- Email: [email protected]
A method for downscaling global climate model calculations by a statistical weather generator
ELSEVIER
Ecological Modelling 82 (1995) 199-204
Short Communication
A method for downscaling global climate model calculations by a statistical weather generator Burkhard Oelschliigel UFZ Centre for Environmental Research Leipzig-Halle Ltd., Department of Soil Science, Hallesche Str. 44, Bad Lauchstiidt, D-06246, Germany
Received 30 November 1993;accepted 10 May 1994
Abstract A method is presented for deriving weather scenarios for studies of the impacts of possible climate change on the carbon-nitrogen dynamics in soil at places of interest. The future development of these soil processes is examined with a simulation model requiring weather data as input. The weather data are produced by a "stochastic weather generator" which is parametrised with meteorological observations. The information about possible climate change come from general circulation model (GCM) calculation results at defined grid points on the earth surface. A spatial interpolation procedure is used to combine the GCM information with the weather generation procedure by updating the generator parameters in dependence on the special moment in future.
Keywords: Carbon; Climate; Nitrogen; Soil ecosystems; Weather 1. Introduction
This paper presents a method for deriving future weather scenarios at specific places based on global climate model (general circulation models, GCM) calculation results. The produced synthetic weather series should be input for a soil simulation model which requires, at minimum, daily mean values of air temperature and global radiation as well as daily precipitation sums as climate input variables. GCMs are presently regarded to be the best tools for getting large-scale scenarios which reflect the expected anthropogenic climate change, but GCM results cannot be used at the local scale without use of downscaling procedures (yon Storch et al., 1992, p. 5). After downscaling the GCM results, the mentioned soil simulation
model is used to examine and forecast the consequences for the carbon-nitrogen dynamics at the local scale. The procedures used are demonstrated below, using Bad Lauchst~idt, a city in the middle part of eastern Germany, as an example. Similar procedures apply to other locations, where several years of weather records are available.
2. The G C M run used
A simulation run for 1985 to 2034 of the general circulation model ECHAM1/LSG from the German Climate Computer Centre in Hamburg was used for a large-scale climate change scenario. The run used was based on a 'businessas-usual' scenario for the increase of climate-rele-
0304-3800/95/$09.50 © 1995 Elsevier Science B.V. All rights reserved SSDI 0304-3800(94)00090-5
200
B. Oelschliigel~Ecological Modelling 82 (1995) 199-204
vant atmospheric trace gases. The simulation results were available at a spatial grid with a horizontal resolution of 5.625° x 5.625° (mesh size in a spherical coordinate system) (Fig. 1). The ECHAM1/LSG-model is described in Hasselmann (1990) and Roeckner et al. (1992). Use of the global climate model results has the following three problems: (1) The available climate scenario is only one realisation from a set of all possible outcomes because it is impossible to define all starting conditions exactly in accordance with reality. The possibilities for a statistical treatment are limited because only few of such scenarios exist. It has to be assumed that all essential features of climate change are reflected in the simulation run under consideration. (2) The model's mesh is too coarse to resolve the finer features of landscape that determine local climate and therefore it provides a weak basis for inferring daily weather patterns. However, the possibility for refinement of mesh size is severely constrained by the associated sharp increase of computing time. (3) The values of the climatic state variables at the grid points must be understood as an areal mean of the appropriate grid cell. Therefore a simple spatial interpolation of these values for downscaling is not recommended (von Storch et al., 1992, p. 8). Ignoring this fact and trying to parametrise regression relations one confronts the problem that there are only eight years of overlap between climate forecasts and
real weather observations. The reliability of farreaching forecasts based on so little data is uncertain.
3. T h e stochastic weather generator used
Problems in simulating the time courses of slowly changing soil state variables typically arise because of the necessity to supply meteorological observations as model input. This problem can be avoided by use of a so-called statistical weather generator. This is a simple tool for generation of yearly weather courses. Such a generator consists of a model of weather variables as stochastic processes, it must be parametrised with weather records (as a rule of thumb with data over 20-30 years) under the assumption of statistical second-order stationarity. In this paper a generator was used that - assumes constant monthly mean and variance of the underlying probability distributions of daily air temperature, amount of daily precipitation and daily global radiation - models the daily precipitation event as a product of a Markov chain variable (0 for dry days, 1 for wet days) and the two-parameter Weibull distribution which specifies precipitation amount
Pt = °',Yt
(1)
Europe
• 59.1°N 53.4°N 47.80N
grid points of the global climate model, used in this paper
D target site Bad Lauchstadt 51.4°N 11.9°E • continent [] ocean 8.4°E 14.1°E 19.7°E Fig. 1. Grid points of the ECHAM1/LSO model used in the computations.
B. Oelschliigel~EcologicalModelling82(1995)199-204 where Pt: precipitation for the day with number t; crt: occurrence of precipitation for day t, Markov chain variable; Yt: Weibull distributed precipitation amount for day t (independent of ~rt). - calculates daily air temperature and global radiation as a two-dimensional stationary Gaussian process (Richardson and Nicks, 1990)
~/El't
TSt = A ( Tst-1 + B Gst) GSt_l) )e2,t
201
to reflect the climate at the target location and combine it with the forecast information of the GCMs model results about the climate time drift. This principle is also used by other authors, for example - S c m c n o v and Porter (1993), where a good overview of literature, concerning the climatic variability and the modelling of crop yields, is given; but the algorithm used is not described in detail. - Wilks (1992), which treats exhaustively the possibilitiesof changing the generator parameters as a reflection of climatic changes. Wilks uses mean earth surface increments of the monthly means respectively sums of the weather variables, stemming from G C M calculations, and corrects them for the intra-annual and geographic dependence to update the generator parameters. The geographic correction value procedure is not described in any detail. M y approach is to predict changes in the generator parameters at the target location is by spatial interpolation of continuous state variables defined on a spherical surface. This method is applicable to any G C M mesh size. The climate model results at the grid points wcrc first aggregated to mean monthly values (for air temperature and global radiation) and monthly sums (for water equivalent of precipitation) in accordance with the weather generator structure. Time trends in monthly values were identified and used as
(2)
where Ts t, TSt_l: standardized daily values of air temperature for day t and t - 1; Gs t, Gst_l: standardized daily values of global radiation for day t and t - 1; A, B: matrices, calculable from the serial and cross correlation coefficients; {e2,t}: sequences of independent, standard Gaussian variables. Weather records of the place Bad Lauchst~idt were taken from 1958-90 for the generator parametrisation procedure. This set of parameter values is assumed to represent the actual state of climate. A climate change will have the result that these generator parameters drift away from their initial states over time.
{el,t},
4. Deriving climate change information for a specific location
The general principle for making local climate predictions is to parametrise a weather generator air temperature °C 25 20
• &
• 15
•
••
• &
•&•A
•
•&
•
•
•
. --•
• AA
•
• •••L"
! •
I0
•
monthly mean ~
trend fit
5
0
1985
1995
2005
2015
2025
2035
year
Fig. 2. M e a n June air temperature for the grid point 53.4 ° N, 14.1° E, as predicted in business-as-usual scenario.
202
B. Oelschliigel~Ecological Modelling 82 (1995) 199-204
indicators of climate change. Over the interval from 1985 to 2034, the postulate of linear time trends seems to be satisfactory, given the data amount and the high data variability level (e.g. Fig. 2). Note, the principle described below can be applied without modification if another type of
trend is found, e.g. a quadratic or cubic polynomial. The regression lines were estimated by least squares. Other methods which are more robust for outliers, for instance resistant line regression (Hartung, 1991, pp. 852-853), might be more appropriate but were not used in this paper. For
Climatic scenario from G C M run * scenario 'business as usual' * grid with horizontal resolution 5.625 ° x 5.625 c * period 1985-2034 * air temperature T, precipitation P and global radiation G
t, accumulation to monthly means and sums at the grid points
I
estimation of time trends
l adjustment at the target location * spatial interpolation on the earth sphere
Lf
weather generator for daily values at the target location monthwise constant parameters yearly updating of the means
I generation of several synthetic weather series as input for the simulation model
t statistical analysis of the simulation results probability inferences
I
i
I
Fig. 3. Procedure for the downscaling procedure of GCM information.
B. Oelschliigel~Ecological Modelling 82 (1995) 199-204
some monthly weather variables, non-zero values of the regression slope coefficient were not statistically significant at the 5% error level. Subdivision of the monthly data into two parts of 25 years' data segments and calculation of the empirical variance led to the conclusion that time trends in the variance of the data could be ignored, given the imprecision inherent in variance representation of the weather generator. Because monthly precipitation data for Bad Lauchst~idt for 1985-92 bore little relationship to that of the model's four nearest grid points, time trends in the transition probabilities of the Markov chain were not analysed, and were assumed to be constant. Similarly, cross-correlation parameters for air temperature and global radiation were assumed to be constant due to lack of correspondence in the first lags of the cross-correlation functions. In sum, the climate model results were only taken into account for updating the means of monthly air temperature, precipitation and global radiation. Variances and other weather generator parameters remained unchanged. Finally it was necessary to convert variable trends at the GCM grid points into a trend at the target location. This was done using the method of Willmott et al. (1985), a procedure for interpolation of variables defined on spherical surfaces as specified in Eqs. 1-4. The algorithm includes a definition of a radius of influence and a distance and directional weighting, which are in accordance with geostatistical principles (Ripley, 1988, p. 3; Henley, 1981, p. 87) K
K
Zd= E (W~Zk)/ E Wk k=l
Wk=S
(3)
k=l
l + rk
St
1/dk Sk=l~7(dk/R-1)2/(4R)
(4) for d k <_ R/3 for R/3 < d k <_R
203
where Zd: interpolated value at target location; Zk'. value at the global climate model grid, k = I(1)K, K = 4; K: number of grid points included in interpolation; R: radius of influence; Wk: weighting factor; Sk: distance-depending part of weighting factor Wk; Tk: direction-depending part of weighting factor Wk; dk: distance between grid point k and target location; Ok,t: spherical angle between grid point k and 1 with corner at target location. Only the four nearest grid points around the target location were used for the interpolation procedure (definition of the radius of influence). For air temperature and global radiation, the yearly increments of the monthly means at the grid points were interpolated and the result was added to the means at the target location. For precipitation constant individual relationships between grid points and target location were assumed. Precipitation portions were calculated and summed based on these relationships and the interpolation procedure. The use of different procedures for handling precipitation versus temperature and radiation follows the advise of Richardson and Nicks (1990). To summarise, the following steps were undertaken to derive climate change scenarios for inclusion in a soil simulation model (Fig. 3): - yearly update of means of the climate variables in the weather generator parameters through the desired year in the future (2034 maximum) using the time trend estimation results - generation of desired number of synthetic series for climate variables draws from the statistical distribution described above - input of these generated synthetic weather data in the soil process simulation model CANDY - statistical interpretation of the results to show features of interest, for instance gaseous nitrogen losses, leached nitrate and humus accumulation in form of frequency distributions or their characteristics.
for d k > R
(5)
5. R e s u l t s a n d c o n c l u s i o n s
K
Tk = E Sk(1 -- coSOkt) l=1
(6)
A method is presented for deriving weather scenarios for climate impact studies under the
204
B. Oelschliigel~Ecological Modelling 82 (1995) 199-204
Table 1 Yearly characteristics of air temperature, precipitation and global radiation at Bad Lauchsfiidt
Air temperature Precipitation sum Global radiation
Mean value for 1958-91
Mean value for 2034
9.0° C 457 mm 897 J cm -2
10.1° C 379 mm 940 J cm -2
Center of Environmental Research Leipzig-Halle Ltd. for fruitful discussions. This research was supported by the Federal Ministry of Research and Technology of the FRG by the project 01 LK 9106/2.
References assumption of continuity for the considered climate state variables and applied to a target location in eastern Germany. Climate projections came from a GCM simulation run superimposed on the knowledge of the actual climate state from weather observations. The results of the simulation of 200 yearly courses of air temperature, precipitation and global radiation simulation for Bad Lauchst~idt are shown in Table 1. As shown for the businessas-usual scenario the climate will become warmer and dryer. Climate scenarios based on other scenarios other than business-as-usual need to be considered to understand the range of possible changes in the soil carbon-nitrogen dynamics. This can, likewise, be done with the method described above.
Acknowledgements I would like to thank H. von Storch, U. Cubasch and A. Hellbach from the German Climate Computer Centre Hamburg for the possibility of using the GCM simulation results and their helpful comments. Further I am grateful to J. Robinson,
Hartung, J., 1991. Statistik. Oldenbourg Verlag, Miinchen, 975 pp. Hasselmann, K., 1990. Ocean Circulation and Climate Change. Report No. 58, MPI fiir Meteorologic, Hamburg, 23 pp. Henley, S., 1981. Nonparametric Geostatistics. Applied Science Publishers, London. Richardson, C.W. and Nicks, A.D., 1990. Weather generator description. In: A.N. Sharpley and J.R. Williams, (Editors), EPIC. The Erosion-Productivity Impact Calculator. 1. Model Description. United States Department of Agriculture, Agricultural Research Service, Technical Bulletin no. 1768, Washington, pp. 93-104. Ripley, B.D., 1988. Statistical Inference for Spatial Processes. Cambridge University Press, Cambridge. Roeckner, E. et al., 1992. Simulation of the Present-Day Climate with the ECHAM Model: Impact of Model Physics and Resolution. Report No. 93, MPI fiir Meteorologie, Hamburg, 179 pp. Semenov, M.A. and Porter, J.R., in press. Climatic variability and the modelling of crop yields: Agric. For. Meteorol. yon Storch, H., Zorita, E. and Cubasch, U., 1992. Downscaling of Global Climate Change Estimates to Regional scales: An Application to Iberian Rainfall in Wintertime. Report No. 64, MPI ffir Meteorologic, Hamburg, 36 pp. Willmott, CJ., Rowe, C.M. and Philpot, W.D., 1985. Smallscale climate maps: a sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring. Am. Cartogr., 12 (1): 5-16. Wilks, D.S., 1992. Adapting stochastic weather generation algorithms for climate change studies. Climatic Change, 22: 67-84.
Ecological Modelling 82 (1995) 199-204
Short Communication
A method for downscaling global climate model calculations by a statistical weather generator Burkhard Oelschliigel UFZ Centre for Environmental Research Leipzig-Halle Ltd., Department of Soil Science, Hallesche Str. 44, Bad Lauchstiidt, D-06246, Germany
Received 30 November 1993;accepted 10 May 1994
Abstract A method is presented for deriving weather scenarios for studies of the impacts of possible climate change on the carbon-nitrogen dynamics in soil at places of interest. The future development of these soil processes is examined with a simulation model requiring weather data as input. The weather data are produced by a "stochastic weather generator" which is parametrised with meteorological observations. The information about possible climate change come from general circulation model (GCM) calculation results at defined grid points on the earth surface. A spatial interpolation procedure is used to combine the GCM information with the weather generation procedure by updating the generator parameters in dependence on the special moment in future.
Keywords: Carbon; Climate; Nitrogen; Soil ecosystems; Weather 1. Introduction
This paper presents a method for deriving future weather scenarios at specific places based on global climate model (general circulation models, GCM) calculation results. The produced synthetic weather series should be input for a soil simulation model which requires, at minimum, daily mean values of air temperature and global radiation as well as daily precipitation sums as climate input variables. GCMs are presently regarded to be the best tools for getting large-scale scenarios which reflect the expected anthropogenic climate change, but GCM results cannot be used at the local scale without use of downscaling procedures (yon Storch et al., 1992, p. 5). After downscaling the GCM results, the mentioned soil simulation
model is used to examine and forecast the consequences for the carbon-nitrogen dynamics at the local scale. The procedures used are demonstrated below, using Bad Lauchst~idt, a city in the middle part of eastern Germany, as an example. Similar procedures apply to other locations, where several years of weather records are available.
2. The G C M run used
A simulation run for 1985 to 2034 of the general circulation model ECHAM1/LSG from the German Climate Computer Centre in Hamburg was used for a large-scale climate change scenario. The run used was based on a 'businessas-usual' scenario for the increase of climate-rele-
0304-3800/95/$09.50 © 1995 Elsevier Science B.V. All rights reserved SSDI 0304-3800(94)00090-5
200
B. Oelschliigel~Ecological Modelling 82 (1995) 199-204
vant atmospheric trace gases. The simulation results were available at a spatial grid with a horizontal resolution of 5.625° x 5.625° (mesh size in a spherical coordinate system) (Fig. 1). The ECHAM1/LSG-model is described in Hasselmann (1990) and Roeckner et al. (1992). Use of the global climate model results has the following three problems: (1) The available climate scenario is only one realisation from a set of all possible outcomes because it is impossible to define all starting conditions exactly in accordance with reality. The possibilities for a statistical treatment are limited because only few of such scenarios exist. It has to be assumed that all essential features of climate change are reflected in the simulation run under consideration. (2) The model's mesh is too coarse to resolve the finer features of landscape that determine local climate and therefore it provides a weak basis for inferring daily weather patterns. However, the possibility for refinement of mesh size is severely constrained by the associated sharp increase of computing time. (3) The values of the climatic state variables at the grid points must be understood as an areal mean of the appropriate grid cell. Therefore a simple spatial interpolation of these values for downscaling is not recommended (von Storch et al., 1992, p. 8). Ignoring this fact and trying to parametrise regression relations one confronts the problem that there are only eight years of overlap between climate forecasts and
real weather observations. The reliability of farreaching forecasts based on so little data is uncertain.
3. T h e stochastic weather generator used
Problems in simulating the time courses of slowly changing soil state variables typically arise because of the necessity to supply meteorological observations as model input. This problem can be avoided by use of a so-called statistical weather generator. This is a simple tool for generation of yearly weather courses. Such a generator consists of a model of weather variables as stochastic processes, it must be parametrised with weather records (as a rule of thumb with data over 20-30 years) under the assumption of statistical second-order stationarity. In this paper a generator was used that - assumes constant monthly mean and variance of the underlying probability distributions of daily air temperature, amount of daily precipitation and daily global radiation - models the daily precipitation event as a product of a Markov chain variable (0 for dry days, 1 for wet days) and the two-parameter Weibull distribution which specifies precipitation amount
Pt = °',Yt
(1)
Europe
• 59.1°N 53.4°N 47.80N
grid points of the global climate model, used in this paper
D target site Bad Lauchstadt 51.4°N 11.9°E • continent [] ocean 8.4°E 14.1°E 19.7°E Fig. 1. Grid points of the ECHAM1/LSO model used in the computations.
B. Oelschliigel~EcologicalModelling82(1995)199-204 where Pt: precipitation for the day with number t; crt: occurrence of precipitation for day t, Markov chain variable; Yt: Weibull distributed precipitation amount for day t (independent of ~rt). - calculates daily air temperature and global radiation as a two-dimensional stationary Gaussian process (Richardson and Nicks, 1990)
~/El't
TSt = A ( Tst-1 + B Gst) GSt_l) )e2,t
201
to reflect the climate at the target location and combine it with the forecast information of the GCMs model results about the climate time drift. This principle is also used by other authors, for example - S c m c n o v and Porter (1993), where a good overview of literature, concerning the climatic variability and the modelling of crop yields, is given; but the algorithm used is not described in detail. - Wilks (1992), which treats exhaustively the possibilitiesof changing the generator parameters as a reflection of climatic changes. Wilks uses mean earth surface increments of the monthly means respectively sums of the weather variables, stemming from G C M calculations, and corrects them for the intra-annual and geographic dependence to update the generator parameters. The geographic correction value procedure is not described in any detail. M y approach is to predict changes in the generator parameters at the target location is by spatial interpolation of continuous state variables defined on a spherical surface. This method is applicable to any G C M mesh size. The climate model results at the grid points wcrc first aggregated to mean monthly values (for air temperature and global radiation) and monthly sums (for water equivalent of precipitation) in accordance with the weather generator structure. Time trends in monthly values were identified and used as
(2)
where Ts t, TSt_l: standardized daily values of air temperature for day t and t - 1; Gs t, Gst_l: standardized daily values of global radiation for day t and t - 1; A, B: matrices, calculable from the serial and cross correlation coefficients; {e2,t}: sequences of independent, standard Gaussian variables. Weather records of the place Bad Lauchst~idt were taken from 1958-90 for the generator parametrisation procedure. This set of parameter values is assumed to represent the actual state of climate. A climate change will have the result that these generator parameters drift away from their initial states over time.
{el,t},
4. Deriving climate change information for a specific location
The general principle for making local climate predictions is to parametrise a weather generator air temperature °C 25 20
• &
• 15
•
••
• &
•&•A
•
•&
•
•
•
. --•
• AA
•
• •••L"
! •
I0
•
monthly mean ~
trend fit
5
0
1985
1995
2005
2015
2025
2035
year
Fig. 2. M e a n June air temperature for the grid point 53.4 ° N, 14.1° E, as predicted in business-as-usual scenario.
202
B. Oelschliigel~Ecological Modelling 82 (1995) 199-204
indicators of climate change. Over the interval from 1985 to 2034, the postulate of linear time trends seems to be satisfactory, given the data amount and the high data variability level (e.g. Fig. 2). Note, the principle described below can be applied without modification if another type of
trend is found, e.g. a quadratic or cubic polynomial. The regression lines were estimated by least squares. Other methods which are more robust for outliers, for instance resistant line regression (Hartung, 1991, pp. 852-853), might be more appropriate but were not used in this paper. For
Climatic scenario from G C M run * scenario 'business as usual' * grid with horizontal resolution 5.625 ° x 5.625 c * period 1985-2034 * air temperature T, precipitation P and global radiation G
t, accumulation to monthly means and sums at the grid points
I
estimation of time trends
l adjustment at the target location * spatial interpolation on the earth sphere
Lf
weather generator for daily values at the target location monthwise constant parameters yearly updating of the means
I generation of several synthetic weather series as input for the simulation model
t statistical analysis of the simulation results probability inferences
I
i
I
Fig. 3. Procedure for the downscaling procedure of GCM information.
B. Oelschliigel~Ecological Modelling 82 (1995) 199-204
some monthly weather variables, non-zero values of the regression slope coefficient were not statistically significant at the 5% error level. Subdivision of the monthly data into two parts of 25 years' data segments and calculation of the empirical variance led to the conclusion that time trends in the variance of the data could be ignored, given the imprecision inherent in variance representation of the weather generator. Because monthly precipitation data for Bad Lauchst~idt for 1985-92 bore little relationship to that of the model's four nearest grid points, time trends in the transition probabilities of the Markov chain were not analysed, and were assumed to be constant. Similarly, cross-correlation parameters for air temperature and global radiation were assumed to be constant due to lack of correspondence in the first lags of the cross-correlation functions. In sum, the climate model results were only taken into account for updating the means of monthly air temperature, precipitation and global radiation. Variances and other weather generator parameters remained unchanged. Finally it was necessary to convert variable trends at the GCM grid points into a trend at the target location. This was done using the method of Willmott et al. (1985), a procedure for interpolation of variables defined on spherical surfaces as specified in Eqs. 1-4. The algorithm includes a definition of a radius of influence and a distance and directional weighting, which are in accordance with geostatistical principles (Ripley, 1988, p. 3; Henley, 1981, p. 87) K
K
Zd= E (W~Zk)/ E Wk k=l
Wk=S
(3)
k=l
l + rk
St
1/dk Sk=l~7(dk/R-1)2/(4R)
(4) for d k <_ R/3 for R/3 < d k <_R
203
where Zd: interpolated value at target location; Zk'. value at the global climate model grid, k = I(1)K, K = 4; K: number of grid points included in interpolation; R: radius of influence; Wk: weighting factor; Sk: distance-depending part of weighting factor Wk; Tk: direction-depending part of weighting factor Wk; dk: distance between grid point k and target location; Ok,t: spherical angle between grid point k and 1 with corner at target location. Only the four nearest grid points around the target location were used for the interpolation procedure (definition of the radius of influence). For air temperature and global radiation, the yearly increments of the monthly means at the grid points were interpolated and the result was added to the means at the target location. For precipitation constant individual relationships between grid points and target location were assumed. Precipitation portions were calculated and summed based on these relationships and the interpolation procedure. The use of different procedures for handling precipitation versus temperature and radiation follows the advise of Richardson and Nicks (1990). To summarise, the following steps were undertaken to derive climate change scenarios for inclusion in a soil simulation model (Fig. 3): - yearly update of means of the climate variables in the weather generator parameters through the desired year in the future (2034 maximum) using the time trend estimation results - generation of desired number of synthetic series for climate variables draws from the statistical distribution described above - input of these generated synthetic weather data in the soil process simulation model CANDY - statistical interpretation of the results to show features of interest, for instance gaseous nitrogen losses, leached nitrate and humus accumulation in form of frequency distributions or their characteristics.
for d k > R
(5)
5. R e s u l t s a n d c o n c l u s i o n s
K
Tk = E Sk(1 -- coSOkt) l=1
(6)
A method is presented for deriving weather scenarios for climate impact studies under the
204
B. Oelschliigel~Ecological Modelling 82 (1995) 199-204
Table 1 Yearly characteristics of air temperature, precipitation and global radiation at Bad Lauchsfiidt
Air temperature Precipitation sum Global radiation
Mean value for 1958-91
Mean value for 2034
9.0° C 457 mm 897 J cm -2
10.1° C 379 mm 940 J cm -2
Center of Environmental Research Leipzig-Halle Ltd. for fruitful discussions. This research was supported by the Federal Ministry of Research and Technology of the FRG by the project 01 LK 9106/2.
References assumption of continuity for the considered climate state variables and applied to a target location in eastern Germany. Climate projections came from a GCM simulation run superimposed on the knowledge of the actual climate state from weather observations. The results of the simulation of 200 yearly courses of air temperature, precipitation and global radiation simulation for Bad Lauchst~idt are shown in Table 1. As shown for the businessas-usual scenario the climate will become warmer and dryer. Climate scenarios based on other scenarios other than business-as-usual need to be considered to understand the range of possible changes in the soil carbon-nitrogen dynamics. This can, likewise, be done with the method described above.
Acknowledgements I would like to thank H. von Storch, U. Cubasch and A. Hellbach from the German Climate Computer Centre Hamburg for the possibility of using the GCM simulation results and their helpful comments. Further I am grateful to J. Robinson,
Hartung, J., 1991. Statistik. Oldenbourg Verlag, Miinchen, 975 pp. Hasselmann, K., 1990. Ocean Circulation and Climate Change. Report No. 58, MPI fiir Meteorologic, Hamburg, 23 pp. Henley, S., 1981. Nonparametric Geostatistics. Applied Science Publishers, London. Richardson, C.W. and Nicks, A.D., 1990. Weather generator description. In: A.N. Sharpley and J.R. Williams, (Editors), EPIC. The Erosion-Productivity Impact Calculator. 1. Model Description. United States Department of Agriculture, Agricultural Research Service, Technical Bulletin no. 1768, Washington, pp. 93-104. Ripley, B.D., 1988. Statistical Inference for Spatial Processes. Cambridge University Press, Cambridge. Roeckner, E. et al., 1992. Simulation of the Present-Day Climate with the ECHAM Model: Impact of Model Physics and Resolution. Report No. 93, MPI fiir Meteorologie, Hamburg, 179 pp. Semenov, M.A. and Porter, J.R., in press. Climatic variability and the modelling of crop yields: Agric. For. Meteorol. yon Storch, H., Zorita, E. and Cubasch, U., 1992. Downscaling of Global Climate Change Estimates to Regional scales: An Application to Iberian Rainfall in Wintertime. Report No. 64, MPI ffir Meteorologic, Hamburg, 36 pp. Willmott, CJ., Rowe, C.M. and Philpot, W.D., 1985. Smallscale climate maps: a sensitivity analysis of some common assumptions associated with grid-point interpolation and contouring. Am. Cartogr., 12 (1): 5-16. Wilks, D.S., 1992. Adapting stochastic weather generation algorithms for climate change studies. Climatic Change, 22: 67-84.