Eile Edit Help ee Copy a Print 2 Help ce ES2 Back Reset Settings Maximum 24 hour totals 80 80 70 70 60 60 wn 50 50 3 40 Sa so 1960 89 E a s 2080 99 20 20 dan Feb Tmar T apr Tay Tuun T uu Taug Tsep T oct Tov TDec Figure 11 9 Monthly maximum daily precipitation totals at Blogsville downscaled using HadCM2 predictors under current 1960 1989 and future 2080 2099 forcing xi File Edt Help a Print Help lt ET Back Reset Settings Copy Dry spells 1960 89 2080 99 o NM oas noa 6 5 4 n 3 4 Wwe 24 1 0 Jan T Feb Tmar apr Tmay Tuun Tur Taug Isep oct Tov T Dec Figure 11 10 Monthly mean dry spell lengths at Blogsville downscaled
5. Even with the same inputs i e PAR file Settings and data period the Weather Generator and Generate Scenario operation is not expected to produce identical results if the Random Number Seed is checked in Settings This is because of the stochastic random component that is applied to each downscaled series to compensate for the fact that the deterministic component of the model due to the chosen predictor variables does not explain all of the observed variance Differences between individual runs and or Ensemble Members is likely to be greater for poorly determined predictands such as precipitation than in better constrained predictands such as temperature Q Does SDSM produce realistic results for multiple sites Also what if Pm interested in preserving relationships between variables Both of these questions are the subject of ongoing research However results from previous studies suggest that regression based downscaling does preserve some of the observed inter site correlations provided that models calibrated on a site by site basis are forced by a common set of predictors In other words inter site correlations are implicitly reproduced by virtue of correlated predictor variables rather than by the model structure Alternatively inter site behaviour may be reproduced by employing a conditional resampling approach in which case SDSM is used to downscale a predictand at a benchmark site This series is in turn used to resamp
6. R L Dawson C W and Barrow E M 2001 SDSM a decision support tool for the assessment of regional climate change impacts Environmental and Modelling Software 17 145 157 Wilby R L Hassan H and Hanaki K 1998b Statistical downscaling of hydrometeorological variables using general circulation model output Journal of Hydrology 205 1 19 Wilby R L Tomlinson O J and Dawson C W 2003 Multi site simulation of precipitation by conditional resampling Climate Research 23 183 194 NCEP re analysis and Hadley Centre experiments Gordon C Cooper C Senior C Banks H Gregory J Johns T Mitchell J and Wood R 2000 The simulation of SST sea ice extents and ocean heat transport in a version of the Hadley Centre coupled model without flux adjustments Climate Dynamics 16 147 168 Hadley Centre 1998 Climate Change and its Impacts Some Highlights from the Ongoing UK Research Programme UK Met Office Publication 12pp Johns T C Carnell R E Crossley J F Gregory J M Mitchell J F B Senior C A Tett S F B and Wood R A 1997 The Second Hadley Centre coupled ocean atmosphere GCM Model description spinup and validation Climate Dynamics 13 103 134 Kalnay E Kanamitsu M Kistler R et al 1996 The NCEP NCAR 40 year reanalysis project Bulletin of the American Meteorological Society 77 437 471 Mitchell J F B and Johns T C 1997 On modification of global warming by su
7. Select Select PE 31 12 1965 File Figure 10 1 Time Series Plot screen File Selection Using the Drive Directory and File Selection boxes the User can select up to five files to plot Note two File Selection windows are provided to allow the User to select files from different directories Only a maximum of five files from the two File Selection windows can be selected in total Data Allows the User to select the period that they wish to analyse Note if an attempt is made to plot a period longer than the available data set as defined in the global Settings an error message will appear Save Results To The User can choose to save a summary of the calculated results to a data file This option will not work if plotting Raw Data as no summary statistics are calculated for this option The default format text file that is comma separated CSV that can be opened in a spreadsheet for further analysis Clicking on the Clear button deselects the selected file Time Period Allows the User to select from Raw Data Month Season Annual or Water Year Raw Data simply graphs the data from the chosen file s as a time series plot for the selected period set by the User under Fit Start and Fit End No statistics are derived for Raw Data Note the Water Year runs from October to September and is referred to by the year in which it starts Season is referred to by the year in which it falls Winter December January February is
8. white noise applied to regression model estimates of the local process The default value produces approximately normal variance inflation prior to any transformation Larger values increase the variance of downscaled properties Variance inflation is de activated by setting the parameter to zero Note that for Fourth root and Natural log Model Transformations see above this parameter also affects changes in the mean estimate Bias Correction Compensates for any tendency to over or under estimate the mean of conditional processes by the downscaling model e g mean daily rainfall totals The default value is 1 0 indicating no bias correction Settings File Locates standard and advanced settings held in a User defined reference file and directory A new or updated settings file is created whenever the Save button is clicked at the top of the screen The C SDSM INI settings file is automatically loaded whenever SDSM is run Variance Inflation Bias Correction fie 1 Figure 3 4 The Advanced Settings screen Press Reset at any time to reload the original settings or Back to return to the Settings screen followed by Back again to return to the last open screen 4 QUALITY CONTROL AND DATA TRANSFORMATION Few meteorological stations have complete and or fully accurate data sets Handling of missing and imperfect data is necessary for most practical situations In some cases it is also necessary to transform data prior to mo
9. 11 987 21 829 1 254 12 358 0 000 May 14 920 25 778 3 967 12 116 0 200 June 19 041 29 871 9 017 12 119 4 233 July 19 612 28 749 10 400 10 097 4 733 August 19 946 30 046 10 930 10 582 6 417 September 17 989 27 125 8 819 9 607 1 050 October 14 137 23 270 4 153 10 305 0 000 November 9 696 20 185 0 403 12 872 0 000 December 7 343 19 562 5 138 18 047 0 000 Winter 7 130 18 280 5 043 16 485 0 000 Spring 12 093 23 328 0 630 14 247 0 069 Summer 19 533 29 555 10 116 10 932 5 128 Autumn 13 941 23 526 4 190 10 928 0 350 Annual 13 174 23 672 2 473 13 148 1 387 Figure 11 3 Example results for Blogsville using GCM predictors 1960 1989 Using the Compare Results operation it is possible to compare the frequency of hot days at Blogsville downscaled using observed NCEP and GCM HadCM2 predictor variables For example Figure 11 4 shows the respective monthly mean frequencies produced by each set of predictors with an ensemble size of 20 It is evident that the downscaling forced by GCM predictors introduces a slight cool bias in early summer and slightly over estimates the number of hot day in late summer Note that results for individual SDSM runs or ensemble members will differ slightly even when using the same model parameters and predictors due to the stochastic component of the downscaling x Ele Edit Help 2 8 2 Back _ Reset Settings Copy Print_ Help Frequency of hot days
10. Climate Modelling and Analysis SDSM Version 2 2 was sponsored by the Environment Agency through the National Centre for Risk Analysis and Options Appraisal SDSM Version 2 1 was supported by the Canadian Climate Impacts Scenarios CCIS Group though the Climate Change Action Fund Assistance in kind was provided by A Consortium for the Application of Climate Impact Assessments ACACIA at the National Centre for Atmospheric Research NCAR Boulder CO NCAR is sponsored by the National Science Foundation NCEP Re analysis data were provided by the NOAA CIRES Climate Diagnostics Center Boulder Colorado USA from their Web site at http www cdc noaa gov The Climate Impacts LINK Project is funded by the UK Department of the Environment Transport and the Regions Contract Reference EPG 1 1 124 CONTENTS i Preface ii Acknowledgements 0 TECHNICAL INFORMATION 1 INTRODUCTION 1 1 Downscaling techniques 1 1 1 Dynamical 1 1 2 Weather typing 1 1 3 Stochastic weather generators 1 1 4 Regression 1 2 Relative skill of statistical and dynamical downscaling techniques 1 3 Manual outline 2 OVERVIEW OF SDSM STRUCTURE AND UKSDSM ARCHIVE 2 1 Key functions of SDSM 2 1 1 Quality control and data transformation 2 1 2 Selection of downscaling predictor variables 2 1 3 Model calibration 2 1 4 Weather generator 2 1 5 Data analysis 2 1 6 Graphical analysis 2 1 7 Scenario generation 2 2 UKSDSM data archive 2 3 UKSDSM predictors 2 4 S
11. DAT files in this directory are then listed in the window above To select a predictor simply click on the file name it will be highlighted in blue A brief definition of the chosen variable is given in the Predictor Description window To deselect a file click on it again and it will no longer be highlighted The number of predictor variables chosen is shown beneath this window up to a maximum of 12 The Data menu on the left hand side of the Screen Variables screen allows the start and end dates of the analysis period to be changed The default dates are held in the Settings screen see Section 3 1 in this case 1961 1990 If the start and end dates lie outside the permissible range the User will be prompted to enter new values The User must also choose the seasonal subset from the pull down window under Select analysis period The available options are Annual no seasonal sub setting Winter December February Spring March May Summer June August Autumn September November and individual months Two more actions are necessary before the analysis can take place Firstly the type of Process must be specified If the predictor predictand process is not regulated by an intermediate process as in the case of maximum temperature then click on Unconditional otherwise select Conditional as with precipitation where amounts depend on wet day occurrence Secondly amend the Significance Level as required This value is
12. Series Chart Y axis label x axis label ncepp__uxx dat po me che ea ena cons SSS SS ES ee Figure 10 3 An example of the Time Series Chart Settings form Wilby amp Dawson 2004 Page 44 of 67 The following points explain how each of these options work Lines The User can adjust the width and legend text of each line in the chart After making the required changes the User must click the Make Changes button to apply the changes to the chart Legend The User can choose to show the legend on the chart or remove the legend by clicking the appropriate buttons at the bottom of this screen Y axis ticks These refer to the tick lines drawn across the chart on the y axis The default is no tick lines except for the line y 0 If the User wishes to apply y axis tick lines enter the number required in the text box here and click the Make Changes button Clicking the Clear Y Ticks button removes these tick lines Y axis range The User can adjust the extent of the Y axis by entering appropriate minimum and maximum values on this page and clicking the Make Changes button X axis labels For analysed data year markers are shown on the X axis For raw data it is possible to apply a number of data markers a counter on the X axis X axis label spacing can be specified by entering an appropriate value in the X axis labels gap text box This specifies the interval between successive X axis labels markers The default for analy
13. are written to specific output files for later statistical analysis and or impacts modelling 2 1 5 Data analysis SDSM provides a means of interrogating both derived SDSM scenarios and observed climate data with the Analyse Data screen In both cases the User must specify the sub period and output file name For model output the ensemble member or mean must also be specified In return SDSM displays a suite of diagnostics chosen from the Statistics screen The default statistics are monthly seasonal annual means maxima minima sums and variances 2 1 6 Graphical analysis Two means of graphical analysis are provided by SDSM 3 1 through the Compare Results screen and the Time Series Plot screen The Compare Results screen enables the User to plot monthly statistics produced by the Analyse Data screen Having specified the necessary input file either bar or line charts may be chosen for display purposes The graphing option allows simultaneous comparison of two data sets and hence rapid assessment of downscaled versus observed or current versus future climate scenarios The Time Series Plot screen allows the User to produce a time series plot of chosen data file s up to a maximum of five files can be plotted simultaneously on the same chart The data can be analysed as monthly seasonal annual or water year periods for statistics such as Sum Mean Maximum Winter Summer ratios Partial Duration Series Percentiles and Standardis
14. as required Then click on the Back button to return to the plot To change the colour scheme of the lines or bars double click on the object required A Color palette will appear Select the desired colour then OK to return to the chart Similarly to change the Font double click on title and or y axis title To change the position of the title single click then drag to the required location on the chart By applying the above design preferences it is possible to change the bar chart in Figure 9 3 into the following xi File Edit Help ps Back Copy amp Print Q Help P Reset _ Settings Maximum temperatures at Nottingham Hi Observed I Downscaled Figure 9 5 The same as Figure 9 3 but with customised bar colour scheme tick marks scale and legend Finally to incorporate Line or Bar charts in a Word document first use the Copy button at the top of the screen then in Word use Paste Special Picture 10 TIME SERIES PLOT 10 1 Time Series Chart The Time Series Plot page Figure 10 1 allows the user to produce a time series plot of chosen data file s Up to a maximum of five files can be plotted simultaneously on the same chart 3 Time Series Plot File Edit Analyse Help Plot neepp__vxx dat ncepp__zxx dat neepp500xx dat neepshumxx dat ncepulagss dat ncepvlagxx dat ncepzlagxx dat I EverythingElse Blogsville Data Sivas 01 01 1961
15. deselected File Help TE Back Reset Open a 2 Advanced _ Help Save Year Length Miscellaneous Calendar 366 C Calendar 365 Allow Negative Values M Sia Event Threshold 0 Missing Data Identifier 999 Data Standard Start Date 01 01 1961 Random Number Seed 7 Standard End Date 31 12 2000 Figure 3 3 The Settings screen 3 2 Advanced settings The advanced settings are accessed from the Settings screen by clicking on the Advanced button at the top of the screen The Advanced Settings screen allows the User to change and save further downscaling model preferences Figure 3 4 Model Transformation Specifies the transformation applied to the predictand in conditional models The default None is used whenever the predictand is normally distributed this is often the case for daily temperature The alternatives Fourth root Natural log and Inverse Normal are used whenever data are skewed as in the case of daily precipitation Note that the Inverse Normal transformation employs conditional resampling of the observed predictand see Wilby et al 2003 The transformation type is recorded in PAR and SIM files to ensure that data are consistently handled during subsequent scenario and data analysis routines Variance Inflation Controls the magnitude of variance inflation in downscaled daily weather variables This parameter changes the variance by adding reducing the amount of
16. over dynamical downscaling approaches In situations where low cost rapid assessments of localised climate change impacts are required statistical downscaling currently represents the more promising option In this manual we describe a software package and accompanying statistical downscaling methodology that enables the construction of climate change scenarios for individual sites at daily time scales using grid resolution GCM output The software is named SDSM Statistical DownScaling Model and is coded in Visual Basic 6 0 As far as the authors are aware SDSM is the first tool of its type freely offered to the broader climate change impacts community Most statistical downscaling models are generally restricted in their use to specialist researchers and or research establishments Other software although more accessible produces relatively coarse regional scenarios of climate change both spatially and temporally For example SCENGEN blends and re scales user defined combinations of GCM experiments and then interpolates monthly climate change scenarios onto a 5 latitude x 5 longitude global grid Weather generators such as WGEN LARS WG or CLIGEN see bibliography are widely used in the hydrological and agricultural research communities but do not directly employ GCM output in the scenario construction processes Following a brief overview of downscaling techniques we describe the structure and operation of
17. referred to by the year in which the January February fall When selecting a Month Season Annual or Water Year Time Period SDSM calculates the chosen statistics from the list below Select Statistic for the specified period and plots them on a line graph For example if the User chooses a Time Period of January and selects Sum SDSM will plot the annual series of January Sums for the selected fit period i e the sum for January 1961 sum for January 1962 and so on as a line chart Select Statistic The User selects the summary statistics to be plotted by clicking in the appropriate check button in this section default is Sum SDSM calculates the chosen statistic for the selected Time Period repeated across the range of the fit period and plots these calculated statistics on a time series chart Note Winter Summer ratio is calculated as the sum of the winter data December January February divided by the sum of the following summer data June July August It is referenced to the year in which the summer period falls PDS is the Partial Duration Series and is calculated as the sum of data values less than or equal to the chosen threshold for the selected Time Period The default is the threshold value set in the main Settings screen This value can be adjusted by entering the required threshold in the text box this will not affect the threshold value set in the main Settings screen and applied elsewhere Percentile calcul
18. studies Climatic Change 22 67 84 Wilks D S 1999 Multisite downscaling of daily precipitation with a stochastic weather generator Climate Research 11 125 136 Wilks D S and Wilby R L 1999 The weather generation game a review of stochastic weather models Progress in Physical Geography 23 329 357 Dynamical downscaling methods Christensen J H Machenhauer B Jones R G Sch r C Ruti P M Castro M and Visconti G 1997 Validation of present day regional climate simulations over Europe LAM simulations with observed boundary conditions Climate Dynamics 13 489 506 Giorgi F and Mearns L O 1991 Approaches to the simulation of regional climate change A review Rev Geophys 29 191 216 Giorgi F and Mearns L O 1999 Introduction to special section Regional climate modeling revisited Journal of Geophysical Research 104 6335 6352 McGregor J J 1997 Regional climate modelling Meteorol Atmos Phys 63 105 117 Downscaling comparisons Charles S P Bates B C Whetton P H and Hughes J P 1999 Validation of downscaling models for changed climate conditions case study of southwestern Australia Climate Research 12 1 14 Hay L E Wilby R L and Leavesley G H 2000 A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States Journal of the American Water Resources Association 36 387 397 Kidson J W and Thompson C S
19. the synthesis process is the selection of the appropriate parameter file Click on the Select Parameter File button in the top left hand corner An Open file window appears browse through until the correct directory and file are reached then click on the appropriate file name for example the parameters for maximum daily temperature at Blogsville are held in TMAX PAR The name of the file will then appear beneath the button Next specify the location of the predictor variable files by choosing the correct directory and drive from the window in the bottom left hand corner of the screen under Select Predictor Directory To write synthetic data to a results file it is necessary to select an appropriate directory and output file name Click on the Select Output File button in the top right hand corner An Open file window appears browse through until the correct directory is reached then enter a suitable file name for example TMAX OUT The name of the file will then appear beneath the button Click on the View Details button and the predictand followed by predictor files used in model calibration are listed in the window below The Record Start date and available Record Length number of days are also displayed The User must specify the sub period required for weather generation using the Synthesis Start and Sythesis Length boxes respectively The default values for Synthesis Start and Sythesis Length are used to simulate the
20. the variable is vorticity computed at the 850 hPa geopotential height pS_z and the grid box is Southwest England sw Alternatively the file name h2ggp_thsb dat indicates that the source is HadCM2 greenhouse gas only experiment A2gg the variable is surface wind direction p_th and the grid box is Scottish Boarders sb 2 4 2 SDSM file name protocol With the above prerequisites in mind Table 2 2 lists the file name extensions employed by SDSM and Figure 2 3 shows the associated directory structures All input and output files are text only format Individual predictor and predictand files one variable to each file time series data only are denoted by the extension dat The PAR file records meta data associated with the model calibration model weights and measures of goodness of fit percentage explained variance and standard error of the model The SIM file records meta data associated with every downscaled scenario e g number of predictor variables ensemble size period etc and the OUT file contains an array of daily downscaled values one column for each ensemble member and one row for each day of the scenario Finally TXT files are created whenever statistical analyses are undertaken in SDSM These files record summary statistics for individual ensemble members or for the ensemble mean and are accessed by bar line chart options The data format also enables convenient export to other graphing sof
21. used to test the significance of predictor predictand correlations The default is p lt 0 05 5 Once the above have been specified SDSM is ready to analyse the chosen predictor predictand relationship s for specified sub period s 5 2 Temporal variations in predictor strength The Analyse button is used to investigate the percentage of variance explained by specific predictand predictor pairs The strength of individual predictors often varies markedly on a month by month basis e g Figure 5 2 The User should therefore be judicious concerning the most appropriate combination s of predictor s for a given season and predictand The local knowledge base is also invaluable when determining sensible combinations of predictors CE x File Help 8 9 Back Print Help RESULTS EXPLAINED VARIANCE Analysis Period 01 01 1961 31 12 1990 Significance level 0 05 Total missing values 0 Predictand TMA DAT Predictors JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC ncepp__uee dat 0348 0408 0 251 0 077 0 006 0 010 0 016 0152 0 314 ncepp__vee dat 0 031 0022 0 044 0 189 0169 0163 0 212 0 167 0173 0184 0 158 0 048 ncepp__zee dat 0 030 0 026 0 020 0 062 0 113 0 117 0 067 0 026 0 011 0 010 ncepp500ee dat 0 088 0105 0 232 0 382 0489 0498 0 483 0449 0343 0239 0 166 0135 ncepulagee dat 0 284 0318 0 206 0 038 0 006 0 011 0 007 0 114 0198 ncepvlagee dat 0 030 0 022 0 020 0140 0 152 0 104 0 111 0 093 0 085 0113 0100 0 03
22. 0 ncepzlagee dat 0 013 0013 0 011 0 028 0 077 0128 0 164 0142 0059 0 012 Figure 5 2 The Results screen for the Blogsville example The strongest correlation in each month is shown in red indicating that the relationship between maximum temperature and p500 and p__u are most important Blanks represent insignificant relationships at the chosen Significance Level For the Blogsville example select maximum daily temperatures as the predictand TMAX and the following predictor files p500 p__u p_v andp z see Table 2 1 In addition use the Transform facility Section 4 2 to create lagged values one day for the surface airflow indices The predictand does not depend on an intermediate occurrence process so Unconditional is checked under the Process option Use the default dates for the Data option i e 1961 1990 and choose Annual under Select Analysis Period Use the default Significance Level i e 0 05 then click on the Analyse button at the top of the Screen Variables menu The results in Figure 5 2 suggest that p500 is a potentially useful predictor for April through October maximum temperature and p__u for December through March 5 3 Correlation matrix The Correlation button is used to investigate inter variable correlations for specified sub periods annual seasonal or monthly SDSM also reports partial correlations between the selected predictors and predictand These statistics help to identify the amount of explan
23. 0 days and for the Blogsville example the period 1960 1989 was used to represent current climate forcing Once necessary changes have been made to the Settings click on Back to return to the Generate Scenario screen 11 2 Setup The first step in scenario generation is the selection of the appropriate downscaling model parameter file Click on the Select Parameter File button in the top left hand corner An Open file window appears browse through until the correct directory and file are reached then click on the appropriate file name for example the parameters for maximum daily temperature at Blogsville are held in TMAX PAR The name of the file will then appear beneath the button Next click on the View Details button and the predictand followed by predictor files used in model calibration are listed in the window below Then select the appropriate drive location and directory for the GCM predictors under the GCM Directory header For best practice GCM predictors originating from different experiments or time slices e g 1960 1989 or 2080 2099 should be held in separate folders This is because SDSM will load only files with the same predictor names i e characters 5 to 8 as those used in model calibration see Table 2 1 As in the Weather Generator Section 7 decide how many ensembles members are needed up to a maximum of 100 and enter the appropriate integer in the Ensemble Size box on the left hand side of the screen
24. 03 3 815 Figure 11 5 Example results for the Blogsville using GCM predictors 2080 2099 Once again by using the Compare Results operation it is possible to compare the frequency of hot days at Blogsville under current 1960 1989 and future 2080 2099 climate forcing For example Figure 11 6 shows a significant increase in the frequency of hot days in summer most noticeably in the month of August The downscaling also indicates that hot days could begin to appear as early as May by the end of the 21st century Chart E x File Edit Help lt 4 Back Reset Settings amp Q Copy Print _ Help Frequency of hot days A F 12 12 1980 99 8 8 2080 99 4 4 a AEA 3 dan Feb Mar Apr May Jun Jul Aug Sep oct Nov Dec Figure 11 6 Monthly frequency of hot days gt 25 C at Blogsville downscaled using HadCM2 predictors under current 1960 1989 and future 2080 2099 forcing Dayymonth 11 4 Blogsville example precipitation Precipitation downscaling is necessarily more problematic than temperature because daily precipitation amounts at individual sites are relatively poorly resolved by regional scale predictors and because precipitation is a conditional process i e both the occurrence and amount processes must be specified Figure 11 7 shows a SIM file used to downscale daily precipitation using observed NCEP
25. 493 15 900 3 700 13 194 0 000 232 280 Winter 6 796 17 900 6 700 14 337 0 000 575 100 Spring 12 393 26 100 1 600 18 087 5 000 1140 193 Summer 20 120 34 800 0 000 14 673 172 000 1851 033 Autumn 13 900 27 100 0 600 20 362 5 000 1264 873 Annual 13 331 34 800 6 700 39 332 182 000 4869 540 Figure 8 3 Default statistics for observed daily maximum temperatures at Blogsville during the validation period 1976 1990 The Results screen lists the name of the input file along with the start and end dates of the analysis Monthly seasonal and annual mean statistics are listed beneath for the chosen tests Comparison of the results obtained from the Weather Generator see below gives an indication of model skill See Section 9 for visual comparisons of monthly statistics Results Analysis Start Date 01 01 1961 Analysis End Date 31 12 1990 Ensemble Member s ALL Mean Maximum Variance 10 301 25 149 11 662 25 309 13 146 25 905 14 726 26 450 16 415 27 675 17 002 28 191 15 798 28 605 13 475 26 806 11 572 24 373 10 034 21 390 9 005 9 667 10 509 14 763 15 408 10 202 Figure 8 4 Example output of Analyse Data Modelled showing the mean and standard deviation of diagnostics for a 20 member ensemble 9 GRAPHING MONTHLY STATISTICS The Compare Results operation enables the User to plot monthly statistics produced by the Analyse Model Ouput Other Data operations Section 8 Graphing options allow the comparison of two sets o
26. Back to return to the Calibrate Model screen or on Next to proceed to Analyse Model Output 7 3 Blogsville example Selections for Blogsville are illustrated in Figure 7 1 In this example the Weather Generator synthesized 20 runs of 15 years daily maximum temperature using five regional scale predictors The data were synthesized using independent predictors withheld from model calibration i e for the period 1976 1990 Figure 7 3 shows the first few values of 12 ensemble members held in the TMAX OUT file written to C SDSM Blogsville Results Figure 7 4 shows the corresponding TMAX SIM file which contains meta data associated with the synthesis In both cases the files have been opened in WordPad E IMAX OUT WordPad Figure 7 3 An example of the format of the simulated data file OUT E TMAX SIM WordPad EBX Fie Edit view Insert Format Help Oeil k gt 5 12 366 01 01 1976 5297 F ALSE 20 12 1 1 THAX DAT neepp uee dat neepp zee dat ncepp500ee dat nceplagee dat ncepzlagee dat Figure 7 4 The SIM file produced by the Weather Generator operation for the Blogsville example The output is in line order 1 the number of predictor variables 2 the number of regression models used l annual 4 seasonal 12 monthly 3 the maximum number of days in a year here a calendar year is used so there are up to 366 days in leap years 4 the start date of the data used for model calibr
27. Centre for Environmental Prediction NCEP re analysis data set The data were re gridded to conform to the grid system of HadCM3 Figure 2 2 All predictors with the exception of the geostrophic wind direction see below were normalised with respect to the 1961 to 1990 average However daily predictors are also supplied for the period 1961 2000 For downscaling future climate scenarios four sets of GCM output are available HadCM2 HadCM3 CGCM2 CSIRO Three emission scenarios are available the Hadley Centre s coupled ocean atmosphere models HadCM2 and HadCM3 the greenhouse gas only experiment with CO2 compounded annually by 1 per year HadCM2 only the two SRES scenarios A2 and B2 produced by greenhouse gas sulphate aerosol and solar forcing HadCM3 CSIRO CGCM2 2 3 UKSDSM predictors Table 2 1 lists the daily predictor variables held in the UKSDSM data archive Ideally candidate predictor variables should be physically and conceptually sensible with respect to the predictand strongly and consistently correlated with the predictand and accurately modelled by GCMs For precipitation downscaling it is also recommended that the predictor suite contain variables describing atmospheric circulation thickness stability and moisture content In practise the choice of predictor variables is constrained by data availability from GCM archives The predictors in Table 2 1 therefore represent a compromise between maximum overlap betwee
28. DSM file protocols 2 5 Obtaining SDSM predictors online 3 GETTING STARTED 3 1 Settings 3 2 Advanced settings 4 QUALITY CONTROL AND DATA TRANSFORMATION 4 1 Quality control 4 2 Data transformation 5 SELECTION OF DOWNSCALING PREDICTOR VARIABLES 5 1 Setup 5 2 Temporal variations in predictor strength 5 3 Correlation matrix 5 4 Scatterplot 6 MODEL CALIBRATION 6 1 File handling 6 2 Model type 6 3 Blogsville example 7 WEATHER GENERATOR 7 1 File handling 7 2 Ensemble size 7 3 Blogsville example 8 ANALYSIS OF OBSERVED AND DOWNSCALED DATA 8 1 Overview 9 GRAPHING MONTHLY STATISTICS 9 1 Line chart 9 2 Bar chart 9 3 Customizing charts 10 TIME SERIES PLOTS 10 1 Time Series Chart 10 2 Adjusting chart appearance 11 SCENARIO GENERATION 11 1 Check settings 11 2 Setup 11 2 Blogsville example temperature 11 3 Blogsville example precipitation 12 CAUTIONARY REMARKS BIBLIOGRAPHY APPENDICES A 1 Enhancements since Version 2 2 A 2 Frequently asked questions A 3 Evaluation of GCM predictors GLOSSARY 0 TECHNICAL INFORMATION SDSM version 3 1 runs on PC based systems and has been tested on Windows 98 NT 2000 XP Note on older machines some statistical analyses may take longer to perform and or may exhaust available memory when large data sets are processed 1 INTRODUCTION Even if global climate models in the future are run at high resolution there will remain the need to downscale the results from such models
29. F SDSM STRUCTURE AND UKSDSM ARCHIVE Downscaling is justified whenever GCM or RCM simulations of variable s used for impacts modelling are unrealistic at the temporal and spatial scales of interest either because the impact scales are below the climate model s resolution or because of model deficiencies Downscaling may also be used to generate scenarios for exotic variables that can not be obtained directly from GCMs and RCMs However the host GCM must have demonstrable skill for large scale variables that are strongly correlated with local processes In practice the choice of downscaling technique is also governed by the availability of archived observational and GCM data because both are needed to produce future climate scenarios The SDSM software reduces the task of statistically downscaling daily weather series into seven discrete processes denoted by heavy boxes in Figure 2 1 1 quality control and data transformation 2 screening of predictor variables 3 model calibration 4 weather generation observed predictors 5 statistical analyses 6 graphing model output 7 scenario generation climate model predictors Station data Select predictand Select predictors Set model structure Calibrate model Downscale predictand Quality control Screen variables Transform variables Station and NCEP data Scatter plot Un conditional process NCEP predicto
30. M2 and CSIRO have 365 days and no leap years whereas HadCM2 and HadCM3 have model years consisting of 360 days Failure to set this parameter correctly can lead to system errors due to insufficient data or the production of non sensible output Standard Start End Date Enter the global default start and end date for all input data These dates will appear throughout the operation of SDSM but may be updated from any screen Allow Negative Values The default allows simulation of negative values by the downscaling model e g for minimum temperature deselection truncates values at zero e g for sunshine hours Event Threshold For some variables it is necessary to specify an event threshold For example when calibrating daily precipitation models the parameter might be set to 0 3 mm day to treat trace rain days as dry days Similarly the threshold for sunny versus cloudy days might be set at 1 0 hours day to discriminate between overcast and sunny conditions Missing Data Identifier This is the code assigned to missing data in all input series Whenever SDSM encounters this code the value will be skipped e g during model calibration or calculation of summary statistics The default is 999 Random Number Seed Ensures that the random sequences produced by Weather Generator Section 7 and Generate Scenario Section 11 are different each time the model is run If replicate experiments are preferred the check box should be
31. SDSM archive Secondly the meta data and output files produced by SDSM more generally 2 4 1 UKSDSM file structure and nomenclature Figure 2 3 shows how the directory structure of the UKSDSM data archive relates to ancillary file systems in SDSM The UKSDSM archive is organised into three levels At the highest level are the data sources presently NCEP HadCM2 HadCM3 CSIRO or CGCM2 At the second level are the nine cells shown in Figure 2 2 At the third level are files containing individual predictor variables Archive Scenarios NCEP GCM GCM Calibration Results Station Current 9 grid boxes e g EE Blogsville climate tmax sim tmax dat nceprhumee dat gz nceptempee dat gz tmax out tmax txt tmax par Figure 2 3 SDSM file structure with example file names see Table 2 2 for definitions of file name extension Each file in the archive complies with a generic nomenclature of the form source variable grid box dat The source is denoted by characters 1 4 the variable name by characters 5 8 and the grid box by characters 9 10 All files have the extension dat for example the file name nceprhumee dat indicates that the source is NCEP ncep the variable is near surface relative humidity rhum and the grid box is Eastern England ee Similarly the file name h3b2p8_zsw dat indicates that the source is HadCM3 SRES scenario B2 h3b2
32. SDSM with respect to seven tasks 1 quality control and data transformation 2 screening of potential downscaling predictor variables 3 model calibration 4 generation of ensembles of current weather data using observed predictor variables 5 statistical analysis of observed data and climate change scenarios 6 graphing model output 7 generation of ensembles of future weather data using GCM derived predictor variables The key functions of SDSM will be illustrated using observed and climate model data for Blogsville UK comparing downscaled daily precipitation and temperature series for 1960 89 with 2080 99 1 1 Downscaling techniques The general theory limitations and practice of downscaling have been discussed in detail elsewhere see bibliography Reviews typically group downscaling methodologies into four main types a dynamical climate modelling b synoptic weather typing c stochastic weather generation or d regression based approaches Each family of techniques is briefly described below 1 1 1 Dynamical Dynamical downscaling involves the nesting of a higher resolution Regional Climate Model RCM within a coarser resolution GCM The RCM uses the GCM to define time varying atmospheric boundary conditions around a finite domain within which the physical dynamics of the atmosphere are modelled using horizontal grid spacings of 20 50 km The main limitation of RCMs is that they are as computationally demanding as GCMs p
33. TARTED To launch SDSM click on the Start button on the Windows desktop then on Programs and then on SDSM which will appear as a small rain cloud on the list of available programs The following screen will appear SDSM R L Wilby C W Dawson UK Statistical Downscaling Model e Ea _ n AYIA A Version 3 1 14 May 2004 Figure 3 1 The SDSM splash screen Click on Start to continue to the SDSM main menu Figure 3 2 If further information is required at any time click on the Help button at the top of each screen the User may then search the Help Contents by key word or task 3 SDSM R L Wilby C W Dawson UK File Edit Data Analyse Help Q Literature Contact About Sponsors Transform Settings Help Exit Start Statistical Downscaling Model RYAN Version 3 1 Figure 3 2 Main menu of SDSM Before downscaling the User should check the date ranges type and integrity of all input data To establish the working environment click on the spanner symbol at the top of the main menu or at the top of any other screen to access the Settings screen Figure 3 3 3 1 Settings The Settings screen may be accessed throughout SDSM The following global preferences are available Year Length The default 366 allows 29 days in February every fourth year i e leap years and should be used with observational data The alternatives allow for different numbers of days in GCM data For example CGC
34. Using SDSM Version 3 1 A decision support tool for the assessment of regional climate change impacts SDSM R L Wilby C W Dawson UK Statistical Downscaling Model SDSM Version 3 1 Plot Credit US NatomlOcoaris anl Atmerplosic Admin tration 14 May 2004 User Manual Robert L Wilby and Christian W Dawson August 2004 Climate Change Unit Environment Agency of England and Wales Nottingham NG2 5FA UK Department of Computer Science Loughborough University Leics LE11 3TU UK Sponsors of SDSM A Consortium for the Application of Climate Impact Assessments ACACIA Canadian Climate Impacts Scenarios CCIS Project Environment Agency of England and Wales Preface General Circulation Models GCMs suggest that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales Less certain is the extent to which meteorological processes at individual sites will be affected So called downscaling techniques are used to bridge the spatial and temporal resolution gaps between what climate modellers are currently able to provide and what impact assessors require This manual describes a decision support tool for assessing local climate change impacts using a robust statistical downscaling technique SDSM Statistical DownScaling Model 3 1 facilitates the rapid development of multiple low cost single site scenarios of daily surface weather variables under
35. a through a single interface by means of a simple check button A1 2 Statistics A number of additional statistics have been added to the Analyse Data option in SDSM 3 3 These include Count a simple count of the number of data points in the fit period Maximum N day total calculates the maximum sum of data over an N day period entered by the user for each month season or annual period SD Dry Wet spell length calculates the standard deviation of the dry wet spell lengths Peaks over threshold calculates the number of days over the threshold The threshold is entered as a percentile and is applied to the entire period POT as of total calculates the ratio of the sum of the peaks over a threshold defined by a percentile as above to the total rainfall in the period For example this is useful for exploring changes in the contribution of heavy rainfall events to total precipitation A1 3 Transform An additional option has been added to the Data Transformation screen that allows the user to extract a single ensemble member from a modelled data file The user selects which ensemble member they wish to extract and SDSM produces a single column text file of the specified member Al1 4 Time Series Plot An additional screen has been incorporated into SDSM that enables the User to plot various time series chart for up to five data sets simultaneously Apart from plotting raw data on a line chart for a selected f
36. actors Climatic Change in press Goodess C Osborn T and Hulme M 2003 The identification and evaluation of suitable scenario development methods for the estimation of future probabilities of extreme weather events Tyndall Centre for Climate Change Research Technical Report 4 Hassan H Aramaki T Hanaki K Matsuo T and Wilby R L 1998 Lake stratification and temperature profiles simulated using downscaled GCM output Journal of Water Science and Technology 38 217 226 Jones P D Hulme M and Briffa K R 1993 A comparison of Lamb circulation types with an objective classification scheme International Journal of Climatology 13 655 663 London Climate Change Partnership 2002 A climate change impacts in London evaluation study Final Technical Report Entec UK Ltd Narula S C and Wellington J F 1977 An algorithm for linear regression with minimum sum of absolute errors Applied Statistics 26 106 111 Rubinstein R Y 1981 Simulation and the Monte Carlo method Wiley New York Wilby R L 2003 Past and projected trends in London s urban heat island Weather 58 251 260 Wilby R L and Dettinger M D 2000 Streamflow changes in the Sierra Nevada CA simulated using a statistically downscaled General Circulation Model scenario of climate change In McLaren S J and Kniveton D R Eds Linking Climate Change to Land Surface Change Kluwer Academic Publishers Netherlands pp 99 121 Wilby
37. ar more involved process entailing data extraction re gridding and normalisation techniques For this reason SDSM is supplied with a prepared set of daily predictor variables for selected grid boxes covering the British Isles Figure 2 2 and globally for all land areas via the web Section 2 5 The User simply locates the required grid box and data source from the UKSDSM archive As Figure 2 2 shows the UK is represented by nine grid boxes each measuring 2 5 latitude by 3 75 longitude corresponding to the grid co ordinate system of the Hadley Centre s coupled ocean atmosphere GCMs see below Of the nine cells six are land and three are ocean To obtain more realistic estimates of forcing over land areas that are represented by ocean grid boxes in the GCM data from the two nearest land cells were averaged For example predictor variables for Southwest England SW are the average of data from the Wales WA and Southern England SE grid boxes ee J f SC Scotland ae NI Northern Ireland fe isc SB Scottish Boarders BE d NE Northeast England IR Ireland BAN LENE Sa 2 WA Wales NE 1 l AlE EE Eastern England SW Southwest England ssw s 570N 510N ri SE Southern England as NI SB IR 2 480N i NE SB EE 2 a SW WA SE 2 Figure 2 2 Location and nomenclature of the UK grid boxes in the SDSM archive For model calibration the source is the National
38. as the basis of the decision support tool SDSM 1 3 Manual outline The rest of this manual is organised in six main parts Section 2 provides a brief overview of the key operations in SDSM For a complete description of the model specification interested readers should refer to the articles listed in the Bibliography see below Descriptions of the UKSDSM and Canadian Climate Impacts Scenarios CCIS data archives and file nomenclature are also provided in Section 2 Sections 3 to 11 provide guidance on the practical implementation of the key functions in SDSM for downscaling regional climate change scenarios Application of SDSM is illustrated using a case study for Blogsville UK Section 12 provides a few cautionary remarks concerning the limitations of SDSM and appropriate usage Users are strongly recommended to consider the issues raised here before developing local scenarios using SDSM Next a comprehensive Bibliography is supplied with follow up references for more detailed discussions of the technical basis of SDSM example applications and comparisons with other downscaling methods Enhancements to SDSM since version 2 2 are listed in Appendix 1 A trouble shooting guide and outline of the most common pitfalls is provided in the form of a Frequently Asked Questions FAQs section in Appendix 2 Finally definitions of commonly used technical terms related to statistical downscaling are provided in a Glossary 2 OVERVIEW O
39. ate the Scatterplot in a Word document first use the Copy button at the top of the screen then in Word use Paste Special Picture 6 MODEL CALIBRATION The Calibrate Model process constructs downscaling models based on multiple linear regression equations given daily weather data the predictand and regional scale atmospheric predictor variables The parameters of the regression model are written to a standard format file with the extension PAR along with meta data recording details of the calibration period model type predictors used etc The User specifies the model structure whether monthly seasonal or annual sub models are required whether the process is unconditional or conditional In unconditional models a direct link is assumed between the predictors and predictand e g local wind speeds may be a function of regional airflow indices In conditional models there is an intermediate process between regional forcing and local weather e g local precipitation amounts depend on wet dry day occurrence which in turn depend on regional scale predictors such as humidity and atmospheric pressure Furthermore it is possible to apply standard transformations to the predictand in conditional models see Section 3 2 and or to specific predictors see Section 4 2 To access the model building facility click the Analyse button at the top of any screen then select Calibrate Model from the drag down list Alternatively cl
40. ates the specified percentile for the chosen Time Period The default is 90 but this can be adjusted by entering the required value in the text box SPI is the Standardised Precipitation Index This is calculated for monthly time series only so the Time Period selection is ignored when SPI is chosen It is derived by first calculating the monthly sums of the data then calculating a moving average of these monthly sums smoothing across the time period entered by the User in the adjacent text box The default moving average period is 3 months The smoothed data are then normalised by subtracting the mean of all the data in the fit range and dividing by the standard deviation of the smoothed data for each month POT is the Peaks Over Threshold This counts the number of events greater than the user specified threshold for the chosen time period Plot By clicking the Plot button the selected statistics are displayed as a time series graph A graph such as that shown in Figure 10 2 will be displayed 3 Time Series Plot SDSM Time Series Chart 239 239 Y axis label Prep dat X axis label Figure 10 2 Time series plot of raw data from PRCP DAT file 10 2 Adjusting chart appearance The appearance of the time series chart can be adjusted in several ways For example by clicking on the Settings button the User is presented with a settings form that allows various adjustments to be made Figure 10 3 SDSM Time
41. ation 5 the number of days simulated 6 whether or not the predictand is a conditional TRUE or unconditional FALSE variable 7 the number of ensemble members 8 the variance inflation parameter see Advanced Settings 9 the transformation code for conditional variables 1 none 2 fourth root 3 natural log 4 inverse normal 10 the bias correction parameter see Advanced Settings 11 the predictand file name 12 onward the predictor file name s 8 ANALYSIS OF OBSERVED AND DOWNSCALED DATA 8 1 Overview Statistical tests of observed and downscaled weather data are handled in slightly different ways by SDSM but both are performed in the Analyse Data screen Common diagnostic tests are available for both observed and synthetic data These statistics include the variable mean maximum minimum variance peaks above below thresholds percentiles percent wet days and wet dry day spell lengths computed on a calendar month seasonal or annual basis To evaluate either downscaled data or observed data click on the Analyse button at the top of any screen then select Analyse Data from the drag down list The following screen will appear 3 Analyse Data File Edit Analyse Help S put File t Output File gt ny Select File Save Summary File As File Not selected File Not selected Figure 8 1 An example of the Analyse Data screen The first step in the analysis is to select the Data Source click on eithe
42. atory power that is unique to each predictor For the Blogsville example use the same predictand predictors and set up as in Section 5 2 Then click on the Correlation button at the top of the Screen Variables menu The results are shown in Figure 5 3 File Help 8 Back Print Help CORRELATION MATRIX Analysis Period 01 01 1961 31 12 1990 Annual Missing values 0 Missing rows 0 1 2 3 4 5 6 F 8 TMAX DAT 1 0 083 0 074 0 034 0 679 0 055 0 041 0 068 ncepp_uee dat 0 083 1 0 001 0 043 0 083 0 759 0 037 0 002 ncepp_vee dat 0 074 0 001 1 0 023 0 194 0 057 0 519 0 202 ncepp_zee dat 0 034 0 043 0 023 1 0 562 0 037 0 236 0 561 ncepp500ee dat 0 679 0 083 0 194 0 562 1 0 037 0 055 0 501 ncepulagee dat 0 055 0 759 0 057 0 037 0 037 1 0 001 0 043 ncepvlagee dat 0 041 0 037 0 519 0236 0 055 0 001 1 0 023 ncepzlagee dat 0 068 0 002 0 202 0 561 0 501 0 043 0 023 1 0 wN D N e a N PARTIAL CORRELATIONS WITH TMAX DAT Partial r P value ncepp__uee dat 0 033 0 0015 ncepp_vee dat 0 071 0 0000 ncepp_zee dat 0 526 0 0000 ncepp500ee dat 0 823 0 0000 ncepulagee dat 0 010 0 3421 ncepvlagee dat 0 157 0 0000 ncepzlagee dat 0 244 0 0000 Figure 5 3 The Results screen for the Blogsville example Partial correlations indicate that p500 and p__z have the strongest association with TMAX once the influence of all other predictors has been removed 5 4 Scatterplot The Scatter button is used for visual inspections of i
43. ature of the file but with the GCM extension Q Why do I keep getting an error message when I use GCM data The most likely explanation is that the Year Length in Settings has not been set correctly with respect to the number of days in the GCM simulation For example HadCM2 and HadCM3 have year lengths of 360 days whereas CGCM1 has 365 days in every year i e no leap years Q What s the best way of handling SDSM files outside the software All SDSM output files are written in ASCII format and therefore accessible by any word processor Model results OUT files are tab delimited if the number of Ensemble Members is greater than one and as such can be imported into Excel for further analysis or graphing Q Pve looked at the predictor variable files and the values only range between 5 Is there something wrong with the data No All predictor variables NCEP and GCM are normalised using their respective 1961 1990 means and standared deviations The result is that each predictor variable is dimensionless and will typically vary between 5 and 5 GLOSSARY Where appropriate the following definitions were drawn from the Glossary of terms in the Summary for Policymakers A Report of Working Group I of the Intergovernmental Panel on Climate Change and the Technical Summary of the Working Group I Report Terms in italics are found elsewhere in this Glossary Aerosols Airborne solid or liquid particles with a typical
44. cepzlagee dat i e vorticity on previous days then click on Save Note that the name used for transformed files MUST comply fully with the protocol described in Section 2 4 1 To activate the procedure click on the Transform button at the top of the screen The following confirmation will appear Transformation complete su i 10957 values processed No missing values in file Figure 4 4 The Transformation complete dialogue box Click on the OK button to return to the Transform Data File screen Click on the Reset button to clear the screen entries or to perform a new transformation Click on Back to return to the previous screen then on Home to return to the Main Menu 5 SELECTION OF DOWNSCALING PREDICTOR VARIABLES Identifying empirical relationships between gridded predictors such as mean sea level pressure and single site predictands such as station precipitation is central to all statistical downscaling methods and is often the most time consuming step in the process The purpose of the Screen Variables screen is to assist the User in the choice of appropriate downscaling predictor variables for model calibration Section 6 SDSM performs three supporting tasks seasonal correlation analysis partial correlation analysis and scatterplots Ultimately however the User must decide whether or not the identified relationships are physically sensible for the site s and predictands in question To investigate potentia
45. columns to be handled by the Analyse Data function Section 8 check the box under Create SIM File If the User wishes to extract a single ensemble member from a multi column data file check the Extract box on this screen Enter the number of the ensemble member required and the data will be written to the selected Save File Note in this case no transformation is applied to this extracted member Select the Transformation by checking the appropriate button Available transformations include natural logarithms and log10 squares cubes fourth powers inversion lag interval and binomial together with the inverse transformations of the above where appropriate If Wrap is selected for Lag n the last value is used as the first value in the lag transformation otherwise the Missing Data Identifier is inserted The Backward change button is used to compute variable changes between successive days All transformations can be applied to standard predictor variables prior to Model Calibration Section 6 to produce non linear regression models e g use power transformations for polynomial models For the Eastern England data select Lag n enter 1 in the box and check the Wrap box which will produce a lag I series of the variable with no missing data Click on the Select Output File button An Open file window will appear browse through until the required directory is located enter the Filename for transformed data in this case n
46. control run and available for all variables used in model calibration see Section 2 2 The procedure is identical to that of the Weather Generator operation in all respects except that it may be necessary to specify a different convention for model dates and source directory for predictor variables As in the case of the Weather Generator see Section 7 input files for Generate Scenario need not be the same length as those used to obtain the regression weights during calibration To access this facility click on the Analyse button at the top of any screen then select Generate Scenario from the drag down list The following screen appears 3 Generate Scenario Eile Edit Analyse Help ad BB A Oo Back Home Reset Generate Settings Help Next Select Input File Predictors GCM Directory Select Output File acs Save To OUT File I Documents and I cocwd Desktop lt SDSM3 I Blogsvi EE 96 No of predictors 0 Figure 11 1 The Generate Scenario screen showing selections for the Blogsville example using HadCM2 predictors for the current 1960 1989 period 11 1 Check settings Before starting scenario generation it may be necessary to change some of the options in the Settings menu Click on the Settings button at the top of the screen and check the appropriate Year Length box Also amend the Standard Start End Date in line with the GCM data time slices For example HadCM2 and HadCM3 have year lengths of 36
47. cts results e Requires significant computing resources Ensembles of climate scenarios seldom produced e Initial boundary conditions affect results e Choice of cloud convection scheme affects precipitation results e Not readily transferred to new regions or domains e Typically applied off line therefore results do not always feedback into the host GCM Table 1 1 Main strengths and weakness of statistical and dynamical downscaling The consensus of model inter comparison studies is that dynamical and statistical methods display similar levels of skill at estimating surface weather variables under current climate conditions However because of recognised inter variable biases in host GCMs assessing the realism of future climate change scenarios produced by statistical downscaling methods is problematic This is because uncertainties exist in both GCM and downscaled climate scenarios For example precipitation changes projected by the U K Meteorological Office coupled ocean atmosphere model HadCM2 are over sensitive to future changes in atmospheric humidity Overall the greatest obstacle to the successful implementation of both statistical and dynamical downscaling is the realism of the GCM output used to drive the schemes However because of the parsimony and low tech advantages of statistical downscaling methods over dynamical modelling Table 1 1 a multiple regression based method was chosen
48. current and future climate forcing Additionally the software performs ancillary tasks of data quality control and transformation predictor variable pre screening automatic model calibration basic diagnostic testing statistical analyses and graphing of climate data In addition the manual describes the UKSDSM archive a set of daily predictor variables prepared for model calibration and downscaling at sites across the UK This archive contains variables describing atmospheric circulation thickness stability and moisture content at several levels in the atmosphere under climate conditions observed between 1961 and 2000 Equivalent predictor variables are provided for four GCM experiments of transient climate change between 1961 and 2099 Users seeking to apply SDSM to regions outside the UK may obtain predictor variables online by visiting http www cics uvic ca scenarios index cgi Scenarios Application of SDSM is illustrated with respect to the downscaling of daily maximum temperature and precipitation scenarios for Blogsville UK under current 1961 90 and future 2080 99 climate forcing Acknowledgements SDSM Version 3 1 was supported by the Environment Agency of England and Wales as part of the Climate Impacts and Adaptation Research Programme The UKSDSM data archive was updated by Ian Harris Climate Research Unit and now includes data kindly supplied by the UK Hadley Centre CSIRO Atmospheric Research and the Canadian Centre for
49. d that of individual weather events Conditional process A mechanism in which an intermediate state variable governs the relationship between regional forcing and local weather For example local precipitation amounts are conditional on wet day occurrence the state variable which in turn depends on regional scale predictors such as atmospheric humidity and pressure Deterministic A process physical law or model that returns the same predictable outcome from repeat experiments when presented with the same initial and boundary conditions in contrast to stochastic processes Domain A fixed region of the Earth s surface and over lying atmosphere represented by a Regional Climate Model Also denotes the grid box es used for statistical downscaling In both cases the downscaling is accomplished using pressure wind temperature or vapour information supplied by a host GCM Divergence If a constant volume of fluid has its horizontal dimensions increased it experiences divergence and by conservation of mass its vertical dimension must decrease Downscaling The development of climate data for a point or small area from regional climate information The regional climate data may originate either from a climate model or from observations Downscaling models may relate processes operating across different time and or space scales Dynamical See Regional Climate Model Emission scenario A plausible representation of the future development
50. del calibration SDSM performs both quality control and data transformation 4 1 Quality control To check an input file for missing data and or suspect values click on the Analyse button at the top of the Main Menu then select Quality Control from the drag down menu The following screen will appear oF Quality Control AmE Fle Ed Date Analyse beo a S A Homa Reset Check File Transform Setti Figure 4 1 The Quality Control screen Click on the Select File button An Open file window will appear browse through until you have located the directory and file to be checked in this example the Blogsville maximum daily temperature TMAX DAT Click on the required data file then on Open To activate the quality control procedure click on the Check File button at the top of the screen The following confirmation will appear Quality check complete j x 5 10957 values processed in TMAX DAT Figure 4 2 The Quality check complete dialogue box Click on the OK button to view the quality control information In this example there are 10957 values with no missing data i e no missing value codes of 999 were detected The data range from 6 7 to 34 8 C with mean 13 1871 C see Figure 4 3 Click on the Reset button to clear the screen entries or to perform a new quality check Click on Home to return to the Main Menu or on Next to proceed to Screen Variables Section 5 Quality Control irs Fil
51. e day to day variations in atmospheric pressure may be a useful predictor of daily rainfall occurrence Radiative forcing The change in net vertical irradiance expressed as Watts per square metre at the tropopause due to an internal change or a change in the external forcing of the climate system such as for example a change in the concentration of carbon dioxide or the output of the Sun Random See stochastic Re gridding A statistical technique used to project one co ordinate system onto another and typically involving the interpolation of climate variables A necessary pre requisite to most statistical downscaling because observed and climate model data are seldom archived using the same grid system Regional Climate Model RCM A three dimensional mathematical model that simulates regional scale climate features of 20 50 km resolution given time varying atmospheric properties modelled by a General Circulation Model The RCM domain is typically nested within the three dimensional grid used by a GCM to simulate large scale fields e g surface pressure wind temperature and vapour Regression A statistical technique for constructing empirical relationships between a dependent predictand and set of independent predictor variables See also black box transfer function Relative humidity A relative measure of the amount of moisture in the air to the amount needed to saturate the air at the same temperature exp
52. e Edit Data Analyse Help A Home Check File gt Next Reset Transform Settings Help Figure 4 3 Results of the Quality Control check for TMAX DAT 4 2 Data transformation To transform data click on the Transform button at the top of the Main Menu Alternatively click on the Transform button at the top of the Quality Control screen In either case the following screen will appear Transform Data File x File Edit Data Help lt 2 Back Reset _ Transform Settings _ Help Select Input File Transformation Function Inverse G ser a File Not selected Ln e Clog 710 Columns in Input File cK c X05 Select Output File 1 leas C X 0 33 Save s O KM C X 0 25 File Not entered Create SIM File CIX Pie F Create C Backward change Extract Ensemble Member Clagn aoo F Esaa ft C Binomial 0 Figure 4 4 The Transform Data File screen Click on the Select Input File button An Open file window will appear Browse through until you have located the directory and file to be transformed for example the surface vorticity over Eastern England ncepp__zee dat Click on the required file If there is more than one column of input data as in the case of an ensemble simulation produced by the Weather Generator or Generate Scenario functions see Sections 7 and 11 enter the appropriate number in the Columns in Input File box To enable transformed data with multiple
53. ed Precipitation Index 2 1 7 Scenario generation Finally the Generate Scenario operation produces ensembles of synthetic daily weather series given atmospheric predictor variables supplied by a climate model either for current or future climate experiments rather than observed predictors This function is identical to that of the Weather Generator operation in all respects except that it may be necessary to specify a different convention for model dates and source directory for predictor variables The input files for both the Weather Generator and Generate Scenario options need not be the same length as those used to obtain the regression weights during the calibration phase 2 2 UKSDSM data archive As Figure 2 1 indicates the SDSM procedure commences with the preparation of coincident predictor and predictand data sets Although the predictand is typically an individual daily weather series obtained from meteorological observations at single stations e g daily precipitation maximum or minimum temperature hours of sunshine wind speed etc the methodology is applicable to other environmental variables e g air quality parameters wave heights snow cover etc In any event these data must be supplied by the User in SDSM format see Section 2 4 2 This is single column text only beginning Ist January 1961 if necessary padded with the Missing Data Identifier Assembly of the candidate predictor suite is by comparison a f
54. een allows the start and end dates of the analysis period to be changed The default dates are held in the Settings screen Section 3 1 in this case 1961 1990 If the start and end dates lie outside the permissible range the User will be prompted to enter new values Ideally the model should be calibrated using part of the available data withholding the remainder for independent model validation see Sections 7 and 8 To specify the name of the output parameter PAR file click on the Output File button in the upper right hand corner An Output PAR file window appears For maximum convenience make sure that the parameter file is saved in the same directory as the predictand files in this case C SDSM Blogsville Calibration Enter an appropriate file name in the File name box then click on the Save button The name of the parameter file will then be displayed beneath the Output File button for example TMAX PAR 6 2 Model type To determine the temporal resolution of the downscaling model check either Monthly Seasonal or Annual under the Model Type box In Monthly models different model parameters are derived for each month In Seasonal models all months in the same season e g December January and February for winter have the same model parameters In Annual models all months have the same parameters i e there is no attempt to specify intra annual variations in parameter values Next indicate whether the downscaling proce
55. entially the most serious limitation is that precipitation changes produced by changes in the frequency of weather patterns are seldom consistent with the changes produced by the host GCM unless additional predictors such as atmospheric humidity are employed 1 1 3 Stochastic weather generators Stochastic downscaling approaches typically involve modifying the parameters of conventional weather generators such as WGEN or LARS WG The WGEN model simulates precipitation occurrence using two state first order Markov chains precipitation amounts on wet days using a gamma distribution temperature and radiation components using first order trivariate autoregression that is conditional on precipitation occurrence Climate change scenarios are generated stochastically using revised parameter sets scaled in direct proportion to the corresponding parameter changes in a GCM The main advantage of the technique is that it can exactly reproduce many observed climate statistics and has been widely used particularly for agricultural impact assessment Furthermore stochastic weather generators enable the efficient production of large ensembles of scenarios for risk analysis The key disadvantages relate to the arbitrary manner in which precipitation parameters are adjusted for future climate conditions and to the unanticipated effects that these changes may have on secondary variables such as temperature 1 1 4 Regression Regression based downscaling m
56. ere known as the stratosphere The tropopause is typically located 10km above the Earth s surface Uncertainty An expression of the degree to which a value e g the future state of the climate system is unknown Uncertainty can result from a lack of information or from disagreement about what is known or knowable It can also arise from poorly resolved climate model parameters or boundary conditions Unconditional process A mechanism involving direct physical or statistical link s between a set of predictors and the predictand For example local wind speeds may be a function of regional airflow strength and vorticity Vorticity Twice the angular velocity of a fluid particle about a local axis through the particle In other words a measure of rotation of an air mass Weather generator A model whose stochastic random behaviour statistically resembles daily weather data at single or multiple sites Unlike deterministic weather forecasting models weather generators are not expected to duplicate a particular weather sequence at a given time in either the past or the future Most weather generators assume a link between the precipitation process and secondary weather variables such as temperature solar radiation and humidity Weather pattern An objectively or subjectively classified distribution of surface and or upper atmosphere meteorological variables typically daily mean sea level pressure Each atmospheric circulation pattern should
57. eshold as a percentage of total rainfall Note that the definition of a wet day can be adjusted using the Event Threshold under Settings The default is zero i e wet days are defined as all days with nonzero precipitation totals Note sum is averaged by the number of years in the data set providing the monthly seasonal annual mean sum By checking the appropriate boxes the User selects up to eight statistics for analysis The defaults are the mean maximum minimum sum and variance Click on Back to return to the Analyse Data screen Once all the above selections have been completed click on the Analyse button at the top of the menu After a few seconds the Results screen will appear Eile Help a 9 Back Print Help SUMMARY STATISTICS FOR TMAX DAT Analysis Start Date 01 01 1976 Analysis End Date 31 12 1990 Ensemble Member s Not applicable Month Mean Maximum Minimum Variance POT Sum January 6 443 13 800 6 700 13 899 0 000 199 733 February 6 418 17 900 2 900 15 259 0 000 181 427 March 9 516 20 100 1 600 9 384 0 000 295 007 April 11 865 22 900 2 100 11 937 0 000 355 940 May 15 782 26 100 7 400 12 711 5 000 489 247 June 18 542 31 800 9 900 14 887 39 000 556 273 July 21 148 32 200 13 600 13 415 72 000 655 580 August 20 619 34 800 0 000 12 011 61 000 639 180 September 17 924 26 400 10 700 8 109 4 000 537 713 October 14 141 27 100 7 700 7 949 1 000 438 360 November 9 627 17 100 0 600 10 930 0 000 288 800 December 7
58. ethods rely on empirical relationships between local scale predictands and regional scale predictor s Individual downscaling schemes differ according to the choice of mathematical transfer function predictor variables or statistical fitting procedure To date linear and non linear regression artificial neural networks canonical correlation and principal components analyses have all been used to derive predictor predictand relationships The main strength of regression downscaling is the relative ease of application coupled with their use of observable trans scale relationships The main weakness of regression based methods is that the models often explain only a fraction of the observed climate variability especially in precipitation series In common with weather typing methods regression methods also assume validity of the model parameters under future climate conditions and regression based downscaling is highly sensitive to the choice of predictor variables and statistical transfer function see below Furthermore downscaling future extreme events using regression methods is problematic since these phenomena by definition tend to lie at the limits or beyond the range of the calibration data set 1 2 Relative skill of statistical and dynamical downscaling Given the wide range of downscaling techniques both dynamical and statistical there is an urgent need for model comparisons using generic data sets and model diagnostics U
59. f results and hence rapid assessment of downscaled versus observed or current versus future climate scenarios To access this facility click the Analyse button at the top of any screen then select Compare Results from the drag down list The following screen will appear 3 Compare Results File Analyse Help Input File 2 Select Second File File Not selected File Not selected Figure 9 1 An illustration of the Compare Results screen 9 1 Line chart To choose a results TXT file click on Select First File button An Open file window appears browse through until the correct directory and file are reached then click on the appropriate file name for example observed statistics for maximum daily temperature at Blogsville are held in TMAXOBS TXT The name of the file will then appear beneath the button along with a list of available statistics Repeat the process by clicking on the Select Second File button Then click on the required statistic listed under Select Statistic Finally to show a line chart click on the Line button at the top of the screen E File Edit Help CENIA 8 9 Back _ Reset Settings Copy Print Help SDSM Line Chart 42 42 F TMAXOBS TXT Mean E TMAX TAT Mean gt a ey kan l aa alao bal Jan Feb Mar apr May Jun Jul aug Sep Oct Nov Dec Figure 9 2 Example of the Line chart using observed TMAXOBS and downscaled TMAX monthly mean maximum dai
60. g for an extended period typically decades or longer Climate change may be due to natural internal processes or to external forcings or to persistent anthropogenic changes in the composition of the atmosphere or in land use Climate model A numerical representation of the climate system based on the physical chemical and biological properties of its components their interactions and feedback processes and accounting for all or some its known properties Climate prediction An attempt to produce a most likely description or estimate of the actual evolution of the climate in the future e g at seasonal inter annual or long term time scales Climate projection A projection of the response of the climate system to emission or concentration scenarios of greenhouse gases and aerosols or radiative forcing scenarios often based on simulations by climate models As such climate projections are based on assumptions concerning future socio economic and technological developments Climate scenario A plausible and often simplified representation of the future climate based on an internally consistent set of climatological relationships that has been constructed for explicit use in investigating the potential consequences of anthropogenic climate change Climate variability Variations in the mean state and other statistics such as standard deviations the occurrence of extremes etc of the climate on all temporal and spatial scales beyon
61. ge scenarios in the San Juan River Basin Colorado Journal of Hydrology 225 67 91 Wilby R L Hay L E Gutowski W J Arritt R W Takle E S Pan Z Leavesley G H and Clark M P 2000 Hydrological responses to dynamically and statistically downscaled climate model output Geophysical Research Letters 27 1199 1202 Wilby R L Wigley T M L Conway D Jones P D Hewitson B C Main J and Wilks D S 1998a Statistical downscaling of General Circulation Model output a comparison of methods Water Resources Research 34 2995 3008 Winkler J A Palutikof J P Andresen J A and Goodess C M 1997 The simulation of daily temperature series from GCM output Part I Sensitivity analysis of an empirical transfer function methodology Journal of Climate 10 2514 2532 Zorita E and von Storch H 1999 The analog method as a simple statistical downscaling technique Comparison with more complicated methods Journal of Climate 12 2474 2489 APPENDIX 1 EHNHANCEMENTS SINCE VERSION 2 2 SDSM 3 1 includes a number of enhancements to version 2 2 sponsored by the Environment Agency of England and Wales Most relate to the interrogation of downscaled scenarios the underlying model algorithms are unchanged Al 1 Analyse Data The Analyse Observed and Analyse Modelled Data screens of version 2 2 have been merged into a single Analyse Data screen This screen allows the User to analyse either observed or modelled dat
62. have distinctive meteorological properties e g chance of rainfall sunshine hours wind direction air quality etc Examples of subjective circulation typing schemes include the European Grosswetterlagen and the British Isles Lamb Weather Types Zonal flow An atmospheric circulation in which the dominant flow of air follows the lines of latitude e g the westerlies in contrast to meridional flow
63. he case of all SDSM predictors Biases in large scale patterns of atmospheric circulation in GCMs e g shifts in the dominant storm track relative to observed data or unrealistic inter variable relationships are much harder to accommodate Where possible Users should not therefore restrict themselves to the use of a single GCM or emission scenario for downscaling By applying multiple forcing scenarios via different GCMs ensemble members time slices or emission pathways better insight may be gained into the magnitude of these uncertainties Finally the authors welcome constructive suggestions about the design or application of SDSM particularly from the wider climate change impacts community BIBLIOGRAPHY This Bibliography cites papers containing full technical details of SDSM along with example case studies Additional background material is also recommended for other downscaling methods as well as selected review papers in which various downscaling methods have been compared Note that an JPCC TGCIA Guidance Document on Statistical Downscaling is available via the Data Distribution Centre Technical basis and examples of the application of SDSM Conway D Wilby R L and Jones P D 1996 Precipitation and air flow indices over the British Isles Climate Research 7 169 183 Diaz Nieto J and WILBY R L The impact of climate change on low flows in the River Thames UK A comparison of statistical downscaling and change f
64. he correct directory is reached then enter a suitable file name for example TMAXOBS TXT The name of the file will then appear beneath the button The final step is to select the required diagnostics Click on the Statistics button at the top of the menu The following screen will appear 3 Statistics Selection x Eile Edit Help 2 9 Back Reset Settings Help Generic Tests Precipitation Only Peaks over ob I Percentage wet E threshold 0 r Mean dry spell A Peaks below fo length iv Maimun E threshold a r Mean wet spell length I Minimum I Percentile fas p Maximum diy spell length Inter quartile Maximum wet v Sum a range E spell length r SD dry spell MV Variance I Autocorrelation length r SD wet spell length I Median I Skewness ae thres 2 30 I Count ij Maximum B POT as of N day total E total Figure 8 2 The Statistics Selection screen The screen is divided into two areas The first headed Generic Tests lists statistics that may be applied to any variable mean maximum minimum sum variance median count peaks over below threshold percentile inter quartile range autocorrelation skewness and maximum N day total The second headed Precipitation Only lists statistics that are only applicable to daily precipitation series percentage wet mean dry wet spell length maximum dry wet spell length standard deviation of dry wet spell peaks over threshold as a percentile peaks over thr
65. ick on the Next button at the top of the Screen Variables screen The following screen will appear Calibrate Model Eile Edit Analyse Help i Back Home Reset Calibrate Settings Help Next Select Input File red ables Select Output File Output File Data Fit Start 01 01 1961 Fit End 31 12 1990 Number of Days 10957 Figure 6 1 The Calibrate Model screen 6 1 File handling To begin model building click on the Select Predictand File button in the top left hand corner An Open file window appears browse through until the correct directory and file are reached then click on the appropriate file name for example the maximum daily temperature at Blogsville TMAX DAT The name of the file will then appear beneath the button Follow a similar procedure to locate and select the desired predictor variables by choosing the correct drive from the pull down window in the centre of the screen The directories available on this drive will then appear in the window directly above the drive window For example locate the C SDSM Blogsville NCEP directory All DAT files in this directory are then listed in the window above To select a predictor simply click on the file name it will be highlighted in blue To deselect a file click on it again and it will no longer be highlighted The number of predictor variables chosen is shown beneath this window The Data menu on the left hand side of the Calibrate Model scr
66. ics in England and Wales using atmospheric circulation variables International Journal of Climatology 18 523 539 McCabe G J and Dettinger M D 1995 Relations between winter precipitation and atmospheric circulation simulated by the Geophysical Fluid Dynamic Laboratory General Circulation Model International Journal of Climatology 15 625 638 Schubert S and Henderson Sellers A 1997 A statistical model to downscale local daily temperature extremes from synoptic scale atmospheric circulation patterns in the Australian region Climate Dynamics 13 223 234 Wilby R L 1997 Non stationarity in daily precipitation series implications for GCM downscaling using atmospheric circulation indices International Journal of Climatology 17 439 454 Weather generators Nicks A D Lane L J and Gander G A 1995 Weather generator In Flanagan D C and Nearing M A Eds USDA Water Erosion Prediction Project Hillslope Profile and Watershed Model Documentation USDA ARS National Soil Erosion Research Laboratory Report No 10 West Lafayette IN USA Richardson C W 1981 Stochastic simulation of daily precipitation temperature and solar radiation Water Resources Research 17 182 190 Semenov M A and Barrow E M 1997 Use of a stochastic weather generator in the development of climate change scenarios Climatic Change 35 397 414 Wilks D S 1992 Adapting stochastic weather generation algorithms for climate change
67. ile s the User can also select to plot particular statistics for the data file s over different time periods For example the User can chose to plot mean sums maximums Winter Summer ratios partial duration series percentiles or standard precipitation indices for either monthly seasonal annual or water year periods APPENDIX 2 FREQUENTLY ASKED QUESTIONS The following generic and specific questions are arranged in the order in which they might typically be encountered during a downscaling procedure Q Do I need to perform any re gridding or normalisation of the predictor variables No These tasks have already been performed for the UKSDSM data set released with the software and available to non profit organisations on request All UK data have been re gridded to a standard co ordinate system 2 5 latitude x 3 75 longitude and normalised with respect to the 1961 1990 climatology The User must simply select the nearest grid box es to the site in question For all other regions including the UK gridded predictor variables are available online courtesy of the Canadian Climate Impacts Scenarios Group The web site is accessed from http www cics uvic ca scenarios index cgi Scenarios Q Can I use observational data that lie outside the standard period 1961 to 2000 No Observed predictor variables for SDSM archives are obtained from NCEP and normalised only for the period 1961 to 2000 Station meteorological data pri
68. irculation in which the dominant flow of air is from north to south or from south to north across the parallels of latitude in contrast to zonal flow NCEP The acronym for the National Center for Environmental Prediction The source of re analysis climate model assimilated data widely used for dynamical and statistical downscaling of the current climate Normalisation A statistical procedure involving the standardisation of a data set by subtraction of the mean and division by the standard deviation with respect to a predefined control period The technique is widely used in statistical downscaling to reduce systematic biases in the mean and variance of climate model output Parameter A numerical value representing a process or attribute in a model Some parameters are readily measurable climate properties others are known to vary but are not specifically related to measurable features Parameters are also used in climate models to represent processes that poorly understood or resolved Partial Duration Series Events above a defined threshold that are recorded as a time series or as a frequency distribution Essentially a peaks over threshold approach to describing the occurrence of extreme events Predictand A variable that may be inferred through knowledge of the behaviour of one or more predictor variables Predictor A variable that is assumed to have predictive skill for another variable of interest the predictand For exampl
69. lacing constraints on the feasible domain size number of experiments and duration of simulations The scenarios produced by RCMs are also sensitive to the choice of boundary conditions such as soil moisture used to initiate experiments The main advantage of RCMs is that they can resolve smaller scale atmospheric features such as orographic precipitation or low level jets better than the host GCM Furthermore RCMs can be used to explore the relative significance of different external forcings such as terrestrial ecosystem or atmospheric chemistry changes 1 1 2 Weather typing Weather typing approaches involve grouping local meteorological data in relation to prevailing patterns of atmospheric circulation Future regional climate scenarios are constructed either by re sampling from the observed data distributions conditional on the circulation patterns produced by a GCM or by first generating synthetic sequences of weather patterns using Monte Carlo techniques and re sampling from observed data The main appeal of circulation based downscaling is that it is founded on sensible linkages between climate on the large scale and weather at the local scale The technique is also valid for a wide variety of environmental variables as well as multi site applications However weather typing schemes are often parochial a poor basis for downscaling rare events and entirely dependent on stationary circulation to surface climate relationships Pot
70. le observations at dependant locations using events occurring on the same date See Wilby et al 2003 for more details Preliminary tests of inter variable correlations produced by SDSM e g between downscaled precipitation and temperature series indicate that inter annual variations in the strength of relationships are preserved but there can be differences between the model and observations in individual months Once again it is suspected that inter variable relationships are implicitly preserved by virtue of commonality in the predictor variables used to downscale each predictand However if required it is relatively straightforward to explicitly condition one predictand on another e g daily precipitation occurrence might be used to condition maximum temperatures In this case the conditioning variable precipitation occurrence would be entered as a predictor during model calibration Q Pve calibrated my model How do I now produce values of PRCP TMAX or TMIN using GCM data Provided you have produced a PAR file via Calibrate Model the software will automatically know what predictors are needed Of course you may need to transform some of the GCM files if this was done for calibration For example if Z DAT was transformed to ZSQUARED DAT and then used to train the model the same transformation should be applied to the equivalent GCM file i e Z GCM to ZAQUARED GCM In which case be sure to maintain the same nomencl
71. lly from one function to the next 2 1 1 Quality control and data transformation Few meteorological stations have 100 complete and or fully accurate data sets Handling of missing and imperfect data is necessary for most practical situations Simple Quality Control checks in SDSM enable the identification of gross data errors missing data codes and outliers prior to model calibration In many instances it may be appropriate to transform predictors and or the predictand prior to model calibration The Transform facility takes specified data files and applies selected transformations e g logarithm power inverse lag binomial etc 2 1 2 Selection of downscaling predictor variables Identifying empirical relationships between gridded predictors such as mean sea level pressure and single site predictands such as station precipitation is central to all statistical downscaling methods The main purpose of the Screen Variables operation is to assist the user in the selection of appropriate downscaling predictor variables This remains one of the most challenging stages in the development of any statistical downscaling model since the choice of predictors largely determines the character of the downscaled climate scenario The decision process is also complicated by the fact that the explanatory power of individual predictor variables varies both spatially and temporally Screen Variables facilitates the examination of seasonal variations in
72. lly useful predictor predictand relationships click on the Analyse button at the top of any screen then select Screen Variables from the drag down menu The following screen will appear 3 Screen ariables File Edit Data Analyse Help Analyse Correlation Scatter Transform Settings Data Fit Start 01 01 1961 a CI Desktop stant 31 12 1990 SDSM3 3 Blogsville amp observed1961 90 E Select analysis period Annual fii Figure 5 1 Illustration of the Screen Variables screen using daily maximum temperatures for Blogsville 1961 1990 5 1 Setup The first step in the Screen Variables operation is the selection of the predictand and predictor files The predictand file e g observed daily maximum temperature daily precipitation totals etc must be supplied by the User in SDSM format see Section 2 4 Click on the Select Predictand File button An Open file window will appear browse through until the appropriate directory has been located Click on the predictand data file for example the maximum daily temperature at Blogsville TMAX DAT located in C SDSM Blogsville Calibration Follow a similar procedure locate and select the desired Predictor Variables by choosing the correct drive from the pull down window in the centre of the screen The directories available on this drive will then appear in the window directly above the drive window Browse through again until the appropriate directory is located All
73. lphate aerosols Journal of Climate 10 245 267 Mitchell J F B Johns T C Gregory J M and Tett S 1995 Climate response to increasing levels of greenhouse gases and sulphate aerosols Nature 376 501 504 Tett S F B Johns T C and Mitchell J F B 1997 Global and regional variability in a coupled AOGCM Climate Dynamics 13 303 323 Downscaling general Department of the Environment 1996 Review of the potential effects of climate change in the United Kingdom HMSO London 247 pp Hulme M Jiang T and Wigley T M L 1995 SCENGEN a Climate Change Scenario Generator User Manual Climatic Research Unit University of East Anglia Norwich UK 38 pp IPCC TGCIA 1999 Guidelines on the Use of Scenario data for Climate Impact and Adaptation Assessment Version 1 Prepared by Carter T R Hulme M and Lal M Intergovernmental Panel on Climate Change Task Group on Scenarios for Climate Impact Assessment 69pp Karl T R Wang W C Schlesinger M E Knight R W and Portman D 1990 A method of relating general circulation model simulated climate to the observed local climate Part I Seasonal statistics Journal of Climate 3 1053 1079 Klein W H and Glahn H R 1974 Forecasting local weather by means of model output statistics Bulletin of the American Meteorological Society 55 1217 1227 Leung L R Mearns L O Giorgi F and WILBY R L 2003 Regional climate research needs and opportunitie
74. ly temperatures at Blogsville 1976 1990 9 2 Bar chart Alternatively having selected the required files and statistics from each list as in Section 9 1 click on the Bar button at the top of the Compare Results screen to produce a bar chart x File Edit Help lt 2 m a o Back Reset Settings Copy Print_ Help SDSM Bar Chart 70 Hh UTMAXOBS TXT Maximum E TMAX TXT Maximum Y axis label Figure 9 3 Example of the Bar chart using observed TMAXOBS and downscaled TMAX monthly absolute maximum daily temperatures at Blogsville 1976 1990 9 3 Customizing charts To change or remove tick marks y axis labels chart titles or y axis maximum minimum in either the Line or Bar chart click on the Settings button at the top of the screen The following screen will appear ia Chart Settings x File Help Back Reset Help Enter number of tick points j Chart title Maximum temperatures at Nottingham Enter new Y axis maximum 40 Y axis label Temperature deg C Enter new Y axis minimum fe Legend 1 title Observed Legend 2 title Downscaled Make Changes Apply Ticks Clear Ticks Show Legend Clear Legend Figure 9 4 An illustration of the Chart Settings screen Enter the required values text then click on the Make Changes button to change text and or click on the Apply Ticks button to change tick marks Similarly click on the Clear Ticks Show Legend or Clear Legend buttons
75. n NCEP and HadCM2 HadCM3 as well as a range of choice for downscaling It is envisaged that the list will be extended as further data are released from the Hadley Centre Daily variable Code NCEP HadCM2 HadCM3 CGCM2 CSIRO 1961 GG SRES SRES SRES 2000 1961 1961 1961 1961 2099 2099 2099 2099 Precipitation mm prec x x x x Maximum temperature K tmax x x x x Minimum temperature K tmin x x x x Mean temperature temp x x x x Mean sea level pressure mslp x x x x 500 hPa geopotential height p500 x x x x 850 hPa geopotential height p850 x x x x Near surface relative humidity rhum x x x x Relative humidity at 500 hPa height r500 x x x x Relative humidity at 850 hPa height r850 x x x x Near surface specific humidity shum x x x x Geostrophic airflow velocity FEF x x x x x Vorticity tE x x x x x Zonal velocity component ou x x x x x Meridional velocity component rE x x x x x Wind direction th x x x x x Divergence 7h x x x x x Table 2 1 Daily variables held in the UKSDSM data archive denoted by x Bold type indicates variables that have not been normalised and are provided for comparative purposes Italics indicate secondary airflow variables derived from pressure fields surface 500 and 850 hPa 2 4 SDSM file protocols For convenience the SDSM file protocol is described in two parts Firstly the file name system and file structure of the UK
76. ncritically as a black box This is a very real danger when employing regression based modelling techniques Rather the downscaling should be based upon physically sensible linkages between large scale forcing and local meteorological response Therefore good practice demands rigorous evaluation of candidate predictor predictand relationships using independent data Furthermore the local knowledge base is an invaluable source of information when determining sensible combinations of predictors Daily precipitation amounts at individual stations continue to be the most problematic variable to downscale and research is ongoing to address this limitation This arises because of the generally low predictability of daily precipitation amounts at local scales by regional forcing factors The unexplained behaviour is currently modelled stochastically within SDSM by artificially inflating the variance of the downscaled series to accord better with daily observations Even so the model can produce unrealistic behaviour if the stochastic component is not properly handled This again underlines the importance of independent testing of all model parameters against data withheld from model calibration Ultimately however the plausibility of all SDSM scenarios depends on the realism of the climate model forcing Systematic biases in the mean and variance of GCM predictors can be reduced through normalisation with respect to a control period as in t
77. nter variable behaviour for specified sub periods annual seasonal or monthly The resultant scatterplot s indicate the nature of the association linear non linear etc whether or not data transformation s may be needed and the importance of outliers For the Blogsville example select TMAX as the predictand p_u as the predictor file and February under Select analysis period following the results in Figure 5 2 Check that all other predictors have been deselected and that Unconditional is selected under Process Note that if Conditional is selected all values less than or equal to the Event Threshold in Settings are excluded from the plot Click on the Scatter button at the top of the Screen Variables menu The results are shown in Figure 5 4 3 Scatter Plot d x File Edit Help 2 m 8 Back Reset Settings Copy Print Help February 0 missing value s TMAX DAT oe N wo 5 ncepp__uee dat Figure 5 4 The Scatterplot for the Blogsville example showing the association between TMAX and p __u in February The results suggest that during February higher maximum daily temperatures are associated with stronger westerly airflows The presentation quality of the Scatterplot may be customized as required by doubling clicking on any of the axis legends titles or data points Additional windows enable changes to be made to chart font style size colour etc To incorpor
78. ntil recently these studies were restricted to statistical versus statistical or dynamical versus dynamical model comparisons However a growing number of studies are undertaking statistical versus dynamical model comparisons and Table 1 1 summarises relative strengths and weaknesses that have emerged Statistical downscaling Dynamical downscaling boundary forcing e Choice of domain size and location affects results e Requires high quality data for model calibration e Predictor predictand relationships are often non stationary e Choice of predictor variables affects results e Choice of empirical transfer scheme affects results e Low frequency climate variability problematic e Always applied off line therefore results do not feedback into the host GCM Strengths Station scale climate information from 10 50 km resolution climate GCM scale output information from GCM scale output e Cheap computationally undemanding Respond in physically consistent ways and readily transferable to different external forcings e Ensembles of climate scenarios permit e Resolve atmospheric processes such as risk uncertainty analyses orographic precipitation e Applicable to exotic predictands such Consistency with GCM as air quality and wave heights Weakness Dependent on the realism of GCM e Dependent on the realism of GCM boundary forcing e Choice of domain size and location affe
79. of emissions of substances that are potentially radiatively active e g greenhouse gases aerosols based on a coherent and internally consistent set of assumptions about driving forces and their key relationships Ensemble member A set of simulations members in which a deterministic climate model is run for multiple climate projections each with minor differences in the initial or boundary conditions Conversely weather generator ensemble members differ by virtue of random outcomes of successive model simulations In either case ensemble solutions can be grouped and then compared with the ensemble mean to provide a guide to the uncertainty associated with specific aspects of the simulation External forcing A set of factors that influence the evolution of the climate system in time and excluding natural internal dynamics of the system Examples of external forcing include volcanic eruptions solar variations and human induced forcings such as changing the composition of the atmosphere and land use change Extreme weather event An event that is rare within its statistical reference distribution at a particular place Definitions of rare vary from place to place and from time to time but an extreme event would normally be as rare or rarer than the 10th or 90th percentile General Circulation Model GCM A three dimensional representation of the Earth s atmosphere using four primary equations describing the flow of energy firs
80. or of the model is 2 2 C Calibration Completed E xj Gi Unconditional R Squared 0 546 Unconditional Standard Error 2 246 Figure 6 2 The Calibration Completed dialogue box Click on the OK button to return to the Calibrate Model screen then on Home to return to the Main Menu on Back to return to Screen Variables or on Next to proceed to the Weather Generator 7 WEATHER GENERATOR The Weather Generator operation produces ensembles of synthetic daily weather series given observed or NCEP re analysis atmospheric predictor variables and regression model weights produced by the Calibrate Model operation see Section 6 The Weather Generator enables the verification of calibrated models assuming the availability of independent data as well as the synthesis of artificial time series representative of current climate conditions The Weather Generator can also be used to reconstruct predictands or to infill missing data To access this facility click the Analyse button at the top of any screen then select Weather Generator from the drag down list Alternatively click on the Next button at the top of the Calibrate Model screen The following screen will appear Weather Generator E 215 x Eile Edit Analyse Help fA KS G Select Input File Select Output File Select Parameter File Save To OUT File amp c IBM_PRELOAD Figure 7 1 The Weather Generator screen 7 1 File handling The first step in
81. or to Ist January 1961 or after 31st December 2000 will have no pre prepared predictor variables The software also assumes that meteorological data provided by the User commences on Ist January 1961 i e has the same start date as the predictors If this is not the case the User should pad the station data with the Missing Data Identifier Q How important is the selection of predictor variables Identifying sensible predictor predictand relationships is the most critical procedure in all statistical downscaling methods The Screen Variables screen is designed to assist the User in the choice of appropriate downscaling predictor variables for model calibration via seasonal correlation analysis partial correlation analysis and scatterplots Ultimately however the User must decide whether or not the identified relationships are physically sensible for the site s in question Q How can I determine if I have chosen the correct predictor variables for the predictands that I require The correlation statistics and P values indicate the strength of the association between two variables Higher correlation values imply a higher degree of association Smaller P values indicates that this association is less likely to have occurred by chance A P value lt 0 05 is routinely used as the cut off so a P value of 0 37 would indicate that the predictor predictand correlation is likely to be due to chance However even if P lt 0 05 the result can be
82. period of record used for model calibration If however model verification is to be undertaken using a set of independent data withheld from the calibration process then the two values should be amended accordingly If simulation of observed data based on the complete predictor record is needed then the Record Start and Record Length values should be used 7 2 Ensemble size Decide how many ensembles of synthetic data are needed up to a maximum of 100 and enter the appropriate value in the Ensemble Size box at the bottom right hand corner of the screen the default is 20 Individual ensemble members are considered equally plausible local climate scenarios realised by a common set of regional scale predictors The extent to which ensemble members differ depends on the relative significance of the deterministic and stochastic components of the regression models used for downscaling For example local temperatures are largely determined by regional forcing whereas precipitation series display more noise arising from local factors The former will yield similar looking ensemble members the latter large differences between individual members Once the above selections have been completed click the Synthesize button at the top of the menu After a few seconds the follow dialogue box will appear Si x 1 Synthesis completed Figure 7 2 The synthesis completed dialogue box Click on OK to return to the Weather Generator then on
83. predictor skill 2 1 3 Model calibration The Calibrate Model operation takes a user specified predictand along with a set of predictor variables and computes the parameters of multiple linear regression equations via the efficient dual simplex algorithm forced entry method The User specifies the model structure whether monthly seasonal or annual sub models are required whether the process is unconditional or conditional In unconditional models a direct link is assumed between the predictors and predictand e g local wind speeds may be a function of regional airflow indices In conditional models there is an intermediate process between regional forcing and local weather e g local precipitation amounts depend on the occurrence of wet days which in turn depend on regional scale predictors such as humidity and atmospheric pressure 2 1 4 Weather generator The Weather Generator operation generates ensembles of synthetic daily weather series given observed or NCEP re analysis atmospheric predictor variables The procedure enables the verification of calibrated models using independent data and the synthesis of artificial time series representing current climate conditions The User selects a calibrated model and SDSM automatically links all necessary predictors to regression model weights The User must also specify the period of record to be synthesised as well as the desired number of ensemble members Synthetic time series
84. predictors A Preps sioix C 4 12 366 01 01 1961 10957 TRUE 20 18 2 0 8 PRCP DAT neepp vee dat neepp zee dat nceepp5SO00ee dat ncepshumee dat LA Lel 4 Figure 11 7 The SIM file for downscaling precipitation at Blogsville 1961 1990 Figure 11 7 shows that four predictors were employed line 1 to simulate 12 months line 2 using calendar years line 3 beginning in 01 01 1961 line 4 and lasting 10957 days line 5 The model was conditional TRUE line 6 had 20 ensemble members line 7 variance inflation line 8 a fourth root transformation of the predictand line 9 and bias correction line 10 The four predictors were p__ v p_ z p500 and shum lines 12 onwards With the above specifications the Weather Generator was used to downscale observed NCEP predictors and Generate Scenario to downscale GCM HadCM2 predictors representing the current climate Note that a Year Length of 366 days should be checked in Settings when working with NCEP and 360 when using HadCM2 predictors Downscaled scenarios were evaluated firstly using the Analyse Data and then Compare Results Figure 11 8 shows for example that the downscaling produced similar monthly mean 24 hour totals under observed NCEP and GCM HadCM2 forcing for the current climate Cn xl Ele Edt Help s 2 6 2 Back Reset Settings Copy Print_ Help Mean 24 hour totals z
85. r Modelled for downscaled data analysis or Observed for observed data analysis The second step is the selection of an appropriate data file Click on the Select Input File button on the left hand side An Open file window appears browse through until the correct directory and file are reached then click on the appropriate file name for example actual maximum daily temperatures at Blogsville are held in TMAX DAT The name of the file will then appear beneath the button If using Modelled output click on View Details to check basic information about the downscaling experiment such as the number of predictors start date etc Next specify the sub period required for analysis using the Analysis start date and Analysis end date windows under the Analysis Period header The default values are the Standard Data Start Date and Standard Data End Date held in the global Settings Section 3 1 The default Use Ensemble Mean box produces mean diagnostics for all ensemble members and the standard deviation of the ensembles see Figure 8 4 However diagnostics for individual members may be extracted by deselecting Use Ensemble Mean and entering the required Ensemble Member in this case integers to 100 in the Ensemble Member box To save the analysis results it is necessary to select an appropriate directory and file name Click on the Save Summary File As button on the right hand side An Open file window appears browse through until t
86. ressed as a percentage Resolution The grid separation of a climate model determining the smallest physical feature that can be realistically simulated Scenario A plausible and often simplified description of how the future may develop based on a coherent and internally consistent set of assumptions about driving forces and key relationships Scenarios may be derived from projections but are often based on additional information from other sources sometimes combined with a narrative story line Specific humidity The ratio of the mass of water vapour in grams to the mass of moist air in kilograms in a given volume of air Station The individual site at which meteorological measurements are systematically observed and recorded Stochastic A process or model that returns different outcomes from repeat experiments even when presented with the same initial and boundary conditions in contrast to deterministic processes See weather generator Transfer function A mathematical equation that relates a predictor or set of predictor variables to a target variable the predictand The predictor s and predictand represent processes operating at different temporal and or spatial scales In this case the transfer function provides a means of downscaling information from coarse to finer resolutions Tropopause The boundary between the lowest part of the atmosphere known as the troposphere and the highly stratified region of the atmosph
87. rs Weather generator Generate scenario Model output Compare results Chart results Figure 2 1 SDSM Version 3 1 climate scenario generation Analyse results Impact assessment Full technical details of SDSM and downscaling prototypes are provided in the Bibliography Within the taxonomy of downscaling techniques SDSM is best described as a hybrid of the stochastic weather generator and regression based methods This is because large scale circulation patterns and atmospheric moisture variables are used to linearly condition local scale weather generator parameters e g precipitation occurrence and intensity Additionally stochastic techniques are used to artificially inflate the variance of the downscaled daily time series to better accord with observations To date the downscaling algorithm of SDSM has been applied to a host of meteorological hydrological and environmental assessments as well as a range of geographical contexts including Europe North America and Southeast Asia The following sections outline the software s seven core operations along with the UKSDSM data archive and recommended file protocols 2 1 Key functions of SDSM As noted previously SDSM performs seven key functions The following paragraphs outline the purpose of each Full technical explanation and User guidance are provided in Sections 3 to 10 Next and Back arrows at the top of each screen guide the User sequentia
88. s Bulletin of the American Meteorological Society 84 89 95 Wilby R L and Wigley T M L 2000 Precipitation predictors for downscaling observed and General Circulation Model relationships International Journal of Climatology 20 641 661 Wilby R L and Wigley T M L 1997 Downscaling general circulation model output a review of methods and limitations Progress in Physical Geography 21 530 548 Xu C Y 1999 From GCMs to river flow a review of downscaling methods and hydrologic modelling approaches Progress in Physical Geography 23 229 249 Statistical downscaling methods Bardossy A and Plate E J 1992 Space time model for daily rainfall using atmospheric circulation patterns Water Resources Research 28 1247 1259 Burger G 1996 Expanded downscaling for generating local weather scenarios Climate Research 7 111 128 Crane R G and Hewitson B C 1998 Doubled CO2 precipitation changes for the Susquehanna Basin downscaling from the GENESIS general circulation model International Journal of Climatology 18 65 76 Hay L E McCabe G J Wolock D M and Ayers M A 1991 Simulation of precipitation by weather type analysis Water Resources Research 27 493 501 Huth R 1999 Statistical downscaling in central Europe evaluation of methods and potential predictors Climate Research 13 91 101 Kilsby C G Cowpertwait P S P O Connell P E and Jones P D 1998 Predicting rainfall statist
89. s a simple way of classifying trace rainfall days as dry days Therefore different values for the Event Theshold will yield different results in Screen Variables correlation values and scatterplots are both affected will produce different parameters in Calibrate Model and different results from the two Analyse Data operations Note however that the Weather Generator and Generate Scenario operations will still produce values in the range 0 to 0 3 even if the threshold is set at 0 3 Q What are the advantages and disadvantages of using the monthly seasonal or annual Model Type in Calibrate Model The Model Type button in Calibrate Model determines whether individual downscaling models will be calibrated for each calendar month climatological season or entire year The monthly button should be selected whenever the predictand is known to have a strong seasonal cycle noting that even the annual button can produce the same result provided that one or more predictor variables have strong seaonality Annual models are more parsimonious in the sense that they have only one set of regression weights instead of twelve in the case of the monthly models Seasonal models might be used in situations where data are too sparse at the monthly level for model calibration for example a low incidence of summer precipitation Q I am trying to model precipitation and have chosen the fourth root transformation in Advanced Settings What else must I do
90. sed data is 1 i e year markers appear every year The default for raw data is 0 i e no markers appear If the User enters 0 no markers are applied to the X axis Note that when plotting SPI data series the X axis labels are determined by the total number of months available In this case it may be better to remove the labels entirely to avoid overcrowding on the X axis The User implements chart settings by clicking the Make Changes button X axis labels are removed by clicking the Clear X Labels button Labels The User can adjust the text appearing on the X and Y axis and also the chart title by typing in the appropriate text on this screen and clicking the Make Changes button The User can also make adjustments to the chart directly For example by double clicking on the lines the User can adjust their colour Double clicking on the title and axis labels allows the User to change the text font The axis labels chart title or legend are removed by clicking on them and hitting delete or backspace To incorporate the Chart in a Word document first use the Copy button at the top of the window then in Word use Paste Special Picture 11 SCENARIO GENERATION The Generate Scenario operation produces ensembles of synthetic daily weather series given daily atmospheric predictor variables supplied by a GCM either for current or future climate experiments The GCM predictor variables must be normalised with respect to a reference period or
91. size between 0 01 and 10um that reside in the atmosphere for at least several hours Aerosols influence the climate directly through scattering and absorbing radiation and indirectly through the formation and optical properties of clouds Airflow index Trigonometric measures of atmospheric circulation obtained from surface pressure or geopotential height fields Commonly derived indices include vorticity zonal flow meridional flow and divergence Certain indices have been used to replicate subjective classifications of daily weather patterns or as predictor variables in statistical downscaling schemes Anthropogenic Resulting from or produced by human beings Atmosphere The gaseous envelope surrounding the Earth comprising almost entirely of nitrogen 78 1 and oxygen 20 9 together with several trace gases such as argon 0 93 and greenhouse gases such as carbon dioxide 0 03 Black box Describes a system or model for which the inputs and outputs are known but intermediate processes are either unknown or unprescribed See regression Climate The average weather described in terms of the mean and variability of relevant quantities over a period of time ranging from months to thousands or millions of years The classical period is 30 years as defined by the World Meteorological Organisation WMO Climate change Statistically significant variation in either the mean state of the climate or in its variability persistin
92. ss should be Unconditional or Conditional by checking the appropriate option in the Process box Note that for conditional processes in which the distribution of predictand values is skewed it is possible to apply one of several transformations in Advanced Settings see Section 3 2 For example the Fourth root might be selected for daily precipitation amounts Finally click the Calibrate button at the top of the screen 6 3 Blogsville example For the Blogsville example five predictor files p500 p__u vlag p_z and zlag might be selected to downscale daily maximum temperatures TMAX see Figure 5 3 There is clearly a seasonal cycle in the regional forcing Figure 5 2 so Monthly is checked in the Model Type box The Unconditional option is checked in the Process box because a direct link is assumed to exist between the regional scale predictors and local temperature The date range in the Data menu is set at 1961 to 1975 ensuring that the second half of the data i e 1976 to 1990 is retained for model validation Once the appropriate selections have been made click on the Calibrate button The process may take several seconds and on completion a dialogue box will report the percentage of explained variance R squared value and the Standard Error for the model Figure 6 2 shows the results for the Blogsville example In this case about 55 of the variance in the local predictand is explained by regional forcing and the standard err
93. statistically significant but not be of practical significance there s a difference Even if a high correlation and low P value is returned the Scatterplot indicates whether this result is due to a few outliers or is a potentially useful downscaling relationship The Scatterplot may also reveal that one or both of the variables should by modified using the Transform operation to linearise the relationship Q How do I build memory autocorrelation into the model There are TWO ways of incorporating memory in the downscaling model Firstly it is possible to use the Transform facility to create lagged predictands For example TMAXIlag1 i e the value of TMAX on preceding days is used as a predictor of TMAX when calibrating the model Secondly the Transform facility is to create a selection of lagged predictor variables For example if V is correlated with PRCP then consider Vlag 1 Vlag 2 Vlag 3 etc It is better practise to employ lagged predictors not lagged predictands in the model calibration This makes greater physical sense and avoids many problems when it comes to generating synthetic data Q What does the Event Threshold parameter in Settings do The Event Threshold parameter specifies the boundary between the two states in a Conditional process model For example if the Conditional process is precipitation changing the Event Threshold from 0 to 0 3 will result in more dry days and fewer wet day
94. t law of thermodynamics and momentum Newton s second law of motion along with the conservation of mass continuity equation and water vapour ideal gas law Each equation is solved at discrete points on the Earth s surface at fixed time intervals typically 10 30 minutes for several layers in the atmosphere defined by a regular grid of about 200km resolution Couple ocean atmosphere general circulation models O AGCMs also include ocean land surface and sea ice components See climate model Geopotential height The work done when raising a body of unit mass against gravity i e acceleration due to gravity at a given level in the atmosphere multiplied by distance divided by the value of gravity at the Earth s surface Greenhouse gas Gaseous constituents of the atmosphere both natural and anthropogenic that absorb and emit radiation at specific wavelengths within the spectrum of infrared radiation emitted by the Earth s surface the atmosphere and clouds The primary greenhouse gases are water vapour H20 carbon dioxide CO2 nitrous oxide N20 methane CH4 and ozone 03 Grid The co ordinate system employed by GCM or RCM to compute three dimensional fields of atmospheric mass energy flux momentum and water vapour The grid spacing determines the smallest features that can be realistically resolved by the model Typical resolutions for GCMs are 200km and for RCMs 20 50km Meridional flow An atmospheric c
95. the default is 20 Finally to save the scenario data to a results file it is necessary to select an appropriate directory and file name Click on the Select Output File button in the top right hand corner An Open file window appears browse through until the correct directory is reached then enter a suitable file name for example TMAXCCF OUT maximum temperature current climate forcing The name of the file will then appear beneath the button Once all the above selections have been completed click on the Generate button at the top of the screen After a few seconds a dialogue box will appear Figure 11 2 Click on OK to return to the Generate Scenario screen SS xi fe lt gt Scenario Generated Figure 11 2 The Scenario Generated dialogue box 11 3 Blogsville example temperature For the Blogsville example the Generate Scenario operation was applied twice First predictors from the HadCM2 GS experiment for the period 1960 1989 were used to emulate current climate forcing Figure 11 3 shows the Results screen for this scenario using the Analyse Model Output operation see Section 8 Te 6 6 xl Eile Back Print SUMMARY STATISTICS FOR TMAXCCF OUT Analysis Start Date 01 01 1961 Number of Days 7200 Ensemble Member s ALL Month Mean Maximum Minimum Variance POT January 7 061 18 116 5 528 17 729 0 000 February 6 986 17 161 3 462 13 680 0 000 March 9 371 22 377 3 330 18 267 0 008 April
96. to individual sites or localities for impact studies DOE 1996 p34 General Circulation Models GCMs indicate that rising concentrations of greenhouse gases will have significant implications for climate at global and regional scales Unfortunately GCMs are restricted in their usefulness for local impact studies by their coarse spatial resolution typically of the order 50 000 km and inability to resolve important sub grid scale features such as clouds and topography As a consequence two sets of techniques have emerged as a means of deriving local scale surface weather from regional scale atmospheric predictor variables Figure 1 1 Firstly statistical downscaling is analogous to the model output statistics MOS and perfect prog approaches used for short range numerical weather prediction Secondly Regional Climate Models RCMs simulate sub GCM grid scale climate features dynamically using time varying atmospheric conditions supplied by a GCM bounding a specified domain Both approaches will continue to play a significant role in the assessment of potential climate change impacts arising from future increases in greenhouse gas concentrations Climate Model Grid Scale g 3 5 RCM 2 bb E aN 5 lt va Precipitation SDS Topography Vegetation pe Figure 1 1 A schematic illustrating the general approach to downscaling Statistical downscaling methodologies have several practical advantages
97. tware and spreadsheets Extension Explanation Directory DAT Observed daily predictor and predictand files employed by SDSM Scenarios the Calibrate and Weather Generator operations input Calibration PAR Meta data and model parameter file produced by the SDSM Scenarios Calibrate operation output and used by the Weather Calibration Generator and Generate Scenario operations input SIM Meta data produced by the Weather Generator and SDSM Scenarios Generate Scenario operations output Results OUT Daily predictand variable file produced by the Weather SDSM Scenarios Generator and Generate Scenario operations output Results TXT Summary statistics produced by the Analyse operations SDSM Scenarios output Results Table 2 2 SDSM file names and recommended directory structure 2 5 Obtaining SDSM predictors online SDSM predictors may be obtained for any global land area courtesy of a data portal maintained by the Canadian Climate Impacts Scenarios Group The web site is accessed from http www cics uvic ca scenarios index cgi Scenarios Having registered by e mail address the User then selects predictors from the available GCMs currently HadCM3 and CGCM2 given the latitude and longitude of the nearest grid box es to the study region All data files including NCEP predictors may then be downloaded directly to Users PC for immediate deployment by SDSM 3 GETTING S
98. using HadCM2 predictors under current 1960 1989 and future 2080 2099 forcing The exemplar results presented in Figures 11 9 and 11 10 indicate a shift towards more intense summer storms inter spaced by longer dry spells in late summer and autumn Conversely maximum winter intensities do not change significantly but the average duration of dry spells marginally decreases 12 CAUTIONARY REMARKS SDSM is a Windows based decision support tool for the rapid development of single site ensemble scenarios of daily weather variables under current and future regional climate forcing Version 3 1 performs the tasks required to statistically downscale climate model output namely quality control of input data screening of candidate predictor variables model calibration synthesis of current weather data generation of future climate scenarios basic statistical and time series analyses and graphing results SDSM provides a robust and parsimonious technique of scenario construction that complements other methods e g direct use of climate model output dynamical downscaling sensitivity analysis etc Prospective Users should however consider the relative strengths and weaknesses of each category of downscaling to determine whether SDSM is most appropriate for the task in hand As far as the authors are aware no comparable tool exists in the public domain Nonetheless the authors strongly caution that the software should not be used u

Using SDSM Version 3.1 — A decision support tool for the

Contents

Download Pdf Manuals

Related Search

Related Contents

Using SDSM Version 3.1 &mdash; A decision support tool for the

Contents

Download Pdf Manuals

Related Search

Related Contents

Using SDSM Version 3.1 — A decision support tool for the