Surface solar radiation (SSR) is an essential factor in the flow of surface
energy, enabling accurate capturing of long-term climate change and
understanding of the energy balance of Earth's atmosphere system. However, the
long-term trend estimation of SSR is subject to significant uncertainties
due to the temporal inhomogeneity and the uneven spatial distribution of in situ observations. This paper develops an observational integrated and
homogenized global terrestrial (except for Antarctica) station SSR dataset (SSRIHstation) by integrating all available SSR observations,
including the existing homogenized SSR results. The series is then
interpolated in order to obtain a 5∘× 5∘
resolution gridded dataset (SSRIHgrid). On this basis, we further
reconstruct a long-term (1955–2018) global land (except for Antarctica) SSR
anomaly dataset with a 5∘× 2.5∘ resolution
(SSRIH20CR) by training improved partial convolutional neural network
deep-learning methods based on 20th Century Reanalysis version 3 (20CRv3). Based on this, we
analysed the global land- (except for Antarctica) and regional-scale SSR trends
and spatiotemporal variations. The reconstruction results reflect the
distribution of SSR anomalies and have high reliability in filling and
reconstructing the missing values. At the global land (except for
Antarctica) scale, the decreasing trend of the SSRIH20CR (-1.276 ± 0.205 W m-2 per decade) is smaller than the trend of the SSRIHgrid (-1.776 ± 0.230 W m-2 per decade) from 1955 to
1991. The trend of the SSRIH20CR (0.697 ± 0.359 W m-2 per decade)
from 1991 to 2018 is also marginally lower than that of the SSRIHgrid
(0.851 ± 0.410 W m-2 per decade). At the regional scale, the
difference between the SSRIH20CR and SSRIHgrid is more significant
in years and areas with insufficient coverage. Asia, Africa, Europe and
North America cause the global dimming of the SSRIH20CR, while Europe
and North America drive the global brightening of the SSRIH20CR.
Spatial sampling inadequacies have largely contributed to a bias in the
long-term variation of global and regional SSR. This paper's homogenized
gridded dataset and the Artificial Intelligence reconstruction gridded
dataset (Jiao and Li, 2023) are both available at 10.6084/m9.figshare.21625079.v1.
National Natural Science Foundation of China41975105National Key Research and Development Program of China2018YFC15077052017YFC1502301Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung200020 188601Ministero dell'Università e della RicercaFSE – REACT EU, DM 10/08/2021 n. 1062Introduction
Energy flows at the Earth's surface play an essential role in climate change
and human activity and link to physical processes such as global warming,
glacier retreating, the hydrological cycle and the carbon budget (Hoskins and
Valdes, 1990; Peixoto et al., 1992; Trenberth and Fasullo, 2013; Wild,
2012). As a critical factor characterizing surface energy flows, surface
solar radiation (SSR) largely determines the climatic conditions and
ecological environment in which we live. Therefore, a more accurate and
comprehensive analysis of the SSR fluxes will help better understand the
Earth's atmospheric system. In situ observations provide the most accurate baseline
data for measuring SSR. They allow for the first time the detection of
decadal changes in SSR known as “dimming and brightening” (Wild et
al., 2005), especially considering that they cover a longer period
concerning another type of data, e.g. satellite data (Pfeifroth et al., 2018). Even observational data often have uneven
distribution and missing data with respect to the satellite data, especially
in areas with complex orography (Manara et al., 2020).
The sources of in situ SSR observations are mainly collected from the Global Energy
Balance Archive (GEBA) (Wild et al., 2017) and the World Radiation
Data Centre (WRDC) (Tsvetkov et al., 1995). Furthermore, other SSR
station series are obtained from the high-quality Baseline Surface Radiation
Network (BSRN) (Driemel et al., 2018) and the data centres of
individual national hydrometeorological services. However, two issues still
need to be addressed: (1) the inhomogeneity of station data resulting from
station relocations and instrumentation changes severely impacting the climate
change assessment. For the regions with a relatively high density of
stations, like Europe (Manara et al., 2019, 2016;
Sanchez-Lorenzo et al., 2013a, b, 2015), Japan (Ma et al., 2022) and China (Ju et al., 2006;
Wang, 2014; Wang et al., 2015; Wang and Wild, 2016; S. Yang et al., 2018; You
et al., 2013), much previous work has redefined the degree and timing of
dimming and brightening by addressing the inhomogeneity of the SSR data series. For example, in Spain, the average annual homogenized SSR series has
a significant increasing trend (+3.9 W m-2 per decade) during the
1985–2010 period (Sanchez-Lorenzo et al., 2013a). The period of
dimming observed in Italy's homogenized SSR series is not apparent in the
1960s and early 1970s, when the raw series (non-homogenized) are taken into
account (Manara et al., 2016). The direct measurements of SSR show a
level trend from 1961 to 2014 over Japan, while their homogenization series
display a decreasing trend (0.8–1.6 W m-2 per decade) (Ma et al.,
2022). In China, homogenization largely eliminated the dramatic non-climatic
rise of the early 1990s and also reduced the increasing trend from 1990 to
2016 (S. Yang et al., 2018). However, most of the research was still
limited to regional scales. (2) There is the issue of limited spatial sampling of long
observational stations and their uneven distribution, especially over areas
with complex orography. Considerable efforts have been devoted to filling in or interpolating the missing values in climate datasets (“spatial analysis”)
(Collins, 1996; Erxleben et al., 2002; Scudiero et al., 2016). The
traditional spatial interpolation methods commonly used include inverse
distance weighting (Fisher et al., 1993; Shepard, 1968), kriging (Krige, 1951) and thin-plate splines (Bookstein, 1989). Since the 1980s, physical parametric interpolation (Feng and Wang,
2021; Tang et al., 2019) and Bayesian fusion schemes (Aguiar et
al., 2015) based on multi-source observational data have been widely used with the emergence of highly accurate and relatively precise satellite data.
However, the resulting fusion datasets cover too short a period to
investigate their decadal and multi-decadal variations and to study the
underlying causes. The spatial, temporal and spectral coverage of a single
satellite is limited, and multiple satellite data are therefore often used
in tandem with each other; however, such a discontinuity in time and space
can introduce inhomogeneity into a dataset (Evan et al., 2007; Feng and
Wang, 2021; Shao et al., 2022). Reanalysis products are an important
complement containing long-term SSR data and therefore have been widely used in
climate studies (Huang et al., 2018; Jiao et al., 2022; Urraca et al.,
2018; C. Zhou et al., 2018, 2017) due to the dynamically
consistent and spatiotemporally complete atmospheric fields with high
resolution and open access to data. However, existing studies have shown
that reanalysis products generally overestimate multi-year mean SSR values
compared to observations over land (He et al., 2021). With the
continuous development of climate system simulations, model data from the
Coupled Model International Project (CMIP) have become an important resource
for conducting climate change research (Gates et al., 1999; Zhou et al.,
2019). Previous studies have shown that the models used in CMIP6
overestimate the global mean SSR (He et al., 2023; Jiao et al., 2022;
Wild, 2020). The rise of deep-learning and big-data techniques has brought
about an explosion of artificial intelligence (AI). Machine learning is
increasingly being used in spatial interpolation, such as the spatial
reconstruction of surface temperature datasets (Huang et al., 2022; Kadow
et al., 2020) or the spatial and temporal reconstruction of
turbulence resolution (Fukami et al., 2021). Furthermore, it shows high accuracy and low uncertainty in reproducing and predicting SSR
(Leirvik and Yuan, 2021; Tang et al., 2016; L. Yang et al., 2018; Yuan et
al., 2021). However, long-term homogenized SSR datasets with global
terrestrial coverage have yet to be developed, resulting in significant
uncertainties in assessing global SSR variation (Jiao et al., 2022).
Therefore, developing a more homogeneous and comprehensive global long-term
SSR climatic dataset that provides a better benchmark for observational
constraints on the global surface energy balance and budget remains a valuable
and challenging task. This paper first homogenizes and grids the most
extensive collection of available global SSR station observations. Then, the
missing grid boxes and years are spatially interpolated using a convolutional
neural network (CNN) approach to obtain a globally covered land surface SSR
anomaly dataset. Finally, the reconstructed datasets are initially
analysed and evaluated. Thus, the paper is divided into seven main sections.
The data resources are introduced in Sect. 2. Section 3 presents the data
homogenization and the CNN model reconstruction methods. The data
homogenization and verification are shown in Sect. 4. Section 5 gives the
AI reconstruction results. Section 6 is the availability of the datasets.
Conclusions are provided at the end of the paper.
Data
Nine SSR datasets are collected to derive the global SSR variable. In
particular, six datasets contain data from observational stations (Sect. 2.1): two global ground-based measurement datasets (GEBA, WRDC) and four
homogenized products at the regional and country levels (Europe, China, Japan
and Italy). Three of the adopted datasets are reanalysis data (Sect. 2.2.1): fifth-generation European Centre for Medium-Range Weather Forecasts
(ECMWF) reanalysis (ERA5), 20th Century Reanalysis version 3 (20CRv3)
data and the Coupled Model Intercomparison Project Phase 6
(CMIP6) historical simulation output (125). Specifically, the ERA5 data are
used to fill the data over oceans and Antarctica (Sect. 3.2.1), and 20CRv3
data and CMIP6 simulations are used for AI model training (Sect. 5.1) and reconstruction. All are listed in Table 1.
List of information on the various types of data used in this paper.
AbbreviationResolutionTimeReferenceIn situ – rawGEBA (station)Monthly1922–2020Wild et al. (2017)WRDC (station)Monthly1964–2017Tsvetkov et al. (1995)In situ – homoChina (station)Monthly1950–2016S. Yang et al. (2018)Japan (station)Monthly1870–2015Ma et al. (2022)Europe (station)Monthly1922–2012Sanchez-Lorenzo et al. (2015)Italy (station)Monthly1959–2016Manara et al. (2016, 2019)Reanalysis/modelERA5 (grid)Monthly/0.25∘× 0.25∘1950–2020Hersbach et al. (2020)20CRv3 (grid)Monthly/0.7∘× 0.7∘1940–2015Slivinski et al. (2019)CMIP6 (grid)Monthly/–1940–2014Eyring et al. (2016)In situ observational dataGlobal datasets
There are two main sources of raw SSR data (see Table 1): the ETH Zurich
GEBA with monthly data from 2445 globally distributed stations, starting
from 1922 until 2020, and the WRDC dataset with monthly globally distributed
data from 1136 stations since 1964. The first one is available for download
at https://geba.ethz.ch (last access: 2 July 2022). The second
one published the first SSR radiation balance data in 1965, and its
publication has been issued four times a year since 1993 and is available
for download at http://wrdc.mgo.rssi.ru/ (last access: July
2021).
National (regional) homogenized station datasets(1) Chinese homogenized SSR dataset
The China Meteorological Radiation Fundamental Elements Monthly Value Data
Set was downloaded from http://www.nmic.cn (last access: September 2022). The homogenized
SSR dataset in China is released by the National Meteorological Information
Centre (NMIC) of the China Meteorological Administration (CMA) (S. Yang et a., 2018).
The data are available for the period between January 1950 and December 2014, and the
follow-up data are extended with raw observations from the NMIC. They used the sunshine duration (SSD) data from nearby stations to construct an arguably
better reference to identify inhomogeneities in the SSR data. Then, a
combined metadata and maximum-penalty t-test (PMT) method was used to
detect the change points. Finally, they were adjusted by a quantile-matching
(QM) algorithm (Wang and Feng, 2013). The final homogenized SSR station
dataset was converted to gridded data using the first difference method (FDM,
Peterson et al., 1998) and is available for download at
http://www.nmic.cn (last access: September 2022).
(2) Japanese homogenized SSR dataset
Ma et al. (2022) released a Japanese SSR homogenized dataset
in 2022 spanning the period between 1870 and 2015. First, they homogenized
SSD based on PMF (penalized maximal F test) and QM algorithms. They then
used the homogenized SSD from the previous step as a reference series,
combined with metadata and PMT, to detect change points. Finally, they
adjusted the change points by the QM algorithm. For more details on data
descriptions, the adopted methodology and downloading data, see
https://data.tpdc.ac.cn/en/data/45d73756-3f5a-4d27-82a4-952e268c20e8/
(last access: March 2022).
(3) European homogenized SSR data
A homogenized dataset of European SSR stations was developed by
Sanchez-Lorenzo et al. (2015)
and is currently available for full public download at 10.1002/2015JD023321.
They selected the 56 longest central European SSR series available in the GEBA
dataset with data for the period between 1922 and 2012. They adjusted them to ensure temporal homogeneity, homogenizing the data with the standard normal homogeneity test (Alexandersson, 1986) and the Craddock test (Craddock, 1979).
(4) Italian homogenized SSR dataset
The Italian homogenized SSR datasets are those published by Manara et
al. (2019, 2016). As candidate stations to use as reference
series, they selected the 10 series located in the same area of the series to be tested, and that series correlates well with the test one. In particular, they tested the change points with the Craddock test
(Manara et al., 2017), and when a break is identified by more than one reference series, the preceding portion of the series is corrected, leaving the most recent portion unchanged. In this way, the SSR stations
were homogenized, and then the missing values were interpolated.
Other datasetsReanalysis
ERA5 can be used to fill in SSR data from the oceans and Antarctica and
carry out the global reconstruction, taking into account its high spatial
resolution and the reliable performance of SSR (Jiao et al., 2022; Liang
et al., 2022). After the reconstruction, we removed the data for the ocean
reanalysis and maintained the data only in the land area (except for
Antarctica). In addition, two SSR data products (20CRv3, CMIP6) are used to
train AI models. These are the following.
ERA5 (space-filling data). ERA5 is the fifth generation of the European
Centre for Medium-Range Forecasts reanalysis product, which currently
publishes data from 1950 to the present (Hersbach et al., 2020). In
addition, ERA5 has an hourly output and an uncertainty estimate from the
ensemble. The data are based on the Integrated Forecasting Model Cy41r2 run
in 2016, which contains a 4D-Var assimilation scheme. In ERA5, SSR is
obtained from a rapid radiation transfer model (RRTM) (Mlawer et al., 1997). The present study utilizes monthly SSR data for the period
1955–2018 from ERA5 with a resolution of 0.25∘× 0.25∘. They can be downloaded at
https://cds.climate.copernicus.eu (last access: July 2022).
20CRv3 (data for AI model training). The 20CR project is an effort led by the NOAA's Physical Sciences Laboratory and CIRES at the University of Colorado,
supported by the Department of Energy, to produce reanalysis datasets
spanning the entire 20th century and much of the 19th century
(Slivinski et al., 2019). 20CR provides a comprehensive global
atmospheric circulation dataset from 1850 to 2015. Its chief motivation is
to provide an observational validation dataset, with quantified
uncertainties, for assessing climate model simulations of the 20th century.
20CR uses an ensemble filter data assimilation method which directly
estimates the most likely state of the global atmosphere every 3 h and estimates the uncertainty in that analysis. The most recent version of
this reanalysis, 20CRv3, provides 8-times daily estimates of global
tropospheric variability across 75 km grids, spanning 1836 to 2015 (with an
experimental extension from 1806 to 1835). The present study uses monthly
SSR data of 20CRv3 (NOAA/CIRES/DOE 20CR, 80 members) from 1955 to 2015. We
selected all 80 members of the 20CR as input (1 for evaluation and to test
reconstruction, the other 79 for training the CNN model). The SSR of 20CRv3
has a spatial resolution of 0.7∘× 0.7∘. The download is available at https://portal.nersc.gov/archive/home/projects/incite11/ (last
access: May 2022).
CMIP6 model output
CMIP6 model output (data for AI model training). The Coupled Model
Intercomparison Project, driven by the World Climate Research Program, is
now in its sixth phase. Specifically, CMIP6 is considered the current state-of-the-art way of producing future climate simulations, including predicting
future SSR based on different climate scenarios (W. Zhou et al., 2018).
It provides an important resource for studying current and future climate
change (Eyring et al., 2016). The historical simulations of CMIP6 are
designed to reproduce observed climate and climate change constrained by
radiative forcing. CMIP6 historical simulation spans between 1850 and 2014. In
this study, we selected 125 members out of a total of 507 members from
several CMIP6 large-ensemble models (with more than 10 realizations and runs)
with high correlation coefficients with observations as input to train and
validate the CNN model (1 for evaluation and to test reconstruction, the
other 124 for training the CNN model). We selected the monthly downward
shortwave radiation from 1955 to 2014 (see Table S1 in the Supplement). The data can be downloaded at https://esgf-node.llnl.gov/search/cmip6 (last access: July 2022).
MethodsData quality control (QC) and homogenization
The SSR data homogenization method is only applied to the two non-homogenized
in situ observation datasets (GEBA and WRDC). The QC and homogenization flowchart (Fig. 1) is divided into three steps: (1) QC; (2) homogenization; and (3) integration and consolidation.
Flowchart of quality control (QC) (first step), homogenization
(second step) and integration (third step).
QC
The QC of SSR data includes the following steps.
Simple integration is integration of the GEBA (2445) and WRDC (1136)
datasets, removing stations with no data and leaving 2681 stations.
Removing duplicate stations. (a) For stations with similar latitude and
longitude, we consider two stations with totally identical latitude and
longitude to be the same station. (b) For stations less than 10 km apart, we
averaged the duplicate stations in these a and b cases. (c) For special duplicate
stations, we stitched together data of the duplicate stations based on
metadata from the CMA.
Remove stations, years or months for which a climatic analysis cannot be
established: we remove stations with records of less than 10 years and
values more than 3 times (3σ criterion, Olanow and Koller, 1998) the standard deviation of the SSR anomalies.
Candidate stations (487) with a record length greater than 15 years in
the period 1971–2000 are selected. We added stations (715) with more than 10 years of SSR records to increase the number of available stations for a
better homogenization of the candidate stations (Fig. 2).
Spatial distribution of candidate stations (“*”) and added
stations (“+”). The different colour bars represent the length of the
station record in months.
Station series homogenization
This paper uses the RHtestV4 software package to test and adjust the SSR
station data for homogeneity (http://etccdi.pacificclimate.org/software.shtmlm last access: July 2021) (Wang and Feng, 2013).
The package is based on the empirical penalty functions PMF (Wang,
2008a) and PMT (Wang, 2008b; Wang et al., 2007) for the
homogenization test. It takes into account the lag-1 autocorrelation of the
time series. It embeds a multiple linear regression algorithm to
significantly reduce the problem of an unbalanced distribution of
pseudo-identification rates and test efficacy. Also, RHtestV4 uses the QM
algorithm (Vincent et al., 2012; Wang et al., 2010) and mean adjustments to adjust the identified change points.
The specific steps are as follows.
Building the reference series
We processed the data from all station series (715) into the annual
first difference (FD) series ei (Eq. 1) (Peterson et al., 1998).
We calculated the correlation of the annual FD series between the series
from the potential reference pool and the candidate stations.
We calculated the distance between the potential reference pool stations
and candidate stations.
We selected potential stations according to the correlation coefficient
(CC >= 0.6) between the series from potential reference pool
and candidate stations. The potential stations also satisfy the limits
in distances (<= 500 km) between the potential pool stations and
candidate stations.
We obtain the reference FD series (Re) based on the m potential reference
series (Pei) and the CCs (ci) between the
potential reference series (Pei) and candidate
station series (Eq. 2).
The synthesized reference FD series (Re) (Eq. 2), plus the average of
all potential reference series (R‾), yields the final annual reference series (R) (Eq. 3).
1Pei=xi-xi+1i=1,2,…,n-12Re=∑i=1mPei⋅ci2∑i=1mci23R=Re+R‾xi is the raw observational station SSR in the year i,
Re is the final reference series, Pei is the potential reference series, and ci is the CC between the potential reference series and the candidate station series.
Testing and adjusting the candidate series
The homogenization test algorithm used in this paper is the PMT. This method
is a reference series-dependent test for a normalized candidate series. It
assumes that the linear trend of the time series is zero and uses the degree
of mean deviation at different points in the series to find change points.
Furthermore, it eliminates the effect of different sample lengths on the
test results. At the same time, the method introduces an empirical penalty
factor, which effectively improves detection. We used the PMT to test the
homogeneity of the candidate series based on the reference series
established in (1). We then adjusted the statistically
significant (p>0.05) change points obtained using the mean
adjustment method (p>0.05). We homogenized the monthly series for 66 stations (see Fig. S1 in the Supplement).
Integration and consolidation
As can be seen from Fig. 1, the candidate stations (487) are relatively
sparse. To better adapt deep-learning methods for the dataset reconstruction
later, we adjusted, added and integrated station series based on the results
of homogenized data from other scholars. (1) We added stations with more than
10-year overall (1955–2018) records but no more than 15 years during the 1971–2000
period and removed those stations that were clearly inhomogeneous (25) and
some years of station (3). (2) We subsequently integrated monthly SSR series for 116 stations based on the results of homogenization from other
scholars: China (56), Japan (8), Europe (2) and Italy (50). After the above
steps, we ended up with a homogenized dataset containing 944 stations (Fig. 3). The details of the processing and classification are shown in Table S2
(see the Supplement).
Spatial distribution of stations after homogenization (unit
months). Different colours represent the length of the station records in months.
CNN model reconstruction methods
The CNN deep-learning model network architecture uses a U-shaped structure
similar to U-Net (Ronneberger et al., 2015). The advantage of using this model is that (1) both high- and low-frequency information of the picture can be retained, and when reconstructing the SSR data, not only will the grid point information close to the missing measurement point be considered, but information from more distant locations will be too (which may be remotely correlated with that missing measurement point). (2) This makes the
model convergence faster and more economical in terms of computational
resources. The upper part of the U-shaped structure, which has no downsamples or a low number of downsamples, represents the high-frequency information of the graph. These sections contain much of the detail in the
graph, and the relationships between similar grid points are conveyed by this
section. The lower half of the U-shaped structure is downsampled more often
and represents the lower-frequency information of the graph. The global
radiation of a wide range of undulations is transmitted by it, and then the
information at the various levels of the U-shaped structure is connected and
transmitted through the skip connection, allowing the whole network to
remember all the information of the picture very well. The model uses
nearest-neighbour upsampling in the decoding phase, and the skip links will
concatenate two feature maps and two masks as the feature and mask inputs
for the next part of the convolution layer. The input to the last part of
the convolution layer will contain the original input image concatenated
with the holes and the original mask, allowing the model to replicate the
gap-free pixels. The complex and variable nature of the sea–land boundary
then has a significant impact on the reconstruction when we reconstruct the
global land SSR data. Therefore, we use partial convolution at the image
boundaries with a suitable image padding, ensuring that the padding content
at the image boundaries is not affected by values outside the image. The
deep-learning models' convolutional layers and loss functions are described in the Supplement.
We further reconstruct a long-term (1955–2018) global SSR anomaly dataset
(SSRIH20CR) by using improved partial CNN deep-learning methods based
on a “perfect” dataset. A CNN consists of three parts: a convolutional layer
to reduce the number of weights by extracting local features, a pooling
layer to reduce peacekeeping and prevent overfitting, and a fully connected
layer to output the desired result. In this paper, a modified CNN is used to model the reconstruction of the SSR data, with the convolutional
layer replaced by a partial convolution method and mask update. This method
is the latest in image restoration effects and can restore irregular holes,
an advantage over other image restoration methods that can only restore
rectangular holes. Therefore, this paper uses the modified CNN model
(Kadow et al., 2020) to recover the missing part of the global
terrestrial SSR (except Antarctica). The specific reconstruction steps and
processes are as in Fig. 4.
Flowchart of AI reconstruction.
Data pre-processing
The homogenized station data are converted to grid box anomalies using the
climate anomalies method (CAM) (Jones et al., 2001). CAM is a commonly
used method for converting station anomaly data to gridded data. We divide
all global areas into a 5∘× 5∘ grid, after
which we calculate the SSR anomalies (relative to 1923–2020) within the grid
box by averaging the anomalies of all the stations (at least one station in it).
If more than one site exists in the same grid box, the record length of this grid box is the total length of all sites in that grid box.
Finally, we removed the values that were more than 3 times the standard
deviation of the SSR anomaly time series after gridding. SSRs are all
processed as daily average anomalies, i.e. monthly anomalies divided by 30
(each month is approximated as 30 d). We multiply all the values by 30 again when the reconstruction is complete. The global land (except for
Antarctica) distribution and coverage of SSRs after gridding are shown in
Fig. 5a, b.
(a) Spatial distribution of 5∘× 5∘ grid boxes
(SSRIHgrid) obtained by interpolating the homogenized global land (except
for Antarctica) SSR series. The different colours represent the length (the
sum of all the records) of the station record in unit years. (b) Grid box
coverage for the homogenized global land (except for Antarctica) SSR
(SSRIHgrid) except for Antarctica.
As seen in Fig. 5a, the SSR is spatially sparsely distributed across South
America and Africa. As shown in Fig. 5b, SSR coverage increased yearly
from 1950 until the mid-1970s, when it slowly decreased. In 2013, the
coverage rate decreased sharply due to untimely data submission. Considering
the SSR coverage above, we only kept the years (1955–2018) with data
coverage of more than 8 % of global land (except for Antarctica) areas.
Comparisons show that ERA5 has a high spatial resolution and relatively
reliable performance in the temporal variations and long-term trends
(Liang et al., 2022; Jiao et al., 2022). To obtain a higher data
coverage and ensure that the AI model runs well, we used the ERA5 to fill
the SSR of the homogenized global gridded SSR in the Antarctic and ocean areas.
However, if we use the SSR of ERA5 to directly fill the SSR of the homogenized
global gridded SSR (SSRIHgrid) in the Antarctic and in the ocean areas,
then the relatively weaker ocean SSR variations (variabilities, decadal
changes, trends) from ERA5 will inevitably introduce certain
systematic biases in land SSR reconstruction due to the SSRs having the lower
coverage on the land. Therefore, we designed an algorithm to avoid excessive
diffusion of SSR system bias in terrestrial areas: we first calculated the
ratios γi(i=1,2,3,…,n) between the SSR from ERA5 and from SSRIHgrid
on the land in all n years. For a single grid box, the γi have
small changes and are regarded as a constant γmedian (Eq. 4),
and the γmedian vary by latitude and longitude in both the marine
and land areas. We then extrapolated the γmedian for all the
grid boxes along the land and sea boundaries. If there is no observation
there, then the adjacent ocean ERA5 SSR is used to take its place after it
is adjusted according to the differences between the SSR variations
(represented by the linear trends) for the different underlying surfaces (Eq. 5):
4γmedian=medianOBSi_landERA5i_land,5OBSi_O&Lland=ERA5i_O&Locean⋅γmedian⋅TOTLi=1,2,3,n.
γmedian is the median value of the ratios of observation (OBS) and ERA5 land SSR series. OBSi_land is the land SSR for the year i from the SSRIHgrid in a single grid. ERA5i_land is the land SSR for the year i from ERA5 in a single
grid.
OBSi_O&L(land) is the land SSR along the sea–land boundary (land)
for the year i from the SSRIHgrid.
ERA5i_O&L(ocean) is the ocean SSR along the sea–land boundary for the year i from ERA5.
TO is the trend of the ERA5 SSR in ocean areas in all n years, and
TL is the trend of the ERA5 SSR in areas in all n years.
AI model reconstruction
We use a server (configured with processor Intel (R) Core (TM) i7-8700 CPU @
3.20 GHz 3.19 GHz, RAM 32G, 64-bit OS, GPU model 516.94, NVIDIA GeForce 1080T
version, Python 3.9.12 64-bit, CUDA 10.1) for AI model training. The
specific training steps are as follows.
A total of 768 missing-value masks (monthly masks between 1955 and 2018)
were prepared for training and validation using “1” for existing and “0” for
missing values.
The 20CRv3–CMIP6 training set (monthly values between 1955 and 2015/2014) and missing-value masks are fed into the 20CR-AI and CMIP6-AI model for
training.
We perform 1 500 000 training sessions with an interval of 10 000
sessions for the training output model.
Afterwards, the two AI models are validated against the root mean squared
error (RMSE) and CCs of the reconstructed SSRs (SSR20CR/SSRCMIP6).
The validation set SSRs, and the optimal number of training cycles is
1 100 000 (see Figs. S2, S3 and S4 in the Supplement). The initial
hyperparameters of the model are set as follows: a learning rate of 2×10-4 and
learning fine-tuning of 5×10-5. First, we set the batch size to 16 in the first
500 000 iterations and fine-tune it to 18 in the last 10 000 000 iterations,
for a total of 1 500 000 iterations, to suppress the overfitting phenomenon
generated during the training process. We validate the model every 10 000
times and with early stopping if the validation shows a decreasing trend, and the
final number of training times used is 1 100 000. Second, L2 (ridge regression) regularization is also added to regulate the loss function (see Eq. S9 in the Supplement).
The training result models generated by the different AI models are obtained
separately for the different training sets. The model is first used to
reconstruct a reanalysis validation set with the same missing-value mask as
the original observation dataset. This is followed by a validation of the
reconstruction against the original reanalysis dataset (calculation of CC
and RMSE) to understand the discrepancies in the model reconstruction.
Data homogenization and verification
We homogenized the original monthly station or gridded SSR time series
(SSRIHstation or SSRIHgrid) using the method in Sect. 3.1.2. We
selected six continental regions, excluding Antarctica and the Arctic, from
the eight continents of the world defined by Xu et al. (2018) (Asia, Africa, South America, Europe, North America, Australia,
Antarctica and the Arctic). The decreasing trend of the SSRIHgrid is
consistent with the original gridded SSR series (SSRIgrid) during
1955–1991, while the increasing trend during 1991–2018 is weaker. At the
regional scale, the SSRIHgrid has a generally similar variation to the
SSRIgrid, and the SSRIHgrid is usually more representative of climate
change than the SSRIgrid at individual stations.
Figure S5 (see the Supplement) illustrates the long-term variations of global
(Fig. S5a in the Supplement) and continental land SSR (Fig. S5b in the Supplement)
from the SSRIgrid and SSRIHgrid (except for Antarctica) during
1955–2018. The most prominent change revolves around the adjustment around
1992: the SSR anomalies were systematically adjusted upward from 1987 to
1992, while the SSR anomalies were systematically adjusted downward from
1993 onwards. Thus, there is a significant decreasing trend for both the global
land SSRIgrid (-1.995 ± 0.251 W m-2 per decade) and global
land SSRIHgrid (-1.776 ± 0.230 W m-2 per decade) (except for
Antarctica) from 1955 to 1991, while the increasing trend of the global land
SSRIHgrid from 1991 to 2018 is 0.851 ± 0.410 W m-2 per
decade, slightly smaller than the increasing trend of the SSRIgrid
(0.999 ± 0.504 W m-2 per decade). It is worth noting that 1992
happened to be the second year of the eruption of Mount Pinatubo, and the
homogenized SSR data integrated in this paper may be affected by this event.
However, overall, the homogenization also has limited effects on the global SSR
variations from Fig. S5 (see the Supplement), which is consistent with the
influence of data homogenization on a wide range of surface air temperatures
(Brohan et al., 2006; Xu et al., 2013).
On the regional scale, the differences between the SSRIHgrid and
SSRIgrid are more pronounced in Asia and Europe (see Fig. S5b in the
Supplement). Asia's homogenized SSR shows that the regional average SSR has been
declining significantly over the period 1958–1990; this dimming trend mostly
diminished over the period 1991–2005 and was replaced by a brightening trend
in the recent decade. The SSRIHgrid in Asia is higher than the
SSRIgrid from 1985 to 1990 and lower than the SSRIgrid from 2012
to 2015. The SSRIHgrid shows a more moderate short-term increase in
Europe from 1960 to 1980. Note also that the Australian raw data prior to
1988 were artificially detrended because at the time the Australia Weather
Service was afraid that the instruments would drift. Therefore, they
detrended them and unfortunately did not store the raw data, and the SSR
evolution in Australia is artificial with no trend (Wild et al.,
2005). In addition, the SSRIstation and SSRIHstation comparisons
for all 66 stations are shown in Fig. S1 (see the Supplement).
AI reconstruction and comparisonTraining of the AI model
We produce two (20CRv3 and CMIP6) separate training and validation sets: we
select the first member data of the reanalysis data and the model data,
respectively, as the validation set, and the remaining 79 (124) ensemble
members as the training sets, where each ensemble member included 732 (720)
months of SSR data. Each validation set included 732 (720) samples, while
the training sets contained 57 828 (89 280) ensemble members. All the above
data, including the in situ observations, are then resampled to monthly anomalies
of 5∘× 2.5∘. We reconstruct the SSR of 20CRv3 and CMIP6 with missing values based on the 20CRv3 and CMIP6 datasets using the method in Sect. 3.2 and obtain two
reconstructions, SSR20CR and SSRCMIP6, respectively. The SSR of
20CRv3 and CMIP6 with missing values uses the SSRIHgrid mask between 1955
and 2015/2014. We compare the global land (except for Antarctica) or regional
annual anomaly variation of SSR20CR or SSRCMIP6. The results show
that SSR20CR is significantly more consistent with the validation set
than SSRCMIP6.
Reconstruction capabilities of the AI model.
Figure 6a shows that the RMSE and CC of the SSR20CR (0.247 W m-2/0.970 W m-2) are smaller or larger than those of the SSRCMIP6 (0.518 W m-2/0.937 W m-2) with the original 20CR and CMIP6 dataset. The 20CR-AI model has a better reconstruction ability for SSR on the global land (except for Antarctica) scale. The RMSEs of the SSR20CR (SSRCMIP6)
are 1.460 (2.413) W m-2, 1.109 (1.829) W m-2, 2.219 (2.596) W m-2 and 1.286 (2.235) W m-2 in North America, Europe, Asia and the Northern Hemisphere, whereas these values are 1.116 (1.766) W m-2, 0.622
(1.602) W m-2, 1.877 (1.839) W m-2 and 0.772 (1.679) W m-2 in
South America, Africa, Australia and the Southern Hemisphere, respectively, concerning the
original 20CR and CMIP6 dataset. In other words, the RMSEs of the
SSR20CR are smaller than those of the SSRCMIP6 for the original 20CR and CMIP6 dataset except for Australia. In addition, the CCs of the
SSR20CR (SSRCMIP6) are 0.958 (0.830) W m-2, 0.958 (0.987) W m-2, 0.886 (0.669) W m-2, 0.930 (0.965) W m-2, 0.938 (0.930) W m-2, 0.943 (0.916) W m-2, 0.936 (0.875) W m-2 and 0.903
(0.822) W m-2 in North America, Europe, Asia, the Northern Hemisphere, South
America, Africa, Australia and the Southern Hemisphere, respectively, with respect to the
original 20CR and CMIP6 dataset. That is, the CCs of the
SSR20CR are larger than those of the SSRCMIP6 with the original 20CR and CMIP6 dataset except for Europe.
Based on the above comparison, the higher uncertainty for CMIP6 model output
possibly biases the CMIP6-AI method. Thus, the accuracy of the SSR20CR
is higher than that of the SSRCMIP6 at both global land (except for
Antarctica) and regional scales. Therefore, we choose the reconstruction
results of the 20CR-AI model as the final AI reconstruction dataset, and
subsequent analysis in the following sections is only based on this dataset.
Comparison of the spatial and temporal variation characteristics
We investigate the long-term trends and spatial and temporal variation of
the SSRIH20CR, compare the differences between the SSRIH20CR and
SSRIHgrid, and suggest that the area and magnitude of the high and low
centres of the SSRIH20CR are the same as those of the SSRIHgrid. The results of the global land (except for Antarctica) reconstruction are
consistent with dimming and brightening; the global dimming is primarily
dominated by decreasing trends in Asia, Europe, Africa and North America,
whereas Europe and North America are contributors to the increasing trends.
Figure 7 shows the spatial distribution of the SSRIHgrid and
SSRIH20CR for the 3 months (July 1960, July 1980 and July 2000).
Figure S6 (see the Supplement) displays the spatial distribution of the annual
SSRIHgrid and SSRIH20CR from 1955 to 2018. Figure 7 also shows that the area and the magnitude of the high and low centres in the SSRIH20CR are
the same as in the SSRIHgrid. The SSRIH20CR has mainly positive
anomalies in Africa and the Eurasian continent in July 1960, especially in
India and the Middle East. Afterwards, India showed a continuous and steady
decline in SSR. This confirms the well-known phenomenon of global dimming
over India (Wild et al., 2009; Soni et al., 2016, 2012;
Padma Kumari et al., 2007; Kambezidis et al., 2012). In Australia, the
SSRIH20CR is dominated by negative anomalies in July 1980 and positive
anomalies in July 1960 and July 2000. In Greenland, the SSRIH20CR shows
a large positive anomaly during 3 months. In northern Russia, there is a
high value in July 2000. The reconstruction can better reflect the anomaly
distribution of observation information, and the grid boxes with the missing
values are infilled and reconstructed, which has high reliability.
Spatial distribution of the SSRIHgrid(a1–3) and
SSRIH20CR(b1–3) in typical months: 1–3 are July 1960, July 1980, and
July 2000, respectively.
Global land (except for Antarctica) time series of the annual
anomaly variations' SSR (relative to 1971–2000) before and after reconstruction.
Figure 8 illustrates the global land (except for Antarctica) annual anomaly
variation and long-term trend of the SSRIH20CR for the periods of
1955–2018, 1955–1991 and 1991–2018. Table S3 in the Supplement demonstrates the
trends of global SSR change evaluation for various data sources on different
scales. Also, we compare the differences between the SSRIH20CR and
SSRIHgrid. The minimum value of the SSRIH20CR occurred in 1991
(-2.411 W m-2). The decreasing trend of the SSRIH20CR from 1955 to
1991 (-1.276 ± 0.205 W m-2 per decade) is slightly lower than that
of the SSRIHgrid (-1.776 ± 0.230 W m-2 per decade). After
that, the SSRIH20CR turns to an increasing trend of 0.697 ± 0.359 W m-2 per decade from 1991 to 2018. This suggests that the difference
between the SSRIH20CR and SSRIHgrid may be caused by the results
observed in limited data coverage (such as in Africa and North America)
(Fig. 9). After homogenization and reconstruction, the trend (-1.276 W m-2 per decade) from 1955 to 1991 corresponds to an overall reduction
of -4.6 W m-2 over the dimming period, while that (0.697 W m-2 per
decade) from 1991 to 2018 corresponds to an overall increase of 2 W m-2
over the brightening period. This is in amazing agreement with the -4 W m-2 for the dimming period and the 2 W m-2 for the brightening
period based on an overall surface energy budget assessment (Wild,
2012; see their Fig. 1). Also, similar conclusions (incomplete coverage of
observational data leads to an underestimation of global warming trends) have
been confirmed in global warming research (Gulev et al., 2021; Li et al.,
2021).
Same as Fig. 8 but for regional annual anomaly variations.
Figure 9 demonstrates the long-term annual anomaly variations of the
SSRIH20CR in different regions and its results compared to the
SSRIHgrid. Table S4 in the Supplement shows the evaluation in continental and
hemispheric SSRIH20CR change trends on different scales. The
SSRIH20CR shows a similar annual anomaly variation to the global land
(except for Antarctica) average trend in North America and Asia, reaches a
minimum in the late 1970s or early 1990s, and follows a moderate reversal.
In Europe, the SSRIH20CR shows a decrease (-2.180 ± 1.866 W m-2 per decade) between 1963 and 1978 before turning to brightening
(1.081 ± 0.312 W m-2 per decade). In South America and Australia
(Southern Hemisphere), the SSRIH20CR shows no significant variation. In
Africa, the SSRIH20CR has a dimming trend (-1.506 ± 0.496 W m2 per decade) from the 1950s to the 1990s, after which it remains
levelled off (0.340 ± 0.998 W m-2 per decade). The SSRIH20CR
shows a decreasing trend (-1.457 ± 0.246 W m-2 per decade) until
the 1990s in the Northern Hemisphere and a brightening (0.887 ± 0.415 W m-2 per decade) afterwards. The annual average anomaly variations in
regions and globally show that Asia, Africa, Europe and North America are
the four contributors to the global dimming, while Europe and North America
are two major contributors to the brightening. This is in general agreement with the results obtained by previous machine learning (Yuan
et al., 2021). In addition, the discrepancy between the SSRIH20CR and
SSRIHgrid is more significant in low-coverage areas (right) than in
high-coverage regions (left). It is particularly pronounced before 1980 and
in South America. This suggests that the limited surface observations are
not representative of the continental variation in SSR.
The sources of error in the observational dataset can be divided into three
types. (1) Station errors are the uncertainties of individual station anomalies, including measurement errors (which are not the focus of the considerations
in this paper) and errors due to homogenization. The errors due to
homogenization adjustment are always approximately normally distributed
(Jones et al., 2008; see their Fig. 5 and Fig. S9 in the Supplement) and therefore have limited impacts on the global average SSR change
(Fig. S5a, b). (2) Sampling errors are the uncertainties in a grid box mean
caused by estimating the mean from a small number of point values
(Jones et al., 1997). (3) Bias error generally refers to systematic errors such as urbanization, which has not been discussed here. However, even the sum of the above errors is much smaller
than the errors due to limited data coverage (Li et al., 2010; see their Fig. 5). So, the focus of this study is to eliminate this kind of
error through the CNN reconstruction.
Data availability
Both the SSRIHgrid (the homogenized monthly gridded SSR data over
1923–2020) and the SSRIH20CR (the monthly 20CR-AI model reconstructed
SSR data for 1955–2018) are currently publicly available on the figshare
website at 10.6084/m9.figshare.21625079.v1 (Jiao and Li, 2023). These datasets are also available at http://www.gwpu.net (last access: May 2023)
for free.
Conclusions
In this study, we integrate global station observations based on the raw
observational SSRs from GEBA and WRDC, combined with existing homogenized
SSR datasets from other scholars. Also, we homogenize the globally
distributed station data using the RHtestV4 software package. An improved
CNN deep-learning algorithm is subsequently used to reconstruct the SSR
anomalies. Thus, a reconstructed SSR anomaly dataset, SSRIH20CR, is
obtained based on training sets (20CRv3) for the years 1955–2018, with a
resolution of 5∘× 2.5∘. The main results are
as follows.
The first integrated and homogenized global SSR monthly dataset is
developed, which contains 944 stations in total and covers the longest
periods (from the 1920s to recent years). A 5∘× 5∘ grid box version of the monthly SSR anomaly dataset is derived.
This paper develops 5∘× 2.5∘ full-coverage
monthly land (except for Antarctica) SSR anomaly reconstructed datasets
based on the above observations, using 20CRv3 to train the AI model.
Comparative validations and evaluations show that the SSRIH20CR provides a
reliable benchmark for global SSR variations.
On average, the global annual SSR variations based on the SSRIHgrid
are not significantly different, except that the increasing (brightening)
trend after 1991 is a little smaller for the latter. The short-term
brightening SSR in Europe from the 1970s to the 1980s disappears at the
regional scale. At the same time, the brightening SSR after the 1990s in
Asia slowed or was delayed.
The supplement related to this article is available online at: https://doi.org/10.5194/essd-15-4519-2023-supplement.
Author contributions
BJ: software, data curation, writing – original draft preparation,
visualization, investigation. YS: software, data curation.
QL: methodology, supervision, conceptualization, validation, writing – review and editing.
VM: provision of the homogenized Italian dataset, writing – review and editing.
MW: writing – review and editing.
Competing interests
At least one of the (co-)authors is a member of the editorial board of
Earth System Science Data. The peer-review process was guided by an independent editor, and the authors also have no other competing interests to declare.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
This research has been supported by the Natural Science Foundation of China (grant no. 41975105) and the National Key R&D Program of China (grant nos. 2018YFC1507705 and
2017YFC1502301). The Global Energy Balance Archive (GEBA) is co-funded by the Federal Office of Meteorology and Climatology
(MeteoSwiss) within the framework of GCOS Switzerland. Global dimming and brightening research at ETH Zurich is supported by the Swiss National Science Foundation (grant no. 200020 188601). Veronica Manara was supported by the Ministero dell'Università e della Ricerca of Italy (grant FSE – REACT EU, DM 10/08/2021 n.
1062).
Financial support
This study is supported by the Natural Science Foundation of China (grant no.
41975105) and the National Key R&D Program of China (grant nos.
2018YFC1507705 and 2017YFC1502301). The Global Energy Balance Archive (GEBA) is
co-funded by the Federal Office of Meteorology and Climatology (MeteoSwiss)
within the framework of GCOS Switzerland. Global dimming and brightening
research at ETH Zurich is supported by the Swiss National Science Foundation (grant no. 200020 188601). Veronica Manara was supported by the
Ministero dell'Università e della Ricerca of Italy (grant FSE – REACT EU, DM 10/08/2021 n. 1062).
Review statement
This paper was edited by Jing Wei and reviewed by two anonymous referees.
ReferencesAguiar, L. M., Pereira, B., David, M., Díaz, F., and Lauret, P.: Use
of satellite data to improve solar radiation forecasting with Bayesian
Artificial Neural Networks, Sol. Energy, 122, 1309–1324,
10.1016/j.solener.2015.10.041, 2015.Alexandersson, H.: A homogeneity test applied to precipitation data, J.
Climatol., 6, 661–675, 10.1002/joc.3370060607, 1986.Bookstein, F. L.: Principal warps: Thin-plate splines and the decomposition
of deformations, IEEE T. Pattern Anal., 11, 567–585, 10.1109/34.24792, 1989.Brohan, P., Kennedy, J. J., Harris, I., Tett, S. F. B., and Jones, P. D.:
Uncertainty estimates in regional and global observed temperature changes: A
new data set from 1850, J. Geophys. Res.-Atmos., 111, D12106m
10.1029/2005JD006548, 2006.Collins, F. C.: A comparison of spatial interpolation techniques in
temperature estimation, The 3rd International Conference/Workshop on
Integrating GIS and Environmental Modeling, Santa Barbara, Santa Fe, NM;
Santa Barbara, CA, 21–26 January 1996.Craddock, J. M.: Methods of comparing annual rainfall records for climatic
purposes, Weather, 34, 332–346, 10.1002/j.1477-8696.1979.tb03465.x,
1979.Driemel, A., Augustine, J., Behrens, K., Colle, S., Cox, C., Cuevas-Agulló, E., Denn, F. M., Duprat, T., Fukuda, M., Grobe, H., Haeffelin, M., Hodges, G., Hyett, N., Ijima, O., Kallis, A., Knap, W., Kustov, V., Long, C. N., Longenecker, D., Lupi, A., Maturilli, M., Mimouni, M., Ntsangwane, L., Ogihara, H., Olano, X., Olefs, M., Omori, M., Passamani, L., Pereira, E. B., Schmithüsen, H., Schumacher, S., Sieger, R., Tamlyn, J., Vogt, R., Vuilleumier, L., Xia, X., Ohmura, A., and König-Langlo, G.: Baseline Surface Radiation Network (BSRN): structure and data description (1992–2017), Earth Syst. Sci. Data, 10, 1491–1501, 10.5194/essd-10-1491-2018, 2018.Erxleben, J., Elder, K., and Davis, R.: Comparison of spatial interpolation
methods for estimating snow distribution in the Colorado Rocky Mountains,
Hydrol. Process., 16, 3627–3649, 10.1002/hyp.1239, 2002.Evan, A. T., Heidinger, A. K., and Vimont, D. J.: Arguments against a
physical long-term trend in global ISCCP cloud amounts, Geophys. Res.
Lett., 34, L04701m 10.1029/2006gl028083, 2007.Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, 10.5194/gmd-9-1937-2016, 2016.Feng, F. and Wang, K.: Merging high-resolution satellite surface radiation
data with meteorological sunshine duration observations over China from 1983
to 2017, Remote Sens., 13, 602, 10.3390/rs13040602, 2021.Fisher, N. I., Lewis, T., and Embleton, B. J.: Statistical analysis of
spherical data, Cambridge University Press, 10.1017/CBO9780511623059,
1993.Fukami, K., Fukagata, K., and Taira, K.: Machine-learning-based
spatio-temporal super resolution reconstruction of turbulent flows, J.
Fluid Mech., 909, A9-1–A9-14, 10.1017/jfm.2020.948, 2021.Gates, W. L., Boyle, J. S., Covey, C., Dease, C. G., Doutriaux, C. M.,
Drach, R. S., Fiorino, M., Gleckler, P. J., Hnilo, J. J., Marlais, S. M.,
Phillips, T. J., Potter, G. L., Santer, B. D., Sperber, K. R., Taylor, K.
E., and Williams, D. N.: An Overview of the Results of the Atmospheric Model
Intercomparison Project (AMIP I), B. Am. Meteorol.
Soc., 80, 29–55, 10.1175/1520-0477(1999)080<0029:Aootro>2.0.Co;2, 1999.Gulev, S. K., Thorne, P. W., J. Ahn, F. J. D., Domingues, C. M., Gerland,
S., Gong, D., Kaufman, D. S., Nnamchi, H. C., Quaas, J., Rivera, J. A.,
Sathyendranath, S., Smith, S. L., Trewin, B., von Shuckmann, K., and Vose, R.
S.: Changing State of the Climate System. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Masson-Delmotte, V., Zhai, P., Pirani, A., Connors, S. L.,
Péan, C., Berger, S., Caud, N., Chen, Y., Goldfarb, L., Gomis, M. I.,
Huang, M., Leitzell, K., Lonnoy, E., Matthews, J. B. R., Maycock, T. K.,
Waterfield, T., Yelekçi, O., Yu, R., and Zhou, B., Cambridge
University Press, 287–422, 10.1017/9781009157896.004, 2021.He, J., Hong, L., Shao, C., and Tang, W.: Global evaluation of simulated
surface shortwave radiation in CMIP6 models, Atmos. Res., 292, 106896,
10.1016/j.atmosres.2023.106896, 2023.He, Y., Wang, K., and Feng, F.: Improvement of ERA5 over ERA-Interim in
simulating surface incident solar radiation throughout China, J.
Climate, 34, 3853–3867, 2021.Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A.,
Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D.,
Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P.,
Biavati, G., Bidlot, J. R., Bonavita, M., Chiara, G. D., Dahlgren, P., Dee,
D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R. G., Fuentes, M.,
Geer, A. J., Haimberger, L., Healy, S. B., Hogan, R. J., Holm, E. V.,
Janisková, M., Keeley, S. P. E., Laloyaux, P., Lopez, P., Lupu, C.,
Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and
Thepaut, J.-N.: The ERA5 global reanalysis, Q. J. Roy.
Meteor. Soc., 146, 1999–2049, 10.1002/qj.3803, 2020.Hoskins, B. J. and Valdes, P. J.: On the existence of storm-tracks, J.
Atmos. Sci., 47, 1854–1864,
10.1175/1520-0469(1990)047<1854:OTEOST>2.0.CO;2,
1990.Huang, B., Yin, X., Menne, M. J., Vose, R., and Zhang, H.-M.: Improvements
to the Land Surface Air Temperature Reconstruction in NOAAGlobalTemp: An
Artificial Neural Network Approach, Artificial Intelligence for the Earth
Systems, 1 1–35, 10.1175/AIES-D-22-0032.1, 2022.Huang, J., Rikus, L. J., Qin, Y., and Katzfey, J.: Assessing model
performance of daily solar irradiance forecasts over Australia, Sol.
Energy, 176, 615–626, 10.1016/j.solener.2018.10.080, 2018.Jiao, B. and Li, Q.: Global Integrated and Homogenized Solar surface
Radiation Datasets, figshare [data set], 10.6084/m9.figshare.21625079.v1, 2023.Jiao, B., Li, Q., Sun, W., and Martin, W.: Uncertainties in the global and
continental surface solar radiation variations: inter-comparison of in-situ
observations, reanalyses, and model simulations, Clim. Dynam., 59, 2499–2516,
10.1007/s00382-022-06222-3, 2022.Jones, P., Osborn, T., Briffa, K., Folland, C., Horton, E., Alexander, L.,
Parker, D., and Rayner, N.: Adjusting for sampling density in grid box land
and ocean surface temperature time series, J. Geophys. Res.-Atmos., 106, 3371–3380, 10.1029/2000JD900564, 2001.Jones, P. D., Osborn, T. J., and Briffa, K. R.: Estimating Sampling Errors
in Large-Scale Temperature Averages, J. Climate, 10, 2548–2568,
1997.Jones, P. D., Lister, D. H., and Li, Q.: Urbanization effects in large-scale
temperature records, with an emphasis on China, J. Geophys.
Res., 113, D16122, 10.1029/2008jd009916, 2008.Ju, X., Tu, Q., and Li, Q.: Homogeneity test and reduction of monthly total
solar radiation over China, J. Nanjing Inst. Meteorol., 29, 336–341, 2006.Kadow, C., Hall, D. M., and Ulbrich, U.: Artificial intelligence
reconstructs missing climate information, Nat. Geosci., 13, 408–413,
10.1038/s41561-020-0582-5, 2020.Kambezidis, H. D., Kaskaoutis, D. G., Kharol, S. K., Moorthy, K. K.,
Satheesh, S. K., Kalapureddy, M. C. R., Badarinath, K. V. S., Sharma, A. R.,
and Wild, M.: Multi-decadal variation of the net downward shortwave
radiation over south Asia: The solar dimming effect, Atmos.
Environ., 50, 360–372, 2012.Krige, D. G.: A statistical approach to some basic mine valuation problems
on the Witwatersrand, J. S. Afr. I. Min.
Metall., 52, 119–139, 10.10520/AJA0038223X_4792,
1951.Leirvik, T. and Yuan, M.: A machine learning technique for spatial
interpolation of solar radiation observations, Earth Space Sci., 8,
e2020EA001527, 10.1029/2020EA001527, 2021.Li, Q., Dong, W., Li, W., Gao, X., Jones, P., Kennedy, J., and Parker, D.:
Assessment of the uncertainties in temperature change in China during the
last century, Chinese Sci. Bull., 55, 1974–1982,
10.1007/s11434-010-3209-1, 2010.Li, Q., Sun, W., Yun, X., Huang, B., Dong, W., Wang, X. L., Zhai, P., and
Jones, P.: An updated evaluation of the global mean land surface air
temperature and surface temperature trends based on CLSAT and CMST, Clim.
Dynam., 56, 635–650, 10.1007/s00382-020-05502-0, 2021.Liang, H., Jiang, B., Liang, S., Peng, J., Li, S., Han, J., Yin, X., Cheng,
J., Jia, K., and Liu, Q.: A global long-term ocean surface
daily/0.05∘ net radiation product from 1983–2020, Sci.
Data, 9, 1–17, 10.1038/s41597-022-01419-x, 2022.Ma, Q., Wang, K., He, Y., Su, L., Wu, Q., Liu, H., and Zhang, Y.: Homogenized century-long surface incident solar radiation over Japan, Earth Syst. Sci. Data, 14, 463–477, 10.5194/essd-14-463-2022, 2022.Manara, V., Brunetti, M., Celozzi, A., Maugeri, M., Sanchez-Lorenzo, A., and Wild, M.: Detection of dimming/brightening in Italy from homogenized all-sky and clear-sky surface solar radiation records and underlying causes (1959–2013), Atmos. Chem. Phys., 16, 11145–11161, 10.5194/acp-16-11145-2016, 2016.Manara, V., Brunetti, M., Maugeri, M., Sanchez-Lorenzo, A., and Wild, M.:
Homogenization of a surface solar radiation dataset over Italy, AIP
Conference Proceedings, 1810, 090004, 10.1063/1.4975544, 2017.Manara, V., Bassi, M., Brunetti, M., Cagnazzi, B., and Maugeri, M.:
1990–2016 surface solar radiation variability and trend over the Piedmont
region (northwest Italy), Theor. Appl. Climatol., 136, 849–862,
10.1007/s00704-018-2521-6, 2019.Manara, V., Stocco, E., Brunetti, M., Diolaiuti, G. A., Fugazza, D.,
Pfeifroth, U., Senese, A., Trentmann, J., and Maugeri, M.: Comparison of
Surface Solar Irradiance from Ground Observations and Satellite Data
(1990–2016) over a Complex Orography Region (Piedmont—Northwest Italy),
Remote Sens, 12, 3882, 10.3390/rs12233882, 2020.Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S.
A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated
correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682,
10.1029/97JD00237, 1997.Olanow, C. W. and Koller, W. C.: An algorithm (decision tree) for the
management of Parkinson's disease: Treatment guidelines, Neurology, 50,
S1–S88, 1998.Padma Kumari, B., Londhe, A. L., Daniel, S., and Jadhav, D. B.:
Observational evidence of solar dimming: Offsetting surface warming over
India, Geophys. Res. Lett., 34, L21810,
10.1029/2007GL031133, 2007.Peixoto, J. P., Oort, A. H., and Lorenz, E. N.: Physics of climate,
Springer, ISBN 978-0-88318-712-8, 1992.Peterson, T. C., Karl, T. R., Jamason, P. F., Knight, R., and Easterling, D.
R.: First difference method: Maximizing station density for the calculation
of long-term global temperature change, J. Geophys. Res.-Atmos., 103, 25967–25974, 10.1029/98JD01168, 1998.Pfeifroth, U., Sanchez-Lorenzo, A., Manara, V., Trentmann, J., and Hollmann,
R.: Trends and Variability of Surface Solar Radiation in Europe Based On
Surface- and Satellite-Based Data Records, J. Geophys. Res.-Atmos., 123, 1735–1754, 10.1002/2017JD027418, 2018.Ronneberger, O., Fischer, P., and Brox, T.: U-net: Convolutional networks
for biomedical image segmentation, International Conference on Medical image
computing and computer-assisted intervention, arXiv [preprint], 234–241,
10.48550/arXiv.1505.04597, 2015.Sanchez-Lorenzo, A., Calbó, J., and Wild, M.: Global and diffuse solar
radiation in Spain: Building a homogeneous dataset and assessing their
trends, Global Planet. Change, 100, 343–352,
10.1016/j.gloplacha.2012.11.010, 2013a.Sanchez-Lorenzo, A., Wild, M., and Trentmann, J.: Validation and stability
assessment of the monthly mean CM SAF surface solar radiation dataset over
Europe against a homogenized surface dataset (1983–2005), Remote Sens.
Environ., 134, 355–366, 10.1016/j.rse.2013.03.012, 2013b.Sanchez-Lorenzo, A., Wild, M., Brunetti, M., Guijarro, J. A., Hakuba, M. Z.,
Calbó, J., Mystakidis, S., and Bartok, B.: Reassessment and update of
long-term trends in downward surface shortwave radiation over Europe
(1939–2012), J. Geophys. Res.-Atmos., 120, 9555–9569,
10.1002/2015JD023321, 2015.Scudiero, E., Corwin, D. L., Morari, F., Anderson, R. G., and Skaggs, T. H.:
Spatial interpolation quality assessment for soil sensor transect datasets,
Comput. Electron. Agr., 123, 74–79,
10.1016/j.compag.2016.02.016, 2016.Shao, C., Yang, K., Tang, W., He, Y., Jiang, Y., Lu, H., Fu, H., and Zheng,
J.: Convolutional neural network-based homogenization for constructing a
long-term global surface solar radiation dataset, Renew. Sust.
Energ. Rev., 169, 112952, 10.1016/j.rser.2022.112952, 2022.Shepard, D.: A two-dimensional interpolation function for irregularly-spaced
data, Proceedings of the 1968 23rd ACM national conference, 517–524,
10.1145/800186.810616, 1068.Slivinski, L. C., Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Giese,
B. S., McColl, C., Allan, R., Yin, X., Vose, R., and Titchner, H.: Towards a
more reliable historical reanalysis: Improvements for version 3 of the
Twentieth Century Reanalysis system, Q. J. Roy.
Meteor. Soc., 145, 2876–2908, 10.1002/qj.3598, 2019.Soni, V. K., Pandithurai, G., and Pai, D. S.: Evaluation of long-term
changes of solar radiation in India, Int. J. Climatol.,
32, 540–551, 10.1002/joc.2294, 2012.Soni, V. K., Pandithurai, G., and Pai, D. S.: Is there a transition of solar
radiation from dimming to brightening over India, Atmos. Res., 169,
209-224, 2016.Tang, W., Qin, J., Yang, K., Liu, S., Lu, N., and Niu, X.: Retrieving high-resolution surface solar radiation with cloud parameters derived by combining MODIS and MTSAT data, Atmos. Chem. Phys., 16, 2543–2557, 10.5194/acp-16-2543-2016, 2016.Tang, W., Yang, K., Qin, J., Li, X., and Niu, X.: A 16-year dataset (2000–2015) of high-resolution (3 h, 10 km) global surface solar radiation, Earth Syst. Sci. Data, 11, 1905–1915, 10.5194/essd-11-1905-2019, 2019.Trenberth, K. E. and Fasullo, J. T.: Regional energy and water cycles:
Transports from ocean to land, J. Climate, 26, 7837–7851,
10.1175/JCLI-D-13-00008.1, 2013.Tsvetkov, A., Wilcox, S., Renne, D., and Pulscak, M.: International solar
resource data at the World Radiation Data Center, American Solar Energy
Society, Boulder, CO (United States), ISBN 0-89553-167-4, 1995.Urraca, R., Huld, T., Martinez-de-Pison, F. J., and Sanz-Garcia, A.: Sources
of uncertainty in annual global horizontal irradiance data, Sol. Energy,
170, 873–884, 10.1016/j.solener.2018.06.005, 2018.Vincent, L. A., Wang, X. L., Milewska, E. J., Wan, H., Yang, F., and Swail,
V.: A second generation of homogenized Canadian monthly surface air
temperature for climate trend analysis, J. Geophys. Res.-Atmos., 117, D18110, 10.1029/2012JD017859, 2012.Wang, K.: Measurement biases explain discrepancies between the observed and
simulated decadal variability of surface incident solar radiation,
Sci. Rep., 4, 1–7, 10.1038/srep06144, 2014.Wang, K., Ma, Q., Li, Z., and Wang, J.: Decadal variability of surface
incident solar radiation over China: Observations, satellite retrievals, and
reanalyses, J. Geophys. Res.-Atmos., 120, 6500–6514,
10.1002/2015JD023420, 2015.Wang, X. L.: Accounting for autocorrelation in detecting mean shifts in
climate data series using the penalized maximal t or F test, J.
Appl. Meteorol. Clim., 47, 2423–2444,
10.1175/2008JAMC1741.1, 2008a.Wang, X. L.: Penalized maximal F test for detecting undocumented mean shift
without trend change, J. Atmos. Ocean. Tech., 25,
368–384, 10.1175/2007JTECHA982.1, 2008b.Wang, X. L. and Feng, Y.: RHtestsV4 user manual, Climate Research Division,
Atmospheric Science and Technology Directorate, Science and Technology
Branch, Environment Canada, 28, 2013.Wang, X. L., Wen, Q. H., and Wu, Y.: Penalized maximal t test for detecting
undocumented mean change in climate data series, J. Appl.
Meteorol. Clim., 46, 916–931, 10.1175/JAM2504.1, 2007.Wang, X. L., Chen, H., Wu, Y., Feng, Y., and Pu, Q.: New techniques for the
detection and adjustment of shifts in daily precipitation data series,
J. Appl. Meteorol. Clim., 49, 2416–2436,
10.1175/2010JAMC2376.1, 2010.Wang, Y. and Wild, M.: A new look at solar dimming and brightening in China,
Geophys. Res. Lett., 43, 11777–711785, 10.1002/2016GL071009,
2016.Wild, M.: Enlightening global dimming and brightening, B.
Am. Meteorol. Soc., 93, 27–37, 10.1175/BAMS-D-11-00074.1,
2012.Wild, M.: The global energy balance as represented in CMIP6 climate models,
Clim. Dynam., 55, 553–577, 10.1007/s00382-020-05282-7, 2020.Wild, M., Gilgen, H., Roesch, A., Ohmura, A., Long, C. N., Dutton, E. G.,
Forgan, B., Kallis, A., Russak, V., and Tsvetkov, A.: From dimming to
brightening: Decadal changes in solar radiation at Earth's surface, Science,
308, 847–850, 10.1126/science.1103215, 2005.Wild, M., Trüssel, B., Ohmura, A., Long, C. N., König-Langlo, G.,
Dutton, E. G., and Tsvetkov, A.: Global dimming and brightening: An update
beyond 2000, J. Geophys. Res.-Atmos., 114, D011382,
10.1029/2008JD011382, 2009.Wild, M., Ohmura, A., Schär, C., Müller, G., Folini, D., Schwarz, M., Hakuba, M. Z., and Sanchez-Lorenzo, A.: The Global Energy Balance Archive (GEBA) version 2017: a database for worldwide measured surface energy fluxes, Earth Syst. Sci. Data, 9, 601–613, 10.5194/essd-9-601-2017, 2017.Xu, W., Li, Q., Wang, X. L., Yang, S., Cao, L., and Feng, Y.: Homogenization
of Chinese daily surface air temperatures and analysis of trends in the
extreme temperature indices, J. Geophys. Res.-Atmos.,
118, 9708–9720, 10.1002/jgrd.50791, 2013.Xu, W., Li, Q., Jones, P., Wang, X. L., Trewin, B., Yang, S., Zhu, C., Zhai,
P., Wang, J., and Vincent, L.: A new integrated and homogenized global
monthly land surface air temperature dataset for the period since 1900,
Clim. Dynam., 50, 2513–2536, 10.1007/s00382-017-3755-1, 2018.Yang, L., Zhang, X., Liang, S., Yao, Y., Jia, K., and Jia, A.: Estimating
surface downward shortwave radiation over China based on the gradient
boosting decision tree method, Remote Sens., 10, 185,
10.3390/rs10020185, 2018.Yang, S., Wang, X. L., and Wild, M.: Homogenization and trend analysis of
the 1958–2016 in situ surface solar radiation records in China, J.
Climate, 31, 4529–4541, 10.1175/JCLI-D-17-0891.1, 2018.
You, Q., Sanchez-Lorenzo, A., Wild, M., Folini, D., Fraedrich, K., Ren, G.,
and Kang, S.: Decadal variation of surface solar radiation in the Tibetan
Plateau from observations, reanalysis and model simulations, Clim.
Dynam., 40, 2073–2086, 10.1007/s00382-012-1383-3, 2013.Yuan, M., Leirvik, T., and Wild, M.: Global trends in downward surface solar
radiation from spatial interpolated ground observations during 1961–2019,
J. Climate, 34, 9501–9521, 10.1175/JCLI-D-21-0165.1, 2021.Zhou, C., Wang, K., and Ma, Q.: Evaluation of Eight Current Reanalyses in
Simulating Land Surface Temperature from 1979 to 2003 in China, J.
Climate, 30, 7379–7398,
10.1175/JCLI-D-16-0903.1, 2017.Zhou, C., He, Y., and Wang, K.: On the suitability of current atmospheric reanalyses for regional warming studies over China, Atmos. Chem. Phys., 18, 8113–8136, 10.5194/acp-18-8113-2018, 2018.Zhou, W., Gong, L., Wu, Q., Xing, C., Wei, B., Chen, T., Zhou, Y., Yin, S.,
Jiang, B., Xie, H., Zhou, L., and Zheng, S.: Correction to: PHF8
upregulation contributes to autophagic degradation of E-cadherin,
epithelial-mesenchymal transition and metastasis in hepatocellular
carcinoma, J. Exp. Clin. Canc. Res., 37, 445,
10.1186/s13046-018-0944-7, 2018.Zhou, W., Gong, L., Wu, Q., Xing, C., Wei, B., Chen, T., Zhou, Y., Yin, S.,
Jiang, B., Xie, H., Zhou, L., and Zheng, S.: Correction to: PHF8
upregulation contributes to autophagic degradation of E-cadherin,
epithelial-mesenchymal transition and metastasis in hepatocellular
carcinoma, J. Exp. Clin. Canc. Res., 38, 445, 10.1186/s13046-019-1452-0, 2019.