Simulation of the hydrological cycle over Europe: Model validation
and impacts of increasing greenhouse gases
Klaus Arpe
*, Erich Roeckner
Max-Planck-Institute for Meteorology, Bundesstr. 55, D-20146 Hamburg, Germany
Received 17 September 1997; received in revised form 24 July 1998; accepted 6 April 1999
Abstract
Dierent methods of estimating precipitation area means, based on observations, are compared with each other to investigate their usefulness for model validation. For the applications relevant to this study the ECMWF reanalyses provide a good and comprehensive data set for validation. The uncertainties of precipitation analyses, based on observed precipitation or from nu-merical weather forecasting schemes, are generally in the range of 20% but regionally much larger. The MPI atmospheric general circulation model is able to reproduce long term means of the main features of the hydrological cycle within the range of uncertainty of observational data, even for relatively small areas such as the Rhine river basin. Simulations with the MPI coupled general circulation model, assuming a further increase of anthropogenic greenhouse gases, show clear trends in temperature and precipi-tation for the next century which would have signi®cant implications for human activity, e.g. a further increase of the sea level of the Caspian Sea and less water in the Rhine and the Danube. We have gained con®dence in these results because trends in the tem-perature and precipitation in the coupled model simulations up to the present are partly con®rmed by an atmospheric model simulation forced with observed SSTs and by observational data. We gained further con®dence because the simulations with the same coupled model but using constant greenhouse gases do not show such trends. However, doubts arise from the fact that these trends are strong where the systematic errors of the model are large. Ó 1999 Elsevier Science Ltd. All rights reserved.
Keywords:Precipitation; River discharge; Scenario simulation; Europe; ECHAM; Impacts from anthropogenic greenhouse gases
1. Introduction
Redistribution of incoming solar energy is the key process in the climate system and is closely connected with the global hydrological cycle. The radiation budget of the earth is characterized by an exchange between the earth as a whole including the oceans and the atmo-sphere and the outer space while we can regard the earth as a closed system with respect to water. Retention pe-riods of water molecules range from a few days in the atmosphere to several thousand years in continental ice sheets. The water available for life on land is only a very tiny fraction of the total amount of water on earth. This part is characterized by a short retention period of a few days and a high variability in time and space.
The distribution of dierent components of the hy-drological cycle has a very large margin of uncertainty. Therefore, international regional and global programs
and observational campaigns have been initiated in re-cent years such as the Baltic Sea Experiment (BALTEX) or the tropical rainfall measuring mission (TRMM). Enhanced observations, data collection and coordinated research will lead to both a better understanding of physical processes within the hydrological cycle and serve as an additional database for validating atmo-spheric models. In Section 2 we shall demonstrate the uncertainties of observed and analysed precipitation on dierent scales in time and space and give an overview of possible data sources for validation. Only data sets which are produced in the environment of numerical weather forecasts provide the complete range on all scales in time and space needed for the validation of climate models. Therefore the use of, and problems as-sociated with, ECMWF reanalysis data will be stressed. Coupled ocean-atmosphere general circulation models (CGCMs) have been used to simulate the climate of the next century by assuming dierent scenarios of green-house gases according to the suggestions by the Inter-governmental Panel on Climate Change (IPCC) [9±11]. Before interpreting such scenario runs, one needs to *
Corresponding author.
know how far the models are able to reproduce the mean climatology and the temporal variability of meteorological parameters under present climate conditions. Due to limited computer resources, long-term CGCM simulations can presently be performed only at a relatively low resolution, typically up to a horizontal grid resolution of 2.5°or T42 spectral reso-lution (T42 means that any horizontal variability which is shorter than a wavelength of 360°/42 on any great circle cannot be represented). Smaller scale processes like convection have to be parameterized which may result in another model limitation.
In Section 3 it will be shown how far atmospheric models are able to simulate the present day climatology with prescribed observed sea surface temperatures (SSTs). Impacts of horizontal resolutions and of pa-rameterizations are studied in a similar way as in Ref. [1]. The ability of the older atmospheric general circu-lation model (AGCM) version of the Max-Planck-In-stitute for Meteorology (MPI), ECHAM3 T42, to simulate the large scale circulation and its variability has been shown by Bengtsson et al. [2] and improvements due to the more recent model version are described by Roeckner et al. [19]. Therefore, we shall restrict our study here to the hydrological cycle and we shall con-centrate on Europe.
In Section 4 the realism of the coupled model will be investigated. Roeckner et al. [20] have shown that their coupled model is able to reproduce the ENSO phe-nomenon which is the most dominant interannual variability of the ocean-atmosphere system. This is a requisite for a more detailed investigation.
Results from scenario simulations of the next century with the MPI CGCM [20,21] are shown in Section 5. The resolution in coupled models may be insucient to distinguish between regions which may have distinctly dierent climatologies due to orographic eects. In such situations the time-slice concept is widely used in the climate modelling community. For time-slice experi-ments one calculates a long term average (approximately 10 years) of SSTs in a CGCM simulation with a selected scenario e.g. CO2 concentration twice present day
val-ues. Then, simulations with an AGCM are carried out using the resulting SST as a lower boundary forcing and keeping the greenhouse gas concentration from that time. Such simulations can be run with a resolution appropriate to the application and for as long as needed for statistical signi®cance. Some results are shown in Section 6.
Information on even higher resolutions can be gained by statistical methods or by running limited area models of a higher resolution (up to 20 km) which are forced at their boundaries with values from a global model. This ``dynamical down scaling'' is still under development. A signi®cant problem results from systematic errors in the global and in the limited area model. These are discussed
in Section 6. The main results of this study are sum-marized in Section 7.
2. Uncertainties of estimates of actual precipitation and evaporation
For the validation of the hydrological cycle of the models observational data are required which were av-eraged in time and space over a grid which is compa-rable to the scales in the models. For evaporation, none of the observations come near to this requirement. Area mean precipitation estimates over land are mostly based on observations at a few stations which are analysed to obtain area means [22]. Currently the highest feasible resolution for an analysis of precipitation on a global scale is a month in time and 2.5° in space. Because of these limitations in data sets based on direct observa-tions, precipitation and evaporation data from analysis schemes which are used for numerical weather forecasts are useful alternatives. Such schemes use a very large range of observations (wind, temperature, pressure, humidity etc.) from all possible platforms but not cipitation or evaporation. The latter quantities are pre-dicted in an atmospheric model which is run within the analysis cycle to provide a ®rst guess for the next anal-ysis time step. In this study we will rely often on the data from the ECMWF reanalysis (ERA) for the period 1979±1993 [6]. It is called reanalysis as it has been done recently using the same state-of-the-art scheme for the whole period. Variabilities in time arise mainly from atmospheric variabilities but also from changes in quality and distribution of the observational data. Comparisons of these reanalysis data with observational data or estimates based on precipitation observations or reanalyses by other centres below will show the limita-tions of the ERA data but will also provide some con-®dence in their usefulness.
A signi®cant problem of the ERA scheme results from a spin-up in forecasts during the ®rst days [24]. Especially during the ®rst hours of a forecast, an adjustment be-tween the wind, mass and humidity ®elds is taking place and fronts are sharpened. Precipitation is generally weakened during this period of the forecast. In the fol-lowing discussion we shall display mostly two ERA values, one gained from the 6 h forecasts 4 times a day (ERA06) and the other gained from the 12±24 h forecast range twice a day (ERA24). The dierence between these two data sets is a manifestation of the spin-up.
gener-ally leading to an underestimation, especigener-ally in windy conditions and when precipitation is falling in the form of snow. Errors of more than 100% have been reported [22].
2.1. Comparisons of dierent climatological estimates of precipitation
In Fig. 1 we compared the precipitation analyses ar-rived at using dierent methods for the summer (June± August) of 1988 as well as the climatological estimate from Legates and Willmott [13]. All data sets are in-terpolated to a T42 grid before plotting. The analysis by Schemm et al. [23] was available only over land and therefore the contours are suppressed over water. All analyses show similar patterns with low values over North Africa, the Mediterranean Sea and the Norwe-gian Sea. High values are found over southern Norway, the Alps and eastern Europe. However, there is a large uncertainty in precipitation amounts. The very high values of the NCEP (US National Centers for Envi-ronmental Prediction) reanalysis [12] are probably un-realistic. Stendel and Arpe [24] have shown that the excessive summer precipitation can be found over most of the northern continents. The GPCP (Global Precip-itation Climatology Project [22]) analysis shows very large values at the northern coast of Spain which is not analysed by any of the other schemes. For the year 1988, shown here, these values are extreme. Smaller dierences of the same character can, however, be seen in several years and this relative maximum is also present in the long-term means of the GPCP data shown in Fig. 4. These high values could result from a few storms in this mountainous region of which reports are only available in the more comprehensive data base of GPCP and not in the data base of Schemm et al. [23]. The reanalysis schemes might have missed such events because of a too smooth orography in their models. Whatever the reason, we do not know which of the analyses is correct in this respect. In other areas, smaller dierences occur which can exceed 20%.
The precipitation in ERA24 is generally slightly higher than in ERA06. On the whole both ERA data sets are more similar to each other than to any of the analyses based solely on precipitation observations. A decision which of the two is superior is not easy for this season but below it will be shown for winter (see Fig. 2(a) and Fig. 3) that the higher values of ERA24 are generally more similar to analyses based on observed precipitation data.
2.2. Variability of precipitation on dierent time scales
2.2.1. Day by day variability
Precipitation is highly variable in time and space and it is therefore very dicult to obtain representative
area-averaged precipitation values on a daily basis. Such data are, however, needed e.g. for calculating statistics of dry or wet spells. In Belgium exists a very dense observational network and we have used it for comparison with reanalysis data in Table 1. We have chosen the T106 grid element ``central Belgium'' (3.94°± 5.06°E, 50.47°±51.59°N). In this area there are 31 ob-servational stations. These were averaged and used to test the ability of the reanalysis schemes to represent the day by day variability. The NCEP reanalysis is produced with a T62 model (corresponding to a grid size of 1.9°) and therefore it is not clear whether one can use the nearest grid element in this resolution for the comparison. To test the impact of using a dierent grid element size or position on the scores we have included in Table 1 also ERA06 data after an inter-polation to a T42 grid and using the nearest grid ele-ment from that data set. The correlations between the reanalyses and the observations are quite high in all data sets.
A very good data coverage is also available for the area along the northern Alps [5] and in Table 2 the correlations for the grid element ``Basel'' (7.31°±8.44°E, 47.10°±48.22°N) are given. The results are quite en-couraging. The largely convective summer precipitation can less easily be simulated than the winter precipitation which is more connected with fronts. By choosing larger areas and exact averaging periods the scores can prob-ably be improved.
The scores for the 6 h forecast range (ERA06) are higher than for the 12±24 h forecast range (ERA24). For this time scale the use of the shorter range forecasts seems to be superior. Statistics of days with no precip-itation or with precipprecip-itation in certain ranges of amounts are quite realistic in the ERA data (see Table 7).
2.2.2. Annual cycle
The ERA06 values are clearly lower than the other ones, especially in winter. That is one reason why we believe that the ERA24 data are superior to the ERA06
data. Otherwise the dierences are in the range of 20 mm/month which means a 30% uncertainty. The other panels in Fig. 2 are discussed below.
2.2.3. Interannual variability
In Fig. 3 we display the interannual variability of precipitation averaged for two river catchment areas for the summer and winter seasons as estimated by dierent methods. For the Volga we have included also obser-vational data from Hulme [8]. For winter the ERA06 data clearly underestimate the amounts compared to the others. The GPCP analysis [22] is based on the largest data base and the precipitation has been checked most carefully and therefore we believe that the GPCP data are the best available estimates. As these are very similar to the ERA24 data we get again con®dence in the quality of the ERA24 data. All estimates produce the same interannual variability and correlations between dierent data sets exceed mostly 80%. Higher correla-tions are found for the Volga probably due to its larger catchment area. The data from the NCEP reanalysis deviate most from the other estimates during summer due to problems with this data set mentioned above. Also the analysis by Schemm et al. [23], which are based on a much smaller data base than the GPCP analysis,
deviate from the other estimates in some points, strongest during summer for the Volga with correlations below 70% with respect to the other data sets.
2.3. Use of river discharges
For long-term time averages one can validate the dierence between precipitation and evaporation (P±E)
Fig. 2. Annual cycle of precipitation in the Rhine catchment area by dierent analyses (panel a) and dierent simulations with the ECHAM model. Panel b shows the impact of resolution, panel c the impact of parameterization and panel d the evolution in a scenario simulation. The heavy line in each panel gives values from the climatology of Legates and Willmott [13].
Fig. 3. Interannual variability of precipitation for the catchment basins of the Rhine and the Volga river during summer and winter as esti-mated by dierent analyses.
Table 1
Correlations between means of observations in an area of a T106 grid element in central Belgium and values in the reanalysis data for the two months of December and July of 1981±1988
obs-ERA06 obs-ERA24 obs-NCEP obs-ER06/T42
December 73% 68% 53% 66%
July 70% 66% 60% 68%
Table 2
Same as Table 1 for a T106 grid-element near Basel for January and July of 1983±1991
obs-ERA06 obs-ERA24 obs-NCEP obs-ER06/T42
January 79% 78% 80% 57%
with river discharge data. With reanalysis data one can carry out a simple consistency test by checking long-term means of P±E for negative values over land. There are quite large land areas in the ERA data where the evaporation exceeds the precipitation by more than 0.2 mm/d, especially in the ERA06 data over almost all arid areas. Below we shall see that this de®ciency can also aect Europe. P±E will eventually result in a river discharge.
For several rivers, the observed monthly mean dis-charge is available [4] and we have used these data to check the long-term mean of P±E from the reanalyses. The Amazon and the Mackenzie river are selected here for demonstration primarily because of their very large basins. The precipitation amounts (not shown) for the two catchment basins in the ERA data agree reasonably well with other climatological estimates while the NCEP data provide an overestimate, especially in summer for the Mackenzie river. We have no observational data of evaporation available and therefore we can only com-pare the two reanalyses. NCEP provides clearly more evaporation than ERA, and this compensates for the higher precipitation in this data set.
Because of the size of these rivers, the maximum discharge at their mouths happens a few months after peak precipitation upstream. For the Mackenzie, the snow melt in May±June dominates the peak discharge. In the present data sets, these delays of discharge are not modelled and therefore only the annual means of P±E and the river discharge can be compared. Re®nements in this respect are expected with the work by Hagemann and Dumenil [7]. The annual mean P±E of the reana-lyses is compared with observed river discharge in Table 3. The river discharge data are generally reported in km3/month, these units are converted here to the units
mm/month by dividing the discharge values by the ac-tual catchment area. For both rivers, the ERA24 pro-vides nearly exact estimates of the observations while NCEP and ERA06 values are on the low side. For the Mackenzie river basin, the ERA06 values are clearly too low, which con®rms our ®nding from above that in the extra tropics a longer forecast range gives more realistic values of precipitation.
For the smaller catchment basins of the European rivers the comparisons are less favourable. In the 6 h
forecast values from ERA even negative values are found. The NCEP values look realistic for the wrong reasons as it has been shown above that this data set has unrealistically high precipitation values over Europe in summer. The ERA24 data give the best estimate but values are still too low.
2.4. Summary
It has been shown that the ERA data provide a good estimate of the real precipitation, at least with respect to the applications for this study. Stendel and Arpe [24] have shown that this data set has considerable problems in the tropics. For Europe, the use of precipitation in the forecast range of 12±24 h is generally preferable to a shorter range except where the day by day variability is concerned. The uncertainties are in the range of 20±30%. It is of course preferable to validate model simulations with analysis data based on observed precipitation and probably the best available data set in this respect is that from GPCP [22] but it is less comprehensive than the ERA data set.
3. The realism of atmospheric climate models
Atmospheric climate models presently used are able to simulate the main large-scale components of the hy-drological cycle satisfactorily. Important components are evaporation at the surface of the continents and oceans and condensation of water vapour in the atmo-sphere which leads to the generation of clouds and further to precipitation in the form of rain and snow. Over land the precipitation is on occasions accumulated as snow, used by vegetation, evaporated again into the atmosphere, stored in the upper layers of the soil or ®-nally discharged by the rivers into the oceans. Present models represent all these processes but do not account for very slowly varying components of the hydrological cycle, e.g. the change of inland ice and the changes of water reserves in the deep ground which is used, e.g. for irrigation in arid regions.
Limitations in modelling the climate result from a coarse resolution in time and space and from a need to parameterize small scale processes, e.g. the evaporation and condensation of water. These limitations result partly from restrictions in computer resources and partly from our incomplete knowledge of the processes. It has been found that the models can reproduce large-scale features better than small-large-scale features in space and in time.
3.1. Large-scale aspects
On a large-scale and in long-term means over the oceans evaporation exceeds precipitation so that the
Table 3
The annual means of observed river discharge or P±E for river basins using dierent reanalysis data. Units mm/mon
obs. discharge P±E in NCEP ERA06 ERA24
Amazon 80.5 61.3 79.2 80.4
Mackenzie 13.5 8.6 4.9 12.8
Rhine 32.1 12.7 9.5 22.0
Danube 19.5 10.2 ÿ1.8 10.4
Volga 12.0 0.4 ÿ2.4 5.2
oceans lose water to the atmosphere. Over the conti-nents, however, precipitation exceeds evaporation and the atmosphere is losing water to the land. Transport from oceans to continents by the atmosphere and back through rivers leads to a closed water budget. It is shown in Table 4 that the ECHAM4 model is able to simulate the global hydrological cycle within the margin of observational uncertainty. The model data are taken from several simulations with the ECHAM4 T42 model [19] which were forced with varying observed SSTs for the period 1979±1994.
Table 4 shows clearly that a fundamental quantity such as the global long-term mean of precipitation is only known with an accuracy of about 10%. This cor-responds to an uncertainty in the global energy budget of about 10 W/m2for the atmosphere as well as for the
surface of the earth because precipitation contributes eciently to the energy exchange between the earth and the atmosphere.
On continental scales (Table 5) the dierences be-tween simulations and observations are larger, as might be expected. The precipitation over Africa and North America seems to be overestimated by the model.
The precipitation distribution for Europe in summer is shown in Fig. 4. The patterns of the AGCM (EC-HAM4 T42) are close to those of the GPCP analysis with maxima over Scandinavia and the Alps and mini-ma over the Baltic and the Mediterranean Sea. How-ever, there is a bias to lower values in the simulation except over eastern Spain. Fig. 5 displays the precipi-tation distribution over Europe for winter. The AGCM is very similar, in patterns and amounts, to the GPCP analysis, except over Spain where the model gives con-siderably less precipitation. This error is connected with an eastward shift of the Azores high in winter towards
the Mediterranean, an error which can be found in several AGCMs.
3.2. Annual cycle
The annual cycle of precipitation for the catchment area of the river Rhine is shown in Fig. 2(b). The cli-matological values of Legates and Willmott [13] are compared with values from the ECHAM4 model of dierent resolutions. For central Europe there is a maximum of precipitation during July/August which is less pronounced during the last two decades as discussed above. The models are not producing this summer maximum and they overestimate the winter maximum. The model with the highest resolution is worst in this respect. However, in many places of the world the higher resolution models provide often the best simulation of precipitation [18]. Also this increase of systematic errors over Europe with increased resolution has been found in other AGCMs.
Climate models are under constant review to reduce their systematic errors. Also the ECHAM models have undergone several stages of development [18,19]. Changes of the convection schemes, in particular, have shown in some places dramatic improvements of the precipitation distribution. To show the impact of such changes in the schemes for central Europe we have in-cluded in Fig. 2(c) the annual cycle of precipitation for the Rhine catchment area using the two latest model versions of ECHAM with a T42 resolution. It demon-strates that improving a climate model is a very painful process and that an improvement of large-scale features does not necessarily mean an improvement of local-scale features everywhere on the globe. Here, we see an im-provement of the summer precipitation maximum but a deterioration in winter.
3.3. Validation of river discharge
In Table 6 we compare the long-term annual mean river discharge of several rivers with long-term means of precipitation minus evaporation for the corresponding basins in the ECHAM4 models as done above for the reanalysis data (cf. Table 3). For the European rivers the T21 values are not provided because these rivers can hardly be resolved by such a coarse resolution. In a T42 resolution the Rhine river basin is represented by only
Table 4
Comparison of simulated and observed (estimated) water transports. Observed (estimated) values are given in brackets. Units: 1015kg/year.
The range given for the observations results from 10 dierent clima-tologies but only the minimum and maximum values are given. The model results are gained with the ECHAM4 T42 model
Precipitation (P) Evaporation (E) P±E
Oceans 408 (380±426) 445 (410±441) ÿ37 (ÿ26 toÿ40) Continents 113 (109±121) 76 (71±95) 37 (26±40) Global 521 (489±547) EP 0
Table 5
Comparison of simulated and observed (estimated) precipitation over dierent continents. Units: 1015kg/year. The range given for the observations
result from 3 dierent climatologies but only the minimum and maximum values are given. The model results are gained with the ECHAM4 T42 model
Africa N. America S. America Asia Australia Europe
Model 24.5 17.2 27.5 28.2 4.1 6.7
3 grid points. Nevertheless it seems that the climate model is reproducing the river discharges at least as well as the reanalysis schemes and the performance is gen-erally better using the higher resolution model, despite the worse performance in the annual cycle of precipi-tation shown above.
3.4. Summary
It has been shown that the AGCMs are able to re-produce the main features of the hydrological cycle within the range of uncertainty of observational data, even for relatively small areas such as the Rhine river basin. The ECHAM4 models overestimate winter pre-cipitation over central Europe and miss the summer maximum but the summer maximum is also missing in recent precipitation analyses, probably due to interde-cadal variability.
4. Coupling ocean and atmospheric models
The CGCM used in this study is based on the EC-HAM4 T42 AGCM, which was mentioned already above and on the OPYC-3 ocean model [15]. Because of systematic errors in both models there is still a ¯ux correction needed but improvements in both models have made it possible to restrict this correction to an-nual means of water and heat. A short description of the models and of the coupling method is given by Roeckner et al. [20].
It takes a long time for ocean models to adjust to atmospheric forcings even when starting from an initial ®eld which is close to the observed climatological mean. For this spin-up period the ocean was forced with present day atmospheric data which were either ob-served or gained from an AGCM with obob-served SSTs, because the comprehensive data needed for the spin-up
are only available for recent periods. Also during the phasing-in of the coupling between the oceanic and the atmospheric models, present day data were used for controlling both models. Moreover, in the so-called control experiment (CTL), the concentrations of green-house gases like carbon dioxide, methane etc. are ®xed at the observed 1990 values so that the simulated CTL climate does represent modern climate. In the green-house gas scenario experiment (GHG), the concentra-tions of the greenhouse gases are prescribed as a
function of time. Between 1860 and 1990, the concen-tration changes are prescribed as observed and from 1990 onward, according to IPCC scenario IS92a [10,11]. Because of the lack of pre-industrial ocean data, the GHG experiment was initialized with data of the CTL experiment. The associated shift in greenhouse gas concentrations is taken into account by enhancing the observed/projected concentrations of these gases in an appropriate way [21]. Although this approach allows for a correct computation of the radiative forcing, it does
Table 6
Long-term annual means of observed river discharge or P±E for river basins in the ECHAM4 models of dierent resolution. Units: mm/mon
observed discharge P±E T106 P±E T42 P±E T30 P±E T21
Amazon 80.5 86.4 51.7 83.5 45.5
Mackenzie 13.5 14.4 19.9 26.5 28.9
Rhine 32.1 27.5 36.2 40.3
Danube 19.5 11.4 16.8 18.3
Volga 12.0 11.6 13.6 19.8
Elbe 17.1 16.5 21.2 32.7
not account for the warm bias in the initial state. This initial warm bias, compared to the observations, is maintained throughout the simulation and, therefore, does not aect climate trends (cf. Fig. 6).
Roeckner et al. [20] demonstrate that their CGCM, in the CTL mode, is able to simulate most large-scale features of the atmosphere and the ocean. They also show a realistic simulation of the ENSO phenomenon. In Fig. 2(d) it is shown that this model produces an annual cycle of precipitation for the Rhine catchment area of the present climate (curve ``now'') which is of similar or better quality than in the uncoupled AGCM simulation. This applies also for the precipitation dis-tribution for Europe in summer which is shown in Fig. 4. The patterns of the AGCM (ECHAM4 T42) are very close to those of the GHG for the present day cli-mate (GHG now). Fig. 5 shows the precipitation dis-tribution over Europe for winter. There is a large-scale over estimation of precipitation in the AGCM and GHG of the present day climate when compared with GPCP data, however, with realistic distributions of maxima and minima. A similar overestimation in winter was already found above when discussing Fig. 2.
5. Scenario simulations
5.1. Global aspects
In the GHG scenario experiments with the CGCM, assuming a continuation of emissions of anthropogenic CO2and other greenhouse gases for the next 100 years
(see Section 4.) an increase of global mean surface air temperatures of about 3°C is simulated [21]. The tem-perature changes are, however, regionally very dierent. Continents will be heated much more than the oceans and the arid tropical regions more than the tropical forest regions. Simulations by several models show similar temperature increases [11]. In Fig. 6, time series of 2m temperature for all land points, all ocean points and for Germany are shown. The data are smoothed with a 9 year running mean. Before the 1980s there is a slight warming in the GHG run in agreement with the AGCM simulation using observed SSTs [17] as forcing from the ocean. After the 1980s the temperatures in-crease more rapidly. The fact that the CTL run, i.e. the same model as used for the GHG run but with constant greenhouse gas concentration, does not show any trend, suggests that the increase of temperature for the next century is solely due to the change of the greenhouse gas concentration and not due to a climate drift in the CGCM. The bias between the AGCM and the GHG run has been explained by the method of creating the initial data for the CGCM in Section 4.
In the GHG simulations this increase of temperature is accompanied by an increase of precipitation globally
by 2%. The increase is mainly due to an increase over the continents (10%). Very often the precipitation increases in areas where there is already considerable precipitation while arid regions do not pro®t from the general pre-cipitation increase. Some trends in the prepre-cipitation are demonstrated in Fig. 7. As in Fig. 6 we see that the AGCM simulation reaches the early GHG values only in the 1990s because of the procedure for creating the initial data for the GHG, which was explained in Sec-tion 4. Over land there is a small precipitaSec-tion increase up to the 1980s which agrees with the AGCM simula-tion while the CTL run with constant greenhouse gases keeps the same values for the whole 180 year period. Over the oceans the variability of precipitation is very low, perhaps 0.5% and an interpretation of trends or dierences seems to be unjusti®ed.
5.2. Regional aspects
For Germany, despite the smoothing by a 9 year running mean there are still large variabilities which conceal any possible trend. This missing trend over Europe is partly due to a compensation between an in-crease of precipitation during winter and a dein-crease during summer. This can be seen in Fig. 2(d) for the Rhine area where the scenario for 2070±2100 (curve ``100+'') shows increased precipitation in winter and decreased precipitation in summer compared to the
scenario run of the present (``now'') or 100 years ago (``100ÿ''). There are also compensating trends within Europe, e.g. there is a decrease of precipitation over Spain in winter while there is an increase over northern Europe. The latter is studied further by investigating the trends over Scandinavia for summer and winter in Fig. 8. Here also 90 years of observational data from Hulme [8] are available. We have averaged the observed precipitation from 29 stations and compare them with model data of all land points for the area 5°±28°E, 56°± 61°N. Biases between those data may result partly from a systematic error of the model as discussed above (see Figs. 4 and 5) or from an unrepresentativeness of the observational data. The scenario simulations indicate a strong increase of precipitation in winter and a smaller decrease in summer, starting perhaps in the present de-cade. For winter there is an increase during the last decades also in the AGCM run and in the observations. They reach high values during the last years which were never found before thus supporting the scenario simu-lation. The scenario run suggests, however, that this recent increase may also be part of an interdecadal variability. The decrease during summer is much weaker and hardly supported by observations, but one might see support in the AGCM run.
The fact that the CTL run does not show any trend in contrast to the scenario run gives some con®dence in these results. On the other hand increases occur where the systematic error of the model shows an overestimate of precipitation and decreases occur where the model bias is negative. There is also a large variation in the trends of precipitation when comparing scenario simu-lations with dierent models [11].
A further trend in precipitation can be found for the Iberian Peninsula where the winter precipitation is pre-dicted to decrease considerably in the next century (Fig. 5). This decrease is connected with a strengthening of an anticyclone over the area (not shown). Again we have a connection with a systematic error of the model [18] as the model is shifting the Azores anticyclone to-wards the Iberian Peninsula and this is strengthened in the scenario run which casts some doubts on the ro-bustness of the results.
5.3. River discharge
Because the global water budget is closed, an increase of precipitation has to be accompanied by an increase of evaporation at least for a global mean. Therefore the impact of increased CO2on soil moisture and river ¯ow
cannot be assumed just from trends in precipitation. In Fig. 9 the annual mean P±E values, which can be interpreted as river discharges, of three European river basins are shown for two centuries as simulated with the GHG run and compared with observed river discharges. The data are smoothed with a 9 year running mean. We have to remember that the Rhine river basin consists only of three grid points in the T42 resolution, used in
Fig. 7. Same as Fig. 6 for precipitation. Units: mm/month.
this simulation. The Volga river basin is the largest basin of Europe and it is best suited for this investigation for this reason but the CGCM did not know about the Caspian Sea which is a major drawback. The Rhine and the Danube show a clear decrease of discharge for the next century while the Volga shows an increase. If these scenarios become true it will have large impacts on so-ciety. The decrease of the Rhine and Danube discharge indicates a water shortage in areas which already have problems with water supply and the increase of the Volga would result in a further increase of the level of the Caspian Sea which has already recently led to ¯ooding of coastal towns.
The dierent behaviour in the West-European rivers and the Volga results from a change in the winter circu-lation with an intensi®ed trough over eastern Europe and strengthening of the Mediterranean anticyclone, which brought mainly more precipitation to the Volga basin. During summer all rivers, strongest the Rhine river, have a decline of precipitation which is connected with an intensi®ed extension of the Azores high into Europe.
To help in judging the realism of the these simula-tions we have included in Fig. 9 the time series of ob-served river discharges and the P±E values of an AGCM run using variable observed SSTs [17] on the lower boundary for the period 1903±1994. For the Volga river we used in Fig. 9 observational data from Polonskii and Gorelits [16] and for the other rivers data from Dumenil et al. [4]. For the Rhine there is a general overestimation in the simulations during this century while the dis-charge of the Danube is simulated best. In all three data sets we ®nd similarly large interdecadal variabilities which make it dicult to see when decreases or increases of river discharge start and if a trend during recent years in the observations is already a manifestation of the impact of increased CO2.
For the Rhine and the Volga there are some simi-larities in the interdecadal variability between observa-tions and the AGCM run using variable observed SSTs. This suggests that the SST is responsible for these in-terdecadal variations. The ability of the model to re-produce such variabilities boosts the con®dence in the quality of the atmospheric model.
5.4. Summary
Simulations with the MPI CGCM assuming a further increase of anthropogenic greenhouse gases show clear trends in temperature and precipitation for the next century which would have a strong impact on society, e.g. a further increase of the sea level of the Caspian Sea and less water in the Rhine and Danube. We have gained con®dence in these results because trends in temperature and precipitation in the coupled model simulations up to the present are at least partly con-®rmed by an atmospheric model simulation forced with observed SSTs and by observational data. It is also encouraging that simulations with the same coupled model, but using constant greenhouse gases, do not show any trends. However, some doubts arise from the fact that these trends are strong where the systematic errors of the model are large. While temperature trends due to the increase of greenhouse gases have been sim-ulated similarly by dierent models in many respects, the same cannot be said for precipitation.
6. Regionalization
The resolution in coupled models is often insucient to distinguish climatic dierences between regions due to orographic eects. We have also seen above that catch-ment areas of important rivers such as the Rhine are represented only by 3 grid points in the T42 resolution which is presently the highest feasible resolution for a CGCM run. Therefore time-slice experiments have been used with global atmospheric models of a higher
resolution than that used in coupled mode to get in-formation on a smaller scale. At present the highest feasible horizontal resolution is a 1° or T106 spectral resolution which is still too low for many applications. It has been discussed above that going from a T42 to a T106 resolution has presently more a large-scale eect while improvements on the small scale are less obvious, at least for long term means. For Europe even a large-scale deterioration was found. On the other hand higher resolution simulations allow sophisticated diagnostics, like calculating the percentage of dry days, which are not useful on a coarser grid. Cubasch et al. [3] give an example of such a method based on a scenario run with an older model. With such a method they are able to investigate the change of drought periods or wet spells. In Table 7 a frequency distribution of days with dier-ent classes of precipitation using these experimdier-ents in comparison with observations and ERA data is shown. A dramatic increase of dry days in the scenario of 2*CO2concentration can be seen. A similar statistic for
winter (not shown) is less dramatic in this respect. Information of an even higher resolution can be gained by statistical methods or by running limited area models (LAMs) of higher resolution (up to 20 km) which are forced at their boundaries with values from a CGCM or a time-slice experiment. ``Dynamical down scaling'' requires that the global model provides realistic forcings of the large-scale general circulation. System-atic errors in the circulation of the global model will force large-scale systematic errors into the LAM which may stay similar to those in the driving model, except for modi®cations due to the LAM's higher resolution. Machenhauer et al. [14] report about a comparison in which several LAM simulations driven by dierent CGCM simulations of the present day climate are evaluated against observations over Europe. Time slices were performed for periods of 5±30 years. The LAMs gave some local improvements of the simulation of precipitation compared to the CGCMs especially in connection with orographic forcings, e.g. along the west coast of Scandinavia. However, they also show consid-erable biases (systematic errors) on a larger scale of
similar or in some cases even larger magnitude than in the CGCMs. It is shown that these biases are statisti-cally signi®cant when compared with the decadal vari-ability. The biases are due to errors in the driving global atmospheric model or errors in the SSTs. Enhancements of biases in the LAMs are due to de®ciencies in their physical parameterization schemes.
Also climate change time-slice LAM experiments which were based on CGCMs after reaching double CO2
concentrations were analysed. Large-scale temperature changes over Europe were found to be signi®cant in the LAMs similar to that in the CGCMs. Smaller-scale cli-mate changes in the CGCMs and the LAMs did, how-ever, generally not pass a signi®cance test and they were generally of the same order of magnitude as the biases in the present day climate simulations. Cases of interac-tions between the systematic model errors and changes in the circulation due to increased greenhouse gases were shown which indicate that reliable regional climate change estimates can only be achieved with improved models which have fewer systematic errors than the models presently available.
7. Conclusion
Estimates of precipitation based on observational data are compared with each other to investigate their usefulness for model validation. It is shown that the ERA data provide a good estimate of the truth, at least with respect to the applications in this study. However, Stendel and Arpe [24] have shown that this data set has considerable problems in the tropics. For Europe the use of precipitation in the forecast range 12±24 h is generally preferable to a shorter range. The uncertainties are in the range of 20% but regionally much larger.
The MPI atmospheric general circulation model is able to reproduce the main features of the hydrological cycle within the range of uncertainty of observational data. For relatively small areas like the Rhine river basin some biases are shown but interannual or interdecadal variability seems to be realistic.
Table 7
Frequency distribution in % of days with dierent classes of precipitation in July for the T106 grid point ``central Belgium'' and ``Basel''
Class 0±0.01 0.01±0.1 0.1±0.2 0.2±0.5 0.5±1 1±2 >2 mm/d
Simulations with the MPI coupled general circulation model, assuming a further increase of anthropogenic greenhouse gases, show clear trends in temperature and precipitation for the next century which would have a strong impact on society, e.g. a further increase of the sea level of the Caspian Sea and less water in the Rhine and Danube. We have gained con®dence in these results because some of the trends in the temperature and the precipitation in the coupled model simulations up to the present are con®rmed by an atmospheric model simu-lation forced with observed SSTs and by observational data. We gained further con®dence because the simu-lations with the same coupled model but using constant greenhouse gases do not show such trends. However, some doubts arise from the fact that these trends are strong where the systematic errors of the model are large. While the temperature trends due to the increase of greenhouse gases have been simulated similarly by dierent models in many respects, such a similarity cannot be found for the precipitation, suggesting that the impacts shown here may be model dependent.
The simulations of the next century's climate in this study are based solely on assumptions of increased greenhouse gases. Recent investigations suggest that these may be modi®ed considerably by the expected increase in anthropogenic sulphur emissions discussed by Roeckner et al. [21]. A further modi®cation may arise from contrails of air planes by increasing in some areas the occurrence of cirrus clouds and by in¯uencing the O3
concentration through the production of NOx.
Acknowledgements
The authors are indebted to their colleagues at the Max-Planck-Institute for Meteorology in Hamburg for constructive discussions. We thank the anonymous re-viewers for improving our English. This study has been made possible by the technical support of the German Climate Computing Centre (DKRZ) and ®nancial sup-port of the German Ministry for Education, Science, Research and Technology (BMBF) under grant 07VKV01/1 and by the EC project No. EV5V-CT97-0640.
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