Ter
ial vveegeta
tion and its ef
lima
te dur
ing the la
test Cr
etaceous
errrestr
estrial
etation
efffects on cclima
limate
during
latest
Cretaceous
G.R. Upc
hur
Upchur
hurcch, Jr
Jr..
Department of Biology, Southwest Texas State University, San Marcos, Texas, 78666 U.S.A.
[email protected]
B.L. Otto-Bliesner
Climate Change Section, National Center for Atmospheric Research, P.O. Box 3000, Boulder,
Colorado, 80307-3000, U.S.A. [email protected]
C.R. Scotese
Department of Geological Sciences, University of Texas at Arlington, Arlington, Texas, 76019
U.S.A.
Geological Society of America Special Papers (in press)
ABSTRA
CT
ABSTRACT
Vegetation cover is a factor that can strongly influence climate but has not been considered in
modeling studies of Cretaceous climates. This study reconstructs vegetation for the latest Cretaceous
(Maastrichtian) with a global data base of paleoclimatic indicators, then evaluates the role of vegetation
in warm Cretaceous climates through modeling experiments and comparison of model output with
paleoclimatic indicators. Baseline simulation of Maastrichtian climate with the land surface coded as
bare soil produces high-latitude temperatures that are too cold to explain the documented paleogeographic distribution of forest and woodland vegetation. In contrast, simulations that include forest
vegetation at high latitudes show significantly warmer temperatures that are sufficient to explain the
widespread geographic distribution of high-latitude forests. These warmer temperatures result from
decreased albedo associated with forest vegetation and feedbacks between the land surface and adjacent
oceans. The best agreement between model simulations and paleoclimatic indicators is when the simulation uses a realistic distribution ofpaleo vegetation, though excessively cold winter temperatures still
occur in the interior of North America. Positive feedbacks between high-latitude forests, the atmosphere, and ocean contributed significantly to high-latitude warming during the latest Cretaceous and
imply that forest vegetation may have been an important source of high-latitude warmth during other
periods of warm global climate.
INTRODUCTION
One major problem in paleoclimatology is understanding how warm polar temperatures can be
maintained during periods of global warmth. Two mechanisms are commonly proposed to explain high
polar temperatures: 1) increased atmospheric pCO(Barron and Washington, 1985), and 2) increased
ocean heat transport (Rind and Chandler, 1991). Increased atmospheric pCOwarms global climate by
absorbing outgoing longwave radiation and reradiating it back to the surface, which causes surface
temperatures to increase at both equatorial and high latitudes (Crowley, 1991). Increased ocean heat
transport, in contrast, removes heat from equatorial latitudes and transports it to high latitudes, simultaneously cooling the tropics and warming the poles (Crowley, 1991). The most commonly proposed
mechanism for increasing ocean heat transport during the Cretaceous is generation of warm saline
bottom water at low latitudes and upwelling of bottom water at high latitudes (Brass et al., 1982). Both
mechanisms create positive feedbacks that further warm the climate: these include inhibition of sea ice
development, which increases absorption of solar radiation through decreased ocean albedo; decreased
snow accumlation and earlier snow melt, and increased concentrations of atmospheric water vapor at
high latitudes, which increase the greenhouse effect on a regional scale (but may also increase clouds)
(Rind and Chandler, 1991). Both mechanisms are supported by quantitative geochemical models
(Berner, 1991, 1994), conceptual oceanographic models (Brass et al., 1982), and geochemical proxies
for atmospheric pCOand ocean surface temperature (Freeman and Hayes, 1992; Andrews et al., 1995;
Huber et al., 1995).
Neglected in discussions of warm climate dynamics is the role of the land surface in maintaining
high-latitude warmth, and in particular the role of vegetation and soils. Vegetation alters surface albedo,
surface roughness, and the balance between sensible and latent heat flux from the land surface (Sellers
et al., 1986; Dickinson and Henderson-Sellers, 1988), parameters that affect both absorption of solar
radiation and transfer of heat, momentum, and water vapor between the land and atmosphere. Vegetation, especially forest and woodland vegetation, increases both the amount of solar radiation absorbed
by the land surface and the efficiency with which heat and water vapor are transferred from the land to
the atmosphere (Dorman and Sellers, 1989). Soil color can significantly affect surface albedo in vegetation types characterized by low fractional cover( Dickinson et al., 1986). The effects of vegetation are
especially pronounced at high latitudes, where boreal forests exert an effect on regional climate comparable to a doubling of pCOor changes in orbital forcing (Bonan et al., 1992; Foley et al., 1994). Climatic effects of high-latitude forests include reduced snow depth, reduced fractional coverage of snow,
and controlling the position of the polar front due to increased absorption of solar radiation by trees and
increased sensible heat flux from the land surface (Bryson, 1966; Foley et al., 1994; Pielke and Vidale,
1995). These factors must have played a role in maintaining warm polar climates during the Late
Cretaceous, because high-latitude forests occupied extensive regions that today are occupied by tundra
and glacial ice (Creber and Chaloner, 1985; Wolfe and Upchurch, 1987; Horell, 1990; Spicer and
Parrish, 1990).
This study explores the importance of forests in maintaining high-latitude warmth during the
Late Cretaceous and feedbacks between the land, atmosphere, and ocean. This importance is evaluated
through a combination of numerical climate modeling and comparison of model results with a global
data base of climatic indicators. To examine the importance of vegetation in the climate system we
simulate latest Cretaceous (Maastrichtian) climate under different types of vegetation cover, then examine the extent to which modeled climates resemble inferred climates. We focus on the Maastrichtian
because of its importance for understanding extinctions at the Cretaceous-Tertiary boundary. However,
many conclusions can be extrapolated to other stages of the Cretaceous, because the Late Cretaceous
was characterized by areally extensive high-latitude forests (Wolfe and Upchurch, 1987; Spicer and
Parrish, 1990).
METHODS OF AN
AL
YSIS
ANAL
ALYSIS
The model
The model used in this study is the National Center for Atmospheric Research (NCAR) GENESIS Global Climate Model, Version 1.02 (Thompson and Pollard, 1995a, b). The model consists of
submodels for the atmosphere, ocean, and land surface. The atmospheric submodel is based on the
NCAR Community Climate Model version 1 (CCM1). The atmosphere is described by equations of
fluid motion, thermodynamics, and mass continuity along with representations of radiative and convective processes, cloudiness, precipitation, and the orographic influence of terrain. The CCM1 code has
been extensively modified to incorporate new physics for solar radiation, water vapor transport, convection, boundary layer mixing, and clouds. The horizontal resolution is R15, which corresponds to a 4.5°
latitude by 7.5° longitude spectral transform grid, on average. The atmosphere is subdivided vertically
into 12 levels.
The ocean submodel of GENESIS contains a 50-m deep mixed-layer ocean coupled to a thermodynamic sea-ice model. The ocean representation crudely captures the seasonal heat capacity of the
ocean mixed layer but ignores salinity, upwelling, and energy exchange with deeper layers. Poleward
ocean heat transport is parameterized using the “0.5 x OCNFLX” case of Covey and Thompson (1989).
A six-layer sea-ice model, patterned after Semtner (1976), predicts local changes in sea ice thickness
through melting of the upper layer and freezing or melting of the bottom surface.
The land-surface submodel of GENESIS (Pollard and Thompson, 1995a, b) incorporates a landsurface transfer component (LSX) to account for the physical effects of vegetation, a six-layer soil
component, and a three-layer thermodynamic snow component. Soils are categorized by color and
texture. Vegetation is represented by two layers: an upper layer of trees or shrubs, and a lower layer of
grass or bare soil. Vegetation prescription includes type, leaf area index (LAI), fractional coverage of
plants, canopy height, solar transmittance/reflectance, and stomatal resistance. Stomatal resistance is
the resistance of leaves to the loss of water, which varies with plant species, vegetation type, atmospheric humidity, and atmospheric pCO(Dorman and Sellers, 1989; Pollard and Thompson, 1995a).
Boundary conditions
Boundary conditions prescribed in model simulations include pCO, solar luminosity, land-sea
distribution, and elevation. Atmospheric pCOis set at 2 times pre-industrial levels (580 ppm), a conservative figure that is slightly higher than the “best estimate” of geochemical models (Berner, 1994) and
significantly lower than the best estimates of isotopic proxies (Freeman and Hayes, 1992; Andrews et
al., 1995). Atmospheric pCOwas set at this conservative level so that the sensitivity of high-latitude
climate to vegetation cover could be readily evaluated using a coarse-resolution model of
paleovegetation (see below). Solar insolation conforms to present-day solar orbital configuration, with
the solar constant adjusted to .664% less than at present (Endal, 1981; Crowley and Baum, 1992).
Paleogeography is based on a new reconstruction for the latest Cretaceous (66 m.y.a.). Plate
positions represent an update of earlier plate-tectonic models (e.g., Scotese et al., 1988), while land-sea
distributions were determined from recently published regional paleogeographies and our working
knowledge of regional geology. We reconstructed paleogeography for the very late Maastrichtian, a
period of major regression and eustatic drawdown, because of its relevance to modeling climatic change
across the Cretaceous-Tertiary boundary. Differences between our reconstruction for the very late
Maastrichtian and previously published reconstructions for the early Maastrichtian include: 1) a narrower Western Interior Seaway that is no longer continuous between the northern and southern Western
Interior, and 2) a narrower Trans-Saharan seaway that is no longer continuous across north Africa (Fig.
1). Paleoelevations are set to 250 m for lowlands and 1,000 and 2,000 m for uplands, a compromise
between two published paleogeographic formulas (Hay, 1984; Ziegler et al., 1985). A map that illustrates paleoelevation and details of coastlines is published in Upchurch et al. (1998, Fig. 1).
Simulations
Four model simulations explore sensitivity to vegetation. BARESOIL supplies benchmark
statistics for no vegetation feedback and is the boundary condition used in earlier published simulations
of Cretaceous climate. Soil texture and color are set to intermediate values in the range given by the
Biosphere-Atmosphere Transfer Scheme (BATS) with a land albedo that ranges from approximately
15% under snow-free conditions to 84% under fresh snow cover (Dickinson et al., 1986). EVERGREEN TREE provides maximum vegetation feedback to encompass the range of climatic effects
exerted by areally extensive high-latitude forests during the Cretaceous. Broad-leaved evergreen trees
(tropical rainforest) are prescribed over all land surfaces at a fractional coverage of 98%; surface albedo
over high-latitude land varies only slightly from 12% in summer to 18% in winter. An additional simulation (not described in this paper), EVERGREEN TREE-OHT, adds tripled ocean heat transport to the
effects of vegetation to explore the sensitivity of a fully vegetated Earth to warm Cretaceous polar
oceans. Land-surface conditions in EVERGREEN TREE-OHT are identical to those in EVERGREEN
TREE.
The final two simlations reported here are based on best estimates of vegetation (Table 1, Fig. 1,
and below). BESTGUESS uses the best estimate of vegetation, which is based on a variety of lithologic
and paleontologic indicators (see below). These indicators are supplemented by model output from the
EVERGREEN TREE-OHT simulation for regions where geologic data are absent. Fossil vegetation
was assigned physiological parameters based on values listed for the most similar extant vegetation by
Dorman and Sellers (Dorman and Sellers 1989). When a type of fossil vegetation had no extant counterpart, averages were made for the two or three most similar extant vegetation types (Table 1).
BESTGUESS-RS2 is similar to BESTGUESS but doubles the resistance of leaf stomata to diffusion of
water vapor. Unlike a previous modeling study (Pollard and Thompson ,1995b) our study doubles only
the minimum resistance of leaves to diffusion of water vapor and not maximum resistance. Doubling of
stomatal resistance is geologically realistic because extant plants show an approximate doubling of
stomatal resistance when grown under 2 times pre-industrial levels of pCO(Pollard and Thompson,
1995b). No understory layer was recognized for any vegetation type because LSX only recognizes grass
for the understory. Grasses do not appear in the fossil record until the Paleocene (Muller, 1981; Crepet
and Feldman, 1991), and grassland vegetation does not appear in the fossil record until the MiocenePliocene (Cerling et al., 1994).
BARESOIL, EVERGREEN TREE, and BESTGUESS simulations are integrated for 20 years. The
BESTGUESS-RS2 simulation is integrated for 18 years. Years 14–18 are averaged for the monthly
temperature maps. Also computed is the standard deviation (S.D.) of the monthly average temperatures
for the individual years from the 5-year statistics. The standard deviations can be used in conjunction
with the Student t-test (Panofsky and Brier, 1965) to separate the signal from the noise (Chervin and
Schneider, 1976) when comparing differences between simulations. For this set of simulations, the
difference between two simulations is statistically significant at the 95% level if the change is greater
than 1.46 S.D. That is, there is a 5% probability that this difference could occur by chance. Those
differences less than these limiting values are not statistically significant and fall within the year-to-year
variability of model results.
Paleo
vegeta
tion and paleoc
lima
tic da
ta
aleov
etation
paleoclima
limatic
data
All model simulations were compared to a global data base of paleovegetation indicators to evaluate the success of each simulation in reproducing Maastrichtian climate (see listing of major references
in the appendix). Paleobotanical indicators of vegetation and climate include: 1) the physiognomy of
leaf megafossils (Wolfe and Upchurch, 1987), 2) life form as inferred from leaf megafossils and fossil
woods (Upchurch and Wolfe, 1993), 3) the physiognomy of dispersed leaf cuticles (Upchurch, 1995),
and 4) the anatomy of fossil woods (e.g., the presence/absence of growth rings) (Creber and Chaloner,
1985). The relationship between these indicators and climatic variables such as temperature and precipitation has been documented to various degrees for representatives from Recent vegetation (Creber,
1977; Wolfe, 1979; Box, 1981; Upchurch, 1995).
Palynologic data were restricted to select environmental indicator species, rather than whole
assemblages or large numbers of indicators. This was done for two reasons. First, determining floristic
patterns from floral lists is risky because of incomplete sampling of the flora and inconsistent taxonomic
concepts. Second, problems exist in associating many palynomorphs with individual life forms because
of incomplete systematic comparisons between fossil and modern plants. Because of these problems we
favored a conservative approach at this stage of the study and restricted our analysis to the following
palynological indicators: 1) the spores of Sphagnum moss, which indicate wet soils and the tendency of
the landscape towards acidification, 2) the spores of aquatic ferns, which indicate ponded water, 3) the
presence of at least two or three pollen types assignable to extant tree genera, which together indicate
forest or woodland vegetation, 4) structurally complex spores of tree ferns, and 5) structurally complex
pollen of palms that can be assigned to an extant subfamily. Structurally simple spore and pollen genera
that are assigned by many stratigraphic palynologists to tree ferns and palms (in particular, Cyathidites
and Arecipites) were not used because other plant families with different life forms produce structurally
identical spore and pollen types (Erdtman, 1971; Skog, 1988). Tree ferns and palms are important as
paleoclimatic indicators because plants with their growth habit cannot live in climates with prolonged
hard freezes or below-freezing cold-month means (e.g., Box, 1981).
Sediment types provided both corroborative and complementary data. Analyzed sediment types
include coals, which indicate wet soils and favorable rainfall regime; and evaporites and calcretes,
which indicate a seasonal or annual moisture deficit. Data for the paleovegetation reconstruction, some
of which are listed in the appendix, were derived from published paleobotanical and paleolithological
compilations (Wolfe and Upchurch, 1987; Horell, 1990; Upchurch and Wolfe, 1993), new sources in the
primary literature, examination of unpublished fossil assemblages, and an unpublished compilation of
lithologic climatic indicators for the Phanerozoic (Boucot et al., in prep). Published compilations for
megafossils and lithology were checked for accuracy against the primary literature sources. Table 1
outlines the criteria used to recognize regional vegetation types, here considered the equivalent of
biomes. Data points tend to be concentrated in North America and have been reduced to fewer than 100
plotted points per map for purposes of clarity.
A global vegetation map (Fig. 1) was used for simulating climate with realistic vegetation. This
map divides the world into seven distinct biomes, some of which will be subdivided as more data become available. Grid squares with site data were used to construct the map. Model output from the
EVERGREEN TREE-OHT simulation was used to interpolate between individual data sites; we considered this preferable to subjective methods or intuitive models of atmospheric circulation. The EVERGREEN TREE-OHT simulation (not considered in detail here) was used for interpolation because it fit
the proxy data better than BARESOIL or EVERGREEN TREE.
Model simulations were evaluated for goodness of fit using the Köppen classification of climate,
which provides a familiar and qualitative means of visualizing regional climatic variation. We modified
the Köppen classification after Wolfe (1979) so that: 1) the boundary between subtropical (C) and boreal
(D) climates is delimited by a cold-month mean of 1°C, and 2) the boundary between subtropical (Cfa)
and marine (Cfb) climates is delimited by a warm-month mean of 20°C. Paleobotanical and
paleolithological indicators were superimposed over Köppen maps to determine areas of agreement and
disagreement. The most strongly emphasized paleoclimatic indicator is the distribution of forest and
woodland vegetation, which is controlled by both warm-month temperature and precipitation (Wolfe,
1979). Also used are the distribution of plant types that indicate above-freezing cold-month means,
such as palms and gingers (Box, 1981; Wing and Greenwood, 1993). More precise paleoclimatic
estimators, such as CLAMP (Wolfe, 1993), have been applied to a much more geographically limited
suite of assemblages (Wolfe, 1990); these are not considered in this paper because global data are not
yet available.
RESUL
TS
RESULTS
Bar
esoil and Ev
er
green Tree Sim
ula
tions (Figs. 2–4, Table 2)
Baresoil
Ever
erg
Simula
ulations
In the BARESOIL simulation January surface air temperatures (temperatures simulated at 2 m
above the surface) are extremely cold at high latitudes in the Northern Hemisphere. The freezing isotherm extends equatorward to 45°N over North America and 38°N over eastern Asia. The Western
Interior Seaway of North America is almost completely frozen, and temperatures as low as –34°C are
modeled over the Asian interior. Most of Europe has mean winter surface temperatures below freezing.
Antarctica and Australia are above freezing during the austral summer. Antarctica has no permanent ice
cover; all snowcover melts during the summer months, precluding ice sheet development. In July,
modeled surface temperatures are well below zero over Antarctica, and sea ice fringes the continent.
Subfreezing temperatures also occur over the southern portions of Australia.
Cold winter temperatures at high latitudes in both hemispheres result from extensive snowcover
and its high reflectivity. Mean land surface albedo ranges from 0.39–0.40, with values as high as 0.84
during the winter. Mean tropical temperatures range from 25–35°C. Global mean surface temperature
in the BARESOIL simulation is 15.8°C, warmer than what the model predicts for the present day
(14.8°C).
In the EVERGREEN TREE simulation coverage of all land surface with evergreen forests
produces both negative and positive feedbacks. These interact to modify modeled surface temperature.
The presence of trees at high latitudes increases both sensible and latent heat flux from the land surface,
which in the absence of other changes would cause a cooling of the land surface. However, the presence
of these evergreen trees also causes snow intercepted by the canopy to blow off with an e-folding time
of one day. This decreases surface albedo and increases absorbed solar radiation by allowing the sun to
see green tree tops during the winter, rather than snow-covered ground. The net result is surface warming due to the greater magnitude of albedo effects. In January, land surface temperatures are as much as
20°C warmer over high northern latitudes (e.g. Alaska), and the average position of the freezing isotherm shifts from 42°N to 48°N. Subfreezing temperatures still exist over the northern interior regions
of North America and Asia. In July, the Antarctic interior is 4–8°C cooler in the EVERGREEN TREE
simulation, though this cooling is generally not significant statistically. Mean tropical surface temperature warms by 1.2°C. Global mean surface temperature warms by 2.5°C to a value of 18.3°C in a late
Maastrichian world covered with evergreen trees.
Terrestrial vegetation has an important influence on oceanic climate. Surface temperatures over
the tropical and mid-latitude oceans warm by up to 4°C in the EVERGREEN TREE simulation. This
warming results from advection of warmer temperatures from the nearby land. High-latitude oceans
warm by as much as 16°C. This results in a substantially lower fractional coverage by sea ice in the
EVERGREEN TREE simulation than in the BARESOIL simulation (0.17 vs. 0.38 in the Southern
Hemisphere and 0.06 vs. 0.17 in the Northern Hemisphere). The average winter sea-ice position (sea ice
freezes at approximately –2°C) retreats from 60°N to 70°N in January and from 65°S to 70°S in July.
Bestguess and Bestguess-RS2 Sim
ula
tions (Figs. 5–7, Table 2))
Simula
ulations
In the BESTGUESS simulation the late Maastrichian land surface is covered by a range of
vegetation types whose distribution is controlled by temperature and precipitation. Broad-leaved evergreen forests and woodlands are restricted to narrow equatorial bands in the tropics and broad midlatitudinal bands that range from 30° to 55–70° in North America, Europe, Asia, and Australia. Deserts
occupy significant areas between 10° and 40° in Asia, Africa, and South America. Deciduous forests
cover all high-latitude land areas. The net result is substantial cooling at high northern latitudes in
January relative to the EVERGREEN TREE simulation. The land surfaces cool on average by 9°C,
with cooling of up to 16°C over northern Asia. The temperate deciduous forests that cover these regions
in the BESTGUESS simulation provide less masking of snowcover than evergreen forests because of
lower fractional cover and the absence of winter foliage. Surface albedo increases to an average of 0.19,
a value still considerably below that of the BARESOIL simulation. The northern oceans cool slightly
(0–4°C), but this cooling is not statistically significant except in the Pacific Ocean. The freezing isotherm moves equatorward to 45°N over land and 65°N over the northern oceans, losing about half of
what was gained in the EVERGREEN TREE experiment.
Africa and South America have some regions that undergo warmingin January and other regions
that undergo cooling. Land covered with semi-deciduous tropical forest warms by as much as 8°C
relative to the EVERGREEN TREE experiment. This warming is due to a decrease in clouds and a
decrease in evaporation, which cause a corresponding increase in absorbed solar radiation and increase
in sensible heating. Land covered with desert, in contrast, undergoes cooling. This cooling is caused by
increased surface albedo, which causes a corresponding decrease in absorbed solar radiation.
The EVERGREEN TREE and BESTGUESS simulations of surface temperature are more similar
for July than for January. The southern continents are generally cooler in the BESTGUESS experiment
during the winter. The northern continents are generally warmer in the BESTGUESS experiment during
the summer, but temperatures changes are generally small (±8°C) and are statistically significant in only
limited regions.
Doubling stomatal resistance of the best-guess vegetation produces only small changes. In
January, warming over Asia negates some of the cooling in the BESTGUESS experiment, but over
North America the BESTGUESS-RS2 has additional cooling. In July, the northern continents are
generally warmer, but the changes are small and not statistically significant.
Var
ia
tion in rreegional cclima
lima
te (Fig. 8)
aria
iation
limate
The BARESOIL and EVERGREEN TREE simulations provide marked contrasts with each other
and with the present day. In the BARESOIL simulation (Fig. 8a) marine climates (Cfb, Cfc) with cool
summers, mild winters, and temperate evergreen forests cover southern Africa and South America,
central Europe, and parts of North America, Australia, and New Zealand. Cool temperate (D) climates
with below-freezing winters and broad-leaved deciduous or boreal forests occupy most of Antarctica
and major regions of Australia, North America, and Asia. Tundra (ET) is predicted for much of Asia
and on the margins of North America and Antarctica. No permanent ice cover is predicted in the
BARESOIL simulation.
In the EVERGREEN TREE simulation (Fig. 8b) warmer summer temperatures cause subtropcial
(Cfa) climates to replace marine (Cfb, Cfc) and boreal (D) climates in North America, Australia, and
parts of southern Europe. Marine climates migrate poleward and replace colder boreal (D) climates in
parts of Europe, Australia, North America, and Asia. Tundra (ET) climates are replaced by marine or
boreal climates in Asia, North America, and Antarctica. Tundra climate is restricted to 2% of the land
area and occupies narrow fringes of North America, Asia, and the southern Antarctic Peninsula. Tropical rainforest appears in southern Mexico and increases in areal extent in Africa and Southeast Asia.
Both the BARESOIL and EVERGREEN TREE simulations produce extensive areas of desert
(BW) and semi-desert (BS) climate (Figs. 8a, 8b). Deserts and semi-deserts occupy large regions of
southern Asia, Africa, and central to southern South America. The general location of dry climates
remains the same between the simulations, which indicates that the general location of deserts and semideserts is controlled by factors other than vegetation. However, the areal extent of dry climates and the
location of boundaries between dry (B) and other climates are sensitive to prescribed plant cover.
The BESTGUESS and BESTGUESS-RS2 simulations (Figs. 8c, 8d) strongly resemble the
EVERGREEN TREE simulation but show climatic boundaries that can shift in response to increased
rainfall or increased summer warming. In the BESTGUESS simulation summer warming causes tundra
to disappear along the southern Antarctic peninsula and become restricted to small fringes along the
Arctic Ocean. In the BESTGUESS-RS2 simulation further summer warming causes only one grid
square of tundra to remain along the Arctic Ocean. Summer warming also causes subtropical climates
to expand farther poleward and replace marine climates. Changes in rainfall under realistic vegetation
cause tropical rainforest climates to expand slightly in South America and Africa but occupy slightly
less area in Southeast Asia. Deserts and semi-deserts increase in areal extent in Africa and Asia under
realistic vegetation, with desert replacing semi-desert in southwest Asia.
On a global scale, inclusion of progressively more realistic vegetation in model simulations
increases the areal extent of predicted forest climate. At high latitudes this increase is due to increased
summer temperatures, which result from increased absorbed solar radiation by low-albedo deciduous
forests and decreased clouds resulting from decreased evapotranspiration. At low latitudes tropical
rainforest climates increase in areal extent at the expense of savanna climates due to the increased areal
extent of year-round precipitation. Dry climates also show a slight increase in areal extent under realistic vegetation, especially in Africa.
Compar
ison of model sim
ula
tions with paleo
vegeta
tion (Figs. 8, 9)
Comparison
simula
ulations
paleov
etation
Comparison of Maastrichtian forest distribution with Köppen climates predicted by model
simulations (Fig. 8) indicates that vegetation greatly improves agreement between predicted and actual
distribution of forest vegetation, especially at high latitudes. The BARESOIL simulation (Fig. 8a)
incorrectly predicts tundra (ET) climates for large regions of Alaska, Siberia, and coastal Antarctica,
regions where fossil assemblages indicate forests (and hence warm-month means >10°C). The areal
distribution of tropical rainforest is also underestimated relative to proxy data, especially for east Africa.
The EVERGREEN TREE simulation (Fig. 8b) shows a better fit with paleoclimatic data by correctly
predicting forest vegetation for most high-latitude sites, especially in North America and mainland
Antarctica. Tropical rainforest vegetation is also correctly predicted for east Africa. However, some
coastal sites in eastern Siberia and the southern Antarctic Peninsula are incorrectly predicted as tundra,
and the areal extent of tropical rainforest is underestimated in west Africa and South America.
The BESTGUESS and BESTGUESS-RS2 simulations (Figs. 8c, 8d) show the best fit with
indicators of forest vegetation. Both simulations correctly predict forest vegetation for the southern
Antarctic Peninsula. Most tropical rainforest sites are also correctly predicted. The BESTGUESS-RS2
simulation is somewhat better than BESTGUESS in that it correctly predicts forest vegetation for all
coastal sites in eastern Siberia. One major distinction between the two simulations is that tropical
rainforest forms a continuous equatorial band in Africa in BESTGUESS-RS2. However, no data currently exist from the African interior to determine whether this represents an improvement in simulating
tropical hydrology.
Paleobotanical indicators of above-freezing winter temperatures conflict with predicted coldmonth means in continental interiors at high middle latitudes (Figs. 8, 9). Cold-month means of 5° and
1° C, which predict the occurrence of biologically significant freezing on a global scale (Prentice et al.,
1992) and in East Asia (Wolfe, 1979), respectively, occur as much as 15° south of above-freezing indicators in all simulations. Indicators of above-freezing winters include Western Interior plants such as
palms and gingers, which cannot tolerate below-freezing cold-month means and prolonged exposure to
hard freezes (Box, 1981; Wing and Greenwood, 1993), and Greenland leaf assemblages that indicate
mean annual temperatures greater than 20° C (lower Atanikerdluk leaf assemblages) (Wolfe and
Upchurch, 1987). The EVERGREEN TREE simulation shows a slight improvement relative to
BARESOIL because of low winter surface albedo due to masking of snow by evergreen leaves. However, this slight improvement in the Western Interior and Greenland is lost in the BESTGUESS and
BESTGUESS-RS2 simulations because of the absence of winter foliage and somewhat increased winter
surface albedo. EVERGREEN TREE-OHT, which increases ocean heat transport by a factor of three
relative to other simulations, provides a much better simulation of cold-month means. This could
indicate that, at high latitudes, increased polar heat transport by oceans exerted a major control over
winter temperatures (Herman and Spicer, 1996) and that vegetation exerted a major control over summer temperatures. However, recent simulations of Cenomanian climate (Valdes et al., 1996) indicate
that higher (5x pre-industrial) levels of atmospheric pCOalso can cause warm polar oceans; thus, the
combination of high pCOand vegetation-ocean feedbacks might provide an alternative explanation for
the high-latitude winter warmth documented by latest Cretaceous paleobotanical indicators.
DISCUSSION
Our simulations indicate that vegetation played an important role in maintaining high-latitude
warmth during the latest Cretaceous and in maintaining high global temperatures. The presence of highlatitude forests significantly decreases albedo, which increases absorbed radiation and warms the land
surface. Warming occurs both in summer and winter, which removes some of the major discrepancies
between model output and the paleobotanical record. Replacing evergreen forest with deciduous forest
results in slightly warmer summer temperatures but lower winter temperatures. These warmer summer
temperatures help explain the widespread occurrence of forests at high latitudes during the latest Cretaceous and the apparent absence of tundra vegetation at low elevations. Tundra at lower elevations is not
documented until the Miocene, at least in the Northern Hemisphere.
Model simulations demonstrate an important climatic linkage between the oceans and the land
surface during the latest Cretaceous. High-latitude forests, with their low surface albedo relative to bare
soil, warm adjacent oceans by as much as 16°C. This oceanic warming results initially from warming of
the adjacent land surface through increased absorbed solar radiation, followed by advection of warm air
to the oceans. Ocean warming, in turn, initiates positive feedbacks that further increase high-latitude
temperatures. Sea ice development is inhibited by warmer oceans, which delay the formation of sea-ice
in winter and reduce both its thickness and fractional cover. Sea ice melts faster and earlier in the spring
because of its reduced thickness and fractional cover, and because the land surface warms more in the
spring from the presence of forest vegetation. Earlier melting of sea ice allows greater ocean warming
during the spring and summer, which further inhibits sea ice development during the fall and winter.
Inhibition of sea ice development lowers the average albedo of the ocean, which in turn absorbs more
solar radiation and undergoes warming.
Our model results indicate that warm high-latitude temperatures during the Late Cretaceous can
result from the interaction of several factors. This, in turn, implies that the absence of glacial ice at high
latitudes could have resulted from the combined effect of increased pCO, paleogeography, and reduced
surface albedo due to forest vegetation. Our BARESOIL simulation for 66 m.y.a. indicates that paleogeography and elevated pCOare sufficient to preclude development of glacial ice at high latitudes under
present-day orbital configuration. This reinforces results obtained by Crowley et al. (1986) with a much
simpler energy budget model (EBM), which indicates that the greater areal extent of continuous highlatitude landmass during the Cretaceous and early Tertiary inhibited the development of glacial ice by
enhancing the seasonal cycle and increasing summer temperature, the most critical factor in the initiation of glaciation. The presence of high-latitude forests amplifies the “anti-glaciation” effects of paleogeography and elevated pCObecause forest vegetation increases temperature, especially during the
summer. Areally extensive polar forests, therefore, provide a “margin of safety” against snow accumulation and glaciation that may have been important during periods of cold summer temperatures resulting from Milankovitch climatic cyclicity.
Inclusion of realistic vegetation in model simulations of latest Cretaceous climate still cannot
completely reconcile model simulations with the paleobotanical record. Summer temperatures at high
latitudes still appear too cool based on the diversity of woody plants (Frederiksen et al., 1988) and the
subtropical affinities of some high-latitude plant groups (Askin,1989). Winter temperatures are definitely too cold at middle latitudes of the Western Interior of North America and in Greenland, regions
where the megafloras indicate above-freezing cold-month means. This discrepancy is important, because our global data base of paleoclimatic indicators conservatively estimates winter temperatures.
Such conservatism results from excluding many pollen types that other authors use to indicate warm
winter temperatures (e.g., Arecipites and Pandaniidites), which extend farther poleward than the
megafossil indicators. Therefore, the temperature discrepancy between the model and paleobotanical
indicators requires a mechanistic explanation.
We propose four possible reasons for the excessively cold winter temperatures produced by our
simulations. First, prescribed boundary conditions may have included excessively low levels of atmospheric pCO. Isotopic proxies indicate the possibility of 5–6 times pre-industrial levels of atmospheric
pCOduring the latest Cretaceous (Freeman and Hayes, 1992; Andrews et al., 1995). Increased
pCOwould increase absorbed infra-red radiation, which would warm summer and winter temperatures
and move the freezing isotherm poleward. Second, uncertainties exist regarding the position of the
Western Interior Sea during the latest Cretaceous. Increased areal extent of the Western Interior Sea
would increase the buffering of adjacent land areas against low temperatures during the winter. Third,
we may have underestimated poleward heat transport in the oceans: our increased poleward heat transport simulation moves the freezing isotherm poleward and reduces the area of discrepancy. Finally,
many discrepancies between model simulations and paleobotanical indicators of winter temperature
occur near the margin of the Western Interior Sea, a region of sharp thermal contrast in the model between adjacent oceanic and land grid cells. This could indicate problems with our model in simulating
meso-scale climatic variation, because coarse-resolution atmospheric general circulation models can
poorly simulate regional climates in areas of strong thermal contrast.
So what was the mechanism of high-latitude warmth during the latest Cretaceous? We suggest
that several variables interacted to create the warm conditions of the Late Cretaceous. Increased atmospheric pCOincreased absorption of infra-red radiation. Areally extensive high-latitude landmasses
enhanced the seasonal cycle and inhibited the development of permanent snow cover. Areally extensive
polar forests increased absorption of solar radiation at high latitudes and warmed temperatures yearround, enhancing the tendency of areally extensive high-latitude landmasses towards warm summer
temperatures. Increased ocean heat transport may have increased high-latitude winter temperatures
while simultaneously preventing the tropics from overheating under strong greenhouse forcing. Positive
feedbacks between the land, ocean, and atmosphere further increased high-latitude temperatures. A
multi-variable mechanism combined with positive feedbacks is intuitively appealing and explains many
seemingly unrelated boundary conditions for the Late Cretaecous. However, the ultimate validity of this
hypothesis will be determined by future modeling studies performed in conjunction with more stringent
comparisons of model output and the paleoclimatic record.
Table 1 Latest Cretaceous vegetation types and their defining features*
_____________________________________________________________________________
Biome
number
Biome name
Recognition criteria
Closest
SiB
Biomes
Canopy
Height
Ar
eal
Areal
ver
cov
co
Stomatal
Resistance
(s m-1)
____________________________________________________________________________________________
1
Tropical rainforest
2
Tropical semideciduous forest/
woodland
3
Subtropical broadleaved evergreen
forest and woodland
4
Desert and semidesert
5
Temperate evergreen
coniferous and
broad-leaved forest
7
Polar deciduous
forest
8
Bare soil (used to
code cold deserts)
Equatorial latitude plus one
or more of the following:
coals, megafloras with large
leaves and drip tips, tree
ferns
Tropical latitudes plus the
absence of coals and
evaporites; calcretes may be
present
Middle latitudes; angiosperm
dominance in megafloras
and palynofloras plus:
megafloraswith small
evergreen leaves and few or
no drip tips, presence of
palms or gingers, fossil
wood without growth rings,
thick angiosperm leaf
cuticles, cold-sensitive
fungal taxa
Low and middle latitudes;
Evaporites, calcretes, or
evidence for treeless
conditions
High middle latitudes;
high relative abundance and
diversity of conifers in
palynofloras and cuticle
assemblages plus: numerous
probable evergreens in
palynofloras and cuticle
assemblages, cold-sensitive
fungal taxa
High latitudes; megafloras
without tundra leaf form and
dominated by deciduous
angiosperm and conifer
leaves, fossil tree stumps
with annual growth rings,
at least 2–3 tree genera in
pollen record
Calcretes present, associated
with extremely low-diversity
palynofloras
1
35 m
0.98
154
1, 6
26 m
0.64
160
1, 2
30 m
0.72
163
9
0.5 m
0.10
855
4
17 m
0.75
233
2, 5
17 m
0.62
206
11
0.05 m
0.00
100
*Our biome numbers correspond to those used in Fig. 1. Our biome 6, tropical savanna, is not mentioned in the
table because grasses do not appear in the fossil record until the Tertiary. SiB refers to the Simple Biosphere Model
(Sellers, Mintz et al. 1986; Dorman and Sellers 1989). SiB biomes are as follows: 1 = Broadleaf-evergreen trees, 2
= Broadleaf-deciduous trees, 4 = Needleleaf-evergreen trees, 5 = Needleleaf-deciduous trees, 6 = Broadleaf trees
with groundcover (tropical savanna, only the upper story used), 9 = Broadleaf shrubs with bare soil, 11 = No
vegetation, bare soil.
All biophysical parameters estimated for fossil vegetation represent unweighted averages of values listed for the
closest SiB biomes (Dorman and Sellers 1989). The exception is our biome 3, which is weighted as follows to
increase the importance of broadleaf evergreens: SiB biome [1 + 1 + 2]/3. Fractional cover for our biome 3 is based
on the weighted average multiplied times 0.8. This created the more open canopy conditions inferred from foliar
physiognomy and in situ megafossil assemblages preserved in volcanic ash (Wolfe and Upchurch 1987; Wing,
Hickey et al. 1993).
Table 2 Simulated mean annual climate statistics*
_____________________________________________________________________________________________
Experiment
BARESOIL
EVERGREEN
BESTGUESS
BESTGUESS
TREE
-RS2
____________________________________________________________________________________________
Surface temperature (°C)
Global
60°N-90°N
15°N-15°S
60°S-90°S
15.8
-5.5
26.1
–0.8
18.3
0.7
27.3
2.7
18.0
-1.4
27.6
2.6
0.65
0.40
0.65
0.13
0.63
0.19
0.62
0.19
72.1
98.3
93.1
94.7
33.1
40.6
41.1
42.2
5.8
12.7
12.8
13.6
32.6
0.16
–7.5
0.62
45.1
0.31
–0.9
0.48
39.2
0.36
–3.3
0.52
38.8
0.37
–2.9
0.52
0.70
0.39
0.68
0.14
0.69
0.18
0.69
0.17
64.8
86.8
84.0
84.9
30.5
38.7
37.7
38.1
4.7
9.1
9.4
10.3
29.7
0.14
–5.8
0.63
39.6
0.24
–2.3
0.51
37.2
0.26
–2.5
0.52
36.9
0.29
–2.1
0.50
Ocean 60°N-90°N
Surface temperature (°C)
Ice fraction
–3.3
2.5
0.17
0.7
0.23
0.7
0.23
Ocean 60°S-90°S
Surface temperature (°C)
Ice fraction
–1.8
0.17
5.3
0.06
5.3
0.06
5.7
0.05
Ocean 15°S-15°N
Surface temperature (°C)
25.8
26.9
26.9
27.0
Land 60°N-90°N
Total cloudiness
Surface albedo
Surface absorbed
solar radiation (W m-2)
Net surface infrared
radiation (W m-2)
Surface sensible
heat flux (W m-2)
Surface latent
heat flux (W m-2)
Bowen ratio
Surface temperature (°C)
Fractional snowcover
Land 60°S-90°S
Total cloudiness
Surface albedo
Surface absorbed
solar radiation (W m-2)
Net surface infrared
radiation (W m-2)
Surface sensible
heat flux (W m-2)
Surface latent
heat flux (W m-2)
Bowen ratio
Surface temperature (°C)
Fractional snowcover
0.38
18.3
-1.2
27.7
3.1
__________________________________________________________________________________________________________
*Averages for years 14-20 of the BARESOIL, EVERGREEN TREE, and BESTGUESS simulations and
years 14-18 of the BESTGUESS-RS2 simulation. All Cretaceous simulations use pCO2 of 580 ppm, or
2X the pre-industrial value. Temperature values are for 2 m above the land or ocean surface. The Bowen
ratio is the ratio of sensible to latent heat flux.
FIGURE 1. BESTGUESS estimate of Maastrichtian vegetation. Lithologic and paleontologic
indicators were used as primary data, and output from the EVERGREEN TREE-OHT simulation (not
reported in this paper) was used to code regions between data points. Vegetation catetories and criteria
for recognition are explained in Table 1. Polar deciduous forest is inferred for Antarctica based on the
prevalence of such vegetation at high latitudes in the Northern Hemisphere. Bare soil is used to code for
cold deserts with extremely low floristic diversity that are located near the Laramide front in the Western Interior of North America. Subtropical woodlands have indicators of above-freezing winters such as
palms. No Maastrichtian equivalent of Mediterranean woodlands and chapparal is used in
BESTGUESS simulations, though some model simulations predict the occurrence of such vegetation in
restricted regions. Legend: 1 = Tropical rainforest, 2 = Tropical semi-deciduous forest, 3 = Subtropical
broadleaved evergreen forest and woodland, 4 = Desert and Semi-desert, 5 = Temperate evergreen
broadleaved and coniferous forest, 6 = Tropical savanna (not used here), 7 = Polar deciduous forest, 8 =
Bare soil.
FIGURE 2. Surface air temperature (at height of 2 m) simulated by the GENESIS model for (a)
January BARESOIL, (b) January EVERGREEN TREE, (c) July BARESOIL, and (d) July EVERGREEN TREE experiments. Units are degrees Centigrade.
FIGURE 3. Changes in surface air temperature (°C): (a) January EVERGREEN TREE minus
BARESOIL, (b) July EVERGREEN TREE minus BARESOIL. Regions where the differences are
significant at the 95% level are shaded. Those changes not shaded could have occurred by chance
within a realization due to year-to-year variability in the simulations. Statistical significance is evaluated using the Student t-test.
FIGURE 4. Zonally averaged surface temperatures (°C) for (a) January land and ocean and (b)
July land and ocean. Solid line is the BARESOIL experiment and dashed line is the EVERGTREE
experiment.
FIGURE 5. Surface air temperature (°C) simulated by the GENESIS model for (a) January
BESTGUESS, (b) January BESTGUESS-RS2, (c) July BESTGUESS, and (d) July BESTGUESS-RS2
experiments.
FIGURE 6. Changes in surface air temperature (°C): (a) January BESTGUESS minus
EVERGreen TREE, (b) July BESTGUESS minus EVERGTREE, (c) January BESTGUESS-RS2
minus BESTGUESS, and (d) July BESTGUESS-RS2 minus BESTGUESS. See text for a discussion of
statistical significance.
FIGURE 7. Changes in zonally averaged surface temperatures (°C) for (a) January land and ocean
and (b) July land and ocean. Dashed line is BESTGUESS minus EVERGTREE and solid line is
BESTGUESS-RS2 minus BESTGUESS.
FIGURE 8. Predicted climates and the distribution of forest/woodland vegetation, (a) BARESOIL (b) EVERGREEN TREE, (c)
BESTGUESS, and (d) BESTGUESS-RS2 simulations. Climates correspond to the Köppen scheme (Köppen 1936) but with the boundary
between C and D climates set at a cold-month mean of 1°C and the boundary between Cfa and Cfb climates set at a warm-month mean of
20°C (after Wolfe, 1979). The boundary between Humid (A or C) and Subhumid (B) climates has been modified compensate for overestimation of precipitation in GENESIS version 1.02. Tundra (ET) and boreal (D) forests are separated by a warm-month mean temperature of
10°C; subtropical (Cfa) and marine (Cfb, Cfc) climates are separated by a warm-month mean temperature of 20°C. Latest Cretaceous tropical
forests are inferred largely from the presence of equatorial coals. Other fossil forests are inferred from the presence of leaf assemblages with
forest-type physiognomy, fossil tree stumps >0.5 m in diameter, or at least 2–3 pollen types assignable to extant tree genera. At middle
latitudes subtropical (C) forests are inferred on the basis of plant megafossils that indicate either cold-month means >1°C (e.g., palms, gingers, large-leaved evergreen dicots, woods without annual rings) or mean annual temperatures >20°C. Vegetation data are derived from
(Wolfe and Upchurch 1987; Horrell 1991; Upchurch and Wolfe 1993) and unpublished work of Upchurch and Scotese. Diamonds = tropical
rain forest, circles = subtropical evergreen forest/woodland, triangles = polar deciduous forest (probably equivalent to boreal or cool subtropical forest).
FIGURE 9. Predicted distribution of 1°C and 5°C cold-month means and the distribution of abovefreezing paleobotanical indicators, Northern Hemisphere, (a) BARESOIL and (b) BESTGUESS simulations. Note how the above-freezing indicators extend poleward of the isotherms in the Western Interior
of North America and Greenland.
ACKNOWLEDGMENTS
We thank S. Thompson and D. Pollard for providing us with the GENESIS climate model, and E.
Becker and T. Morgan for assistance with graphics. Research was supported by the Texas Higher
Education Coordinating Board, Advanced Research Program, Grant 3615–029, and a Research Enhancement Grant, Southwest Texas State University. Computer simulations were performed at the
University of Texas Center for High-Performance Computing and the National Center for Atmospheric
Research, which is supported by the U.S. National Science Foundation.
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