Global hydrological changes associated with a perturbation of the
climate system: the role of atmospheric feedbacks, their uncertainty
and their validation
Herve le Treut
*Laboratoire de Meteorologie Dynamique du CNRS, Universite Paris VI, Case courrier 99, 4 Place Jussieu, 75252 Paris Cedex 05, France
Received 5 March 1998; received in revised form 14 September 1998; accepted 6 April 1999
Abstract
The anthropogenic increase of the atmospheric greenhouse eect is expected to bring important perturbations of the climate system during the next century. The models which are used to compute scenarios of this future climate change nevertheless suer from important uncertainties which make impossible the detailed prediction of regional impacts. Characterizing these uncertainties as precisely as possible constitutes a necessary step to assess a climate risk and realize local impact studies. We describe the manifestation of water vapour and cloud feedbacks in the present models, and show that satellite data, in particular, may constitute an important source of information to constrain more eciently the models. Ó 1999 Elsevier Science Ltd. All rights reserved.
1. Introduction
The atmospheric concentration of greenhouse gases has been increasing since the beginning of the century at a rate which has no equivalent over the last millenia. The concentration of carbon dioxide (CO2), for exam-ple, has risen from the preindustrial value of 280 ppmv (parts per million in volume) to more than 360 ppmv, whereas measurements throughout the last glacial/in-terglacial oscillation show a range of variation between 180 and 300 ppm roughly. Similarly the methane (CH4) concentration has risen from 0.8 to 1.6 ppm, while, again, paleoclimate records from ice core data show that the preindustrial value did not exceed 0.8 ppmv. Other gases (N2O, CFCs) have also seen their concentration increase dramatically due to human in¯uence. Alto-gether these gases are responsible for an increased ra-diative forcing ± de®ned as the perturbation of the Earth radiative balance at the tropopause ± of about 2.5 W mÿ2 [22]. This value may appear modest compared to
the mean absorbed solar radiation, which is about 240 W mÿ2. But this 1% perturbation of the Earth
energet-ics, although small in relative value, is able to bring about important consequences. The diminution in the
incident solar radiation which might have caused the XVIIth Century Little Ice Age was about half of this value [38]. Moreover the anthropogenic greenhouse ef-fect is expected to increase importantly in the future, as greenhouse gases have generally a long residence time, and tend to accumulate within the atmosphere. Due to this long residence time, the greenhouse gases are also well mixed within the atmosphere, and instead of being considered individually, their eect is often summarized through an equivalent-CO2concentration. The hypoth-esis of an equivalent CO2-doubling, which has been used for some of the scenarios reviewed below, corresponds roughly to a forcing of 4 W mÿ2[8] and may be attained
during the ®rst part of the next century [21,23], irre-spective of the reductions in the greenhouse gas emis-sions which are presently considered.
Evaluating the possible impacts of this anthropogenic greenhouse forcing is therefore of immediate concern. But model predictions are not free from uncertainties. Although all models coincide in that they show a sig-ni®cant change of the climate system to the anthropo-genic forcing, a signi®cant divergence also appears in the quanti®cation or geographical distribution of these im-pacts. This re¯ects the very subtle balance between op-posite feedback processes which control the climate system. In Section 2 we review a few model experiments which show how eective this control is. We then review some of the methods which are available to validate the
*Corresponding author. Tel.: 8406; fax: +33-01-4427-6272.
models, focusing in Section 3 on the use of contempo-raneous satellite data, and concluding in Section 4 with the use of past data, in particular over the last century.
2. The climate response to anthropogenic forcing: global features
2.1. Generalities
Many model simulations of the climate response to an anthropogenic forcing have used atmospheric Gen-eral Circulation Models (GCM) coupled to simple slab ocean representations.
GCMs use the Navier±Stokes equations to describe the general motion of the atmosphere over the globe. In addition to the components of the wind, the models also predict the pressure and temperature ®eld, and hydro-logical or surface variables, such as atmospheric water vapour and cloud water content, or soil moisture esti-mate and snow cover. The equations are solved over discretization grids whose horizontal resolution varies strongly from model to model, from a grid size of about 500 km for coarser models, to a grid size of about 50 km, for the higher resolution models used for weather fore-cast. The lack of representation of the smaller spatial scales is therefore always a ®rst limitation of the GCMs. The actual numerical solving of the equations may be done in a spectral domain, or through ®nite dierencing. The number of vertical levels may vary from about 10 to 30. The AMIP experiment [16] has provided an ex-haustive review of the models currently available.
Whereas for weather forecast applications, the spec-i®cation of the initial atmospheric state from which the model is run is very obviously of utmost importance, this is no longer true for climatic applications. In this case one is mostly interested in the statistical behaviour of the atmosphere over long periods of time, which is primarily controlled by the energetics of the system. All models include a representation of the radiative transfer in the solar and terrestrial domains, of the energy ex-changes with the surface, of the main atmospheric hy-drological features, such as latent heat release within clouds, or cloud/radiation interaction. An accurate and balanced representation of those energy and water sources and sinks is of speci®c importance for climato-logical studies.
Most of these physical processes, however, corres-pond to unresolved spatial scales: the energy exchanges over continental surfaces are controlled by the vegeta-tion cover, and by movegeta-tions in the boundary layer which are organized at the scale of about 100 m; the tropical convection is one of the main sources of energy for the atmosphere, but corresponds to motions organized pri-marily at the scale of a few kilometers. All those pro-cesses need to be represented in a simpli®ed,
parametrical manner in the GCMs. But there is a large variety of possible approaches to these parameterization problems, a situation which is responsible for the large number of existing climate models (about 30 groups have participated in the AMIP project).
For climate sensitivity experiments, the role of the ocean component is also crucial. Climate change can occur and organize itself over long periods of time es-sentially because of the thermic inertia of the ocean. There is a whole hierarchy of ocean models used for climate studies. Slab ocean models constitute one of the simplest approaches, in which the ocean passively stores heat in a slab layer of about 50 m, with no representa-tion of the changes in the ocean vertical and horizontal energy transport. These models can describe the equi-librium response of the atmosphere/surface ocean sys-tem to prescribed changes in the climate forcing. They cannot take into account the eects of ocean dynamics ± which are represented in the newer generation of cou-pled models using ocean general circulation models. But comparison between those two approaches [35,36] has shown that, if the equilibrium response fails to represent the patterns which are associated with a slower ocean heating, particularly in the regions of deep water for-mation around Antarctica or in the Norwegian Sea, it nevertheless constitutes a good approximation of the climate perturbation in most areas. These simpler models are also very precious, because they isolate the contribution of the atmosphere in the climate response to an anthropogenic forcing, and therefore the contri-bution of the atmosphere to current uncertainties in our evaluation of future climate changes.
2.2. Examples using the LMD GCM
In the following we use results obtained with the LMD (Laboratoire de Meteorologie Dynamique) GCM. The ®rst version of the model was developed by Sadourny and Laval [44]. Since then, the model has evolved continuously, but some of its original features are unchanged: the model uses a ®nite dierence dis-cretization of the Navier±Stokes equations over an Ar-akawa C-grid. In the versions considered in the present paper, the horizontal grid is regular in longitude (with a number of points ranging from 48 to 96 depending on the model versions) and in sine of the latitude (with a number of points ranging from 36 to 72). The vertical coordinate is the pressure normalized by its surface value (r coordinate) and the number of vertical levels
varies from 11 to 19 depending on model version. In addition to this treatment of the dynamical equa-tions, the model includes a comprehensive representa-tion of the ``physical'' processes: subgrid-scale processes, radiative transfer, hydrological features and exchanges with the surface. In the examples which follow, the LMD Cycle 4 version is being used [28]. The shortwave (solar) radiation transfer is parameterized using a modi®ed version of Fouquart and Bonnel [15] where two spectral bands corresponding to the visible and near-infrared are distinguished. In the longwave (ter-restrial) part of the spectrum, the scheme developed by Morcrette [37] is used and six spectral bands are con-sidered. The treatment of convection is still the original one, where a moist adjustment is combined with a scheme derived from the Kuo [25] parameterization. A diusive approach is used to represent vertical mixing within the atmospheric boundary layer. The treatment of the surface conditions, although comprehensive, is rather simple, with soil moisture being treated following a single bucket approach: the inclusion of a more ad-vanced scheme including the eects of vegetation has been considered in LMD Cycles 5 and 6 only. This could be problematic if we were to discuss regional impacts. The treatment of clouds is comparatively more ad-vanced; a prognostic cloud water budget equation is included in the model, with a corresponding parame-terization of the source and sink terms: condensation, evaporation, conversion to precipitable water.
The results of simulations testing the response of the LMD GCM to CO2 and aerosol forcing are shown in Fig. 1. The response to the two perturbations are ®rst considered separately, a third diagram showing their combined eect. The representation of the sulphate ef-fect within the LMD GCM, which involve both the di-rect and indidi-rect eects, as de®ned above, gives a global forcing of about ÿ1.2 W mÿ2, which happens to be
approximately similar in amplitude but opposite in sign to the CO2 forcing since the beginning of the industrial era. A striking feature is the symmetry between the two
responses to sulphate aerosol and CO2. In both cases the surface temperature change is characterized by an am-pli®cation over the Polar regions and over the conti-nental areas. In a latitude±altitude zonal mean section (Fig. 2), one can also recognize a well-known feature of the model response [46], which provides part of the ex-planation for this polar ampli®cation: at high or middle latitudes, the temperature change is larger near the ground, whereas at low latitudes, it reaches its maximum at about 12±15 km. This latter eect has been attributed for a long time to the convective activity of the low latitude regions [46]. At the same time the stratosphere responds with a cooling (in the case of a CO2-induced tropospheric warming) or a warming (in the case of an aerosol-induced tropospheric cooling). Such a strato-spheric response, in the case of the aerosol forcing, can be the consequence of internal atmospheric feedbacks only, and re¯ects in particular the change in water va-pour associated with climate cooling. Of course, the last panel of Fig. 1, where both the CO2 and sulphate aerosol eects are imposed on the climate system, shows the limit of this symmetry between the response to the two forcings. In this case (which is largely an academic one since the role of the other greenhouse gases and aerosols has been ignored), the Northern Hemisphere (NH) is cooling whereas the Southern Hemisphere (SH) is warming. This eect has been invoked to explain the slower warming of the Northern Hemisphere through-out the century [45]. But even in this case, each of the Hemispheres shows a very speci®c spatial pattern with an enhancement of both the NH cooling and the SH warming at high latitudes and over continental areas. Our results indicate that the response to any form of pollution is largely non-local. This is important politi-cally, because the countries responsible for the pollution may not be those who suer the most from its conse-quences, and also for the detection of the ®rst signs of a climate change, because they will not be related in a simple manner to the forcing.
[40] have shown that the response to the condition of the Last Glacial Maximum can be qualitatively dierent, probably because of the strong modi®cation of the Pole to equator gradient of temperature characterizing this period. But the atmospheric feedback eects appear to exert a strong control not only on the general geo-graphical pattern of any climate change, but also on its amplitude.
Unfortunately, if, for a given model, the response of the climate system to an external perturbation seems
well behaved, the involved feedback eects are very dicult to simulate accurately, and the response is very model-dependent. It is well known for example that the equilibrium temperature response to a doubling of the CO2, may range from 1.9 to 5.3 degrees [47]. The re-gional distribution of the climate change is also a deli-cate issue. On one side the geographical distribution of the temperature and precipitation changes associated with a CO2doubling, displayed for 3 dierent models by the ®rst IPCC report [21], or for 8 models as part of a
more recent intercomparison held within the framework of CLIVAR (McAvaney and Le Treut et al., 1998, personal communication), reveals some general consis-tency at a very large scale, with an ampli®cation of the surface warming over the polar regions, over the conti-nents and in winter, and with a general increase in the precipitation, both in the equatorial and mid-latitude regions. On the other side, the regional response diers largely from one model to the other: in particular some models show a displacement of the precipitation zones, and associated risks of drought, which are not con-®rmed by other simulations.
3. The main feedback processes and their validation through satellite data
The main feedback eects which aect the climate response to anthropogenic perturbations have been identi®ed for a long time (see for example the review of Schlesinger and Mitchell [46]). The role of the atmo-sphere was stressed in the preceding paragraph, but oceanic, biochemical or chemical processes are also important. The representation of all those eects within climate models is subject to considerable uncertainty.
The purpose of the present section is to show that each of these feedbacks may be and must be studied individually, if one wishes to quantify the domain of uncertainties associated with the estimation of future climate changes. We focus on the water vapour and cloud feedbacks, because they are probably the pro-cesses which respond more directly to any climate forcing, and are related to the changes in precipitation which are also discussed. They have also been the sub-ject of many studies, triggered in particular by the ®rst model intercomparisons of Cess et al. [7].
3.1. Water-vapour feedbacks
The water vapour eect may appear simple in es-sence: as the climate gets warmer, the saturation value of the atmospheric water vapour concentration increases. In all models this translates into an increase in the water vapour itself, which increases the greenhouse eect, and almost doubles the climate response [21]. The accuracy with which models simulate this change in water vapour
Fig. 3. Summary of equilibrium sensitivity experiments carried out with the LMD GCM Cycle 4, coupled to a slab ocean. These experi-ments are described in [38,28,30]. As indicated by the legend they in-clude sensitivity experiments to a change in the solar constant, to a doubling of the CO2, and to changes in CO2, aerosols, or tropospheric ozone concentration from preindustrial to present conditions. The plot gives the mean global surface temperature response (in degrees) as a function of the mean global radiative forcing (in W mÿ2). Regardless of
the geographical distribution of the forcing, the points tend to line along a curve whose slope de®nes a global `climate sensitivity', which appears larger for colder climate.
is of course dependent on the model ability to control eciently the relative humidity of the atmosphere, and in particular the contrast between the very humid re-gions near the InterTropical Convergence Zone (ITCZ) and the very dry areas of the subtropical areas, in the descending part of the Hadley±Walker cells. It is also linked to the eciency of the convective parameteriza-tions, which contribute to extend this increase in hu-midity to the higher levels of the troposphere, in low latitude regions. The data that can be used to check the simulated variations of the water vapour content are all subject to limitations:
(i) the radiosonde data extend over several decades, but their spatial coverage is limited. The quality of the data as one gets higher into the atmosphere may also be questioned [18,17]. Also their quality may have varied from decade to decade.
(ii) the satellite data cover a restricted period of about a decade. They provide indirect measure-ments of the radiative eect of water vapour, in dif-ferent spectral bands, and need to be interpreted with care. Indications on the behaviour of the water vapour within the atmosphere may be obtained from the clear-sky longwave measurements of Earth Radiation Budget Experiment (ERBE), from micro-wave radiometers such as Special Sensor Micromicro-wave Imager (SSMI) (over the oceans only), from the ver-tical pro®lers such as Tiros-N Operational Verver-tical Sounder (TOVS), or from speci®c infrared channels such as the Meteosat water vapour channel. Those data are enough to reveal some systematic errors of the models. The LMD GCM for example is both too cold and too dry in the higher troposphere [2]. But we also want to use them in order to assess the strength of the atmospheric water vapour feedbacks, which means to diagnose the derivative of the water vapour when the climate (and therefore the surface temperature) changes. This is a more complex problem: the only observed climate changes are the seasonal variations, or interannual ¯uctuations such as the op-position between El Nino and La Nina conditions in 87±88, for which a complete set of satellite measure-ments is available, and the patterns associated with those ``short-term'' ¯uctuations are very dierent from those which characterize the long-term climate evolu-tions. The water vapour feedbacks are correspondingly dierent. For example the seasonal changes of water vapour over the oceans, as may be analysed from the SSMI data [3], depend strongly on the changes of the vertical gradients of temperature: over the oceans the seasonal changes of temperature are smaller at the surface, due to ocean inertia, than at higher altitudes, where the in¯uence of the continents is being felt more strongly. As a consequence the greenhouse eect may in some case diminish with increasing surface tempera-ture, because the stronger longwave emission from the
higher and warmer atmospheric layers dominate the increased water vapour absorption. This eect is faithfully reproduced by models and must not in any case be interpreted as a negative water vapour feed-back, as could be from a hasty interpretation. It merely re¯ects the complexity of the seasonal response. In general, in spite of some quantitative dierences the models reproduce correctly the seasonal cycle or inter-annual ¯uctuations of the clear-sky greenhouse eect [4]. The results of Roca et al. [41], from satellite mea-surements in the Meteosat water vapour channel, also show the ability of the models to successfully reproduce the negative correlation between the surface of the as-cending and desas-cending branches of the Hadley-Walker circulation, measured in monthly averages throughout the seasonal cycle. All those features add some credi-bility to the capacity of the models to simulate the water vapour feedbacks in scenarios of future climate changes.
But ultimately no perfect validation is yet possible. In particular the radiative eect of the water vapour changes depend on the changes of the vertical stability of the atmosphere, which are very dierent at the sea-sonal or interannual time scale, or for climate sensitivity experiments. This is illustrated in Fig. 4. The sensitivity of the clear-sky greenhouse eect to surface temperature is diagnosed from the output of the LMD GCM, using the GCM radiative code in o-line mode. The sensitivity at the seasonal and interannual scales have been veri®ed using observed data, and the model is in good qualita-tive agreement with these observations. The very large dierences between the dierent cases, illustrated in the upper panel, are reduced when the o-line computations are carried out with a ®xed relative humidity and a ®xed temperature lapse rate. The sensitivity value of 2 W mÿ2
Kÿ1, is characterizing the water vapour feedback in
climate scenarios, and it corresponds to a weak per-turbation of the atmospheric vertical strati®cation in temperature and humidity. That such a weak change should characterize a modi®ed climate is however a model prediction that cannot be veri®ed from observa-tions.
3.2. Cloud feedbacks
The largest uncertainty as regards the evolution of future climate is probably the behaviour of clouds. Cloud feedbacks include a wide range of eects which have been discussed over the years. The eect of cloud altitude, for example, has been described by Stephen and Webster [48], who have illustrated through a simple
model that an increase in low cloudiness is dominated by the eect of cloud albedo, and contributes to a climate cooling, whereas an increase in high cloudiness, is dominated by the cloud greenhouse eect and contrib-utes to a climate warming. But they have also shown that changes in cloud optical properties have to be considered simultaneously. For a low cloud whose eect in the infrared is already saturated, an increase in water content results in an increased albedo eect and there-fore a cooling, whereas for high clouds the competition between the cloud albedo and the cloud greenhouse ef-fect is more uncertain. All those eects have been rec-ognized as essential since the ®rst model studies of the greenhouse eect [34].
Since then, new processes have emerged which have added even more to the complexity of the problem. In particular the importance of the microphysical structure of the clouds has been recognized. First the size of the hydrometeors has a great importance for the scattering of solar radiation: for the same cloud water content, a dimininution of the droplet size increases both the number of droplets and the surface which they cover. This eect is responsible for the sulphate aerosol indirect eect, but may also be aected by natural processes, such as the ocean biology which produces di-methyl-sulphate [9]. The transition between ice and liquid phases is another microphysical process of very large importance. Liquid water clouds tend to precipitate more eciently, but only when a certain threshold necessary for an ecient coalescence of cloud droplets is attained. Senior and Mitchell [47] have demonstrated that a global warming could then be damped by the transformation of ice clouds into more re¯ective liquid water clouds. Li and Le Treut [31] have shown that the choice of the temperature range throughout which the liquid/ice transition takes place may aect the sign of this radiative feedback.
Most of these processes have slowly been integrated into models and a number of qualitative features are now well understood. For a CO2increase, in most cases, the models show a decrease of the cloudiness in the low and middle troposphere and an increase in the upper troposphere (results of the LMD GCM are shown in Fig. 5 as an example). This partly re¯ects the fact that, as the water vapour saturation level increases with in-creasing temperature in the lower troposphere, it be-comes more dicult to reach saturation and form a cloud through water vapour condensation. This low level cloudiness decrease is therefore by no way con-tradictory to the increase in the water vapour concen-tration noted above. The increase in high cloudiness proceeds from the reverse mechanism and is also asso-ciated with a higher altitude of the tropopause level. In the ITCZ, in addition, most models show an increase in convective clouds. This eect can be easily understood: although the smaller Pole-to-Equator temperature
gradients associated with a warmer climate generally tend to slow the Hadley cell meridional circulation [1,43,38], the low-level convergence of both warmer and moister air creates more favourable conditions for convection. Finally a last feature is shown by most models, at least those including a physical representation of cloud for-mation: cloud fraction and cloud liquid water content increase at the liquid/ice phase transition, for a warmer climate. This eect is due to the very dierent micro-physical properties of liquid water and of ice clouds.
The mechanisms which contribute to cloud modi®-cation are also very largely the ones that are responsible for rain modi®cations. The increase in equatorial con-vective cloudiness is accompanied by a corresponding increase in equatorial rain. At mid-latitudes the trans-port of water vapour, depends on the latitudinal gradi-ent of humidity, which is dominated by the expongradi-ential increase in the saturation level of water vapour, fol-lowing the Clausius±Clapeyron law.
The combination of these dierent processes, in spite of the qualitative convergence of the model simulations, leads to a very large scatter in the quantitative radiative eect. This, again, raises the problem of validation.
As for water vapour, two types of data sets can be used.
(i) Cloudiness data from conventional in situ mea-surements at weather stations. Such data have been gathered through a very long eort by Warren et al. [51]. Henderson-Sellers [20] has shown some long-term trends in cloud data, which may relate to cli-mate change.
(ii) Satellite data, and most notably the ISCCP cli-matology (International Cloud Satellite Climatolo-gy Project) which was developed speci®cally for the purpose of model validation and covers the whole period from July 1983 up to now. A large va-riety of other instruments give a relevant informa-tion about clouds. One should perhaps give a special mention to broad-band measurements such as the ERBE measurements, which provide some ac-cess to the cloud radiative impact. Also, the cloud
water content is measured through microwave radi-ometers such as the SSMI one, but the uncertainty of the retrieval is very large: the annual mean over the oceans is 0.081 kg mÿ2in the estimate of
Green-wald et al. [19], compared to 0.059 kg mÿ2in the
es-timate of Weng and Grody [52].
An interesting and early example of how those data may be used to point out possible errors in the design of models can be found in the work of Tselioudis et al. [50]. They have used correlations between cloud optical thickness and cloud temperature to show that empirical relations where cloud condensed water content increases with temperature may be wrong in many situations and may introduce arti®cial feedbacks in the model. Other examples [12,13] show how useful the study of correla-tions between parameters may be to validate the models. But again an important diculty comes from the pe-culiarities of the seasonal and interannual changes to which observed data give access: at these time scales cloud changes are both due to large displacements of the
Fig. 5. The changes in the mean zonal cloudiness (in percents) asso-ciated with a CO2 increase from preindustrial to present condition (from [30]).
meteorological patterns, and to thermodynamical feed-backs such as those relevant to study a global warming. Bony et al. [5] show a method to separate those eects: when studying the regression between dierent cloud parameters they stratify the relation using the vertical velocity as an index of the circulation and its changes (Fig. 6). The consistency of the results when they con-sider dierent cloud parameters, and therefore dierent satellite instruments, is a good indication of their rele-vance for model validation.
4. Other approaches to model validation
The reference to past climates oers other possibilities to evaluate the models. We review them brie¯y in this last section. In all cases these methods allow a better insight into the qualitative behaviour of the climate system, but also bring new elements of quantitative uncertainty (linked for example to the measure of past SSTs, of past radiative forcing). Also, the atmospheric processes can only be considered indirectly.
For long time scales, one may consider the climate evolution over the last glacial cycle [33] as a way to determine a climate sensitivity: both the forcings (as-tronomical changes of the insolation, greenhouse gases concentration) and the SST evolutions are known from astronomical calculations and ice or deep-sea records. Alternatively one may also reconstruct climate extrema, such as the last glacial maximum (21 000 yr Before Present), or the warmer conditions of the Holocene (6000 yr Before Present): this is in particular the objec-tive of the PMIP programme [24].
But the near past is also an important reference pe-riod for the models. Whether the global temperature increase of about 0.6°C since the beginning of the
in-dustrial area constitutes a ®rst sign of the warming to come, or is due to natural ¯uctuations of the climate, is a matter of debate [23]. To interpret the changes that oc-curred since the beginning of the industrial area, however, one has to add ± or substract ± the eect of at least two other anthropogenic perturbations: the changes in ozone chemistry and the increased aerosol loading of the at-mosphere. A characteristic feature of both ozone and aerosols is that they have a short life within the atmo-sphere, and, as a result, are very unevenly distributed. Ozone is principally formed in the stratosphere, through speci®c photochemistry, whose perturbation by CFC leads to an ``ozone hole'' mostly apparent over Antarctica during the Southern Hemisphere Spring, and also to a weaker diminution of stratospheric ozone at all latitudes. This stratospheric depletion causes a negative perturba-tion of the atmospheric system (of the order of 0.2 W mÿ2
± from IPCC [23]. But ozone is also formed in the lower layers of the atmosphere, though the transformation of nitrous oxides (NOx) in the presence of hydrocarbons
such as methane. This tropospheric ozone formation, which depends upon temperature and is felt strongly during the Northern Hemisphere summer, over the con-tinental areas, causes a positive forcing of the climate system, whose present estimation is about 0.3 W mÿ2[23].
Aerosols are also largely released over the Northern Hemisphere continents (although this image may be biased because the aerosols taken into account so far in climate simulations are sulphate aerosols: aerosols from biomass burning, for example, have a very dierent distribution). Their main impact is to cool the climate system by increasing the re¯ection of solar radiation, both directly by Mie scattering, or indirectly because they contribute to the nucleation of cloud droplets, which are then more numerous, and re¯ect the solar radiation more eectively (for a given amount of cloud water) [10]. Smaller droplets also diminish the eciency of precipitation and lead to thicker clouds. A consider-able uncertainty aects our estimations of the negative aerosol forcing since the beginning of the industrial era: it is known with a factor 2 or 3 at best, but might be important, underÿ1 W mÿ2 [22,6,30].
The history of the climate over the last century, as can be reconstructed by models is therefore the product of an uncertain forcing by an uncertain climate sensitivity. Many processes are generally ignored in the models: the possible non-linear response of the ocean circulation, which has been observed in the past and is beyond the reach of present models, because they do not represent the dynamics of continental glaciers; the possible release of methane from the permafrost; the role of solar ¯uc-tuations, of volcanic eruptions. As in the case of the atmospheric feedbacks discussed above, the progress of our understanding will most certainly reduce those un-certainties.
5. Conclusions
approach, which is slow and unglamorous, may never-theless constitute the main source of information to-wards more useable scenarios.
Acknowledgements
As evidenced by the citations in the text, this review owes a lot to discussion with, and work from S. Bony, Z.X. Li and J.P. Durel.
References
[1] Boer GB. Some dynamical consequences of greenhouse gas warming. J Atmos Sci 1995;62:123±234.
[2] Bony S, Le Treut H, Duvel JP, Kandel R. Satellite validation of GCM-simulated annual cycle of the earth radiation budget and cloud forcing. J Geophys Res 1992;97(D16):18061±81.
[3] Bony S, Duvel JP. In¯uence of the vertical structure of the atmosphere on the seasonal variation ofprecipitable water and greenhouse eect. J Geophys Res 1994;99(12):963±12980. [4] Bony S, Duvel JP, Le Treut H. Observed dependence of the water
vapor and clear-sky greenhouse eect on sea surface temperature. comparison with climate warming experiments. Climate Dynam-ics 1995;11:307±20.
[5] Bony S, Lau KM, Sud YC. Sea-Surface Temperature and Large-cale Circulation In¯uences on Tropical Greenhouse Eect and Cloud Radiative Forcing. J Climate 1997;10:2055±77.
[6] Boucher O, Anderson TL. GCM assessment of the sensitivity of direct climate forcing by anthropogenic sulfate aerosols to aerosol size and chemistry. J Geophys Res 1995;100:26117±134. [7] Cess RD, Potter GL, coauthors. Interpretation of Cloud-Climate
Feedback as Produced by 14 Atmospheric General Circulation Models. Science 1989;245:513±51.
[8] Cess RD, 29 co-authors. Uncertainties in carbon dioxide radiative forcing in atmospheric general circulation models. Science 1993;262:1252±55.
[9] Charlson RJ, Lovelock JE, Andreae MO, Warren SG. Oceanic phytoplankton, atmospheric sulphur, cloud albedo, and climate. Nature 1987;326:655±61.
[10] Charlson RJ, Schwartz SE, Hales JM, Cess RD, Coakley JA, Hansen JE, Hofmann DJ. Climate forcing by anthropogenic aerosols 1992;255:423±30.
[11] Cox SJ, Wang WC, Schwartz SE. Climate response to radiative forcings by aerosols and greenhouse gases. Geophys Res Lett 1995;22:2509±12.
[12] Del Genio AD, Kovari Jr, W, Yao MS. Climatic implications of the seasonal variation of upper troposphere water vapor. Geophys Res Lett 1994;21:2701±4.
[13] Del Genio AD, Yao MS, Kovari Jr, W, Lo KKW. A prognostic cloud water parameterization for global climate models. J Clim 1995.
[14] Fouquart Y, Isaka H. Sulfur emission, CCN, clouds and climate: A review. Ann Geophysicae 1992;10:462±71.
[15] Fouquart Y, Bonnel B. Computations of solar heating of the Earth's atmosphere: A new parameterization. Beitr Phys Atmos 1980;53:35±62.
[16] Gates WL. AMIP: The Atmospheric Model Intercomparison Project. Bull Amer Meteor Soc 1992;73:1962±70.
[17] Gaen DJ. Temporal inhomogeneities in radiosonde temporal records. J Geophys Res 1994;99:3667±3676.
[18] Gaen DJ, Barnett TP, Elliott WP. Space and time scales of global tropospheric moisture. J Clim 1991;4:989±1008.
[19] Greenwald TJ, Stephens GL, Vonder Haar TH, Jackson DL. Aphysical retrieval of cloud liquid water above the global oceans using special sensor microwave/imager (SSM/I) observations. J Geophys Res 1993;98:18471±89.
[20] Henderson-Sellers A. Continental cloudiness changes this century. GeoJournal 1992;27.3:255±62.
[21] Houghton JT, Jenkins GJ, Ephraums JJ, editors. Intergovern-mental Panel on Climate Change (IPCC). Climate Change: the IPCC Scienti®c Assessment. Cambridge University Press, Cam-bridge, UK 1990:356.
[22] Houghton JT, Meira Filho LG, Bruce J, Hoesung Lee, Callander BA, Haites E, Harris N, Maskell K, editors. Intergovernmental Panel on Climate Change (IPCC). Climate Change: Radiative Forcing of Climate Change and an Evaluation of the IPCC IS92 Emission Scenarios. Cambridge University Press, Cambridge, UK 1994:339.
[23] Houghton JT, Meira Filho LG, Callander BA, Harris N, Kattenberg A, Maskell K, editors. Intergovernmental Panel on Climate Change (IPCC). Climate Change: The science of Global Change. Cambridge University Press, Cambridge, UK 1995:572. [24] Joussaume S, Taylor K. Status of the paleoclimatic modeling intercomparison project. In Proceeding First International AMIP Science Conference, WCRP-92 1995;415±30.
[25] Kuo HL. On formation and intensi®cation of tropical cyclones through latent heat release by cumulus convection. J Atmos Sci 1965;22:40±63.
[26] Langner J, Rodhe H. A global three-dimensional model of the tropospheric sulfur cycle. J Atmos Chem 1991;13:225±63. [27] Langner J, Rodhe H, Crutzen PJ, Zimmermann P. Anthropogenic
in¯uence of the distribution of tropospheric sulphate aerosol. Nature 1992;359:712±16.
[28] Le Treut H, Li ZX, Forichon M. Sensitivity of the LMD general circulation model to greenhouse forcing associated with two dierent cloud water parameterizations. J Clim 1994;7:1827±41. [29] Le Treut H, Forichon M, Boucher O, Li ZX. Equilibrium climate
response to CO2 and sulfate aerosol forcings. In: Le Treut H, editors. Climate Sensitivity to Radiative Perturbations: Physical Mechanisms and their Validation. NATO ASI Series I, Berlin: Springer 1996;34:267±80.
[30] Le Treut H, Forichon M, Boucher O, Li ZX. Sulfate aerosol indirect eect and CO2greenhouse forcing: equilibrium response of the LMD GCM and associated feedback eects. J Clim 1998;11:1673±84.
[31] Li ZX, Le Treut H. Cloud-radiation feedbacks in a general circulation model and their dependence on cloud modelling assumptions. Clim Dyn 1992;7:133±39.
[32] Lindzen RS. Can increasing carbon dioxide cause climate change. Proc Natl Acad Sci USA 1997;94:8335±42.
[33] Lorius C, Jouzel J, Raynaud D, Hansen J, Le Treut H. Greenhouse warming, climate sensitivity and ice core data. Nature 1990;347;139±45.
[34] Manabe S, Wetherald RT. The eects of doubling the CO2 concentration on the climate of a general circulation model. J Atmos Sci 1975;32:3±15.
[35] Mitchell JFB, Davis RA, Ingram WJ, Senior CA. On surface temperature, greenhouse gases and aerosols: Models and obser-vations. J Clim 1995a;8:2364±86.
[36] Mitchell JFB, Johns TC, Gregory JM, Tett SFB. Climate response to increasing levels of greenhouse gases and sulphate aerosol. Nature 1995b;376:501±4.
[38] Nesmes-Ribes E, Ferreira EN, Sadourny R, Le Treut H, Li ZX. Solar dynamics and its impact on solar irradiance and the terrestrial climate. J Geophys Res 1993;98:18923±35.
[39] Penner JE, Charlson RJ, Hales JM, Laulainen NS, Leifer R, Novakov T, Ogren J, Radke LF, Schwartz SE, Travis L. Quantifying and minimizing uncertainty of climate forcing by anthropogenic aerosols. Bull Am Met Soc 1994;75:375±400. [40] Ramstein G, Sera®ni V, Le Treut H, Forichon M, Joussaume S.
Cloud processes associated with past and future climate changes. Clim Dyn 1998:14:233±47.
[41] Roca R, Picon L, Desbois M, Le Treut H, Morcrette JJ. Direct comparison of Meteosat water vapor channel and general circulation model results. Geophys Res Lett 1997;24:147±150. [42] Roeckner E, Siebert T, Feichter J. Climatic response to
anthro-pogenic sulfate forcing simulated with a general circulation model. In: Charlson R, Heintzenberg J, editors. Aerosol Forcing of Climate. New York: Wiley 1995;349±62.
[43] Sadourny R. Maunder Minimum and the little ice age: impact of a long-term variation of the solar ¯ux on the energy and water cycle. In Duplessy JC, Spyridakis MT, editors. Long term climatic variations, NATO ASI Series. Berlin, Heidelberg, New York: Springer 1992;533±50.
[44] Sadourny R, Laval K. January and July performance of the LMD general circulation model. In: Berger A, Nicolis C, editors. New
Perspectives in Climate Modelling. Amsterdam: Elsevier 1984;173±98.
[45] Santer BD, Taylor KE, Wigley TML, Penner JE, Jones PD, Cubasch U. Towards the detection and attribution of an anthropogenic eect on climate. Clim Dyn 1995;12:77±100. [46] Schlesinger ME, Mitchell JFB. Climate model simulations of the
equilibrium climatic response to increased carbon dioxide. Rev Geophys 1987;25:760±98.
[47] Senior CA, Mitchell JFB. Carbon dioxide and climate: The impact of cloud parameterization. J Clim 1993;6:393±418. [48] Stephen GL, Webster PJ. Sensitivity of radiative forcing to
variable cloud and moisture. J Atmos Sci 1979;36:1542±56. [49] Taylor KE, Penner JE. Response of the climate system to
atmospheric aerosols and greenhouse gases. Nature 1994;369: 734±37.
[50] Tselioudis G, Rossow WB, Rind D. Global Patterns of cloud optical thickness variation with temperature. J Clim 1992;5:1484± 95.
[51] Warren SG, Hahn CJ, London J, Chervin RM, Jenne RL. Global distribution of Total Cloud Cover and Cloud Type amounts over the Ocean. NCAR Technical Note TN-317+STR 1988;42±170. [52] Weng F, Grody NC. Retrieval of cloud liquid water using the
