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Projections of future changes in solar radiation in China based on CMIP5 climate models
Volume 1 Number 4 October 2018 (452-459) DOI: 10.14171/j.2096-5117.gei.2018.04.005
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Projections of future changes in solar radiation in China based on CMIP5 climate models Liwei Yang1, Junxia Jiang1, 2, Tian Liu1, 2, Yujie Li1, 2, Ya Zhou1, 2, Xiaoqing Gao1 1. Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Scan for more details
Lanzhou 730000, P.R. China. 2. University of Chinese Academy of Sciences, Beijing 100049, P.R. China
Abstract: Surface solar radiation research is important for understanding future climate change and the application of largescale photovoltaic systems. We used the coupled model intercomparison project phase 5 (CMIP5) under the RCP8.5 scenario to project potential changes in surface solar radiation, surface temperatures, and cloud fractions between 2006 and 2049 in China, as well as how these changes may affect photovoltaic power generation. The results show that the following. (1) For surface temperatures, the median trends of all considered models show warming in China of 0.05 K/year. The maximum positive trends for all-sky radiation appear in the southeast of China, reaching 0.4 W/m2/year. Cloud cover exhibits a mainly decreasing trend in the overall region of China. (2) The all-sky radiation of most selected regions shows a decreasing trend. The maximum negative value (−0.08 W/m2/year) appears in Qinghai. (3) Compared with the average photovoltaic power output from 2006 to 2015, the photovoltaic power output in western China will decrease by −0.04 %/ year, while photovoltaic power output in southeastern China will increase by 0.06–0.1%/year. Keywords: Solar radiation changes, CMIP5 models, Climate change, PV output, Projections.
1 Introduction Solar radiation is the most important energy source on the earth’s surface. It determines the thermodynamic and dynamic state of the earth’s atmospheric systems and Received: 9 August 2018/ Accepted: 20 August 2018/ Published: 25 October 2018 Xiaoqing Gao [email protected]
Tian Liu [email protected]
Liwei Yang [email protected]
Yujie Li [email protected]
Junxia Jiang [email protected]
Ya Zhou [email protected]
Open access under CC BY-NC-ND license. 452
is an important signal reflecting climate change [1, 2]. In addition, as global industrialization progresses, human needs for energy continue to grow. Solar photovoltaic power is clean and environmentally friendly energy, and has therefore become the cornerstone of long-term strategies for the future production of energy by human societies [3-5]. Variations in solar radiation and the possible factors affecting that variation in China or in the world have been studied by many scholars using observed data. Variations in surface solar radiation in northern Europe showed a significant decreasing trend from the 1950s to the 1980s, followed by a slight increase, and this study pointed out that cloud cover and atmospheric circulation were the
Liwei Yang et al. Projections of future changes in solar radiation in China based on CMIP5 climate models
main influencing factors [6]. Surface solar radiation in southeastern China decreased significantly from 1961 to 1989, and then increased from 1989 to 2008 [7]. Fang Shibo et al. analyzed the variations in total surface solar radiation in eastern and western China from 1961 to 2011, and found that the total surface solar radiation in both eastern and western China showed a decreasing trend, with sunshine percentages and air pollution as the main influencing factors [8]. Current methods for predicting future solar radiation change scenarios mainly involve the extension of statistics and model estimations based on mathematical science [9-11]. However, few scholars have previously predicted the surface solar radiation in China or specific regions within China. The coupled model inter-comparison project phase 5 (CMIP5), which is a global climate model, can effectively simulate average climate states and change characteristics [12]. In this paper, 38 CMIP5 climate models are used to predict changes in surface solar radiation and their effect on photovoltaic power generation in China over the next 40 years. Additionally, several regions that are the key to the development of photovoltaic power stations currently and in the future, are specifically analyzed: Xinjiang, Qinghai, Inner Mongolia, and Shaanxi. This study will be of great significance for understanding future climate change in China, and will help optimize the macro locations of photovoltaic power plants, providing a reference for the future development of solar energy resources.
2 Data and Methods 2.1 Data Data were selected from the CMIP5 simulation data (http://cmip-pcmdi.llnl.gov/cmip5). In the emission scenario, the CMIP5 trial used four representative concentration pathways (RCPs): RCP2.6, RCP4.5, RCP6.0, and RCP8.5 [13]. Emissions scenarios are not chosen to accurately predict the future, but to reduce prediction uncertainty and provide a reference for decisions. In this study, the RCP8.5 high radiative forcing scenario is considered. This scenario is improved relative to the CMIP3 model, because it establishes a spatial distribution of atmospheric pollution estimations and strengthens the estimation of land use and land surface changes [14]. The RCP8.5 scenario does not include any specific tasks of alleviating climate change pressures. Under the RCP8.5 scenario, radiative forcing will rise to 8.5 W/m2, and the CO2 concentrations in the air will reach 1370 ppm (1 ppm =1 μL/L) by 2100, which is 3–4 times higher than CO2 concentrations prior to the industrial revolution. The basic information for the 38 CMIP5 climate patterns selected is shown in Table 1. The selected ensemble
run for each pattern was rlilp1 (r2ilp1 was selected for CESM1-WACCM and FGOALS-s2). The analyzed variables are surface downward solar radiation (rsds, for all weather conditions), surface downward solar radiation under cloudless conditions (rsdscs; the CMCC-CESM, CMCC-CM and CMCC-CMS models are not available for rsdscs), near-surface air temperature (tas, also known as ambient temperature), and total cloud cover (clt). The time period analyzed in this study ranges from 2006 to 2049 (photovoltaic panel lifespans are approximately 40–50 years). The 2006–2015 period is used as the reference period, representing current climatic conditions. We performed a bilinear interpolation of global data in the selected model to a horizontal resolution of 0.75°×0.75°. All data were processed into annual average data.
2.2 Methods To forecast the effects of climate change on future photovoltaic generation, we use the method proposed by Crook et al. [10], in which photovoltaic generation largely depends on battery materials, sunshine, and ambient temperatures. Without considering other loss factors and system components such as inverters, the power output of a photovoltaic panel is PPV = Gtotη cell (1) Where Gtot is the downward solar radiation received by the earth’s surface under all weather conditions and ηcell is the efficiency of the photovoltaic panel (Photovoltaic conversion rate). ηcell is calculated as follows:
η
cell = 1 − β (Tcell − Tref ) + γ log10 Gtot (2) ηref Where η ref is the reference efficiency and β and γ depend on the material and structure of the battery. Parida et al. [15] showed that the monocrystalline silicon cell dominates production, that β =0.0045, and that γ =0.1. Tref = 25 ℃ is the reference temperature and Tcell is the panel temperature, which is determined as follows [10]: (3) Tcell = c1 + c2T + c3Gtot where T is the ambient air temperature (℃). c1, c2 and c3 depend on the properties of the battery and c1= -3.75 ℃, c2= 1.14, c3= 0.0175 ℃m2W-1 for a monocrystalline silicon cell. We calculate the change ( ∆P / P ) in future photovoltaic power generation relative to that under current climatic conditions, because the value of η ref is unknown. In addition, we consider only horizontal irradiance, disregarding the fact that slant panels or tracing ray panels could receive more radiation.
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Table 1 Basic information on the climate models in CMIP5 Model
Unit, country
Resolution
Model
Unit, country
Resolution
ACCESS1-0
CSIRO-BOM, Australia
192 × 145
GFDL-ESM2G
NOAA GFDL, America
144 × 90
ACCESS1-3
CSIRO-BOM, Australia
192 × 145
GFDL-ESM2M
NOAA GFDL, America
144 × 90
BCC-CSM1-1
BCC, China
128 × 64
GISS-E2-H
NASA GISS, America
144 × 90
BCC-CSM1-1-m
BCC, China
320 × 160
GISS-E2-R
NASA GISS, America
144 × 90
BNU-ESM
GCESS, China
128 × 64
HadGEM2-AO
NIMR/KMA, Korea/ England
192 ×145
CanESM2
CCCMA, Canada
128 × 64
HadGEM2-CC
MOHC, England
192 ×145
CCSM4
NCAR, America
288× 192
HadGEM2-ES
MOHC, England
192 ×145
CESM1-BGC
NSF-DOE-NCAR, America
288 × 192
INMCM4
INM, Russia
180 × 120
CESM1-CAM5
NSF-DOE-NCAR, America
288 × 192
IPSL-CM5A-LR
IPSL, France
96 × 96
CESM1-WACCM
NSF-DOE-NCAR, America
144 × 96
IPSL-CM5A-MR
IPSL, France
144 × 143
CMCC-CESM
CMCC, Italy
96 × 48
IPSL-CM5B-LR
IPSL, France
96 × 96
CMCC-CM
CMCC, Italy
480 × 240
MIROC5
MIROC, Japan
256 × 128
CMCC-CMS
CMCC, Italy
192 × 96
MIROC-ESM
MIROC, Japan
128 × 64
CNRM-CM5
CNRM-CERFACS, France
256 × 128
MIROC-ESM-CHEM
MIROC, Japan
128 × 64
CSIRO-Mk3-6-0
CSIRO-QCCCE, Australia
192 × 96
MPI-ESM-LR
MPI-M, Germany
192 × 96
FGOALS-g2
LASG-CESS, China
128 × 60
MPI-ESM-MR
MPI-M, Germany
192 × 96
FGOALS-s2
LASG-IAP, China
128 × 108
MRI-CGCM3
MRI, Japan
320 × 160
FIO-ESM
FIO, China
128 × 64
NorESM1-M
NCC, Norway
144 × 96
GFDL-CM3
NOAA GFDL, America
144 × 90
NorESM1-ME
NCC, Germany
144 × 96
3 Results 3.1 Estimated projections of radiation, temperature, and cloud cover in China by CMIP5 model We use linear regression to analyze the evolution of climate elements on a long-term scale [16]. Fig. 1 shows a spatial distribution of the linear trends of various climates elements from 2006–2049: (a) downward solar radiation received by the earth’s surface; (b) downward solar radiation received by the earth’s surface under cloudless conditions; (c) near-surface air temperatures; (d) total cloud fractions. The regression coefficients are confirmed by a test of significance for the linear regression equation (t-test, α = 0.05). We used a median collection scheme (that is, the median of the 38 pattern data values at each grid point for each variable); Fig. 1 shows the synthesis of the 38 pattern data. 454
According to Fig. 1c, near-surface layer air temperatures are warming throughout China at a rate of 0.05 K/year. Panel efficiency decreases with the increasing panel temperatures induced by increased air temperatures, assuming the same amount of radiation [17]. Fig. 1b shows that surface downward solar radiation under cloudless conditions has a linear trend close to zero or a small negative trend over western China, and a maximum positive trend that is approximately 0.3 W/m2/year over southeastern China. Previous studies have shown that atmospheric turbidity dominates surface solar radiation in eastern China [8]. Therefore, the maximum positive trend in southeastern China may result from a decrease in aerosol concentrations. Positive and negative trends in the spatial distribution of surface downward solar radiation under all weather conditions (rsds), which mainly includes the effects of clouds, is consistent with that under cloudless conditions
Liwei Yang et al. Projections of future changes in solar radiation in China based on CMIP5 climate models
(a) Surface downward solar radiation (rsds, units: W/m2/yr)
(c) Near surface air temperature (tas, unit: K/yr)
(b) Surface downward clear-sky solar radiation (rsdscs, unit: W/m2/yr)
(d) Total cloud fraction (clt, unit: %/yr)
Fig. 1 Distribution of median trends of all considered models between 2006 and 2049 in China
(rsdscs), as shown in Fig. 1a. Cloud cover exhibits a negative trend throughout China; in the southeast, that negative trend is up to -0.1%/year. However, rsds increases, with a trend of 0.4 W/m2/year (Fig. 1d). The results are consistent with those of Wild et al. [9].
3.2 E stimates of selected areas of radiation, temperature and cloud cover by CMIP5 model Combined with the National Energy Administration’s 12th Five-Year Plan and 13th Five-Year Plan for developing solar power, the eastern part of Xinjiang (86°E ~ 95°E, 42°N ~ 44°N), Qinghai (91°E ~ 101°E, 34°N ~ 38°N), Inner Mongolia Municipal (101°E ~ 112°E, 39°N ~ 42°N) and the northern part of Shaanxi (108°E ~ 110.7°E, 36°N ~ 39°N) are selected to analyze, because these regions will develop photovoltaic rapidly in the future (Fig. 2).
The average annual variations in climatic factors in the selected area (deviation from the average of ten years from 2006 to 2015) are shown in Fig. 3. The black lines in the figure represent annual variations in the deviation values of the 38 models (rsds, rsdscs, tas, clt). The red lines in the figure show a linear trend fit of the median of 38 model deviation values. As shown in Fig. 3, the linear temperature trends (tas) increase in selected regions. The increase rates are only slightly different, and all approximately 0.49 K/ year. Downward solar radiation under cloudless conditions (rsdscs) exhibits negative trends in Xinjiang, Qinghai, and Inner Mongolia. The maximum negative trend (-0.051 W/ m2/year) occurs in Xinjiang. In Shaanxi, the rsdscs trends before 2025 are not significant, and increase after 2025. The surface downward solar radiation (rsds) in Xinjiang, Qinghai and Inner Mongolia exhibit negative trends. The maximum negative trend (-0.080 W/m2/year) appears in 455
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Qinghai. In Shaanxi, the rsds increases at a rate of 0.030 W/m2/year. The amount of clouds selected in the figure is negative, and the maximum negative trend (-0.025 %/year) appears in Qinghai. Previous studies have shown that total cloud cover has an irregular correlation with surface solar radiation in western China [8]. The surface downward solar radiation in the western region is mainly characterized by a decreasing trend, which may be related to atmospheric turbidity and the percentage of sunshine. In short, surface downward solar radiation in the northwestern region of China mainly exhibits a negative trend. The temperature is warming in China. Those all negatively affect power generation by photovoltaic power plants in northwest China. 5
0 -10 -20 2000
2025 time [year]
0
-5 2000
2050
tas [ K ]
rsdscs [W ⋅m-2]
rsds [W ⋅m-2]
10
4
m= -0.032
m= -0.036
2025 time [year]
m= -0.001
5
2
0
0 -2 2000
2050
10
m= 0.050
clt [%]
20
Fig. 2 Four regions of China selected in this study
-5
2025 time [year]
2050
-10 2000
2025 time [year]
2050
(a) Rsds, rsdscs, tas, and clt in Xinjiang 5
0 -10 2025 time [year]
0
-5 2000
2050
2025 time [year]
10 m= 0.048
0 -2 2000
2050
m= -0.025
5
2
tas [ K ]
10
-20 2000
4 m= -0.051
clt [%]
m= -0.080
rsdscs [W ⋅m-2]
rsds [W ⋅m-2]
20
0 -5
2025 time [year]
-10 2000
2050
2025 time [year]
2050
(b) Rsds, rsdscs, tas, and clt in Qinghai
-10 2025 time [year]
0
-5 2000
2050
2025 time [year]
5
2 0
-2 2000
2050
10
m= 0.049
clt [%]
0
4 m= -0.018
tas [ K ]
10
-20 2000
5
m= -0.032
rsdscs [W ⋅m-2]
rsds [W ⋅m-2]
20
0 -5
2025 time [year]
2050
-10 2000
m= -0.002 2025 time [year]
2050
(c) Rsds, rsdscs, tas, and clt in Inner Mongolia
-2 rsdscs [W⋅m ]
-2 rsds [W ⋅m ]
4
10 0 -10
5
15 m = 0.048
m = 0.032
0
10
2
clt [%]
m= 0.030
20
tas [ K ]
30
10
0
-5
5 0 -5
-20 -30 2000
-10 2025 time [year]
2050
-10 2000
2025 time [year]
2050
-2 2000
2025 time [year]
2050
-15 2000
(d) Rsds, rsdscs, tas, and clt in Shaanxi
Fig. 3 Annual variation of rsds [W/m2], rsdscs [W/m2], tas [K] and clt [%] for the four focal regions (m is the regression coefficient, units are per year) 456
m= -0.012
2025 time [year]
2050
Liwei Yang et al. Projections of future changes in solar radiation in China based on CMIP5 climate models
3.3 P otential impact of climate elements on solar photovoltaic power plants Power generation in photovoltaic systems is greatly affected by natural environmental conditions. The study of the prediction on PV power generation by using meteorological factors needs to be further improved [18]. Lv et al. [19] found that radiation and temperature are the two main factors affecting photovoltaic power generation. As is known from formulas (1, 2, and 3) in section 1.2, in this study we analyze the effects of surface solar radiation and near-surface temperatures on photovoltaic power generation. We hope this will provide a reference for predicting the output of photovoltaic power stations. Previous studies have shown that the surface solar radiation and temperature values simulated by climate models deviate somewhat from observed values, mainly because of the difficulty of cloud parameterization [20]. Therefore, some studies [21] have corrected the temperature and radiation before calculating the amount of photovoltaic power generation. To evaluate the effects of systematic deviations in surface solar radiation and temperature on the calculation of photovoltaic power generation, Wild et al. [9] performed a sensitivity experiment. However, the researchers did not find that the correction of their data has a significant impact on their estimations of photovoltaic power generation trends. Therefore, we have not corrected the surface solar radiation and temperature data. In this study, we used irradiance data for the horizontal plane. If the panel is slanted or is a chasing photovoltaic panel, the effects of radiation and temperature changes on photovoltaic power generation will be greater. For example, chasing panels are affected twice as much as horizontal panels [9, 22]. We calculated the relative deviations in the annual photovoltaic power generation for 2006–2049 according to 38 models, and calculated the average photovoltaic power generation from 2006–2015 at each grid point. Then we fit linear trends for the relative deviation data for the 38 models. Finally, the median of 38 model calculation results (regression coefficients) is selected for each grid point (Fig. 4). Fig. 4 is very similar to Fig. 1a, because the trends in temperature changes throughout China are relatively consistent. Formula (1) indicates that temperature increases will lead to decreases in photovoltaic power generation. Additionally, the decline in surface solar radiation (rsds) in western China will lead to a decrease in photovoltaic power generation. Compared with the average photovoltaic power generation from 2006–2015, photovoltaic power generation in western China will decrease by -0.04 %/year.
In southeastern China, photovoltaic power generation will increase by 0.06-0.1%/year. The effect of surface solar radiation on power generation is dominant. Comparing the relative changes in photovoltaic power generation in the eastern and western regions of China over the next 40 years, we find that the eastern region shows a larger change than the western region, mainly because the variation of surface solar radiation in eastern China is significantly greater than that in the west. This is partly because of the effects of cloud cover. In addition, smog and air pollution are the main factors affecting surface solar radiation in the eastern region. Over the past 50 years, the frequency of haze in eastern China has increased significantly, while the frequency in the west has not changed significantly. Therefore, in the next 40 years, smog and other polluted weather in eastern China will decrease. Additionally, wind speeds are negatively correlated with aerosol thickness. Therefore, whether the wind speeds will increase in the future need further research. Although the western part of China shows a negative trend, the changes are relatively small, and the solar energy resources are abundant [23, 24]. The development of photovoltaic power stations is also relatively stable.
Fig. 4 Spatial distribution of median trends of the relative changes in photovoltaic generation capacity in China from 2006–2049 (Unit: %/year)
4 Conclusions In this study, CMIP5 global climate model data are used to predict surface solar radiation, temperature, and cloud cover in China over the next 40 years. Additionally, the effects of surface solar radiation and temperature changes on future photovoltaic power generation are analyzed. The main conclusions are as follows: (1) For surface temperatures, the median trends of all considered models show a warming trend in China, at a rate 457
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of about 0.05 K/year. The maximum positive trend for allsky radiation appears in the southeast of China, reaching about 0.4 W/m 2/year. Cloud cover mainly decreases throughout China. (2) The all-sky radiation for most selected regions shows a decreasing trend. The maximum negative value (-0.08 W/ m2/year) appears in Qinghai. (3) Compared with the average photovoltaic power output from 2006 to 2015, the photovoltaic power output in western China will decrease by -0.04 %/year, while the photovoltaic power output in southeastern China will increase by 0.06–0.1%/year. The new-generation CMIP5 model represents a significant improvement on past climate models. However, there are still some problems associated with the simulation of solar radiation in current climate models, which makes long-term solar radiation predictions uncertain, especially in local regions. The models should be further improved to address these issues.
Acknowledgements This work was supported by the National Natural Science Foundation of China (No. 41805085, 41475066 and 41575112), the State Grid Science and Technology Project (SGGSKY00FJJS1700304), and the Foundation for Excellent Youth Scholars of Northwest Institute of EcoEnvironment and Resources, Chinese Academy of Sciences. Thanks for the simulation test data to the CMIP5 model groups shown in Table 1. Thanks to Doris Folini for his guidance.
References [1] Wild M, Gilgen H, Roesch A, et al (2005) From dimming to brightening: decadal changes in solar radiation at earth’s surface. Science, 308(5723): 847-850 [2] Wild M (2012) Enlightening Global Dimming and Brightening. Bulletin of the American Meteorological Society, 93(1): 7-37 [3] Zekai S (2004) Solar energy in progress and future research trends. Progress in Energy & Combustion Science, 30(4): 367-416 [4] Chen F, Shen L (2012) Regional Differences and Their Cause Analysis of China’s Solar PV Industry. Journal of Basic Science and Engineering, 20(s1): 108-118 [5] Tong X, Wang W, Li M (2015) Recycling Modes of Photovoltaic Modules. Journal of Basic Science and Engineering, (s1): 59-67 [6] Stjern CW, Kristjánsson JE, Hansen AW (2009) Global dimming and global brightening - an analysis of surface radiation and cloud cover data in northern Europe. International Journal of Climatology, 29(5): 643-653 [7] Shen Y, Wang B (2011) Effects of surface solar radiation variations on temperature in southeast China during recent 50 458
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years. Chinese Journal of Geophysics, 54(6): 1457-1465 [8] Qi Y, Fang S, Zhou W (2015) Correlative analysis between the changes of surface solar radiation and its relationship with air pollution, as well as meteorological factor in East and West China in recent 50 years. Acta Physica Sinica, 64(8): 89201-089201 [9] Wild M, Folini D, Henschel F et al (2015) Projections of longterm changes in solar radiation based on CMIP5 climate models and their influence on energy yields of photovoltaic systems. Solar Energy, Vol. 116, pp: 12-24 [10] Crook JA, Jones LA, Forster PM et al (2011) Climate change impacts on future photovoltaic and concentrated solar power energy output. Energy & Environmental Science, 4(9): 3101-3109 [11] Gaetani M, Huld T, Vignati E et al (2014) The near future availability of photovoltaic energy in Europe and Africa in climate-aerosol modeling experiments. Renewable & Sustainable Energy Reviews, 38(5): 706-716 [12] Taylor KE, Stouffer RJ, Meehl GA (2011) An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93(4), 485-498 [13] Moss RH, Edmonds JA, Hibbard KA et al (2010) The next generation of scenarios for climate change research and assessment. Nature, 463(7282): 747−756 [14] Riahi K, Rao S, Krey V et al (2011) RCP8.5: a scenario of comparatively high greenhouse gas emissions. Climatic Change, 109(1): 33-57 [15] Parida B, Iniyan S, Goic R (2011) A review of solar photovoltaic technologies. Renewable & Sustainable Energy Reviews, 15(3): 1625-1636 [16] Fengying W (2007) Modern Diagnosis and Prediction of Climate Statistics. Beijing: Meteorological Press [17] Gao X, Yang L, Lü F et al (2016) Study on the impact of large solar farm on radiation field in desert areas of Golmud. Acta Energiae Solaris Sinica, 37(11): 2909-2915 [18] Yangna L, Xian S, Lu C et al (2016) Analysis of the impact of meteorological factors on photovoltaic power output. Solar energy, Vol. 6, pp: 33-36 [19] Lü X, Sun Z, Cao Z (2014) A trend surface analysis on effect of battery plate temperature and radiation levels on PV power output. Renewable Energy Resources, 32(7): 922-927 [20] Bony S, Dufresne JL (2005) Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophysical Research Lettters, 32(20): L20806 [21] Haerter JO, Hagemann S, Moseley C et al (2010) Climate model bias correction and the role of timescales. Hydrology & Earth System Sciences, 7(5): 301-308 [22] Müller B, Wild M, Driesse A et al (2014) Rethinking solar resource assessments in the context of global dimming and brightening. Solar Energy, 99(1): 272-282 [23] Han X, Liao S, Wen J, et al (2016) Grid integration of solar power in northwest China. In: IEEE International Conference on Power System Technology, pp: 1-6 [24] Sherman P, Chen X, McElroy MB (2017) Wind-generated electricity in China: Decreasing potential, inter-annual variability, and association with climate change. Scientific Reports
Liwei Yang et al. Projections of future changes in solar radiation in China based on CMIP5 climate models
Biographies Liwei Yang received her bachelor degree in Atmospheric Science from Yunnan University, Kunming, 2012. She received her master degree in meteorology from Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, 2015, where she currently works. Her research interests are climate change and climatic resources. Junxia Jiang received her bachelor degree from Lanzhou University. She received her master degree from the university of the Chinese Academy of Sciences. She is working towards a doctoral degree at the Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences. Her research interests are climate resources and numerical simulations. Tian Liu received her bachelor degree from the Nanjing University of Information Science and Technology, 2016. She is working towards a master degree at the Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Her research interests are climate change and land surface processes.
Yujie Li received her bachelor degree from Nanjing University, Nanjing, 2013. She is working towards her master degree at the Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou. Her research interests include climate change and monsoons. Ya Zhou received her master degree from university of the Chinese Academy of Sciences. She is a Ph.D. of the Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences. Her research interests are land surface processes and numerical simulations. Xiaoqing Gao received his bachelor and Ph.D. degrees from Lanzhou University. He received a master’s degree from the Lanzhou Institute of Plateau Atmospheric Physics, Chinese Academy of Sciences. He is a professor and doctoral supervisor of the Key Laboratory of Land Surface Processes and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences. His research interests are land surface processes, climate change, and climatic resources. (Editor Chenyang Liu)
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Projections of future changes in solar radiation in China based on CMIP5 climate models Liwei Yang1, Junxia Jiang1, 2, Tian Liu1, 2, Yujie Li1, 2, Ya Zhou1, 2, Xiaoqing Gao1 1. Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Scan for more details
Lanzhou 730000, P.R. China. 2. University of Chinese Academy of Sciences, Beijing 100049, P.R. China
Abstract: Surface solar radiation research is important for understanding future climate change and the application of largescale photovoltaic systems. We used the coupled model intercomparison project phase 5 (CMIP5) under the RCP8.5 scenario to project potential changes in surface solar radiation, surface temperatures, and cloud fractions between 2006 and 2049 in China, as well as how these changes may affect photovoltaic power generation. The results show that the following. (1) For surface temperatures, the median trends of all considered models show warming in China of 0.05 K/year. The maximum positive trends for all-sky radiation appear in the southeast of China, reaching 0.4 W/m2/year. Cloud cover exhibits a mainly decreasing trend in the overall region of China. (2) The all-sky radiation of most selected regions shows a decreasing trend. The maximum negative value (−0.08 W/m2/year) appears in Qinghai. (3) Compared with the average photovoltaic power output from 2006 to 2015, the photovoltaic power output in western China will decrease by −0.04 %/ year, while photovoltaic power output in southeastern China will increase by 0.06–0.1%/year. Keywords: Solar radiation changes, CMIP5 models, Climate change, PV output, Projections.
1 Introduction Solar radiation is the most important energy source on the earth’s surface. It determines the thermodynamic and dynamic state of the earth’s atmospheric systems and Received: 9 August 2018/ Accepted: 20 August 2018/ Published: 25 October 2018 Xiaoqing Gao [email protected]
Tian Liu [email protected]
Liwei Yang [email protected]
Yujie Li [email protected]
Junxia Jiang [email protected]
Ya Zhou [email protected]
Open access under CC BY-NC-ND license. 452
is an important signal reflecting climate change [1, 2]. In addition, as global industrialization progresses, human needs for energy continue to grow. Solar photovoltaic power is clean and environmentally friendly energy, and has therefore become the cornerstone of long-term strategies for the future production of energy by human societies [3-5]. Variations in solar radiation and the possible factors affecting that variation in China or in the world have been studied by many scholars using observed data. Variations in surface solar radiation in northern Europe showed a significant decreasing trend from the 1950s to the 1980s, followed by a slight increase, and this study pointed out that cloud cover and atmospheric circulation were the
Liwei Yang et al. Projections of future changes in solar radiation in China based on CMIP5 climate models
main influencing factors [6]. Surface solar radiation in southeastern China decreased significantly from 1961 to 1989, and then increased from 1989 to 2008 [7]. Fang Shibo et al. analyzed the variations in total surface solar radiation in eastern and western China from 1961 to 2011, and found that the total surface solar radiation in both eastern and western China showed a decreasing trend, with sunshine percentages and air pollution as the main influencing factors [8]. Current methods for predicting future solar radiation change scenarios mainly involve the extension of statistics and model estimations based on mathematical science [9-11]. However, few scholars have previously predicted the surface solar radiation in China or specific regions within China. The coupled model inter-comparison project phase 5 (CMIP5), which is a global climate model, can effectively simulate average climate states and change characteristics [12]. In this paper, 38 CMIP5 climate models are used to predict changes in surface solar radiation and their effect on photovoltaic power generation in China over the next 40 years. Additionally, several regions that are the key to the development of photovoltaic power stations currently and in the future, are specifically analyzed: Xinjiang, Qinghai, Inner Mongolia, and Shaanxi. This study will be of great significance for understanding future climate change in China, and will help optimize the macro locations of photovoltaic power plants, providing a reference for the future development of solar energy resources.
2 Data and Methods 2.1 Data Data were selected from the CMIP5 simulation data (http://cmip-pcmdi.llnl.gov/cmip5). In the emission scenario, the CMIP5 trial used four representative concentration pathways (RCPs): RCP2.6, RCP4.5, RCP6.0, and RCP8.5 [13]. Emissions scenarios are not chosen to accurately predict the future, but to reduce prediction uncertainty and provide a reference for decisions. In this study, the RCP8.5 high radiative forcing scenario is considered. This scenario is improved relative to the CMIP3 model, because it establishes a spatial distribution of atmospheric pollution estimations and strengthens the estimation of land use and land surface changes [14]. The RCP8.5 scenario does not include any specific tasks of alleviating climate change pressures. Under the RCP8.5 scenario, radiative forcing will rise to 8.5 W/m2, and the CO2 concentrations in the air will reach 1370 ppm (1 ppm =1 μL/L) by 2100, which is 3–4 times higher than CO2 concentrations prior to the industrial revolution. The basic information for the 38 CMIP5 climate patterns selected is shown in Table 1. The selected ensemble
run for each pattern was rlilp1 (r2ilp1 was selected for CESM1-WACCM and FGOALS-s2). The analyzed variables are surface downward solar radiation (rsds, for all weather conditions), surface downward solar radiation under cloudless conditions (rsdscs; the CMCC-CESM, CMCC-CM and CMCC-CMS models are not available for rsdscs), near-surface air temperature (tas, also known as ambient temperature), and total cloud cover (clt). The time period analyzed in this study ranges from 2006 to 2049 (photovoltaic panel lifespans are approximately 40–50 years). The 2006–2015 period is used as the reference period, representing current climatic conditions. We performed a bilinear interpolation of global data in the selected model to a horizontal resolution of 0.75°×0.75°. All data were processed into annual average data.
2.2 Methods To forecast the effects of climate change on future photovoltaic generation, we use the method proposed by Crook et al. [10], in which photovoltaic generation largely depends on battery materials, sunshine, and ambient temperatures. Without considering other loss factors and system components such as inverters, the power output of a photovoltaic panel is PPV = Gtotη cell (1) Where Gtot is the downward solar radiation received by the earth’s surface under all weather conditions and ηcell is the efficiency of the photovoltaic panel (Photovoltaic conversion rate). ηcell is calculated as follows:
η
cell = 1 − β (Tcell − Tref ) + γ log10 Gtot (2) ηref Where η ref is the reference efficiency and β and γ depend on the material and structure of the battery. Parida et al. [15] showed that the monocrystalline silicon cell dominates production, that β =0.0045, and that γ =0.1. Tref = 25 ℃ is the reference temperature and Tcell is the panel temperature, which is determined as follows [10]: (3) Tcell = c1 + c2T + c3Gtot where T is the ambient air temperature (℃). c1, c2 and c3 depend on the properties of the battery and c1= -3.75 ℃, c2= 1.14, c3= 0.0175 ℃m2W-1 for a monocrystalline silicon cell. We calculate the change ( ∆P / P ) in future photovoltaic power generation relative to that under current climatic conditions, because the value of η ref is unknown. In addition, we consider only horizontal irradiance, disregarding the fact that slant panels or tracing ray panels could receive more radiation.
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Table 1 Basic information on the climate models in CMIP5 Model
Unit, country
Resolution
Model
Unit, country
Resolution
ACCESS1-0
CSIRO-BOM, Australia
192 × 145
GFDL-ESM2G
NOAA GFDL, America
144 × 90
ACCESS1-3
CSIRO-BOM, Australia
192 × 145
GFDL-ESM2M
NOAA GFDL, America
144 × 90
BCC-CSM1-1
BCC, China
128 × 64
GISS-E2-H
NASA GISS, America
144 × 90
BCC-CSM1-1-m
BCC, China
320 × 160
GISS-E2-R
NASA GISS, America
144 × 90
BNU-ESM
GCESS, China
128 × 64
HadGEM2-AO
NIMR/KMA, Korea/ England
192 ×145
CanESM2
CCCMA, Canada
128 × 64
HadGEM2-CC
MOHC, England
192 ×145
CCSM4
NCAR, America
288× 192
HadGEM2-ES
MOHC, England
192 ×145
CESM1-BGC
NSF-DOE-NCAR, America
288 × 192
INMCM4
INM, Russia
180 × 120
CESM1-CAM5
NSF-DOE-NCAR, America
288 × 192
IPSL-CM5A-LR
IPSL, France
96 × 96
CESM1-WACCM
NSF-DOE-NCAR, America
144 × 96
IPSL-CM5A-MR
IPSL, France
144 × 143
CMCC-CESM
CMCC, Italy
96 × 48
IPSL-CM5B-LR
IPSL, France
96 × 96
CMCC-CM
CMCC, Italy
480 × 240
MIROC5
MIROC, Japan
256 × 128
CMCC-CMS
CMCC, Italy
192 × 96
MIROC-ESM
MIROC, Japan
128 × 64
CNRM-CM5
CNRM-CERFACS, France
256 × 128
MIROC-ESM-CHEM
MIROC, Japan
128 × 64
CSIRO-Mk3-6-0
CSIRO-QCCCE, Australia
192 × 96
MPI-ESM-LR
MPI-M, Germany
192 × 96
FGOALS-g2
LASG-CESS, China
128 × 60
MPI-ESM-MR
MPI-M, Germany
192 × 96
FGOALS-s2
LASG-IAP, China
128 × 108
MRI-CGCM3
MRI, Japan
320 × 160
FIO-ESM
FIO, China
128 × 64
NorESM1-M
NCC, Norway
144 × 96
GFDL-CM3
NOAA GFDL, America
144 × 90
NorESM1-ME
NCC, Germany
144 × 96
3 Results 3.1 Estimated projections of radiation, temperature, and cloud cover in China by CMIP5 model We use linear regression to analyze the evolution of climate elements on a long-term scale [16]. Fig. 1 shows a spatial distribution of the linear trends of various climates elements from 2006–2049: (a) downward solar radiation received by the earth’s surface; (b) downward solar radiation received by the earth’s surface under cloudless conditions; (c) near-surface air temperatures; (d) total cloud fractions. The regression coefficients are confirmed by a test of significance for the linear regression equation (t-test, α = 0.05). We used a median collection scheme (that is, the median of the 38 pattern data values at each grid point for each variable); Fig. 1 shows the synthesis of the 38 pattern data. 454
According to Fig. 1c, near-surface layer air temperatures are warming throughout China at a rate of 0.05 K/year. Panel efficiency decreases with the increasing panel temperatures induced by increased air temperatures, assuming the same amount of radiation [17]. Fig. 1b shows that surface downward solar radiation under cloudless conditions has a linear trend close to zero or a small negative trend over western China, and a maximum positive trend that is approximately 0.3 W/m2/year over southeastern China. Previous studies have shown that atmospheric turbidity dominates surface solar radiation in eastern China [8]. Therefore, the maximum positive trend in southeastern China may result from a decrease in aerosol concentrations. Positive and negative trends in the spatial distribution of surface downward solar radiation under all weather conditions (rsds), which mainly includes the effects of clouds, is consistent with that under cloudless conditions
Liwei Yang et al. Projections of future changes in solar radiation in China based on CMIP5 climate models
(a) Surface downward solar radiation (rsds, units: W/m2/yr)
(c) Near surface air temperature (tas, unit: K/yr)
(b) Surface downward clear-sky solar radiation (rsdscs, unit: W/m2/yr)
(d) Total cloud fraction (clt, unit: %/yr)
Fig. 1 Distribution of median trends of all considered models between 2006 and 2049 in China
(rsdscs), as shown in Fig. 1a. Cloud cover exhibits a negative trend throughout China; in the southeast, that negative trend is up to -0.1%/year. However, rsds increases, with a trend of 0.4 W/m2/year (Fig. 1d). The results are consistent with those of Wild et al. [9].
3.2 E stimates of selected areas of radiation, temperature and cloud cover by CMIP5 model Combined with the National Energy Administration’s 12th Five-Year Plan and 13th Five-Year Plan for developing solar power, the eastern part of Xinjiang (86°E ~ 95°E, 42°N ~ 44°N), Qinghai (91°E ~ 101°E, 34°N ~ 38°N), Inner Mongolia Municipal (101°E ~ 112°E, 39°N ~ 42°N) and the northern part of Shaanxi (108°E ~ 110.7°E, 36°N ~ 39°N) are selected to analyze, because these regions will develop photovoltaic rapidly in the future (Fig. 2).
The average annual variations in climatic factors in the selected area (deviation from the average of ten years from 2006 to 2015) are shown in Fig. 3. The black lines in the figure represent annual variations in the deviation values of the 38 models (rsds, rsdscs, tas, clt). The red lines in the figure show a linear trend fit of the median of 38 model deviation values. As shown in Fig. 3, the linear temperature trends (tas) increase in selected regions. The increase rates are only slightly different, and all approximately 0.49 K/ year. Downward solar radiation under cloudless conditions (rsdscs) exhibits negative trends in Xinjiang, Qinghai, and Inner Mongolia. The maximum negative trend (-0.051 W/ m2/year) occurs in Xinjiang. In Shaanxi, the rsdscs trends before 2025 are not significant, and increase after 2025. The surface downward solar radiation (rsds) in Xinjiang, Qinghai and Inner Mongolia exhibit negative trends. The maximum negative trend (-0.080 W/m2/year) appears in 455
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Qinghai. In Shaanxi, the rsds increases at a rate of 0.030 W/m2/year. The amount of clouds selected in the figure is negative, and the maximum negative trend (-0.025 %/year) appears in Qinghai. Previous studies have shown that total cloud cover has an irregular correlation with surface solar radiation in western China [8]. The surface downward solar radiation in the western region is mainly characterized by a decreasing trend, which may be related to atmospheric turbidity and the percentage of sunshine. In short, surface downward solar radiation in the northwestern region of China mainly exhibits a negative trend. The temperature is warming in China. Those all negatively affect power generation by photovoltaic power plants in northwest China. 5
0 -10 -20 2000
2025 time [year]
0
-5 2000
2050
tas [ K ]
rsdscs [W ⋅m-2]
rsds [W ⋅m-2]
10
4
m= -0.032
m= -0.036
2025 time [year]
m= -0.001
5
2
0
0 -2 2000
2050
10
m= 0.050
clt [%]
20
Fig. 2 Four regions of China selected in this study
-5
2025 time [year]
2050
-10 2000
2025 time [year]
2050
(a) Rsds, rsdscs, tas, and clt in Xinjiang 5
0 -10 2025 time [year]
0
-5 2000
2050
2025 time [year]
10 m= 0.048
0 -2 2000
2050
m= -0.025
5
2
tas [ K ]
10
-20 2000
4 m= -0.051
clt [%]
m= -0.080
rsdscs [W ⋅m-2]
rsds [W ⋅m-2]
20
0 -5
2025 time [year]
-10 2000
2050
2025 time [year]
2050
(b) Rsds, rsdscs, tas, and clt in Qinghai
-10 2025 time [year]
0
-5 2000
2050
2025 time [year]
5
2 0
-2 2000
2050
10
m= 0.049
clt [%]
0
4 m= -0.018
tas [ K ]
10
-20 2000
5
m= -0.032
rsdscs [W ⋅m-2]
rsds [W ⋅m-2]
20
0 -5
2025 time [year]
2050
-10 2000
m= -0.002 2025 time [year]
2050
(c) Rsds, rsdscs, tas, and clt in Inner Mongolia
-2 rsdscs [W⋅m ]
-2 rsds [W ⋅m ]
4
10 0 -10
5
15 m = 0.048
m = 0.032
0
10
2
clt [%]
m= 0.030
20
tas [ K ]
30
10
0
-5
5 0 -5
-20 -30 2000
-10 2025 time [year]
2050
-10 2000
2025 time [year]
2050
-2 2000
2025 time [year]
2050
-15 2000
(d) Rsds, rsdscs, tas, and clt in Shaanxi
Fig. 3 Annual variation of rsds [W/m2], rsdscs [W/m2], tas [K] and clt [%] for the four focal regions (m is the regression coefficient, units are per year) 456
m= -0.012
2025 time [year]
2050
Liwei Yang et al. Projections of future changes in solar radiation in China based on CMIP5 climate models
3.3 P otential impact of climate elements on solar photovoltaic power plants Power generation in photovoltaic systems is greatly affected by natural environmental conditions. The study of the prediction on PV power generation by using meteorological factors needs to be further improved [18]. Lv et al. [19] found that radiation and temperature are the two main factors affecting photovoltaic power generation. As is known from formulas (1, 2, and 3) in section 1.2, in this study we analyze the effects of surface solar radiation and near-surface temperatures on photovoltaic power generation. We hope this will provide a reference for predicting the output of photovoltaic power stations. Previous studies have shown that the surface solar radiation and temperature values simulated by climate models deviate somewhat from observed values, mainly because of the difficulty of cloud parameterization [20]. Therefore, some studies [21] have corrected the temperature and radiation before calculating the amount of photovoltaic power generation. To evaluate the effects of systematic deviations in surface solar radiation and temperature on the calculation of photovoltaic power generation, Wild et al. [9] performed a sensitivity experiment. However, the researchers did not find that the correction of their data has a significant impact on their estimations of photovoltaic power generation trends. Therefore, we have not corrected the surface solar radiation and temperature data. In this study, we used irradiance data for the horizontal plane. If the panel is slanted or is a chasing photovoltaic panel, the effects of radiation and temperature changes on photovoltaic power generation will be greater. For example, chasing panels are affected twice as much as horizontal panels [9, 22]. We calculated the relative deviations in the annual photovoltaic power generation for 2006–2049 according to 38 models, and calculated the average photovoltaic power generation from 2006–2015 at each grid point. Then we fit linear trends for the relative deviation data for the 38 models. Finally, the median of 38 model calculation results (regression coefficients) is selected for each grid point (Fig. 4). Fig. 4 is very similar to Fig. 1a, because the trends in temperature changes throughout China are relatively consistent. Formula (1) indicates that temperature increases will lead to decreases in photovoltaic power generation. Additionally, the decline in surface solar radiation (rsds) in western China will lead to a decrease in photovoltaic power generation. Compared with the average photovoltaic power generation from 2006–2015, photovoltaic power generation in western China will decrease by -0.04 %/year.
In southeastern China, photovoltaic power generation will increase by 0.06-0.1%/year. The effect of surface solar radiation on power generation is dominant. Comparing the relative changes in photovoltaic power generation in the eastern and western regions of China over the next 40 years, we find that the eastern region shows a larger change than the western region, mainly because the variation of surface solar radiation in eastern China is significantly greater than that in the west. This is partly because of the effects of cloud cover. In addition, smog and air pollution are the main factors affecting surface solar radiation in the eastern region. Over the past 50 years, the frequency of haze in eastern China has increased significantly, while the frequency in the west has not changed significantly. Therefore, in the next 40 years, smog and other polluted weather in eastern China will decrease. Additionally, wind speeds are negatively correlated with aerosol thickness. Therefore, whether the wind speeds will increase in the future need further research. Although the western part of China shows a negative trend, the changes are relatively small, and the solar energy resources are abundant [23, 24]. The development of photovoltaic power stations is also relatively stable.
Fig. 4 Spatial distribution of median trends of the relative changes in photovoltaic generation capacity in China from 2006–2049 (Unit: %/year)
4 Conclusions In this study, CMIP5 global climate model data are used to predict surface solar radiation, temperature, and cloud cover in China over the next 40 years. Additionally, the effects of surface solar radiation and temperature changes on future photovoltaic power generation are analyzed. The main conclusions are as follows: (1) For surface temperatures, the median trends of all considered models show a warming trend in China, at a rate 457
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of about 0.05 K/year. The maximum positive trend for allsky radiation appears in the southeast of China, reaching about 0.4 W/m 2/year. Cloud cover mainly decreases throughout China. (2) The all-sky radiation for most selected regions shows a decreasing trend. The maximum negative value (-0.08 W/ m2/year) appears in Qinghai. (3) Compared with the average photovoltaic power output from 2006 to 2015, the photovoltaic power output in western China will decrease by -0.04 %/year, while the photovoltaic power output in southeastern China will increase by 0.06–0.1%/year. The new-generation CMIP5 model represents a significant improvement on past climate models. However, there are still some problems associated with the simulation of solar radiation in current climate models, which makes long-term solar radiation predictions uncertain, especially in local regions. The models should be further improved to address these issues.
Acknowledgements This work was supported by the National Natural Science Foundation of China (No. 41805085, 41475066 and 41575112), the State Grid Science and Technology Project (SGGSKY00FJJS1700304), and the Foundation for Excellent Youth Scholars of Northwest Institute of EcoEnvironment and Resources, Chinese Academy of Sciences. Thanks for the simulation test data to the CMIP5 model groups shown in Table 1. Thanks to Doris Folini for his guidance.
References [1] Wild M, Gilgen H, Roesch A, et al (2005) From dimming to brightening: decadal changes in solar radiation at earth’s surface. Science, 308(5723): 847-850 [2] Wild M (2012) Enlightening Global Dimming and Brightening. Bulletin of the American Meteorological Society, 93(1): 7-37 [3] Zekai S (2004) Solar energy in progress and future research trends. Progress in Energy & Combustion Science, 30(4): 367-416 [4] Chen F, Shen L (2012) Regional Differences and Their Cause Analysis of China’s Solar PV Industry. Journal of Basic Science and Engineering, 20(s1): 108-118 [5] Tong X, Wang W, Li M (2015) Recycling Modes of Photovoltaic Modules. Journal of Basic Science and Engineering, (s1): 59-67 [6] Stjern CW, Kristjánsson JE, Hansen AW (2009) Global dimming and global brightening - an analysis of surface radiation and cloud cover data in northern Europe. International Journal of Climatology, 29(5): 643-653 [7] Shen Y, Wang B (2011) Effects of surface solar radiation variations on temperature in southeast China during recent 50 458
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years. Chinese Journal of Geophysics, 54(6): 1457-1465 [8] Qi Y, Fang S, Zhou W (2015) Correlative analysis between the changes of surface solar radiation and its relationship with air pollution, as well as meteorological factor in East and West China in recent 50 years. Acta Physica Sinica, 64(8): 89201-089201 [9] Wild M, Folini D, Henschel F et al (2015) Projections of longterm changes in solar radiation based on CMIP5 climate models and their influence on energy yields of photovoltaic systems. Solar Energy, Vol. 116, pp: 12-24 [10] Crook JA, Jones LA, Forster PM et al (2011) Climate change impacts on future photovoltaic and concentrated solar power energy output. Energy & Environmental Science, 4(9): 3101-3109 [11] Gaetani M, Huld T, Vignati E et al (2014) The near future availability of photovoltaic energy in Europe and Africa in climate-aerosol modeling experiments. Renewable & Sustainable Energy Reviews, 38(5): 706-716 [12] Taylor KE, Stouffer RJ, Meehl GA (2011) An overview of CMIP5 and the experiment design. Bulletin of the American Meteorological Society, 93(4), 485-498 [13] Moss RH, Edmonds JA, Hibbard KA et al (2010) The next generation of scenarios for climate change research and assessment. Nature, 463(7282): 747−756 [14] Riahi K, Rao S, Krey V et al (2011) RCP8.5: a scenario of comparatively high greenhouse gas emissions. Climatic Change, 109(1): 33-57 [15] Parida B, Iniyan S, Goic R (2011) A review of solar photovoltaic technologies. Renewable & Sustainable Energy Reviews, 15(3): 1625-1636 [16] Fengying W (2007) Modern Diagnosis and Prediction of Climate Statistics. Beijing: Meteorological Press [17] Gao X, Yang L, Lü F et al (2016) Study on the impact of large solar farm on radiation field in desert areas of Golmud. Acta Energiae Solaris Sinica, 37(11): 2909-2915 [18] Yangna L, Xian S, Lu C et al (2016) Analysis of the impact of meteorological factors on photovoltaic power output. Solar energy, Vol. 6, pp: 33-36 [19] Lü X, Sun Z, Cao Z (2014) A trend surface analysis on effect of battery plate temperature and radiation levels on PV power output. Renewable Energy Resources, 32(7): 922-927 [20] Bony S, Dufresne JL (2005) Marine boundary layer clouds at the heart of tropical cloud feedback uncertainties in climate models. Geophysical Research Lettters, 32(20): L20806 [21] Haerter JO, Hagemann S, Moseley C et al (2010) Climate model bias correction and the role of timescales. Hydrology & Earth System Sciences, 7(5): 301-308 [22] Müller B, Wild M, Driesse A et al (2014) Rethinking solar resource assessments in the context of global dimming and brightening. Solar Energy, 99(1): 272-282 [23] Han X, Liao S, Wen J, et al (2016) Grid integration of solar power in northwest China. In: IEEE International Conference on Power System Technology, pp: 1-6 [24] Sherman P, Chen X, McElroy MB (2017) Wind-generated electricity in China: Decreasing potential, inter-annual variability, and association with climate change. Scientific Reports
Liwei Yang et al. Projections of future changes in solar radiation in China based on CMIP5 climate models
Biographies Liwei Yang received her bachelor degree in Atmospheric Science from Yunnan University, Kunming, 2012. She received her master degree in meteorology from Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences, Lanzhou, 2015, where she currently works. Her research interests are climate change and climatic resources. Junxia Jiang received her bachelor degree from Lanzhou University. She received her master degree from the university of the Chinese Academy of Sciences. She is working towards a doctoral degree at the Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences. Her research interests are climate resources and numerical simulations. Tian Liu received her bachelor degree from the Nanjing University of Information Science and Technology, 2016. She is working towards a master degree at the Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, Her research interests are climate change and land surface processes.
Yujie Li received her bachelor degree from Nanjing University, Nanjing, 2013. She is working towards her master degree at the Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou. Her research interests include climate change and monsoons. Ya Zhou received her master degree from university of the Chinese Academy of Sciences. She is a Ph.D. of the Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences. Her research interests are land surface processes and numerical simulations. Xiaoqing Gao received his bachelor and Ph.D. degrees from Lanzhou University. He received a master’s degree from the Lanzhou Institute of Plateau Atmospheric Physics, Chinese Academy of Sciences. He is a professor and doctoral supervisor of the Key Laboratory of Land Surface Processes and Climate Change in Cold and Arid Regions, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences. His research interests are land surface processes, climate change, and climatic resources. (Editor Chenyang Liu)
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