land cover changes in eastern China using multi-model ensembles

Accepted Manuscript Simulation of the climatic effects of land use/land cover changes in eastern China using multi-model ensembles

Xianliang Zhang, Zhe Xiong, Xuezhen Zhang, Ying Shi, Jiyuan Liu, Quanqin Shao, Xiaodong Yan PII: DOI: Reference:

S0921-8181(16)30486-6 doi: 10.1016/j.gloplacha.2017.05.003 GLOBAL 2586

To appear in:

Global and Planetary Change

Received date: Revised date: Accepted date:

2 November 2016 3 May 2017 13 May 2017

Please cite this article as: Xianliang Zhang, Zhe Xiong, Xuezhen Zhang, Ying Shi, Jiyuan Liu, Quanqin Shao, Xiaodong Yan , Simulation of the climatic effects of land use/land cover changes in eastern China using multi-model ensembles, Global and Planetary Change (2017), doi: 10.1016/j.gloplacha.2017.05.003

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Simulation of the climatic effects of land use/land cover changes in eastern China using multi-model ensembles Xianliang Zhang1*, Zhe Xiong2, Xuezhen Zhang3, Ying Shi4, Jiyuan Liu3, Quanqin Shao3,

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Xiaodong Yan5*

College of Forestry, Shenyang Agricultural University, Shenyang 110866, China

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CAS Key Laboratory of Regional Climate–Environment for East Asia, Institute of

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Atmospheric Physics, Chinese Academy of Sciences, Beijing 10029, China Institute of Geographic and Natural Resources Research, Chinese Academy of

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Sciences, Beijing 100101, China

National Climate Center, China Meteorological Administration, Beijing 100081,

State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing

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China

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Normal University, Beijing 100087, China

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Corresponding authors: Xianliang Zhang, Shenyang Agricultural University, Shenyang, 110866,

China. Email: [email protected]; Xiaodong Yan, Beijing Normal University, Beijing 100087, China. Email: [email protected]. 1

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Abstract Human activities have caused substantial land use/cover change (LUCC) in China, especially in northeast China, the Loess Plateau and southern China. Three high-resolution regional climate models were used to simulate the impacts of LUCC on climate through one control experiment and three land use change experiments

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from 1980 to 2000. The results showed that multi-regional climate model ensemble

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simulations (the arithmetic ensemble mean (AEM) and Bayesian model averaging

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(BMA)) provide more accurate results than a single model in over 70% grid cells of

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study regions. Uncertainty was reduced when using the two ensemble methods. The results of the AEM and BMA ensembles showed that the temperatures decreased by

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0.2–0.4C in northeast China, the Yangtze river valley and the north of the Loess Plateau, and by 0.6–1.0C in the south of the Loess Plateau in spring, autumn and

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winter. The AEM precipitations changed by -40–40 mm in in spring and winter, and by -100–100 mm in summer and autumn, while the BMA precipitations changed by

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-20–20 mm in spring, autumn and winter, and by -50–50 mm in summer. The seasonal precipitation decreased in northeast China and the Yangtze river valley, and increased

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in the Loess Plateau in most grid cells of study regions. Winter and spring precipitation decreased more in the Yangtze river valley and the Loess Plateau than in northeast China. Keywords: Land use change; Eastern China; multi-model ensemble; WRF; RegCM3; RIEMS

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ACCEPTED MANUSCRIPT 1. Introduction Land use/cover changes (LUCCs) make a significant contribution to regional climate change (Pielke 2005; Dirmeyer et al. 2010; Pielke et al. 2011; Pitman et al. 2012). As the human population has increased, more than a third of the natural land

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area worldwide has been transformed by human activities (Ramankutty et al. 2002;

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Eills et al. 2010, 2011). LUCC alters the properties of the land surface and influences

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the Earth’s climate thorough biogeophysical and biogeochemical effects (Turner et al.

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1993; Brovkin et al. 1999). Climate change effects have been observed in many LUCC hotspots – for instance, a warmer and drier climate has been induced by

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deforestation in tropical regions (Sud et al. 1996; Zhang et al. 1996). The regional climate may be highly influenced by LUCC, especially in regions where there have

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been large-scale changes in land use.

Land use in China has experienced great changes as a result of the high

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population and long agricultural history of the region. Most of the population and croplands are concentrated in eastern China and the most significant changes in land

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use have occurred in this region. Under the influence of government rules, land use in China has changed remarkably in the early 21st century (Liu et al. 2010; Li et al. 2017). The main LUCCs from 1980 to 2000 were changes from forest to cropland in northeast China, the replacement of forest by cropland around the Yangtze river valley, and changes from grassland to cropland around the farming–grazing transitional zone of the Loess Plateau (Liu et al. 2003, 2005). Large areas of LUCC in these regions 3

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may have resulted in regional climate changes. The effect of LUCCs on climate can be simulated by regional climate models (RCMs). High-resolution RCMs such as the Regional Climate Model (RegCM), the Weather Research and Forecasting (WRF) model, and the Regional Integrated

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Environmental Model System (RIEMS) have been proved to perform well in

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simulating regional climate change in China, and are commonly used to simulate the

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climatic effects of LUCCs (Xiong et al. 2006, 2009; Gao et al. 2007, Ge et al. 2014).

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For instance, the replacement of forest by farmland may reduce regional temperatures when simulating the effect of LUCCs with RegCM (Gao et al. 2007). The influence of

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LUCC on climate has been simulated using a single RCM over eastern China, where there had been much LUCC (Zhang et al. 2015; Hu et al. 2014). However, different

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RCMs showed different effects of LUCC on the climate when simulating the same LUCC (Fu et al. 2005; Pitman et al. 2009; Déqué et al. 2012; Mearns et al. 2012). The

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effects of LUCC simulated by WRF on climate were found to be very different from the RegCM3 simulations (Zhang et al. 2015; Zhang et al. 2016a). Multi-RCM

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comparison experiments showed that the ranges and trend of LUCC-induced regional climate change were different when simulated by different RCMs (e.g. Pitman et al. 2009; Mearns et al. 2012; Zhang et al. 2016a). Large uncertainties were found when simulating the effects of LUCC on climate by a single RCM (Pitman et al. 2009). Multi-model ensemble simulations improve the results of simulation (Zhang and Yan 2014, 2015; Zhang et al. 2016b). Multi-RCMs provided more accurate results 4

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than single RCMs for the effects of LUCC on climate in northeast China (Li et al. 2013; Zhang et al. 2016a). Although the effects of LUCC on climate have been simulated with a single RCM in previous studies (e.g. Zhang et al. 2015; Hu et al. 2014), the range and trend of the effects on climate were not well captured. We

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therefore aimed to detect the effects of LUCC on climate using multi-RCM ensembles

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over areas of eastern China where there have been large LUCCs. The main goals of

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this study were (1) to compare the simulated effects of LUCC on climate using

ensemble simulations over eastern China.

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2. Data and methods

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different RCMs and (2) to detect the effects of LUCC on climate based on multi-RCM

2.1. Model descriptions

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The climatic effects of LUCC were simulated by three high-resolution RCMs (RegCM3, WRF and RIEMS) and the differences between these models were

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investigated. 2.1.1 RegCM3

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RegCM3 is a third-generation regional-scale climate model (Pal et al. 2007). It was developed by the International Center for Theoretical Physics. RegCM3 includes the Biosphere–Atmosphere Transfer Scheme (BATS) (Dickinson et al. 1993), the Community Climate Model version 3 (CCM3), the same dynamic core as MM5 and a radiative transfer package (Kiehl et al. 1996). RegCM3 is a primitive equation, sigma vertical coordinate regional climate model. The vertical resolution of RegCM3 is 18 5

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levels, with seven levels below 800 hPa. The documentation and source code of RegCM3 are available at www.ictp.triest e.it/RegCNET/model.html 2.1.2. WRF The WRF model is a next-generation regional weather research and forecasting

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model. It was developed by the National Center for Atmospheric Research, the

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National Centers for Environmental Prediction and the Forecast Systems Laboratory,

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the Air Force Weather Agency, the Naval Research Laboratory, the University of

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Oklahoma and the Federal Aviation Administration. WRF includes two dynamic cores, a data assimilation system and a software architecture. A detailed description of the

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WRF model can be found at http://www.wrf-model.org/index.php 2.1.3. RIEMS

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RIEMS was developed by the Regional Center for Temperate East Asia, Chinese Academy of Sciences (Fu et al. 2000). RIEMS includes the land surface physics

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scheme BATS1e (Dickinson et al. 1993), a Grell cumulus parameterization, the same

1996).

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dynamic component as MM5 and a CCM3 radiative transfer package (Kiehl et al.

2.2. Experimental design A control (CTL) experiment and three land use change (LUC) experiments were designed to reveal the effects of LUCC on climate from 1981 to 2000 in eastern China. Recently developed land cover data with a spatial resolution of 1×1 km2 (Liu et al. 2002, 2005), which are more representative of the land cover in China than the US 6

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Geological Survey (USGS) data, were used in both experiments. Land use categories were defined by Liu et al. (2002). Because the RCMs used the USGS land cover categories in their model design, the land use categories of Liu et al. (2002) were transformed to match the USGS land use categories by spatial aggregation and type

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conversions according to the criteria in Table 1.

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Land cover data for the year 2000 (Fig. 1) were used as a fixed vegetation

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parameter in the CTL experiment. In the three LUC experiments, the land use types in

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the year 2000 were replaced by the land use types in the year 1980 in northeast China and by farming–pasture transitions around the Loess Plateau and the Yangtze river

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valley (Fig. 1). The same simulation domains and periods, lateral boundary conditions and settings of physical parameterizations were used in the CTL and LUC

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experiments, except for the prescribed land cover parameter in three LUCC regions. Driven by the same initial and boundary conditions (6-h interval NCEP II reanalysis

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data), the models WRF, RegCM3 and RIEMS were used to perform four experiments. The continuous 21-year simulations from 1980 to 2000 were run by the three RCMs,

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with the initial simulations on 1 January 1980. The first year was used as the spin-up period and was not considered in the analysis. Although the resolutions of the three RCMs were different, the simulated domains of the different RCMs were roughly located over the same region of China (Fig. 1). RegCM3 had a horizontal resolution of 50 km and a domain central point at (36° N, 116° E), with 159 grid cells in the west–east direction and 128 grid cells in the 7

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south–north direction. The domain of WRF had a central point at (37° N, 117° E) and a horizontal resolution of 30 km, with 95 grid cells in the west–east direction and 139 grid cells in the south–north direction. For RIEMS, the model domain had a central point at (36° N, 117.5° E) and a horizontal resolution of 30 km, with 79 grid cells in

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the west–east direction and 139 grid cells in the south–north direction.

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2.3 Multi-model ensemble methods

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Every RCM output the simulated climatic effects of LUCC in every grid cell of

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study regions for four experiments. The ensemble results were calculated by averaging the final simulations of three RCMs by the following two methods.

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2.3.1. Arithmetic ensemble mean method

The arithmetic ensemble mean (AEM) is defined by: 1

(1)

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𝑌(𝑡) = 𝐾 ∑𝐾 𝑖=𝑘 𝐹𝑘 (𝑡),

where Y(t) is an ensemble prediction for time t, K is the total number of models and

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Fk(t) is a forecast of the kth model for time t. 2.3.2. Bayesian model averaging

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Bayesian model averaging (BMA) was developed based on the Bayesian formula. Using K predictors x1, … , xK to forecast y, the Bayesian forecast PDF p(y|x1 … xK) is calculated as 𝑝(𝑦|𝑥1… 𝑥𝑘 ) = ∑𝐾 𝑘=1 𝑤𝑘 𝑝𝑘 (𝑦|𝑥𝑘 ),

(2)

where wk is the weight of the kth model and pk(y|xk) is an approximate conditional PDF by a normal distribution, centered at a linear function of the predictor ak+bkxk. 8

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The deterministic BMA ensemble prediction is the conditional expectation of y given the forecast and is calculated as 𝐸(𝑦|𝑥1… 𝑥𝑘 ) = ∑𝐾 𝑘=1 𝑤𝑘 (𝑎𝑘 + 𝑏𝑘 𝑓𝑘 ),

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where ak and bk can be obtained by the regression between xk and y in the training

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period. The weights wk are recommended to be calculated using the expectation–

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maximization (EM) algorithm (Raftery et al. 2005). A detailed description of EM

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algorithm could be found in Raftery et al. (2005) and Zhang and Yan (2015), and a

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detailed description of BMA is given in Raftery et al. (2005), Zhang and Yan (2015) and Zhang et al. (2016b).

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The rules used in BMA, which weight every model according to single model’s performance, are also used in many data mining and machine learning studies to

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classify land use, and proved to improve the classification performance (e.g. Guan et al. 2012; Senf et al. 2012; Li et al. 2013). Generally, BMA obtained better results than

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AEM for it weighs the RCMs by their contributions to the observations (e.g. Zhang and Yan 2014, 2015; Zhang et al. 2016a, b). However, the calculation of BMA is

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complicated. AEM is a better choice for those who want to improve the simulations of single RCMs based on an easy and simple method. 2.4 Analysis of simulations Monthly temperature and precipitation data were downloaded from the Climate Research Unit TS 3.22 dataset (Harris et al. 2014) and used as the observed data. The observed data had a spatial resolution of 0.5×0.5 and therefore the simulated data 9

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were interpolated to, and masked with, the observed grid. The CTL simulations were verified with the observations to evaluate the performance of the three RCMs and their ensemble simulations. The system biases (mean of 1981–2000) and correlation with the observations were calculated using CTL simulations.

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Three LUC experiments were used to illustrate the climatic effects of LUCC. The

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differences between the CTL and the three LUC simulations (northeast China, the

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Loess Plateau and the Yangtze river valley) were calculated to show the impacts of

3. Results

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3.1. Evaluation of the CTL simulations

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LUCC on climate in eastern China.

The bias and correlation in the spatial patterns of the annual mean temperature

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were calculated to show the performance of each RCM and their ensembles in simulating the changes in temperature in the study regions (Fig. 2). The bias was

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<4C for every RCM. A cold bias was found in most grid cells in the study regions, which is a common problem in models (Gao et al. 2007). However, RegCM3 showed

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a warm bias in the west of northeast China. Compared with other two RCMs, WRF had the highest bias in most grid cells of the study regions. The AEM and BMA ensembles provided a lower bias in more grid cells in the study regions than a single RCM. The simulated CTL annual mean temperatures were significantly correlated with the observations in most grid cells for the three RCMs and their AEM and BMA ensembles. However, relative low correlation coefficients were found in the central 10

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and northeastern regions for RegCM3, in the northeastern and southeastern regions for WRF and in the southern regions for RIEMS. The AEM and BMA ensembles had high correlation coefficients with the observations in almost all grid cells (>70%). Therefore, the temperature could be well simulated by the three RCMs, and their

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AEM and BMA ensembles gave better results than the single RCMs in simulating the

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temperature changes in the research regions.

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The bias and correlation were also calculated for the annual total precipitation

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(Fig. 3). The bias ranged from 200 to −1000 mm for every RCM. A larger bias was found in the southern regions than in the northern regions because the total annual

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precipitation was higher in the southern regions. RegCM3 and RIEMS had a positive bias in most grid cells in the research regions, whereas WRF had a positive bias in the

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northern regions and a negative bias in the southern regions. RegCM3 had the lowest bias and RIEMS had the highest bias among the three RCMs. The AEM and BMA

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ensembles had a lower bias than any single RCM in most grid cells. The simulated CTL annual total precipitation had no significant correlation with the observations in

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more than half of the grid cells for every RCM. Compared with other regions, higher correlation coefficients were found in the southern regions for WRF, in the southern and northeastern regions for RegCM3, and in the northern, central and southern regions for RIEMS. A significant correlation between the AEM and BMA ensembles and the observations was found in more grid cells (>70%) than between single RCM simulations and observations. Thus, the AEM and BMA ensembles improved the 11

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results of the simulation, although precipitation was not well simulated by the RCMs in many grid cells. 3.2. Climatic effects of LUCC The main LUCC was the replacement of grassland and forest with cropland in

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northeast China, the replacement of grassland with cropland in the Loess Plateau and

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the replacement of forest with cropland or woodland in the Yangtze river valley (Table

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2). The annual mean temperature in the RegCM3 simulations increased by 0.1–0.2C

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in the west of northeast China and the central part of the Loess Plateau, and decreased by 0.1–0.2C in the central part of the Yangtze river valley (Fig. 4). The mean

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temperature in the RegCM3 simulations increased by 0.1–0.4C in the west of northeast China and in the north of the Loess Plateau, whereas it decreased by 0.2–

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0.6C in the Yangtze river valley in spring, summer and autumn. The winter temperature in the RegCM3 simulations increased by 0.05–0.1C in northeast China

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and 0.1–0.25C in the Loess Plateau, and decreased by 0.05–0.2C in the Yangtze river valley. In the RIEMS simulations, the mean temperatures decreased by 1–2C in

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most grid cells of the three research regions in every season. In the WRF simulations, the temperature changes mainly occurred in the grid cells affected by LUCC. The mean temperatures decreased by 0.2–1.0C in northeast China and 1.0–2.0C in the Loess Plateau, whereas they increased by 0.2–1.0C in the Yangtze river valley in every season when simulated by WRF. The AEM obtained similar results to the BMA. The AEM temperatures decreased by 0.2–0.4C in northeast China, the Yangtze river 12

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valley and the north of the Loess Plateau and by 0.6–1.0C in the south of the Loess Plateau in spring, autumn and winter. A greater decrease was found in summer than in other seasons based on the AEM temperatures. For the RegCM3 simulations, the annual total precipitation decreased by 50–100

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mm in about half of the grid cells of the research regions, whereas it increased by 50–

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100 mm in the other half of the grid cells (Fig. 5). The autumn precipitation in the

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RegCM3 simulations increased by 10–20 mm in most grid cells in northeast China

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and in the eastern part of the Yangtze river valley. The spring precipitation simulated by RegCM3 in northeast China increased by 10–20 mm, and the summer precipitation

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decreased by 25–75 mm. For the RIEMS simulations, the range of the annual total precipitation was 100–200 mm in northeast China and 300–500 mm in the Loess

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Plateau and the Yangtze river valley. The summer precipitation varied in the range −100 to 100 mm. The precipitation changed more in the Yangtze river valley than in

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northeast China in the spring, winter and autumn. In the WRF simulations, the total precipitation increased in most grid cells of the three research regions in every season.

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The precipitation changed more in summer than in the other seasons. The winter precipitation changed more in the Yangtze river valley than in the other regions. For the AEM and BMA ensembles, the precipitation changed more in the Yangtze river valley and the Loess Plateau than in northeast China. The range of precipitation was highest in summer and lowest in winter. The AEM precipitations changed by -40–40 mm in spring and winter, and by -100–100 mm in summer and autumn, while the 13

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BMA precipitations changed by -20–20 mm in spring, autumn and winter, and by -50–50 mm in summer. The AEM evapotranspiration (ET) rate increased by around 0.1 mm/day in most grid cells of northeast China and increased by around 0.1 mm/day in most grid cells of

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the Yangtze river valley in spring and autumn. The AEM ET increased more in

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summer than in other seasons. The ET increased by about 0.1–0.3 mm/day in most

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grid cells of the Loess Plateau. The winter ET changed little in winter, but increased

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by about 0.1–0.2 mm/day in most grid cells of the Loess Plateau and the Yangtze river valley. A decrease in the AEM net radiation (NR) as a result of LUCC was detected in

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every season. The NR decreased by 2–4 W/m2 in most grid cells in the three research regions.

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4. Discussion

Four experiments were designed to illustrate the effects of LUCCs on the climate

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in eastern China using three RCMs and their ensemble simulations. By verifying the CTL simulations with the observations, we concluded that the AEM and BMA

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ensemble simulations gave more accurate results than a single RCM when simulating temperature and precipitation in eastern China for they produced lower bias and higher correlation coefficients than single RCMs in most grid cells (over 70%) of the study regions. Zhang et al. (2016a) have previously shown that ensemble simulations produce better results than single simulations in northeast China when simulating the annual mean temperature. Apart from temperature, our results showed that the 14

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simulation of precipitation could also be improved by ensemble simulations in eastern China. The benefit of using ensemble simulations was shown in the reduction of bias and the increase in the correlation coefficients with the observations. The multi-model ensemble simulation was a post-processing of the model

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simulations to improve the single simulations. The multi-model superiority is caused

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not only by error compensation but also by its greater consistency and reliability

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(Hagedorn et al. 2005). The success of multi-model ensemble is based on the

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differences in model assumptions, structure and land use characteristics, which make the simulation biases from different models could partly cancel out (Reifen and Toumi

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2009).

The use of multi-model ensemble simulations could reduce uncertainties in

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simulating the climatic effects of LUCC. The trends in temperature were different when simulated with different RCMs. For instance, the temperature decreased in the

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Yangtze river valley when simulated with RegCM3, whereas it increased when simulated with WRF and RIEMS. These results are in agreement with those of Zhang

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et al. (2016a), in which an opposite trend of LUCC-induced temperature changes was detected by different models in many grid cells. The range in variation was also different when simulated with different RCMs. When simulating the same LUCC effect, RIEMS output larger climate changes than the other two RCMs. Some results were unreliable when the LUCC-induced climate changes were either extremely large or extremely small. The changes in climate were more obvious in the LUCC grid cells 15

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than in the grid cells without LUCC when simulated by WRF, which is very different from the results of the other two RCMs. Thus, there were large uncertainties in the simulated climatic effects of LUCC provided by a single RCM. Ensemble simulations should be used to reduce the uncertainties in the single RCM simulations. Although

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running more RCMs requires more time to obtain results, more accurate results are

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obtained using multi-RCM ensembles.

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The main LUCC in eastern China was the transformation of forest, grassland and

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a mixed land use of forest and cropland to cropland and dryland. Seasonal temperatures decreased in northeast China, the Loess Plateau and the Yangtze river

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valley as a result of the LUCC in eastern China when using the AEM and BMA ensembles. These results are in agreement with previously reported research. Gao et al.

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(2003, 2007) conducted a number of investigations into the influence of LUCC on the climate of China using RegCM. They showed that the annual mean temperature

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decreased in northeastern and southern China when the potential vegetation cover was changed to the current land use. A cooling effect in central and northern China was

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detected when land use changed from broadleaf forest to irrigated cropland (Suh and Lee 2004). The ET rates increased as a result of LUCC in northeast China and transferred heat from the surface to the upper atmosphere. The albedo increased when forest was replaced by cropland (Betts 2000; Matthews et al. 2004; Jackson et al. 2008), which led to a decrease in the NR. The temperature decreased as a result of the increase in the rate of ET and the decrease in the NR. There was a decrease in the NR, 16

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whereas there was an increase in the ET rate, which explained why the temperature decreased as a result of LUCC. Seasonal precipitation (AEM and BMA) increased in the Loess Plateau as a result of LUCC, whereas it decreased in northeast China and the Yangtze river valley.

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The effects of LUCC on climate varied both spatially and temporally with the type of

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conversion. The mechanisms were difficult to explain when several different

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conversions between vegetation types occurred. Irrigation increased after the land use

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changed from grassland and forest to cropland, which resulted in the availability of more soil water. The rate of ET increased and led to an increase in rainfall, which

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explained why the precipitation increased in many grid cells. However, other conversions lead to a decrease in precipitation. LUCC had more influence on climate

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in the south than in the north, especially in winter. The south of China has higher precipitation than the north, so changes in precipitation may also be larger in the south.

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Precipitation is mainly in the form of snow in winter in northeast China. The land surface physical attributions such as albedo and net radiation which influence

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precipitation were not changed significantly although land use had changed in winter because the snow covers the ground throughout the winter in Northeast China. Therefore, LUCCs had little influence on snow. In conclusion, land use was strongly affected by human activities in eastern China. Three regions (northeast China, the Loess Plateau and the Yangtze river valley) that had large LUCC were selected as the research regions. Multi-RCM ensemble 17

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simulations outperformed single RCM simulations and provided more accurate results in more than 70% grid cells of study regions. The results of the AEM and BMA simulations showed that the seasonal temperature and precipitation decreased as a result of LUCC in the three study regions. The seasonal temperatures decreased more

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in the Loess Plateau than in northeast China and the Yangtze river valley. Seasonal

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precipitation showed a larger decrease in the Yangtze river valley and the Loess

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Plateau in northeast China and the winter precipitation decreased more in the Yangtze river valley and the Loess Plateau than in northeast China.

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Acknowledgments

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This work was funded by the National Natural Science Foundation of China (41601045, 31570632, 41571094 and 31570473, 91425304, 41471171, 41271066)

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and Kezhen Outstanding Young Scholars from IGSNRR (No. 2015RC101).

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Caption of Figures and Tables Fig. 1. Land use/cover types in eastern China in 2000 (LU2000) and 1980 (LU1980) and their differences (land use in 2000 minus land use in 1980). Fig. 2. System bias (upper panels, C) and correlation (lower panels) between the

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observed and simulated annual temperature by the three regional climate models

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(RegCM3, WRF and RIEMS) and their ensembles (AEM and BMA). The blank areas

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in the correlation results represent insignificant correlations between the model

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simulations and the observed data.

Fig. 3. System bias (upper panels, mm) and correlation (lower panels) between the

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observed and simulated annual precipitation by the three regional climate models (RegCM3, WRF and RIEMS) and their ensembles (AEM and BMA). The blank areas

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Fig. 4. Differences in seasonal mean temperatures (C) during the 20 years from 1981 to 2000 between the control and three land use change (northeastern China, the Loess

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Plateau and the Yangtze river valley) simulations by the three regional climate models and their ensembles (AEM and BMA). Fig. 5. Differences in seasonal total precipitation (mm) during the 20 years from 1981 to 2000 between the control and three land use change (northeastern China, the Loess Plateau and the Yangtze river valley) simulations by the three regional climate models and their ensembles (AEM and BMA). 26

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Fig. 6. Effects of land use/cover change on evapotranspiration (mm/day) and net radiation (W/m2) by the AEM ensemble simulations of three regional climate models (RegCM3, WRF and RIEMS). Table 1. Rules for spatial aggregation and land use conversions from the land cover

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data of Liu et al. (2002) to USGS land cover categories.

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Table 2. Changes in area of land use types during 1980–2000. Negative numbers

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represent a reduction in area, whereas positive numbers represent an increase in area.

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Fig. 1. Land use/cover types in eastern China in 2000 (LU2000) and 1980 (LU1980)

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and their differences (land use in 2000 minus land use in 1980).

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Fig. 2. System bias (upper panels, C) and correlation (lower panels) between the observed and simulated annual temperature by the three regional climate models (RegCM3, WRF and RIEMS) and their ensembles (AEM and BMA). The blank areas in the correlation results represent insignificant correlations between the model simulations and the observed data. 29

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T P

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Fig. 3. System bias (upper panels, mm) and correlation (lower panels) between the observed and simulated annual precipitation by the three

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regional climate models (RegCM3, WRF and RIEMS) and their ensembles (AEM and BMA). The blank areas in the correlation results represent insignificant correlations between the model simulations and the observed data.

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Fig. 4. Differences in seasonal mean temperatures (C) during the 20 years from 1981 to 2000 between the control and three land use change (northeastern China, the Loess Plateau and the Yangtze river valley) simulations by the three regional climate models and their ensembles

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Fig. 5. Differences in seasonal total precipitation (mm) during the 20 years from 1981 to 2000 between the control and three land use change (northeastern China, the Loess Plateau and the Yangtze river valley) simulations by the three regional climate models and their ensembles

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Fig. 6. Effects of land use/cover change on evapotranspiration (mm/day) and net radiation (W/m2) by the AEM ensemble simulations of three

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Table 1. Rules for spatial aggregation and land use conversions from the land cover data of Liu et al. (2002) to USGS land cover categories. Liu land use

Liu land use category

Code

USGS

Name/description

USGS category Name

Code

One-to-one conversion; any single land use (left-hand side) accounts for more than half of the model grid

1

12

Dry cropland

2

11

Irrigated cropland

3

31–33

Grassland

22

Shrub

41–43

Water body

45–46

Wetland

61–67

Bare land

44

Land ice/snow cover

Urban and built-up land

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Building area

Dry cropland and pasture

Irrigated cropland and pasture

7

Grassland

8

Shrubland

16

Water body

17

Herbaceous wetland

19

Barren or sparsely vegetated

24

Snow or ice

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51–53

One to more conversion; total area of land use (left-hand side) accounts for more than half of the model grid

Sparse woodland

24

Cut-over land

10

Savanna

D

23

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More to one conversion; using the vegetation regionalization map to determine forest type and the single type of forest accounts for more than half of the model grid

Forest

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21

11

Deciduous broadleaf forest

12

Deciduous needleleaf forest

13

Evergreen broadleaf forest

14

Evergreen needleleaf forest

Mixed type of land cover, excluding the above conditions

Total area of dryland (code 12) and irrigated (code 11) cropland

4

Mixed dryland/irrigated cropland and pasture

accounts for more than half of the model grid Forest (code 21) accounts for more than half of the model grid

15

Mixed forest

5

Cropland/grassland mosaic

6

Cropland/woodland mosaic

and no dominant USGS forest could be found Grassland (code 31–33) and cropland (code 11–12) are the largest two land use types in the model grid Woodland (code 21–24) and cropland (code 11–12) are the largest two land use types in the model grid

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Woodland (code 21–24) and grassland (code 31–33) are the

Mixed shrubland/grassland

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largest two land use types in the model grid

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Table 2. Changes in area of land use types during 1980–2000. Negative numbers represent a reduction in area, whereas positive numbers represent an increase in area. Area of changed land use (103 km2)

Land use type

Loess Plateau

Yangtze river valley

Dryland cropland and pasture

63

27

19

Irrigated cropland and pasture

0

19

13

Cropland/grassland mosaic

7

44

Cropland/woodland mosaic

26

15

Grassland

−14

−50

Shrubland

0

−13

−9

Savanna

−25

−20

−3

Deciduous broadleaf forest

−8

−17

−21

Evergreen broadleaf forest

0

0

−61

Evergreen needleleaf forest

0

0

−25

Mixed forest

−38

−1

−10

Herbaceous wetland

−7

0

0

−4

0

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Barren or sparsely vegetated

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Northeast China

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-1

98 0

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Multi-RCMs produced more accurate results than any single RCMs Seasonal temperatures decreased in three regions of eastern China due to LUCC Seasonal precipitation decreased in northeast China and the Yangtze river valley LUCC leads to precipitation increase in the Loess Plateau

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