We present a homogenized surface air temperature (SAT) time series at 2 m height for the
city of Qingdao in China from 1899 to 2014. This series is derived from three
data sources: newly digitized and homogenized observations of the German
National Meteorological Service from 1899 to 1913, homogenized observation
data of the China Meteorological Administration (CMA) from 1961 to 2014 and a
gridded dataset of Willmott and Matsuura (2012) in Delaware to fill the gap
from 1914 to 1960. Based on this new series, long-term trends are described.
The SAT in Qingdao has a significant warming trend of
0.11
Surface air temperature at 2 m (hereinafter referred to as SAT) is one of the most important climate elements influencing the biosphere and human activities. Systematical observations in China on a national scale started in 1951. However, the 60-year length of the SAT dataset seems insufficiently long to understand the long-term trend and interdecadal variability. For detecting changes beyond the range of natural variations (e.g., Zorita et al., 2008) and for attributing such a change to plausible drivers (a concept introduced by Hasselmann, 1979, known as “detection and attribution”), longer observational series are needed. Therefore, the changes in temperatures in China in the past more than 100 years need to be investigated in more detail (Qian and Zhu, 2001; Qian et al., 2011; Soon et al., 2011).
Positions of the 14 stations of the German Marine Observatory in
China
Several annual mean SAT series for China commencing in the late 19th century
have been constructed (Lin et al., 1995; Mitchell et al., 2002; Wang et
al., 2004; Tang and Ren, 2005; Tang et al., 2009; Soon, et al., 2011; Compo
et al., 2011). Currently, four sets of long-term time series exist that have
been widely used in climate change studies in China (Wang and Gong, 2000;
Wang et al., 2004; Lin et al., 1995; Tang and Ren, 2005; Tang et al., 2009).
However, these studies exhibit widely different linear trends of
nationally averaged SAT namely 0.03 and 0.11
The International Atmospheric Circulation Reconstructions over the Earth
(ACRE) project was set up in 2008. One aim of ACRE is to link international
meteorological organizations for the recovery, quality control and
consolidation of global terrestrial and marine instrumental surface data of
the last 250 years (Allan et al., 2011, 2016). Among others, the German
Meteorological Service (Deutscher Wetterdienst, DWD) in Hamburg also supports this
project with huge archives of historical handwritten journals of weather
observation. Archived data from about 1500
Here, two questions need to be considered: (1) how can we make good use of these data (2) what can be obtained from these data for climate change studies? This study attempts to use the Qingdao station as an example to objectively establish a new homogenized monthly mean SAT series back to the year 1899. Then, the derived time series are used to analyze the characteristics of climate variability in Qingdao where rapid industrial developments have taken place.
Several data sources are used in the study. The observed sub-daily SAT records and the associated metadata of Qingdao (1899–1913; Table 1) have been archived by DWD. Note that these SAT records from 1905 to 1914 have a high temporal resolution of 24 records for each day. The homogenized monthly SAT of Qingdao station (WMO station number is 54857) from 1961 to 2013 is selected from CMA, which has developed the first national homogenized temperature dataset (Li and Yan, 2009) and its updated version (Xu et al., 2013). Moreover, three gridded SAT datasets with high spatial resolution starting from the late 19th century have been used in the construction: (1) the monthly mean SAT from the global precipitation and temperature of Willmott and Matsuura, which is developed in the Department of Geography, University of Delaware (referred to as SAT W&M v4.01; Willmott and Matsuura, 2012); (2) the monthly mean SAT data of the Climatic Research Unit (Harris et al., 2013; referred as CRU TS3.230); and (3) the monthly mean SAT data of the 20th Century Reanalysis version 2c (referred to as 20CR v2c; Compo, et al., 2011). More details about the three datasets are shown in Table 2.
Coordinates, heights and daily observation times of Qingdao and Schatsykou in the earlier time.
Three gridded SAT datasets that are used in this study.
When we test the significance of correlation between two time series and the
significance of the presence of trends in a time series, an important
question needs to be considered, that is, how large sample correlations and
sample trends could be, even if the stochastic processes, which generate the
series, are not correlated at all and exhibit no trends. Firstly, we have to
make an assumption, namely the processes
In the case of correlations, the assumption is that the underlying processes
are stationary (free of systematic trends) and serially independent, i.e.,
A practical remedy for avoiding such errors is to deal with normalized series
(mean “detrend” the time series before testing for correlations between
two time series “prewhiten” the time series, by first determining the sample
autocorrelation
The standard routines are applied to both cases. If the null hypothesis is
rejected at the stipulated significance level of 5 %, then the sample
trend
In this paper, four seasonal mean SAT time series are defined by calculating
the average of each three-month period: December(
The earlier SAT data from 1899 to 1913 have been digitized manually and passed through a quality check. The quality checking routine of DWD starts with a formal check, followed by climatological, temporal, repetition and consistency checks (Leiding et al., 2016). From April to December 1901, the original observations of Qingdao are not available. These missing values are filled in using the SAT time series of a neighboring station. We find that the value and variability in SAT monthly time series from July 1900 to December 1901 in Schatsykou exhibits a good agreement with these in Qingdao (Fig. 2). Consequently, the SAT data in Schatsykou is merged into the SAT data of Qingdao to fill the missing data.
Comparison of the monthly mean SAT in Qingdao (solid line) with that in Schatsykou (dashed line) during July 1900 to December 1901.
Here, we have to point out that these quality checks above account for errors in coding and archiving, but are not efficient in dealing with inhomogeneities (Karl et al., 1993). In fact, changes in station height and changes in daily observational times (Table 1) can affect the SAT during 1899–1913. It is important in observational studies that the data used should be homogeneous (Trewin, 2010; J. F. Wang et al., 2014; Li et al., 2016). And in this study, homogenization of the data is the key factor. Thus, the homogenization of the SAT time series from 1899 to 1913 needs to be done in the next step.
Inhomogeneities in land-based observations of air temperature may dampen or introduce noise to estimates of long-term air temperature trends. SAT data from 1961 to 2014 have been homogenized by CMA (Xu, et al., 2013). Here, we pay more attention to the detecting and adjusting of the SAT homogeneity from 1899 to 1913, which is newly digitized without homogenization.
Details of non-climatic factors were recorded in the metadata back to January
1899 (Table 1). Among these factors, the changes in observation height and
daily observation times are the main causes of inhomogeneities. In the
earlier time from January 1899 to April 1905, SAT was observed at
36
In order to adjust the inhomogeneities caused by observation height change
and observation times change, the best way is to find neighboring reference
series and then modify the candidate series based on several mathematical
methods. But actually it is hard and even impossible to find a reference
series in such early times. In this case, air temperature in Qingdao was
transformed into temperature at sea level using an average environmental
lapse rate (6.0
Distribution of difference between the daily mean temperature
Annual mean SAT series (
Generally, the SAT calculated by
Annual mean SAT time series in Qingdao from 1899 to 2013 (black solid line: SAT OBS; dashed line and cross: 20CR v2c; dashed line and circles: SAT W&M v4.01; dashed line and diamond: CRUTS 3.230).
A previous study pointed out that the observation SAT from 1914 to 1960 was
discontinuous with many missing data (Cao et al., 2013). For Qingdao station
before 1960, the missing times of records were in July 1914 to March 1915, September
1937 to January 1938, and January 1951 to December 1960. However, all data from
1916 to 1950 has not been homogenized. In light of this, we attempt to use some
grid-box datasets to fill with the gap from 1914 to 1959. Here, each of the
three gridded SAT datasets have been evaluated for consistency with
homogenized observations. Then the best one was used to estimate the SAT from
1914 to 1959. We calculated the correlation coefficient between the three SAT
series with homogenized observations from DWD and CMA in Qingdao station
(hereinafter referred to as SAT OBS) in two periods (1899–1913 and
1960–2014) after detrending described in Sect. 2.2. Results show that all of
the correlation coefficients are statistically significant on the 95 %
confidence interval (Table 3). The highest correlation coefficients
(
Correlation coefficients between the three SAT time series and the observational SAT time series in Qingdao. All of these time series have been detrended. The largest correlation coefficients are in bold.
Then, we compare the three annual mean SAT time series with the SAT OBS in
Fig. 5. It can be seen that except for the 20CR v2c, the annual mean SAT series
of W&M v4.01 and CRUTS 3.230 both have the similar climate variability
with that of the SAT OBS. Interestingly, the difference between 20CR v2c and
CRUTS 3.230 shows marked non-stationarities. In the first years, both series
are similar, but exhibit relatively smaller interannual variability. Since
about 1920 until 1950, the temperatures of 20CR v2c are mostly smaller than
the CRUTS 3.320. But since about 1960 until 2005, the yearly means of 20CR v2c
are strongly larger than CRUTS 3.320. The abrupt change in 20CR v2c around 1960
is not replicated in the W&M v4.01 series, and we suggest that this jump is
an artifact in the analysis of 20CR v2c. Other non-stationarities have been
found in the 20CR analyses (e.g., Krueger et al., 2014) and we suggest to
rely more on the other two descriptions of past temperature variations.
However, there is a relatively large systematic difference between the SAT OBS
and the CRUTS 3.230 data and the difference even exceeds 3.5
Using a linear regression method, each monthly mean SAT OBS in each period can
be estimated from SAT W&M v4.01. Take SAT OBS in January for example,
SAT OBS
Construction of annual mean SAT (
The newly constructed annual mean SAT in Qingdao from 1899 to 2014
(Fig. 6) exhibits a warming rate in Qingdao over the last 116 years of
Since 2000, the SAT undergoes a decreasing trend, with a rate of
It is also interesting to note that during 1899–1910 there is another
decreasing trend, with the rate of
Averaged annual mean SAT anomalies for each decade (
Constructed seasonal mean SAT series (
Definitions of temperature indices used in this study.
Differences in TX, TN and DTR between the period 1907–1913 and the
period of 2007–2013 (
The constructed 10-year annual mean SAT from 1899 to 2014 are shown in Fig. 7.
Five main periods are associated with larger than normal SAT. The three
maximum warm periods occurred during 1989–1998, 1999–2008 and 2009–2014. The
average anomaly SAT of 1999–2008 is the largest which is higher than normal
of about 0.96
The seasonal mean SAT time series are also shown in Fig. 8. The linear change
trends and 95 % uncertainty ranges are also calculated, with the warming
rate of about
The homogenized monthly mean surface air temperature for
Qingdao from 1899 to 2014 is provided and archived by the Deutscher
Wetterdienst (DWD) web page under overseas stations of the Deutsche Seewarte
(
Construction of a long-term homogeneous meteorological time series is essential for research in the field of climate change. Using quality control, interpolation and homogeneity methods, we objectively establish a set of homogenized monthly mean SAT series in Qingdao, China from 1899 to 2014. Three datasets were combined in this study, including the newly digitized observations of Qingdao station from the German National Meteorological Service from 1899 to 1913, adjusted SAT W&M v4.01 from Delaware University during 1914–1959 and homogenized SAT dataset from CMA during 1960–2014.
Based on the monthly SAT data, long-term changes in Qingdao, China are
analyzed for the period 1899–2014. The main conclusions are as follows: (1) the SAT in
Qingdao has a significant warming trend of
Frankly, hourly observations in the early 20th century make it possible
to compare the extreme temperature and diurnal cycle of temperature with
present-day observations. Here we finally chose the period from 1 January
1907 to 31 December 1913 with little missing data. Then we compare the
maximum temperature (TX), minimum temperature (TN) and diurnal temperature
range (DTR; Table 4) in the period from 1 January 1907 to 31 December 1914
to those in the period from 1 January 2007 to 31 December 2013 (100-year interval). Hourly data from 1 January 2007 to 31 December
2014 are provided by CMA. Yearly mean daily TX (TN/DTR) temperature is
defined by calculating the average of each daily TX (TN/DTR) temperature in a
year. Then the differences in TX, TN and DTR between the two periods are shown
in Fig. 9. In Fig. 9, the TX and TN are found to have significantly increased
at the range of
From this study, we have also noticed that reconstruction and digitization of historical weather observations is important for extending time series or filling gaps and improving the gridded or reanalysis dataset. Furthermore, it is essential to be aware that metadata is important for homogenization of the time series, especially in the earlier times without reference series. We therefore agree with Allan et al. (2011, 2016) that longer and more spatially and temporally complete historical weather records could be recovered, imaged and digitized to expand the observational database. There is still a long way to go.
The authors declare that they have no conflict of interest.
This work was conducted by the lead author during a stay as visiting scientist at the German Meteorological Service (Deutscher Wetterdienst, DWD) and Federal Maritime and Hydrographic Agency (BSH) in Germany. We thank Hamburg University's Cluster of Excellence CliSAP (Integrated Climate System Analysis and Prediction) for funding the stay. Edited by: David Carlson Reviewed by: two anonymous referees