Geophys Res Lett
Supporting Information for
China experiences the recent warming hiatus
Qingxiang Li1，Su Yang1，Wenhui Xu1，Xiaolan L. Wang2，Phil Jones3，David Parker4,
Liming Zhou5, Yang Feng2, Yun Gao6
1 China Meteorological Administration, National Meteorological Information Center, 46 Zhongguancun
South Avenue, Haidian District, Beijing, China 100081
2 Climate Research Division, Science and Technology Branch, Environment Canada, Toronto, Ontario,
Canada.
3 Climatic Research Unit, School of Environmental Sciences, University of East Anglia, Norwich NR4 7TJ,
UK;
Also Center of Excellence for Climate Change Research, Department of Meteorology, King Abdulaziz
University, Jeddah 21589, Saudi Arabia
4 Met Office Hadley Centre, Exeter, EX1 3PB, UK
5 Department of Atmospheric and Environmental Sciences, University at Albany, State University of New
York, Albany, NY 12222, USA
6 China Meteorological Administration, 46 Zhongguancun South Avenue, Haidian District, Beijing, China
100081
S1. Temperature data
The temperature data set has been collected and processed by the National
Meteorological Information Center (NMIC) of the China Meteorological Administration
(CMA). Using CMA’s classification in 2012, the 825 National Reference or Basic
Stations (NRBS) stations are shown in Fig. S1. The data set covers the period from 1951
to 2012. According to “the Rules of Surface Observations in China”, stated in the China
Meteorological Industry Standards, any change at a national meteorological station shall
be documented in a standard form, the “Meteorological Station History data File
1
Format”. These station history data files (i.e., metadata) are checked and stored by the
provisional meteorological information centers (PMICs) and also by NMIC. Table 1
reports the statistics of station relocations. Among the 825 stations analyzed in this study,
a total of 669 stations have been moved at least once. Previous research showed that a
number of station temperature series are inhomogeneous1-3, and some homogenized
temperature datasets have been developed to correct the bias in climate trends detection
over China1-5
The observed values were quality controlled using the NMIC conventional
procedures, including the climatological limit check, the station or regional extremes
check, the internal consistency check, and the temporal and spatial consistency checks.
We then homogenized time series of daily maximum and minimum temperatures
recorded at 825 stations in China, separately, over the period from 1951 to 2010, using
both metadata and the penalized maximum t test with the first order autocorrelation being
accounted for to detect changepoints, and using the quantile-matching algorithm to adjust
data time series to diminish discontinuities (Wang and Feng, 2010)6. Station relocation
was found to be the main cause for discontinuities, followed by station automation. The
effects of discontinuities on estimation of long-term trends in the annual mean and
extreme indices of temperature are illustrated. The data homogenization is shown to have
improved the spatial consistency of estimated trends 5.
2
S2. Surface Solar Radiation (SSR) data
The SSR data is also quality controlled and processed by NMIC. There are about
130 sites over mainland China where SSR has been monitored and we used 93 stations
with high SSR data completeness7. (Fig S2).
Similar to temperature, the station relocations and instrumentations change cause
inhomogeneity in SSR series, but it is more difficult to detect their discontinuities and
adjust them, because the sparse station distribution makes it difficult to construct
reference series of SSR data from nearby stations when homogenizing the temporal
series. However, Ju et al.（2006）employed sunshine duration (SSD) totals as reference
series to identify the change points of SSR data. Sunshine duration series have a much
higher density of observational sites than SSR and are generally highly correlated with
SSR. The results showed that most of the SSR potential change points were in the 1970s
and 1990s8. In addition, we check the metadata for SSR stations (NMIC, 2013), and find
that the instrumentation and observational rules have remained the same since the last
change of instruments in the 1990s9. So we believe that the data in 1998-2012 are more
stable and fit for analyzing in this paper.
S3. Trend analysis for 1998-2012
Since we are analyzing extreme temperatures (which are not normally distributed),
we used the Kendall method developed by Wang and Swail (2001)10-12 to estimate linear
trends and test for their statistical significance at the 5% level: the procedures and steps
are given in APPENDIX A of Wang and Swail (2001) . Table 1 shows trends in seasonal
3
and annual mean maximum (Tx) and minimum temperatures (Tn) and DTR, and Fig S3
shows comparisons of the trends for different periods.
S4. Surface air temperature (SAT) trends from 20CR
We plotted surface air temperature trends from the 20th Century Reanalysis
(20CR)13, which is driven by atmospheric pressure data, and for 1998-2012 it reproduced
cooling over China in winter, in agreement with observations, with little change in
summer. As 20CR does not use air temperature data or aerosol forcings, the winter
cooling must have come from the atmospheric circulation, i.e. an increase in winds from
the north responding to the assimilated atmospheric pressure data. But 20CR provides
output only on a 6-hourly basis, not true maximum and minimum temperatures, so we
only looked at mean temperatures.
It is noteworthy that the SAT change trends derived from 20CR are similar to those
from observational SAT data. This means the SAT change in JJA and DJF are related to
regional atmospheric circulations. (Fig S4)
Acknowledgement
20th Century Reanalysis V2 data were provided by the NOAA/OAR/ESRL PSD,
Boulder, Colorado, USA, from their Web site at http://www.esrl.noaa.gov/psd/. Support
for the 20th Century Reanalysis Project dataset is provided by the U.S. Department of
Energy, Office of Science Innovative and Novel Computational Impact on Theory and
Experiment (DOE INCITE) program, and Office of Biological and Environmental
4
Research (BER), and by the National Oceanic and Atmospheric Administration Climate
Program Office.
5
50
40
30
20
80
90
100
110
120
130
140
Fig.S1. The 825 stations analyzed in this study, including 682 National Basic Stations (NBS) (red
circle) and 143 National Reference Stations (NRS) (blue star) stations.
6
Fig.S2. The 130 SSR stations over China, and 93 of them (the length of SSR time series is longer
than 10 years) analyzed in this study.
7
(a)
8
(b)
9
(c)
10
(d)
11
(e)
Fig.S3. The annual (a), Spring (b), Summer (c), Fall (d) and winter (e) trends calculated by
Kendall’s slope estimator developed by Wang and Swail (2001)10-12.
12
13
14
15
Fig.S4. The Summer (JJA) and Winter (DJF) surface air temperature trends during 1961-2012
and 1998-2012 from the 20th Century Reanalysis (20CR)13 and homogenized data (below panel
for each 20 CR plots)
16
Table.S1. The statistics of station relocations (up to 31 December 2009).
Frequency
Never
Once
Twice
>= 3 times
Total
Number of
156
231
203
235
825
stations
(18.9%)
(28.0%)
(24.6%)
(28.5%)
relocated
17
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