Directory UMM :Data Elmu:jurnal:A:Advances In Water Resources:Vol23.Issue3.1999:
Advances in Water Resources 23 (1999) 105±119
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 numerical 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 precipitation 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 temperature 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 atmosphere and the outer space while we can regard the earth
as a closed system with respect to water. Retention periods 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 hydrological cycle has a very large margin of uncertainty.
Therefore, international regional and global programs
*
Corresponding author.
and observational campaigns have been initiated in recent 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 atmospheric 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 associated 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 greenhouse gases according to the suggestions by the Intergovernmental Panel on Climate Change (IPCC) [9±11].
Before interpreting such scenario runs, one needs to
0309-1708/99/$ - see front matter Ó 1999 Elsevier Science Ltd. All rights reserved.
PII: S 0 3 0 9 - 1 7 0 8 ( 9 9 ) 0 0 0 1 5 - 9
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K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
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, longterm 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 resolution (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 parameterizations are studied in a similar way as in Ref.
[1]. The ability of the older atmospheric general circulation model (AGCM) version of the Max-Planck-Institute 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 concentrate 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 phenomenon 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 experiments 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 values. 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 summarized 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 averaged in time and space over a grid which is comparable 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 observations, 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 precipitation or evaporation. The latter quantities are predicted in an atmospheric model which is run within the
analysis cycle to provide a ®rst guess for the next analysis 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 limitations 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 between 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 following 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.
A main problem with analyses based solely on observations of precipitation results from having too
sparse a network. In mountainous areas the observations are often carried out in valleys while most of the
precipitation occurs in the mountains, resulting in an
underestimation of area means of precipitation. Observational data also suer from systematic errors, gener-
K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
ally leading to an underestimation, especially 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 arrived 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 interpolated 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 Norwegian 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 Environmental Prediction) reanalysis [12] are probably unrealistic. Stendel and Arpe [24] have shown that the
excessive summer precipitation can be found over most
of the northern continents. The GPCP (Global Precipitation 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
107
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 observational 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 interpolation to a T42 grid and using the nearest grid element 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 encouraging. 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 probably 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 precipitation or with precipitation in certain ranges of
amounts are quite realistic in the ERA data (see
Table 7).
2.2.2. Annual cycle
In Fig. 2(a) the annual cycle of precipitation averaged
over the catchment area of the river Rhine (3 grid points
in a T42 resolution) is shown using dierent climatological estimates. The largest dierence can be found
between the estimates of Legates and Willmott [13] and
the other estimates during August. The long-term means
used here present mostly the period 1979±1993 while the
climatology of Legates and Willmott [13] is using longterm means as long as possible and it can be assumed
that this is often based on the 1931±1960 climatological
normals [25]. In this data set we found many stations
with a clear summer maximum as in the climatology of
Legates and Willmott [13] and therefore the large difference for August is probably a manifestation of an
interdecadal variability. The NCEP reanalysis data exhibit an annual cycle which looks unrealistic. It becomes
evident also in Fig. 3.
108
K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
Fig. 1. June to August 1988 precipitation of dierent analyses and the June to August climatology of Legates and Willmott [13]. Units: mm/d,
shading for values of more than 3 mm/d and less than 1 mm/d.
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.
109
K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
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].
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 observational 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 correlations 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,
Fig. 3. Interannual variability of precipitation for the catchment basins
of the Rhine and the Volga river during summer and winter as estimated 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
December
July
obs-ERA06
obs-ERA24
obs-NCEP
obs-ER06/T42
73%
70%
68%
66%
53%
60%
66%
68%
Table 2
Same as Table 1 for a T106 grid-element near Basel for January and
July of 1983±1991
January
July
obs-ERA06 obs-ERA24 obs-NCEP
obs-ER06/T42
79%
66%
57%
56%
78%
64%
80%
56%
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)
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K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
with river discharge data. With reanalysis data one can
carry out a simple consistency test by checking longterm 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 discharge 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 compare 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 D
umenil [7]. The annual mean P±E of the reanalyses 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 actual catchment area. For both rivers, the ERA24 provides 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
Table 3
The annual means of observed river discharge or P±E for river basins
using dierent reanalysis data. Units mm/mon
Amazon
Mackenzie
Rhine
Danube
Volga
Elbe
obs. discharge
P±E in NCEP
ERA06
ERA24
80.5
13.5
32.1
19.5
12.0
17.1
61.3
8.6
12.7
10.2
0.4
8.5
79.2
4.9
9.5
ÿ1.8
ÿ2.4
ÿ3.3
80.4
12.8
22.0
10.4
5.2
6.1
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 hydrological cycle satisfactorily. Important components
are evaporation at the surface of the continents and
oceans and condensation of water vapour in the atmosphere 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 largescale features better than small-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
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K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
oceans lose water to the atmosphere. Over the continents, 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 corresponds to an uncertainty in the global energy budget
of about 10 W/m2 for 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 between 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 (ECHAM4 T42) are close to those of the GPCP analysis
with maxima over Scandinavia and the Alps and minima over the Baltic and the Mediterranean Sea. However, there is a bias to lower values in the simulation
except over eastern Spain. Fig. 5 displays the precipitation 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 considerably less precipitation. This error is connected with
an eastward shift of the Azores high in winter towards
Table 4
Comparison of simulated and observed (estimated) water transports.
Observed (estimated) values are given in brackets. Units: 1015 kg/year.
The range given for the observations results from 10 dierent climatologies but only the minimum and maximum values are given. The
model results are gained with the ECHAM4 T42 model
Oceans
Continents
Global
Precipitation (P)
Evaporation (E)
P±E
408 (380±426)
113 (109±121)
521 (489±547)
445 (410±441)
76 (71±95)
EP
ÿ37 (ÿ26 to ÿ40)
37 (26±40)
0
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 climatological 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 included 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 demonstrates 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 improvement 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 5
Comparison of simulated and observed (estimated) precipitation over dierent continents. Units: 1015 kg/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
Model
Observation
Africa
N. America
S. America
Asia
Australia
Europe
24.5
19.1±21.9
17.2
11.0±15.2
27.5
26.1±29.7
28.2
23.9±30.7
4.1
3.0±4.1
6.7
5.5±7.1
112
K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
Fig. 4. Long-term mean summer precipitation as analysed (GPCP), as simulated by an AGCM, by a GHG run under present conditions and by a
GHG run under conditions in 100 years. Units: mm/d, shading for values more than 2.5 mm/d and less than 1 mm/d. Contours at 0.5, 1.0, 1.5, 2.0,
2.5, 3.0, 4.0, 5.0, 6.0 mm/d.
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 generally better using the higher resolution model, despite
the worse performance in the annual cycle of precipitation shown above.
3.4. Summary
It has been shown that the AGCMs are able to reproduce 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 precipitation over central Europe and miss the summer
maximum but the summer maximum is also missing in
recent precipitation analyses, probably due to interdecadal variability.
4. Coupling ocean and atmospheric models
The CGCM used in this study is based on the ECHAM4 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 annual 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 observed or gained from an AGCM with observed SSTs,
because the comprehensive data needed for the spin-up
113
K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
Fig. 5. Same as Fig. 4 but for winter and shading for values more than 4 mm/d and less than 2 mm/d.
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
Amazon
Mackenzie
Rhine
Danube
Volga
Elbe
observed discharge
P±E T106
P±E T42
P±E T30
P±E T21
80.5
13.5
32.1
19.5
12.0
17.1
86.4
14.4
27.5
11.4
11.6
16.5
51.7
19.9
36.2
16.8
13.6
21.2
83.5
26.5
40.3
18.3
19.8
32.7
45.5
28.9
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 greenhouse 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 greenhouse gas scenario experiment (GHG), the concentrations of the greenhouse gases are prescribed as a
function of time. Between 1860 and 1990, the concentration 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
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K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
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 distribution 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 climate (GHG now). Fig. 5 shows the precipitation distribution 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
Fig. 6. Time evolution of annual mean 2m temperature for all land
points, all sea points and for Germany. A 9 year running mean has
been applied. A CGCM simulation with increasing greenhouse gas
concentration (GHG) is compared with an AGCM simulation which
was forced with observed SSTs and a CGCM simulation with constant
greenhouse gas concentration (CTL).
5.1. Global aspects
In the GHG scenario experiments with the CGCM,
assuming a continuation of emissions of anthropogenic
CO2 and 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 temperature 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 increase 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 precipitation increase. Some trends in the precipitation 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 Section 4. Over land there is a small precipitation increase
up to the 1980s which agrees with the AGCM simulation 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 increase of precipitation during winter and a decrease
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
K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
115
Fig. 8. Same as Fig. 7 but for Scandinavia separately for summer and
winter. Observational data are from Hulme [7].
Fig. 7. Same as Fig. 6 for precipitation. Units: mm/month.
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 decade. 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 simulation. 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 simulations with dierent models [11].
A further trend in precipitation can be found for the
Iberian Peninsula where the winter precipitation is predicted 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 towards the Iberian Peninsula and this is strengthened in
the scenario run which casts some doubts on the robustness 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 CO2 on 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
116
K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
To help in judging the realism of the these simulations we have included in Fig. 9 the time series of observed 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 D
umenil
et al. [4]. For the Rhine there is a general overestimation
in the simulations during this century while the discharge 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 similarities in the interdecadal variability between observations and the AGCM run using variable observed SSTs.
This suggests that the SST is responsible for these interdecadal variations. The ability of the model to reproduce such variabilities boosts the con®dence in the
quality of the atmospheric model.
5.4. Summary
Fig. 9. Time evolution of annual mean river discharge (precipitation
minus evaporation) for three river basins. A 9 year running mean has
been applied. GHG simulations with increasing greenhouse gas concentration are compared with an AGCM simulation which was forced
with observed SSTs and observed river discharge data (obs). 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 society. 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 circulation 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.
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 simulated 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 catchment 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
117
K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
resolution than that used in coupled mode to get information 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 largescale 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 dierent classes of precipitation using these experiments in
comparison with observations and ERA data is shown.
A dramatic increase of dry days in the scenario of
2*CO2 concentration 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. Systematic 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 considerable 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 statistically signi®cant when compared with the decadal variability. 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 climate changes in the CGCMs and the LAMs did, however, 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 interactions 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
Central Belgium
obs
ERA06
AGCM
SCEN
50
39
67
81
16
28
17
9
8
11
5
2
12
12
5
2
9
8
3
3
3
20
1
2
1
0
0
Basel
obs
ERA06
AGCM
SCEN
46
37
55
77
11
15
9
15
10
10
5
3
13
16
15
3
10
12
9
1
6
5
5
0
4
4
1
2
118
K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
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 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,
some doubts arise from the fact that these trends are
strong where the systematic errors of the model are
large.
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 numerical 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 precipitation 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 temperature 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 atmosphere and the outer space while we can regard the earth
as a closed system with respect to water. Retention periods 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 hydrological cycle has a very large margin of uncertainty.
Therefore, international regional and global programs
*
Corresponding author.
and observational campaigns have been initiated in recent 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 atmospheric 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 associated 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 greenhouse gases according to the suggestions by the Intergovernmental Panel on Climate Change (IPCC) [9±11].
Before interpreting such scenario runs, one needs to
0309-1708/99/$ - see front matter Ó 1999 Elsevier Science Ltd. All rights reserved.
PII: S 0 3 0 9 - 1 7 0 8 ( 9 9 ) 0 0 0 1 5 - 9
106
K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
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, longterm 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 resolution (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 parameterizations are studied in a similar way as in Ref.
[1]. The ability of the older atmospheric general circulation model (AGCM) version of the Max-Planck-Institute 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 concentrate 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 phenomenon 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 experiments 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 values. 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 summarized 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 averaged in time and space over a grid which is comparable 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 observations, 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 precipitation or evaporation. The latter quantities are predicted in an atmospheric model which is run within the
analysis cycle to provide a ®rst guess for the next analysis 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 limitations 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 between 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 following 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.
A main problem with analyses based solely on observations of precipitation results from having too
sparse a network. In mountainous areas the observations are often carried out in valleys while most of the
precipitation occurs in the mountains, resulting in an
underestimation of area means of precipitation. Observational data also suer from systematic errors, gener-
K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
ally leading to an underestimation, especially 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 arrived 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 interpolated 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 Norwegian 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 Environmental Prediction) reanalysis [12] are probably unrealistic. Stendel and Arpe [24] have shown that the
excessive summer precipitation can be found over most
of the northern continents. The GPCP (Global Precipitation 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
107
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 observational 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 interpolation to a T42 grid and using the nearest grid element 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 encouraging. 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 probably 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 precipitation or with precipitation in certain ranges of
amounts are quite realistic in the ERA data (see
Table 7).
2.2.2. Annual cycle
In Fig. 2(a) the annual cycle of precipitation averaged
over the catchment area of the river Rhine (3 grid points
in a T42 resolution) is shown using dierent climatological estimates. The largest dierence can be found
between the estimates of Legates and Willmott [13] and
the other estimates during August. The long-term means
used here present mostly the period 1979±1993 while the
climatology of Legates and Willmott [13] is using longterm means as long as possible and it can be assumed
that this is often based on the 1931±1960 climatological
normals [25]. In this data set we found many stations
with a clear summer maximum as in the climatology of
Legates and Willmott [13] and therefore the large difference for August is probably a manifestation of an
interdecadal variability. The NCEP reanalysis data exhibit an annual cycle which looks unrealistic. It becomes
evident also in Fig. 3.
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K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
Fig. 1. June to August 1988 precipitation of dierent analyses and the June to August climatology of Legates and Willmott [13]. Units: mm/d,
shading for values of more than 3 mm/d and less than 1 mm/d.
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.
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K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
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].
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 observational 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 correlations 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,
Fig. 3. Interannual variability of precipitation for the catchment basins
of the Rhine and the Volga river during summer and winter as estimated 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
December
July
obs-ERA06
obs-ERA24
obs-NCEP
obs-ER06/T42
73%
70%
68%
66%
53%
60%
66%
68%
Table 2
Same as Table 1 for a T106 grid-element near Basel for January and
July of 1983±1991
January
July
obs-ERA06 obs-ERA24 obs-NCEP
obs-ER06/T42
79%
66%
57%
56%
78%
64%
80%
56%
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)
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K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
with river discharge data. With reanalysis data one can
carry out a simple consistency test by checking longterm 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 discharge 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 compare 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 D
umenil [7]. The annual mean P±E of the reanalyses 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 actual catchment area. For both rivers, the ERA24 provides 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
Table 3
The annual means of observed river discharge or P±E for river basins
using dierent reanalysis data. Units mm/mon
Amazon
Mackenzie
Rhine
Danube
Volga
Elbe
obs. discharge
P±E in NCEP
ERA06
ERA24
80.5
13.5
32.1
19.5
12.0
17.1
61.3
8.6
12.7
10.2
0.4
8.5
79.2
4.9
9.5
ÿ1.8
ÿ2.4
ÿ3.3
80.4
12.8
22.0
10.4
5.2
6.1
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 hydrological cycle satisfactorily. Important components
are evaporation at the surface of the continents and
oceans and condensation of water vapour in the atmosphere 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 largescale features better than small-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
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oceans lose water to the atmosphere. Over the continents, 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 corresponds to an uncertainty in the global energy budget
of about 10 W/m2 for 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 between 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 (ECHAM4 T42) are close to those of the GPCP analysis
with maxima over Scandinavia and the Alps and minima over the Baltic and the Mediterranean Sea. However, there is a bias to lower values in the simulation
except over eastern Spain. Fig. 5 displays the precipitation 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 considerably less precipitation. This error is connected with
an eastward shift of the Azores high in winter towards
Table 4
Comparison of simulated and observed (estimated) water transports.
Observed (estimated) values are given in brackets. Units: 1015 kg/year.
The range given for the observations results from 10 dierent climatologies but only the minimum and maximum values are given. The
model results are gained with the ECHAM4 T42 model
Oceans
Continents
Global
Precipitation (P)
Evaporation (E)
P±E
408 (380±426)
113 (109±121)
521 (489±547)
445 (410±441)
76 (71±95)
EP
ÿ37 (ÿ26 to ÿ40)
37 (26±40)
0
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 climatological 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 included 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 demonstrates 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 improvement 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 5
Comparison of simulated and observed (estimated) precipitation over dierent continents. Units: 1015 kg/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
Model
Observation
Africa
N. America
S. America
Asia
Australia
Europe
24.5
19.1±21.9
17.2
11.0±15.2
27.5
26.1±29.7
28.2
23.9±30.7
4.1
3.0±4.1
6.7
5.5±7.1
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Fig. 4. Long-term mean summer precipitation as analysed (GPCP), as simulated by an AGCM, by a GHG run under present conditions and by a
GHG run under conditions in 100 years. Units: mm/d, shading for values more than 2.5 mm/d and less than 1 mm/d. Contours at 0.5, 1.0, 1.5, 2.0,
2.5, 3.0, 4.0, 5.0, 6.0 mm/d.
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 generally better using the higher resolution model, despite
the worse performance in the annual cycle of precipitation shown above.
3.4. Summary
It has been shown that the AGCMs are able to reproduce 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 precipitation over central Europe and miss the summer
maximum but the summer maximum is also missing in
recent precipitation analyses, probably due to interdecadal variability.
4. Coupling ocean and atmospheric models
The CGCM used in this study is based on the ECHAM4 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 annual 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 observed or gained from an AGCM with observed SSTs,
because the comprehensive data needed for the spin-up
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K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
Fig. 5. Same as Fig. 4 but for winter and shading for values more than 4 mm/d and less than 2 mm/d.
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
Amazon
Mackenzie
Rhine
Danube
Volga
Elbe
observed discharge
P±E T106
P±E T42
P±E T30
P±E T21
80.5
13.5
32.1
19.5
12.0
17.1
86.4
14.4
27.5
11.4
11.6
16.5
51.7
19.9
36.2
16.8
13.6
21.2
83.5
26.5
40.3
18.3
19.8
32.7
45.5
28.9
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 greenhouse 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 greenhouse gas scenario experiment (GHG), the concentrations of the greenhouse gases are prescribed as a
function of time. Between 1860 and 1990, the concentration 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
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K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
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 distribution 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 climate (GHG now). Fig. 5 shows the precipitation distribution 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
Fig. 6. Time evolution of annual mean 2m temperature for all land
points, all sea points and for Germany. A 9 year running mean has
been applied. A CGCM simulation with increasing greenhouse gas
concentration (GHG) is compared with an AGCM simulation which
was forced with observed SSTs and a CGCM simulation with constant
greenhouse gas concentration (CTL).
5.1. Global aspects
In the GHG scenario experiments with the CGCM,
assuming a continuation of emissions of anthropogenic
CO2 and 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 temperature 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 increase 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 precipitation increase. Some trends in the precipitation 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 Section 4. Over land there is a small precipitation increase
up to the 1980s which agrees with the AGCM simulation 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 increase of precipitation during winter and a decrease
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
K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
115
Fig. 8. Same as Fig. 7 but for Scandinavia separately for summer and
winter. Observational data are from Hulme [7].
Fig. 7. Same as Fig. 6 for precipitation. Units: mm/month.
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 decade. 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 simulation. 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 simulations with dierent models [11].
A further trend in precipitation can be found for the
Iberian Peninsula where the winter precipitation is predicted 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 towards the Iberian Peninsula and this is strengthened in
the scenario run which casts some doubts on the robustness 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 CO2 on 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
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K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
To help in judging the realism of the these simulations we have included in Fig. 9 the time series of observed 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 D
umenil
et al. [4]. For the Rhine there is a general overestimation
in the simulations during this century while the discharge 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 similarities in the interdecadal variability between observations and the AGCM run using variable observed SSTs.
This suggests that the SST is responsible for these interdecadal variations. The ability of the model to reproduce such variabilities boosts the con®dence in the
quality of the atmospheric model.
5.4. Summary
Fig. 9. Time evolution of annual mean river discharge (precipitation
minus evaporation) for three river basins. A 9 year running mean has
been applied. GHG simulations with increasing greenhouse gas concentration are compared with an AGCM simulation which was forced
with observed SSTs and observed river discharge data (obs). 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 society. 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 circulation 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.
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 simulated 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 catchment 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
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K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
resolution than that used in coupled mode to get information 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 largescale 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 dierent classes of precipitation using these experiments in
comparison with observations and ERA data is shown.
A dramatic increase of dry days in the scenario of
2*CO2 concentration 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. Systematic 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 considerable 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 statistically signi®cant when compared with the decadal variability. 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 climate changes in the CGCMs and the LAMs did, however, 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 interactions 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
Central Belgium
obs
ERA06
AGCM
SCEN
50
39
67
81
16
28
17
9
8
11
5
2
12
12
5
2
9
8
3
3
3
20
1
2
1
0
0
Basel
obs
ERA06
AGCM
SCEN
46
37
55
77
11
15
9
15
10
10
5
3
13
16
15
3
10
12
9
1
6
5
5
0
4
4
1
2
118
K. Arpe, E. Roeckner / Advances in Water Resources 23 (1999) 105±119
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 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,
some doubts arise from the fact that these trends are
strong where the systematic errors of the model are
large.