Land-surface characterization in greenhouse climate simulations

INTERNATIONAL JOURNAL OF CLIMATOLOGY, VOI.. 14. 1065-1094 (1994) 551.581 . I .551.588.6(4/9)

LAND-SURFACE CHARACTERIZATION IN GREENHOUSE CLIMATE SIMULATIONS

ABSTRACT

A simplified Holdridge-type vegetation prediction scheme has been coupled to a version of the NCAR community climate model (CCM 1 - 0 z ) that includes the biosphere-atmosphere transfer scheme (BATS) and a mixed-layer ocean. This interactive vegetation climate model has been used to conduct two complementary C0,-doubling experiments: an instantaneous 2 x CO, simulation (15 years in total) and a fast, transiently increasing CO, simulation (45 years in total). There are some differences in the predicted vegetation distributions and areas. However, there is agreement that in a warmed world the vegetation type termed ‘agriculture’ increases in area at the expense of deciduous needle-leaf trees and short grass; and the tundra extent, already underestimated, decreases further whereas deserts and the deciduous broadleaf tree areas expand. The overall vegetation areas predicted are not particularly sensitive to initialization, although effects of different initialization can be monitored for 1-2 years. On the other hand, when the sensitivity of the modclled climate to the inclusion of some aspects of an interactive biosphere is examined, it is found that annually updated continental characteristics do not disrupt the climate simulation but do modify zonal temperatures and precipitation and increase continental evaporation. The latter intensifies the Hadley circulation, especially in July, and, thus. leads to increased evaporation globally. These results, if corroborated by other similar studics, indicate that simple, post firm application of vegetation diagnostic schemes once climatic equilibrium is achieved may be diagnosing vegetation from an incorrect climatic state.

K F Y WORDS Vegetation Climate variation Coupled models Greenhouse Interactive biosphere

1. INTRODUCTION

1.1 Why try to link vegrtution und c1irnutt.Y

To date, all model predictions of the likely climatic impact of doubling atmospheric CO, have assumed that the continental vegetation remains fixed, distributed as for present-day simulations. In general, the only continental surface parameter that has varied interactively has been the soil moisture (e.g. Meehl and Washington, 19881, whereas other equally important variables (cf. Henderson-Sellers, 1993a), such as albedo and vegetation roughness length, have been fixed at present-day values. This unrealistic assumption has been justified by arguing that inclusion of an interactive biosphere is too complex; because the time-scales of ecosystem and climate changes differ significantly; because a continental surface submodel would greatly increase the variability of the simulated climate; o r on the grounds that human activities are at least as important as climate for climatically determined continental vegetation. These arguments, although partially correct, can be applied to some extent t o other components of the climate system that are simulated interactively. Cloud amount and type used to be specified in global climate models (GCMs) at values believed to be appropriate for the present-day (e.g. London, 1957) but such specification is unacceptable in modern models. Initial introduction of predicted rather than specified cloud characteristics was based upon rather simple schemes (e.g. J. M. Slingo, 1980). Incorporation of more realistic parameterizations has

CCC 0899-841 8/94/101065-30 c) 1994 by the Royal Meteorological Society

1066 A. HENDERSON-SELLERS AND K . MCGUFFIE

greatly modified reported model sensitivity to doubling of atmospheric carbon dioxide (e.g. Mitchell et a/., 1989; Washington and Meehl, 1992) and, very recently, the effects of human activities on cloud characteristics have begun to be incorporated into GCMs (Charlson et al., 1987, 1991; Schwarz, 1988 (and ‘responses’, 1989); Erickson and Oglesby, 1992).

The desirability of examining interactive predictive schemes for land-surface specification has been underlined by the need to incorporate biochemical processes into global climate models. At this stage, these schemes are highly simplistic, incorporating only very coarse relationships between climate and the continental vegetation. None the less, at a global scale and in the case of climate equilibrium, bioclimatic classification schemes have been shown to be fairly effective (e.g. Koppen, 1900; Thornthwaite, 1933; Holdridge, 1947; Box, 1978; Emanuel el al., 1985a; Henderson-Sellers, 1990; K. C. Prentice, 1990). These schemes use simple climatic parameters such as precipitation, temperature, and potential evapotranspiration to predict vegetation distributions. (In this paper, the term ‘vegetation’ will be used to describe the continental ecologies or landscapes being defined and the term ‘prediction’ will be used to indicate the derivation of continental land-type classes from simple climatic data. In using both terms, it must be recognized that the global climate model has no ‘knowledge ’ of vegetation. Its climate is affected only by surface parameters such as albedo and roughness length. The term vegetation is therefore a convenient short-hand denoting a look-up table of surface characteristics.) Generally, the large-scale distribution of the continental vegetation is reasonably well captured but there is less success in replicating finer distinctions of ecotypes. K. C. Prentice (1990) demonstrated that refinements and redefinitions of transition zones could achieve 77 per cent agreement between observed and predicted vegetative landscapes.

The issue of mismatched time-scales is of paramount importance in any attempt to link subcomponents of the climate systems. The oceans (and the cryosphere) share the longer time-scales of continental vegetation and soils and so can be linked only to the short-time-scale atmosphere with great care. In this paper, an equilibrium, diagnostic scheme is applied on the shortest possible time-scale: 1 year. It is, of course, possible to conceive of the need for vegetation-climate coupling on even shorter time-scales (say a month) but all the available ‘ecotype prediction’ models require annual characterization of climate. This is a dangerous, perhaps foolhardy, attempt at coupling but it mimics the early stages of ocean-atmosphere model linking and has been undertaken intentionally to investigate sensitivities at this very short (for the vegetation) time-scale. However, even the conventional applications of Holdridge are inherently flawed. Vegetation does not depend only on climate-climate (at least near the continental surface) also depends on vegetation, e.g. Charney (1975), Shukla and Mintz (1982), Mintz (1984), Dickinson and Henderson-Sellers (1988), and Nobre et al. (1991). No GCM modeller should ‘diagnose’ (the one-way street) an equilibrium vegetation and be content with the prediction if it differs in any substantial way from the vegetation/land-surface characteristics used to obtain the climate prediction.

The aim of this paper is to investigate the joint sensitivities of a coarse (pseudo) vegetation model and a climate model. If the atmospheric climate is exceedingly sensitive to imposed vegetation changes then the simplistic coupling used here will have to be modified in the future. If, at the other extreme, the GCM copes with the ‘shocks’ of annually changing vegetation but exhibits no, or very little, sensitivity to different continental characteristics, then there is little need to continue the search for improved coupling methods. Thus, this investigation is offered as an extreme case test of the means of coupling vegetation and atmospheric climate.

The research presented here will appear unconventional to ecologists who recognize, for example, that episodic disturbances, such as fire, occur and modify vegetation. This is true but this study deals with coarse resolution GCMs with ‘points’ 450 km by 750 km. Within them, fires can rage and opportunistic species proliferate but our GCM will ‘see’ only the grid-aggregate albedo, roughness, etc. In this application, it is possible to argue that many, although not all, of these disturbances are spatially subgrid scale. The, interdisciplinary, circumstances demand the old tricks of trading space for time.

Of greatest concern to us was whether climatic discontinuities would be caused by the ‘shock’ of uegetation change. This possibility was tested by coupling at 1 x CO, and for a (relatively) short integration (5.6 years). It seems that, remarkably, the GCM ‘copes’ with the shock; and the results are real, tracking an uncoupled integration, i.e. we are not ‘analysing noise’.

LAND-SURFACE CHARACTERISATION 1067

I . 2. Methorlologicul issues und time-scales

Climate modelling contains abundant examples of mismatched time-scales. The problem of coupling the deep ocean to the atmosphere (Gates et ul., 1992) has to overcome similar time-scale differences as the ecosystem-atmosphere coupling attempted here. The former has not yet been successfully accomplished with either the coupled climate drifting or the addition of an arbitrary flux at the surface interface (the q-flux) (Washington and Meehl, 1989; cf. Manabe et ul., 1991a,b). It is, of course, absurd to imagine that a forest can spring up on 31 December simply because the year’s climate was hospitable to its growth, although i t can be persuasively argued that vegetation can be removed by frost, drought or fire in less than a year. Thus, the research reported here captures vegetation degradation and removal on about the right time-scale but unreasonably introduces positive discontinuities in biomass that cannot occur on such short t ime-scales.

Transitions between different ecotypes are a function of a wide range of processes that operate on many time- and space-scales: rapid shifts (< 5 years) involve decreases in biomass prompted by, for example, severe drought, frost, hurricanes, and fire (Noble and Slatyer, 1980; Doyle, 1981; Tucker et a/., 1991); medium-term (10-50 years) ‘recovery’ can be observed and much longer term (5Cr500 years) ecological succession can be simulated assuming a fixed or prescribed climate (Shugart, 1984; Shugart er ul., 1986; I . C. Prentice et ul., 1993). In simulations of future enhanced greenhouse conditions the climate cannut be assumed to be fixed or known and human influence cannot be denied; rapid as well as longer term vegetation changes will occur as climate changes. Changing continental surface characteristics can be: (i) ignored; (ii) prescribed as today’s vegetation and a new ecology diagnosed when climate equilibrates; (iii) diagnosed at intervals during climatic change capturing faster changes (i.e. reductions in biomass) but speeding up slower (increasing biomass) changes; (iv) computed in a succession model assuming constant or known climatic conditions while the succession model equilibrates (&SO0 years) and then iterating.

All GCM simulations to date have used technique (i) (J. T. Houghton et al., 1990, 1992). Many impact models (Tegart et al., 1990; Smith er al., 1992) have adopted technique (ii), delivering a climate unmodified by the new vegetation. I t is widely assumed that technique (iv) is optimum but the time-scales remain mismatched and the effect on the simulated climate of sudden imposed changes in prescribed vegetation has not been explored. Here we explore this imposition in the context of technique (iii). There seems to be no obvious ‘best choice’ of meshing time period. We select 1 year in order to minimize central processing unit (cpu) time and because averages of 5, 10, or even 50 years have little meaning in a transient climate and deny shorter scale feedbacks of disturbed vegetation on climate (see Shukla and Mintz, 1982; Dickinson and Henderson-Sellers, 1988; Bonan et a/., 1992). We seek to answer two questions: can a climate model withstand transient vegetation change ‘shocks’, and do such imposed changes have any effect on the ensuing climate? The ‘predictions’ of vegetation extent and distribution described here depend only on the near-surface air temperature and precipitation. There is no effect due to the fertilization of plant growth by the increased atmospheric CO, nor is any effect caused by other climate-related parameters known to modify plant growth.

This paper describes the effect of doubling the concentration of atmospheric carbon dioxide using a coarse resolution GCM in which the vegetation, but not the soil, characteristics are defined dynamically within the model as an interactive function of the predicted climate. The simulations reported here represent the first attempt to include the terrestrial biosphere as an interactive component of a global model applied to estimating the sensitivity of the climate to a doubling of the atmospheric concentration of carbon dioxide.

2. MESHING THE GLOBAL CLIMATE AND VEGETATION PREDICTION MODELS

2.1. Glubul climate model and land-surjucr scheme

CCM1-0z is a modified version of the NCAR community climate model version 1 (Williamson and Williamson, 1987; Williamson et al., 1987), which includes the current version of the biosphere-atmosphere transfer scheme (BATSIE) and a mixed-layer, slab ocean of 50m depth. The mixed-layer ocean model


includes a three-layer ice model subcomponent and a standard y-flux scheme to correct for ocean advection of energy and the prescription of a fixed mixed-layer depth. CCM1-0z includes a number of modifications to the physics subroutines, including cloud prediction and radiation updates being tested for the next version of the model: CCM2 (A. Slingo, 1989). The model simulates full seasonal and diurnal cycles and a review of a number of standard global fields shows that the general circulation of the atmosphere is well simulated (Dickinson and Kennedy, 1992). The model is used at a spatial resolution of about 4.5" latitude by 7.5" longitude (a spectral truncation at rhomboidal wavenumber 15).

The biosphere-atmosphere transfer scheme (BATS), first described by Dickinson (1 984), incorporates a single vegetation, or canopy, layer, a multiple-layer soil scheme and provision for snow cover on the land surface. The scheme has been subjected to stability and sensitivity tests both with the NCAR community climate model (e.g. Wilson et ul., 1987b; Dickinson and Henderson-Sellers, 1988) and in off-line mode (e.g. Wilson et ul., 1987a; Pitman et ul., 1990; Henderson-Sellers, 1994). The BATS scheme has evolved as a result of these experiments so that the current version (BATSlE), which is used here, although substantially the same as that described in Dickinson rt a/. (1986), does incorporate corrections and improvements (Dickinson et ul., 1993).

The BATS uses 16 distinct vegetation types (plus inland water and ocean) when coupled to CCM1-Oz, representing both natural and agricultural ecologies (Figure I). This global vegetation classification was

I I

.. ............................................ ... ............................................ .............................................................................................................................................................................................. ................... ............................................................................................................................. 8 8 8 ... ...

Evergreen Needleleaf @. Evergreen Shrub Declduous Shrub

@ Tall Grass @ Mixed Woodland Irrigated Crop

@ Sernl Desert

........... Decid. Needleleaf Tree :&::!: ...... Ice Cap ..... ....... ....... - .............. - - - Decid. Broadleaf Tree jli$ljtj$iii ....... Crop

= Short Grass

IIIII Tundra

lllillllllll Desert - - - - - -

.......

Evergreen Broadleaf -

Figure 1 . The vegetation distribution specified for the BATS land-surface scheme when it is used in the NCAR Community Climate Model. All 18 BATS classes are shown

LAND-SIJRFACE CHARACTERISATION 1069

generated originally from the data sets developed by Matthews (1983), Wilson and Henderson-Sellers (1985) and Olson et al. (1983); the latter being to x 4" and the other two 1" x 1" archives.

2.2. The vegetation purdiction scheme

In these simulations, the Holdridge (1947) life zone classification is used to generate the global distribution of only 1 I vegetation types. This coarse ecological resolution has been selected primarily because the characterization of the vegetation parameters is rather poorly understood so that predicting a larger number of vegetation types would not necessarily increase the confidence with which the continental surface characteristics could be specified.

The Holdridge life zone classification system (Holdridge, 1947) is a scheme which aggregates the general character of natural vegetation into 37 classes defined by simple climatic indices: the total annual precipitation, the mean annual biotemperature, and the potential evapotranspiration ratio (Figure 2(b)). Two of these three primary variables, for example, total annual precipitation and the biotemperature (which is a representation of the growing season temperature), are required to define a location within the life zone triangle (e.g. Holdridge, 1964). The average 'biotemperature' represents conditions important for the growing plant. Thus the effects of temperatures below (and sometimes above) prescribed thresholds are modified in the calculation of average conditions. Biotemperature is defined as the sum of the temperatures over a year, with each temperature value (daily, weekly, monthly, or seasonal) set to 0°C if i t is less than or equal to 0 'C and this sum then divided by the total number of values (i.e. 12 for monthly temperatures) (Holdridge, 1947).

Emanuel et ul. (1985a,b) and Henderson-Sellers (1990, 1993b) showed that this life zone classification produced a reasonable representation of the undisturbed present-day patterns of ecosystem types when the input parameters were climatological temperatures and precipitation. The current classification is very similar to that used by Henderson-Sellers (1993b) (Figure 2(c)) who described the application of such a scheme to present-day climate simulations. Here the scheme has been tuned (see section 2.3) to improve its prediction of present-day vegetation distributions.

The final range of biotemperature and mean annual precipitation for each vegetation class is given in Table I . (Scheme tuning is described below). There are 1 1 predicted vegetation classes (Figure 2(a)), which have been associated with 1 1 of the standard BATS land-use types. The crop (or approximate agriculture) class was introduced by Henderson-Sellers (1993b) to try to reduce the large discrepancies identified by Henderson-Sellers (1 990) between the 'natural' vegetation predicted by this type of simple scheme and the human effects of large-scale land-use change, particularly the introduction of agriculture. The biotemperature and annual total precipitation ranges selected for this class: 8"-21"C and 65C2000 mm are somewhat arbitrary but are designed to identify areas of the globe where agricultural activities could be pursued readily.

The vegetation prediction scheme cannot predict ice-caps or inland water areas so these are retained in situ from the global distribution of land-use normally used in BATS. Inland water areas do not change and in the global maps presented here are equated to ocean areas. The ice-cap/glacier regions (BATS land-use type 12) are permitted to change to one of the vegetation classes if the annually averaged biotemperature exceeds 1,5"C, i.e. the ice-cap/glacier area cannot be extended but it can be diminished as vegetation encroaches upon it. Its maximum distribution is that in the standard BATS prescription (Figure I ) . There are thus I 1 land-use types generated by the vegetation prediction scheme.

When this scheme is coupled into CCM 1-Oz, the continental vegetation distribution becomes a diagnostic component of the global climate model. However, no attempt is made here to simulate alterations in soil texture, colour, and drainage. These characteristics are held static over the 15-50-year simulations undertaken. The implementation of the vegetation prediction scheme is thus dynamic. The climatic parameters (Stevenson screen air temperature and total precipitation) used as predictors are accessed at each time step (i.e. every 30 minutes) in the model computation and accumulated. At the end of each 12-month period, which occurs on 31 December each year in these simulations, the mean annual biotemperature and total precipitation are calculated and a new global vegetation distribution is diagnosed. This distribution is then used for the next year of climate simulation.


Henderson-Sellers (1993b) tested the sensitivity of CCMI-0z to the coupling of a dynamic vegetation model. In a 5.6-year interactive simulation of the present-day climate, she found that there was no discernible trend in the land-surface climatic parameters and no indication of disturbances caused by the instantaneous change in vegetation distribution at the end of each year. For these present-day simulations, including a dynamically coupled vegetation, the continental surfaces were warmer by up to 1.5"C; they were found to be evaporating more (up to 5 W m-l ) and absorbing about the equivalent additional solar radiation, and producing less surface and total runoff. The largest differences persistently occurred in July.

2.3. Tuning the vegetation prediction scheme to CCMI-Oz

Henderson-Sellers ( 1 993b) made no attempt to tune the vegetation prediction scheme, although she noted differences between its present-day climate predictions and both observed distributions and the standard BATS prescribed distributions. There are significant differences between the specified BATS classification (Figure 1) and her predicted ensemble-average vegetation distribution.

Tall Grass

Tundra

Semi Desert

Desert

Mixed Woodland

Decid. Needleleaf Tree

Decid. Broadleaf Tree

Short Grass

...... ..... ...... ......

ice Cap

Approx. Agriculture

Evergreen Broadleaf Tree

Figure 2. (a) The 10 derived vegetation classes defined by the climate parameters precipitation (P) and biotemperature (T). These vegetation classes are named to coincide with I I of the BATS ecotypes (see Table I); (b) Holdridge life zones; (c) the untuned scheme

used by Henderson-Sellers (1992)

1071

Trrnprmturr Unr

(c) Untuned

Fig. 2. Continued

Table I1 also lists t w o estimates of global vegetation distributions appropriate for around 1700 and 1980 and taken, respectively, from R. A. Houghton et ul. (1983) and R. A. Houghton and Skole (1990). In generating these percentages from the areal distributions of ecosystems given by these authors, classes have been generalized and (somewhat) grouped to approximate the BATS vegetation types used here. In particular, an additional class of ice-caps/glaciers taken directly from R. A. Houghton et al. (1983) has been inserted into the R. A Houghton and Skole (1990) areas. Incidentally, the former is remarkably close to the percentage cover specified in BATS. The desert (type 8) and semi-desert (type 11) areas were difficult to differentiate in the two observational data sets so they have been grouped together: for 1700 into semi-desert (their class ‘desert scrub’) and for 1980 into desert. The resulting ‘observed’ distributions should not be taken as sound ecology, only as an attempt to evaluate the performance of the interactive vegetation prediction scheme and, incidentally, the present-day BATS prescription of vegetation.

1072 A . HENDERSON-SELLERS AND K . MCGUFFlE

Table 1. The biotemperaturc and precipitation ranges delimiting the 11 generalized vegetation classes used and their association with 1 1 of the vegetation/land cover types specified in BATS.

Also listed are the roughness length (cm) and shortwavelength vegetation albedos (percentage)

BATS specified vegetation type (code) roughness length (cm)/albedo (per cent) Limits of predicted vegetation classes"

T 2 21 and P 2 2000

T 2 6 and T < 21 and P 2 2000

T 2 21 and P 2 650 and P < 2000

T 2 4 and T < 8 and P 2 650 and P < 2000

T 2 8 and T < 21 and P 2 650 and P < 2000 or T 2 4 and T < 6 and P 2 2000

T 2 21 and P 2 300 and P < 650

T 2 4 and T < 6 and P 2 I25 and P < 300

T 2 21 and P < 300 or T 2 4 and T < 21 and P 2 300 and P < 650

T 2 4 and T < 21 and P < 125

T < 4 or T 2 6 and T < 21 and P < 300 and P > 125

Evergreen broadleaf tree (6) 200/4 Mixed woods (18) 80/6 Deciduous broadleaf tree ( 5 ) 80/8 Deciduous needle-leaf tree (4) 1 oop Crop (approximate agriculture ) ( I ) 6/10 Tall grass (7) 10/8 Short grass (2) 2/10 Desert (8) 5/20 Semi-desert (1 1) 10/17 Tundra (9) or 419 Ice-cap/gIacier ( I 2)" I /80

* Specification of classes given as a function of: T = mean annual biotemperature ( 'C) (bioternperature is defined in the text): P = total annual precipitation (rnm).

BATS class 12 retained in prediction scheme unless biotemperatures rise above I.5"C.

Table I I . Percentage areas of the I I land-use classes examined here; as specified for BATS; as observed; and as predicted ( i ) using an untuned version of this scheme; (i i ) using the tuned version and 5-year averages of temperature

and precipitation from a 1 x CO, simulation

BA7i-s BATS code type

Prescribed Observations" Observations" Untunedb Tuned, Comments (per cent) 1700 1980 predicted predicted on tuned

(per cent) (per ccnt) distribution

1 2 4 5 6 7 8 1 1 9 12 18

Agriculture Short grass Deciduous needle Deciduous broadleaf Evergreen broadleaf Tall grass Desert Semi-desert Tundra Glacier Mixed woods

Roughness length (cm) Shortwave albedo ("i)

11.6 12.3

1.1 4.0 9.7

I 1.0 4.3

10.7 8.1

10.6 4.3

3 7.2 17.0

5.3 13.8 11.6 9.7 9.0 9.6

14.3 4.8

10.5 11.4

10.9 10.4 10.3 6.3 7.1

169 I 2 5

7.6 10.5 5.3

-

11.3 5.1

26.4 19.0 7. I 6. I 6.5 0.7 3.2

10.6 1.4

58.9 15.7

14.5 14-9 13.4 15.2 8.6 7.2 6.7 1.2 3.9

i 0.6 1.2

46.4 16.4

Too much Better Better Too much Good A little bctter Too little Too little Too little Good Too little

Smoother Brighter

Observations after R . A. Houghton ct t i / . (1983) for 1700 and after R. A. Houghton and Skole (1990) for 1980. * llntiined predictions after Henderson-Sellers (1992).


Despite the coarseness of the grouping, there is some agreement between both observed distributions and the model-prescribed and model-predicted vegetation distributions (Table 11). The untuned agricultural class is remarkably close to the 1980 estimate and the evergreen broadleaf tree area is well represented. Other areas differ from the observed patterns for 1700 and 1980. Overall, the untuned vegetation prediction scheme used by Henderson-Sellers (1 993b) underpredicted desert and semi-desert areas; overpredicted the deciduous broadleaf tree extent; slightly underpredicted both short- and tall-grass areas; and greatly overpredicted the extent of deciduous needle-leaf tree.

The biotemperature and total precipitation boundaries of the 11 vegetation types were shifted in an iterative tuning in which some of these poor predictions were improved. Figure 3 shows the resultant (tuned) vegetation distribution for the 1 x CO, control climate. This has been computed, off-line, following the averaging of the biotemperature and precipitation from 5 years of the 1 x CO, control climate. Slight differences are likely to occur using this off-line method but these do not significantly affect the vegetation distribution or relative areas (Henderson-Sellers, 1993b). Figure 3 illustrates the success of the tuning especially the reduction in the areas of deciduous needle-leaf tree. Table I 1 also lists the percentage land-areas for a present-day (1 x CO,) climate generated using the tuned scheme. As can be seen, the area of deciduous needle-leaf tree has been substantially reduced, the area of deciduous broadleaf tree somewhat reduced and the predicted area of short grass increased. I t proved to be very much harder to increase the predicted areas of desert, semi-desert and tundra, which have barely been modified by the tuning.

............................................ ............................................ .. ...................... ................................................................. ............................................................................................................................. .............................................................................................................................

Tall Grass @ Mixed Woodland

Tundra

@ Seml Desert

........... ...... @ Decid. Needleleaf Tree 2222i.i ..... Ice Cap

....... - - - - ....... ....... - Decid. Broadleaf Tree g$ Approx Agrlculture - - - - - .......

Evergreen Broadleaf - = Short Grass - llllllllllll Desert - Figure 3. Vegetation distribution predicted, off-line, from ensemble means of biotemperature and precipitation from 5-years of the

1 x CO, control climate


Careful inspection of Figures 2(a) and 2(c) reveal that the boundaries of the two vegetation types in the right-hand corner of the Holdridge triangle have not been altered in the tuning. These are the evergreen broadleaf tree and mixed woodland. The very small differences seen in Table IT for these types are due to differences between the two control climates used. The earlier simulations of Henderson-Sellers (1993b) used a version of CCM1-Oz in which the seasonal variation in ocean surface temperature was too small.

Two vegetation types in particular warrant further comment here because of the resulting areal extents and distributions computed in the doubled-CO, experiments (section 3). These are the ‘approximate agriculture’ class and the ‘evergreen broadleaf tree’ class. The total area of the latter, which represents the tropical forests, seems to be well simulated but comparison of Figures 1 and 3 shows that its geographical distribution is rather poor in Africa. On the other hand, the area of ‘approximate agriculture’ vegetation seems to be overestimated in comparison to the observations and BATS prescribed distributions. This can be seen (Figure 3, cf. Figure 1 ) to be primarily the result of the greater areal extent of the ‘agriculture’ class in North America. The paucity of agricultural grid elements in the Southern Hemisphere is in agreement with the prescribed BATS distribution, although where it occurs (South Africa, South Australia, and South America) the agreement is good. The distribution in Europe and Asia (with the exception of India) is good. Bearing in mind that the predicted agriculture class represents only an estimate of where farming could be conducted and that some other BATS prescribed classes (especially irrigated crop and short grass) also indicate the presence of agricultural activities, the agreement is remarkably good.

Table I1 also lists two measures used by Henderson-Sellers (1993b) to assess the global impact of vegetation changes: the land-averaged vegetation roughness length and shortwave ( < 0.7 pm) albedo. These have been calculated using the values associated with individual BATS classes (Table I). There is no globally averaged observed values for these measures but it seems reasonable to assume that the prescribed BATS vegetation distributions give reasonable values because the global climate predictions using BATS in CCM 1 are adequate (e.g. Wilson et a/., 1987a; Dickinson and Henderson-Sellers, 1988). The tuning has brought both values closer to the BATS prescription, i.e. the continental surface is smoother and brighter using the tuned scheme.

3. TWO DOUBLED-CO, EXPERIMENTS INCORPORATING INTERACTIVE CONTINENTAL VEGETATION CHARACTERISTICS

3.1. Instantaneous and fast, transient doubling of CO,

The version of CCM1-0z that incorporates the dynamic continental biosphere has been used for two complementary doubled-CO, simulations. The first is a ‘standard’ instantaneous doubling experiment in which the atmospheric concentration of CO, is doubled, from its present-day value of 330 ppmv, and the GCM allowed to equilibrate. In the second experiment, the atmospheric concentration of CO, is increased gradually over a number of years until the amount has doubled and then the climate model is allowed to equilibrate with this CO, level held constant. Other modelling groups who have undertaken transient CO, increase experiments have used a variety of time-scales for doubling, ranging from 50 to over 100 years. Washington and Meehl(l989) assumed a 1 per cent per annum linear increase in atmospheric CO,, giving a 100-year doubling time; GFDL used 1 per cent compound increase per year, taking 70 years to double (Stouffer et al., 1989); the Hamburg group (e.g. Cubasch, 1989) used the IPCC scenarios (J. T. Houghton et a/., 1990) of which scenario A (also termed the ‘Business as Usual’ scenario) takes about 100 years to rise from 330 to 660 ppmv of equivalent CO, and about 60 years to double ‘equivalent CO,’ from 1990 values; and Hansen ef ul. (1988) assume a ‘worst (A) case’ increase of 1.5 per cent per annum, compound, but comment that this is less than the typical rate of increase in the past century ($4 per cent per annum) (Hansen et a[., 1988, p. 9345).

Here a somewhat shorter time frame than that used in previous experiments had to be used because of the constraints of computer resources. This fast, transient doubling of CO, occurred over a 35-year time period, i.e. using a 2 per cent per annum compound rate of increase of atmospheric CO, concentration.


Although this is a faster increase in radiative forcing than has been used elsewhere, it should be noted that in constructing the 'Business as Usual' scenario, IPCC Working Group 111 commented that aggregation of national projections gave 10-20 per cent higher emissions of both carbon dioxide and methane by 2025 than is reflected in this scenario (Policy Makers Summary, IPCC1, 1990). In addition, the estimates of the net release of CO, due to tropical deforestation have been revised upwards since the development of the IPCC scenarios. R. A Houghton (1991) gives the most likely range of net annual flux to the atmosphere as 1.5 to 3.0 GtC as compared to the IPCCl estimate of 1.6 GtC year-'.

The instantaneous doubling simulation ran for a total of 15 years, whereas the fast, transient simulation was integrated for 10 years beyond the point at which the atmospheric concentration of 660ppmv was achieved, i.e. a total of 45 years' simulation. Figure 4 shows the globally averaged surface temperature and daily averaged total precipitation for the 15- and 45-year experiments and Table 111 lists some global mean quantities from both experiments and from the 1 x CO, control experiment. The CCM1-Oz response for instantaneous doubling (temperature +2.55"C, precipitation +6.1 per cent) is towards the low end of the current sensitivity range to doubling of C 0 2 but is well within accepted estimates (1.9"-5-2" and 3-15 per cent) (e.g. Cess et nl., 1990; J. T. Houghton er nl., 1990, 1992; Boer et al., 1992). The main interest here is in the evaluation of the vegetation changes during the two warming simulations, rather than in the climate changes themselves. It can be seen from Table 111 that the fast, transient experiment produces a very similar climate to that generated by the instantaneous doubling; the global and annually averaged temperature and precipitation increases being 2.46"C and + 6.0 per cent respectively. There are slight differences between the precipitation and evaporation terms in the two simulations and the predicted sea-ice extent is larger in the fast, transient simulations in the annual and January and July cases.

Tempcnture

Figure 4. Evolution of screen temperature (K) and daily total precipitation over the two doubled-CO, experiments. The time-scales are not commensurate (15 years for the instantaneous and 45 years for the fast transient)


Table I I I . Climatic parameters derived from the two doubled-CO, simulations (5-years ensemble means), the 14-year ( 1 x CO,) control integration with the same model but without the interactive vegetation, and a 5-year simulation at 2 x CO, conditions but also with the 1 x CO, prescribed vegetation. In all cases, the land area is 34.17 per cent of

the globe

1 x CO, Doubled CO,

Constant Control Instantaneous Fast, transient vegetation

Globe Land Globe Land Globe Land Globe Land

Annuul Screen temperature ( K ) Precipitation (mm day- I )

Evaporation (mm day- ' ) Planetary albedo (per cent) Cloud amount (per cent) (OOZ) Sea-ice area (percentage of globe) Relative root zone soil moisture (OOZ)

Junuurj* Screen temperature ( K ) Precipitation (mm day- ' ) Evaporation (mm day"') Planetary albedo (per cent) Cloud amount (per cent) (OOZ) Sea-ice area (percentage of globe) Relative root zone soil moisture (OOZ)

July Scrccn temperature ( K ) Precipitation (mm day- I )

Evaporation (mm day-') Planetary albedo (per cent) Cloud amount (per cent) (002) Sea-ice area (percentage of globe) Rclativc root zone soil moisture (OOZ)

288.88 2.972 2.967

30.2 45.7

6.96 -

286.84 2.883 2.886

30.4 46.0

7.78

290.89 3.129 3.151

28.6 45.7

6.24

283.99 2.579 1.469

35.4 43.1

0.750

277.3 1 2.255 0.979

39.4 43.3 -

0.764

290.37 3.002 2.1 09

28.3 43.3

0.723

29 1.43 3.153 3.148

28.8 44.5 4.05 -

28953 3.04 1 3.040

29.0 44.1

3.47 -

293.35 3.309 3.335

27-4 44.4

3.67 -

286.98 2.736 1.593

34.1 42.0

0.623

280.74 2.326 1.066

37.8 42.1 -

0.63 1

293.05 3.140 2.267

27.3 42.0

0.595

291.34 3.150 3.145

28.9 44.7

4.35

289.47 3.049 3.052

29.1 44.8

4.19 -

293.24 3.340 3.369

27.6 44.7

3.75

286.89 2.763 1.598

34.1 42.3

0.624

280.78 2.449 1,108

37.8 42.5

0.643

292.90 3.279 2.286

27.4 42.8

0.593

29 I .40 3.139 3.134

28.9 44.6 4.19 -

289.50 3.045 3.046

29.0 44.8

3.58 -

293.3 I 3.307 3.334

27.5 44-8

3.68 -

286.99 2.7 14 1.552

34.2 42.2

0.734

280.72 2.336 1.027

37.7 41.8

0.752

293.09 3.173 2.209

27.3 42.4

0.702

I t can be seen that the climates achieved in the two experiments conducted here are very similar. I t is possible that neither simulation has quite equilibrated but any continuing climate change is small (Figure 4). In constructing the 5-year ensemble means used in Table 111 the last 3 years of these two simulations have been used together with two final years (coincident with the last 2 years of both simulations) from two additional sensitivity experiments, which are described below (sections 4.2 and 4.3).

3.2. Continenful vegeturion churctcteristic response to doubled CO,

Figures 5 and 6 show the predicted distribution of continental vegetation at the termination of the two doubled-CO, simulations, i.e. after 15 years in the case of the instantaneous doubling and after 45 years in the fast, transient experiment. Table IV lists the percentages occupied by the 1 1 land-use classes. There are broad-scale agreements between the predictions in the two 'warmed worlds': for example, agriculture expands, tundra contracts. On the other hand, there are differences in the amount (Table IV) and distribution (Figures 5 and 6) of a number of vegetation types. In particular, the instantaneous 2 x CO, experiment generates a larger area of agriculture than the fast, transient experiment and a significantly smaller extent of deciduous needle-leaf tree. The distribution of vegetation in the arid regions, especially the Sahara and central Australia, also differ in Figures 5 and 6.


i\ =T-=

%-- .:.:.:=

r_. . ... ... ...:.- - e= ... ....... .... ... .... ... .... ... ....

.... ... .... ... ....

.. ............................................ .................................................................................................................. ................................................................................... ....... .............................................................................................................................

8 Toll Grass

@ Semi Desert

@ Mixed Woodland a Decid. Needleleaf Tree zii:; Ice Cap

- Decld. Broadleaf Tree iiiii:i:;:i ....... Approx Agrlculiure

= Short Grass

...... ..... ...... ..... ....... ....... ....... - - ....... - -

//I l l Tundra

llllllllllll Desert - - ....... - - - - Evergreen Broadleaf -

Figure 5 . The 11 vegetation classes (10 predicted and BATS class 12: ice-cap/glacier) predicted at the end of the last year (year 15) of the instantaneous doubled-CO, simulation

Table IV underlines the conclusions of Henderson-Sellers (1993b) who found that there is usually less than cu. 2 per cent (absolute) difference in total land-area coverage of individual classes from year to year, or as seen in Table IV, between an individual year and the 5-year ensemble from which it came. Thus the distribution maps in Figures 5 and 6 offer a good representation of the areal cover of all classes, although there clearly are differences in the predictions from the two simulations and, as discussed in detail in section 4, year-to-year changes do occur on the boundaries between vegetation classes.

Taken as a whole, Table IV represents the predicted vegetation distribution (together with a measure of its uncertainty) appropriate to a doubled-CO, climate. Comparing the percentages with those listed in Table 11, substantial changes can be seen. The agricultural area has increased by about 5 per cent and there is about a 5 per cent decrease in the area of deciduous needle-leaf trees. There are smaller changes in deciduous broadleaf trees ( c a 3 per cent increase), short grass (ca. 2 per cent decrease), tundra (cu. 3 per cent decrease), and desert (cu. 1.5 per cent increase). Other differences may be too small to be deemed significant except for the decrease in the area of glacier extent, e.g. vegetation on the south and east coasts of Greenland (Figures 5 and 6, cf. Figure I) .

Figures 7 and 8 show the time evolution of the percentage cover of the predicted continental vegetation from both doubled-CO, simulations, and Figure 7 also shows the changes in the land-average vegetation roughness length and shortwave vegetation albedo. I t can be seen that, although the increase in the area of crop (Figure 8) is roughly balanced by the decreases in short grass and deciduous needle-leaf trees, in both simulations the extent of the changes differs. Other vegetation types show much smaller changes.


............................................ ... ............................................ .. ................................................................................................................................................ ................................................................. .............................................................................................................................

Tall Grass 8 Mixed Woodland

Tundra

8 Semi Desert

...... ..... ...... @ Decid. Needleleaf Tree <<<<<: ...... Ice Cap ..... ... . . . . . . . __ ....... - . . . . .

- - - - - - - Decld. Broadleaf Tree jijii;;? Approx Agriculture .......

Evergreen Broadleaf = Short Grass - -

Figure 6 . As for Figure 5 except from the last year (year 45) of the fast, transient doubled-CO, simulation

Table IV. Percentage areas of the 1 I land-use classes predicted for the last year and a 5-year average for both the instantaneous and fast, transient doubled-CO, simulations. The 5-year averages were constructed using the last 3 years of the two base simulations each combined with the last 2 (overlapping) years from sensitivity experiments

described in the text

5-year Year 45 5-year BATS BATS Year 15 mean of of fast, mean of code t Y Pe of instantaneous instantaneous transient fast, transient

1 2 4 5 6 7 8 9 I I 12 18

Agriculture Short grass Deciduous needle Deciduous broadleaf Evergreen broadleaf Tall grass Desert Tundra Semi-desert GI acie r Mixed woods

Roughness length (cm) Shortwave albedo ( O h )

22.2 13.1 5.9

19.0 7.5 8.0 8.9 0.7 0.2

10.2 1.2

40.2 16.6

19.3 13.2 7.9

18.8 8.6 7.7 8.7 0.7 0.6

10.2 1.7

44.2 16.5

17.8 13.0 9.0

19.5 8.3 8.2 8.1 0.9 0.5

10.2 1.8

45.4 16.3

18.8 11.9 9.1

18.8 8.4 8.7 8.0 0.8 0.9

10.2 1.7

45.2 16.4


Although a few vegetation types show significant trends in areal coverage through the course of both doubled-CO, experiments (Figure 8), overall the vegetation distributions are stable (Figure 7) and exhibit rather small interannual variability. The shortwave vegetation albedo exhibits very little change over time, although all the land-averaged estimates of this parameter (Table IV) are slightly lower than the BATS prescribed value of 17 per cent (Table II), a result which is in agreement with Henderson-Sellers (1993b) who found a lower albedo in 2 x CO, conditions. Although there is also a slight tendency for the vegetation roughness length to decrease, especially towards the end of the instantaneous doubled-CO, simulation (Figure 7(a)), the land-averaged estimates given in Table IV are all greater than the BATS prescribed value of 37.2 cm (Table 11). Overall, vegetation albedo has decreased slightly, and remains stable, and vegetation roughness length has increased by about 7 cm but may be decreasing in the instantaneous doubled-CO, simulation.

Figure 8, which emphasizes the vegetation types showing the largest changes, suggests that there is divergence between the areas resulting from the two, different 2 x CO, simulations. In particular, the total crop areas predicted for years 15 and 45 differ by 4.4 per cent and those for deciduous needle-leaf tree by 3.1 per cent (absolute). Despite these differences, there are large areas of agreement between Figures 5 and 6. Figure 9 shows the land points, and their interactively predicted vegetation types, for which the two 2 x CO, simulations agree. It can be seen that this agreed core vegetation covers most of the continents.

- . ' : - . .:-. I I . : 1 . * I 1 J I 7 9 11 1J 15 17 19 21 23 25 27 29 31 J3 35 J7 39 41 4 45

YEAR . .

Figure 7. (a) Vegetation roughness length (cm), shortwavelength (<0.7 pm) vegetation albedo (per cent) and the 1 1 vegetation classes predicted for the 15 years of the instantaneous doubled-CO, experiment (BATS vegetation codes are related to vegetation types in,

e.g., Table 11). (b) As for (a) except from the 45 years of the fast, transient doubled-CO, simulation


Figure 8. Vegetation areas predicted for the 15 years (instantaneous) and 45 years (fast, transient) of the doubled-CO, experiments for crop, deciduous needle-leaf tree, tropical forest (evergreen broadleaf tree), and tundra. The two interactive simulations of vegetation areas have roughly the same interannual variability and for some types (e.g. tundra and tropical forest) final areas are very similar. On the other hand, the results for crop and deciduous needle-leaf tree agree in sign but not in magnitude. The initial values differ because these represent the end of the first year of two experiments in which the imposed CO, amounts ( + 100 per cent and + 2 per

cent) differ considerably

I I 0 ' I I I I I.:.:.:.:.:.:.:.:.:.:.;.;.; .I., .............I. 1. .....

- - -- -- - - - .... ... .... ... ....

.. ............................................ ............................................ ..................................................... :.:.: ....................................................................................................................................................... .............................................................................................................................

Tall Grass

Tundra

8 Seml Desert

111111111111 Desert

Mixed Woodland ...... ..... ...... @ Decid. Needleleaf Tree iiiiiiiiiii Ice Cap

- Decid. Broadleaf Tree iiWilri; :.:.:.:.:.:.:. Approx Agrlculture

= Short Grass

....... - ....... - - - ....... .......

- - - - - Evergreen Broadleaf - -

Figure 9. As for Figures 5 and 6 except that only land points are plotted when both predictions agree


3.3. Clirntitic inipuct of (in interactive biosphere in a doubled-CO, climate

The simulations reported here are the first global model sensitivity studies of the climatic response to doubling atmospheric COz to include an interactive terrestrial vegetation component. Previous GCM experiments have fixed the distribution and characteristics of the continental biosphere at present-day conditions. There are significant differences between the normal (i.e. present-day) prescription of land-surface conditions for CCM1-0z (Figure 1) and the vegetation distribution that the model computes for itself in a doubled-CO, climate (Figures 5 and 6). The most important differences are that the interactive scheme predicts only 1 1 of the standard 16 BATS vegetation types and that the distribution of the common 11 types differs considerably. In particular, there is less tundra and semi-desert and more agriculture in the predicted vegetation in the warmed-world than in the present-day specified distribution. In addition to these differences the predicted vegetation can, and often does, change each year so that there is a greater natural variability in the warmed-world surface state (section 4.1). Thus it might be anticipated that the near-surface continental climate predicted by this coupled model for 2 x CO, will differ in both mean values and in variability from a 2 x CO, simulation in which the vegetation is held constant in the present-day state.

To examine the impact on the simulated climate of this interactive vegetation scheme, an additional 5 years simulation was undertaken in 2 x CO, conditions but prescribing the vegetation to present-day state (i.e. Figure 1). The 5-year simulation was started from the atmospheric and oceanic conditions prevailing at the end of the twelfth year of the instantaneous doubled-CO, experiment. As can be seen from Figure 4, climatic equilibration was essentially complete at this time. The vegetation was set equal to the present-day values (Figure 1 ) and held fixed throughout this experiment. As it was believed that altering the continental vegetation distribution is likely to be a second-order effect, cf. doubling CO,, an additional 5 years of simulation was deemed adequate. This assumption is seen to be reasonable by comparing Figure 10 with Figure 5. Both represent the same year ofclimatic simulation: year 3 of the fixed BATS vegetation simulation (which started at year 12), cf. year 15 of the instantaneously doubled CO, simulation. Although there are differences between the posf,fucto calculated vegetation in Figure 10, cf. the interactive vegetation in Figure 5, overall the distributions are rather similar.

Geographical differences do occur; for example, in northern Africa (the desert is more extensive in Figure 5), in the Amazon region (the rainforest is less extensive in Figure 5) , and in western Australia (the desert is less extensive in Figure 5). In all these cases, it could be argued that the specification of vegetation (Figure 1 ) is producing a second order, regional effect on climate, which is compatible with the vegetation distributions that result from the off-line calculation (Figure 10). For the three examples cited, Figure 1 has less desert in the Sahara than Figure 5, more rainforest in the Amazon Basin and more semi-desert in western and central Australia. Overall, when vegetation is viewed as a diagnostic of climate, it can be seen that interactive vegetation produces noticeable effects but that these are secondary to the impact of doubling atmospheric COz.

Table I l l lists a range of globally averaged parameters derived from this fixed vegetation, 2 x CO, simulation. I t can be seen that the global-scale impact of differing vegetation is fairly small (cf. Henderson-Sellers, 1993b). In particular, the predicted temperature increases in the instantaneously doubled CO, simulation are 2.55"C and 2.99"C for the globe and the continents, respectively, with an interactive biosphere, and 2.52"C and 3.00"C with prescribed (present-day) vegetation. The difference in the hydrological cycle responses are a little greater: +6.1 per cent and + 5.7 per cent for the globe and continents, respectively, with the interactive vegetation, cf. + 5.6 per cent and + 5.0 per cent increases in precipitation with prescribed, present-day vegetation.

Although there is no significant difference between the globally or continentally averaged surface-air temperatures predicted with interactive, cf. prescribed ( 1 x CO,), vegetation (291.43 K, cf. 291.40 K and 286.98 K, cf. 286.99 K, respectively see Table Ill), there are striking regional differences in temperature. Figure 1 1 shows the 5-year ensemble mean surface-air temperature differences (interactive vegetation-prescribed). There are four locations where the interactive vegetation climate is 2-3°C warmer: Greenland and the Canadian Arctic, the Siberian Arctic, the Tibetan plateau and the Arabian Gulf. In all four locations, the prescribed vegetation was short vegetation, which has been replaced in the interactive case by taller


= ...

% - a= -.-

w .:.:., ...

...... C-- ......- -ez

.:.:.:. .... ....... ....... t.. ....

.. ............................................ ..................................................... :.:.: ........................................................ :.:.:.:.:.:.:*::-:.:.:.:.:.:.::.:.:.:.::.:.:.:.:.:.:.:.:.:.:.:.~.~..:.:.~:.~..~..: ........ .............................................................................................................................

Tall Grass @ Mixed Woodland

Tundra

8 Semi Desert

...... ........... @ Decid. Needleleaf Tree ;;$;;: Ice Cap

- - Decid. Broadleaf Tree isi: Approx Agrlculture

= Short Grass

..... - ....... .............. ....... - - - ....... - - - @ Evergreen Broodleaf - llllll~llll Desert - Figure 10. As for Figure 5 except calculated off-line at the end of the third year of the fixed-vegetation 2 x C 0 2 simulation

vegetation types. In the Gulf, the predominant change was from semi-desert (prescribed; Figure 1) to tall grass (predicted; Figure 5). In the other three areas, tundra, or short grass in Tibet (prescribed), has been replaced by deciduous needle-leaf trees, and mixed woodland. It seems likely that this shift from short to taller vegetation has decreased the surface albedos and hence warmed the climate. In the Gulf region the lower vegetation albedo of tall grass (cf. the prescribed semi-desert) could have the same effect.

The seasonality of the changes in Figure 11 are examined further in zonal averages of temperature and precipitation in Figure 12. Figure 12 shows how the inclusion of an interactive biosphere results in a different climate simulation. In this figure, the results from 2 x CO, simulations with interactive and non-interactive biospheres are plotted as differences from the control simulation. There are notable differences in precipitation, although the peak at ca. ION in July represents only ca. 22 mm month-'. July near-surface temperatures are modified in the Northern Hemisphere by 1-2°C when the interactive biosphere is included. The results in Figure 12 indicate that allowing vegetation in the model to respond to climate forcing results in a redistribution of the C0,-induced warming within the hemisphere.

Such changes in surface temperature may influence the atmospheric circulation. To examine this possibility, the meridional stream function, which indicates the strength of the meridional circulations (e.g. the Hadley circulation), is analysed. The most important changes in Figure 12 are in the Northern Hemisphere summer (July). Figure 13 shows the meridional stream function differences for July caused by doubling CO, (Figure 13(b)) and by introducing an interactive biosphere (Figure 13(c)). The dominant feature of the meridional circulation in July is the winter Hadley cell, which has its strongly ascending branch at ca. 10"N (Figure 13(a)). Doubling of CO, results (Figure 13(b)) in a weaker winter branch

LAND-SURFACE CHARACTERISATION

Screen Temperature Differences

1083

90"N

0"

90"s 180°W 0" 180"E

Contour from -2 to 2.8 by .4

Figure 11. Screen temperature diRerences (K): interactive vegetation minus prescribed vegetation for 5-year annual averages

Januarv .2QJ

Precip. dff. (%)

20

10

0

-10

-20 J I

51 July

Temperature diff. (K) I I I I I

90N EQ 90s

Temperature diff. (K) I I I I I 1.

90N EQ 90s

Figure 12. Zonally averaged differences for temperature and precipitation for January and July ( 2 x CO,) for 5-year ensemble averages with prescribed and interactive vegetation. Zonal values are shown as absolute (temperature) and percentage (precipitation) differences

from the prescribed vegetation control simulation ( I x CO,)

1084 A. HENDERSON-SELLERS A N D K . MCGUFFIE

Contour from -220 to 20 by 20

32 16

8 H to 2

4 -

h

3

Contour from -27.5 to 7.5 by 2.5

Contour from -14 to 10 by 2

Figure 13. (a) Mean meridional stream function for July for the 1 x CO, control simulation. The main feature is the Southern Hemisphere (winter) branch of the Hadley circulation with the ascending branch at cu. IO’N. (b) Differences in meridional stream function due to doubling of CO, (2 x CO, - 1 x CO,) with no change in the biosphere. (c) Differences in meridional stream function

(interactivc-fixed) due to the introduction of an interactive biosphere at 1 x CO,


(decreases between the Equator and 30"s) but a slightly stronger summer branch (increases from the Equator to 40"N), indicating enhanced circulation. In Figure 13(c), it can be seen that the presence of an interactive biosphere produces similar changes in the meridional circulation. The winter branch of the Hadley circulation is diminished and the summer branch is enhanced (area of positive difference L'U. 20"N). Differences at higher latitudes are much smaller and harder to identify clearly. Changes in meridional circulation induced by allowing the biosphere to respond to the climate are similar in character and of the same magnitude as changes induced by doubled CO,.

These experiments have shown that if the vegetation does change as the climate warms (in response to the warming, because of human activities, or for any other reason), at least the near-surface continental climate will be modified. Thus it is probably unwise to generate maps of vegetation off-line once climate has equilibrated as a result of increasing atmospheric CO,.

4. VEGETATION MODEL SENSITIVITY

4. I. Etlaluating the robustness of intertictive oeyetution prediction

Incorporating a predictive scheme for the distribution of continental vegetation into the CCM 1 - 0 2 has, almost by definition, increased the variability of the land-surface state. Two questions arise: how much does the predicted vegetation vary and does this variation alter the climate'? The latter question was touched on in section 3.4 although the simulations have not been conducted for a long enough period to permit a statistical evaluation of the changes in either the mean or variance of the near-surface climate.

Figures 7 and 8 indicate that, in addition to the trends exhibited in the areal extent of some vegetation types as (20,-induced warming occurs, there is also year-to-year variability in the predicted areas. This variation in vegetation distribution is also seen in the simulations with 'constant' climates (see also Henderson-Sellers, 1993b). For example, Figure 14 shows two global maps of an index of vegetation variation generated from two 5-year constant-climate simulations. In each case, the index of variation is the number of year-to-year changes in predicted vegetation expressed as a percentage of the maximum possible changes. Since these figures are for 5 years, a maximum of four changes is possible and, thus, only five categories can occur. (The sixth keyed category, 1-20 per cent, will be used in later maps.)

Figure 14(a) shows the vegetation variation index for 5 years comprising the last 3 years of the instantaneously doubled CO, experiment together with the latter 2 years of a tropical deforestation disturbance in 2 x CO, conditions (see section 4.3). There is a considerable degree of vegetation variation, with over half the land area exhibiting some change in the 5 years. A slightly smaller (Table V(a)), but still notable, degree of variation is seen in Figure 14(b), which is taken from the first interactive vegetation experiment conducted for present-day conditions and described in Henderson-Sellers (1 993b). The reduced vegetation variability in Figure 14(b), cf. Figure 14(a), could be due to a number of causes including: (i) the tuning of the vegetation prediction scheme to improve the present-day area distributions (section 2.2) may have shifted predicted vegetation changes to more sensitive precipitation/biotemperature boundaries; ( i i ) the I x CO, climate simulation of Henderson-Sellers (1993b) may have been more stable than the 5 years for 2 x CO, used here, which may still be equilibrating (cf. Figure 4); and (iii) the need to combine 2 years from a deforestation experiment with 3 years from the standard instantaneous 2 x CO, simulation may have introduced additional vegetation variability (cf. section 4.3).

There are 10 grid points in Figure 14(a) that exhibit 100 per cent variation (the highest keyed class must be 100 per cent in this case of only four possible changes in 5 years of simulation). The BATS vegetation types occurring at these 10 locations are listed in Table V(b). Seven of these 10 locations exhibit a biannual oscillation between adjacent (in the Holdridge sense see Figure 2) vegetation types. The three remaining locations (termed Florida, North Arabia, and Thailand in Table V(b)) also exhibit recurrence of vegetation types but the changes are slightly more varied. I t is also interesting to note the relatively small degree of change at these locations from the vegetation calculated off-line for the I x CO, climate (cf. Figure 3). Only one (termed South Arabia) has all five 2 x CO, predicted vegetation types different from the 1 x CO, vegetation. These small changes and the fairly plausible nature of the predicted 2 x CO, vegetation (e.g.


............................................ ............................................... :.:.: ..... :.x.=.:.x:s:.:.: ........................... ~ . ~ . : . : . : . : . x . ~ . ~ . ~ : : ~ . ~ . ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ . .:.:.;.; - - - - - 41% - 602

IIIII 21% - 402

.............. 81% - 100%:

.......... 61% - 802

gixg ::*::.:. 12 - 20% ............ ..... .......... ..... ..... .......... ..... Nochange

.. ............................................ ..................................................... :.:.: ........................................................ ................................................................................. ........ ............................................................................................................................. - - - - - 41% - 60%

IIIII 21% - 40% cq<:*;f. ....... 1% - 20%

.......... Nochange

... >..:< .:.:.: ........ ..A .A:.:.:

.......... ..........

......,... :.:.:.:.:. .....

812 - 100%

61% - 80%

Figure 14. (a) Vegetation variation index calculated as the number of times each grid element alters vegetation as a percentage of the possible number of changes in the simulated period. Time period here is 5 years of a composite instantaneous 2 x CO, simulation.

(b) As for (a) except from the 1 x C 0 2 untuned predictions of Henderson-Sellers (1992)


Table V(a). Percentage of land area appearing in the six plotted vegetation variation index classes in Figures 14 and 15

Vegetation-variation class

No change 1-20 (per cent) 21-40 (per cent) 41-60 (per cent) 61-80 (per cent) 81-100 (per cent) ~

Figure 14(a) 57.6 0.0 12.0 19.7 8.5 2.2

Figure 15(b) 24.1 29.7 31.2 13.4 1.5 0.0

Figure 14(b) 65.0 0.0 14'4 13-8 5.4 1.4 Figure 15(a) 38.1 14.9 21.2 20.4 5.0 0.3

Table V(b). Year to year variations in predicted vegetation in the 5 years of 2 x CO, (instantaneous doubling) used to construct Figure 14(a). The 10 locations listed are those exhibiting a vegetation-variation index of 100 per cent in Figure 14(a) (The BATS types are identified in Table

1 .)

2 x CO, years I x co, Approximate Location 5-year geographical area (48 x 40) grid 13 14 15 14d 15d off-line

Arctic coast Kazakhstan Florida North Arabia Nepal South Arabia Thailand Niger Sahel West Amazon

2 4 1 2 6 5 8 7 1 4 5 7

18 5 7 5 5 7 5 6

2 1 1 5 1 5 1 7 5 5

4 2 6 2 4 7

18 5 7 6

2 1 5 7 1 5 1 7 5 5

2 2 1 2 4 1 1 7 7 6

Table VI. Impacts of forcing a disturbance in the predicted vegetation during the last 10 years of the equilibration of the fast, transient doubled-CO, simulation

Following years (control) BATS BATS Previous Disturbed code type year year: 39 40 43 45

1 Agriculture 18.8 23qin.3) 19.6( 20.2) 19.7( 19.1 ) I9.2( 17.8) 2 Short grass 12.4 10.9( 1 3.4) 11~1(11~5) 1 I '3( 12.2) 12.4( 13.0) 4 Deciduous needle 9.2 6.4(9.0) 10.4(8.6) 8.9(9.0) 8.2(9.0) 5 Deciduous broadleaf 18.3 20.0( 18.4) 18.0( 16.4) 1 S.o(l9.1) 20.3( 19.5) 6 Evergreen broadleaf 8.6 8.9( 8.0) 9.1 (8.3) 9.2(7.5) 7.8(8.3) 7 Tall grass 8. I 6-9(9.2) 8.8( 10.0) 8.q8.5) 8.5(8.2) 8 Desert 8.5 7.9(7.7) 7.6(8.8) 9.1(8.9) 7.3(8.1) 9 Tundra 1 .o 0.7( 0.7) 0.7(0.8) O.S(O.9) 0.7)( 0.9) 11 Semi-desert 0.6 0-5(0.9) 0,5(09) 0.5(0.4) l.l(O.5)

18 Mixed woods 1.6 1.6(( 1.6) 1 '4( 1'6) 1.8( 1.6) 1 '6( 1.8) 12 Glacier 10.2 10-2( 10.2) 10.2( 10.2) 10.2( 10.2) 10.2( 10.2)

Roughness length (cm) Shortwave albedo (YO)

44.3(43.9) 47.q42.6) 45.9(43.4) 44.2(45,4) 1 6 4 16.4) I6.2( 16.5) 16.4( 16.5) 16.4( 16.3)

1088 A HENDERSON-SELLERS AND K. MCGUFFIE

evergreen or deciduous broadleaf trees in the Amazon; crop or short grass in Kazakhstan), offers some confidence in the vegetation model.

Examination of other high vegetation-variability locations reveals that the majority of these also exhibit oscillation amongst two or, less often, three vegetation types. Thus much of the high variability seen in Figure 9 is the result of individual grid areas having a climate that is on, or very close to, a precipitation-biotemperature Holdridge boundary (Figure 2).

Figure 15 shows the vegetation-variation index for the two doubled-CO, experiments described in section 3.1: Figure 15(a) being averaged over 15 years and Figure 15(b) over 45 years. Overall, it can be seen that about half of the land area shows less than a 20 per cent change in vegetation type throughout both simulations (Table V(a)), the regions that remain fairly fixed being the tropical forest (especially in South-east Asia), parts of the deserts (especially the Sahara), and the agricultural areas in eastern North America and the eastern former Soviet Republics. The pattern of vegetation variability in these simulations differs from that of the ‘constant climates’ maps (Figure 14) in keeping with the expectation that a gradual shift in vegetation types is occurring as the climate warms. There are, however, some differences between the two maps of vegetation variation (Figures 15(a) and 15(b)). Fewer grid points fall into the 61-80 per cent variation class in the fast, transient simulation (Figure 15(b)) than in the instantaneous experiment (Figure 15(a)) and none in the 81-100 per cent class.

Overall, there is noticeable year-to-year variation in predicted vegetation but the changes are often associated with near-boundary transitions rather than unidirectional changes. The degree of variation recognized here is not so large that ‘core’ changes in vegetation following both doubled-CO, simulations could not be found (Figure 9).

4.2. Vegetation sensitivity to the initial vegetation distribution

A n important question in any set of sensitivity studies is the importance of initialization. In the two doubled-CO, simulations described here, the initial vegetation distribution was the BATS standard specification for the present-day (Figure 1). However, because the vegetation affects local climate, it is possible that different initial conditions might result in major differences in the simulations.

This problem was examined by prompting a disturbance in the continental vegetation during a rerun of the last 10 (equilibration) years of the fast, transient doubled-CO, simulation. The disturbance was caused during year 3 of the rerun by using only the last 5 months of the year as input to the ‘annually’ averaged biotemperature and precipitation used in the vegetation prediction scheme. This disturbance was selected in preference to an arbitrary modification because it also permitted an evaluation of the sensitivity of the vegetation prediction scheme to reduced climatic input.

This vegetation distribution should be compared with the previous year’s vegetation, which served as the land-surface for this year’s calculation. I t can be seen (Table VI) that the largest changes occur in vegetation types 1-5. There are substantial increases in the areas of crop and deciduous broadleaf tree and decreases in short grass and deciduous needle-leaf tree. These differences occur in areas likely to be affected by restricting climatic input to only 5 months (August-December) whereas tropical and subtropical regions remain largely unaffected, presumably because their climates change little over the seasonal cycle. There are variations even in low latitudes (e.g. evergreen broadleaf tree) but these are generally within the normal variability of the predicted vegetation (cf. Figure 14).

The total areas listed for the disturbed vegetation (Table VI) give a better view of the extent of the disturbance. I t can be seen that the impact of the imposed disturbance in vegetation distribution is felt for only the first 1-2 years following the disturbance. Certainly by year 45 the ‘disturbed’ distribution differs no more from the control than the year-to-year variation exhibited by this coupled biosphere-climate model.

4.3. The impact (?f’ land-use change: instantaneous tropical deforestation

The second sensitivity test of the vegetation model is based upon a 3-year simulation. In this experiment, the global model was restarted from the climatic and vegetation conditions prevailing at the end of the twelfth year of the instantaneously doubled CO, experiment except that the tropical moist forest (evergreen


.I I ......

............................................ ..................................................... ........................................................ ....................................................................... ........ ............................................................................................................................. - - - - - 41% - 60%

I I l I I 21% - 40%

.............. 81% - 100% .............. ..............

..... ..... ..... ..... 61% - 80%

:m ::::::::::::s 1 x - 20%

:-x-x. .......... ..... No change

- I t Ill ...

-,.I.:.: - ....... ... .. ... ... .. ...

- - - - - !.!!I ....... I ...... ....... ....... ............. .............. ...... ..... ...... ...... ..... .......... ..... ..... ..... ..... ..... ..... .....

Figure 15. (a) As for

41% - 60%

21% - 40%

1% - 20%

No change

81% - 100% 61% - 80%

Figure 14(a) except from 15 years of the instantaneous doubled-CO, simulation. (b) As for (a) except from 45 years of the fast, transient doubled-CO, simulation


broadleaf tree) in the current vegetation distribution was replaced by a new BATS vegetation classification, termed scrub grassland. Twenty-eight 4.5" by 7.5" grid elements were modified. This experiment is similar to the tropical deforestation experiment described in detail in Dickinson and Henderson-Sellers (1988) and Henderson-Sellers rf al. (1993) in the specifications of the land-use change although, of course, here the vegetation is interactive.

The evergreen broadleaf trees were widely re-established within 1 year although the original area of tropical forest was not fully recovered in Africa until the end of the second year of simulations. The total number of grid squares occupied by the evergreen broadleaf trees (and the associated percentage of land area) are as follows: 28 in year 12 of the instantaneous doubled-CO, simulation (7.3 per cent); 29 (7.5 per cent), 35 (9.0 per cent), and 35 (9.1 per cent) in years 13, 14, and 15, respectively, following tropical deforestation at the end of year 12, which can be compared with the same years during the instantaneous doubled-CO, simulation when the forest was untouched---32 (8.6 per cent), 32 (8.6 per cent) and 29 (7.5 per cent), respectively. There is a slight tendency for the percentage of land area occupied to be greater following the prescribed deforestation, but this variation, mostly confined to Africa, is within the natural variability of the prediction model (cf. areas in Table IV).

These results suggest that the doubled-CO, climate of the GCM is only transitorially sensitive to an instantaneous deforestation. In a similar experiment with prescribed tropical deforestation but for present-day atmospheric conditions, the evergreen broadleaf trees removed throughout South-east Asia were rapidly regenerated, but the disturbance of the climate of South America was such that very little evergreen broadleaf tree was predicted as ' regrowing' by the vegetation classification scheme and almost none of it occurred in the deforested Amazon Basin (Henderson-Sellers, 1993b). Thus, within the limits of the coarse resolution of the global model and the simplistic nature of the vegetation prediction scheme, it appears that removal of tropical forest in 2 x CO, conditions does not cause as large a disturbance as for the present-day. In a C0,-warmed world, the tropical forest is predicted to recur in most, if not all, locations. An additional test of this result would be to prescribe the scrub grassland and force it to remain over many years to ascertain the climatic effect of continued degradation and subsequent attempts at reforestation. Such a study will be pursued in the future.

I t is interesting to note that in both this sensitivity study and the 'disturbed' (globally) vegetation study reported in section 4.2, the land area of tropical forest in all the 2 x CO, climates never exceeded 9.2 per cent, i.e. i t had barely increased over the 1 x CO, area of 8.6 per cent (Table 11). This is in contrast to the predictions of K. C. Prentice and Fung (1990) who found a large increase in the area of tropical forest (+ 1 1 per cent absolute) using, off-line, their version of the Holdridge scheme. However, the GISS GCM, upon which their results were based, exhibited a more marked response to doubled-CO, than the CCM1-0z used here. K. C. Prentice and Fung (1990) report that globally, land temperatures rose by 54°C and precipitation increased by 17.5 mm day-' (although this precipitation increase seems to be given in the wrong units) as compared with the continental climatic changes reported here of +2.99"C and + 0.157 mrn day- (Table 111). Thus their larger increase in area of tropical forest, and the associated increase in carbon in vegetation, may be the result of greater continental temperature rises and commensurate rainfall increases.

5. SUMMARY AND CAVEATS

There are many caveats on these results that must be clearly stated. The most important of these is the fact that the vegetation 'prediction' scheme used is highly simplistic and occurs here on time-scales much shorter than ecosystem response times. Indeed, it could be argued that it would be most usefully used as a means of diagnosing differences amongst simulations of the near-surface continental climate. Moreover, these 'predictions' of vegetation extent and distribution described here depend only on the near-surface air temperature and precipitation (see Figure 2 and Table I). There is no effect due to the fertilization of plant growth by the increased atmospheric CO, nor is any effect caused by other climate-related parameters known to modify plant growth. In particular, soil moisture is not part of the vegetation prediction scheme. On the other hand, some feedbacks are permitted by this coupling of a vegetation prediction model to a global climate model. For example, the BATS scheme specifies different


soil depths, rooting factors, stomata1 resistances, roughness lengths, etc. for different vegetation types (Dickinson et a/., 1993). Thus when the vegetation is changed, following a local change in climate, this change is very likely to cause a modification in the climate. In the experiments performed here such feedbacks have been observed manifested in the form of vegetation ‘oscillations’ (e.g. Table V(b)). This result suggests that any interactive vegetation scheme must incorporate a ‘climatic stress’ index generated over a number (5-50 say) of years upon which predicted vegetation changes would also depend.

The version of the vegetation ‘prediction’ scheme used here has been tuned to improve the predicted distribution of present-day vegetation (section 2) . Despite this, there are significant discrepancies between the present-day calculation and observed distributions (Table 11). These result, in part, from the very simple ecological classification used ( 1 1 vegetation types only) and, in part, from the less than ideal performance of the climate model, CCM1-0z.

Perhaps the most important caveat, especially in view of the areal expansion identified after doubling CO,, is on the definition of the region in the Holdridge life zone triangle termed ‘approximate agriculture’ (Figure 2(a)). This region has biotemperatures and precipitation totals that suggest that agriculture could be sustained but the occurrence of a ‘prediction’ of ‘crop’ cannot necessarily be equated to successful agricultural activity.

A series of doubled-CO, sensitivity experiments has been undertaken in which a simple continental vegetation prediction scheme became an integral part of the CCM I-Oz global climate model. Two complementary C0,-doubling experiments, an instantaneous 2 x CO, simulation (1 5 years in total) and a fast, transiently increasing CO, simulation (45 years in total), gave rise to very similar predictions of vegetation distribution and areal extent in a warmed world. The vegetation type termed ‘agriculture’ increased in area at the expense of deciduous needle-leaf trees and short grass. The tundra extent, already underestimated, decreased further, whereas desert and deciduous broadleaf tree areas expanded. The performance of this interactive vegetation prediction scheme for a doubled-CO, climate has been compared with earlier, but off-line, applications of similarly derived schemes. The agreement between these results and those derived from Emanuel ef a/. (1985b) are very good except for the changes in areal extents of short grass and agriculture, which are roughly equivalent but of opposite sign. However, Emanuel ef al. (1 985a,b) used only predicted temperature changes, not precipitation (as here), and employed post farto output from a different GCM. There is agreement in the direction of change in the carbon in the terrestrial standing biomass (cf. K . C . Prentice and Fung, 1990). However, their increases are much larger than those computed here (ca. 260 GtC, cf. 6 GtC). This discrepancy is due primarily to the very different predictions of areal extent of tropical rainforest in the two simulations: + 11 per cent in K. C. Prentice and Fung (1990), cf. +0.5 per cent here. Off-line calculations of areal extent of vegetation using two I-year, snap-shot climates from the coupled ocean-atmosphere model of Washington and Meehl (1989) suggest a decrease in agricultural areal extent and an increase of tropical forest of magnitude roughly mid-way between the (near-zero) change reported here and the increases predicted by K. C. Prentice and Fung (1990).

The experiments reported here indicate that the climate is sensitive to the incorporation of changing land-surface characteristics. The evaporation from the continents is noticeably larger when the vegetation is interactive than when it is prescribed at the present-day distribution, and in the annual mean the global (oceans as well as land) evaporation is greater when the vegetation is interactive and the climate warmer. This seems to be the result of the interactive biosphere results in the sustained strengthening of, predominantly, the summer Hadley cell, associated especially with increased evaporation over the Northern Hemisphere continents. Increased evaporation in the interactive biosphere case provides additional energy to the Hadley circulation, which leads to an increase in evaporation over the oceans, producing a positive feedback via changes in the low-level tropical convergence. These circulation changes, seen clearly in plots of the meridional stream function, also prompt disturbances distant from the continents. The sea-ice area is found to be sensitive to both the inclusion of an interactive biosphere and the mode of CO, increase, the sea-ice area remaining larger (i.e. the smallest decrease) in the case of the transiently increasing CO,. Large temperature differences are seen in July in high-latitude locations, where sea-ice differences occur, whereas tropical precipitation is sensitive to the incorporation of interactive vegetation.

We conclude that at least one current GCM copes with vegetation altered at regular intervals. Although the exact nature of the vegetation ‘evolution’ has little effect on the resulting climate (this may, possibly,

1092 A. HENDERSON-SELLERS AND K. MCGUFFIE

be a fortuitous result of altering vegetation in the Northern Hemisphere mid-winter), an interactive vegetation does modify the climatic change due to doubling of atmospheric CO,. On the basis of the results presented here, it could be argued that impact simulations could generate different results if the ‘control’ simulation used 1 x CO, ‘predicted’ vegetation rather than today’s ‘observed’ vegetation. The most direct changes are enhanced continental evaporation, which prompts intensification of the atmospheric circulation in the tropics, enhanced oceanic evaporation, and other disturbances. This response suggests that different outcomes might be anticipated from future simulations of a greenhouse-warmed world when changing continental characteristics are included.

The differences between the results generated by the two interactive vegetation simulations described here and other, off-line, calculations are especially important in that the interactive results suggest an increase in areas available for agriculture but no change in tropical forest regions in a greenhouse-warmed world. Because the future increase in world population must demand greater agricultural exploitation, the former finding is positive although the world geography of agricultural productivity changes. On the other hand, the tropical moist forests are already under great pressure from, so called, ‘development’. The finding that even their potential area is not increased in a greenhouse-warmed world means that the sustained future of the tropical forests may be in jeopardy.

Additional sensitivity tests have been conducted in 2 x COz conditions, which were designed to try to establish: (i) the dependence of the resulting (equilibrium) predicted vegetation on the initialization and (ii) the confidence associated with the predictions of tropical forest areal extent. It was found that the extent of evergreen broadleaf trees in doubled-CO, climates (tropical forest) is very little affected by disturbances including a prescribed replacement of the forest by scrub grassland and a different initialization of vegetation distribution. Similarly, the overall vegetation areas predicted are not particularly sensitive to vegetation initialization although effects can be monitored for 1-2 years. On the other hand, there are quite significant differences between the predicted extents of agriculture and deciduous needle-leaf trees generated by the two different realizations of a warmed world. Although these differences could be reconciled by longer simulations, especially by extending the instantaneous 2 x CO, experiment, they may also indicate that dynamic prediction of vegetation is not a transient component of the climate system (e.g. Lorenz, 1963).

ACKNOWLEDGEMENTS

We wish to thank Warren Washington for hospitality in NCAR’s Global Climate Dynamics Division during the early stages of this work. This research was funded in part by grants from the Model Evaluation Consortium for Climate Assessment, the Department of Environment, Sport and Territories and by the Australian Research Council. This is Contribution 93/8 of the Climate Impacts Centre.

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Land-surface characterization in greenhouse climate simulations