Transient climate simulation forced by natural and anthropogenic climate forcings

INTERNATIONAL JOURNAL OF CLIMATOLOGY

Int. J. Climatol. 22: 623–648 (2002)

Published online in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/joc.738

TRANSIENT CLIMATE SIMULATION FORCED BY NATURAL ANDANTHROPOGENIC CLIMATE FORCINGS

CEDRIC BERTRAND* and JEAN-PASCAL VAN YPERSELE1

Institut d’Astronomie et de Geophysique G. Lemaıtre, Universite catholique de Louvain, chemin du cyclotron, 2 B-1348Louvain-la-neuve, Belgium

Received 2 March 2001Revised 25 September 2001Accepted 5 October 2001

ABSTRACT

Numerical experiments have been carried out with a two-dimensional sector-averaged global climate model coupled toa diffusive ocean in order to assess the potential impact of four hypothesized mechanisms of decadal to century-scaleclimate variability, both natural and anthropogenically induced: (1) solar variability; (2) variability in volcanic aerosolloading of the atmosphere; (3) anthropogenic increase of sulphate aerosols’ concentration; (4) anthropogenic increase ofgreenhouse gas concentrations.

Our results suggest that neither the individual responses nor the combined natural or anthropogenic forcings allow oneto reproduce all of the recorded major temperature fluctuations since the latter half of the 19th century. They show thatthese temperature variations are the result of both naturally driven climate fluctuations and the effects of industrialization.

By contrast, the dominant cause of decade-to-century-scale variability of the 21st century is likely to be changes inatmospheric trace-gas concentrations. Indeed, when the solar, volcanic, and tropospheric aerosols forcings used in ourexperiments are extended into the future, they are unable to counter the expected greenhouse warming. Copyright 2002Royal Meteorological Society.

KEY WORDS: transient climate simulations; natural and anthropogenic climate forcings; emission scenarios

1. INTRODUCTION

Over the past century the climate has changed (Houghton et al., 2001). The global mean air surface temperaturehas increased by 0.4–0.8 °C, and by about 0.2–0.3 °C over the last 40 years, the period with the most credibledata. Parallel to these observed temperature changes, it is now widely acknowledged (Houghton et al., 2001)that human activities have been modifying the composition of the atmosphere since the onset of the industrialrevolution. A number of factors are usually postulated as possible causes of decadal to century-scale climatevariations, such as: the natural unforced variability, associated with either atmospheric dynamics or interactionswithin the climate system; changes in ocean circulation; modifications in the Sun’s radiant energy output;variations in the atmospheric greenhouse trace gas concentration, which have likely generated a cumulativeclimatic impact since the pre-industrial era; increases of the aerosol loading in the atmosphere, also due tohuman activities (e.g. Free and Robock, 1999; Crowley, 2000). Therefore, whether these recent temperaturechanges may be attributed to human activities is difficult to determine, since, even if the external forcings arekept constant, the non-linearity of the climate system causes the climate to exhibit ‘internal variability’, ofwhich the El Nino–southern oscillation is a striking example (Philander, 1990). Any change due to mankindwill be superimposed on natural variations. Moreover, though it is possible to formulate hypotheses aboutthe nature of climatic change, there is no physical laboratory, save the actual climatic system itself, in which

* Correspondence to: Cedric Bertrand, Royal Meteorological Institute of Belgium, Department of Observations, Avenue Circulaire 3,B-1180, Brussels, Belgium; e-mail: [email protected] E-mail: [email protected]

Copyright 2002 Royal Meteorological Society

624 C. BERTRAND AND J.-P. VAN YPERSELE

quantitative evaluation of such hypotheses can be performed. Two approaches, based on statistical analysisand physical modelling, are usually used to investigate the relative importance of these factors on the climateof the past century. The statistical approach tries to verify the postulated relationship between a particularforcing and climate (especially temperature) time series using various correlation techniques. In particular,multiple regression analysis allows one to study the signal-to-noise ratio for combinations of various forcingparameters (e.g. Cress and Schonwiese, 1992; Schonwiese and Staler, 1991; Schonwiese, 1994; Schonwieseet al., 1997).

On the other hand, in order to determine in a physically based manner the relative importance of someof these forcings in producing the observed temperature record, climate models of various complexitieshave been used to simulate the time evolution of the global mean surface temperature of the Earth overthe last century. Though detection and attribution of recent climate change (e.g. the search for evidence ofanthropogenic climate change) is still an open question, some answers resulting from this second approachare now becoming available (e.g. Hegerl et al., 1997; Barnett et al., 1999; Tett et al., 1999, Stott et al.,2000; Houghton et al., 2001), and these suggest that most of the warming observed over the last 50 years isattributable to human activities. This means that the mankind-induced changes are now progressively emergingfrom the natural variability of the climate system and that global warming could accelerate as greenhouse-gasforcing begins to dominate over all other externally driven climate forcings.

It is now recognized that fully coupled global circulation models (GCMs) of the atmosphere and oceanare needed to predict the transient response of the climate system. However, climate simulations carried outwith such models still require large computer resources, and it is not generally feasible to perform separatesimulations for a large number of forcing scenarios or to conduct a variety of experiments to see howsensitive their results are to changes in climate sensitivity, forcing reconstructions, etc. Here, therefore, wepropose to use the two-dimensional zonally averaged (latitude, altitude) global climate model developed at theInstitut d’Astronomie et de Geophysique G. Lemaıtre (Gallee et al., 1991) extended to both hemispheres asa surrogate for the Earth’s real climate system. We performed a number of ‘hypothesis-testing’ experimentsin order to investigate the potential impact of four hypothesized external factors of decadal to century-scale climate variability, both natural and anthropogenically induced: (1) solar variability; (2) variability involcanic aerosol loading in the atmosphere; (3) anthropogenic increase of greenhouse gas concentrations; (4)anthropogenic increase of sulphate aerosol concentration. This study is thus an extension to our previouswork, which was devoted solely to externally driven natural climate variability (Bertrand and van Ypersele,1999; Bertrand et al., 1999). First, we present and discuss the ability of our climate model to reproduceclimate changes over the period of the instrumental temperature record (from the latter half of the 19thcentury to the present) using different climate forcing reconstructions and model sensitivities to CO2. Thesecond part of this paper is devoted to future climate change due to projected modification in greenhousegases and sulphate aerosol concentrations, as well as to hypothetical scenarios concerning natural climateforcing. Indeed, were the natural climatic variations of the sort that have characterized the last centuries torecur in the next 100 years or so, they could modify the expected effects of increased greenhouse gases, eitherby masking an underlying upward trend during the early stages of a greenhouse warming or by acceleratingthe rate at which it occurs.

2. CLIMATE MODEL DESCRIPTION

All simulations were carried out using the two-dimensional sector-averaged climate model of Gallee et al.(1991) extended to both hemispheres as used in Bertrand and van Ypersele (1999) and Bertrand et al. (1999).This model is latitude-, altitude-, and time-dependent and is designed for simulating the seasonal cycle of bothhemispheres. The surface is represented on a 5° latitudinal grid and, at each latitude band, the model surfaceis resolved into continental and oceanic portions, each of which interacts separately with the subsurface andthe atmosphere. The oceanic surfaces are ice-free ocean and sea ice, while the continental surfaces are snow-covered, snow-free land and ice-covered land. The mean altitude of the ice-free land and the elevation andextent of ice sheets are specified from data. Sea ice can exist at the ocean surface and vary in amount at theexpense of the open-water area.

Copyright 2002 Royal Meteorological Society Int. J. Climatol. 22: 623–648 (2002)

TRANSIENT CLIMATE SIMULATION 625

The atmospheric component of the model is based on the zonally averaged form of the two-level quasi-geostrophic potential vorticity system of equations (including diabatic heating and frictional dissipation)written in spherical and pressure coordinates (Sela and Wiin-Nielsen, 1971; Ohring and Adler, 1978) andapplied independently on each hemisphere. The basic output consists of the latitudinal distributions of thetemperature at 500 hPa and of the zonal winds at 250 and 750 hPa. Meridional transport of quasi-geostrophicpotential vorticity is accomplished by an eddy mixing process using exchange coefficients parameterized asin Gallee et al. (1991). It is well known that the quasi-geostrophic approximation leads to an underestimationof the strength of the Hadley cell at low latitudes and, therefore, to an overestimation of the temperatures inthese regions (e.g. White and Green, 1984). Partly to remedy this problem, a parameterization of the verticallyintegrated Hadley heat transport (Peng et al., 1987) has been introduced in the model. A parameterization ofthe interhemispheric Hadley sensible heat transport has also been included. This allows one to simulate thelatitudinal variation of the intertropical convergence zone. Separate surface energy balances are calculatedover the various surface types at each latitude, and the heating of the atmosphere due to the verticalheat fluxes is the weighted average of the convergences of these fluxes above each kind of surface. Thevertical heating processes considered are: solar radiation, long-wave radiation, convection, and latent heatrelease.

Given the model-predicted zonally averaged temperature at 500 hPa, the vertical temperature profile in thetroposphere is determined by assuming that the product of the static stability parameter σ and the pressurep is constant throughout that layer. Near the surface the temperature profile is linearly modified so that theair has the same temperature as the surface. Such correction is necessary to allow, for example, thermalinversion over cold surfaces. The stratosphere is supposed isothermal with a temperature identical to that ofthe tropopause. Tropopause height is parameterized as a function of the zonally averaged surface temperaturefollowing Sellers (1983).

The model explicitly incorporates detailed short-wave (Fouquart and Bonnel, 1980) radiative transfercomputations. The following processes are included: absorption by H2O, CO2, and O3, Rayleigh scattering,absorption and scattering by cloud droplets and aerosols, and reflection by the Earth’s surface. The long-wave radiation computations are based on Morcrette’s (1984) wide-band formulation of the radiative transferequation. Absorption by H2O, CO2, and O3 is explicitly treated and the cloud cover is supposed to behave as ablackbody, as is the Earth’s surface. In both schemes, account is taken of variation in surface topography and,unlike the atmospheric dynamics, the radiative transfer computation uses 10 to 15 layers, the exact numberdepending on the surface pressure over each surface type.

A single cloud layer is assumed to exist in each latitude belt, with monthly cloud amount prescribed frommean climatology. The base and top altitudes of the cloud layer and its optical thickness are kept fixedthroughout the year. The surface fluxes of sensible and latent heat are parameterized according to Saltzmanand Ashe (1976) and Saltzman (1980) respectively. Latent heat release in the atmosphere is obtained fromobserved zonal and monthly mean precipitation rates uniformly scaled to ensure that global evaporation equalsglobal precipitation at each time step. Above ice sheets the precipitation rates are modified to incorporate theeffects induced by the distance to the moisture source and the surface slope and elevation of the ice sheet(Oerlemans, 1982). In contrast to the Northern Hemisphere, where continents are today largely free of ice, thezonally averaged precipitation rates for the Antarctic already include the impact of such correction factors,which are therefore not applied.

Over the land free of snow and ice, the surface temperature is determined by an energy-balance equationaccounting for the subsurface heat storage (Taylor, 1976). The groundwater budget is computed by the so-called ‘bucket method’ of Manabe (1969). The soil is assumed to have a water-holding capacity of 40 cm.If the calculated soil moisture exceeds this value, then the surplus is supposed to run off. Changes in soilmoisture depend on the rates of rainfall, evaporation, snow melt, and run-off. The fraction of the ice-free landarea above which precipitation falls as snow, ffs, is parameterized as a function of the surface temperaturefollowing Harvey (1988). As the surface temperature decreases, ffs increases, but only a single average snowdepth is stored. When ffs decreases from one time step to the next, the snow is uniformly distributed over theexisting snow-covered area. The surface temperature of the snow layer is obtained by assuming an equilibriumbetween the internal conductive heat flux and the other surface heat fluxes. When the predicted temperature



reaches 0 °C, snow melt occurs. This melting is partitioned between a decrease in the snow extent and adecrease in the snow depth, so that the snow area is gradually reduced during the melting period. The icesheets are supposed to be completely covered by perennial snow. The proportion of precipitation falling assnow over their surfaces is a function of the surface temperature as in Ledley (1985). An energy-balanceequation similar to the one applied for snow on land provides the temperature at the air–snow interface. Thenet accumulation rate of snow is derived from an explicit surface mass budget.

The upper ocean (up to a depth of 150 m) is represented by the variable depth and temperature mixed-layermodel of Gaspar (1988). Meridional convergence of heat due to oceanic currents is modelled by a diffusivelaw according to Sellers (1973). The surface temperature of the ice pack and the vertical ice growth anddecay rates are computed by the zero-layer model of Semtner (1976). Snow on top of the ice layer is nottaken into account and no dynamic effect, such as ice drifting caused by wind, is considered. The openingand closing of leads and the open-water temperature are calculated following the technique of Parkinson andWashington (1979), subject to small modifications described in Gallee et al. (1991). The heat flux from theocean to the ice is computed by assuming that sea ice is in thermodynamic equilibrium with the water justbelow. Therefore, both the temperature at the base of the ice and the temperature of the under-ice water aresupposed to be equal to the freezing point of sea water. In the model, the only physical mechanism that candisturb this equilibrium is a partial mixing between the under-ice water and the lead water. To maintain theice-covered water at the freezing point, the heat flux from the ocean to the ice has to compensate exactly forthat perturbation.

Separate albedos are calculated for each surface type. The albedo of the land not covered by snow or iceis a function of the soil moisture content according to Saltzman and Ashe (1976). Over continental areascovered by forests, the snow albedo is set equal to 0.40 (Robock, 1980). Elsewhere, the snow albedo isdependent on the surface temperature and the snow age as in Danard et al. (1984). Values of 0.85 and 0.65are assigned to the maximum and minimum albedos of dry snow respectively. For melting snow, the upperand lower limits of the albedo are assumed to be 0.70 and 0.50 respectively. These albedos are correctedlater on to include the effects of snow depth and solar zenith angle. The albedo of ice-free ocean under clearsky is parameterized as a function of the solar zenith angle after Briegleb and Ramanathan (1982). Undercloudy conditions, the sea surface has an albedo of 0.07. The albedo of sea ice is supposed to vary linearlywith surface temperature, from 0.75 at −10 °C to 0.40 at 0 °C. A solar zenith angle correction is also appliedhere (Robock, 1980). The model time step is 3 days, except for the mixed-layer and sea-ice computations,for which it is 1 day.

In order to perform our transient climate simulations, the model is coupled to a diffusive deep ocean inwhich the uptake of heat perturbations by the deep ocean was approximated by a vertical diffusion processfollowing Hansen et al. (1988):

∂�T

∂t= k(φ)

∂2�T

∂z2 (1)

where the effective diffusion coefficient k(φ) represents the ocean heat uptake, which is a critical elementof the climate model response — e.g. see Levitus et al. (2001) and Barnett et al. (2001). For example, alarge value of diffusion will slow and reduce the response of the surface temperature. Our latitude-dependentvalues of k(φ) have been computed from the geographical distribution of effective thermocline diffusioncoefficient derived by Hansen et al. (1984) and range from 0.2 cm2 s−1 near the equator to 5 cm2 s−1 inhigh latitudes. Such values result from the low rate of exchange (k < 0.2 cm2 s−1) in the eastern equatorialPacific, where upwelling and the resulting high stability at the base of the mixed layer inhibit vertical mixing,and the rapid exchange rate (k > 10 cm2 s−1) in the North Atlantic — Norwegian Sea area — and SouthernOceans, where convective overturning occurs. The deep ocean, taken to be the water below the maximumallowable mixed-layer depth (150 m), is divided into 13 layers of geometrically increasing thickness, witha total thickness of 3850 m. Note that the k(φ) values we use are kept constant in time and in the verticaldirection.

As we see, our climate model, which is of intermediate complexity between energy balance models (EBMs)in their one-dimensional or two-dimensional versions and GCMs, allows a response varying with latitude



and takes into account the main climatic processes while still being simple enough to require reasonablecomputer time. Indeed, the principal drawback of the quasi-geostrophic equations is the poor representationof the atmospheric dynamics in the tropical latitudes, but their major advantage over primitive equationsis that zonal averaging of these equations allows a 3 day time step, comparable to that used in the EBMs.Moreover, the explicit treatment of the thermal wind relation allows the simulation of the zonal wind speedand, consequently, a more realistic coupling between atmosphere and the oceanic mixed layer than in EBMs.The advantage of using such a model type is that it is much simpler than a full GCM, and therefore mucheasier to understand and analyse. It allows the major features of the GCM response to be captured using afew basic parameters, which can be varied to carry out sensitivity experiments. Its main limitations are that itis too simple to represent the full range of interactions and non-linear feedbacks occurring in a GCM. As anexample, the use of our simplified deep ocean representation prohibits any investigation about the potentialimpact of fluctuations in ocean circulation.

3. SIMULATION OF CHANGES IN CLIMATE SINCE THE LATTER HALF OF THE 19TH CENTURY

3.1. Climate forcings reconstruction

In the absence of a full physical theory able to explain the origin of the observed Sun’s radiant energyoutput variations, three different total solar irradiance (TSI) reconstructions have been considered. First aTSI change due to the photospheric effects incorporated in the Willson and Hudson (1988) parameterizationaccounting for a potential range in solar variability at the centennial time scale of 0.05% (hereafter referredto as TSI WH). Second, a solar reconstruction as computed by Lean et al. (1995), which derives from recentsatellite observations and historical recordings, as well as stellar intensity fluctuations (hereafter referred to asTSI Lean). Such a reconstruction is constrained to agree with a TSI variability of 0.24% from the MaunderMinimum (from 1645 to 1715) to the present-day mean. Third, the Reid (1997) series, which accounts for anirradiance reduction of about 0.65% below the 1980 level during the Maunder Minimum (hereafter referredto as TSI Reid). The three TSI reconstructions are displayed in Figure 1(a) for comparison.

The potential impact of large volcanic eruptions on the Earth’s climate variations has been taken into accountin our numerical simulation by the short-wave radiative transfer perturbations resulting from the changes inthe latitudinal and temporal stratospheric aerosol optical depths as compiled by Sato et al. (1993; updated).Indeed, although volcanic aerosols also absorb and emit terrestrial radiation and reflect and absorb in the nearinfrared, these effects on the Earth’s surface temperature are an order of magnitude smaller than the coolingeffect due to reduction of short-wave radiation (Harshvardhan, 1979). The volcanic time series (expressed interm of annual and hemispherical mean stratospheric aerosol optical depths) is given in Figure 1(b).

The response of the Earth’s climate to the perturbation in radiative forcing due to increased concentrationsof infrared-active (greenhouse) gases in the atmosphere over the industrial era has then been investigatedby means of an effective-CO2 method (e.g. Tricot and Berger, 1987; Delobbe et al., 1997). Indeed, CO2

is not the only greenhouse gas that has increased steadily in the atmosphere during this century. Severalother greenhouse gases are also observed to be increasing in concentration in the atmosphere because ofhuman activities (especially biomass burning, landfills, rice paddies, agriculture, animal husbandry, fossilfuel and industry). They tend to reinforce the changes in radiative forcing from increased CO2. Therefore,starting with the observed variations in greenhouse gas concentration from 1850, we have expressed thisforcing in terms of changes in equivalent CO2 concentration with the formulations given in Houghton et al.(1990, 1992, 1995). The time evolution of the atmospheric equivalent CO2 concentration is provided inFigure 1(c). Here, we take advantage of the low computer cost of our model and the possibility to investigateits transient response according to two different sensitivities to a doubling of CO2 concentration. Modelsensitivities of 1.9 and 2.5 °C have been considered. Note that these sensitivities are not prescribed in ourmodel, but rather result from the multi-layer radiative scheme (for further information, see Delobbe et al.(1997)).

To generate the spatial and temporal distributions of sulphate aerosol concentrations for use in our transientclimate simulation, we used the monthly mean sulphate abundances simulated by the Moguntia model (Langner



Figure 1. Forcing terms used in model runs. (a) Comparison of three TSI reconstructions. TSI Reid and TSI Lean refer respectively tothe Reid (1997) and Lean et al. (1995) reconstructions, and TSI WH is an irradiance reconstruction as provided by using the Willsonand Hudson (1988) parameterization. (b) Reconstructed stratospheric aerosol optical depths due to volcanic activity (from Sato et al.(1993), updated). (c) Time evolution of the atmospheric equivalent CO2 concentration. (d) Estimated sulphur emissions since 1850

(from Orn et al. (1996))

and Rodhe, 1991): first, for the pre-industrial case (1850, only natural sources), and second for the industrialcase (1980, natural and anthropogenic sources). Then, the time dependence and sectorial distribution ofsulphate concentration in each model latitudinal band for the intermediate period was obtained by the use ofan historical worldwide anthropogenic sulphur emission inventory, as recorded by Orn et al. (1996). Startingfrom the gridded (5° × 5° resolution) sulphur inventory, sulphur emissions trajectories were determined aboveeach sector in each model latitudinal band. Such trajectories were then used to compute the sulphate fieldsconcentration using linear interpolation linking the two Moguntia outputs of sulphate fields concentration and



sulphur emission. The estimated hemispherical mean sulphur emissions over the industrial era are given inFigure 1(d).

In contrast to an increasing number of GCM studies that represent the direct effect of sulphate aerosols intheir transient climate simulation by an increase in the clear sky surface albedo (e.g. Mitchell et al., 1995a,b;Mitchell and Johns, 1997; Manabe and Stouffer, 1997), we compute the short-wave radiative flux perturbationsby using a full radiative code. Based on the work of Bertrand and van Ypersele (1997), we determine theoptical properties of sulphate aerosols (assumed for practical convenience as consisting of a sulphuric acidsolution in water) as a function of sulphate concentration and relative humidity in each model layer and aboveeach surface type. Finally, though the radiative forcing provided by sulphate aerosol particles is not limitedjust to direct effects (scattering of sunlight radiation), in that it also includes an indirect effect (they act ascloud condensation nuclei, and increasing their concentration may lead to more cloud droplets and hence tobrighter clouds (Twomey, 1974)), only the direct sulphate aerosol forcing has been taken into account in thisstudy.

3.2. Model results

Starting from the present-climate initial conditions, the model was first run until the seasonal cycle reachedequilibrium. This equilibrium is assumed to be achieved when the annual global mean radiative balance at thetop of the atmosphere varies by less than 0.01 W m−2 (which takes 100 years of integration). During the first100 years of the simulation (equilibrium run), the solar irradiance and the equivalent CO2 concentration werekept fixed to their reconstructed values in 1850. The monthly mean pre-industrial case tropospheric sulphateabundances simulated by the Moguntia model (Langner and Rodhe, 1991) and a background stratosphericaerosol optical depth value deduced from Deepak and Geber (1983) were also used during the equilibriumpart of our climate simulations. No exchange of heat at the base of the upper mixed-layer ocean was allowedat this stage of the simulation. Using this solution as a new initial state, the model was then integrated from1850 to 1995 with no interactive ice-sheets (Antarctica and Greenland). In this transient run, the flux oftemperature anomalies from the upper ocean into the deep ocean was represented as a diffusion process,and the solar irradiance, the greenhouse gas concentration, and the tropospheric and stratospheric aerosolconcentrations were allowed to vary as described above.

The analysis of the transient air surface temperature response to the forcings considered (see Figures 2,3, 4 and 6, and Table I) shows that the greenhouse gas forcing and the solar forcing contribute to a long-term warming (largely dominated by the greenhouse gas forcing, as highlighted in Figures 4(c) and 6) andthat volcanic and sulphate forcings contribute to a long-term cooling (dominated by anthropogenic sulphateaerosols forcing and episodic volcanic events, as displayed in Figures 3 and 4). More specifically, whateverthe TSI reconstruction may be, the solar-induced global (or hemispherical) mean warming simulated overthis time period is insufficient to reproduce the recorded temperature change. And, though solar activity hasrisen systematically through the past 80 years, only the model response to the Reid (1997) reconstructionpresents a substantial temperature increase (about 0.46 °C in global mean from 1915 to 1960). By contrast,the maximum temperature variation associated with the Schwabe radiation cycle (as given by the use ofthe Willson and Hudson (1988) parameterization) only accounts for a global mean temperature increase ofabout 0.05 °C over the same period; an intermediate temperature change of about 0.18 °C was simulatedin response to the Lean et al. (1995) reconstruction. One can note that, though the model response tothe Reid (1997) reconstruction exhibits temperature changes in the range of that observed over the timeperiod (1915–60), such an optimistic solar irradiance reconstruction is clearly unable to engender the rapidwarming as recorded over the last 40 years. The different amounts and distributions of land and oceanbetween the two hemispheres lead to a larger temperature response to the solar forcing in the NorthernHemisphere than in the Southern Hemisphere, as shown in Figure 2(b) and (c). The transient response ofthe zonally averaged annual mean surface air temperature response to the solar forcing is displayed inFigure 4(a). Note that only the model response to the TSI Reid time series is provided, since similar patternsare found in response to the TSI Lean and TSI WH reconstructions, and the amplitude of the temperatureresponse is less in these last two cases. The time series of the modelled air surface temperature change



Table I. Air surface temperature changes simulated by the model between the 1854–63mean (T 1) and the 1986–95 mean (T 2) in response to the solar irradiance (TSI), volcanic(VOLC), tropospheric sulphate (SUL), and greenhouse gas (GHG) forcings, as well as totheir ‘natural’ (N = TSI + VOL) and ‘anthropogenic’ (A = SUL + GHG), and their sum(S = N + A). Subscripts R, L, and W refer to the total solar irradiance reconstruction asprovided respectively by Reid (1997), Lean et al. (1995), and the Willson and Hudson(1988) parameterization. Subscripts 1.9 and 2.5 refer to the model sensitivity to a ofdoubling of CO2 concentration. Subscripts NH and SH refer to the Northern and Southern

Hemispheres respectively

Forcing �T = T 2 − T 1 ( °C)

TNH TSH TGLOBAL

TSIR 0.229 0.179 0.204TSIL 0.128 0.103 0.115TSIW 0.022 0.021 0.022

GHG2.5R 0.915 0.695 0.805GHG2.5L 0.936 0.721 0.828GHG2.5W 0.925 0.704 0.814GHG1.9L 0.639 0.493 0.566

VOL −0.161 −0.135 −0.147SUL −0.287 −0.161 −0.225

NR 0.105 0.093 0.099NL 0.000 0.012 0.006NW −0.145 −0.119 −0.132

A2.5 0.714 0.621 0.668A1.9 0.410 0.393 0.402

S2.5R 0.791 0.687 0.739S2.5L 0.724 0.645 0.684S2.5W 0.610 0.553 0.582S1.9R 0.499 0.463 0.480S1.9L 0.425 0.417 0.421S1.9W 0.313 0.326 0.320

indicates that the larger temperature variations are located at high latitudes during both warming and coolingperiods. This behaviour is related to the amplification of the climate response due to the ice, and to alesser extent to the snow-albedo–temperature feedback (see Bertrand and van Ypersele (1999) for a furtherdiscussion).

The air surface temperature response to the volcanic forcing (see Figure 3) is characterized by a suc-cession of coolings. The transient cooling tendency at the surface induced by such a negative forcing hasthe potential to dominate the global and hemispheric surface temperature for a few years. However, theeffect of the aerosols from a single eruption is marked only for a short period, as clearly shown in thezonal average in Figure 4(b), unlike the greenhouse gas effect, which persists (see Figure 4(c)). More-over, because the aerosol cloud resulting from a single eruption is flushed out of the stratosphere in afew years, and because of the thermal inertia of the climate system, the Earth’s temperature responseto such a transient forcing is rather modest, except to some extent in the high latitudes due to theamplification of the climate response occurring at these latitudes. Nevertheless, volcanic activity maybe important in explaining some of the interdecadal variation in surface temperature during the instru-mental record. When the volcanic forcing vanishes, the climate model recovers progressively, as bestillustrated in Figures 2 and 4(b) between 1925 and 1960 due to the lack of volcanism during this timeperiod.



Figure 2. Comparison between the transient response of the annual globally (a) and hemispherically (Northern Hemisphere in (b) andSouthern Hemisphere in (c)) averaged air surface temperature to solar forcing reconstructions and the recorded temperature fluctuations(Jones, 1994, updated; Parker et al., 1995, updated) from 1850 to 1995. TSI Reid refers to the model response to the Reid (1997)reconstruction, TSI Lean to the Lean et al. (1995) reconstruction, and TSI WH to the Willson and Hudson (1988) parameterization.

The temperature anomalies are calculated relative to the 1854–63 average

The air surface temperature response to the combined solar and volcanic forcings is displayed in Figure 5and compared with the recorded temperature changes. These two forcings are clearly unable to induce therapid warming observed after 1970. Nevertheless, our result indicates that the lack of volcanism from 1925to the 1960s, combined with the upward stage in the Gleissberg cycle, certainly could account, at least partly,for the warming trend in this period. Another large discrepancy between the model response to the naturalforcings and the observation occurs in the model response to the Krakatau eruption (Indonesia, 1883). Sucha discrepancy is reduced to some extent when forcing the model with half a Krakatau forcing, as illustratedin Figures 3 (dotted line). Justification for this sensitivity experiment could be found in Sato et al. (1993),which subjectively estimated a potential error in their reconstructed stratospheric aerosol optical depth ofabout 50% for the period 1850–1915. Nevertheless, owing to the amplification of the climate response bythe albedo–temperature feedback, the model (forced with half a Krakatau perturbation), by contrast to the



Figure 3. Comparison between the transient response of the annual globally (a) and hemispherically (Northern Hemisphere in (b) andSouthern Hemisphere in (c)) averaged air surface temperature to aerosols forcings and the recorded temperature fluctuations (Jones,1994, updated; Parker et al., 1995, updated) from 1850 to 1995. SUL refers to the transient model response to the anthropogenic sulphateforcing, and VOL to the volcanic forcing (the dashed line provides the model response to half a Krakatau forcing). The temperature

anomalies are calculated relative to the 1854–63 average

observed surface temperature, is still perturbed when the following volcanic events occur: Tarawera, 1886;Ritter Island, 1888; Bandai San, 1888; Awu, 1892. This amplification of the climate response could be acharacteristic of climate models that do not realistically portray the interactions between the surface climateand either the dynamics of the ocean and/or the stratosphere. But, owing to the episodic nature of the volcanicforcing, this larger cooling does not significantly affect the rest of our transient climate response.

The individual responses of the annual global and hemispherical mean transient air surface temperatureto the direct sulphate forcing and greenhouse-gas forcing are displayed in Figures 3 and 6 respectively andtheir combined effects are shown in Figure 7. The estimated 1850s TSI values differ from one reconstructionto another (i.e. 1366.12 W m−2 for Lean et al. (1995), 1367.34 W m−2 for the Willson and Hudson (1988)parameterization, and 1369.80 W m−2 for the Reid (1997) reconstruction), and we have investigated our modelresponse to greenhouse-gas forcing by initializing the model with each of these irradiance values, during theequilibrium part of our simulations. As indicated in Table I, these differences in the 1850s irradiance value,



1850

90

60

30

0

–30

–60

–90

1900 1950

(a) Solar (TSI Reid) – Control

(b) Volcanic – Control

1850

90

60

30

0

–30

–60

–90

1900 1950

(c) GHG (2.5, Lean) – Control

Latit

ude

(Deg

ree)

Latit

ude

(Deg

ree)

1850

90

60

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Figure 4. Time evolution of the annual mean latitudinal distribution of the simulated air surface temperature response to (a) the solar(TSI Reid), (b) the volcanic, (c) the greenhouse gas (using a model sensitivity of 2.5 °C for a doubled CO2 concentration), and (d) the

direct sulphate forcings relative to the equilibrium state. Units are °C



Figure 5. Comparison between the transient response of the annual globally (a) and hemispherically (Northern Hemisphere in (b) andSouthern Hemisphere in (c)) averaged air surface temperature to the natural climate forcings (solar and volcanic) and the recordedtemperature fluctuations (Jones, 1994, updated; Parker et al., 1995, updated) from 1850 to 1995. N Reid, N Lean, and N WH referrespectively to a model response accounting for a solar forcing reconstruction as provided by Reid (1997), Lean et al. (1995), and the

Willson and Hudson (1988) parameterization. The temperature anomalies are calculated relative to the 1854–63 average

and thus in the equilibrium temperature (of about 0.4 °C in global mean between the Reid (1997) and the Leanet al. (1995) estimations), lead to a simulation of net global warming for the time period 1850–1995 rangingfrom +0.908 to +0.922 °C when a model sensitivity of 2.5 °C for a doubling in the CO2 concentration isused. However, this temperature range difference of 0.014 °C at the end of our transient simulations (largelyresulting from larger sea-ice and snow extents in the colder climate at the beginning of the transient simulation)is negligible in view of the change in the model results when a model sensitivity of 1.9 °C for a doubling CO2

concentration is used; a net global warming reduction of 1.5% (or 0.014 °C) versus 31.3% (or 0.286 °C) occurswhen the lower model sensitivity to CO2 is chosen. Nevertheless, whatever the model sensitivity to CO2 maybe, the observed warming trend is not entirely consistent with the greenhouse-gas atmospheric concentrationchange, as illustrated in Figure 6. A model sensitivity of 2.5 °C overestimates the recorded warming, whereasa model sensitivity of 1.9 °C underestimates it. Moreover, part of the recorded temperature increase occurredbefore 1940, after which the Earth started to cool until the early 1970s, when warming resumed. Greenhouse



Figure 6. Comparison between the transient response of the annual globally (a) and hemispherically (Northern Hemisphere in (b) andSouthern Hemisphere in (c)) averaged air surface temperature to the greenhouse-gas forcing and the recorded temperature fluctuations(Jones, 1994, updated; Parker et al., 1995, updated) from 1850 to 1995. GHG1.9 and GHG2.5 refer to the model sensitivity to adoubling CO2 concentration of 1.9 °C and 2.5 °C respectively. The subscripts Reid, Lean, and WH indicate that the equilibrium runwas performed by using a solar irradiance value for 1850 as provided by Reid (1997), Lean et al. (1995), and the Willson and Hudson

(1988) parameterization respectively. The temperature anomalies are calculated relative to the 1854–63 average

gases such as CO2, CH4, and N2O, on the other hand, have been increasing steadily throughout the pastcentury. The corresponding latitudinal distributions of temperature change between 1850 and 1995 are givenin Figure 4(c). This figure shows that the largest warming is simulated in the high latitudes (because of thesea-ice and snow-albedo–temperature feedback), whereas a relatively uniform warming appears over tropicaloceans. Figure 4(c) also exhibits a larger warming over land areas compared with that over the oceans,resulting from the greater thermal inertia of the oceans. This ocean–land asymmetry contributes to a morerapid warming in the Northern Hemisphere compared with that in the Southern Hemisphere, as illustrated inFigure 6(b) and (c). These large-scale patterns of temperature change remain essentially fixed with time, andthey become more evident as the simulations progress.

When the estimated historical forcing due to anthropogenic sulphate aerosol is included, then an improvedagreement with the observational record of global and hemispherical surface temperature appears (at least



Figure 7. Comparison between the transient response of the annual globally (a) and hemispherically (Northern Hemisphere in (b) andSouthern Hemisphere in (c)) averaged air surface temperature to the anthropogenic climate forcings (anthropogenic sulphate aerosoland greenhouse gas) and the recorded temperature fluctuations (Jones, 1994, updated; Parker et al., 1995, updated) from 1850 to 1995.

A1.9 and A2.5 refer to the model sensitivity to a doubling of CO2 concentration of 1.9 °C and 2.5 °C respectively

for the highest model sensitivity to CO2), as shown in Figure 7. Because of their short atmospheric lifetime,aerosol concentrations are largest near their source region. Hence, in contrast to the well-mixed greenhousegases, they have the potential to produce strong latitudinal and sectorial radiative forcing of climate. As aresult, their relative impact on the Earth’s surface temperature will be greater than given by considering onlyglobal mean quantities. Nevertheless, Figure 3 and Table I indicate that the anthropogenic sulphate forcingis sufficiently large to induce a difference in the hemispherical mean temperature responses, the net coolingsimulated by the model being twice as larger in the Northern Hemisphere as in the Southern Hemisphere(−0.31 versus −0.17 °C). This is illustrated in Figure 4(d), which presents the transient response of the zonallyaveraged annual mean surface air temperature to the anthropogenic sulphate forcing. The direct sulphateaerosol forcing produces a cooling which is most pronounced over the northern mid-latitude continentalsurfaces and downwind oceanic sector, as illustrated between 30 and 60 °N. However, it is important tonote that, though the anthropogenic sulphate forcing is larger over the continental surfaces, the maximumtemperature decrease simulated by the model appears in the vicinity of sea-ice areas in the oceanic sector.



This is because the aerosol cooling is spread by the atmospheric circulation and amplified through sea-ice feedbacks. This cooling illustrates the high sensitivity of our climate model to the polar amplification,since this structure is caused rather by the feedback mechanisms than the forcing itself. A similar coolingpattern is also found in more sophisticated climate models (e.g. Mitchell et al., 1995b; Mitchell and Johns,1997), except that there the simulated cooling in polar regions is less than over mid-latitude continentalsurfaces.

Whereas simulations forced by anthropogenic factors only lead to a better agreement with observationsmainly during the rapid warming after 1970, none of these simulations involving anthropogenic factors isable to reproduce the steep rise of around 0.3 °C in global mean temperature during the 1930s and 1940s.Furthermore, statistical analysis of global temperature trends from 1861 to 1987 have shown that, whiletemperatures have been increasing in both hemispheres, there was a pronounced decrease in the NorthernHemisphere temperatures between 1940 and 1970, which did not occur in the Southern Hemisphere (Joneset al., 1986; Jones, 1988). Given the conclusion reached above, i.e. that the impact of anthropogenic aerosolson climate predominantly affects the Northern Hemisphere, it is tempting to attribute the cooling of theNorthern Hemisphere relative to the Southern Hemisphere to sulphate aerosols, especially since this periodcoincides with the time in which sulphur emissions exceeded natural levels in the Northern Hemisphere andgrew rapidly before being checked by emission controls. Nevertheless, as shown in Figure 7(b), this coolingtrend is lacking in the transient response of the Northern Hemispheric annual mean surface temperature tothe greenhouse-gas and aerosol forcings combined. The simulated changes in climate that result from humanactivities occur gradually in response to steadily increasing climate forcing by greenhouse-gas increases andanthropogenic aerosol production. The global mean anthropogenic sulphate aerosol forcing has increasedcontinuously since 1850, and most rapidly from 1950 to 1970, as illustrated in terms of the transient surfacetemperature response in Figure 3. And while this increase in aerosol forcing has been able to reduce theNorthern Hemisphere warming rate significantly over a few years, it is, however, unable to explain theobserved cooling trend. However, the increasing Northern Hemisphere aerosol concentration reduces theasymmetry between the hemispheres found in greenhouse-gases simulations (Figure 6), though the warmingremains greater in the Northern Hemisphere owing to the lower thermal inertia of the continents (Figure 7).The rapid warming after 1970 is the response to accelerated greenhouse warming and a slower rate ofincrease in cooling from sulphate aerosols. These model results suggest that global warming could accelerateas greenhouse-gas forcing begins to dominate over sulphate aerosol forcing.

Previous simulations performed by using an upwelling diffusion-energy balance (Houghton et al., 1996)present a period of relatively constant temperature from 1950 to 1970 when forced by the historical increase inatmospheric greenhouse gas concentrations and sulphate aerosols’ loading. This longer plateau in temperaturein comparison with our results is probably linked to the large uncertainty in the estimation of the past radiativeforcing due to anthropogenic sulphate aerosols and to its representation in climate models (e.g. Heintzenberget al., 1996). As an example, the indirect anthropogenic sulphate forcing is lacking in our simulations, andthis forcing could have been at least twice as large as the direct one in 1990, as reported by Harvey et al.(1997).

As we see, neither the individual responses nor the combined natural or anthropogenic forcings allowus to reproduce all of the recorded major temperature fluctuations since the latter half of the 19th century.Indeed, whereas prior to about 1930 the solar and volcanic activities were the major forcings of the climatesystem, these two forcings are clearly unable to induce the rapid warming observed after 1970. By contrast,the combination of the cooling attributed to the sulphate aerosols and the warming due to the greenhousegases gives a simulation close to the observations in recent decades; prior to that, the modelled surfacetemperature response to anthropogenic forcings only is too smoothed to reproduce the recorded surfacetemperature fluctuations. Therefore, it seems impossible to simulate the 20th century climate warming withoutthe inclusion of the greenhouse-gas releases related to human activities. An alternative explanation wouldbe that this warming is due to the internally driven natural variability of the climate system, which cannotbe simulated with the simple diffusive deep-ocean model used here. However, numerical simulations carriedout using GCMs (e.g. Stouffer et al., 1994; Manabe and Stouffer, 1997) indicated that it is not likely thatthe ocean–atmosphere–land interaction in either a coupled or mixed-layer model could randomly generate a



substantial long-term warming trend, such as that observed since the end of the 19th century. Instead, it hasmore likely been induced by a sustained trend in thermal forcing.

Figures 8 and 9 display the comparison between observations and the simulated temperature variationswhen all the inferred climate forcings are introduced together in the model. Figure 8 presents the resultswhen a climate sensitivity of 1.9 °C for a doubling of CO2 concentration is used, and the model resultswhen a climate sensitivity of 2.5 °C is chosen are given in Figure 9. Clearly, the model has succeeded inreproducing the major trends in temperature since the middle of the 19th century. The temperatures arerelatively constant from 1860 to 1920, and show a steep rise of around 0.3 °C during the 1930s and 1940s,then another period of relative stability until 1970. Since then, temperatures have climbed more or lesscontinuously, and are currently around 0.3 °C higher than the 1951 to 1980 average. Figures 8 and 9, as

Figure 8. Comparison between the transient response of the annual globally (a) and hemispherically (Northern Hemisphere in (b) andSouthern Hemisphere in (c)) averaged air surface temperature to the combined anthropogenic (anthropogenic sulphate aerosol andgreenhouse gas) and natural (solar and volcanic) climate forcings when a climate sensitivity of 1.9 °C for a doubled CO2 concentrationis chosen, with the recorded temperature fluctuations (Jones, 1994, updated; Parker et al., 1995, updated) from 1850 to 1995. Thesubscripts Reid, Lean, and WH indicate that the solar forcing considered comes from the Reid (1997) reconstruction, the Lean et al.(1995) reconstruction, and the Willson and Hudson (1988) parameterization respectively. The temperature anomalies are calculated

relative to the 1854–63 average



Figure 9. Comparison between the transient response of the annual globally (a) and hemispherically (Northern Hemisphere in (b) andSouthern Hemisphere in (c)) averaged air surface temperature to the combined anthropogenic (anthropogenic sulphate aerosol andgreenhouse gas) and natural (solar and volcanic) climate forcings when a climate sensitivity of 2.5 °C for a doubled CO2 concentrationis chosen, with the recorded temperature fluctuations (Jones, 1994, updated; Parker et al., 1995, updated) from 1850 to 1995. Thesubscripts Reid, Lean, and WH indicate that the solar forcing considered comes from the Reid (1997) reconstruction, the Lean et al.(1995) reconstruction, and the Willson and Hudson (1988) parameterization, respectively. The temperature anomalies are calculated

relative to the 1854–63 average

well as Table I, also indicate that both the choice of the STI reconstruction and the model sensitivity to adoubling of CO2 concentration greatly affect the model’s ability to simulate the observed temperature change.First, for each of the three TSI reconstructions used, a model sensitivity of 1.9 °C for a doubling of CO2

concentration (Figure 8) leads to an underestimation of the observed warming trend. Second, when the modelsensitivity to a doubling of CO2 concentration is fixed to 2.5 °C (Figure 9), forcing the model with the Reid(1997) TSI reconstruction overestimates the 20th century warming in both hemispheres, whereas the use ofthe Willson and Hudson (1998) parameterization underestimates it. The overestimation related to the use ofthe Reid (1997) reconstruction might come from his omission to take the anthropogenic sulphate forcing intoaccount when he derived his reconstruction. On the other hand, it seems reasonable to consider that recentsatellite measurements have failed to capture the full range of solar variability and thus the photospheric



effects incorporated in the Willson and Hudson (1988) parameterization, and could account only for a (minor)part of the Sun’s variability at the secular time scale.

Therefore, by using a model sensitivity of 2.5 °C for a doubling of CO2 concentration and consideringthe Lean et al. (1995) time series as the most representative reconstruction of the TSI variations, the modelsimulations suggest that the observed temperature trends are the result of a subtle combination betweennaturally driven climate fluctuations and the effects of industrialization. For example, the warming trend from1920 to 1940, which coincides with a warm stage of the Gleissberg cycle (an 80 to 90 year quasi-periodicvariation in sunspot number and other solar indices) and with the lack of large volcanic events after theKatmai eruption (Alaska) in 1912, is strengthened by industrialization in Western Europe and the USA. Theperiod of relatively low volcanic activity holds until the Gunung Agung eruption (Bali) in 1963, which can beclearly identified in the Southern Hemisphere (Figure 9(c)) and global (Figure 9(a)) annual mean temperaturechange. This resurgence of volcanic activity, combined with a decreasing solar output, allows one to offsetand overcome the greenhouse warming signal (partly counteracted by sulphate aerosols) during some years.In this way, we can easily explain the cooling trend exhibited in the Northern Hemisphere (Figure 9(b))during the period from the 1950s through to the early 1970s. The rapid warming after 1970 is the responseto accelerated greenhouse warming and a slower rate of increase in cooling from sulphate aerosols. Indeed,this last period is clearly dominated by the greenhouse warming, which seems to be the largest climateforcing except for short time periods linked with volcanic events like El Chichon (Mexico, 1982) or Pinatubo(Philippines, 1991).

Discrepancies between the simulation and the observations could result from uncertainties in theobservations, inaccurate specification of the climate forcings or inaccurate sensitivity of the climate model,and from the incapacity of our simple climate model to generate the high-frequency internal natural climatevariability. We will investigate this last point in the future by replacing the simple diffusive deep-ocean modelemployed here by a zonally averaged three-basin ocean circulation model (Fichefet et al., 1994). Finally, itmust be mentioned that the choice of the deep ocean heat diffusion values in the model (k(φ) in Equation (1))can also play a critical role in the model’s ability to reproduce the observations, and could probably be tunedto do so. Here, no attempt was made to tune this parameter, and the latitude-dependent values of k(φ) werekept fixed to the estimated 1980 values during all our transient climate simulations.

4. PROJECTIONS OF CLIMATE CHANGE

Projections of future anthropogenic climate change depend, amongst other things, on the scenarios used toforce the climate model. Future changes in the forcing due to sulphate aerosols and greenhouse gases willdepend on how the corresponding emissions vary. The most important difference between greenhouse gasesand aerosols with regard to their climatic role is their atmospheric residence time. Because of the shortatmospheric lifetime of sulphate and its precursors, atmospheric concentrations will adjust within weeks tochanges in emissions. This is a very different situation than for most greenhouse gases, which have effectivelifetimes of decades to centuries. As a consequence, the amount of greenhouse gases present in the atmosphereresult from an accumulation over decades to centuries, and can be approximated by the integral of the sourcestrength over time. Therefore, scenarios of greenhouse-gas emissions play an important role in the analysisof potential climate change.

Until recently, the IPCC ‘business as usual’ scenario, IS92a (Houghton et al., 1996), was the reference formodel projections of future climate change. This and the other scenarios of the Second Assessment Report(SAR) have been revised for the Third Assessment Report (TAR). The IPCC Special Report on EmissionsScenarios (SRES) (Nakicenovic et al., 2000) indeed describes a new set of emission scenarios for CO2,other greenhouse gases, and for sulphate. Each scenario corresponds to a ‘storyline’ for population, economicgrowth, energy, land use, etc. Rather than providing a central value like the IS92a ‘business as usual’ case ofthe SAR, SRES is intended to display the uncertainty range of emission forecasts. It must be noted that noneof these scenarios includes additional climate initiatives, which means that no scenarios explicitly assumeimplementation of the United Nations’ Framework Convention on Climate Change on the Kyoto Protocol.



Here, we will present climate projections up to 2100 that explore the response of the climate system tothe six SRES Illustrative Marker Scenarios (A1B, A1T, A1FI, A2, B1, and B2). The model results basedon the previous scenario used by IPCC (IS92a) will also be used for comparison. The CO2 concentrationswere calculated from the SRES Illustrative Marker Scenarios using the ‘reference’ version of the Berncarbon cycle–climate (Bern-CC) model (Joos et al., in press). The sulphur emissions were converted toconcentrations of sulphate aerosol particles using a monthly mean sulphate distribution for 2050 simulatedby the Moguntia model (H. Rodhe and U. Hansson, personal communication). The aerosol spatial andtemporal distributions were then obtained using the same methodology as done earlier over the industrialperiod.

Knowing the projected concentration change referring to these emission scenarios for the differentgreenhouse gases (Houghton et al., 2001), we have evaluated the associated change in effective CO2

concentration with respect to the year 1850. Indeed, since the climate system responds slowly to externalchanges, the future climate response depends to some degree on the forcing history prior to now. Sensitivitytests (e.g. Fichefet and Tricot, 1992) have shown that this ‘history effect or cold start’ is a vital considerationduring the first few decades, but becomes unimportant after 30–40 years of simulation. Given the lowcomputer time requirements of our climate model, all our transient climate simulations have been startedin 1850 in order to include the historical increase in equivalent CO2.

Figure 10 displays the transient response of the global annual mean air surface temperature to thegreenhouse-gas forcing with (GHG + SO4) and without (SO4 = 90), including changes in aerosol beyond1990 simulated by the model using a climate sensitivity of 2.5 °C for a doubled CO2 concentration. Themodel response to the A1 and A2 SRES families is given in Figure 10(a), the response to families B1 andB2 is provided in Figure 10(b), and, finally, the model response to the IPCC IS92a scenario is shown inFigure 10(c) for comparison. In this last panel, the curve labelled SAR refers to the model response to theIS92a scenario with the carbon cycle model used in the IPCC SAR, whereas the curve IS92a is the modelresponse to the IS92a emissions but using the new generation of chemistry and carbon cycle models (e.g.new feedbacks on the lifetimes) to determine the atmospheric greenhouse-gas concentration. The net globalwarmings relative to the year 1990 simulated by the model in response to these different scenarios aresummarized in Table II. As we see, the SRES emission range leads to an envelope of global warming whoseaverage is greater than the IS92a reference case. The largest warming simulated by the model, i.e. 3.6 °C,is for the fossil fuel intensive (A1FI) scenario of the A1 SRES family; the lowest temperature change, ofabout 1.8 °C, is simulated for the B1 scenario family, which accounts for rapid changes in economic structurestoward a service and information economy, with reductions in material intensity, and the introduction of cleanand resource-efficient technologies. Joos et al. (in press) found essentially similar results, i.e. a net globallyaveraged surface air temperature increase of 1.6 °C (B1) to 3.5 °C (A1FI) from 2000 to 2100 when evaluatingthe climatic consequences of the six marker scenarios with the standard version of the Bern-CC model (witha climate sensitivity of 2.5 °C for a doubling of atmospheric CO2 concentration). In comparison, the range ofglobal mean temperature change from 1990 to 2100 simulated by the simple climate model used by Houghtonet al. (2001) (used as a tool to simulate the results of seven AOGCMs) in response to the six illustrativescenarios is 2.0 to 4.5 °C for a mean climate sensitivity of 2.8 °C (which is the average of the effective climatesensitivity of the seven AOGCMs on which the simple IPCC climate model has been tuned). Note that whenthe climate sensitivity used in this simple model is allowed to range from 1.7 to 4.2 °C, the globally averagedsurface temperature is projected to increase by 1.4 to 5.8 °C over the period 1990 to 2100 in response to thefull range of 35 SRES scenarios. Figure 10 shows that our model projections for 2100 lie in the middle ofthe IPCC range. The slight differences in the projected warming range can be explained by the larger climatesensitivity of the IPCC model (2.8 °C versus 2.5 °C in our model) and the different carbon cycle models used(the model of Wigley (1993) with some adaptations in the new IPCC report versus the Bern-CC model (Jooset al., in press) in this study — see above) to compute the atmospheric CO2 concentration from the SRES.

In all cases, Table II indicates that the average rate of warming would probably be greater than any seen inthe last 10 000 years. Clearly, if anthropogenic sulphate forcing has been able to mask part of the greenhouse-gas forcing over the last decades, its influence expected from the SRES and IS92a simulations on the globalmean temperature from 2000 to the end of the 21st century is insignificant compared with the warming



Figure 10. Annual and global mean air surface temperature changes from 1990 to 2100 for the SRES Illustrative Marker Scenarios andIS92a scenario with changing (dotted lines) aerosol concentration (GHG + SO4) and constant (solid lines) (SO4 = 1990) beyond 1990.(a) The model response to the A1 and A2 SRES families; (b) the model response to families B1 and B2; (c) the model response to

IS92a scenario

Table II. Global mean air surface temperature changes simulated by the model between 1990 and the end of the 21stcentury in response to the SRES Illustrative Marker Scenarios (A1B, A1T, A1FI, A2, B1, and B2) with (GHG + SO4)and without (SO4 = 90) including changes in aerosol beyond 1990. Model responses to IPCC IS92a emission scenarioare also given for comparison. The SAR column refers to the IS92a scenario with the carbon cycle model used in theIPCC Second Assessment. Also displayed are the global mean air surface temperature changes simulated by the modelover the same period in response to ±0.65% variations in TSI in 2100 relative to 1990 value (Soup and Sodown) or byassuming a volcanic perturbation similar to that of Pinatubo every 6 years (vol) when combined with a greenhouse gas

perturbation as provided by the A1F1, B1 and A1B SRES Illustrative Marker Scenarios

Model Run Temperature change ( °C)

A1FI A1B A1T A2 B1 B2 IS92a SAR

GHG + SO4 3.600 2.552 2.103 3.179 1.807 2.236 2.396 2.524GHG (SO4 = 90) 3.508 2.445 1.973 3.155 1.700 2.167 2.605 2.743

GHG (SO4 = 90) + Soup 4.127 3.074 2.321GHG (SO4 = 90) + Sodown 2.896 1.814 1.033GHG (SO4 = 90) + vol 2.900 1.823 1.046



induced by the greenhouse-gas forcing. This is especially true when forcing the model with the SRES ratherthan with the IS92 family scenarios. Indeed, emissions of sulphur dioxide, which produce sulphate aerosols,are substantially lower in SRES than in IS92. As sulphur emission controls in power plants are projectedto reduce the aerosol cooling, the global warming resulting from greenhouse gases is ‘unmasked’ in SRESsimulations to a greater extent than in IS92a during the 21st century. This is highlighted in Figure 10 bythe additional warming seen in SRES simulations when changes in sulphate concentration after 1990 (dottedlines) are taken into account, compared with simulations where the sulphate is kept fixed to its 1990 value(solid lines). By contrast, when including changes in sulphur emissions after 1990 in the IS92a emissionscenario, the global warming is reduced by about 0.2 °C, as indicated in Figure 10(c) and Table II.

An important issue here is whether projected changes in greenhouse gases and aerosols due to humanactivity are significant compared with natural factors. Indeed, any human-induced effect on climate will besuperimposed on the background ‘noise’ of natural climate variability. Were natural climatic variations of thesort that have characterized the last centuries to recur in the next 100 years, they could modify the expectedeffects of increased anthropogenic perturbations: either masking an underlying upward trend or acceleratingthe rate at which it occurs. Indeed, it is important to realize that future impacts of the Sun on otherwise-risingtemperatures could be either negative or positive. As an example, solar changes could also accelerate, for atime, an underlying warming trend.

Nevertheless, future variations in natural forcings due to volcanic aerosols and solar variability cannot beforecast. Observations of Sun-like stars suggest that solar variability could be larger in the future (Zhanget al., 1994), but current understanding of the Sun indicates that this variability might not exceed the changesinferred for the Sun during the past few centuries (Houghton et al., 1995). Therefore, the climate responseto two extreme scenarios concerning the solar forcing have been investigated here. Based on the Reid(1997) reconstruction, which allowed for the largest solar variability, we have forced the model with asolar irradiance of ±0.65% in 2100 relative to the 1990 value. The increase (decrease) was obtained bysimple linear interpolation between the STI values in 1990 and 2100. As volcanic eruptions are distributedunevenly in time, future volcanic perturbations are therefore unpredictable. Nevertheless, two hypotheticalcases have been investigated here. We first assumed no volcanic perturbations over the next century, whichleads then to simulate the same temperature change that was previously presented in this section. In asecond step, we forced the model with an extreme scenario assuming a volcanic perturbation similar to thePinatubo eruption every 6 years. It is worth pointing out that the possible importance of the carbon cyclefeedbacks in the atmospheric CO2 concentration computation is not taken into account in the followingsensitivity experiments. In other words, the atmospheric CO2 concentration time series simulated by theBern-CC model (standard version) have been used as they are, regardless of the potential changes in surfaceair temperatures and in the hydrological cycle due to the inclusion of the natural climate forcings in ourtransient climate simulations. Such an additional warming, for example by comparison with simulations inwhich only anthropogenic forcings were considered, may cause increased respiration of the carbon storedin soil and litter owing to higher bacterial activities at higher temperatures, reduced net primary productionbecause of excessively high temperatures and/or reduced water availability, and dieback of existing forestsin response to heat or drought stress, thereby offsetting the carbon uptake stimulated by the increase inatmospheric CO2 and, in some regions, by climate change (e.g. Joos et al., in press). Therefore, althoughthere is still considerable uncertainty about the relative magnitudes of these processes (Houghton et al.,2001), models show they are in the direction of greater temperature changes giving greater atmosphericCO2 concentration. The atmospheric CO2 concentration time series we used could probably be modifiedby the temperature changes resulting from the natural forcings added to the anthropogenic forcing in oursimulations.

Figure 11 presents the model response to these natural climate forcings scenarios when combinedwith the SRES A1FI scenario (Figure 11(a)), the SRES B1 scenario (Figure 11(b)), and SRES A1Bscenario (Figure 11(c)). Clearly, natural climate forcings such as solar variability and volcanic perturbation,which have probably modulated the global and hemispheric surface temperature variations over the pastcenturies (e.g. Overpeck et al., 1997; Briffa et al., 1998; Bertrand et al., 1999; Bertrand and van Ypersele,



Figure 11. Annual and global mean air surface temperature changes from 1990 to 2100 in response to ±0.65% variations in TSI in2100 relative to 1990 value (GHG(SO4 = 90) + Soup and GHG(SO4 = 90) + Sodown) or by assuming a volcanic perturbation similarto that of Pinatubo every 6 years (GHG(SO4 = 90) + vol) combined with a greenhouse gas perturbation as provided by the A1FI (a),

B1 (b), and A1B (c) SRES Illustrative Marker Scenarios

1999; Free and Robock, 1999; Crowley and Kim, 1999; Crowley, 2000), appear either weak or short-lived compared with projected greenhouse-gas forcing. Indeed, the transient model response to thesechanges, expressed in terms of global surface temperature change, appears like background noise (about±0.6 °C) compared with the model response to the greenhouse-gas forcing over the same period (seeTable II).

5. FINAL DISCUSSION AND CONCLUSION

A two-dimensional sector-averaged global climate model has been used to assess the potential impact ofgreenhouse gases, tropospheric and stratospheric (volcanic) sulphate aerosols, and solar irradiance changesin climate change since 1850. Our results indicate that each of these are needed to explain the observedtemperature change since the latter half of the 19th century. Such a result is, to some extent, contrary to theprevious conclusion of Rowntree (1998), that there is no need for the contribution from solar variability toexplain the recent climate record; however, it is in line with the conclusions of Cubasch et al. (1997) andBertrand and van Ypersele (1999), that if the solar irradiance estimates we used are valid, then the changesinduced by solar variability contribute, at least partly, to the climate changes on the multi-decadal time scale.



Sensitivity experiments suggest a climate sensitivity of 2.5 °C for a doubled CO2 concentration, which is inthe middle of the range of the IPCC estimation. Moreover, concerning the solar forcing, our results indicatethat the total solar irradiance reconstruction as provided by Lean et al. (1995) seems more realistic than thelargest one suggested by Reid (1997) or obtained by the sole photospheric effects incorporated in the Willsonand Hudson (1988) parameterization.

Although our results suggest that there is an anthropogenic component in the observed temperature record,they cannot be considered as compelling evidence of a clear cause-and-effect link between anthropogenicforcing and changes in the Earth’s surface temperature. Indeed, it is difficult to achieve attribution of all orpart of a climate change to a specific cause using global and hemispheric mean changes only (Houghtonet al., 1996). The difficulties arise due to uncertainties in natural internal variability and in the historiesand magnitudes of natural and human-induced climate forcings, so that many possible forcing combinationscould yield the same curve of observed global and hemispheric mean temperature change. Moreover, oursimulations included only the direct forcing due to sulphate aerosols and neglected other potentially largesources of natural and anthropogenic forcing, such as the forcings associated with large-scale land-use changes(Tegen and Fung, 1995), the carbonaceous aerosols generated by biomass burning (Penner et al., 1992, 1994),and the changes in stratospheric ozone (Santer et al., 1995). It is certainly possible that qualitative agreementcould be due to compensating errors, such as a climate sensitivity that is too high being partially offset bycooling due to a residual drift, or by an overestimated aerosol effect.

More convincing evidence for the attribution of a human effect on climate is expected to come by shiftingour focus from studies of global mean changes to comparisons of modelled and observed spatial and temporalpatterns of climate change. Indeed, different forcing mechanisms must have different patterns of response orcharacteristic ‘fingerprints’, particularly if the response is considered in three or even four dimensions (e.g.temperature changes as a function of latitude, longitude, height and time) (e.g. Hegerl et al., 1996). Therefore,a good match between observed and modelled multi-dimensional patterns of climate change increases thelikelihood that the ‘cause’ (forcing change) used in the model experiment is, in part, responsible for producingthe observed effect. The detection studies clearly require the use of more sophisticated climate models thanour simple two-dimensional quasi-geostrophic model. Nevertheless, our results show that it is possible toexplain past changes in a global-scale property of the climate system in a plausible way.

Finally, our results suggest that the dominant cause of climate change over the next century is likelyto be changes in atmospheric trace-gas concentrations. Indeed, if the solar, volcanic, and troposphericaerosol forcings used in our experiments, as well as our climate model sensitivity, are appropriate for futureprojections, our simulations show that they are unlikely to counter the expected greenhouse warming, exceptperhaps for brief intervals in the next couple of decades, before the human-induced component of changeswamps natural sources of change.

ACKNOWLEDGMENTS

We would like to thank H. Rodhe and U. Hansson for having provided the data from the MOGUNTIAmodel, M. Sato for reconstructed stratospheric aerosol optical depth perturbations, J. Lean and G. C. Reidfor their TSI time series, F. Joos for the CO2 concentration, P. D. Jones for recorded temperature time series,M. Hoffert and an anonymous referee for stimulating remarks. This research was sponsored by the ImpulseProgramme ‘Global Change et developpement durable’ (Belgian State, Prime Minister’s Services, SciencePolicy Office, contract CG/DD/242). Cedric Bertrand is a scientific research worker with the Belgium FondsNational de la Recherche Scientifique.

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Transient climate simulation forced by natural and anthropogenic climate forcings

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