Articles | Volume 15, issue 10
https://doi.org/10.5194/essd-15-4519-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-4519-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
06 Oct 2023
Data description paper |
| 06 Oct 2023
| 06 Oct 2023
An integrated and homogenized global surface solar radiation dataset and its reconstruction based on a convolutional neural network approach
Boyang Jiao
School of Atmospheric Sciences, Sun Yat-sen University, and Key
Laboratory of Tropical Atmosphere–Ocean System, Ministry of Education,
Zhuhai 519082, China
Southern Laboratory of Ocean Science and Engineering (Guangdong
Zhuhai), Zhuhai 519082, China
Yucheng Su
Department of Public Meteorological Service Center, Meteorological Bureau of Zhuhai, Zhuhai 519082, China
School of Atmospheric Sciences, Sun Yat-sen University, and Key
Laboratory of Tropical Atmosphere–Ocean System, Ministry of Education,
Zhuhai 519082, China
Southern Laboratory of Ocean Science and Engineering (Guangdong
Zhuhai), Zhuhai 519082, China
Veronica Manara
Department of Environmental Science and Policy, Università degli
Studi di Milano, via Celoria 10, 20133, Milan, Italy
Martin Wild
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich,
Switzerland
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This study investigates changes in extreme hourly precipitation across Italy using a high-resolution reanalysis, a dataset that combines observations and weather models to reconstruct past atmospheric conditions. By analysing over 35 years of hourly data, the study identifies an increase in extreme precipitation events in Alpine areas during summer and southern coastal regions in autumn, providing insights into evolving precipitation patterns and supporting climate resilience planning.
Sihao Wei, Qingxiang Li, Qiya Xu, Zicheng Li, Hanyu Zhang, and Jiaxue Lin
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This study introduces the update to the C-LSAT 2.1 station data and its gridded dataset (5° × 5°). Based on this, we develop high-resolution (0.5° × 0.5°) LSAT (C-LSAT HRv1) and DTR (C-LDTR HRv1) datasets. The C-LSAT 2.1 station data integrates over 3000 additional global stations, significantly improving spatial coverage. The global and regional variations in C-LSAT HRv1 and C-LDTR HRv1 align well with their 5° × 5° datasets (C-LSAT 2.1 and C-LDTR).
Lucas Ferreira Correa, Doris Folini, Boriana Chtirkova, and Martin Wild
Atmos. Chem. Phys., 24, 8797–8819, https://doi.org/10.5194/acp-24-8797-2024, https://doi.org/10.5194/acp-24-8797-2024, 2024
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We investigated the causes of the decadal trends of solar radiation measured at 34 stations in Brazil in the first 2 decades of the 21st century. We observed strong negative trends in north and northeast Brazil associated with changes in both atmospheric absorption (anthropogenic) and cloud cover (natural). In other parts of the country no strong trends were observed as a result of competing effects. This provides a better understanding of the energy balance in the region.
Junli Yang, Weijun Quan, Li Zhang, Jianglin Hu, Qiying Chen, and Martin Wild
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Due to the difficulties involved in the measurements of the Downward long-wave irradiance (DnLWI), the numerical weather prediction (NWP) models have been developed to obtain the DnLWI indirectly. In this study, a long-term high time-resolution (1 min) observational dataset of the DnLWI in China was used to evaluate the radiation scheme in the CMA-MESO model over various underlying surfaces and climate zones.
Zengyun Hu, Xi Chen, Deliang Chen, Zhuo Zhang, Qiming Zhou, and Qingxiang Li
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-82, https://doi.org/10.5194/gmd-2024-82, 2024
Preprint withdrawn
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ERC firstly unified the evaluating, ranking, and clustering by a simple mathematic equation based on Euclidean Distance. It provides new system to solve the evaluating, ranking, and clustering tasks in SDGs. In fact, ERC system can be applied in any scientific domain.
Weijun Quan, Zhenfa Wang, Lin Qiao, Xiangdong Zheng, Junli Jin, Yinruo Li, Xiaomei Yin, Zhiqiang Ma, and Martin Wild
Earth Syst. Sci. Data, 16, 961–983, https://doi.org/10.5194/essd-16-961-2024, https://doi.org/10.5194/essd-16-961-2024, 2024
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Radiation components play important roles in various fields such as the Earth’s surface radiation budget, ecosystem productivity, and human health. In this study, a dataset consisting of quality-assured daily data of nine radiation components is presented based on the in situ measurements at the Shangdianzi regional GAW station in China during 2013–2022. The dataset can be applied in the validation of satellite products and numerical models and investigation of atmospheric radiation.
Qiuyan Wang, Hua Zhang, Su Yang, Qi Chen, Xixun Zhou, Bing Xie, Yuying Wang, Guangyu Shi, and Martin Wild
Atmos. Chem. Phys., 22, 15867–15886, https://doi.org/10.5194/acp-22-15867-2022, https://doi.org/10.5194/acp-22-15867-2022, 2022
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The present-day land energy balance over East Asia is estimated for the first time. Results indicate that high aerosol loadings, clouds, and the Tibet Plateau (TP) over East Asia play vital roles in the shortwave budgets, while the TP is responsible for the longwave budgets during this regional energy budget assessment. This study provides a perspective to understand fully how the potential factors influence the diversifying regional energy budget assessments.
Johannes Quaas, Hailing Jia, Chris Smith, Anna Lea Albright, Wenche Aas, Nicolas Bellouin, Olivier Boucher, Marie Doutriaux-Boucher, Piers M. Forster, Daniel Grosvenor, Stuart Jenkins, Zbigniew Klimont, Norman G. Loeb, Xiaoyan Ma, Vaishali Naik, Fabien Paulot, Philip Stier, Martin Wild, Gunnar Myhre, and Michael Schulz
Atmos. Chem. Phys., 22, 12221–12239, https://doi.org/10.5194/acp-22-12221-2022, https://doi.org/10.5194/acp-22-12221-2022, 2022
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Pollution particles cool climate and offset part of the global warming. However, they are washed out by rain and thus their effect responds quickly to changes in emissions. We show multiple datasets to demonstrate that aerosol emissions and their concentrations declined in many regions influenced by human emissions, as did the effects on clouds. Consequently, the cooling impact on the Earth energy budget became smaller. This change in trend implies a relative warming.
Wenbin Sun, Yang Yang, Liya Chao, Wenjie Dong, Boyin Huang, Phil Jones, and Qingxiang Li
Earth Syst. Sci. Data, 14, 1677–1693, https://doi.org/10.5194/essd-14-1677-2022, https://doi.org/10.5194/essd-14-1677-2022, 2022
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The new China global Merged Surface Temperature CMST 2.0 is the updated version of CMST-Interim used in the IPCC's AR6. The updated dataset is described in this study, containing three versions: CMST2.0 – Nrec, CMST2.0 – Imax, and CMST2.0 – Imin. The reconstructed datasets significantly improve data coverage, especially in the high latitudes in the Northern Hemisphere, thus increasing the long-term trends at global, hemispheric, and regional scales since 1850.
Xinyuan Hou, Martin Wild, Doris Folini, Stelios Kazadzis, and Jan Wohland
Earth Syst. Dynam., 12, 1099–1113, https://doi.org/10.5194/esd-12-1099-2021, https://doi.org/10.5194/esd-12-1099-2021, 2021
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Solar photovoltaics (PV) matters for the carbon neutrality goal. We use climate scenarios to quantify climate risk for PV in Europe and find higher PV potential. The seasonal cycle of PV generation changes in most places. We find an increase in the spatial correlations of daily PV production, implying that PV power balancing through redistribution will be more difficult in the future. Thus, changes in the spatiotemporal structure of PV generation should be included in power system design.
Peng Si, Qingxiang Li, and Phil Jones
Earth Syst. Sci. Data, 13, 2211–2226, https://doi.org/10.5194/essd-13-2211-2021, https://doi.org/10.5194/essd-13-2211-2021, 2021
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This paper documents the various procedures necessary to construct a homogenized daily maximum and minimum temperature series starting in 1887 for Tianjin. The newly constructed temperature series provides a set of new baseline data for the field of extreme climate change at the century-long scale and a reference for construction of other long-term reliable daily time series in the region.
Marcia Akemi Yamasoe, Nilton Manuel Évora Rosário, Samantha Novaes Santos Martins Almeida, and Martin Wild
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Spatio-temporal disparity to assess global dimming and brightening phenomena has been a critical topic. For instance, few studies addressed surface solar irradiation (SSR) long-term trend in South America. In this study, SSR, sunshine duration (SD) and the diurnal temperature range (DTR) are analysed for São Paulo, Brazil. We found a dimming phase, identified by SSR, SD and DTR, extending till 1983. Then, while SSR is still declining, consistent with cloud increasing, SD and DTR are increasing.
Kine Onsum Moseid, Michael Schulz, Trude Storelvmo, Ingeborg Rian Julsrud, Dirk Olivié, Pierre Nabat, Martin Wild, Jason N. S. Cole, Toshihiko Takemura, Naga Oshima, Susanne E. Bauer, and Guillaume Gastineau
Atmos. Chem. Phys., 20, 16023–16040, https://doi.org/10.5194/acp-20-16023-2020, https://doi.org/10.5194/acp-20-16023-2020, 2020
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In this study we compare solar radiation at the surface from observations and Earth system models from 1961 to 2014. We find that the models do not reproduce the so-called
global dimmingas found in observations. Only model experiments with anthropogenic aerosol emissions display any dimming at all. The discrepancies between observations and models are largest in China, which we suggest is in part due to erroneous aerosol precursor emission inventories in the emission dataset used for CMIP6.
Cited articles
Aguiar, 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,
https://doi.org/10.1016/j.solener.2015.10.041, 2015.
Alexandersson, H.: A homogeneity test applied to precipitation data, J.
Climatol., 6, 661–675, https://doi.org/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, https://doi.org/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
https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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 https://doi.org/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, https://doi.org/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, https://doi.org/10.3390/rs13040602, 2021.
Fisher, N. I., Lewis, T., and Embleton, B. J.: Statistical analysis of
spherical data, Cambridge University Press, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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,
https://doi.org/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, https://doi.org/10.1002/qj.3803, 2020.
Hoskins, B. J. and Valdes, P. J.: On the existence of storm-tracks, J.
Atmos. Sci., 47, 1854–1864,
https://doi.org/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, https://doi.org/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, https://doi.org/10.1016/j.solener.2018.10.080, 2018.
Jiao, B. and Li, Q.: Global Integrated and Homogenized Solar surface
Radiation Datasets, figshare [data set], https://doi.org/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,
https://doi.org/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, https://doi.org/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, https://doi.org/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,
https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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,
https://doi.org/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, https://doi.org/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,
https://doi.org/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,
https://doi.org/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, https://doi.org/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, https://doi.org/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,
https://doi.org/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,
https://doi.org/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, https://doi.org/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,
https://doi.org/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,
https://doi.org/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, https://doi.org/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,
https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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,
https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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,
https://doi.org/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,
https://doi.org/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, https://doi.org/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, https://doi.org/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,
https://doi.org/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, https://doi.org/10.1002/2016GL071009,
2016.
Wild, M.: Enlightening global dimming and brightening, B.
Am. Meteorol. Soc., 93, 27–37, https://doi.org/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, https://doi.org/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, https://doi.org/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,
https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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,
https://doi.org/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, https://doi.org/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, https://doi.org/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, https://doi.org/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,
https://doi.org/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, https://doi.org/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,
https://doi.org/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, https://doi.org/10.1186/s13046-019-1452-0, 2019.
Short summary
This paper develops an observational integrated and homogenized global-terrestrial (except for Antarctica) SSRIH station. This is interpolated into a 5° × 5° SSRIH grid and reconstructed into a long-term (1955–2018) global land (except for Antarctica) 5° × 2.5° SSR anomaly dataset (SSRIH20CR) by an improved partial convolutional neural network deep-learning method. SSRIH20CR yields trends of −1.276 W m−2 per decade over the dimming period and 0.697 W m−2 per decade over the brightening period.
This paper develops an observational integrated and homogenized global-terrestrial (except for...
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