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Global Climate Responses to Land Use and Land Cover Changes Over the Past Two Millennia Mi Yan 1,2 , Jian Liu 1,2,3, * and Zhiyuan Wang 1 1

2 3

*

Key Laboratory of Virtual Geographic Environment of Ministry of Education & State Key Laboratory Cultivation Base of Geographic Environment Evolution of Jiangsu Province, School of Geography Science, Nanjing Normal University, Nanjing 210023, China; [email protected] (M.Y.); [email protected] (Z.W.) Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China Jiangsu Provincial Key Laboratory for Numerical Simulation of Large Scale Complex Systems, School of Mathematical Science, Nanjing Normal University, Nanjing 210023, China Correspondence: [email protected]; Tel.: +86-186-6272-5225

Academic Editor: Robert W. Talbot Received: 26 December 2016; Accepted: 18 March 2017; Published: 23 March 2017

Abstract: A reconstructed land use/land cover change (LUCC) dataset was used with the Community Earth System Model (CESM) to conduct a climate sensitivity analysis over the past two millennia. Compared to a controlled experiment conducted with the CESM, the LUCC showed significant biogeophysical effects on global climate on multi-decadal to centennial time scales. The global annual mean temperature and precipitation show clear decadal and multi-centennial scale oscillations when the LUCC effect was considered in the CESM simulation. With increased crop acreage and decreased natural vegetation over the past two millennia, the reflected terrestrial solar radiation has increased and the net terrestrial radiation has decreased, leading to a decrease in the global annual mean temperature. Global annual mean precipitation has also decreased along with decreased evaporation and atmospheric humidity. Our simulation suggests that LUCC mainly influences convective precipitation and has little influence on large-scale precipitation. The impact of LUCC has latitudinal and seasonal differences. The largest response of temperature to LUCC has occurred in the middle latitudes of the Northern Hemisphere (NH), while the largest precipitation response occurred at lower latitudes of the NH. The responses of temperature and precipitation to LUCC is stronger in winter and spring than in summer and autumn. Keywords: two millennia; historical LUCC; biogeophysical effect; global climate; climate simulation

1. Introduction The impacts of land use/land cover change (LUCC) on climate change are components of global climate change research [1,2]. Tropical forests can reduce atmospheric CO2 concentrations [3], slow down global climate change [4], and reduce warming by evaporative cooling. The low albedo of northern forests can generate a positive radiative forcing, but the evaporative effects of temperate forests remains unclear [5]. Therefore, the net climate effects of these processes require further study. Many studies have focused on the impact of LUCC on climate since the late 1970s [6–13]. These studies demonstrate that on a regional scale, the influence of LUCC on temperature can be comparable to that of increased CO2 [14]. The influence of LUCC on regional hydrometeorology is also equivalent to the influence of other climate factors (e.g., the El Niño-Southern Oscillation, ENSO) [15,16]. Studies of tropical forests such as the Amazon rainforest, show that deforestation can significantly influence local precipitation [17–19]. Li et al. [20] and Gao et al. [21] used numerical simulations of modern and contemporary land use changes in China to demonstrate that land use Atmosphere 2017, 8, 64; doi:10.3390/atmos8040064

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change had a large influence on precipitation in different areas. However, other studies reported that the influence of vegetation change on precipitation in China was not significant [22]. Although LUCC may significantly affect regional climate, the magnitude of its effects is controversial. Research results regarding LUCC effects on global climate are limited and debatable. Brovkin et al. [23] concluded that the influence of land cover change (predominantly deforestation) on the global climate was equivalent to the effects of factors such as greenhouse gases and solar radiation. However, Findell et al. [24] determined that the influence of anthropogenic land cover change (e.g., forest conversion to grassland) on the global climate change was trivial. Gibbard et al. [25] conducted a simulation study and concluded that actions such as the replacement of current vegetation by trees on a global basis may increase global temperature by up to 1.3 ◦ C. Arora and Mentenegro [26], in another simulation study, found that returning the area presently occupied by crops to forests could decrease global warming by 0.45 ◦ C. Clearly, the impacts of LUCC on global and regional climate are largely uncertain and therefore warrant further study. Most of the previous literature has involved various sensitivity experiments. For example, Zhang et al. [27] studied the impact of future grassland changes on surface climate in Mongolia. Brovkin et al. [28] evaluated the climate response to different greenhouse gas concentration scenarios with and without land-use changes. Notaro et al. [29] investigated the influence of fractional change in vegetation cover on the monsoon climate. However, studies on climate that consider the effects of transient LUCC are relatively rare, especially on centennial and millennial scales. Research on inter-annual LUCC needs to be more detailed [30], and more studies on the climate impacts of LUCC over longer time-scales are required. These studies may be significant in estimating climate change during the 21st century. Previous studies on the effects of LUCC have been limited to either modern times [30,31] or the past 300 years [2,32]. The climatic effect of LUCC at decadal to centennial time-scales is rare and requires further study. We have collected a LUCC dataset covering a time span of 2000 years from 1 to 2000 AD [33]. The last 2000 years has been a key research period of the Past Global Changes (PAGES) project [34], and climate simulation over 2000 years is an effective method for studying the climate effects of LUCC and quantitatively differentiating the impacts of natural factors and human activities on climate change. The features and mechanisms of long-term variability in global climate change and regional climate change have been reconstructed in References [35–37] and simulated in References [38–42]. However, the climate effects of LUCC on longer time-scales over the past 2000 years remain unclear. This paper used the reconstructed LUCC dataset in the Community Earth System Model (CESM) to conduct LUCC single-factor climatic sensitivity experiments over the past two millennia. By comparing the results to a control experiment with fixed land cover, we analyzed the global climate effects of historical LUCC, with an emphasis on the global climate effects of LUCC on multi-decadal to centennial time-scales. 2. Model and Data 2.1. Model and Experiment We conducted climate simulation experiments using Community Earth System Model (CESM) developed by the US National Center for Atmospheric Research (NCAR) [43]. Compared to the simple coupling model utilized in previous research, the CESM model integrates the physical and chemical processes of various sub-systems relating to the land, ocean, atmosphere, sea ice, and land ice [39], which provides better conditions for studying the climate effects of LUCC. Furthermore, the land model used in this study is the Community Land Model Version 4.0 (CLM4) and includes transient LUCC and developed parameters, which can study the role of land processes in climate change [44,45]. We performed two climate simulation experiments, where each was integrated for more than 2000 years. One experiment was control run using fixed external forcing conditions of 1850 (CTRL run), and the other was a single-factor climatic sensitivity experiment using transient land use and cover change (LU run). The CTRL run started from model year 1, the ocean state in 2010 AD (provided


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by NCAR) was used as the initial condition, and the experiment was integrated 2400 years [46]. The energy balance at the top of atmosphere (TOA) showed that the coupled climate system reached its balance after 400 years’ spin-up (Figure S1). The LU run, initiated from the last year of the control run, was integrated over 2000 years with the reconstructed yearly LUCC data during the period from 1 to 2000 AD with a dynamic vegetation process. The first 200-year simulations of the LU run were discarded for spin-up, with only the last 1800-year simulations used for analysis. To facilitate comparison, the CTRL run used results for the last 1800 years. The T31 atmosphere model selected had a horizontal resolution of approximately 3.75◦ (latitude) × 3.75◦ (longitude), and the ocean model had a resolution of about 1.5◦ (latitude) × 3.6◦ (longitude). Our climate change focus was on a 10–100-year time scale, and unless noted, a 31-year moving-average process was used for the time series. The carbon-nitrogen (CN) biogeochemical model was not considered in our experimental design and focused only on the biogeophysical impacts of the LUCC on global climate. Furthermore, although the CTRL and LU experiments did not represent actual climate states, the results from the LU experiment could reveal a climate response to the LUCC. Finally, we only considered how global climate would be affected by the reconstructed LUCC over the past two millennia. 2.2. LUCC Data The external forcing data used to conduct the LUCC single-factor sensitivity experiment was from the reconstructed results (KK10) provided by Kaplan et al. [47]. Yan et al. [33] had also previously collected and compared several LUCC reconstructed datasets with scales >1000 years, and adopted the KK10 dataset. This dataset was reconstructed using a deforestation model based on the relationship between population and land use, and covers the variation, in fractions, of 14 vegetation types over the past 2000 years (1–2000 AD). The temporal resolution was one year, and the spatial resolution was 0.5◦ × 0.5◦ . The fraction of a specific vegetation type refers to the percentage of that specific vegetation in the unit grid. For example, if 50% of the unit grid are crops, then the crop ratio was specified as 50%. The land surface model of CESM divides the vegetation into 16 different types [48] (Table 1), so the reconstructed data was re-categorized and processed. A one-to-one mapping method was primarily used to rearrange the KK10 data or split the original data as per the ratio of 50% (Kaplan and Chen, pers. comm.) (Table S1), and this approach provided vegetation type data suitable for the CESM land component. It was noted that the Evergreen shrubs, C3 pasture and C4 pasture in the KK10 had no comparative types in CLM4, thus, the anthropogenic land cover change in CLM4 was only represented by the change of the Crop 1 fraction. Table 1. Vegetation types in Kaplan data and the Community Earth System Model (CESM) required data. No.

Vegetation Type (Kaplan)

Vegetation Type (CESM)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Tropical evergreen forest Tropical deciduous forest Temperate evergreen broad-leaved forest Temperate/boreal evergreen broad-leaved forest Temperate/boreal evergreen needle-leaved forest Temperate/boreal deciduous coniferous forest Evergreen shrubs Deciduous shrubs C3 natural grassland C4 natural grassland Tundra Crop C3 pasture C4 pasture

Temperate evergreen forest Boreal evergreen coniferous forest Boreal deciduous coniferous forest Tropical evergreen broad-leaved forest Temperate evergreen broad-leaved forest Tropical deciduous broad-leaved forest Temperate deciduous broad-leaved forest Boreal deciduous broad-leaved forest Temperate evergreen broad-leaved shrubs Temperate deciduous broad-leaved shrubs Boreal deciduous broad-leaved shrubs C3 polar grass (tundra) C3 grassland C4 grassland Crop 1 1 Crop 2

1

The surface data applied in the model only used the first type of crop.


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The change of the fractions of 16 vegetation types (including crops, grassland, and other natural Atmosphere 2017, 8, 64 4 of 14 vegetation types) used in the LU sensitivity experiments in the past two millennia are shown in Atmosphere 2017, 8, 64 4 of 14 Figure In the the past past two millennia, the the ratio ratio of of the the crops, crops, reflecting reflecting the the impacts impacts of of human human activities, activities, Figure 1. 1. In two millennia, have (dashed line in 1). After the the industrial revolution, revolution, the fractions fractions of of crops crops rapidly rapidly have increased increased in Figure Figure 1).ratio After Figure 1. In the (dashed past twoline millennia, the of theindustrial crops, reflecting the the impacts of human activities, increased, reaching 15.5% in 2000. The fractions of natural vegetation decreased during this period increased, reaching 15.5% The natural vegetation during thisrapidly period have increased (dashed lineinin2000. Figure 1).fractions After theof industrial revolution,decreased the fractions of crops (solid line in Figure 1), except for aa slight increase in the C3 and C4 grassland fractions. (solid line in Figure 1), except for slight increase in the C3 and C4 grassland fractions. increased, reaching 15.5% in 2000. The fractions of natural vegetation decreased during this period Compared to fixed data of 1850 used inin thethe control run, regional differences existed in LU to the the data 1850AD AD used control run, regional differences existed in (solidCompared line in Figure 1), fixed except for of a slight increase in the C3 and C4 grassland fractions. during the past twotwo millennia. In most world regions, the ratios of multi-year average crops in the LU during the past millennia. In most world regions, the ratios of multi-year average crops in Compared to the fixed data of 1850 AD used in the control run, regional differences existed in LU runrun were higher than in the control run, which was related totothe massive deforestation caused the LU were higher than in the control run, which was related the massive deforestation caused LU during the past two millennia. In most world regions, the ratios of multi-year average crops in by activities. However, However, the ratios crops in middle regions of and by human human thecontrol ratios of of crops in the the middle of the the US US and partial partial regions regions the LU run activities. were higher than in the run, which was relatedregions to the massive deforestation caused in mid-Asia decreased slightly and the ratio of natural vegetation increased, which may may be due in mid-Asia decreased slightly and the ratio of natural vegetation increased, which be due to to by human activities. However, the ratios of crops in the middle regions of the US and partial regions reforestation and afforestation. Therefore, compared to the average conditions in 1850 used by the reforestation and afforestation. Therefore, compared to vegetation the averageincreased, conditions in 1850 used by the in mid-Asia decreased slightly and the ratio of natural which may be due to control run, the ratio of forest cover used in the LU sensitivity experiment increased in central North reforestation and afforestation. Therefore, compared to the average conditions in 1850 used by the America, Central Eurasia, and Equatorial Asia. However, However, the the ratio ratio of crops in most world regions control run, the ratio of forest cover used in the LU sensitivity experiment increased in central North remained higher than the value produced in the control run (Figure 2 2and Figure S2). value produced in the control run (Figures and S2). America, Central Eurasia, and Equatorial Asia. However, the ratio of crops in most world regions remained higher than the value produced in the control run (Figures 2 and S2).

Figure 1. changes of of 16 16 vegetation types used in LU experiments over Figure 1.Time Timeseries seriesofofthe thefraction fraction changes vegetation types used in sensitivity LU sensitivity experiments the past 2000 years. Dashed lines indicate the time series of the fraction changes of Crop 1 (type 15) and over the past series 2000 years. Dashed lines indicate the time series ofinthe changes of Crop 1 Figure 1. Time of the fraction changes of 16 vegetation types used LUfraction sensitivity experiments over Crop 2 (type 16), and the solid lines are the time series of fraction changes of various natural vegetation. (type 15) and Crop 2 (type 16), and the solid lines are the time series of fraction changes of various the past 2000 years. Dashed lines indicate the time series of the fraction changes of Crop 1 (type 15) and natural vegetation. Crop 2 (type 16), and the solid lines are the time series of fraction changes of various natural vegetation.

Figure 2. Spatial distributions of anthropogenic vegetation fractions (crops) during four periods. (a–d) represent 1 AD, 1700 AD, of 1850 AD and 2000 vegetation AD, respectively, used in the LU run.four periods. Figure 2. Spatial Figure 2. Spatial distributions distributions of anthropogenic anthropogenic vegetation fractions fractions (crops) (crops) during during four periods. (a–d) represent 11 AD, AD, 1700 1700 AD, AD, 1850 1850 AD AD and and 2000 2000 AD, AD, respectively, respectively,used usedin inthe theLU LUrun. run. (a–d) represent

3. Results

3. Results 3.1. Global Climate Effects 3.1. Global Climate Effects 3.1.1. Temperature Response 3.1.1.The Temperature Response global mean surface temperature (including land- and sea surface temperatures) showed a decreasing trendmean in the LU experiment over the pastland1800 and years (Figure This was particularly The global surface temperature (including sea surface3a). temperatures) showed a decreasing trend in the LU experiment over the past 1800 years (Figure 3a). This was particularly


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3. Results 3.1. Global Climate Effects 3.1.1. Temperature Response The global mean surface temperature (including land- and sea surface temperatures) showed a decreasing trend in the LU experiment over the past 1800 years (Figure 3a). This was particularly obvious prior to 700 AD, where the surface temperature remained at a relatively high level. During this period, LUCC, especially the change in the ratio of crops reflecting human activities, was relatively Atmosphere 2017, 8, 64 700 and 1100 AD, cooling-warming-cooling fluctuations with relatively 5 of 14 small; however, between large amplitudes occurred, and from 1200 to 1800 AD, the temperature changed little (blue line in Figure 3a). obvious prior to 700 AD, where the surface temperature remained at a relatively high level. During After 1800this AD,period, the ratio of especially crops rapidly increased ratioreflecting of the natural vegetation LUCC, the change in the and ratio the of crops human activities, wasdecreased. During thisrelatively period,small; temperature surface temperature reached the lowest however, decreased between 700 and and global 1100 AD, cooling-warming-cooling fluctuations with level of relatively large amplitudes andin from 1200 to and 1800 AD, the temperature changed little past 1800 years. LUCC caused aoccurred, decrease annualglobal-mean temperature by(blue 0.004 ◦ C per line in Figure 3a). After 1800 AD, the ratio of crops rapidly increased and the ratio of the natural hundred years (p < 0.05). vegetation decreased. During this period, temperature decreased and global surface temperature Figure 3b,c the illustrates theofresults a power spectrum analysis of the annual mean reached lowest level past 1800ofyears. LUCC caused a decrease in annualandglobal global-mean surface temperature 31-yearyears moving average). As the annual periodicity of both surface temperature (without by 0.004 °C the per hundred (p < 0.05). Figure 3b,c illustrates the results of asensitivity power spectrum analysis ofwas the global annual mean surface decadal temperatures in the control run and the LU experiment significant, to highlight (without the 31-year moving average). As the annual periodicity of both surface periodicity,temperature Figure 3b,c shows periods longer than 20 years. From these two figures, the approximate temperatures in the control run and the LU sensitivity experiment was significant, to highlight 200-year period significant the periods 95% confidence level (p From < 0.05) intwo thefigures, controltherun, while decadal was periodicity, Figure above 3b,c shows longer than 20 years. these the approximate 200-year period in the LU sensitivity experiment was(pnot significant. In contrast, approximate 200-year period was significant above the 95% confidence level < 0.05) in the control run, while the approximate 200-year period in the50-year, LU sensitivity experiment was not in the LU sensitivity experiment, the approximate 100-year, 300-year andsignificant. 400-yearInperiods all contrast, in the LU sensitivity experiment, the approximate 50-year, 100-year, 300-year and 400-year exceeded the 95% confidence level (p < 0.05). periods all exceeded the 95% confidence level (p < 0.05).

Figure 3. (a) Time series of the global annual mean surface temperature anomalies in the past 1800 years

Figure 3. (a) Time series of the global annual mean surface temperature anomalies in the past 1800 years (compared to the 201–2000 AD). After the 31-year running mean process, the red line indicates the (comparedresult to the 201–2000 AD). the is31-year running meansensitivity process,experiment, the red line the of the control run, the After blue curve the result of single-factor andindicates the result of the control blue curve trend is theofresult of single-factor experiment, and the blue blue dashedrun, line the is the variation the surface temperature sensitivity in the single-factor sensitivity (b,c) Power spectrum global annual average in surface temperature, which here only dashed lineexperiment; is the variation trend of the of surface temperature the single-factor sensitivity experiment; displays periodic variation conditions over 20 years; where (b) shows the results of the control run; (b,c) Power spectrum of global annual average surface temperature, which here only displays periodic and (c) the results of the LU sensitivity experiment. The red dashed line indicates the 95% confidence level. variation conditions over 20 years; where (b) shows the results of the control run; and (c) the results of the LU sensitivity red dashed lineof indicates the 95% confidence In the LUexperiment. experiment, The the decreasing trend global temperatures in winter level. and spring was more apparent than that summer and autumn. The decreasing trend of the temperatures in winter and spring were 0.0053 °C per 100 years and 0.005 °C per 100 years, respectively, and the decreasing In therates LU in experiment, decreasing trend of 100 global in100 winter summer and the autumn were 0.0034 °C per yearstemperatures and 0.0038 °C per years.and spring was more apparent thanRegions that summer and autumn. Thein decreasing trend of the temperatures inthe winter and with a relatively large increase crops were mainly located at the mid-latitudes in Northern Hemisphere (NH) (30–50° N) and the temperature responses at different latitudes were shown in Figure 4a,b and the spatial distribution of the temperature change in Figure S3. Though the


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spring were 0.0053 ◦ C per 100 years and 0.005 ◦ C per 100 years, respectively, and the decreasing rates in summer and autumn were 0.0034 ◦ C per 100 years and 0.0038 ◦ C per 100 years. Regions with a relatively large increase in crops were mainly located at the mid-latitudes in the Northern Hemisphere (NH) (30–50◦ N) and the temperature responses at different latitudes were shown in Figure 4a,b and the spatial distribution of the temperature change in Figure S3. Though Atmosphere 2017, 8, 64 6 of 14 the amplitude of the temperature change in the high-latitude NH (60–90◦ N) was the greatest, the ◦ trend wasamplitude not. Theofmaximum temperature was inN)the the temperature change in thereduction high-latitude NH seen (60–90° wasmid-latitude the greatest, theNH trend(30–60 N) (0.01 ◦ C per whichtemperature corresponded to was theseen latitude zone showing a–maximum increase in 60° N) (0.01 °C was100 not.years), The maximum reduction in the mid-latitude NH (30 100 years),The which corresponded reduction to the latitude zone showing maximum increase crops ◦and crops andper pastures. temperature trends in thealow-latitude NHin(0–30 N) and the ◦ S) were andspatial the Southern pastures. The temperature trends in the low-latitude NH (0–30° N)the Southern Hemisphere (SH) (0–90reduction relatively small. Furthermore, distribution of – 90° S) were relatively small. Furthermore, the spatial distribution of the annual Hemisphere (SH) (0 the annual mean temperature trend also illustrated that the temperature decreased more over the NH mean temperature trend also illustrated that the temperature decreased more over the NH midmid-latitudes, particularly over the Eurasian continent, under the LUCC forcing (Figure 4c). latitudes, particularly over the Eurasian continent, under the LUCC forcing (Figure 4c).

Figure 4. Time series of annual average surface temperature anomalies over different latitudes in the

Figure 4. Time series of annual average surface temperature anomalies over different latitudes in the past 1800 years. (a) LU run; (b) control (CTRL) run; and (c) the spatial distribution of temperature past 1800 years. (a) LU run; (b)run, control (CTRL) run; and spatiallevel distribution trend derived from the LU only areas that exceeded the(c) 99%the confidence are plotted. of temperature trend derived from the LU run, only areas that exceeded the 99% confidence level are plotted. In addition to the impacts of LUCC on the long-term trend of temperature, LUCC also leads to multi-decadal or even multi-centennial variation periods of temperature. In different seasons, the In addition the occurred impactsprimarily of LUCC onfirst the long-term trend and of temperature, LUCC also leads impact ofto LUCC in the half of the year (winter spring), and was relatively larger compared to the second half of the year (summer and autumn). The temperature response to seasons, to multi-decadal or even multi-centennial variation periods of temperature. In different LUCC in the mid-latitude NH was the strongest. the impact of LUCC occurred primarily in the first half of the year (winter and spring), and was

relatively 3.1.2. larger compared to the second half of the year (summer and autumn). The temperature Precipitation Response response to LUCC in the mid-latitude NH was the strongest.

In the LU experiment, the global annual mean precipitation showed a decreasing trend associated with multi-decadal to centennial variabilities over the past 1800 years (indicated by the 3.1.2. Precipitation blue line inResponse Figure 5a). The reduced rate of precipitation was 0.1 mm per 100 years, and global precipitation reached its lowest level at 1800 AD. In relation to global surface temperature, In the LU experiment, the global annual mean precipitation showed a decreasing trend associated precipitation changed 0.06 mm/day per degree of the temperature change. with multi-decadal to centennial variabilities themean pastprecipitation 1800 years(Figure (indicated by thethe blue line in The power spectrum analysis of globalover annual 5b,c, without Figure 5a).31-year The moving reduced rate indicated of precipitation 0.1 mm percontrol 100 years, andLUglobal precipitation average) that global was precipitation in the run and the experiment showed inter-annual variabilities. There no surface significant periodicities longer than the changed reached itsboth lowest level at 1800 AD. In relation towere global temperature, precipitation inter-annual time-scale in the control run; however, in the LU sensitivity experiment, significant 0.06 mm/day per degree of the temperature change. 50-year, 100-year, and 400-year periods occurred. The power spectrum analysis of global annual mean precipitation 5b,c, without The global annual mean temperature and precipitation were reduced (Figure to 0.05 K and 0.01 mm/day,the 31-year moving average) indicated that global precipitation in from the control and LU run. experiment both respectively, when compared to the results derived the LU run run to thethe CTRL The

showed inter-annual variabilities. There were no significant periodicities longer than the inter-annual time-scale in the control run; however, in the LU sensitivity experiment, significant 50-year, 100-year, and 400-year periods occurred.


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The global annual mean temperature and precipitation were reduced to 0.05 K and 0.01 mm/day, respectively, when compared to the results derived from the LU run to the CTRL run. The precipitation Atmosphere 2017, 8, 64 7 of 14 changed 7.5% per degree of temperature change, which exceeded the thermodynamic effect governed by the Clausius-Clapeyron equation (7% moisture change per degree of temperature precipitation changed 7.5% per degree of temperature change, which exceeded the thermodynamic change). effect governed by thewould Clausius-Clapeyron equation (7% primarily moisture change per degree of temperature We assumed that the LUCC affect global climate through the thermodynamic effect. change). We assumed that the LUCC would affect global climate primarily through the thermodynamic Along with increased crops and a decrease in natural vegetation, the differences between the LU and effect. Along with increased crops and a decrease in natural vegetation, the differences between the CTRL run became larger and more significant on a centennial time scale (Table S2). LU and CTRL run became larger and more significant on a centennial time scale (Table S2).

Figure 5. (a) Time series of the global annual mean precipitation rate anomalies in the past 1800 years

Figure 5. (a) Time series of the global annual mean precipitation rate anomalies in the past 1800 years (compared to the 201–2000 AD). After the 31-year running mean process, the red line is the result of (comparedthe tocontrol the 201–2000 AD). After the 31-year running mean process, the red line is the result of run, the blue curve is the result of the single-factor sensitivity experiment, and the blue the controldashed run, the curve is the result of the single-factor experiment, and the blue lineblue is the variation trend of the surface temperaturesensitivity in the single-factor sensitivity (b,c) Power spectrum global annual average precipitation rate, which only displays dashed lineexperiment; is the variation trend of theofsurface temperature in the single-factor sensitivity experiment; the periodic variation conditions over 20 years; where (b) are the results of the control run; and (c) are (b,c) Power spectrum of global annual average precipitation rate, which only displays the periodic the results of the LU sensitivity experiment. The red dashed line is the 95% confidence level. variation conditions over 20 years; where (b) are the results of the control run; and (c) are the results of the LU sensitivity experiment. The red dashed line the 95% confidence level. latitudes under Figure 6a,b show the 1800-year time series ofisannual precipitation at different the effect of LUCC and in the CTRL run. Precipitation showed a stronger variability in the low latitudes relative to the middle and high latitudes, and its long-term trend was also relatively large Figureat6a,b show the 1800-year time series of annual precipitation at different latitudes under the 0.146 mm per 100 years. The trend of precipitation decline in the mid-latitude NH was also effect of LUCC and in the CTRL run. Precipitation showed a stronger variability in the low latitudes relatively large at 0.153 mm/100 years. The spatial distribution of the annual mean precipitation change also showed that the precipitation changes were larger over the low latitudes of NH relative to the middle and high latitudes, and its long-term trend was also relatively large at 0.146 mm (Figure S4). The long-term trends of precipitation in the NH and the SH were relatively small, with large at per 100 years. The trend of precipitation decline in the mid-latitude NH was also relatively values of 0.084 mm per 100 years and 0.066 mm per 100 years, respectively. However, in the LU 0.153 mm/100 years. The spatial distribution of the annual mean precipitation change also showed that experiment, the variation amplitude and rates of convective precipitation were both greater than the precipitation larger over the of during NH (Figure The the long-term those ofchanges large-scalewere precipitation (Figure 6c).low For latitudes precipitation differentS4). seasons, impacts trends of of LUCC precipitation winter mm persmall, 100 years) and spring of (0.107 mmmm per 100 precipitation in theonNH and the in SH were(0.128 relatively with values 0.084 peryears) 100 years and slightly greater than the impacts in summer mmexperiment, per 100 years) and (0.091 mm 0.066 mm were per 100 years, respectively. However, in(0.086 the LU the autumn variation amplitude and per 100 years).

rates of convective precipitation were both greater than those of large-scale precipitation (Figure 6c). For precipitation during different seasons, the impacts of LUCC on precipitation in winter (0.128 mm per 100 years) and spring (0.107 mm per 100 years) were slightly greater than the impacts in summer (0.086 mm per 100 years) and autumn (0.091 mm per 100 years).

Atmosphere 2017, 8, 64 Atmosphere 2017, 8, 64

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Figure 6. Time series of annual average precipitation rate anomalies over different latitudes in the

Figure 6. Time series of annual average precipitation rate anomalies over different latitudes in the past past 1800 years. (a) LU run; (b) for CTRL run; and (c) the time series of global annual mean convective 1800 years. (a) LU run; (b) for CTRL run; and (c) the time series of global annual mean convective precipitation rate anomaly (red line) and large-scale precipitation rate anomaly (blue line). precipitation rate anomaly (red line) and large-scale precipitation rate anomaly (blue line).

3.2. Analysis of Mechanisms

3.2. Analysis of Mechanisms 3.2.1. Mechanisms of Temperature Variation

3.2.1. Mechanisms of Temperature Variation

On a multi-decadal to centennial time scale over the last 1800 years, crops increased and natural

vegetation decreased,toespecially duetime to ascale reduction of NH Whilecrops this occurred, solar On a multi-decadal centennial over the lastforests. 1800 years, increasedthe and natural 2 per 100 years (Figure 7). The radiation reflected by the ground surface increased at a rate of 0.02 W/m vegetation decreased, especially due to a reduction of NH forests. While this occurred, the solar net surface radiation had a decreasing trend of 0.03 W/m2 per 100 years. Under2the effect of LUCC, the radiation reflected by the ground surface increased at a rate of 0.02 W/m per 100 years (Figure 7). average global ground surface reflection radiation increased by 0.22 W/m2, surface ground net radiation The net surface radiation had2 a decreasing trend of 0.03 W/m2 per 100 years. Under the effect of LUCC, was reduced by 0.22 W/m , and surface heat flux was reduced by 0.08 W/m2. Furthermore, the surface the average global ground surface reflection radiation increased by 0.22 W/m2 , surface ground net latent flux was reduced by 0.18 W/m2, and the surface sensible flux increased by 0.1 W/m2, where global 2 . Furthermore, radiation was reduced by 0.22showed W/m2 ,aand surface trend. heat flux wascaused reduced by 0.08 in W/m temperature consequently decreasing LUCC a decrease the surface net 2 2 2 as well the surface latent reduced by 0.18 W/m , and thebysurface radiation by 1flux W/mwas as a decrease in temperature 0.15 °C.sensible flux increased by 0.1 W/m , where global temperature consequently a decreasing trend. LUCC decreaseofin the Among the different latitudes, theshowed regions showing the strongest groundcaused surfaceareflection 2 as well as ◦ C. solar were the mid-latitude NH (Figure 8),inwhich were the by regions the greatest surface netradiation radiation by in 1 W/m a decrease temperature 0.15 showing croplandthe expansion. the influence of LUCC on the temperature relatively direct, and was Among differentThus, latitudes, the regions showing strongestwas ground surface reflection of solar realized via a thermodynamic mechanism. A previous study in Reference [49] suggested that radiation were in the mid-latitude NH (Figure 8), which were the regions showing the greatest cropland converting crops in the mid-latitude NH to deciduous coniferous forest led to an increase in the expansion. Thus, the influence of LUCC on temperature was relatively direct, and was realized via a absorbed solar radiation and the sensible heat flux, which in turn resulted in temperature increases thermodynamic mechanism. A previous study in Reference [49] suggested that converting crops in in these areas. These results were consistent with the conclusions of this paper. the mid-latitude NH to deciduous coniferous forest led to an increase in the absorbed solar radiation and the sensible heat flux, which in turn resulted in temperature increases in these areas. These results were consistent with the conclusions of this paper.


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Atmosphere 2017, 8, 64 Atmosphere 2017, 8, 64

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Figure 7. Time series of annual mean surface reflected solar radiation anomalies. The description of Figure 7. Time series of annual mean surface reflected solar radiation anomalies. The description of Figure seriesasofthat annual meaninsurface solar radiation anomalies. The description of the lines7.isTime the same provided Figurereflected 2a. the lines is the same as that provided in Figure 2a. the lines is the same as that provided in Figure 2a.

Figure 8. Time series of the annual mean surface reflected solar radiation anomalies over different Figure 8.(the Time series of the annual mean surface reflected solar radiation anomalies over different latitudes difference themean resultssurface derived from the LU sensitive and the over control run). Figure 8. Time series of between the annual reflected solar radiationrun anomalies different latitudes (the difference between the results derived from the LU sensitive run and the control run). latitudes (the difference between the results derived from the LU sensitive run and the control run).

In winter and spring, the albedo of the crops covered by snow was greater than the albedo of In winter andbyspring, the albedo of theand crops covered wascrops greater than the albedo of the forest covered snow [50,51]. Summer autumn tendby tosnow be when grow vigorously, and In winter and by spring, the albedo of the crops coveredtend by snow was greater than the albedo of and the the forest covered snow [50,51]. Summer and autumn to be when crops grow vigorously, the albedo of the vegetation was reduced compared to those of winter and spring. The difference in forest covered by vegetation snow [50,51]. Summer autumn toofbewinter whenand crops growThe vigorously, and the albedo albedo of different the reducedand compared totend those spring. difference in the of types was of vegetation varied from one season to another, which was one of the the albedo of the vegetation was reduced compared to those of winter and spring. The difference in the albedo differentvegetation types of vegetation seasonontothe another, which was one ofand the reasons whyofdifferent types can varied have a from large one influence temperatures in spring the albedo of different different vegetation types of vegetation varied from one seasonon to the another, which was one of and the reasons why types can have a large influence temperatures in spring winter. Hua and Chen [30] also demonstrated that on an inter-annual scale, the influence of LUCC reasons why vegetation types can havethat a largean influence on thescale, temperatures in spring and winter. Hua different and in Chen also demonstrated theand influence LUCC on temperatures the [30] mid-latitude regions can beon moreinter-annual significant in winter springofthan in winter. Hua and Chen [30] also demonstrated that on an inter-annual scale, the influence of LUCC on on temperatures in the mid-latitude regions can be more significant in winter and spring than in summer and autumn. temperatures the mid-latitude regions can be more significant in winter and spring than in summer summer and in autumn. To further investigate the multi-decadal periodicity of temperature, we applied a 30–50 year and autumn. To further investigate the multi-decadal periodicity of temperature, we appliedtoa the 30–surface 50 year band-pass filter investigate and then applied an Empirical Orthogonal (EOF) To further the multi-decadal periodicity of Function temperature, weanalysis applied a 30–50 year band-pass filter and then applied antemperature). Empirical Orthogonal Function (EOF) analysis to the surface temperature (including surfacean We foundFunction an Atlantic multi-decadal oscillation band-pass filter and thensea applied Empirical Orthogonal (EOF) analysis to the surface temperature (including sea surface temperature). We found an Atlantic multi-decadal oscillation (AMO) pattern and a Pacific decadaltemperature). oscillation (PDO) patternan inAtlantic the LU run, which wasoscillation absent in temperature (including sea surface We found multi-decadal (AMO) pattern and a Pacific decadal oscillation (PDO) pattern in the LU run, which was absent in the CTRL run (Figure S5). Therefore, we considered that the enhanced multi-decadal variation (AMO) pattern and a Pacific decadal oscillation (PDO) pattern in the LU run, which was absent inmay the therelated CTRL to run (Figure S5). Therefore, we considered that the enhanced multi-decadal variation may be both the and AMO. CTRL run (Figure S5).PDO Therefore, we considered that the enhanced multi-decadal variation may be be related to both the PDO and AMO. related to both the PDO and AMO. 3.2.2. Mechanism of Precipitation Variation 3.2.2. Mechanism Mechanism of of Precipitation Precipitation Variation Variation 3.2.2. As the ratio of the global crops increased and the ratio of natural vegetation decreased, surface As the ofofthe global crops increased the ratio of natural vegetation decreased, surface evaporation in the past 1800 years shows a and decreasing (Figure 9a). vegetation Correspondingly, the As the ratio ratio the global crops increased and thetrend ratio of natural decreased, evaporation in the past 1800 years shows a decreasing trend (Figure 9a). Correspondingly, the concentration of water vapor in the atmosphere has declined, and atmospheric humidity also shows surface evaporation in the past 1800 years shows a decreasing trend (Figure 9a). Correspondingly, concentration of water vapor in the atmosphere has declined, and atmospheric humidity also shows a clear decreasing trend (Figure 9b), after 1850 when surface evaporationhumidity and the large the concentration of water vapor inespecially the atmosphere hasAD declined, and atmospheric also a clear decreasing trend (Figure 9b), especially after 1850 AD when surface evaporation and the large reduction in atmospheric humidity resulted in a reduction in global precipitation. The dramatic shows a clear decreasing trend (Figure 9b), especially after 1850 AD when surface evaporation and reduction in atmospheric resulted in the a reduction in global Theofdramatic decreasing trend of surfacehumidity evaporation during last 100 years wasprecipitation. likely the result LUCC, decreasing trend of surface evaporation during the last 100 years was likely the result of LUCC, especially after 1850 AD when the temperate forests were largely replaced by cropland (Figures 2 especially after 1850 AD when the temperate forests were largely replaced by cropland (Figures 2


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the large reduction in atmospheric humidity resulted in a reduction in global precipitation. The dramatic decreasing trend of surface evaporation during the last 100 years was likely the result Atmosphere 2017, 8, 64 10 of 14 of LUCC, especially after 1850 AD when the temperate forests were largely replaced by cropland Atmosphere 2017, 8, 64 10 of 14 (Figure 2 and Figure S1). With increased crops and decreased natural vegetation, the reflected terrestrial and S1). With increased crops and decreased natural vegetation, the reflected terrestrial solar solar the terrestrial radiation decreased, leading toaadecreasing decreasing trend in and radiation S1). increased Withincreased increased crops and decreased natural vegetation, the to reflected terrestrial solar radiation and and the net net terrestrial radiation decreased, leading trend in evaporation during that period. In addition, the decreased surface temperature may have decreased radiation increased and the net terrestrial radiation decreased, leading to a decreasing trend in evaporation during that period. In addition, the decreased surface temperature may have decreased evaporation, especially over the ocean, which may have contributed to a decrease in precipitation. evaporation during thatover period. addition, decreased surface temperature mayinhave decreased evaporation, especially the In ocean, whichthe may have contributed to a decrease precipitation. Thus, LUCC to the sensible heat in past years, evaporation, especially over the trend ocean, may have contributed decrease in precipitation. Thus, LUCC leads leads to aa decreased decreased trend in inwhich the surface surface sensible heat flux fluxto inathe the past 1800 1800 years, which which corresponded to a weakening of the upwelling and atmosphere convection convergence [52], which Thus, LUCC leads to a decreased trend in the surface sensible heat flux in the past 1800 years, which corresponded to a weakening of the upwelling and atmosphere convection convergence [52], which hampered the of especially precipitation. corresponded to a weakening of the upwelling andconvective atmosphere convection convergence [52], which hampered the formation formation of precipitation, precipitation, especially convective precipitation.

hampered the formation of precipitation, especially convective precipitation.

Figure 9. (a) Time series of annual mean evaporation anomalies; and (b) specific humidity anomalies Figure 9. (a) Time series of annual mean evaporation anomalies; and (b) specific humidity anomalies Figure 9. (a) Time series of annual mean at 850 hPa. The demonstration of the linesevaporation is the same anomalies; as in Figureand 2a. (b) specific humidity anomalies at 850 hPa. The demonstration of the lines is the same as in Figure 2a. at 850 hPa. The demonstration of the lines is the same as in Figure 2a.

Figure 10 shows that the zonal wind field at 500 hPa in the mid-latitude NH has increased since Figure 10 shows that that the the zonal windfield field at in the mid-latitude NH hasshowed increased 10 shows wind atregions, 500500 hPahPa in the mid-latitude has increased since 1850. Figure With increased crops inzonal the mid-latitude the middle latitudeNH westerlies an since 1850. With increased crops in the mid-latitude regions, the middle latitude westerlies showed 1850. Withtrend, increased crops in inwinter the mid-latitude the middle latitude westerlies showed an increasing and winds and spring regions, were relatively stronger. When the middle latitude an increasing trend, in winter and spring were relatively When the middle increasing trend, andand winds in winter and spring were relatively stronger. When the middle latitude westerlies were stronger inwinds East Asia during winter, cold weather eventsstronger. frequently occurred, which latitude westerlies were stronger in Asia during winter, coldprecipitation weather westerlies were in East during winter, cold weather events events frequently which may have led tostronger reductions in Asia theEast surface temperature and in frequently Eastoccurred, Asia occurred, [53]. We which may have led to reductions in the surface temperature and precipitation in East Asia may have the led to reductions in the surface temperature andmean precipitation in East Asia hPa [53].[53]. We calculated correlation coefficient between the annual zonal wind at 500 and We calculated the correlation coefficient betweenthe theannual annual mean zonal wind at atrelated 500 and calculated the correlation coefficient mean wind 500 hPa hPa and temperature (precipitation). The results between demonstrated that they werezonal significantly to each temperature (precipitation). The results demonstrated they were significantly to each temperature (precipitation). The results demonstrated that they were toother each other under LUCC forcing. The correlation coefficientthat was −0.45 (−0.23) insignificantly the LUrelated run, related and it appears under LUCC forcing. The correlation coefficient was − 0.45 ( − 0.23) in the LU run, and it appears that other under LUCC forcing. The coefficient was −0.45 (−0.23) in the LU run, and it appears that the LUCC mainly affects thecorrelation middle latitude westerly winds, thereby influencing precipitation the LUCC mainly affects the middle latitude westerly winds,winds, thereby influencing precipitation in the that the LUCC mainly affects the middle latitude westerly thereby influencing precipitation in the mid-latitude regions. mid-latitude regions. in the mid-latitude regions.

Figure 10. The annual mean zonal wind anomalies at 500 hPa at different latitudes (the difference Figure10. 10. The annual mean zonal wind anomalies atand 500the hPa atdifferent different latitudes(the (thedifference difference between theThe results derived from thewind LU sensitive runat control run). latitudes Figure annual mean zonal anomalies 500 hPa at between the results derived from the LU sensitive run and the control run). between the results derived from the LU sensitive run and the control run).

At NH low latitudes, precipitation change may also be related to the changed Walker and Atcirculations. NH low latitudes, precipitation change may also be to the changed Walkerover and Hadley For example, the temperature decreased andrelated the ascending flow weakened Hadley circulations. For example, thethe temperature decreased andweakened the ascending weakened over the Western Pacific warm pool and descending flow also over flow the Eastern Pacific the Western warm pool and circulation the descending flow alsoPacific weakened over EasternWalker Pacific Ocean, leadingPacific to a weakened Walker over the entire Ocean. Thethe weakened Ocean, leading a weakened Walker circulation over entire Pacific Ocean. The weakened Walker circulation alongtowith decreased specific humidity ledthe to the decreased precipitation. circulation along with decreased specific humidity led to the decreased precipitation.


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At NH low latitudes, precipitation change may also be related to the changed Walker and Hadley circulations. For example, the temperature decreased and the ascending flow weakened over the Western Pacific warm pool and the descending flow also weakened over the Eastern Pacific Ocean, leading to a weakened Walker circulation over the entire Pacific Ocean. The weakened Walker circulation along with decreased specific humidity led to the decreased precipitation. 4. Discussion Between 1200 AD and 1700 AD, variables such as surface radiation, temperature and precipitation showed low variation, and the variations in the crop ratio was relatively small. After the industrial revolution, the crop ratio showed considerable variation, resulting in dramatic changes in factors such as net surface radiation, surface temperature, evaporation, and precipitation. Before 700 AD, the ratio and the variation in the crop ratio were both relatively small, and the change in each corresponding climatic factor was also relatively small. However, between 700 AD and 1200 AD, there was a greater variation amplitude of the ratio of crops, resulting in dramatic changes in each climatic factor during this period. On a multi-decadal scale, LUCC has had a significant effect on the variation amplitude of the global climate, which is consistent with the conclusion that the LUCC effect would increase global temperature. This was also consistent with the occurrence of an approximate 400-year multi-decadal variation period in precipitation. Prior to 700 AD, human population was low, and technology and the use of natural vegetation was minor. The variation amplitude of the crop ratio was low, and its climatic influence was also relatively small. The interval between 700 AD and 1200 AD, represented a Medieval Warm Period where the climate was benign, population growth was rapid, crop cultivation increased as did the use of natural vegetation and resulted in a rapid increase in the crop ratio. Therefore, the global climate showed a relatively large variation. Between 1200 AD and 1700 AD, the world entered the Little Ice Age, when population growth slowed, and the ratio of crops varied only slightly within a narrow range. As a result, climate change variation during this period was relatively moderate. Between 1700 AD and 2000 AD, the climate once again entered a warming period, the human population rapidly increased, as did development and land utilization. The crop ratio increased rapidly, resulting in another period of climate change amplitude increase. The LUCC shows an interactive influence on climate, where climate change will influence human activities, thus influencing LUCC. LUCC also has a relatively large influence on the variation amplitude of the climate. During the 20th century, in contrast to the well-recognized global warming trend, LUCC appears to be associated with a decreasing trend in global temperature. By comparing the time series of the ratio of global crops in the past 1800 years, as well as the time series of global temperature and precipitation, it is clear that in the relatively stable stages of global temperature and precipitation variation (e.g., 200–700 AD, and 1200–1700 AD), the crop ratio changed less than 1%. During periods showing relatively larger variation in the amplitude of the global temperature and precipitation (e.g., 700–1200 AD, and post 1700 AD), the variation in the ratio of crops exceeded 1%. It is unclear at this stage whether a threshold must be reached before the LUCC shows significant influence on the global climate, which will require additional research. 5. Conclusions A comparison of the LU sensitive run to the CTRL run showed that the LUCC had a significant biogeophysical impact on global climate. In the past 1800 years, as croplands increased and natural vegetation decreased, global solar radiation reflected by the ground surface on multi-decadal and centennial scales showed an increasing trend. Additionally, decreased surface net radiation and a reduction in surface heat flux resulted in reduced global temperatures. The precipitation also showed a significant decreasing trend related to the changes of temperature, evaporation and wind field etc. Both the global annual mean temperature and precipitation showed significant oscillations at multi-decadal and centennial time-scales.


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The conclusions in this study are resulted from a single model and a single experiment. Whether the results are robust and model dependent will require further study. Additionally, the reconstructed LUCC datasets also contained large uncertainties [32], which may also have influenced the simulated results based on these datasets. Supplementary Materials: The following is available online at www.mdpi.com/2073-4433/8/4/64/s1. Figure S1: Time series of energy balance at top of atmosphere (TOA) (W/m2) from CTRL run. The blue line represents annual mean TOA; the red line represents its linear trend in the last 2000 years. Figure S2; Spatial distributions of natural vegetation types during four periods, where (a–d) represent 1 AD, 1700 AD, 1850 AD and 2000 AD, respectively, used in LU run. Figure S3: Spatial distributions of the annual mean surface temperature derived from (a) the CTRL run; and (b) the difference between the LU run and CTRL run. Only the areas exceeding the 95% confidence level are shown in (b). Figure S4: Spatial distributions of the annual mean precipitation rate derived from (a) the CTRL run; and (b) the difference between the LU run and CTRL run. Only those areas exceeding the 95% confidence level are shown in (b): Figure S5. Spatial pattern of the leading two EOF modes of annual surface temperature, after a 30–50 year band-pass filter. Left panel is derived from the LU run, right panel from the CTRL run. Table S1: IDs of vegetation type in the CESM land component and in Kaplan’s classification. Table S2: Climatology of temperature and precipitation derived from the CTRL and LU runs and the difference for every 100 years during the last 1800 years. Acknowledgments: We thank several reviewers for their constructive comments that assisted in clarifying and improving this paper. This work was jointly supported by the National Key Research and Development Program of China (Grant No. 2016YFA0600401), the National Natural Science Foundation of China (Grant Nos. 41302137, 41671197, 41371209 and 41420104002), and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD, Grant No. 164320H116). Author Contributions: M.Y. and J.L. designed the work. Z.W. ran the simulations. M.Y. performed analysis and wrote the initial manuscript. J.L. and M.Y. contributed to manuscript improvement and revisions. Conflicts of Interest: The authors declare no conflicts of interest.

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