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  • View in gallery

    (a) Land-cover types and the surface stations used in this study and (b) differences in the NDVI between June and May. The box indicates the NCP, and closed and open circles indicate the stations that are selected as DCR and SCR, respectively. (c) Time series of NDVI averaged over the DCR and SCR for the period 1996–2005.

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    Differences of the monthly surface temperature between May and June (June minus May): (a) ΔTMODIS for 2000−06 and (b) ΔTstation for 1996−2005. (c) Scatterplots of ΔTMODIS vs ΔTstation with linear regression slope and the 95% prediction band.

  • View in gallery

    Monthly-mean values of (a) Tmax and Tmin and (b) daily mean specific humidity (q) averaged over the DCR and SCR stations. Prior to the domain averages, a 5-month average (April–August) is removed from each station data to eliminate latitudinal influence on the calculation. (bottom) Differences of the three variables between DCR and SCR (DCR minus SCR).

  • View in gallery

    Scatterplot of ΔNDVI vs (a) ΔTmax, (b) ΔTmin, and (c) Δq for June minus May. Closed circles represent DCR, and open circles represent SCR stations. The slopes are shown.

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Observational Evidences of Double Cropping Impacts on the Climate in the Northern China Plains

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  • 1 School of Earth and Environmental Sciences, Seoul National University, Seoul, South Korea
  • 2 Department of Geosciences, Princeton University, Princeton, New Jersey
  • 3 University of California, Los Angeles, Los Angeles, California
  • 4 School of Earth and Environmental Sciences, and Research Institute of Oceanography, Seoul National University, Seoul, South Korea
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Abstract

The impacts of harvested cropland in the double cropping region (DCR) of the northern China plains (NCP) on the regional climate are examined using surface meteorological data and the satellite-derived normalized difference vegetation index (NDVI) and land surface temperature (LST). The NDVI data are used to distinguish the DCR from the single cropping region (SCR) in the NCP. Notable increases in LST in the period May–June are found in the area identified as the DCR on the basis of the NDVI data. The difference between the mean daily maximum temperature averaged over the DCR and SCR stations peaks at 1.27°C in June. The specific humidity in the DCR is significantly smaller than in the SCR. These results suggest that the enhanced agricultural production by multiple cropping may amplify regional warming and aridity to further modify the regional climate in addition to the global climate change. Results in this study may also be used as a quantitative observed reference state of the crop/vegetation effects for future climate modeling studies.

Corresponding author address: Chang-Hoi Ho, School of Earth and Environmental Sciences, Seoul National University, Seoul 151-742, South Korea. E-mail: hoch@cpl.snu.ac.kr

Abstract

The impacts of harvested cropland in the double cropping region (DCR) of the northern China plains (NCP) on the regional climate are examined using surface meteorological data and the satellite-derived normalized difference vegetation index (NDVI) and land surface temperature (LST). The NDVI data are used to distinguish the DCR from the single cropping region (SCR) in the NCP. Notable increases in LST in the period May–June are found in the area identified as the DCR on the basis of the NDVI data. The difference between the mean daily maximum temperature averaged over the DCR and SCR stations peaks at 1.27°C in June. The specific humidity in the DCR is significantly smaller than in the SCR. These results suggest that the enhanced agricultural production by multiple cropping may amplify regional warming and aridity to further modify the regional climate in addition to the global climate change. Results in this study may also be used as a quantitative observed reference state of the crop/vegetation effects for future climate modeling studies.

Corresponding author address: Chang-Hoi Ho, School of Earth and Environmental Sciences, Seoul National University, Seoul 151-742, South Korea. E-mail: hoch@cpl.snu.ac.kr

1. Introduction

Increased food demand due to increasing population and higher living standards in China in recent years has resulted in the expansion of cropland and multiple cropping practices in the northern China plains (NCP). The expansion of cropland results in land-use and land-cover changes by transforming grasslands and/or forests into croplands. The impacts of land-cover changes on climate have been an important topic of a number of earlier studies. Feddema et al. (2005) showed that agricultural expansion can lead to very different climates in many regions, making land-cover changes among the key features that must be considered in projecting future climate. Gao et al. (2007) studied the land-use effects on climate in China using a regional climate model (RCM) with and without dynamic vegetation feedback under the present-day land use to show that anthropogenic land-use changes affect local temperature, precipitation, and atmospheric circulation via altering the surface energy budget.

The expansion of croplands and the associated land-use changes (e.g., large-scale deforestation and irrigation) can affect long-term climate variations and has been a topic of intense research. The agricultural practice of double cropping, widely used to increase crop production, causes short-term but systematic variations in land surface characteristics during a period between two adjacent growing seasons. Double cropping is composed of two growing seasons separated by a short harvest season in a year, resulting in two seasons of maximum vegetation activity separated by a harvested postharvest/preplanting period of near-bare-ground conditions. The surface energy budget in the harvested period is different from that in the growing season, primarily due to the absence of transpiration. The effects of the sudden changes in land use due to double cropping on the regional climate characteristics can be large over major agricultural regions but largely remain to be understood. The possibility of the cropland–atmosphere feedback was investigated by Cooley et al. (2005) using a coupled regional atmosphere–land model. Comparing the regional climate of early harvested cropland with that of late harvested cropland, they showed that near-bare-soil conditions between the two growing seasons lead to an increase of the surface air and soil temperatures by magnitudes comparable to that induced by land-cover changes.

Previous observational studies that systematically examine the relationship between subseasonal land-use changes and regional climate in double cropping regions (DCRs) do not exist to the authors’ knowledge. The lack of observational data leaves large uncertainties in the occurrence and magnitude of such impacts that are also crucial for evaluating modeling studies and interpreting the underlying processes. Observations taken under ideal conditions and analyzed carefully would help us to assess the cropping effects in the real world. The NCP is an ideal region for such investigations as it is composed of huge croplands with flat and relatively homogeneous agricultural practices. In the target region, the cropland density is very high (>70%), as shown in satellite data (Liu et al. 2005), and the seasonal variation of vegetation occurs almost simultaneously because the same crop is cultivated during the same period. Therefore, double cropping may have significant and systematic impacts on the regional climate, which is the major motivation for this study. This study aims to examine the climate impact of short-term land-use change associated with a harvested cropland of the NCP DCR from available observations. Descriptions of the surface data and satellite-observed vegetation data are given in section 2. Section 3 explains the features of the vegetation and the identification of the DCR compared to the single cropping region (SCR), and presents climate impacts revealed by the temperature and humidity observations. A summary and discussion are given in section 4.

2. Data

Brown et al. (2010) showed that the variations in annual crop activities can be determined from the normalized difference vegetation index (NDVI), obtained from the difference of the absorbed solar energy in the visible and near-infrared wavelengths measures; healthy vegetation absorbs most visible light and reflects a large portion of the near-infrared light and thus is associated with a higher NDVI. Here, we analyze the NDVI data from the Advanced Very High Resolution Radiometers (AVHRRs) on National Oceanic and Atmospheric Administration (NOAA) polar-orbiting satellites recently produced by the National Aeronautics and Space Administration (NASA)’s Global Inventory Modeling and Mapping Studies group. These data come in a 8 km × 8 km grid and monthly in the spatial and temporal resolutions, respectively, for the period 1996–2005. Compared to the previous NDVI dataset (Zhou et al. 2001), the dataset used in this study has been improved by minimizing the effects of volcanic, aerosol, cloud, solar zenith angle, and sensor degradation, and has been carefully optimized (Tucker et al. 2005). This improved dataset has been widely used in studies of regional (e.g., Piao et al. 2006; Jeong et al. 2009a) and global (e.g., Jeong et al. 2011a; Piao et al. 2011) ecology and land surface vegetation processes.

Climate impacts caused by double cropping are evaluated by analyzing two surface variables from the Moderate Resolution Imaging Spectroradiometer (MODIS) and ground stations. The MODIS data include daytime land surface temperature (LST) for the period 2000–06, and the ground station data include daily maximum temperature (Tmax), daily minimum temperature (Tmin), and specific humidity over all of China for 1996–2005. The specific humidity values are obtained by considering the daily mean temperature, atmospheric pressure, and relative humidity at the surface. These ground observation data have been checked for quality and used in previous studies (e.g., Gong et al. 2006; Jeong et al. 2009a).

3. Results

a. Double cropping in the northern China plains

The DCR is separated from the SCR in the cropland to examine the climatic impacts of double cropping. First, croplands are identified using the land-cover classification from the Global Land Cover Facility at the University of Maryland (Hansen et al. 2000). Figure 1a depicts various types of land cover in the NCP (marked as a box, 30°−40°N, 112°−122°E) and adjacent regions including forests, woodlands, grasslands, shrubs, bare ground, urban, and cropland. The analysis presented below is confined to the cropland stations in the NCP. Then, the change in the NDVI from May to June for the analysis period 1996−2005 is calculated to separate the DCR from the SCR because the land-cover classifications do not distinguish the DCR from the SCR. The separation of the DCR from the SCR is based on the fact that the harvest of the first crop (e.g., winter wheat in the NCP) ends in early June and the planting of the second crop (e.g., maize) starts in mid-June (Mingwei et al. 2008). The harvest and replanting timing causes the DCR to be characterized by near-bare-soil conditions in June. Thus, the NDVI differences between May and June (ΔNDVI = NDVIJune minus NDVIMay) in the DCR should have large negative values as in Fig. 1b; large negative values of ΔNDVI smaller than −0.1 appear widely in the NCP. We classify a station as a DCR if the ΔNDVI < −0.1 and as a SCR if the ΔNDVI ≥ 0. Among the total of 64 surface stations in the NCP, 51 stations are grouped into the DCR (25 stations, closed circles in the figure) and the SCR (26 stations, open circles) stations. The remaining 13 stations, which have −0.1 ≤ ΔNDVI < 0, are excluded in our analysis to emphasize the contrast between the DCR and SCR stations.

Fig. 1.
Fig. 1.

(a) Land-cover types and the surface stations used in this study and (b) differences in the NDVI between June and May. The box indicates the NCP, and closed and open circles indicate the stations that are selected as DCR and SCR, respectively. (c) Time series of NDVI averaged over the DCR and SCR for the period 1996–2005.

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00224.1

The temporal variations of double or single cropping are clearer in the NDVI time series. The annual cycles of monthly NDVI values averaged over the DCR and SCR stations are presented for the period 1996–2005 (Fig. 1c). The double and single NDVI peaks in the DCR and SCR, respectively, can be clearly identified from the NDVI time series. For the double NDVI peaks, the early maxima occur in April–May with the late maxima in August. This bimodal distribution of the NDVI is consistent for the entire analysis period, showing that double cropping was already widespread in the mid-1990s (Piao et al. 2010) and has continued until today. In contrast with the DCR, the annual cycle of the NDVI at the SCR stations shows a single peak with a maximum in August and a minimum in winter months.

b. Influence of double cropping on the surface climate

The spatiotemporal variations in the NDVI show prominent decreases of the NDVI in June compared to May over the DCR in contrast to the SCR, which does not show such a decrease. Generally, larger vegetation greenness (i.e., larger NDVI) in the warm season reduces the surface air temperatures and increases the humidity via enhanced evapotranspiration (Jeong et al. 2009a). Climate model simulations with vegetation dynamics also reveal dominant surface evapotranspiration (combining both evaporation from soil and transpiration from vegetation) and the associated cooling effects attribute daytime temperature variations (i.e., Tmax) (Jeong et al. 2009b, 2011b). Thus, the difference in the NDVI between the two crop regions can induce different regional climate responses to vegetation changes. Figure 2 shows differences of monthly surface temperature between May and June (June minus May) in MODIS daytime LST (ΔTMODIS) and station TmaxTstation). As seen in Figs. 2a and 2b, the spatial patterns of two ΔT values (from MODIS and station) are closely related to surface air temperatures and vegetation changes; larger positive temperature differences correspond to large negative ΔNDVI over the DCR, while relatively smaller positive temperature differences correspond to near-zero and/or positive ΔNDVI over the SCR. In general, ΔNDVI and ΔT are negatively correlated in the growing season because active vegetation (i.e., larger NDVI) enhances transpiration to result in cooler surfaces (Kogan et al. 2004; Jeong et al. 2009b, 2011b). The negative relationship between ΔNDVI and ΔT helps us to understand the effects of the bare-soil conditions over the harvested period in the DCR during the summer. Values of ΔT from ΔTMODIS versus ΔTstation show a considerable surface warming in June over the DCR compared to the SCR (Fig. 2c). An examination of the consistency (the 95% prediction band1 in the figure) shows that 49 stations from the entire 51 stations fall within the 95% confidence interval.

Fig. 2.
Fig. 2.

Differences of the monthly surface temperature between May and June (June minus May): (a) ΔTMODIS for 2000−06 and (b) ΔTstation for 1996−2005. (c) Scatterplots of ΔTMODIS vs ΔTstation with linear regression slope and the 95% prediction band.

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00224.1

We have further examined the seasonal variations in the surface air temperatures (Tmax and Tmin) and specific humidity using the monthly values averaged over the DCR and the SCR stations (Fig. 3). To alleviate the effects of the large-scale latitudinal variations in the regional temperatures and specific humidity, at least partially, the 5-month (April–August) mean values of the two variables are removed from the station data prior to the analysis below. The differences between the DCR and SCR stations are shown in the bottom panel of the figure. As anticipated, the anomalous Tmax and Tmin increase monotonically in time from around −10°C in April to 5°C in July in both the DCR and SCR (Fig. 3a). It is noted that Tmax in the DCR is larger than the Tmax in the SCR during June by as much as 1.27°C, significant at the 95% confidence level (Fig. 3a). This may be attributed to the impact of the harvested bare-soil condition on the surface temperatures in the DCR. The smaller cooling effect due to reduced transpiration over the DCR occurs during the daytime, thus mostly affecting Tmax rather than Tmin. This asymmetric response of the two surface air temperatures to the presence/absence of surface vegetation supports the vegetation effects on daytime temperatures. Recently, Jeong et al. (2011b) reported that the dominant cooling effects of land vegetation occur during the daytime, when vegetations open their stomata; thus, vegetation growth can lead to the asymmetric influence on Tmax and Tmin. Bonan (2001) also reported that a decrease in the NDVI through changing from natural forest to cropland leads to Tmax changes over the midwestern United States region.

Fig. 3.
Fig. 3.

Monthly-mean values of (a) Tmax and Tmin and (b) daily mean specific humidity (q) averaged over the DCR and SCR stations. Prior to the domain averages, a 5-month average (April–August) is removed from each station data to eliminate latitudinal influence on the calculation. (bottom) Differences of the three variables between DCR and SCR (DCR minus SCR).

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00224.1

It is noteworthy that the asymmetric temperature response can be affected by aerosols and the recent climate changes over the NCP. Since China has experienced rapid urbanization and industrialization during recent decades, anthropogenic aerosols have become an important climatic factor in regulating surface air temperatures. Like the vegetation effects, the impact of aerosols and the associated cloud formations on temperature is well identified during the daytime, and thus contributes to changes in the diurnal temperature range; however, previous studies revealed that these effects on regional temperature changes over the NCP are minimal (Gong et al. 2006; Xu et al. 2009; Jeong et al. 2009b).

To further verify the impact of the NDVI decrease on the Tmax increase over the harvested region, the monthly specific humidity averaged over the DCR and SCR stations are analyzed. Here, the 5-month average is also removed from each station datum. The impact of reduced transpiration is also evident in the surface specific humidity (Fig. 3b). The moisture decrease in June is consistent with our expectations on the basis of the Tmax increase in the DCR region. Similar to the temperature variations, anomalous humidity shows the maximum value, 5 g kg−1, in July at both the DCR and SCR stations. Also, consistent with the temperature differences between the DCR and SCR, it is clearly seen that the humidity in the DCR is lower than that in the SCR by 0.44 g kg−1 in June, representing different features compared to the mean differences during the adjacent four months. This difference is found to be significant at the 90% confidence level.

The characteristics of temperature and humidity in the DCR during June are further analyzed for the intensity of double cropping (Fig. 4). The differences in temperature and humidity between May and June (June minus May) are compared in terms of the double cropping intensity (i.e., ΔNDVI). Note that the difference between May and June at a single station is plotted in this figure, unlike the difference of the domain-mean values between the DCR and SCR stations in Fig. 3. According to the definition of the DCR and SCR, the DCR stations show a large decrease of the NDVI (<−0.1) from May to June and the SCR stations exhibit a small increase of the NDVI. The large negative slope of ΔTmax versus ΔNDVI indicates the strong impact of crop activity, which is dependent on the intensity of the double cropping. More intense double cropping causes higher ΔTmax. The negative slope of ΔTmin versus ΔNDVI is much smaller than that of ΔTmax versus ΔNDVI, reflecting the clear effect of transpiration during the daytime and the weaker effect of transpiration at night. The positive slope of Δq versus ΔNDVI from May to June represents the fact that more intense double cropping causes a relative drying effect (e.g., less increase of humidity in June) due to the harvested bare-soil condition.

Fig. 4.
Fig. 4.

Scatterplot of ΔNDVI vs (a) ΔTmax, (b) ΔTmin, and (c) Δq for June minus May. Closed circles represent DCR, and open circles represent SCR stations. The slopes are shown.

Citation: Journal of Climate 25, 13; 10.1175/JCLI-D-11-00224.1

4. Summary and discussion

This study presents the observed climate impacts of double cropping using satellite-observed LST, and station temperatures and humidity in the NCP. The NCP region is divided into the DCR and SCR on the basis of the changes in the satellite-retrieved monthly-mean NDVI from May to June. Because of the bare-soil condition due to the harvest of winter wheat in early June, ΔNDVI clearly shows negative values over the DCR. However, even at the surface stations in the NCP, the NDVI difference shows positive values in the SCR, representing continuously increasing crop activities in early summer. In summary, the impact of the harvested cropland in the DCR is clearly observed as larger surface warmings shown in the MODIS LST and station-observed Tmax. Corresponding to the sudden decrease of the NDVI in the DCR in June, the surface temperature, particularly anomalous Tmax, a deviation from the 5-month (April–August) mean in individual stations, shows a significant increase in the DCR compared to the SCR by as much as 1.27°C. Consistent changes are found in the surface humidity in June; the humidity in the DCR is lower than that in the SCR by 0.44 g kg−1. Moreover, the magnitudes of temperature and humidity changes are closely related to the double cropping intensity, defined in terms of the ΔNDVI. The present results could be regarded as an observational reference state of crop/vegetation effects for climate modeling studies. Also, the results suggest that the enhanced agricultural productivity by multiple croppings in a year may amplify the regional warming and drying, and thus modify regional climate circulations.

In general, using observational datasets alone, the separation of the effects of land cover from the effects of other climatic variables is a very difficult task for climate and vegetation studies (Bonan 2001; Jeong et al. 2009a). However, various kinds of observational datasets (e.g., satellite data from different sensors, AVHRR and MODIS, and station-observed meteorological data) confirmed the general conclusion regarding the influence of double cropping on the climate derived in the present study. This suggests that an appropriate combination of data from remote sensing and ground observation are a useful means for analyzing the relationship between the changes in regional climate and surface conditions. Further, those links may be included in the RCM parameterization. For example, recently, an intercomparison study of RCMs (Fu et al. 2005) revealed a cold bias of −4° to −1°C during the summer in China. Considering the present results, this bias could be attributed to the wrong parameterization for the DCR crop condition, such as the leaf area index. Thus, the exact parameterization of the annual cycle of vegetation greenness should be applied in any future modeling studies.

In the previous study of Koster et al. (2004), the NCP was noted as a region with strong land–atmosphere coupling. Thus, the drier surface conditions over the DCR in June may lead to a decrease in precipitation and trigger a positive feedback. It may also worsen the water problems in the NCP, as shown in previous studies on the overuse of water for irrigation in the NCP (Wang et al. 2008) and the decreasing trend of the groundwater table (Kendy et al. 2004). The results in this study imply that climate impacts of double cropping, if combined with the existing water problem, may aggravate the groundwater problem via the positive land–atmosphere feedback over the NCP. A comprehensive understanding of various climate impacts of double cropping in the NCP is crucial to better predict meteorological and hydrological conditions in that region.

Because of the data availability, this study focuses on the impact of double cropping on surface temperatures and humidity, which is expected to reflect the local impacts of the surface conditions well. However, we speculate that the direct impacts of surface temperature and humidity, if they occur systematically over a large area like the NCP, would influence the boundary layer characteristics over extended areas to affect synoptic-scale mass fields and circulations. For example, as a result of removing vegetation during the warm season, the dry surface condition leads to a high sensible heat flux, thus the overlying atmosphere becomes warm and dry. However, the cool surface conditions with the presence of vegetation could further lead to cooler and moister conditions (Jeong et al. 2009b). The noticeable spatial heterogeneity through different types of cropland surface conditions may generate a mesoscale circulation over the region (Pielke et al. 1993). Therefore, the regional climate might experience additional variations in, for example, wind and precipitation due to double cropping in the NCP. The local impacts of these variables are difficult to quantify using solely observations. An RCM study is planned as the next step to examine the climate impact in more detail by analyzing the local and regional features of relevant meteorological variables. The fact that June is the starting month of the East Asian summer monsoon makes a modeling study more urgent because changes in surface conditions (e.g., land-cover type, vegetation leaf area, and soil moisture) in June might continue to affect regional climates in the following months.

Acknowledgments

This work was funded by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (2012-0000850). C.-H. Ho is grateful to Dr. M.-H. Lee and Mr. H.-J. Gim for constructing figures. The authors thank three anonymous reviewers for their helpful comments and suggestions.

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1

The 95% prediction band indicates that an expected data point may fall within the 95% confidence level. That is, if we add one more experiment data point with an independent variable within the independent variable range of the original dataset, then there is a 95% chance that the data point will appear within the prediction band.

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