Introduction
Precipitation and shortwave radiation are the fundamental sources of water
and energy for land-surface biological, physical and chemical processes
(Zhao and Zhu, 2015; Zhang et al., 2010). These fluxes affect moisture and
heat exchange between the atmosphere and the land surface (Pan et al., 2014;
Tian et al., 2007; Fekete et al., 2004; Gottschalck et al., 2005), and are key
meteorological forcing inputs for studies using land process models,
including crop simulation, hydrologic modeling, dryland expansion estimation
and analysis of dust events (Bart and Lettenmaier 2004; Tang et al., 2008;
Huang et al., 2016; Kang et al., 2016). Accurate estimates of precipitation
and shortwave radiation are therefore essential for studies of climate
change and land-surface processes.
Although conventional station-based measurements can obtain the values of a
measured variable with high accuracy and precision, these measurements can
only represent information at local scales (Maurer et al., 2002; Bogh et al., 2004),
and are unable to adequately depict spatial variations given the limited
number and locations of stations (Duan et al., 2012). In the late 1980s, data
assimilation techniques were proposed as a means of reconstructing
historical forcing data at high resolution (Xie et al., 2011; Zhao et al.,
2010). This innovation brought unprecedented opportunities for researchers.
The resulting forcing data sets, which typically include precipitation,
shortwave radiation, temperature, specific humidity, wind speed, surface
pressure and other meteorological data, are derived by assimilating
numerical weather forecast information, ground observation data and remote
sensing data into an analysis product (Xie et al., 2011; Zhao et al., 2010;
Pan et al., 2010). Many forcing data sets are now available, including the
National Centers for Environmental Prediction and the National Center for
Atmospheric Research reanalysis (NCEP/NCAR; Kalnay et al., 1996), the Global
Land Data Assimilation System (GLDAS; Rodell et al., 2004), the European
Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis
(ERA-Interim; Dee et al., 2011) and the Japanese 55-year Reanalysis (JRA-55;
Kobayashi et al., 2015), among many others. In recent years, Chinese
researchers have made great progress in developing forcing data sets.
Through these efforts, two forcing data sets covering China have been
produced, namely the China Meteorological Forcing Dataset (CMFD), released by
the Institute of Tibetan Plateau Research, Chinese Academy of Sciences (He
and Yang, 2011), and the China Meteorological Administration (CMA) Land Data
Assimilation System (CLDAS; Shi et al., 2014). Separate efforts have produced
new gridded analyses of station-based measurements, including the CN05.1
interpolation of CMA rain gauge data released by the National Climate Center
(Wu and Gao, 2013). These forcing data sets are widely used because they have
high spatial resolution, cover a large area over a long period and are
convenient to obtain and process. For example, CMFD forcing data have been
used to simulate permafrost and seasonally frozen ground conditions on the
Tibetan Plateau (Guo and Wang, 2013), to analyze the impacts of
precipitation on springtime vegetation phenology (Shen et al., 2015), to
model the land-surface water and energy cycles in a mesoscale watershed (Xue
et al., 2013) and to assess climatic and human impacts on surface water
resources in the middle reaches of the Yellow River (Hu et al., 2015). The
CMFD and GLDAS forcing data sets have also been used to improve land-surface
temperature modeling for arid regions in China (Chen et al., 2011), while
GLDAS has been applied to analyze long-term variations in terrestrial water
storage in the Yangtze River basin (Huang et al., 2013) and the
recently released CLDAS has been adopted in a recent drought monitoring
study (Han, 2015). The CN05.1 data set also has been used in many fields,
such as simulating climate change over China (Gao et al., 2013) and studying
shifts in the western Pacific subtropical high (Huang et al., 2015).
However, forcing data have considerable uncertainties, regardless of whether
these data are generated by interpolating ground observations or derived
from reanalysis products (Qian et al., 2006). Biases associated with a
forcing data set can propagate into model results (Wang et al., 2016b), which
may in turn be unrealistic if the forcing data are unreliable (Cosgrove et al., 2003). For example, errors in precipitation and shortwave radiation can
have profound impacts on simulations of soil moisture, runoff and heat
fluxes (Luo et al., 2003). It is therefore necessary to evaluate the accuracy
of forcing data sets so that the relevant biases are fully recognized when
they are applied in studies of land-surface processes (Pan et al., 2014).
Several previous studies have evaluated the forcing data sets examined in
this work. Y. Wang et al. (2016) evaluated the performance of CMFD daily
precipitation estimates over the Qinghai–Tibetan Plateau from 2009 to 2012
and found systematic overestimates through much of the year (more than 255 days). Wang et al. (2014, 2016a) assessed the reliability of GLDAS monthly
precipitation data in China from 1979 to 2012 by visual comparison with
direct observations, and found that both GLDAS-1 and GLDAS-2 precipitation
match the direct observations well. Wang et al. (2011) validated GLDAS-1
daily and monthly precipitation data for a mesoscale basin in northeast
China during March 2003 to March 2006, and concluded that both daily and
monthly precipitation estimates from GLDAS were of high quality. Wang and
Zeng (2012) evaluated six reanalysis products (MERRA, NCEP–NCAR, CFSR,
ERA-40, ERA-Interim and GLDAS-1) against in situ measurements from 63
weather stations on the Tibetan Plateau, and found that GLDAS provided the
best overall performance with respect to both daily and monthly
precipitation.
Although the quality of GLDAS data has been validated and confirmed by
previous studies, these data are not bias-free. In particular, the
credibility of GLDAS over continental China in recent years has yet to be
assessed. The CN05.1, CMFD and CLDAS data sets have been developed and
maintained by Chinese scientists and are supposed to be accurate and
reliable because they are more strictly constrained by surface observations.
However, no comprehensive evaluation of these forcing data sets has yet been
conducted over major land areas of China (including mainland China and the two biggest islands off the coast, Hainan and Taiwan). In this study, we present an intercomparison
of precipitation and shortwave radiation products from CN05.1, CMFD, CLDAS
and GLDAS, along with an evaluation against available in situ observations.
The results of this intercomparison will assist researchers in selecting
and understanding meteorological forcing data, and will also help to guide
further innovations and improvements in these data sets.
Evaluation of shortwave radiation data
Comparison against ground measurements
We compare the forcing data sets against station observations to evaluate
their ability to reproduce the ground observations (Fig. 8). The CMFD data
set compares well with station observations from CMA (Fig. 8a), while
CLDAS (Fig. 8d) and GLDAS (Fig. 8g) are biased high relative to the
station observations at about 96 % of the validation points. This
conclusion is supported by a Taylor diagram based on these data (Fig. 9),
which shows that CMFD is highly correlated with a similar variance and an
extremely small unbiased RMSE relative to the CMA stations. Based on the
same metrics, CLDAS performs slightly better than GLDAS in reproducing
observed shortwave radiation fluxes. Although these results strongly suggest
that estimates of shortwave radiation from CMFD are more realistic than
those from CLDAS or GLDAS, their significance is called into question by the
cross-dependence between these site observations and the CMFD shortwave
radiation product (Sect. 2.2.2). To account for this problem, we also use
station observations from CERN that are completely independent of all three
forcing data sets. The results of this comparison are similar to those of
the previous comparison. Shortwave radiation products from CMFD (Fig. 8b)
coincide well with the observations, while most shortwave radiation products
from CLDAS (Fig. 8e) and GLDAS (Fig. 8h) generally overestimate the
observed fluxes. Figure 9 likewise indicates that CMFD outperforms CLDAS and
GLDAS with respect to spatiotemporal variability in these areas, while CLDAS
outperforms GLDAS.
As the CMA and CERN observation stations are relatively sparsely distributed
in western China, we also validate the forcing data sets against
observations of shortwave radiation at eight observation stations in the
Heihe River basin and two observation stations in western Tibetan Plateau.
CMFD (Fig. 8c) again provides the closest match with the in situ
observations, while CLDAS (Fig. 8f) and GLDAS (Fig. 8i) again
overestimate the observed fluxes. The Taylor diagram (Fig. 9) indicates
that the three forcing data sets reproduce the spatiotemporal variability at
these locations comparably well, although CLDAS produces a slightly smaller
unbiased RMSE than the other two data sets and GLDAS shows the worst
agreement with the observed values in all three metrics (correlation,
normalized variance and unbiased RMSE). The RMSE and relative bias in the
mean values at the station locations are substantially larger for CLDAS and
GLDAS than for CMFD, but GLDAS produces the largest linear correlation
coefficient between mean values at the station locations. These results are
consistent with those of previous studies. Wang et al. (2011) showed that
GLDAS estimates of shortwave radiation at the Changchun, Shenyang and Yanji
stations in China were consistently too large during 2000–2006, while Qi et al. (2015) reported that GLDAS systematically overestimated surface
shortwave radiation fluxes in the Biliu Basin (in the coastal region of
northern China) from March 2000 to December 2007.
Spatial distribution of shortwave radiation
As discussed in the previous section, the shortwave radiation fluxes
provided by CMFD correspond well with station-based observations. We
therefore use CMFD as a reference data set to evaluate the broader
performance of CLDAS and GLDAS within major land areas of China.
As shown in Fig. 10, the spatial distributions of shortwave radiation
fluxes based on the three forcing data sets have several common
characteristics. Surface shortwave radiation is consistently greater in
western China than in eastern China, with the largest shortwave
radiation fluxes located over the Tibetan Plateau and relatively small
shortwave radiation fluxes in northeastern China. Based on CMFD (Fig. 10a), the regions where surface shortwave radiation exceeds 200 W m-2
are limited to seven provinces: Xinjiang, Inner Mongolia, Qinghai, Tibet,
Sichuan and Yunnan. Except for Tibet and Qinghai, these areas are relatively
small. By contrast, both CLDAS and GLDAS indicate that the areas where
shortwave radiation exceeds 200 W m-2 extend into northern China, and
particularly Hebei and Shandong provinces. In GLDAS, fluxes exceeding 200 W m-2 even appear in southeastern China. Area-mean fluxes based on CLDAS
and GLDAS are likewise larger than those based on CMFD, although the latter
contains more spatial heterogeneity as indicated by larger values of SD and
CV (Table 5).
Spatial distributions of annual-mean shortwave radiation based on
the three forcing data sets over 2008–2014 (unit: W m-2).
![]()
Spatial distributions of differences in mean shortwave radiation
between (a) CLDAS and CMFD and (b) GLDAS and CMFD. The corresponding
histograms are shown in (c) for CLDAS–CMFD and (d) for GLDAS–CMFD.
![]()
Spatial distributions of the differences among the forcing data sets are
shown in Fig. 11. Surface shortwave radiation fluxes based on CLDAS and
GLDAS are much larger than those based on CMFD over most parts of China.
Positive differences between CLDAS and CMFD cover more than 95 % of the
area of major land areas of China, with particularly pronounced estimates in some areas
of Xinjiang Province and the area bounded by 24–44∘ N and 105–120∘ E. Estimates of surface shortwave
radiation from GLDAS are also significantly higher than those from CMFD
except for over the Tibetan Plateau. The statistical metrics listed in Table 5 show similar absolute values for the average difference, RMSE and relative
bias, although the differences between CLDAS and CMFD are slightly less than
those between GLDAS and CMFD for all three metrics. Correlation coefficients
between the three forcing data sets are consistently around 0.9. Overall,
CLDAS and GLDAS are similar, with overestimates relative to CMFD in most
regions of major land areas of China.
Statistical metrics describing the spatial average and variability
of annual mean shortwave radiation during 2008–2014, along with the average
difference, RMSE, bias and correlation coefficient relative to CMFD.
Metrics
CMFD
CLDAS
GLDAS
Mean (W m-2)
178.60
202.26
203.13
SD (W m-2)
31.13
28.82
20.99
CV
0.17
0.14
0.10
Average difference (W m-2)
–
23.72
24.57
RMSE (W m-2)
–
27.55
28.61
Relative bias
–
0.14
0.15
Correlation coefficient
–
0.89
0.92
Temporal variations in shortwave radiation
Figure 12 shows time series of shortwave radiation anomalies averaged over
mainland China. CMFD and CLDAS generally match each other well, particularly
during the middle part of the record, but with different amplitudes of
month-to-month variations. GLDAS indicates the largest fluctuations relative
to the mean annual cycle among these three data sets. The absolute
difference between GLDAS and CMFD exceeds that between CLDAS and CMFD in 52
of 84 months. CLDAS consistently shows positive anomalies after year 2013
and primarily shows negative anomalies before January 2011. These changes
suggest a potential drift relative to the climatological annual cycle during
the analysis period. No such drift is evident in CMFD. Mean shortwave
radiation from GLDAS was also consistently larger than its climatological
annual cycle through most of 2012–2014. Table 6 summarizes the temporal
variations in shortwave radiation based on CLDAS and GLDAS relative to CMFD.
The two data sets perform comparably with respect to CMFD based on these
summary statistics, with CLDAS showing slightly smaller values of RMSE and
bias and GLDAS showing a slightly higher correlation relative to CMFD.
Time series of monthly mean shortwave radiation anomalies
relative to the mean annual cycle from CMFD (blue), CLDAS (orange) and GLDAS
(green; unit: W m-2).
![]()
Spatial distributions of TCV for shortwave radiation products
from the three forcing data sets.
![]()
The spatial pattern of TCV for shortwave radiation of the three forcing data
sets have some common characteristics: the highest TCV appears in northwest
and northeast China while the smallest TCV can be found in southwest China.
For south of 34∘ N, the TCV of CLDAS is lower than 0.25 while
CMFD is higher than 0.25 in the southeast. In addition, the estimation of
TCV in CMFD and GLDAS has an obviously higher value in the vicinity of
Sichuan Province and Chongqing Province than the surrounding areas. The TCV
values of the three data sets are similar to each other north of 34∘ N
and the difference mainly lies in the south of China. The characteristics
mentioned above are shown in Fig. 13.
Statistical metrics for monthly mean shortwave radiation anomalies
among forcing data sets during 2008–2014.
Metrics
CLDAS-CMFD
GLDAS-CMFD
RMSE (W m-2)
5.14
5.79
Relative bias
1.14
1.66
Correlation coefficient
0.50
0.62
Discussion
Although these forcing data sets have many characteristics in common and can
accurately reflect the broad features of precipitation and shortwave
radiation over major land areas of China, our analysis identifies many important
differences. These differences arise from multiple factors, including
different resolutions, different underlying data and different algorithms
for combining those data.
We have compared the spatial distributions of precipitation in the forcing
data sets and evaluated their quality in the middle and lower reaches of the
Yangtze River. CLDAS performs better at both annual and monthly timescales
because it merges observations from more than 30 000 stations, which has
substantial benefits for the quality of the analysis. CMFD is able to
reproduce annual mean values, but is less realistic at monthly scales. The
sharp decrease in the CMFD precipitation analysis after August 2014 is
particularly surprising. Relative to CN05.1 and CLDAS, CMFD used fewer
precipitation observations from a smaller selection of stations, which may
influence the quality of its analysis in 2014. GLDAS is a global data set.
The accuracy of this data set in major land areas of China may thus be limited by a
dearth of assimilated observations in China. Although both CMFD and GLDAS
use remote sensing data as the initial background state, they differ
considerably due to differences in both the satellite data and the station
data they use. CN05.1 was constructed solely from station data based on a
mathematical interpolation. It is therefore reasonable that CN05.1 does not
perform as well as the other forcing data sets in regions where stations are
sparse.
With respect to shortwave radiation, we find that CMFD significantly
outperforms CLDAS and GLDAS. Only about 100 sparsely distributed radiation
stations have been deployed in China since 1961, and the radiation
observations reported by these stations are often unusable due to erroneous
values or missing data (Shi et al., 2008). The observations of surface solar
radiation available for merging into CLDAS and GLDAS are therefore limited.
By contrast, CMFD assimilated a 50-year reconstruction of daily surface
solar radiation at 716 CMA stations (Sect. 2.1.2). Although these data are
based on model outputs, they have been widely validated and show good
performance throughout China. The shortwave radiation products provided by
CMFD are therefore in better agreement with direct observations.
Conclusions
In recent years, an increasing number of forcing data sets have been
developed to support climatological, agricultural and hydrological research.
In this study, we have presented an intercomparison and evaluation of
precipitation and shortwave radiation products from the CN05.1, CMFD, CLDAS
and GLDAS forcing data sets over major land areas of China. The results provide useful
guidance to the users and producers of these data sets.
For precipitation, all four forcing data sets show similar spatial
distributions, with a positive gradient in precipitation from northwestern
China to southeastern China. Precipitation estimates from CLDAS are
systematically smaller than those from the other data sets in most areas of China. The temporal variability of monthly mean precipitation
anomalies is generally consistent among the four forcing data sets, except
for a sudden decrease in CMFD precipitation after August 2014. The spatial
distribution of temporal variability (as represented by TCV) is inversely
related to the spatial distribution of precipitation amount, with larger
variability in dry regions than in wet regions. Variability is also larger
in CLDAS than in the other data sets, as is the area where TCV exceeds 1.0.
We have validated the forcing data sets against independent rain gauge
observations provided by MWR for the middle and lower reaches of the Yangtze
River valley during 2014. Based on this validation, CLDAS provides the best
performance at both annual and monthly timescales, with the lowest RMSE and
highest correlation coefficient among the four forcing data sets. CMFD
provides good estimates of annual mean precipitation at the MWR stations,
but this agreement is considerably reduced at monthly timescales. CN05.1
greatly overestimates precipitation in this region relative to the MWR
observations.
For shortwave radiation, comparisons against ground-based observations show
that both CLDAS and GLDAS substantially overestimate shortwave radiation.
These data also have much higher RMSEs and biases relative to direct
observations, and relatively low correlation coefficients. CLDAS slightly
outperforms GLDAS; however, CMFD significantly outperforms both with respect
to the validation data. All forcing data sets capture the key features of
the spatial distribution of shortwave radiation, with higher values in
western China than eastern China and the largest values centered over the
Tibetan Plateau. The spatial characteristics of CLDAS and GLDAS are
especially similar, with both data sets indicating much larger shortwave
fluxes than CMFD in most parts of major land areas of China. Differences between CLDAS
and CMFD are generally smaller than those between GLDAS and CMFD. Time
series of anomalies in area-mean shortwave radiation averaged over China
show large fluctuations in GLDAS and a gradual drift in CLDAS. Estimates
from CMFD are more stable. Temporal variability in the three forcing data
sets is more similar north of 34∘ N, with larger differences in
the south. All three data sets indicate that shortwave radiation is more
variable over northern China than over southern China.
In summary, none of these data sets clearly outperforms the others with
respect to both precipitation and shortwave radiation. CLDAS, which has the
highest spatial and temporal resolution, appears to provide the most
realistic estimates of precipitation. However, this data set also
considerably overestimates shortwave radiation. CMFD is also available at
high resolution, and its estimates of shortwave radiation data match well
with station-based observations. Its estimates of annual mean precipitation
are also reliable, but this reliability at annual timescales masks
relatively large errors in monthly precipitation. GLDAS estimates of both
precipitation and shortwave radiation over major land areas of China have considerable
room for improvement, as do CN05.1 estimates of precipitation, particularly
in regions where station data are sparse. These products are widely used and
continually being developed and improved. Our results will help researchers
to make more informed decisions when selecting data, contribute to
uncertainty quantification and provide guidelines that can help data
providers to improve these data sets and others like them. Our results also
highlight the large uncertainties that remain in currently available forcing
data, especially in western China, where the density of ground stations is
low. There is a great need to improve the quality of forcing data in this
region.