Changes in the Asian monsoon climate during
1700 –1850 induced by preindustrial cultivation
Kumiko Takataa,1, Kazuyuki Saitoa,b, and Tetsuzo Yasunaria,caResearch Institute for Global Change, Japan Agency for Marine–Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, Yokohama,
Kanagawa 236-0001, Japan; bInternational Arctic Research Center, University of Alaska Fairbanks, P.O. Box 757340, Fairbanks, AK 99775-7340;
and cHydrospheric Atmospheric Research Center, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi 464-8601, Japan

Preindustrial changes in the Asian summer monsoon climate fromthe 1700s to the 1850s were estimated with an atmosphericgeneral circulation model (AGCM) using historical global land
cover/use change data reconstructed for the last 300 years. Extended cultivation resulted in a decrease in monsoon rainfall overthe Indian subcontinent and southeastern China and an associatedweakening of the Asian summer monsoon circulation. The precipitation decrease in India was marked and was consistent with theobservational changes derived from examining the Himalayan ice
cores for the concurrent period. Between the 1700s and the 1850s,
the anthropogenic increases in greenhouse gases and aerosols
were still minor; also, no long-term trends in natural climate
variations, such as those caused by the ocean, solar activity, or
volcanoes, were reported. Thus, we propose that the land cover/use change was the major source of disturbances to the climate
during that period. This report will set forward quantitative examination of the actual impacts of land cover/use changes on Asianmonsoons, relative to the impact of greenhouse gases and aerosols, viewed in the context of global warming on the interannual,
decadal, and centennial time scales.
atmospheric water balance 兩 climate change 兩historical land-cover change 兩 monsoon rainfallIn general, a monsoon is generated from a thermal contrast
between land and ocean. Thus, the land’s surface condition is
an important factor in determining its climate. Monsoon Asia,
where ⬎50% of the world population is concentrated, hasexperienced large land cover/use changes due to agriculturaldevelopment, particularly during the 18th and 19th centuries. In
India and China between 1700 and 1850, extension of cultivationand habitation activities decreased the percentage of forested
area from 40–50% to 5–10% of the entire territories between
1700 and 1850 (1).
Changing the land cover/use from forest to croplands canaffect the global and regional climate through changes in theenergy and water balance at the earth’s surface (2, 3). Among thevarious effects of vegetation change, 2 factors have been shownto have a major influence on the energy and water balance: (i)an increase in surface albedo leading to a reduction in solar
energy absorption at the surface and (ii) a decrease in surface
roughness, resulting in low-level wind speed intensification. As
a consequence, the partitioning of turbulent heat fluxes into its
sensible and latent heat fluxes would subsequently affect theplanetary boundary layer and deep cumulus convection and,hence, the large-scale atmospheric phenomena (3). Previous
modeling studies that investigated the impact of those effects
used either a model of intermediate complexity with historicalland cover changes (4, 5) or a general circulation model with asimplified setup in which the indigenous forests were totally anduniformly replaced by cultivated land. These latter studies haveshown how different vegetation distributions affect the global
surface energy–water balance (6, 7) or the atmospheric circulation (8, 9). In the Asian tropical region, the effect of reduced
roughness on the summer climate was stronger than the effect ofwww.pnas.org兾cgi兾doi兾10.1073兾pnas.0807346106

increased albedo on a simplified setup (10). Yet, a detailed
examination or discussion of these distinct effects due to thehistorical changes has been insufficient.
Here, we examine the actual impacts of land cover/use changes
using a global historical vegetation map reconstructed for thelast 300 years (1, 11). In the first half of the period, from 1700
to 1850, intensive cultivation activities continually occurred overthe Indian subcontinent and over eastern China in Asia (shaded
areas in Fig. 1). Croplands in India have continued to increasesince the 17th century (1) when the British East India Company
embarked on the colonial management in the Mughal Empire.
Croplands expanded explosively in China in the 1700s (1) in
response to the introduction of new crops (e.g., corn, sweet
potato, peanut) and to the rapid increase in population under thefavorable economic policies of the Qing Dynasty.
We considered that the difference between the climatic
realizations of the 2 periods, in the 1700s and in the 1850s, might
represent the long-term changes that occurred in those 150 years,
and we examined the impacts of the reconstructed historical land
cover/use changes between those 2 periods. Consequently, we
conducted 2 time-slice numerical experiments for the periods
represented by 1700 and 1850, using the historical vegetation
maps of 1700 and 1850 (1,11) [see supporting information (SI)
Fig. S1] as the lower boundary conditions for an atmosphericgeneral circulation model (AGCM) (12), considering thechanges in physical and physiological parameters of vegetation.
Both equilibrium experiments were repeatedly forced by identical climatological annual cycles of the present-day monthly
distributions of sea surface temperature (SST) and sea ice. The
averaged states of summer (June, July, and August) werecomputed for the equilibrated results. These were compared for
examining the impact of the land cover/use changes on the Asiansummer monsoons between the 2 periods.
Results
Changes in Wind Field and Surface Energy–Water Balance Between
1700 and 1850. In the western Indian subcontinent and in south-

eastern China, the summer monsoonal wind blows from theocean to the land in the lower troposphere (streamlines in Fig.
1). Because winds usually decelerate over land because of thelarge surface roughness, atmospheric moisture convergence and,hence, precipitation are large in those regions. Most of the
agricultural cultivation during 1700–1850 occurred on forest (seeSI Text and Fig. S1), causing the surface roughness to decrease
because of the reductions in vegetation height and leaf amount[specified as leaf area index (LAI)]. As a result, the surface windspeed increased (colors in Fig. 2A). This effect reached the lower
Author contributions: T.Y. designed research; K.T. and K.S. performed research; T.Y.
provided validation data; K.T. and K.S. analyzed data; and K.T. and K.S. wrote the paper.
The authors declare no conflict of interest.
This article is a PNAS Direct Submission. R.S.B. is a guest editor invited by the Editorial Board.
1To whom correspondence should be addressed. E-mail: takata@jamstec.go.jp.

This article contains supporting information online at www.pnas.org/cgi/content/full/
0807346106/DCSupplemental.

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Edited by Ray S. Bradley, University of Massachusetts, Amherst, MA, and accepted by the Editorial Board April 15, 2009 (received for review July 31, 2008)

A

B
Fig. 1. Land cover/use changes between 1700 and 1850 (gray shades) and
850-hPa wind in June–August (streamlines with colors in meters䡠s⫺1) in the1850 experiment.

troposphere as seen in the changes in 850-hPa wind field (arrows
in Fig. 2 A, see also SI Text and Fig. S2), which resulted in a
reduction in the moisture convergence and the precipitation in
the western Indian subcontinent (hereafter the IND region) andin southeastern China (hereafter the SCH region) (Fig. 2B). The
reduction in precipitation reduced the surface soil moisture and,hence, the local evaporation and moist convection, creating a
positive feedback to further precipitation reduction.
A vegetation change from forest to cropland resulted in an
increase in the surface albedo (colors in Fig. 2C) due to thedecrease in LAI and the increase in leaf reflectivity. However,effects of the increase in surface albedo were overshadowed bya decrease in clouds in the IND and SCH regions associated withthe significant decrease in precipitation. As a result, net shortwave radiation (i.e., absorbed at the surface) was increased, andthe decrease in soil moisture was further enhanced in thoseregions (contours in Fig. 2C). On the other hand, net short-wave
radiation was decreased in the middle of eastern China (hereafter the ECH region) because of the increase in surface albedo.
The decrease in net short-wave radiation caused a decrease insurface temperature but only in part of the ECH region where
surface soil moisture was not decreased (see SI Text and Fig. S3).
These decreases would have less effect on precipitation change.
Changes in Atmospheric Water Balance. Our results suggest that theatmospheric water balance in those regions reflects whether
surface roughness reduction and albedo increase had a majorrole in the summer climate change. In the IND and SCH regions,the ratio of moisture convergence to the source of precipitation
was high (Table 1). In those regions, the moisture convergencechanged considerably due to a combined effect of the decreased
surface roughness and the decreased soil moisture. However, in
the ECH region, dependence on the moisture convergence waslow, and precipitation changed little (Table 1). Thus, the albedo
effect, in terms of reducing net short-wave radiation (contours
in Fig. 2C), was more apparent in this area. The changes inprecipitable water for the June–August mean were very small
and negligible in each region and each time period (less than a
few millimeters䡠month⫺1).
As a result of the overall changes in precipitation and atmospheric heating induced by agricultural development particularly
in south and southeast Asia, the Tibetan anticyclone in the upper
troposphere weakened in 1850 (Fig. 3) The weakening of the
Asian summer monsoon by cultivation was also interpreted byusing a regional climate model (13). The North Pacific high2 of 4 兩 www.pnas.org兾cgi兾doi兾10.1073兾pnas.0807346106

C

Fig. 2. June–August mean changes between 1700 and 1850. (A) Surface
wind speed (colors, in m s⫺1) and 850-hPa wind (vectors, with unit vector 2 ms⫺1). (B) Precipitation (colors, in mm day⫺1) and vertically integrated vapor flux
(vectors, with unit vector 7 ⫻ 10 kg m⫺1 s⫺1). (C) Surface albedo (colors, unitof legends is nondimensional) and net short-wave radiation absorbed at thesurface (downward positive; contours, with labels in W m⫺2). Shaded regions
are similar to those where the differences in surface wind speed, precipitation,or surface albedo are statistically significant at the 95% confidence level.

weakened in 1850 (see Fig. S4), which was associated with theweakened Tibetan anticyclone (14). This weakening of the North
Pacific high resulted in the slight increases in moisture convergence and precipitation in the ECH region, in contrast to thedecreases in those in the IND and SCH regions.
Discussion
Between the 1700s and the 1850s, the major anthropogenic
disturbance to the climate in Asian Monsoon region was caused
by cultivation. There were no marked trends in the concentrations of greenhouse gases and aerosols between the 1700s andthe 1850s (15). Natural disturbances such as sea surface temTakata et al.

Table 1. Atmospheric moisture budget of the June–August mean in millimeters䡠monthⴚ1
Western Indian
subcontinentSoutheastern
China

Mideastern China

Year

1700

1850

1700

1850

1700

1850

Moisture convergence
Evapotranspiration
Precipitation

146.1
135.8
281.9

77.4
126.6
204.0

139.8
133.2
273.0

108.3
130.7
239.0

22.4
128.3
150.7

33.2
123.1
156.3

Moisture convergence, evapotranspiration, and precipitation in the western Indian subcontinent (72– 80°E,
17–25°N), southeastern China (105–115°E, 20 –27°N) and the middle of eastern China (105–120°E, 27–37°N).

Fig. 3. June–August mean changes in the 200-hPa wind field (streamlines)
and geopotential height (colors, in meters) between 1700 and 1850. Thickgray lines represent those in which the differences are statistically significant
at the 95% confidence level.

Takata et al.

Asian monsoon regions. The simulated changes in precipitationin the Indian subcontinent agreed well with the reconstructedclimate estimates for 1700–1850, when the only significant agent
for the long-term trend that we identified was land cover/use
change. In contemporary times, changes in land cover/land useneed to combine with increases in greenhouse gases and aerosols
to impact the Asian monsoon climate.
Methods
Model Description. We used an AGCM that was the atmospheric component ofa global climate model, Model for Interdisciplinary Research on Climate(MIROC) version 3.2 (12), with a land surface scheme, Minimal Advanced
Treatments of Surface Interaction and Runoff (MATSIRO) (22). Land cover/use
was categorized into 11 vegetation types (see Fig. S1). The vegetation parameters (canopy top/bottom height, leaf reflectance/transmissivity, leaf turbulent exchange coefficients, and photosynthetic parameters) were determinedfor each vegetation type and were prescribed at each grid point according to
the assigned vegetation type. We specified the monthly LAI at each grid point;these values were derived from a satellite observation independent of thevegetation distribution. Surface albedo and surface roughness were estimated from the above parameters, by using a single-layer canopy scheme on
the basis of a multilayer canopy model (23, 24). Stomatal resistance fortranspiration was estimated with a simple photosynthetic scheme (25). Energy
and water balances at leaf and ground surfaces were determined by all of theabove factors and processes, including snow, runoff, ground thermal andhydrological processes, and canopy water balance.
The AGCM used a 2-stream k-distribution method for radiation, a prognostic Arakawa–Schubert cumulus scheme, a prognostic cloud water scheme
for large-scale condensation, a turbulence closure scheme with cloud effects,
and an orographic gravity wave drag scheme. The primitive equations on asphere using a spectral transform method were used for the dynamical part,for prognosticating the wind velocity, temperature, surface pressure, andmixing ratio of water vapor and cloud liquid water. See ref. 12 for the details.
Experimental Settings. The numerical experiments were conducted by using
the AGCM with 20 vertical levels and a horizontal resolution of spectral
triangular 42 (T42, i.e., ⬇2.8°, ⬇300 km). The historical vegetation distribution
was prescribed for each 50-year integration, represented by the years 1700
and 1850 (see Fig. S1). The vegetation map (11) was produced by superimposing the historical croplands ratio (1) onto the present day (i.e., in 1992) land
cover map (U.S. Geological Survey Global Land Cover Characterization, Version 1.2, 2003, http://edc2.usgs.gov/glcc/glcc.php). For any grid point where
the cropland ratio was smaller than the threshold value (11), the potential
vegetation (1) was assigned.
A climatological annual cycle of LAI distribution was produced for thehistorical vegetation map (11), by superimposing the monthly LAI for potential vegetation onto the present-day monthly mean LAI map averaged from1983 to 1995 in International Satellite Land Surface Climatology Project
Initiative 2 (ISLSCP-II) dataset (26). The monthly LAI for potential vegetation
was estimated by averaging the present-day LAI for the same vegetation typeat the same 5° latitude band for each month (11).
Similar to the LAI, climatological annual cycles of SST and sea ice distributions, derived from the observed monthly mean averaged from 1981 to 2000
(27) was prescribed throughout the integrations in this study.
Identical initial conditions for the atmosphere and land were used for both
integrations; these resulted from an equilibrated integration with thepresent-day land use condition. Because the vegetation distribution and theannual cycles of LAI, SST, and sea ice are all fixed, the interannual variability,
after reaching an equilibrated state, is solely induced by the internal variability of the atmosphere–land system. Thus, the differences between the averPNAS Early Edition 兩 3 of 4

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peratures, solar activity, and volcanic eruptions could havecaused climate change, but there are no reports of remarkable
trends or substantial changes in these factors that are comparable to the changes in land cover/use. The solar constant,according to the reconstructed variation for 1713–1996 (16), hada clear decadal solar cycle, but there was no apparent trend from1713 to 1850. A few major volcano eruptions occurred duringthat period (e.g., in 1783, 1809, 1815, and 1883) (17, 18).
However, the effect of an individual eruption could have lasted
no more than a decade, so no significant impact on the 1700s or
1850s climate was expected. These findings suggest that thesimulated differences in the Asian monsoon climate between
1700 and 1850 reflect the results of land cover/use changes
during that period.
We compared our results to the available estimates of preindustrial climate derived from proxy data and/or historical
records. The monsoonal precipitation in the western Indian
subcontinent was negatively correlated to the annual snow
accumulation at Dasuopu in the central Himalayas, according toa glacier ice-core analysis for the past 300 years (19). This
analysis revealed that the monsoon precipitation in the region
decreased by ⬇20% (i.e., ⬇200 mm) from the 1700s to the 1850s(see Fig. S5). Another glacier ice-core analysis at Qomolangma(Mount Everest, Himaraya), spanning 1534–2001, revealed adecrease in annual precipitation from the 1700s to the 1850s thatwas significantly correlated with the all-India precipitation (20).
These results are qualitatively and quantitatively consistent with
our simulated result. This high level of agreement might berelated to the ‘‘hot spots,’’ one of which is located in India, where
the impact of the lower boundary conditions (e.g., soil moisture)on precipitation has been shown to be very high (21).
Using historical vegetation maps, we found that the actual land
cover/use change had a significant impact on the energy andwater budget and on the atmospheric circulation in summer

ages of the 2 experiments, calculated from the equilibrated 40 years of the
50-year integrations, showed that the signal resulted from the land cover/use
change and did not reflect the initial conditions. The statistical significance ofany difference between the 2 experiments was examined by t test, with theinterannual variability as the measure of the noise. The values in the summer
season (July to August) were use in the analysis, which is when the Asianmonsoon activity is at its highest.

ACKNOWLEDGMENTS. We thank Dr. Y. Hirabayashi (Jichi Medical University,
Tochigi, Japan) for providing the historical vegetation maps, and Dr. Y. Kosaka
and Mr. R. Yamashima for data analysis and visualization assistance. The authors
are also grateful to the editor and the 2 anonymous reviewers for providing
valuable comments in the revision of the article. This work was supported by the
Global Environment Research Fund of the Ministry of the Environment, Japan(B-061). All numerical integrations were run on the NEC SX-8 supercomputer at
the Japan Agency for Marine–Earth Science and Technology.

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Supporting Information
Takata et al. 10.1073/pnas.0807346106
SI Text
Global maps of vegetation in 1700 and 1850 are shown in Fig.
S1. The red areas indicate cultivated areas. Most of the cultivatedland was originally forest, except for a few grid points (e.g., on
the western edges of the cultivated area in eastern China). These
areas had originally been grassland, and because vegetation
height and LAI are similar for grasslands and croplands, thesurface wind speed did not change at those points. The surface
albedo was slightly decreased by ⬍0.01 due to the larger LAI for
croplands than for grasslands. However, the albedo decrease had
no discernible effects because its area and magnitude were small.
Large area was also cultivated intensively in Western Europe
during 1700–1850. This land use/cover change may have an
impact in the winter, but is generally insignificant in the summer.
We display the increase in surface wind speed that reached thelower troposphere by the longitude–height cross-sections ofchanges in the horizontal winds, in addition to the changes in
850-hPa wind vectors (Fig. 2 A). The cross-section at 20°N showsthat the westerly wind increased from 75°E to 85°E, whichcorresponds to the western Indian subcontinent (the IND region;
Fig. S2 a, shown by rightward vectors). The cross-section at 30°N
shows that the southerly wind increased from 100°E to 120°E;
this corresponds to southeastern China (SCH region; Fig. S2b,shown by upward vectors).

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The effects of the reduction in clouds in the IND and SCH
regions are clearly indicated by the increase in downward
short-wave radiation (Fig. S3a). This corresponds with theregions where there was an increase in the net short-wave
radiation absorbed at the surface (contours in Fig. 2C). Therefore, the increase in net short-wave radiation was induced by theincrease in downward short-wave radiation, and hence thereduction in clouds, that is associated with a decrease in precipitation. In those regions, the surface soil moisture decreased,
latent heat flux decreased, sensible heat flux increased, and airtemperature at 2-m height increased (Fig. S3 b–e).
The effects of the increase in surface albedo appeared as adecrease in net short-wave radiation in ECH (Fig. 2C), occurring
without a considerable decrease in downward short-wave radiation (Fig. S3a). In addition, outgoing long-wave radiation
increased (Fig. S3f), as a result of the increase in the upward
terrestrial radiation due to the surface temperature increase(Fig. S3g) and as a result of the decrease in downward long-wave
radiation due to the moisture decrease near the surface (Fig.
S3h). Summing those effects shows that the net radiation absorbed at the surface was decreased. A reduction in net radiation
could result in a surface temperature decrease, but this effectappeared only at ⬇30°N, 110–120°E, and was ⬇0.5–1 °C (Fig.
S3e). At this location, surface soil moisture was not decreased(Fig. S3b).

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Fig. S1.

Land cover distribution used in this study. (a) In 1700. (b) In 1850. Index: 1, ice; 2– 6, forest; 7–9, grassland; 10, cropland; and 11, desert.

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Fig. S2. Longitude– height cross-sections of June–August mean changes in horizontal wind speed (shades, in meters䡠s⫺1) and horizontal wind vector (arrows,
unit vector in meters䡠s⫺1) between 1700 and 1850. (a) At 20°N. (b) At 30°N. The gray solid and dotted lines represent the regions in which the differences in zonal(east–west) and meridional (north–south) wind are statistically significant at the 95% confidence level.

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Fig. S3. June–August mean changes between 1700 and 1850. (a) Downward short-wave radiation in W䡠m⫺2. (b) Volumetric soil moisture ratio in the top 0to 5-cm layer. (c) Latent heat flux in W䡠m⫺2. (d) Sensible heat flux in W䡠m⫺2. (e) The 2-m height air temperature in K. (f) Net long-wave radiation (upward positive)in W䡠m⫺2. (g) The surface radiative temperature in K. (h) The 2-m height-specific humidity in kg kg⫺1. Negative values are indicated by blue contours and positive
values by red contours. Shaded regions are those in which the differences are statistically significant at the 95% confidence level.

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Fig. S4. June–August mean changes in sea level pressure (hPa) between 1700 and 1850. Shaded regions represent those in which the differences are statisticallysignificant at the 95% confidence level.

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Fig. S5. Dasuopu snow accumulation (DSA) in Tibet–Himalaya and monsoon rainfall trends in Peninsula India (MRI). Three-year running mean of the DSA isshown by the dotted line, the reconstructed secular trend by the gray line, and the 26-year oscillation ⫹ trend by the solid line. MRI is negatively correlated withDSA (horizontal bars in lower part of the image) at a correlation coefficient of approximately ⫺0.5. After figure 4 of Duan K, Yao T, Thompson LG (August 25,2004) Low-frequency of southern Asian monsoon variability using a 295-year record from the Dasuopu ice core in the central Himalayas. Geophys Res Lett,
10.1029/2004GL020015, modified by permission of the American Geophysical Union.

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