A comparison of tropospheric temperature changes over China revealed by multiple data sets

Transcription

1 JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES, VOL. 118, , doi: /jgrd.50370, 2013 A comparison of tropospheric temperature changes over China revealed by multiple data sets Lixia Zhang 1 and Tianjun Zhou 1 Received 3 October 2012; revised 19 March 2013; accepted 23 March 2013; published 28 May [1] Based on four radiosonde data sets and three reanalysis data sets, the long-term tropospheric temperature changes over China for the period , and the uncertainties are analyzed. The results from all data sets, except for HadAT2 and National Centers for Environmental Protection 20th Century Reanalysis data (NCEP-20CR), show a significant warming trend in the lower troposphere temperature averaged over China during , but this trend decreases with height and is replaced by a cooling tendency at 500 hpa, reaching maximum cooling at 300 hpa. The year-by-year changes of temperature over China are largely in agreement among the radiosonde and the reanalysis data sets. The uncertainties of upper troposphere temperature changes are larger compared with those of the lower troposphere. The uncertainty was relatively small during and large during and from 1990 to the present. The trend uncertainty is large in the Northwest for the lower troposphere and in south China for the upper troposphere, with the largest trend uncertainty over Northwest China at 850 hpa and east to 100 E at 300 hpa. For the average temperature trend over China, the largest uncertainty peaks at 300 hpa for The tropospheric temperature of China at 300 hpa, derived from HadAT2, warms up; this differs greatly from the other data sets. The warming trend is produced mainly by the stations over south China and is due to the choice of neighbor stations during construction. The temperature trend at 300 hpa in NCEP-National Center for Atmospheric Research is cooler than the other data sets due to its abrupt cooling around the early 1990s. NCEP-20CR can partly capture the cooling tendency at 300 hpa over North China but with weaker magnitude. Citation: Zhang, L., and T. Zhou (2013), A comparison of tropospheric temperature changes over China revealed by multiple data sets, J. Geophys. Res. Atmos., 118, , doi: /jgrd Introduction [2] The vertical profile of atmospheric temperature reflects a balance between the radiative, convective, and dynamical heating (cooling) of the surface-atmosphere system. Temperature trends at the surface may be different from temperature trends higher in the atmosphere because of surface types, changes in atmospheric circulation, and many other reasons [Karl et al., 2006]. It is desirable to detect tropospheric temperature changes at all levels. [3] During the period , tropospheric temperature in East Asia featured some unique characteristics, in contrast to warming trends elsewhere. Strong upper tropospheric cooling trends over East Asia were prominent in late spring Additional supporting information may be found in the online version of this article. 1 State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China. Corresponding author: T. Zhou, State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing , China. (zhoutj@lasg.iap.ac.cn) American Geophysical Union. All Rights Reserved X/13/ /jgrd (May) and late summer (July and August), which contributed to the tendency toward deficient rainfall over south China in late spring and increased droughts in northern China and floods along the Yangtze River Valley in summer [Yu et al., 2004; Xin et al., 2006; Yu and Zhou, 2007]. The decadal increase of winter snow depth over the Tibetan Plateau due to the enhanced winter North Atlantic Oscillation (NAO) after the 1970s might contribute to the late spring tropospheric cooling over East Asia [Xin et al., 2010; Ding et al., 2009]. The summer tropospheric cooling is partly ascribed to the tropical warming since the 1970s, especially the warming associated with the tropical interdecadal variability centered over the central and eastern Pacific [Li et al., 2010]. Whether the increasing emission of aerosol precursors which resulted in negative radiative forcing has any effect on the cooling is still an open issue [Qian and Giorgi, 2000;Li et al., 2007]. Further analysis reveals that the tropospheric cooling over East Asia is a regional manifestation of an interdecadal variability mode of tropospheric temperature change across the entire subtropical northern hemisphere [Zhou and Zhang, 2009]. [4] A recent study shows that the interannual variability of the East Asian summer tropospheric temperature closely relates with the different phases of the El Niño Southern Oscillation [Zhang and Zhou, 2012]. Previous studies have shown evidence that climate anomalies in the extratropical 4217

2 North Atlantic, such as NAO and Arctic Oscilattion, might play an important role in modulating the climate changes in East Asia [Wu and Wang, 2002; Yang et al., 2004; Gong and Ho, 2003; Li et al., 2008]. Detecting the long-term changes of tropospheric temperature of East Asia, especially over China, and understanding the potential mechanisms are of great scientific and societal importance. [5] Estimating long-term tropospheric temperature trends often involves large uncertainties. Previous examinations of interdecadal variability in East Asian tropospheric temperature were based mainly on reanalysis data sets and a few radiosonde stations. However, the quality of reanalysis data sets is questionable in East Asia. For example, the reanalysis of pressures and heights over Asia by the National Centers for Environmental Protection-National Center for Atmospheric Research (NCEP- NCAR) is systematically lower than objective analyses and observations before the late 1970s [Yang et al., 2002; Wu et al., 2005]. Moreover, it does not reproduce the actual surface conditions over Mongolia and its vicinity before the 1970s [Inoue and Matsumoto, 2004]. The 40 year European Centre for Medium-Range Weather Forecasts Reanalysis (ERA-40) also has some quality problems, such as lower sea level pressure than that of the observational data before the 1980s [Inoue and Matsumoto, 2004]. The second empirical orthogonal function mode of global tropical tropospheric temperature also exhibits a complex vertical structure that has been demonstrated to be largely spurious and associated with problems in assimilating satellite data [Trenberth and Smith, 2006, 2009]. [6] Uncertainties also exist in radiosonde data sets. Trends and uncertainties in radiosonde temperature records in eastern China have been assessed in many previous works [Zhai and Eskridge, 1996; Guo et al., 2008; Guo and Ding, 2009]. By homogenizing radiosonde temperature time series on six stations located in eastern China using metadata and the two-phase regression method, Guo et al. [2008] documented a large uncertainty in station time series for the six stations. Later, Guo and Ding [2009] constructed a new set of radiosonde temperature time series over China from 1958 to 2005 through quality control and homogenization; they found that average temperatures in China tend to decrease in the upper troposphere, although trend uncertainty still exists. [7] Because all temperature data products involve complex data implementation, observation systems, assimilation systems, and processing algorithms, it is currently difficult to objectively identify one or more reliable data sets [Thorne et al., 2005; Haimberger, 2007; Xu and Powell, 2010]. To ensure robust temperature changes, comparison with more than one data set is necessary. Fortunately, China has a dense radiosonde network, and radiosondes have been launched twice daily within China since the 1950s. Progress has been made in recent years in creating long-term radiosonde temperature data sets [Free et al., 2004; Haimberger et al., 2008; McCarthy et al., 2008; Sherwood et al., 2008]. The availability of these data sets has provided us opportunities to do an extensive comparison of tropospheric temperature changes derived from different data sets and to detect some reliable variability. The main motivations of this study include (1) quantitatively examining the temperature change uncertainty among various temperature data sets and (2) revealing the reliability of the radiosonde and reanalysis temperature data over China. The ultimate objective of the study is to provide a reliable estimate of long-term temperature changes over China. [8] The remainder of the paper is organized as follows. Section 2 describes the radiosonde and reanalysis data and the analyses method. Section 3 compares long-term temperature changes during the period over China. Section 4 compares the seasonal dependency of temperature changes over China. Section 5 presents a discussion on the unique trend of HadAT2. Section 6 gives a summary. 2. Data and Method Description 2.1. Data Description [9] To examine the changes of tropospheric temperature changes over China, the data used in this study include four radiosonde data sets and three reanalysis data sets. Six pressure levels were selected (i.e., 850, 700, 500, 300, 200, and 100 hpa) as primary levels for analysis. [10] For the four radiosonde temperature data sets, the first consists of radiosonde observations from 1958 to 2005, as provided by the Chinese National Metrological Information Center/China Meteorological Administration. Simple quality control is carried out in this radiosonde data set using the hydrostatic method [Parker and Cox, 1995]. However, no homogenization is undertaken. Thus, this data set is termed the Raw data set in the analysis. The second data set is the updated Hadley Center s radiosonde temperature (HadAT2) products ranging from 1958 to the present [Thorne et al., 2005; McCarthy et al., 2008] ( com/hadat/). This radiosonde data set consists of monthly temperature anomaly data relative to the climatology. The third data set consists of Radiosonde Observation Correction using Reanalysis (RAOBCORE) data from 1958 to 2009 ( [Haimberger et al., 2008]. The newest version RAOBCORE1.5 is used. The data sources are the Integrated Global Radiosonde Archive (IGRA) and the ERA-40 radiosonde archives. It employed ERA-40 reanalysis data for homogenization. The last radiosonde data is the RICH data version 1.5, which uses reference series from neighboring radiosonde stations for breakpoint adjustment [Haimberger et al., 2008]. [11] The three reanalysis data sets consist of (1) NCEP- NCAR reanalysis data from 1948 to 2008 [Kalnay et al., 1996], (2) the ERA-40 data set from 1958 to 2001 [Uppala et al., 2005], and (3) the NCEP 20th Century Reanalysis data version2 (NCEP-20CR) [Compo et al., 2011] from 1958 to It assimilates only surface pressure reports and uses observed monthly sea surface temperature and sea ice distribution as boundary conditions. Hence, NCEP- 20CR is independent of the radiosonde data. [12] To discuss the temperature trend at 850 hpa, three surface temperature observational data sets are used here. They are (1) gridded surface temperature data set from 1958 to 2001 at a 1.0 latitude 1.0 longitude grids, obtained from the National Meteorological Information Center of the China Meteorological Administration. It is termed as CMA_TAS here; (2) Goddard Institute for Space Studies Land-Surface Air Temperature Analysis anomalies from 1958 to 2001 (GISSTEMP) [Hansen et al., 2010]; and (3) CRUTEM4 temperature anomalies for [Jones et al., 2012]. In addition, the surface temperature data 4218

3 Figure 1. Distribution of the station locations of the radiosonde data set over China. in ERA-40, NCEP-NCAR, and NCEP-20CR are also examined in this work. Because ERA-40 did not output surface temperature data, the 2 m temperature was used to stand for surface temperature. [13] This work shows that the long-term trend of HadAT2 over China differs from that of the other data sets. To find the potential causes, the data sets for full audit trail of all decisions made in the construction of HadAT2 were also used. These are available at the website metoffice.gov.uk/hadobs/hadat/full_audit.html Method Description [14] The change of station and the inclusion of new station data may affect the derived long-term trend and interdecadal variation of temperature. To reduce the bias induced by station change, we extract the stations with available observational temperature data since The selected stations have continuous or nearly continuous temperature data since An analysis by Guo et al. [2008] showed that a maximum missing rate of 30% is reasonable for examining the reliability of the temperature time series. A subset of stations with a maximum missing rate less than 30% will induce a warmer trend at lower troposphere and a cooler trend at upper troposphere. Thus, stations with the missing data less than 30% of the total observation numbers are employed in this work. Figure 1 shows the selected stations. There are 76 radiosonde stations distributed throughout China, these can well cover the whole China region. [15] When examining the upper tropospheric temperature changes, the reanalysis data sets were interpolated into the above-mentioned 76 station locations by the bilinear interpolation method. Because the trends derived from different interpolation methods did not change much, we used bilinear interpolation here. In the interpolation of reanalysis data sets, we set the data to missing when the pressure level is lower than the height of topography. There are 63 and 71 stations at 850 and 700 hpa, respectively. All levels higher than 700 hpa have 76 stations. The masked stations at 850 and 700 hpa are mostly located at the Tibetan Plateau region. [16] Because HadAT2 is a temperature anomaly relative to , the other six data sets are also calculated relative to the climate mean of Due to the short time period of ERA-40 (ending in 2002), the linear trends of all data sets are calculated for the period of As in Xu and Powell [2010], the standard deviation based on the seven data sets is used here to quantitatively examine the uncertainty in temperature change. 3. Long-Term Changes of Tropospheric Temperature Over China 3.1. Temporal Variability of Tropospheric Temperature of China [17] Figure 2a shows the vertical profiles of the linear trend of monthly temperature anomaly averaged over China for All the seven data sets show that the lower troposphere (850 hpa) is dominated by a warming trend that is statistically significant at the 5% level using Student s t test. The amplitude of the warming trends gradually decrease with height and are replaced by a cooling tendency at 500 hpa in all data sets except for HadAT2 and NCEP- 20CR. The trends derived from Raw, RICH, RAOBCORE, and ERA-40 reach the maximum cooling trend at 300 hpa and then recover above 300 hpa but still dominated by cooling trends. It is clear that the vertical profile of the temperature trend over China is largely in agreement between the radiosonde and the reanalysis data sets for the period , except for the trend in HadAT2. [18] The difference among these data sets is evident. For (Figure 2a), the cooling strength of Raw Figure 2. The vertical profile of the monthly mean temperature trend averaged over China ( C/decade) derived from each data set for (a) , (b) , and (c) The shaded area is the trend spread defined as standard deviation of all data sets. 4219

4 Figure 3. (a) Time series of 6 months running mean temperature anomalies (unit: C) averaged over China at 850 hpa derived from the individual data sets. (b) Same as Figure 3a but for that at 300 hpa. The anomalies are relative to the climatological monthly mean of The shaded area, defined as standard deviation of all data sets, is the spread among all data sets. (NCEP-NCAR) at 300 hpa is stronger than the other data sets, except that the cooling strength of Raw and NCEP- NCAR at 300 hpa is stronger (Figure 2a). The trends derived from HadAT2 for and the NCEP-20CR reanalysis data set for differ greatly from the other data sets. The atmosphere in HadAT2 is warmed up (cooled) below (above) 200 hpa, with a maximum warming at 850 hpa, reaching 0.15 C/decade. The trend derived from NCEP-20CR is positive below 500 hpa, near zero between 500 and 200 hpa and a slight warming at 100 hpa. [19] Did the tropospheric temperature of China get warmer or colder during ? An analysis by Guo and Ding [2009] used metadata and a two-phase regression to show that the cooling trend of China at 300 hpa was robust during , although uncertainties still exist. The cooling trends that do not get stronger above 300 hpa is a sign that the cooling may be real and not the result of a pervasive shift in systematic errors in Chinese temperature soundings since one would expect systematic errors to increase with height. Some other independent observations, such as decreased East Asian summer monsoon, the related precipitation anomalies [Ding et al., 2009; Yu et al., 2004; Xin et al., 2006; Yu and Zhou, 2007; Zhou et al., 2009], and weakened surface wind speed over China derived from ground measurement in boreal summer [Xu et al., 2006], help in backing the upper tropospheric cooling over China for [20] To investigate uncertainties in long-term radiosonde observations, Met Office [Thorne et al., 2005] has launched an automatic radiosonde homogenization system methodology (QUARC) that uses a neighbor-based, iterative approach similar to the manual method employed in HadAT2. Based on this method, Guo et al. [2008] examined the results of temperature changes over China and found that the ENS of 100 experiments in QUARC shows a slight cooling trend at 300 hpa over China but with a large range, which can reach 0.2 K/decade at 300 hpa at most. Thus, the warming trend at 300 hpa over China derived from HadAT2 may be ascribed from the construction method of HadAT2. [21] The period covered in this study is separated into the presatellite era ( ) and the satellite era ( ). Figures 2b and 2c present corresponding linear tropospheric temperature trends over China. The temperature trends for and show higher consistency among these data sets than that for The cooling tendency for is mainly seen from the presatellite era. The whole troposphere in every data set except for NCEP-20CR 4220

5 peaked at around 300 hpa with the largest trend at 0.76 C/decade in NCEP-NCAR and RICH, which is statistically significant at the 5% level. The tropospheric temperature below 200 hpa during shows a warming trend in all data sets except for the NCEP-NCAR reanalysis. Since the NCEP-20CR reanalysis data set is independent of the radiosonde data, the cooling trend in NCEP-20CR that existed during indicated that the cooling trend over China in this era is consistent with the inputs to the NCEP-20CR, but the NCEP-20CR shows much more warming than the other data for 1979 onward. [22] To quantitatively examine the uncertainty of temperature trends in various data sets, the ensemble spreads defined as standard deviation of trends are calculated and shown in Figure 2 as shaded. The trend spreads show overall increases with height, peaking at 300 hpa for and with the intensity at 0.14 C/decade and 0.21 C/decade, respectively. The trend at 850 hpa sees the weakest spread for and at 0.05 C/decade and 0.03 C/decade, respectively. The largest trend spread during is seen at 100 hpa at about 0.29 C/decade. [23] Because the most significant warming and cooling trend occurs at 850 and 300 hpa, respectively, the 6 month running mean temperature anomalies relative to averaged over China at 850 and 300 hpa are shown in Figure 3. For temperature changes at 850 hpa (Figure 3a), the monthly variations of temperature anomaly derived from the radiosonde and the reanalysis data sets show high consistency. Cooling anomalies dominate before 1970, whereas warming anomalies dominate after A warming trend emerges for 1958 to the present (Figure 2, see the trend at 850 hpa), especially since The warming trends during are strongest, with a linear trend of C/decade. The warming trend since 2000 becomes flat. The temperature change spread (Figure 3a, shading) is relatively large during , which can reach 0.37 Cat most, compared with that since 1970 (about 0.02 C). This suggests that the warming trend in China at 850 hpa for is reliable but with larger uncertainty before [24] At 300 hpa (Figure 3b), the monthly changes of temperature anomalies over China are largely in agreement among the radiosonde and the reanalysis data sets. The temperature anomalies derived from all data sets are relatively warmer before the 1970s and cooler since the 1970s. This indicates an overall decreasing temperature trend at 300 hpa, as shown in Figure 2. The temperature spread (Figure 3b, shading) is larger before the 1970s and after the 1990s, with the maximum value around 1967 (0.57 C) and 2002 (0.63 C). This indicates that temperature change uncertainty at 300 hpa over China was larger during the above-mentioned two periods, which correspond to the times of known overall instrument changes in China. The uncertainty after mid-1990s may also relate to the observing systems, data assimilation methods, and numerical models of the reanalysis. [25] To quantitatively depict the difference among the seven data sets, the differences between individual data set and their ensemble mean (ENS) are calculated and termed as difference in the following analysis. The differences at 850 and 300 hpa are shown in Figure 4. At 850 hpa (Figure 4, black lines), the difference shows that the temperatures derived from the three radiosonde data sets Figure 4. The 6 months running mean temperature anomaly difference (unit: C) between individual data set and their ensemble mean. The black and red lines for temperature differences at 850 and 300 hpa, respectively. are cooler than the ENS before 1970, with the maximum magnitude of 0.4 C in 1958 in RAOBCORE and 0.4 C in 1965 in HadAT2. Although the temperature anomalies of ERA-40 and NCEP-NCAR both show coherent changes with radiosonde data sets, they are both warmer than the ENS before 1970, especially before 1960 and around The differences of NCEP-20CR are larger than the other six data sets but change randomly with time. The differences are small and change randomly since 1970, suggesting smaller temperature uncertainty since the 1970s, which can also be seen from the spread among the five data sets (Figure 3a, shading). [26] The difference between the individual data sets and the ENS at 300 hpa is also shown in Figure 4 in red lines. The amplitudes of the differences at 300 hpa of all data sets are larger than that at 850 hpa. The differences of Raw and RICH are consistent with each other, both show obviously warm biases before 1970 and a slightly cold bias during , with a maximum of 0.6 C in The difference of HadAT2 shows strong cooling (warming) anomalies before (after) 1975, exceeding 0.6 C in 1964 (0.5 Cin 2003). The above temperature difference of HadAT2 induces the slightly warming trend of HadAT2 at 300 hpa (Figure 2). The temperature difference derived from ERA- 4221

6 Figure 5. Spatial distribution of the in situ monthly temperature trends ( C/decade) for at 850 hpa over China derived from (a) Raw, (b) HadAT2, (c) RICH, (d) RAOBCORE, (e) ERA-40, (f) NCEP-NCAR, and (g) NCEP-20CR. (h) Trend standard deviation among the seven data sets. Solid circles in Figures 5a 5g denote the trends that are statistically significant at the 5% level. 40 is small and lower than 0.3 C. The difference between NCEP-NCAR and ENS exhibits an overall decreasing trend during The difference is positive before the 1990s. The average mean of difference for is 0.16 C. However, the difference suddenly turns to negative around The cooling trend of the difference results in the stronger cooling tendency of NCEP-NCAR (Figure 2). The difference between the NCEP-NCAR and the ENS changes abruptly from positive to negative at 1992, and it may have induced an artificial interdecadal climate change of China around the early 1990s. Therefore, more attention should be paid to the NCEP-NCAR upper level temperature data quality when studying the interdecadal variation of East Asia around the mid-1990s. The differences of NCEP-20CR are larger than the other data sets and show a warm trend. They are negative at the beginning, then random and positive after the mid-1990s Spatial Trend Patterns of Tropospheric Temperature Over China During [27] Figure 5 illustrates the spatial patterns of the long-term temperature anomalies trend at 850 hpa for In the radiosonde data sets except for HadAT2 (Figures 5a, 5c,and 5d), China is dominated by a warming trend except for Yunnan and Sichuan provinces. Warming trends exceeding the statistically significant 5% level are seen over North China (north to 35 N) and south China (south to 25 N), larger than 0.4 C/decade, while the cooling trend over southwestern China is weak with the magnitude less than 0.1 C/decade. [28] The differences among these data sets are still evident. First, the cooling trend region in Raw covers the whole Yangtze River, which is larger than that in the RICH and RAOBCORE. Second, no cooling trend is seen from the HadAT2 and the NCEP-20CR, and the warming trends are 4222

7 Figure 6. Spatial distribution of the monthly surface temperature trends ( C/decade) for over China derived from (a) CMA_TAS, (b) CRUTEM4, (c) GISTEMP, (d) ERA-40, (e) NCEPNCAR, and (f) NCEP_20CR. The dotted areas denote the trends that are statistically significant at the 5% level. statistically significant at the 5% level over China. Third, the warming trend in the ERA-40 reanalysis shows much weaker intensity than that in the other data sets, and most stations are not statistically significant at 5% level. Fourth, an unrealistic cooling trend over Northwest China and the Tibetan Plateau is derived from NCEP-NCAR; it exceeds the 5% significance level and shows C/decade stronger warming than the other data sets over East China. Due to the differences mentioned above, the trend spread is large over Northwest China (Figure 5h). [29] The cooling trend over Northwest China and the Tibetan Plateau derived from ERA-40 and NCEP-NCAR may be a result from the local high topography. The 850 hpa over Northwest China and the Tibetan Plateau is near surface and is related with the surface temperature changes. Therefore, the surface temperature trend of the observation and the reanalysis data sets are shown in Figure 6. In the observation data sets (Figures 6c), the surface temperatures show an overall significant warming trend over North China and no obvious trend over south China, especially over the Southwest China in situational observation data in China. This is consistent with the temperature trend at 850 hpa in Raw, RICH, and RAOBCORE (Figures 5a, 5c, and 5d). With regard to the ERA-40 and NCEP-NCAR reanalysis data, over Northwest China and the Tibetan Plateau, they (Figures 6e) both show cooled surface temperature. The warming surface temperature trend over Northeast China in NCEP-NCAR is larger than that in the ERA-40. These features are consistent with the temperature trends at 850 hpa in these two reanalysis data sets. It suggests that the cooling trend over Northwest China and the Tibetan Plateau derived from ERA-40 and NCEP-NCAR is related with the local high topography and their reanalysis surface temperature. [30] Figure 7 presents the spatial patterns of the longterm temperature trend at 300 hpa for Differing greatly from that at 850 hpa, a significant cooling trend over the whole China can be seen from all data sets except for HadAT2 and NCEP-20CR.The temperature trend at 300 hpa in HadAT2 (Figure 7b) is greatly distinguished from the other data sets. It shows a significant warming trend south to 35 N ( C/decade) and cooling over North China ( 0.1 to 0.2 C/decade). The cooling signal in NCEP-NCAR is 0.1 C/decade stronger north to 30 N than the cooling trend in the other data sets. The trend derived from ERA-40 is much closer to the RICH and RAOBCORE. The temperature trend derived from NCEP20CR is similar to that in HadAT2, but with stronger cooling 4223

8 Figure 7. Same as Figure 5 but for temperature trend at 300 hpa. tendency over North China. The trend spread at 300 hpa is larger over central China. The trend uncertainty over East China at 300 hpa is much larger than that at 850 hpa. [31] To illustrate the spatial pattern of the uncertainty, Figure 8 shows the spatial distributions of the ensemble trend spread of all data sets at individual pressure level. The spread is smaller at the lower troposphere ( hpa) than at the upper troposphere ( hpa). However, the uncertainty over Northwest China at 850 hpa is the largest among all levels. It resulted from the cooling trend in NCEP-NCAR and ERA-40. From 850 to 500 hpa (Figures 8a 8c), the spread is small over East China ( C/decade) and larger over Northwest China (larger than 0.2 C/decade). The spread magnitude at 300 hpa east to 100 E (Figure 8d) is the strongest, larger than 0.16 C/decade for most areas. At 200 hpa (Figure 8e), the trend spread is small along the Yangtze River and over Northeast China (between 0.02 C and 0.08 C/decade), while it is evident over south China and Northwest China (larger than 0.14 C/decade). The trend uncertainty at 100 hpa (Figure 8f) is large over south China with the magnitude at 0.14 C/decade. The spatial patterns of trend spread suggest that the trend uncertainty is large in the Northwest for the lower troposphere and in south China for the upper troposphere. The trend at 300 hpa east to 100 E shows that largest uncertainty. [32] The above discussion indicates that the radiosonde and the reanalysis data sets have a high consistency in revealing the monthly evolution of tropospheric temperature anomalies over China. All data sets, except for HadAT2 and NCEP-20CR, show consistent vertical profiles of temperature trends over China. The temperature uncertainty is large during and from 1990 to the present. 4. Seasonal Dependence of Tropospheric Temperature Over China 4.1. Time-Height Trend of Tropospheric Temperature Over China for [33] The seasonally varying structures of the trend are investigated in this section. Figure 9 presents the time-height cross-sections of the monthly mean air temperature trend 4224

9 Figure 8. Spatial distribution of the temperature trends standard deviation among the seven data sets ( C/ decade) for over China derived at (a) 850, (b) 700, (c) 500, (d) 300, (e) 200, and (f) 100 hpa. averaged over China in Significant cooling vertically occurs at around 300 hpa and persists from March to October in all data sets except for HadAT2 and NCEP20CR. There are two peaks in the upper tropospheric cooling; these occur in April and August at 300 hpa, with center values of 0.35 C/decade and 0.3 C/decade, respectively. A moderate warming trend is seen in the lower troposphere in December February, centered at 850 hpa with 0.25 C/decade. The cooling trends at 300 hpa in Raw and NCEP-NCAR (Figure 9a) are 0.15 C/decade stronger than those in the other data sets. The cooling trend of 300 hpa derived from NCEP-NCAR is persistent all year round, which is different from the other data sets. In HadAT2 (Figure 9b), a cooling signal cannot be seen at 300 hpa, but can be found at pressure levels higher than 200 hpa, with the strongest cooling at 100 hpa in June (larger than 0.35 C/decade). In HadAT2, a strong warming tendency is seen in the lower troposphere throughout the year and in the upper troposphere in winter (from November to February). NCEP-20CR can partly capture the cooling tendency of the China upper troposphere but with weaker magnitude. [34] Figure 9h shows the temperature trend spreads at each level for individual months. Generally speaking, the spreads at pressure levels higher than 300 hpa are larger than those at hpa. The largest temperature trend spread is seen at 300 hpa in May and July August and at 100 hpa in June and September (exceeding 0.15 C/decade), while the smallest trend spread is seen at 700 and 850 hpa in winter (December February), the minimum value being 0.01 C/decade Long-Term Change of Tropospheric Temperature of China in Different Seasons [35] Figure 10 shows the seasonally varying vertical profiles of temperature trend averaged in China at six levels from 1958 to In March May (MAM), a robust cooling in the upper troposphere and a slightly warming trend in the lower troposphere can be clearly observed in all data sets except for ERA-40, which shows a slight cooling trend at 850 hpa (Figure 10a). The temperature trend profiles from all data sets except for HadAT2 and NNCEP-20CR in June August (JJA) and September November (SON) are similar to that in MAM. In all data sets, except for HadAT2 and NCEP-20CR, the lower troposphere ( hpa) in winter (December February, DJF) is dominated by a warming trend, decreases with height, is replaced by a slight cooling at 300 hpa, and then shows warming at 200 hpa. [36] It is clear that the vertical profiles of the temperature trend are largely in agreement among the radiosonde and the reanalysis data sets, except for HadAT2 and NCEP20CR. The tropospheric temperature from 850 to 300 hpa revealed by HadAT2 is dominated by an increasing tendency from JJA to DJF. The strongest cooling signal in HadAT2 is above 200 hpa in MAM to SON. The temperature trend magnitude in ERA-40 is closer to the RICH and 4225

10 Figure 9. Time-height cross-section of tropospheric temperature trend (unit: C/decade) of each month during averaged over China derived from (a) Raw, (b) HadAT2, (c) RICH, (d) RAOBCORE, (e) ERA-40, (f) NCEP-NCAR, and (g) NCEP-20CR. (h) Trend spread among the seven data sets. The lines in Figures 9a 9g denote the trends that are statistically significant at the 5% level. RAOBCORE compared with the NCEP-NCAR. The cooling intensity at 300 hpa in NCEP-NCAR is strongest among all data sets. The spreads among the different data sets are relatively stable among the four seasons (Figure 11, shading). It varies from 0.03 C/decade (850 hpa in DJF) to 0.15 C/decade (100 hpa in JJA). In general, the temperature trend uncertainty is seasonally dependent and usually increases with height from 850 to 300 hpa. The spreads tend to be large above 300 hpa, with the largest value (0.15 C/decade) at 100 hpa in JJA. [37] Figure 11 illustrates the temperature anomalies at 300 hpa (relative to ) because of the unique cooling trend at 300 hpa in all seasons during Temperature anomalies shift from positive to negative phase around 1975 in MAM and JJA. This indicates the interdecadal shift of the East Asian monsoon system around the late 1970s, which is consistent with previous studies [Xin et al., 2006; Yu and Zhou., 2007]. The year-by-year variations of the radiosonde and the reanalysis data sets show high consistency with each other. Of particular interest is that temperature anomalies derived from HadAT2 are weaker than those in the other data sets before 1975 and stronger after the 1980s. This will induce a less cooling trendofhadat2comparedwiththeotherdatasets,as shown in Figure 10. The negative temperature anomalies since the 1990s in NCEP-NCAR are stronger than in the other data sets in all seasons and induce the stronger cooling trend in NCEP-NCAR, as shown in Figures The spreads among the data sets (as shaded) are shown in Figure 11. The data set spreads are relatively small during and larger during and from the 1990s to the present. 5. Discussion About the Warming Trend at 300 hpa in HadAT2 During [38] This work examined the changes of tropospheric temperature changes over China and found that the long-term trends at 300 hpa of HadAT2 differ greatly from the other radiosonde data sets, i.e., Raw, the data source for all data 4226

11 Figure 10. Vertical profile of temperature trend averaged over China ( C/decade) for (a) MAM, (b) JJA, (c) SON, and (d) DJF during the period The shaded areas indicate the standard deviation of the trend from all data sets. sets, RICH; and RAOBCORE. To identify what causes the unique trend of HadAT2 over China, we examined the audit trail data of decisions made during the construction of HadAT2. [39] In the construction of HadAT2, three crucial processes are carried out: deriving the initial station network and the raw station data, quality control (QC), and homogenization. Because only the seasonal mean raw station data and adjustments for during QC are available at the website, and the summer warming trend at 300 hpa in HadAT2 is mainly distributed over the south to 35 Nof China (figure not shown), we take the summer temperature series at Wuhan (30.62 N, E) (Station ID 57494) as an example in the following discussion. [40] Many previous studies, such as Santer et al. [1999] and Seidel et al. [2004], show that the temperature trend depends critically on the selection and implementation of the data sources. Thus, we examined the raw station data for 57494, which was inputted to the QC procedure in HadAT2. Figure 12a (black line) shows the summer mean time series of raw data at 300 hpa. It exhibits a robust cooling tendency ( 0.2 C/decade), exceeding the 5% significance level. This suggests that the warming trend of HadAT2 is not ascribed to the source data. [41] Then, we checked the final adjustment (Figure 12a, blue line) applied during QC and homogenization, which is negative before 1976 and positive after 1976 and found an overincreasing tendency during The temperature trend after adjustment (Figure 12a, red line) is 0.02 C/decade. This suggests that the warming trend is produced during homogenization. [42] This suggests that the identification of a neighbor time series induces the warming trend of HadAT2. Therefore, we further examined the surrounding neighbor stations and the neighbor coefficients used to calculate the average neighbor series of (Figure 12b) during homogenization. It can be seen that most neighbor stations are located in the tropics outside of China, with coefficients larger than 0.4. During , these tropical stations witnessed warm trends (Figure 12c). The robust warming trend of the neighbor stations for station may induce the adjustment for to show a warming trend and thereby change the tendency of station [43] The neighbor stations for each level in HadAT2 are identified using NCEP-NCAR reanalysis for They are selected based on the correlation coefficient between the temperature time series of the grid point that the 4227

12 (Raw radiosonde data sets provided by China Meteorological Administration, HadAT2, RICH, and RAOBCORE) and three reanalysis temperature data sets (ERA-40, NCEP-NCAR, and NCEP-20CR), the temperatures of six pressure levels were sampled to evaluate the tropospheric temperature change over China during the past 50 years. The ensemble spread, defined by the standard deviation of all data sets, was used here to quantitatively examine the Figure 11. Time series of temperature anomalies (unit: C) averaged over China at 300 hpa derived from individual data sets for (a) MAM, (b) JJA, (c) SON, and (d) DJF during the period The shaded areas indicate the standard deviation of the seven data sets. target station falls in and the temperature of NCEP-NCAR. The neighbor stations are selected from the HadAT0 stations. For each station, the stations in the contiguous surrounding region with correlation greater than 1/e were identified as neighbor stations [Thorne et al., 2005]. The correlation coefficients of Wuhan station are shown in Figure 12b (shaded). It can be seen that most of the stations with coefficients greater than 1/e are located in tropics. Although many stations over Southwest China and south China are identified as the neighbor stations of Wuhan station, these stations are not included in HadAT0 and are not used for neighbor composite series. It may suggest that the identification method of neighbor composite series of China stations needs improvement, or more radiosonde stations over China should be included. 6. Summary [44] The estimation of long-term tropospheric temperature trends often contains large uncertainties, complex data implementation, observation systems, assimilation systems, and processing algorithms. The evaluation of tropospheric temperature change is necessary for climate detection and attribution. Based on four radiosonde temperature data sets Figure 12. (a) Summer mean temperature anomalies (unit: C) time series at 300 hpa for station The black line is the raw station data input to the quality control procedure in HadAT2. The red line is the time series after adjustment, and the blue line is the adjustment applied during the homogenization. (b) Surrounding neighbor stations and the neighbor coefficients used to calculate the average neighbor series of station during homogenization. The shading is the correlation coefficient between summer temperature at 300hPa of the target station and that of NCEP-NCAR during (c) The linear trend (unit: C/decade) of the neighbor stations during The black dot in (b)-(c) is the station being adjusted. 4228

13 temperature change uncertainty. The main findings are listed below:(1) During , a warming trend is seen in the lower troposphere temperature averaged over China; it gradually decreases with height and is replaced by a cooling tendency at 500 hpa, reaching maximum cooling at 300 hpa in all except HadAT2 and NCEP-20CR data sets. The most robust warming trend occurs at 850 hpa, ranging from 0.1 C/decade in ERA-40 to 0.17 C/decade in NCEP- NCAR. The warming trend at 850 hpa is statistically significant at 5% level over North China and Southeast China. The cooling signal at 300 hpa can be seen over the whole of China in all data sets except for HadAT2 and NCEP- 20CR. The strongest cooling vertically occurs at around 300 hpa and persists from March to October, with two peaks occurring in April ( C/decade) and August ( C/decade). A significant warming trend at 850 hpa is obvious in winter (December February), with a magnitude of C/decade.(2) The year-by-year changes of temperature anomalies averaged over China are largely in agreement among the radiosonde and the reanalysis data sets. The uncertainties of upper tropospheric temperature changes are larger compared with those of the lower troposphere. The cooling trend for at 850 hpa (300 hpa) has the weakest (strongest) spread, with an amplitude of 0.03 C/decade (0.14 C/decade). The spread for monthly temperature anomaly changes averaged over China shows that temperature change uncertainty is relatively small during and is larger during and from the 1990s to the present. The uncertainty over Northwest China at 850 hpa and over east to100 Eat300hPa is the largest among all levels. The trend uncertainty is large in the northwest for the lower troposphere and in south China for the upper troposphere.(3) The temperature changes derived from the Raw, RICH, and RAOBCORE radiosonde data sets agree well with each other. However, the upper tropospheric temperature changes derived from HadAT2 differ greatly from the other radiosonde data sets. In HadAT2, a slight warming trend at 300 hpa is seen from the averaged temperature of China; only several stations over North China witness a cooling trend, but the cooling magnitude is only half of that in the ENS. The difference between the HadAT2 and the ENS shows that HadAT2 is cooler (warmer) than the ensemble mean of seven data sets (ENS) before (after) 1975 and induces the warming trend of HadAT2 at 300 hpa. The warming tendency at 300 hpa of HadAT2 over the south may be produced by choosing the neighbor stations during construction of HadAT2.(4) A cooling trend over Northwest China and the Tibetan Plateau is seen from ERA-40 and NCEP-NCAR. This resulted from the local high topography, which is near surface and related with the surface temperature changes. The temperature trend in NCEP-NCAR is cooler than that derived from the other data sets at 300 hpa. NCEP-NCAR is warmer than the ENS before 1990 and suddenly becomes colder than the ENS around early 1990s and results in a stronger cooling trend at 300 hpa. Because the temperature of NCEP- NCAR gets cold abruptly in the early 1990s, it may induce an artificial interdecadal change or at least a stronger variability around the early 1990s. The reasons for the abrupt cooling in the 1990s for NCEP-NCAR remain unknown. Thus, attention should be paid to NCEP-NCAR data quality when studying interdecadal variability of East Asian climate which occurred in the 1990s. NCEP-20CR can partly capture the cooling tendency at 300 hpa over North China, but with weaker magnitude. [45] Acknowledgments. This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA ), the Program of Excellent State Key Laboratory ( ), and the National High-Tech Research and Development Plan of China (2010AA012302), and the National Natural Science Foundation of China (Grant No ). Special thanks to Dr. Leopold Haimberger of the Department of Meteorology and Geophysics at the University of Vienna for providing the original merged radiosonde data from IGRA and ERA-40. ECMWF and IGRA are also acknowledged for providing the radiosonde data sets. Helpful comments from Yanfeng Zhu of the National Meteorology Information Center of China Meteorological Administration are highly appreciated. 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