on the model applied (particularly on the

assumed degree of metal-silicate equilibration

during core formation), resulting in age es-

timates ranging from È30 to 9100 My after

solar system formation (20–22). In contrast,

the Hf-W age of LMO crystallization tightly

constrains the age of the Moon and the final

stage of Earth_s accretion to 30 to 50 My after

the formation of the solar system. The

formation of the Moon significantly later than

that of asteroids and Mars (18, 27) underpins

the Moon_s origin by a unique event, as re-

quired in the giant impact hypothesis.

References and Notes1. R. M. Canup, E. Asphaug, Nature 412, 708 (2001).2. R. W. Carlson, G. W. Lugmair, Earth Planet. Sci. Lett.

90, 119 (1988).3. C. Alibert, M. D. Norman, M. T. McCulloch, Geochim.

Cosmochim. Acta 58, 2921 (1994).4. L. E. Borg et al., Geochim. Cosmochim. Acta 63, 2679

(1999).5. D.-C. Lee, A. N. Halliday, G. A. Snyder, L. A. Taylor,

Science 278, 1098 (1997).6. J. H. Jones, H. Palme, in Origin of the Earth and Moon,

R. M. Canup, K. Righter, Eds. (Univ. Arizona Press,Tucson, AZ, 2000), pp. 197–216.

7. C. K. Shearer, H. E. Newsom, Geochim. Cosmochim.Acta 64, 3599 (2000).

8. I. Leya, R. Wieler, A. N. Halliday, Earth Planet. Sci.Lett. 175, 1 (2000).

9. D. C. Lee, A. N. Halliday, I. Leya, R. Wieler, U. Wiechert,Earth Planet. Sci. Lett. 198, 267 (2002).

10. H. Wanke et al., Proc. Sec. Lunar Planet. Sci. Conf. 2,1187 (1971).

11. C. K. Shearer, J. J. Papike, Am. Mineral. 84, 1469 (1999).12. P. H. Warren, J. T. Wasson, Rev. Geophys. Space Phys.

17, 73 (1979).13. H. Palme, H. Wanke, Proc. Lunar Sci. Conf. 6, 1179

(1975).14. K. Righter, C. K. Shearer, Geochim. Cosmochim. Acta

67, 2497 (2003).15. I. Leya, R. Wieler, A. N. Halliday, Geochim. Cosmochim.

Acta 67, 529 (2003).16. L. E. Nyquist et al., Geochim. Cosmochim. Acta 59,

2817 (1995).17. H. Palme, W. Rammensee, Lunar Planet. Sci. XII, 796

(1981).18. T. Kleine, C. Munker, K. Mezger, H. Palme, Nature

418, 952 (2002).19. Q. Z. Yin et al., Nature 418, 949 (2002).20. S. B. Jacobsen, Annu. Rev. Earth Planet. Sci. Lett. 33,

531 (2005).21. A. N. Halliday, Nature 427, 505 (2004).22. T. Kleine, K. Mezger, H. Palme, E. Scherer, C. Munker,

Earth Planet. Sci. Lett. 228, 109 (2004).23. L. T. Elkins-Tanton, J. A. Van Orman, B. H. Hager, T. L.

Grove, Earth Planet. Sci. Lett. 196, 239 (2002).

24. S. C. Solomon, J. Longhi, Proc. Lunar Sci. Conf. 8, 583(1977).

25. H. Palme, Geochim. Cosmochim. Acta 41, 1791 (1977).26. R. W. Carlson, G. W. Lugmair, Earth Planet. Sci. Lett.

45, 123 (1979).27. T. Kleine, K. Mezger, H. Palme, E. Scherer, C. Munker,

Geochim. Cosmochim. Acta, in press.28. T. Kleine, K. Mezger, C. Munker, H. Palme, A. Bischoff,

Geochim. Cosmochim. Acta 68, 2935 (2004).29. T. Kleine, K. Mezger, H. Palme, E. Scherer, C. Munker,

Earth Planet. Sci. Lett. 231, 41 (2005).30. We thank NASA for providing the samples for this

study and I. Leya, R. Wieler, L. Borg, T. Grove, T.Irving, S. Jacobsen, L. Nyquist, and two anonymousreviewers for their comments. E. Scherer supportedthe MC-ICPMS in Munster, and C. Munker providedaliquots of the whole-rock samples. This study wassupported by the Deutsche Forschungsgemeinschaftas part of the research priority program ‘‘Mars andthe terrestrial planets’’ and by a European UnionMarie Curie postdoctoral fellowship to T.K.

Supporting Online Materialwww.sciencemag.org/cgi/content/full/310/5754/1671/DC1SOM TextTables S1 and S2References

15 August 2005; accepted 10 November 200510.1126/science.1118842

The Importance of Land-CoverChange in Simulating

Future ClimatesJohannes J. Feddema,1* Keith W. Oleson,2 Gordon B. Bonan,2

Linda O. Mearns,2 Lawrence E. Buja,2 Gerald A. Meehl,2

Warren M. Washington2

Adding the effects of changes in land cover to the A2 and B1 transient climatesimulations described in the Special Report on Emissions Scenarios (SRES) bythe Intergovernmental Panel on Climate Change leads to significantly dif-ferent regional climates in 2100 as compared with climates resulting fromatmospheric SRES forcings alone. Agricultural expansion in the A2 scenario re-sults in significant additional warming over the Amazon and cooling of theupper air column and nearby oceans. These and other influences on the Hadleyand monsoon circulations affect extratropical climates. Agricultural expansionin the mid-latitudes produces cooling and decreases in the mean daily tem-perature range over many areas. The A2 scenario results in more significantchange, often of opposite sign, than does the B1 scenario.

As anthropogenic impacts on Earth_s surface

continue to accelerate, the effects of these ac-

tions on future climate are still far from known

(1–3). Historical land-cover conversion by hu-

mans may have decreased temperatures by

1- to 2-C in mid-latitude agricultural regions

(4–9). Simulations of tropical deforestation

(10–12) and potential future human land-

cover impacts project a warming of 1- to 2-Cin deforested areas (13, 14), with possible ex-

tratropical impacts due to teleconnection pro-

cesses (7, 11, 13, 15). However, most of

these experiments have been performed in un-

coupled or intermediate-complexity climate

models and have not followed the proposed

framework of the Intergovernmental Panel

on Climate Change (IPCC) Special Report on

Emissions Scenarios (SRES) (16). The study

described here evaluated whether future land

use decisions, based on assumptions similar to

those used to create the IPCC SRES atmo-

spheric forcing scenarios, could alter the out-

comes of two future IPCC SRES climate

simulations.

Land-cover impacts on global climate can

be divided into two major categories: bio-

geochemical and biogeophysical (2, 14–18).

Biogeochemical processes affect climate by

altering the rate of biogeochemical cycles,

thereby changing the chemical composition of

the atmosphere. To some extent, these emis-

sions are included in the IPCC climate change

assessments (1). Biogeophysical processes di-

rectly affect the physical parameters that

determine the absorption and disposition of

energy at Earth_s surface. Albedo, or the re-

flective properties of Earth_s surface, alters the

absorption rate of solar radiation and hence

energy availability at Earth_s surface (4–19).

Surface hydrology and vegetation transpiration

characteristics affect how energy received by

the surface is partitioned into latent and sen-

sible heat fluxes (4–19). Vegetation structure

affects surface roughness, thereby altering mo-

mentum and heat transport (12). Summarizing

the effects of land-cover change on climate

has been difficult because different biogeo-

physical effects offset each other in terms of

climate impacts (16), and, on global and annual

scales, regional impacts are often of opposite

sign and are therefore not well represented in

annual global average statistics (7, 16).

For this study, we used the fully coupled

Department of Energy Parallel Climate Model

(DOE-PCM) (20, 21) to simulate combined land-

cover and atmospheric forcings for the A2 and

B1 IPCC SRES scenarios (22). Atmospheric

forcings were identical to those used in pre-

vious IPCC SRES scenario experiments, re-

sulting in a 1-C warming for the low-impact

B1 scenario and a 2-C warming for the high-

impact A2 scenario (20). To simulate future

land-cover change, we used the Integrated

Model to Assess the Global Environment

(IMAGE) 2.2 IPCC SRES land-cover projec-

tions (7, 22–24) and DOE-PCM natural veg-

etation data to create land-cover data sets

1Department of Geography, University of Kansas, Law-rence, KS 66045, USA. 2National Center for Atmo-spheric Research, Post Office Box 3000, Boulder, CO80307, USA.

*To whom correspondence should be addressed.E-mail: feddema@ku.edu

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9 DECEMBER 2005 VOL 310 SCIENCE www.sciencemag.org1674

representing SRES B1 and A2 scenarios for

the years 2050 and 2100 (Fig. 1) Efor further

details, see section A of the Supporting Online

Material (25)^. For each SRES scenario, we

ran the model from 2000 to 2033 with present-

day land cover, from 2033 to 2066 with 2050

land cover, and from 2066 to 2100 with 2100

land cover. The model ran in transient mode,

using IPCC atmospheric forcings from 2000 to

2100 (20). For comparison, we ran the same

simulations with identical IPCC SRES atmo-

spheric forcing while holding land cover con-

stant at the present-day conditions (Fig. 1).

To isolate the effects produced by land-cover

change, results are presented as the differ-

ence between the all-forcing scenario (atmo-

spheric and land-cover) and the atmospheric

forcing with constant land cover. To illustrate

the robustness of our results, we conducted a

second A2 scenario simulation that held land

cover constant at present conditions to 2066

and then switched to the A2 2100 land-cover

scenario Efor further details, see section B of

the Supporting Online Material (25)^. This ex-

periment showed almost identical results, with

similar statistical significance, as the initial A2

2100 experiment (fig. S1).

Land-cover change effects on global sur-

face temperatures differ significantly between

the A2 and B1 climate scenarios (Fig. 2).

However, globally averaged annual temper-

ature differences for a given scenario are less

than 0.1-C for all the simulations because of

offsetting regional climate signals. Most sig-

nificant regional climate effects are associated

directly with land-cover conversions in mid-

latitude and tropical areas. At higher latitudes,

temperature responses are not directly linked

to local land-cover change and can change

sign by season (Fig. 2). Compared to surface

temperature responses, land-cover change has

a more significant effect on diurnal tempera-

ture ranges (DTRs) (Fig. 3). All scenarios

show widespread DTR responses to land-cover

change, and many of the changes correspond

directly with areas of land-cover change. In

three of the four scenarios, the DTR decreases

significantly in southern Asia; and in the A2

scenarios, significant portions of the mid-

latitude land areas experience decreases in

DTRs. To better understand the potential ef-

fects and mechanisms of the impacts of land-

cover change, six regions have been selected

to illustrate the nature of the response (Fig. 1).

In the Amazon, the direct effect of con-

verting tropical broadleaf forest to agriculture

in the A2 2100 scenario is a significant warm-

Fig. 1. Representation of present-day land cover and land-cover change for each of the scenarios. Each of the six tropical regions discussed in the textis indicated. B, broadleaf; N, needleleaf; E, evergreen; D, deciduous; and F, forest.

R E P O R T S

www.sciencemag.org SCIENCE VOL 310 9 DECEMBER 2005 1675

ing, well above 2-C (Fig. 2). However, the

same land-cover conversion results in relative-

ly minor temperature responses in Indonesia.

From these observations, it is apparent that

tropical locations with the same land-cover

forcing have different responses, as has been

shown in other studies (12, 13). To assess these

different regional responses, we evaluated

temperature responses in all grid cells that

were converted from tropical broadleaf ever-

green forest to agriculture Efor further details,

see section C of the Supporting Online Material

(25)^. In almost all cases, this land-cover

change has minor effects on daily maximum

temperatures. However, in the Amazon there is

a significant increase in daily minimum tem-

peratures, an effect not observed in Indonesia

(fig. S2). The changes in minimum tempera-

tures are most often associated with dry pe-

riods Efor further details, see section C of the

Supporting Online Material (25)^. Therefore, it

is primarily the increase in daily minimum

temperatures, typically at nighttime, that af-

fects the DTR in tropical regions. Increased

nighttime temperatures are known to cause a

disproportionate human stress response (26).

Further analysis of the tropical regions

shows that in the Amazon, net radiation

changes in the atmospheric forcing scenarios

are primarily offset by increases in latent heat

fluxes when tropical forests are present. These

increases in latent heat fluxes increase cloud

cover and minimize temperature impacts. In

comparable land-cover and atmospheric forcing

simulations, the lower leaf-area index over the

region reduces latent heat flux and cloud

cover, resulting in increased incident radia-

tion. These processes increase surface temper-

atures and sensible heat flux. In the present-day

and A2 atmospheric forcing scenarios, mois-

ture fluxes from canopy evaporation, ground

evaporation, and transpiration are partitioned

as 22, 20, and 58%, respectively. When the A2

2100 land-cover change is included, this

changes to 10, 63, and 26%. In contrast, In-

Fig. 2. JJA and DJF tem-perature differences dueto land-cover change ineach of the scenarios.Values were calculatedby subtracting thegreenhouse gas–onlyforcing scenarios froma simulation includingland-cover and green-house gas forcings.Shaded grid cells aresignificant at the 0.05confidence level. Thetop four panels showJJA; the bottom fourshow DJF. B1 scenarioresults are on the leftand A2 results are onthe right.

R E P O R T S

9 DECEMBER 2005 VOL 310 SCIENCE www.sciencemag.org1676

donesia does not experience a reduction in

latent heat flux even though there is a 20%

reduction in the fraction of latent heat flux that

is transpired. In this case, an increase in local

rainfall provides water to increase evaporation

rates, thereby compensating for increases in

sensible heat flux and temperature. The lack of

response over Indonesia can be attributed to the

effects of the Asian Monsoon circulation and

precipitation regime, which override feedbacks

from local land-cover change.

Although the Asian Monsoon suppresses

the Indonesian response to land-cover forcing,

other large-scale land-cover forcings in East

Africa, Australia, and southern and eastern

Asia appear to affect the strength and timing

of the large-scale Asian Monsoon circulation.

This results in climate impacts over a num-

ber of areas that are influenced by the Asian

Monsoon. For example, both 2050 scenarios

over India in June, July, and August (JJA)

show increased cloud cover and precipita-

tion, resulting in decreased incident radiation

and higher latent heat fluxes. This effect oc-

curs despite local reductions in transpiration

efficiencies due to local land-cover change.

This reverses in the A2 2100 scenario, per-

haps because the effect of African land-cover

change on the monsoon circulation is reduced.

The B1 2100 scenario, with global reforest-

ation, results in significantly dryer and warmer

Indian climates. Similar impacts occur in East

Africa and northern Australia. Temperatures

over the Indian Ocean are also affected, with

possible consequences for the North Atlantic

Oscillation (27).

Compared to Asia, Amazonian land-cover

feedbacks have much greater local impacts.

Although surface temperatures increase dra-

matically in response to land-cover forcing,

temperatures in the air column above show a

significant cooling as compared to the atmo-

spheric forcing scenario. This slows the re-

gional Hadley circulation and has significant

impacts over nearby ocean areas. The Atlantic

Ocean experiences a significant cooling that

extends from the tropical warm pool to much

of the North Atlantic in the A2 2100 JJA sce-

nario. The eastern equatorial Pacific also shows

a significant cooling response in the A2 sce-

nario, suggesting more La NiDa–like condi-

tions. In the B1 scenario, a slight cooling in the

western equatorial Pacific Ocean in 2050 and

slight warming over the eastern Pacific Ocean

in 2100 suggest a more El NiDo–like state.

The impacts of land-cover change on ex-

tratropical climates are in response to a mix-

ture of local land-cover change effects and

changes in the large-scale circulation system.

The conversion of mid-latitude forests and

grasslands to agriculture is generally thought

to cool mean daily maximum temperatures

(28, 29). This direct land-cover effect is evi-

dent in northeast China, where the conver-

sion to agriculture results in relative cooling

(or reduced warming in the all-forcing sce-

nario) and decreased DTR due to increases in

winter albedo and summer evapotranspiration

efficiencies. This contrasts strongly with the

warming, also in southern China, in the B1

scenario when existing agricultural areas are

replaced with forest.

In the A2 2100 scenario, a less direct re-

sponse to land cover is observed in the south-

western United States. There, transpiration

efficiencies increase significantly with local

land conversion to agriculture. But increased

latent heat fluxes are only realized because of

a significant increase in local precipitation, a

result that is opposite to that found in similar

uncoupled studies (15). In this case, the weak-

ened Hadley circulation, caused by Amazon

deforestation and cooler temperatures over

the neighboring ocean areas, allows a greater

northward migration of the Intertropical Con-

vergence Zone (ITCZ) and more moisture

entrainment to intensify southwest monsoon

precipitation in summer. The increase in latent

heat flux, from increased water availability

and transpiration efficiency, results in the cool-

ing of mean daily maximum temperatures. The

same process also explains the cooling over the

eastern Pacific and western Atlantic Oceans,

where increased cloud cover and precipitation

associated with an expanded northward migra-

tion of the ITCZ result in cooler temperatures.

Fig. 3. Changes in the annual average diurnal temperature range due to land-cover change in each of the scenarios. Values were calculated bysubtracting the greenhouse gas–only forcing scenarios from a simulation including land-cover and greenhouse gas forcings. Shaded grid cells aresignificant at the 0.05 confidence level.

R E P O R T S

www.sciencemag.org SCIENCE VOL 310 9 DECEMBER 2005 1677

In higher-latitude areas, particularly in the

Northern Hemisphere, there are significant

temperature changes that do not appear to be

directly related to land-cover change. Al-

though statistically significant, these changes

are relatively small as compared to the pro-

jected atmospheric forcing changes. For ex-

ample, in western Russia there is reforestation

in both scenarios, which should lead to warm-

ing. However, although the additional land-

cover changes have the expected impact on

net radiation, the B1 and A2 scenarios show

strongly opposing temperature signals in De-

cember, January, and February (DJF). These

results appear to be closely linked to changes

in regional precipitation and may be the result

of teleconnections, either linked to the Asian

Monsoon circulation or indirect effects from

temperature changes over the tropical Pacific

and North Atlantic Oceans.

Results from this study suggest that the

choices humans make about future land use

could have a significant impact on regional

and seasonal climates. Some of these effects

are the result of direct impacts of land-cover

change on local moisture and energy balances.

Other impacts appear to be related to signifi-

cant indirect climate effects through telecon-

nection processes. The A2 land-cover scenario

shows that tropical rainforest conversion will

likely lead to a weakening of the Hadley cir-

culation over much of the world and to signif-

icant changes in the Asian Monsoon circulation.

Especially in the A2 2050 scenario, the inter-

play between Asian and African land-cover

change affects the Asian Monsoon circulation.

The Indian Ocean experiences a significant

reduction in surface pressure, resulting in in-

creased cloud cover and precipitation and

warmer surface temperatures, and these effects

extend over most of the Indian subcontinent.

We conclude that the inclusion of land-

cover forcing, thereby accounting for a num-

ber of additional anthropogenic climate impacts,

will improve the quality of regional climate as-

sessments for IPCC SRES scenarios. Although

land-cover effects are regional and tend to offset

with respect to global average temperatures,

they can significantly alter regional climate out-

comes associated with global warming. Beyond

local impacts, tropical land-cover change can

potentially affect extratropical climates and

nearby ocean conditions through atmospheric

teleconnections. In this respect, our fully cou-

pled experiments differ from previous fixed

ocean temperature studies (12, 13, 15). Further

study is needed to determine the exact nature

of these responses. Overall, the results demon-

strate the importance of including land-cover

change in forcing scenarios for future climate

change studies.

References and Notes1. J. J. Houghton et al., Eds., Climate Change 2000: The

Scientific Basis (IPCC Working Group I, CambridgeUniv. Press, Cambridge, 2001).

2. P. Kabat et al., Vegetation, Water, Humans and theClimate Change: A New Perspective on an InteractiveSystem (Springer, Heidelberg, Germany, 2002).

3. W. Steffen et al., Global Change and the EarthSystem: A Planet Under Pressure (Springer-Verlag,New York, 2004).

4. R. A. Betts, Atmos. Sci. Lett. 2, 39 (2001).5. L. R. Bounoua, R. DeFries, G. J. Collatz, P. Sellers, H.

Khan, Clim. Change 52, 29 (2002).6. T. N. Chase, R. A. Peilke Sr., T. G. F. Kittel, R. R.

Nemani, S. W. Running, Clim. Dyn. 16, 93 (2000).7. J. J. Feddema et al., Clim. Dyn. 25, 581 (2005).8. J. Hansen et al., Proc. Natl. Acad. Sci. U.S.A. 95,

12753 (1998).9. H. D. Matthews, A. J. Weaver, K. J. Meissner, N. P.

Gillett, M. Eby, Clim. Dyn. 22, 461 (2004).10. M. H. Costa, J. A. Foley, J. Clim. 13, 18 (2000).11. N. Gedney, P. J. Valdes, Geophys. Res. Lett. 27, 3053

(2000).12. K. McGuffie, A. Henderson-Sellers, H. Zhang, T. B.

Durbidge, A. J. Pitman, Global Planet. Change 10, 97(1995).

13. R. S. DeFries, L. Bounoua, G. J. Collatz, Global ChangeBiol. 8, 438 (2002).

14. S. Sitch et al., Global Biogeochem. Cycles 19,GB2013 (2004).

15. R. Avissar, D. Werth, J. Hydrometeorol. 6, 134 (2005).16. R. A. Pielke Sr. et al., Philos. Trans. R. Soc. London

Ser. A 360, 1705 (2002).17. G. Krinner et al., Global Biogeochem. Cycles 19,

GB1015 (2005).18. P. K. Snyder, C. Delire, J. A. Foley, Clim. Dyn. 23, 279 (2004).19. G. B. Bonan, D. Pollard, S. L. Thompson, Nature 359,

716 (1992).20. G. A. Meehl et al., Science 307, 1769 (2005).21. W. M. Washington et al., Clim. Dyn. 16, 755 (2000).22. N. Nakicenovic et al., Special Report on Emissions

Scenarios (Cambridge Univ. Press, Cambridge, 2000).23. J. Alcamo, R. Leemans, E. Kreileman, Eds., Global Change

Scenarios of the 21st Century. Results from the IMAGE2.1 Model (Pergamon Elsevier Science, London, 1998).

24. IMAGE 2.2 CD release and documentation (RijksInstituut voor Volksgezondheid en Milieu, Bilthoven,Netherlands, 2002). The IMAGE 2.2 implementationof the SRES scenarios: A Comprehensive Analysis ofEmissions, Climate Change and Impacts in the 21stCentury (see www.rivm.nl/image/index.html for fur-ther information).

25. Materials and methods are available as supportingmaterial on Science Online.

26. T. R. Karl, R. W. Knight, Bull. Am. Meteorol. Soc. 78,1107 (1997).

27. M. P. Hoerling, J. W. Hurrell, T. Xu, G. T. Bates, A. S.Phillips, Clim. Dyn. 23, 391 (2004).

28. G. B. Bonan, Ecol. Appl. 9, 1305 (1999).29. G. B. Bonan, J. Clim. 14, 2430 (2001).30. We acknowledge the large number of scientists who

have assisted in the development of the models andtools used to create the simulations used in this study.Special thanks to A. Middleton, T. Bettge, and G.Strand for their assistance in running the model andassistance with data processing and to R. Leemansfor providing the SRES data. This research was sup-ported by the Office of Science (Biological and Envi-ronmental Research Program), U.S. Department ofEnergy, under Cooperative Agreement No. DE-FC02-97ER62402; NSF (grant numbers ATM-0107404 andATM-0413540); the National Center for AtmosphericResearch Weather and Climate Impact AssessmentScience Initiative supported by NSF; and the Centerfor Research, University of Kansas, Lawrence, KS.

Supporting Online Materialwww.sciencemag.org/cgi/content/full/310/5754/1674/DC1Materials and MethodsFigs. S1 and S2References

29 July 2005; accepted 25 October 200510.1126/science.1118160

Equivalent Effects of Snake PLA2Neurotoxins and Lysophospholipid–

Fatty Acid MixturesMichela Rigoni,1 Paola Caccin,1 Steve Gschmeissner,2

Grielof Koster,3 Anthony D. Postle,3 Ornella Rossetto,1

Giampietro Schiavo,2 Cesare Montecucco1*

Snake presynaptic phospholipase A2 neurotoxins (SPANs) paralyze the neuro-muscular junction (NMJ). Upon intoxication, the NMJ enlarges and has a reducedcontent of synaptic vesicles, and primary neuronal cultures show synapticswelling with surface exposure of the lumenal domain of the synaptic vesicleprotein synaptotagmin I. Concomitantly, these neurotoxins induce exocytosis ofneurotransmitters. We found that an equimolar mixture of lysophospholipids andfatty acids closely mimics all of the biological effects of SPANs. These resultsdraw attention to the possible role of local lipid changes in synaptic vesiclerelease and provide new tools for the study of exocytosis.

SPANs are major protein components of the

venom of many snakes (1–3). They block the

NMJ in a characteristic way (3–7). The phos-

pholipase A2 (PLA2) activity varies greatly

among different SPANs, and its involvement

in the NMJ block is still debated (3, 8, 9).

There is only a partial correlation between PLA2

activity and neurotoxicity among SPANs and no

overlap of surface residues required for neuro-

toxicity with those essential for PLA2 activ-

ity (8, 10). Here, we compared the effects of

SPANs on the mouse NMJ hemidiaphragm

preparation and on neurons in culture with those

of their hydrolysis products: lysophospholipids

(LysoPL) and fatty acids (FAs). To conclusive-

1Department of Biomedical Sciences and ConsiglioNazionale Ricerche Institute of Neuroscience, Universityof Padova, Italy. 2Cancer Research UK, London ResearchInstitute, London, UK. 3School of Medicine, Universityof Southampton, UK.

*To whom correspondence should be addressed.E-mail: cesare.montecucco@unipd.it

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9 DECEMBER 2005 VOL 310 SCIENCE www.sciencemag.org1678

www.sciencemag.org/cgi/content/full/310/5754/1674/DC1

Supporting Online Material for

The Importance of Land-Cover Change in Simulating Future Climates

Johannes J. Feddema,* Keith W. Oleson, Gordon B. Bonan, Linda O. Mearns, Lawrence E. Buja, Gerald A. Meehl, Warren M. Washington

*To whom correspondence should be addressed. E-mail: feddema@ku.edu

Published 9 December 2005, Science 310, 1674 (2005)

DOI: 10.1126/science.1118160

This PDF file includes:

Materials and Methods Figs. S1 and S2 References

Supporting online material for:

The importance of land cover change in simulating future climates

by

Johannes J. Feddema1*, Keith W. Oleson2, Gordon B. Bonan2, Linda O. Mearns2, Lawrence E. Buja2, Gerald A. Meehl2 and Warren M. Washington2

1 Department of Geography, University of Kansas Lawrence KS 66045 2 National Center for Atmospheric Research Post Office Box 3000, Boulder, CO 80307, USA.

* To whom correspondence should be addressed. E-mail: feddema@ku.edu This pdf file includes:

Materials and Methods Figures S1 and S2 References

1

Materials and Methods A. Development of the Land Surface datasets

To simulate the future land cover scenarios we obtained land cover information from the IMAGE 2.2 CDROM released by RIVM (S1). These datasets provided digital forms of the land cover conditions presented in the IPCC SRES report for the IMAGE 2.2 scenario simulations (S2). The IMAGE 2.2 datasets provided land cover information based on 18 land cover classes. However, for our simulations we needed to represent land cover information based on the 22 class National Center for Atmospheric Research Land Surface Model (LSM) land cover scheme (S3). When comparing the datasets we found large discrepancies between the natural vegetation distributions used in each (e.g. where one has evergreen needleleaf trees in Siberia the other had deciduous needleleaf trees etc.). These discrepancies result in significantly different climate simulations (S4). The IMAGE 2.2 datasets also included natural vegetation shifts due to simulated Greenhouse Gas (GHG) forced climate change. These temperature changes were obtained from the Upwelling-Diffusion Climate Model (UDCM) model (S1). Temperature change values from UDCM are then used in the Geographical Pattern Scaling model to obtain changes in monthly precipitation (S1). Finally the IMAGE 2.2 natural vegetation distributions for the GHG forcing conditions were determined by a Terrestrial Vegetation Model using these climate inputs (S1, S5).

While the IMAGE conversions are meritorious in the context within which they

were developed, we felt we could not include these vegetation shifts in our simulations. The PCM used in our study differs in its spatial extent and magnitude of GHG warming compared to the values used by the IMAGE team. Hence our experiment could infer vegetation change that was not compatible with the observed GHG warming in the PCM simulations. To avoid any incompatibilities between the IMAGE and LSM datasets and differences between the projected GHG warming trends from PCM and IMAGE, we decided to develop a hybrid methodology for creating our future land cover scenarios. We only used the IMAGE 2.2 human land cover classes that reflect human land use projections based on economic, political and demographic decisions. Background natural vegetation was held constant and used data from the original LSM classification system, reducing the need for translation between different land cover classification systems. Because of large uncertainties in feedbacks associated with dynamic vegetation and the effects of increasing atmospheric CO2 concentration on stomatal conductance we did not include these processes in our simulations.

Determination of the human land cover component for each hybrid land cover

dataset used the IMAGE 2.2 SRES agriculture and degraded grassland classes as a starting point. If the aggregated IMAGE 2.2 agricultural land cover class for a PCM grid cell was dominant, i.e. greater than 50% of the area, the cell was classified as agriculture. If the IMAGE 2.2 degraded grassland was dominant, then the PCM grid cell was assigned the NCAR LSM grassland land cover class. For those locations where a human land cover type was in the minority, and where the original NCAR LSM dataset had a human land cover type, the new natural vegetation type was determined by the dominant natural vegetation type of the surrounding grid cells and checked for consistency against

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the a potential vegetation dataset (S6). Details on the precise methodology and vegetation conversion schemes are given in an earlier study simulating historical land cover impacts on climate (S4).

All our simulations were initiated from a 100 year equilibrium experiment (S4)

using the hybrid present day land cover with 1870 atmospheric forcing conditions. From this equilibrium simulation we ran a 20th century transient atmospheric forcing simulation, while holding land cover constant to present day conditions. This end of this simulation was used as a starting point for each simulation using B1 or A2 atmospheric forcing with present day land cover to 2033. At that point we switched to the 2050 land cover datasets and, with a land model re-initialization, continued the simulation to 2066. At that point we switched to the 2100 land cover dataset and then continued the transient simulation to 2100. B. A second simulation of the A2 2100 land cover change scenario

In order to evaluate the impact of a rapid versus gradual response by the PCM to land cover change we elected to run a second A2 2100 land cover simulation. The simulations discussed in the main text transitions land cover from present day (model years 2000 to 2033) to a 2050 land cover for model years 2033 to 2066, and finally to 2100 land cover for model years 2066 to 2100. This second simulation maintained a constant present day land cover for model years 2000 to 2066, and then switched to 2100 land cover for model years 2066 to 2100. Hence the initial conditions of the simulation are slightly different. However, the results for both these A2 2100 simulations are very similar in terms of climate responses (Fig. S1). C. Geographical variation in climate impacts of Tropical Deforestation.

To evaluate the reason for different temperature responses to deforestation in tropical regions we singled out all grid cells that were converted from tropical broadleaf evergreen forest in the present day to become agriculture in any of the four future scenarios. For each grid cell we assessed the relative impacts of atmospheric and land cover forcing on the grid cell (Fig. S2). The mean annual temperature increases due to atmospheric forcing range from about 0.5-1° C for the 2050 B1 scenario to 2-3° C for the 2100 A2 scenario. The responses are relatively similar for the mean daily minimum and maximum temperatures. The additional climate response due to land cover change has a slightly larger range compared to the atmospheric forcing response, ranging from –0.5 to 2.5° C. Land cover forcing results in little change in mean annual maximum temperatures, while mean annual minimum daily temperature show a much greater range in values varying from –1 to 4° C, with a average increase in temperature. Land cover change responses are similar for both the B1 and A2 scenarios showing that land cover effects are largely insensitive to different atmospheric forcings. From this analysis it is primarily the increase in average daily minimum temperatures that affect the DTR in tropical regions.

When the grid cells are evaluated by season (not shown), and matched to their

locations, it appears that the warming of the grid cells is primarily a dry season event, an effect also found in previous work (S7). For example, compared to other regions, grid

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cells in northern Indochina show a much greater warming for minimum temperatures in DJF versus JJA. In the Congo the greatest response is in DJF (dry season) in the A2 2050 scenario when deforestation is primarily along the northern fringe of the tropical forest area. In the A2 2100 scenario the greatest response is in JJA when deforestation is along the southern fringe of the forested area in its dry season. Figures S1and S2

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Figure S1: JJA and DJF temperature differences and annual average diurnal temperature range due to land cover change in the A2 2100 scenario branched form the present day land cover simulation in 2066. Values are calculated by subtracting the greenhouse gas only forcing scenarios from a simulation including land cover and greenhouse gas forcings. Shaded grid cells are significant at the 0.05 confidence level.

Figure S2: Plots showing the impacts of land cover conversion from tropical broadleaf forest to agriculture and global warming on mean daily maximum, average and minimum temperatures and the annual diurnal temperature range. Grid points are identified by scenario (open symbols) and region (fill color).

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References

S1. RIVM (Rijks Instituut voor Volksgezondheid en Milieu), 2002. IMAGE 2.2 CD release and documentation. The IMAGE 2.2 implementation of the SRES scenarios: A comprehensive analysis of emissions, climate change and impacts in the 21st century. See http://www.rivm.nl/image/index.html for further information.

S2. N. Nakićenović, (lead author), Special Report on Emissions Scenarios, Cambridge

University Press, Cambridge, (2000). S3. G.B. Bonan GB A land surface model (LSM version 1.0) for ecological,

hydrological, and atmospheric studies: technical description and user's guide. NCAR Technical Note NCAR/TN-417+STR. National Center for Atmospheric Research, Boulder, Colorado.

S4. J.J. Feddema et al. Clim. Dyn. Online first, 2005. S5. J. Alcamo, R. Leemans, E. Kreileman (eds), Global change scenarios of the 21st

century. Results from the IMAGE 2.1 model. Pergamon & Elseviers Science, London. (1998).

S6. N. Ramankutty, J.A. Foley (1999) Estimating historical changes in global land cover:

croplands from 1700 to 1992, Global Biogeochem. Cycles 13, 997, (1999). S7. R. Avissar, D. Werth, J. Hydromet. 6, 134 (2005).

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List of Figures Figure S1: JJA and DJF temperature differences and annual average diurnal temperature

range due to land cover change in the A2 2100 scenario branched form the present day land cover simulation in 2066. Values are calculated by subtracting the greenhouse gas only forcing scenarios from a simulation including land cover and greenhouse gas forcings. Shaded grid cells are significant at the 0.05 confidence level.

Figure S2: Plots showing the impacts of land cover conversion from tropical broadleaf

forest to agriculture and global warming on mean daily maximum, average and minimum temperatures and the annual diurnal temperature range. Grid points are identified by scenario (open symbols) and region (fill color).

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