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  • View in gallery

    Distribution of radiosonde stations in China (IGRA stations, black crosses; CMA stations, blue open circles; valid stations, red open circles).

  • View in gallery

    (a) Annual mean APW and (b) APW standard deviation (NWC = northwestern China; NNC = northern and northeastern China; MEC = middle and eastern China; SC = southern China).

  • View in gallery

    (a) Probability density function (PDF) of annual mean APW during 1995–2012 (the two gray dashed lines denote 20.0 and 40.0 mm APW), and (b) regional PDFs of annual mean APW during 1995–2012 (NWC, NNC, MEC, and SC).

  • View in gallery

    Seasonal pattern of APW in mainland China during 1995–2012 for (a) MAM, (b) JJA, (c) SON, and (d) DJF.

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    Seasonal standard deviation pattern of APW in mainland China during 1995–2012 for (a) MAM, (b) JJA, (c) SON, and (d) DJF.

  • View in gallery

    Monthly mean APW variations during 1995–2012 (red line = NWC; blue line = NNC; green line = MEC; pink line = SC; the bar represents standard deviation).

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    (a) Spatial pattern of APW trends during 1995–2012, and (b) spatial pattern of APW trend rates during 1995–2012. Filled circles indicate that the trends are statistically significant at the 95% confidence level.

  • View in gallery

    Regional interannual anomalies of APW in mainland China during 1995–2012 (a) NWC, (b) NNC, (c) MEC, and (d) SC. The black solid line represents the regression line; the bar represents the APW anomaly.

  • View in gallery

    (a) Annual mean APW from radiosonde dataset and NCEP reanalysis dataset in NWC, NNC, MEC, and SC. (b) PDFs of difference (radiosonde dataset minus NCEP reanalysis dataset) in NWC, NNC, MEC, and SC.

  • View in gallery

    As in Fig. 8, but for the NCEP reanalysis dataset.

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    Regional interannual anomalies differences of radiosonde dataset minus NCEP dataset.

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    (a) Spatial pattern of near surface temperature trends during 1995–2012, and (b) spatial pattern of mean temperature trends (1000–300 hPa) during 1995–2012. Filled circles indicate that the trends are statistically significant at the 95% confidence level.

  • View in gallery

    Regional precipitation annual variation (solid line) and linear trends (dashed lines) during 1995–2012 for (a) NWC, (b) NNC, (c) MEC, and (d) SC.

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    Monthly mean APW, monthly precipitation and monthly conversion rate in the four regions: (a) NWC, (b) NNC, (c) MEC, and (d) SC.

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    Annual variation of regional precipitation conversion rate (solid line) and linear trends (dashed lines) during 1995–2012 for (a) NWC, (b) NNC, (c) MEC, and (d) SC. Asterisks indicate that the correlation coefficients are statistically significant at the 95% confidence level.

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Evaluation of Atmospheric Precipitable Water Characteristics and Trends in Mainland China from 1995 to 2012

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  • 1 School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
  • | 2 Key Laboratory of Atmospheric Sciences and Satellite Remote Sensing of Anhui Province, Anhui Institute of Meteorological Sciences, Hefei, China
  • | 3 Anhui Meteorological Information Centre, Anhui Institute of Meteorological Sciences, Hefei, China
  • | 4 Department of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, Florida
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Abstract

Variations and trends of atmospheric precipitable water (APW) are examined using radiosonde data from Integrated Global Radiosonde Archive (IGRA) and China Meteorological Administration (CMA) from 1995 to 2012 in mainland China. The spatial distribution of the climatological mean APW shows that APW gradually decreases from the southern to the northern regions of mainland China. The seasonal mean pattern of APW shows clear regional difference, except for higher APW in summer (June–August) and lower APW in winter (December–February). Four regions show significantly downward trends in APW. Moreover, the trends of APW calculated using reanalysis datasets are consistent with the results of radiosonde data. Furthermore, the relationship between APW and the general circulation is investigated. The summer East Asian monsoon intensity and El Niño events show positive correlations with APW, whereas the North Atlantic Oscillation shows negative correlation with APW. The downward trend of APW is in accordance with the downward trend of mean column temperature (1000–300 hPa) at most stations, which suggests that decreasing mean column temperature results in decreasing APW in mainland China. Additionally, statistical analysis has revealed the regional trends in APW are not consistent with the regional trends in precipitation, implying that not all the variation of precipitation can be explained by APW.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yunfei Fu, fyf@ustc.edu.cn

Abstract

Variations and trends of atmospheric precipitable water (APW) are examined using radiosonde data from Integrated Global Radiosonde Archive (IGRA) and China Meteorological Administration (CMA) from 1995 to 2012 in mainland China. The spatial distribution of the climatological mean APW shows that APW gradually decreases from the southern to the northern regions of mainland China. The seasonal mean pattern of APW shows clear regional difference, except for higher APW in summer (June–August) and lower APW in winter (December–February). Four regions show significantly downward trends in APW. Moreover, the trends of APW calculated using reanalysis datasets are consistent with the results of radiosonde data. Furthermore, the relationship between APW and the general circulation is investigated. The summer East Asian monsoon intensity and El Niño events show positive correlations with APW, whereas the North Atlantic Oscillation shows negative correlation with APW. The downward trend of APW is in accordance with the downward trend of mean column temperature (1000–300 hPa) at most stations, which suggests that decreasing mean column temperature results in decreasing APW in mainland China. Additionally, statistical analysis has revealed the regional trends in APW are not consistent with the regional trends in precipitation, implying that not all the variation of precipitation can be explained by APW.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Yunfei Fu, fyf@ustc.edu.cn

1. Introduction

Water vapor is a variable component in the atmosphere and a dominant greenhouse gas. Although water vapor makes up only 1.5% of the global water cycle system, it can strongly absorb long-wavelength radiation and affect Earth’s radiation budget. Moreover, phase changes of water vapor lead to latent heat exchanges in the atmospheric circulation (Chahine 1992; Held and Soden 2000; Lenderink and van Meijgaard 2010; Sherwood et al. 2010). In general, atmospheric precipitable water (APW) or column integrated water vapor (CWV) is the column-integrated amount of water vapor. Accordingly, APW represents the horizontal distribution of water vapor and provides reference values for initialization parameters in numerical weather models.

APW can be derived from Global Positioning System (GPS) observations, remote sensing retrievals, reanalysis datasets, and radiosonde measurements (Ross and Elliott 2001; Wang et al. 1999; Cai et al. 2004; Trenberth et al. 2005; Liu et al. 2006; Mears et al. 2007; Wang et al. 2007; Radhakrishna et al. 2015; Renju et al. 2015; Wang et al. 2016). APW was estimated by GPS with an accuracy of ±1.3 mm. Moreover, GPS observations are sensitive to the network configuration and the APW results have larger biases in APW observation when the station baselines are longer than 2000 km (Tregoning et al. 1998). Liou et al. (2001) pointed out that error exists in remote sensing measurements and showed that the bias in radiometer measurements of brightness temperature was nearly 0.3 K. Even if there is only an error of 0.3 K in brightness temperature, the maximum uncertainty in two radiometer channel measurements accumulates to a large bias in the process of retrieval and estimation of APW. Reanalysis datasets are also commonly used to analyze weather and climate changes with a long time series. However, atmospheric parameters are more complicated in the real atmosphere, and water vapor data calculated from reanalysis dataset need to be validated (Vey et al. 2010; Zhao et al. 2015). In comparison with other approaches, radiosondes measure temperature and humidity profiles directly with high vertical resolution and long periods. In the past, radiosonde datasets have been applied to estimate the trends in APW and evaluate other measurements, such as GPS and reanalysis datasets (Ross and Elliott 2001; Wang et al. 2007; Wang and Zhang 2008; Durre et al. 2009; Dai et al. 2011; Zhao et al. 2015).

Previous study has revealed that the evident regional variation of water vapor is related to the great latitude difference (Zhai and Eskridge 1997). In addition, China is located within the summer East Asian monsoon (SEAM) district where an obvious water vapor convergence exists. Water vapor is affected by the southwest monsoon through the Bay of Bengal in eastern China and the northern monsoon via the South China Sea (Zhou et al. 2010). This suggests that the SEAM influences water vapor transport path and directly changes the distribution pattern of APW in China. Moreover, water vapor variability is dominated by the El Niño events (Trenberth et al. 2005). Therefore, it is necessary to study the spatial and temporal variations in APW and to understand the relationship between APW and the general circulation.

Water vapor increases with temperature at about 7% °C−1 near the surface when averaging over the largest spatial scales following the Clausius–Clapeyron (C-C) equation (Trenberth et al. 2003). Moreover, positive feedback of water vapor increases the sensitivity of surface temperature (Held and Soden 2000; Zhao 2014) and APW could contribute to the magnitude of long-term temperature changes. These suggest that APW interacts with temperature variation. In addition, variations in APW associated with temperature not only influence climate trends in APW but also change precipitation variation (Wu et al. 2003; Zhao et al. 2012). Thus, the correlation between APW and atmospheric temperature and the connection between APW and precipitation in China are also discussed in this study.

Previous studies rarely focused on the regional variations and trends in APW using radiosonde data in mainland China. In this study, the Chinese radiosonde dataset derived from the Integrated Global Radiosonde Archive (IGRA) and the China Meteorological Administration (CMA) is used to analyze the temporal and spatial variations and trends in APW from 1995 to 2012 in mainland China. Moreover, the relationships between APW and the general circulation, mean column temperature, and precipitation are discussed in detail.

This paper is organized as follows. The data and methods are presented in section 2. Section 3 provides the climatological mean, seasonal variations, and regional trends in APW. The results are also compared with National Centers for Environmental Prediction (NCEP) reanalysis. The relationships among APW, the general circulation indices, column temperature, and regional precipitation are discussed in section 4. Section 5 summarizes the study and presents conclusions.

2. Data and methods

In this study, radiosonde data are obtained from the IGRA (http://www.ncdc.noaa.gov/data-access/weather-balloon/integrated-global-radiosonde-archive) and CMA, consisting of radiosonde and pilot balloon observations with long time records. In China, there are 126 stations belonging to IGRA (black crosses in Fig. 1) and 130 stations belonging to CMA (blue circles in Fig. 1). Gross errors of radiosonde data from the IGRA and CMA are eliminated preliminarily with quality control (Durre et al. 2006; Chen et al. 2014). Because of the lack of continuous data derived from IGRA before 1995, the study period is chosen to be from 1995 to 2012. It is true that the time period is too short for climatological significance. However, to ensure a continuous data record as much as possible, the study focuses on the variation of APW from 1995 to 2012.

Fig. 1.
Fig. 1.

Distribution of radiosonde stations in China (IGRA stations, black crosses; CMA stations, blue open circles; valid stations, red open circles).

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

Radiosonde data are relatively homogeneous after 1970 (Zhai and Eskridge 1996). However, there still exist discontinuous records from radiosonde data (McCarthy et al. 2009; Dai et al. 2011). The CMA dataset was chosen to improve spatial coverage of stations and remove discontinuous records of IGRA. Based on daily sounding profiles, monthly mean values for each station are calculated from IGRA and CMA. Twice daily observations (0000 and 1200 UTC) are included to ensure sufficient samples and at least 15 days in a month must qualify to calculate a monthly mean. In addition, missing profiles of monthly means from IGRA are filled with quality-controlled CMA data and then a new monthly mean dataset for each station is constructed. There are 122 stations in total in this new merged radiosonde data.

Furthermore, the previous studies show that the minimum data requirement (MDR) for calculation is at least 80% for a station (Gaffen et al. 2000; Seidel et al. 2001), whereas an MDR of 70% is sufficient for regional research in China (Guo and Ding 2009). A sensitive analysis is conducted on how the number of stations changes with different MDR values in China. The number of stations in mainland China is 52, 54, and 60 when the MDR is 70%, 50%, and 30%, respectively. It is clear that the number of radiosonde stations increases with decreasing MDR. However, large outliers from radiosonde network still exist when the number of radiosonde stations increases. It suggests that increasing the number of stations does not give more reliable results (Free and Seidel 2005; Guo and Ding 2008). To ensure sufficient samples, an MDR of 70% is selected and the number of radiosonde stations in mainland China reduced to 52 (red circles in Fig. 1). Annual mean APW for each station is then calculated, requiring at least 150 months (or at least 70% of the months) from 1995 to 2012. Seasonal variation and interannual variation of APW are calculated on the basis of the monthly mean and annual mean APW, referred to in previous studies (Xie et al. 2011; Zhao et al. 2012; Zhao 2014). After adjustment, the new radiosonde stations derived from IGRA and CMA are relatively uniform spatially and temporally consistent. The least squares linear regression method is used to calculate APW trend, and t-test analysis at a 95% confidence level is employed to estimate the significance of trend.

APW is calculated from 1000 hPa (or 925 hPa for the higher-altitude stations without a 1000-hPa measurement) to the top of the sounding humidity profiles at standard pressure levels. However, APW is sensitive to missing humidity data at standard levels in the lower troposphere (Wang et al. 2007). The sensitive analysis shows that missing humidity data at 1000 hPa (or 925 hPa) and at 850, 500, and 300 hPa for Chinese stations could cause a dry bias of 47.2% and 5.7%, respectively. Note that APW is significantly sensitive to missing data at 1000 hPa (or 925 hPa) and 850 hPa. Thus, humidity data are required to be available at 1000 hPa (or 925 hPa) and at least five standard levels above 1000 (or 925) hPa for APW calculation (Wang and Zhang 2008).

Rain gauge precipitation data with quality control, obtained by the National Meteorological Information Center (NMIC) of China (Yu et al. 2007), are also included in this work. Note that the locations of radiosonde stations and rain gauges are at the same points and thus share the same station numbers. In this paper, 52 rain gauge stations, which corresponding to valid radiosonde stations, are selected during the same period. Rain gauges measure cumulative precipitation for one month, and monthly cumulative APW is calculated to compare with rain gauge precipitation data. That is, the mean APW for each month is calculated and then multiplied by the number of days in the corresponding month. The precipitation conversion rate is defined as 100% × precipitation/APW. The precipitation conversion rate indicates how much precipitation is converted from APW for one month or one year. Thus, the unit of precipitation conversion rate is % month−1 or % yr−1. Generally, large precipitation conversion rates correspond to more precipitation when the APW is constant.

For comparison, monthly mean specific humidity at standard pressure levels (1000, 925, 850, 700, 600, 500, 400, and 300 hPa) with 2.5° horizontal resolution obtained from the NCEP reanalysis dataset is also utilized during the same time period as the new radiosonde data. Specific humidity monthly means from NCEP grids are extracted from the corresponding radiosonde station locations to evaluate APW.

Atmospheric circulations indices denote the state of general circulation quantitatively and represent atmospheric circulation strong and weak variations. The general circulation is associated with transport path of water vapor (Ding et al. 2008) and influences the long-term trends of APW in China. To investigate the relationship between APW and the general circulation, three indices are used in this paper. 1) The summer East Asian monsoon index (SEAMI) presented by Li and Zeng (2002) is adopted. Li and Zeng presented a dynamical normalized seasonality (DNS) index to reflect the annual fluctuation of the monsoon intensity. 2) The Niño-3.4 region (5°N–5°S, 170°–120°W) index is calculated from sea surface temperature (SST) anomalies and characterizes the nature of El Niño (Trenberth and Stepaniak 2001). 3) The North Atlantic Oscillation (NAO) index is based on rotated principal component analysis (Barnston and Livezey 1987). The NAO is associated with the pattern of moisture transport (Hurrell 1995).

3. Results

a. Climatological pattern of APW

Climatological mean APW can reflect characteristics of regional APW and the nature of climatic status. For example, Dai (2006) examined the climatological pattern of surface specific humidity (q) and found that the q values are the highest over the tropics. To reveal spatial distribution of APW in mainland China, the annual mean APW values and standard deviation (SD) are shown in Fig. 2. Figure 2a shows that the annual mean APW is small (11.46–16.01 and 12.36–19.83 mm) in northwestern China (NWC; 42°–49°N, 80°–95°E) and northern and northeastern China (NNC; 37°–50°N, 110°–130°E). The corresponding SD (as shown in Fig. 2b) ranges are 7.16–7.7 and 10.25–15.48 mm, respectively. The annual mean APW ranges in middle and eastern China (MEC; 24.5°–37°N, 105°–123°E) and southern China (SC; 19°–24.5°N, 105°–120°E) are respectively 23.6–40.04 and 41.03–52.73 mm, resulting in large SD (12.38–18.96 and 10.87–16.48 mm). Because of the higher terrain height in NWC and NNC (Zhai and Eskridge 1997), the atmospheric is relatively drier and the APW value is lower compared with MEC and SC. The spatial distribution pattern shows that the APW values decrease gradually from south to north, which is similar to climatological mean distribution of APW in China over the period of 1979–2005 (Xie et al. 2011).

Fig. 2.
Fig. 2.

(a) Annual mean APW and (b) APW standard deviation (NWC = northwestern China; NNC = northern and northeastern China; MEC = middle and eastern China; SC = southern China).

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

The probability density functions (PDFs) of annual mean APW are used to identify different regions. As shown in Fig. 3a, the APW distribution can be divided into four regions; the first section corresponds to NWC and NNC, the second section corresponds to MEC, and the third corresponds to SC. The PDFs of the four regions (Fig. 3b) are given to verify the scientific rationality of the regional division. Eventually, there are 3, 13, 27, and 9 stations in NWC, NNC, MEC, and SC, respectively. Although radiosonde stations are sparse in NWC, these stations are representative after data processing in section 2a.

Fig. 3.
Fig. 3.

(a) Probability density function (PDF) of annual mean APW during 1995–2012 (the two gray dashed lines denote 20.0 and 40.0 mm APW), and (b) regional PDFs of annual mean APW during 1995–2012 (NWC, NNC, MEC, and SC).

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

b. Seasonal variation in APW

The seasonal mean of APW is shown in Fig. 4. The regional distribution of APW is apparent in four seasons [March–May (MAM), June–August (JJA), September–November (SON), and December–February (DJF)]. The seasonal mean values of APW decrease from south to north, similar to climatological pattern in Fig. 2a, whereas APW values are higher in summer and lower in winter in the four regions. The ranges of APW values are 7.97–55.08, 21.7–69.98, 9.94–52.74, and 3.18–36.03 mm for MAM, JJA, SON, and DJF, respectively. The largest difference among four regions occurs in MAM, and the smallest difference occurs in DJF.

Fig. 4.
Fig. 4.

Seasonal pattern of APW in mainland China during 1995–2012 for (a) MAM, (b) JJA, (c) SON, and (d) DJF.

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

Generally, the intraseasonal large values of APW appear in MEC and SC, while the small values appear in NWC and NNC. In NWC and NNC, APW is generally lower and the seasonal difference is not obvious. MEC and SC are located at lower latitudes and closer to the ocean, and APW increases rapidly after the summer monsoon breaks out, which results in great seasonal differences.

To understand the magnitude of seasonal variation in APW, SD is further calculated as shown in Fig. 5. The seasonal SD range is 3.33–10.0, 2.92–9.22, 3.14–12.43, and 0.73–5.11 mm for MAM, JJA, SON, and DJF, respectively. The largest APW SDs can be found in MEC during SON. Then when APW is influenced by the summer monsoon in JJA, the large SDs shift to NNC. In SON, large SDs again appear in MEC. Finally, the largest SDs appear in SC in DJF. In brief, the seasonal SD of APW, to a great extent, changes according to progression of the summer monsoon from south to north.

Fig. 5.
Fig. 5.

Seasonal standard deviation pattern of APW in mainland China during 1995–2012 for (a) MAM, (b) JJA, (c) SON, and (d) DJF.

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

Similarly, monthly mean APW is calculated (Fig. 6). The peak of the monthly mean appears from July to August. The maximum of APW is 26.26 mm in NWC, 39.05 mm in NNC, 58.94 mm in MEC, and 62.38 mm in SC. It is noted that the peak for SC appears in June, earlier than in the three other regions, which is probably because humidity increases much earlier in SC than in other regions since the monsoon prevails earlier. The SEAM transports water vapor to the south or east of the Yangtze River (Liu and Ding 2007), water vapor accumulates sufficiently, and APW reaches its peak value earlier. Monthly mean APW is the lowest in DJF, with the minimum values reaching 5.24, 3.77, 14.59, and 26.92 mm in January in four regions, respectively.

Fig. 6.
Fig. 6.

Monthly mean APW variations during 1995–2012 (red line = NWC; blue line = NNC; green line = MEC; pink line = SC; the bar represents standard deviation).

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

c. Regional interannual variation and time series

The trend of atmospheric parameters has been become a focus of attention. For example, Feng et al. (2012) analyzed the global thickness of the tropopause layer (TL) and revealed that the thickness of global TL significantly increased during 1965–2004. Fu et al. (2016) examined the trends of summer precipitation in eastern China (EC) from 2002 to 2012 and found that both convective and stratiform increased in southern EC and decreased in northern EC. Therefore, the trends of APW for the each station are calculated as shown in Fig. 7 (the filled circles indicate that the trends are statistically at the 95% confidence level). Figure 7a shows downward trends of APW in 52 stations over mainland China, with the most significant trends (more negative than −2.0 mm decade−1) in MEC and SC. The magnitude of decreasing trends is between −1.0 and −2.0 mm decade−1 in NWC and NNC. Figure 7b shows the trend rate at each station. The trend rate is the APW trend expressed as a percentage of mean climatological APW (Zhai and Eskridge 1997) at each independent station during 1995–2012. APW decreases about −10% decade−1 at most stations, particularly in MEC and SC. APW decreases from −20% to −30% decade−1 in NNC and NWC, and even more than 40% decade−1 at individual stations.

Fig. 7.
Fig. 7.

(a) Spatial pattern of APW trends during 1995–2012, and (b) spatial pattern of APW trend rates during 1995–2012. Filled circles indicate that the trends are statistically significant at the 95% confidence level.

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

Figure 8 shows the time series and linear trends of APW in the four regions. In NWC, the APW decreases by −3.1 mm decade−1. The linear trends for NNC and MEC are negative (−2.0 and −2.1 mm decade−1). The APW trend, with a magnitude of −3.4 mm decade−1 in SC, is larger than that in the three other regions. (All the trends are statistically significant at the 95% confidence level.) Moreover, Fig. 8 identifies that anomaly values in 1997/98 are highly positive in four regions, probably related to El Niño events (Trenberth et al. 2005). Besides, regional APW anomalies are basically consistent with the results of Durre et al. (2009) from 1995 to 2006. Teng et al. (2013) found that the characteristics of APW presented the same decreasing trends in most of China in winter, which is comparable to Fig. 8. In addition, the weakening SEAM in recent years (Wang and Fan 2013; Ding et al. 2013; Lu et al. 2016) has resulted in a decrease in water vapor transport from the ocean to the land, eventually leading to the decrease in APW. To ensure temporal consistency, the study period is chosen from 1995 to 2012. Indeed, this short study period is a limitation of the analysis. However, the significant downward APW trends still reflect water vapor changes in recent 18 years. Moreover, APW has a feedback for climate variability (Soden et al. 2002; Wu et al. 2008). In turn, APW is dominated by temperature, atmospheric circulation, and precipitation. The relationships will be discussed in section 4.

Fig. 8.
Fig. 8.

Regional interannual anomalies of APW in mainland China during 1995–2012 (a) NWC, (b) NNC, (c) MEC, and (d) SC. The black solid line represents the regression line; the bar represents the APW anomaly.

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

d. Comparison with the NCEP reanalysis dataset

As described in section 2, the NCEP reanalysis dataset have been used to compare annual mean APW and APW trends with the new radiosonde dataset merged with IGRA and CMA data. Figure 9a depicts scatterplots of annual mean APW derived from the radiosonde dataset in mainland China and from the corresponding NCEP reanalysis dataset. The results calculated from NCEP data generally agree with radiosonde data and the correlation between the two dataset is robust (correlation coefficient = 0.89). However, the NCEP reanalysis dataset displays systematically higher APW than the radiosonde dataset. Figure 9b shows annual mean APW difference PDFs in four regions. The bias, which is larger than 10 mm, exists in NWC and the peak reaches 66%. The bias is between 0 and 10 mm in NNC, MEC, and SC and the peak is 46%, 37%, and 22%, respectively. Like Fig. 8, Fig. 10 presents APW trends obtained by NCEP in four regions. The trends are 0.0, −1.0, −1.0, and −1.2 mm decade−1, for NWC, NNC, MEC, and SC respectively. Trends in APW are statistically significant at the 95% confidence level in NNC, MEC, and SC but not in NWC. Decreasing trends are consistent with radiosonde results but they are less significant than trends derived from the radiosonde dataset in four regions. Although the APW spatial patterns derived from radiosonde observation and NCEP reanalysis are similar, discrepancies between the two datasets still exist. This is because NCEP assimilated raw humidity records from radiosonde and satellite observations that contain biases (Randel et al. 2000; Zhao et al. 2015), which poorly capture long-term APW changes. Owing to the existing systematic biases, the trends calculated from NCEP are more positive than radiosonde data. Figure 11 shows that interannual anomalies exist for the radiosonde dataset minus the NCEP dataset in four regions. There exist evident discontinuities of in the radiosonde dataset before 1997 in NWC and NNC. This is mainly caused by the MDR of 70%, which removes the invalid months before 1997. However, the time series is complete after 1997 in four regions. Moreover, the anomaly differences are positive in early time series and negative in later time series. This suggests that the anomalies of APW from the radiosonde dataset are larger than those from the NCEP reanalysis dataset.

Fig. 9.
Fig. 9.

(a) Annual mean APW from radiosonde dataset and NCEP reanalysis dataset in NWC, NNC, MEC, and SC. (b) PDFs of difference (radiosonde dataset minus NCEP reanalysis dataset) in NWC, NNC, MEC, and SC.

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

Fig. 10.
Fig. 10.

As in Fig. 8, but for the NCEP reanalysis dataset.

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

Fig. 11.
Fig. 11.

Regional interannual anomalies differences of radiosonde dataset minus NCEP dataset.

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

4. Discussion

a. Relationship between APW and atmospheric general circulation

El Niño–Southern Oscillation (ENSO) and the NAO are associated with anomalous SST and atmospheric circulation and have great influence on APW variation (Trenberth et al. 2005; Willett et al. 2010). The relationships between APW and the indices that indicate the large-scale circulation variations are assessed in the following analyses. Table 1 lists the correlation coefficients between monthly mean APW and the circulation indices. The SEAMI is positively correlated with APW in four regions. In particular, the correlation is significant in MEC. It could be deduced that APW variations are sensitive to the summer monsoon in this region, which results in larger APW SD (Figs. 2b and 5) than the other three regions. Moreover, the summer monsoon intensity has apparently decreased since the 1970s (Lu et al. 2016), which weakens northward water vapor transport. As a consequence, decreasing APW trends could be explained by the weakened summer monsoon in four regions. The Niño-3.4 is significant correlated with APW in four regions. Previous study indicated that APW is related to ENSO with EOF analysis and the spatial and temporal variations of APW are consistent with ENSO (Dai 2006). In addition, the increasing (decreasing) SST anomalies affected by ENSO strengthen (weaken) water vapor transport from the northwest Pacific Ocean to mainland China. In contrast, the NAO is negatively related to APW in four regions. The NAO dominates the climate changes and drives APW variations from Europe (Ross and Elliott 2001; Zveryaev et al. 2008). The moisture transport from the Europe has remote influence on APW in four regions. As a result, variation patterns are significantly opposite between NAO and APW.

Table 1.

The correlation coefficients between monthly mean of the SEAMI, Niño-3.4, NAO, and APW in NWC, NNC, MEC, and SC from 1995 to 2012. Asterisks (*) indicate that the correlation coefficients are statistically significant at the 90% confidence level.

Table 1.

b. Relationship between APW and column temperature

Water-holding capacity varies along with temperature in the troposphere (Trenberth et al. 2007) and the relationship between APW and temperature can be described by the C-C equation when the relative humidity is constant (Lenderink and van Meijgaard 2008; Zhao et al. 2012). To investigate the connection between APW and temperature, the relationship between monthly mean APW and monthly mean column temperature (1000–300 hPa) is shown in Table 2. It shows that monthly mean APW varies with monthly mean column temperature positively. The regional correlation coefficient is 0.91, 0.94, 0.97, and 0.95 for NWC, NNC, MEC, and SC, respectively (the correlation coefficients are statistically significant at the 95% significance level in four regions). The temperature shows downward trends at most of the radiosonde stations during 1995–2012 (Fig. 12), which corresponds to the decreasing trends in APW at most radiosonde stations (Fig. 7a). From the C-C equation, the atmospheric water-holding capacity is a function of temperature. Therefore, APW decreases due to decreasing temperature during the period of 1995–2012. Although APW is highly positively correlated with column temperature in the lower troposphere, interactions between APW and temperature are complicated, especially during extreme events of temperature, and still need to be explored in different regions in the future.

Table 2.

The correlation coefficients between monthly mean APW, monthly mean column temperature (1000–300 hPa) and monthly precipitation from 1995 to 2012 in NWC, NNC, MEC, and SC. Asterisks (*) indicate that the correlation coefficients are statistically significant at the 95% confidence level.

Table 2.
Fig. 12.
Fig. 12.

(a) Spatial pattern of near surface temperature trends during 1995–2012, and (b) spatial pattern of mean temperature trends (1000–300 hPa) during 1995–2012. Filled circles indicate that the trends are statistically significant at the 95% confidence level.

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

c. Relationship between APW and precipitation

In contrast to the previous analyses, the results show that precipitation exhibits a decreasing tendency gradually from southern to northern China (Zhou et al. 2008; Miao et al. 2015); that is, the mean spatial pattern of precipitation is consistent with APW performance (Fig. 2a). Moreover, the correlation between APW and precipitation is robust in NNC, MEC, and SC (0.93, 0.81, and 0.82, respectively) but not in NWC, which is situated in an arid to semiarid area (0.13), as shown in Table 2. The correlation result is partly consistent with the results in previous studies (Zhai and Eskridge 1997).

Figure 13 shows the regional precipitation trends calculated from rain gauge observation. The precipitation trend is upward in NWC, NNC, and MEC but downward in SC. Through linear regression analysis, the precipitation trends are 1.4, 2.9, 1.1, and −3.1 mm yr−1 in NWC, NNC, MEC, and SC (trends in the four regions did not pass the t test; thus, the precipitation trends are not significant in these four regions). Precipitation variation shows an opposite tendency with changes in APW in NWC, NNC, and MEC. Only in SC does the precipitation trend agree with the APW trend. In fact, precipitation is affected by various factors and not only controlled by APW (Lenderink and van Meijgaard 2008). Complicated mechanisms of precipitation and climate changes dominate the inconsistent trend between APW and precipitation in NWC, NNC, MEC, and SC. Because of extreme rain events, the increasing frequency and intensity are associated with increasing precipitation trends in northwestern China (Zhai et al. 2005; Shi et al. 2007). Moreover, large-scale circulation changes may be linked with the increasing precipitation in central and eastern China (Wang and Zhou 2005) and the contribution of typhoon precipitation also cannot be ignored in this region (Chen et al. 2016). Owing to the increase of absorbing black carbon aerosols, precipitation decreases in SC (Menon et al. 2002). On the other hand, increased sea surface temperature in the 1990s over the west Pacific Ocean and South China Sea and west Pacific subtropical high extended westward into South China, which weakened the summer monsoon (Cheng et al. 2005). Consequently, precipitation in SC is affected by the water vapor insignificantly.

Fig. 13.
Fig. 13.

Regional precipitation annual variation (solid line) and linear trends (dashed lines) during 1995–2012 for (a) NWC, (b) NNC, (c) MEC, and (d) SC.

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

As shown in Fig. 14, the precipitation conversion rate reaches the maximum value in summer for NNC (11%), MEC (14%), and SC (14%), whereas a peak value of 10% occurs in winter for NWC. The regional precipitation conversion rate is higher in summer and lower in winter in NNC, MEC, and SC. It can be deduced that the precipitation conversion rate increases with the enhanced summer monsoon and dereases with the weakening summer monsoon in NNC, MEC, and SC. It is noted that the monthly mean precipitation conversion rate is lower in summer than winter in NWC, which is opposite to the results in other three regions. Less precipitation falls on the ground because of the dry atmosphere and high evaporation in NWC. Thus, the precipitation conversion rate is lower in summer in this region. Figure 15 displays regional precipitation conversion rate interannual variations and linear trends from 1995 to 2012. The trends in precipitation conversion rate increase slightly in MEC and SC, whereas the precipitation conversion rate increases significantly in NWC and NNC. The positive precipitation conversion rate trends in four regions indicate that precipitation increases in all four regions in the context of decreasing APW from 1995 to 2012. It implies that the changes in precipitation do not completely follow long-term variations in APW (Wang et al. 2003; Shi and Sun 2008; Simmons et al. 2010).

Fig. 14.
Fig. 14.

Monthly mean APW, monthly precipitation and monthly conversion rate in the four regions: (a) NWC, (b) NNC, (c) MEC, and (d) SC.

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

Fig. 15.
Fig. 15.

Annual variation of regional precipitation conversion rate (solid line) and linear trends (dashed lines) during 1995–2012 for (a) NWC, (b) NNC, (c) MEC, and (d) SC. Asterisks indicate that the correlation coefficients are statistically significant at the 95% confidence level.

Citation: Journal of Climate 30, 21; 10.1175/JCLI-D-16-0433.1

5. Summary

In this study, a new radiosonde dataset based on merging observations from the Integrated Global Radiosonde Archive (IGRA) and China Meteorological Administration (CMA) are used to study the temporal and spatial distributions and trends in atmospheric precipitable water (APW) across mainland China from 1995 to 2012.

The spatial pattern of APW shows obvious regional differences, decreasing from south to north. Four regions are identified according to climatological pattern of APW and the PDFs of annual mean APW: northwestern China (NWC), northern and northeastern China (NNC), middle and eastern China (MEC), and southern China (SC). The seasonal mean pattern of APW is similar to the pattern of annual mean APW. In contrast, the maximum APW appears in June and July in SC, and in July and August in NWC, NNC, and MEC; the minimum occurs in December. The long-term trend of APW is downward in the four regions: −3.1, −2.0, −2.1, and −3.4 mm decade−1 in NWC, NNC, MEC, and SC during 1995–2012. Furthermore, the climatological mean APW calculated from NCEP reanalysis dataset is larger than that from the radiosonde dataset and the decreasing trends of APW from NCEP dataset are consistent with the radiosonde dataset in four regions.

The merged dataset from IGRA and CMA has been adjusted to minimize biases before calculating the APW trend. However, relocation of individual stations, systematic biases of radiosonde humidity instruments, poor performance of humidity sensors at cold temperature, and the changing algorithm of radiosonde humidity instrument (Ross and Elliott 1996; Zhai 1997; McCarthy et al. 2009; Dai et al. 2011; Wang et al. 2013) are not considered in this paper. These problems could present a limitation in the calculation of APW trends with this radiosonde dataset.

In addition, the NCEP reanalysis dataset assimilated humidity records from raw radiosonde data and satellite products, which introduces instrumental biases from the input data. Moreover, the raw radiosonde data are assimilated into NCEP often without homogenization. APW trends calculated by NCEP reanalysis dataset could be spurious (Zhao et al. 2015). Therefore, more work will be devoted to validate APW with data from multiple resources in the future.

The effect of the summer East Asian monsoon, El Niño–Southern Oscillation, and North Atlantic Oscillation on APW is significant. Besides, APW is positively related to the mean column temperature from 1000 to 300 hPa. APW decreases as the column temperature decreases during 1995–2012 in the four regions. Moreover, the regional trends in APW are not consistent with the regional trends in precipitation. This implies that not all the variation of precipitation can be explained by APW.

The summer monsoon intensity has apparently decreased in recent years, weakening water vapor transport from the ocean to mainland China. As a result, APW decreases from 1995 to 2012 in the four regions. On the other hand, the weakening summer monsoon increases aerosol concentrations, which causes a radiative cooling effect in China (Zhu et al. 2012; Fu et al. 2017). Moreover, the increasing aerosol loading because of frequent human activities has resulted in decreasing solar radiation in the past few decades (Cheng et al. 2005; Norris and Wild 2009; Tie and Cao 2009; Tang et al. 2011). The decreasing solar radiation could be the potential reason for the downward temperature trends at most stations in mainland China, which is consistent with previous studies that revealed a warming hiatus since the late 1990s (Li et al. 2015; Xian and Fu 2017; Xie et al. 2017). According to Clausius–Clapeyron equation, the downward temperature trends result in decreasing saturation vapor pressure, which gives rise to decreasing atmospheric water-holding capacity. Consequently, APW shows downward trends in four regions of mainland China in the recent 18-yr period analyzed.

Acknowledgments

This research has been jointly supported by the National Natural Science Foundation of China (Grants 41230419 and 91337213), Special Funds for Public Welfare of China (Grant GYHY201306077), the National Foundation of Natural Science (Grant 41505033), Special Financial Grant from the China Postdoctoral Science Foundation (Grant 2016T90572), and the Fundamental Research Funds for the Central Universities. We appreciate NCDC for providing IGRA data, the China Meteorological Administration for providing radiosonde data, NMIC for providing surface precipitation data, and the NOAA–CIRES Climate Diagnosis Center for providing NCEP reanalysis data. Additional, we appreciate the constructive suggestions by the editor and three anonymous reviewers.

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