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      Global Energy Interconnection

      Volume 1, Issue 4, Oct 2018, Pages 443-451
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      Projection of global wind and solar resources over land in the 21st century

      Feimin Zhang1 ,Chenghai Wang1 ,Guohui Xie2 ,Weizheng Kong2 ,Shuanglong Jin3 ,Ju Hu3 ,Xi Chen4
      ( 1.College of Atmospheric Sciences, Lanzhou University, Key Laboratory for Arid Climatic Change and Disaster Reduction of Gansu Province, Lanzhou 730000, P.R.China , 2.State Grid Energy Research Institute, CO., LTD., Beijing 102209, P.R.China , 3.State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems, China Electric Power Research Institute, Beijing 100192, P.R.China , 4.GEIRI North America, 250 W Tasman Dr., Ste 100, San Jose, California, USA )

      Abstract

      This study modelled projected spatiotemporal changes in global wind and solar resources over land in the 21st century under the RCP2.6 and RCP8.5 climate scenarios using an ensemble mean drawn from 11 Coupled Model Intercomparison Project Phase 5 (CMIP5) models.These models’ performances were verified by comparing historical global near-surface wind speed and downward surface solar radiation over land.Compared to the baseline historical period 1985–2005, the distribution of relative projected changes in global wind and solar resources had great spatial and seasonal discrepancies.Under both climate scenarios, projected wind resources throughout the 21st century presented a decreasing trend in Asia and Europe but an increasing trend in the low-latitude Americas.In comparison, projected global solar resources over land generally showed an increasing trend throughout the 21st century, especially in Europe, eastern Asia,and eastern North America.Moreover, wind resources in the Americas had their most significant decrease and increase in January and July, respectively, while in Asia and Europe the decreasing trend as most prominent in January and October,respectively.The most significant increases in solar resources in the Americas, Asia, and Europe happened in October and July, respectively.Discrepancies between the variation trends of future global wind and solar resources suggest the complexity and nonlinearity of these resources’ responses to future climate change.

      1 Introduction

      Near-surface wind speed and solar resources are important components of global renewable resources that have achieved striking development recently.The Global Wind Energy Council (GWEC) reported that global wind and solar resources have increased significantly in past years and will continue to be developed, especially in Asia,Europe, and North America [1].

      Near-surface wind speed and downward surface solar radiation are the basic components that most directly influence wind and solar resources; these have been broadly investigated by previous studies [2-6].For instance, the mean near-surface wind speed over China has shown a decreasing trend in the past 50 years [7].The wind-power density in China’s northwestern Shanxi province has also shown an obvious decreasing trend in the past 50 years that is closely related to changes in atmospheric circulation and land cover/use [8].Previous studies have also shown that, in past years, mean near-surface wind speeds over land have had a decreasing trend in northern hemisphere mid-latitude and tropical areas but an increasing trend in polar regions[9, 10].Analysis of Chinese meteorological station data has found that the overall downward surface solar radiation has been decreasing in the past years; this is closely related to low cloud activity in the atmosphere [11, 12].In comparison,downward surface solar radiation increased by 5% in Europe from 1990–2009 [13].

      The distribution and amount of global wind and solar resources will be altered by changes in the future global climate.For instance, the annual average near-surface wind speed in China, most of France, and the Mediterranean coast is projected to have a decreasing trend in the 21st century that would become more pronounced with further increases greenhouse gas emission [14, 15].In contrast,regional climate models project that near-surface wind speed in most of the United States would increase under medium greenhouse gas emissions (the A1B scenario) to a maximum value of ~0.4 m s-1 [16].Model data based on the Coupled Model Inter-comparison Project Phase 5 (CMIP5)suggest that the future wind speed increases in South Africa will not exceed 6%, though appropriate wind speeds for power generation in northeastern South Africa will increase modestly [17].Although future wind energy potential is projected to increase in middle and northern Europe while decreasing near the Mediterranean coast under climate change conditions, annual variation trends in European wind energy are not clear [18].Overall, mean future wind energy is projected to decrease in the middle latitudes of the northern hemisphere but increase in tropical areas and the southern hemisphere under global warming scenarios [19].

      Previous studies have also indicated that future downward surface solar radiation and solar resources will be altered by climate change scenarios.For instance, one regional climate model projects that downward surface solar radiation in the United States could decrease by up to 20%at the seasonal scale [20].Statistical modeling projected that the future downward surface solar radiation in Japan would increase in warm seasons but decrease in cold seasons [21].Based on the ensemble mean model results from CMIP3,the projected downward surface solar radiation in Europe should increase in central and southern Europe by 5–10%but decrease in northern and eastern Europe by 5–15%under the A1B emission scenario [22].Downward surface solar radiation in Nigeria is projected to decrease by the end of the 21st century, especially in southern Nigeria [23].

      Overall, projections of near-surface wind speed and downward surface solar radiation have evident regional and seasonal discrepancies along with obvious annual and decadal variations, indicating that climate change has important direct impacts on the wind and solar resources,though they have great uncertainties in response to climate change.Most previous studies of future distributions and variation trends in wind and solar resources have mainly focused on specific regions or countries.Therefore,further study is needed with regard to the future global spatial distribution and temporal variation of wind and solar resources over land during the 21st century.Such results could be important to the proper development and utilization of global wind and solar resources in the future.

      This paper is organized as follows: Section 2 introduces the model, data, and methodology used in this study,Section 3 presents verifications of model accuracy, Sections 4 and 5 discuss the spatial distributions and temporal variations of global wind and solar resources over land in different periods of the 21st century, as well as their seasonal characteristics, and Section 6 presents further discussion and summarizes the conclusions.

      2 Model, data, and methods

      2.1 CMIP5 project models

      The Coupled Model Inter-comparison Project (CMIP)which is dedicated to promoting the development of climate models and includes over 58 models around the world, is becoming an important tool for future climate projection.CMIP5 uses four emission experiments called Representative Concentration Pathways (RCPs) for future climate projection: RCP2.6, RCP4.5, RCP6.0, and RCP8.5,representing radiation forcing of 2.6 W m-2, 4.5 W m-2, 6.0 W m-2, and 8.5 W m-2, respectively, by 2100 (compared to the pre-industrial period).This study specifically analyzed wind and solar resources under the RCP2.6 and RCP8.5 scenarios, since they correspond to lowest and highest climate change intensity due to human activity, respectively.A total of 16 models were selected to project future wind and solar resources in the 21st century, all of which included monthly near-surface temperature, near-surface wind speed,precipitation, and downward surface solar radiation data simultaneously (Table 1).

      Table 1 CMIP5 models used in this study (bold font indicates those used for the ensemble)

      Model name Country Historical integration Future integration Horizonal resolution(longitude × latitude)1.BCC-CSM1.1 China 1850—2012 2006—2300 2.8°×2.8°2.BNU-ESM China 1850—2012 2006—2100 1.3°×1.1°3.CanESM2 Canada 1850—2005 2006—2100 2.8°×2.8°4.CNRM-CM5 France 1850—2005 2006—2100 1.4°×1.4°5.CSIRO-Mk3.6.0 Australia 1850—2005 2006—2300 1.875°×1.875°6.MIROC-ESM Japan 1850—2005 2006—2300 2.8°×2.8°7.MIROC-ESM-CHEM Japan 1850—2005 2006—2100 1.875°×1.25°8.MIROC5 Japan 1850—2012 2006—2100 2.8°×2.8°9.GISS-E2-H United States 1850—2005 2006—2300 2.5°×2.0°10.NorESM1-M Norway 1850—2005 2006—2100 2.5°×1.875°11.NorESM1-ME Norway 1850—2005 2006—2100 2.5°×1.875°12.IPSL-CM5A-LR France 1850—2005 2006—2300 3.75°×1.875°13.IPSL-CM5A-MR France 1850—2005 2006—2100 2.5°×1.25°14.HadGEM2-ES United Kingdom 1859—2005 2006—2299 1.875°×1.25°15.HadGEM2-AO Korean 1859—2005 2006—2299 1.875°×1.25°16.MRI-CGCM3 Japan 1850—2005 2006—2100 1.125°×1.125°

      In this study, the historical (reference/benchmark)period was defined as 1986–2005 according to the Fifth Assessment Report (AR5) issued by the Intergovernmental Panel on Climate Change (IPCC).The 21st century was divided into 3 periods: early (EP: 2016–2045), middle (MP:2046–2065), and late (LP: 2066–2099).

      2.2 Data

      Near-surface temperature and precipitation data were acquired from the Climate Research Unit (CRUv4.01,http://www.cru.uea.ac.uk/data) dataset.Near-surface wind speed and downward surface solar radiation data were acquired from the National Centers for Environmental Prediction (NCEP, https://www.esrl.noaa.gov/psd/data/gridded/).These were used to verify the CMIP5 model performance in the historical period.

      2.3 Methods

      Wind resources were defined as:

      where W is wind resource (W m-2), Hρ is air density (g m-3),and V is near-surface wind speed (m s-1).Air density was calculated by considering the influence of terrain elevation [24]:

      where Hρ is the air density at altitude H, ρ0 =1225 g m-3 as the reference air density, T0 = 273 °K is the absolute temperature, and α=0.0065 °C m-1 denotes the vertical atmospheric temperature decreasing trend.In effect,Eq.(2) defines air density as being smaller at higher altitudes.

      Solar resources were defined as the direct downward surface solar radiation (W m-2); the stronger the downward surface solar radiation, the larger amount of solar resources.

      The following statistical methods were also used:

      Root Mean Square Error (RMSE):

      Correlation coefficient (R):

      Normalized Standard Deviation (NSD):

      where Pi and Oi represent model and observation results at each integration time, andrepresent averaged model and observation results during a period, and n is the sample size.

      To acquire a relatively reasonable comparison, all data and models were interpolated to the same horizontal gird resolution of 2.5° × 2.5° (using bilinear interpolation)before calculations.

      3 Verifying CMIP5 model performance in the historical period

      Fig.1 compares the simulation performance of global near-surface wind speed, temperature, precipitation,and downward surface solar radiation over land among the CMIP5 models.The results show that these models accurately reflected the spatial distribution of these factors with correlation coefficients all passing the 99%significance test.The NSD had the greatest discrepancy,indicating larger uncertainties for a single model.Based on these results, 11 models with the best results were selected for use in an ensemble model (Table 1) as climate projections based on a multiple-model ensemble mean have better performances than any single model.The results of the multiple-model ensemble were used to project future wind and solar resources.

      Fig.2 shows the global near-surface wind speed and downward surface solar radiation over land simulated by the multiple-model ensemble mean.These results accurately reproduced the spatial distributions of historical global near-surface wind speed and downward surface solar radiation over land when compared to a previous study[25], confirming the good performance and reliability of the selected ensemble.Three regions (Asia, the lower-latitude Americas, and Europe) where solar and wind resources have rapidly development in recent years [1]were then further investigated January, April, July and October in northern hemisphere are chosen to represent winter, spring, summer and autumn, respectively.

      Fig.1 Taylor diagram of global (a) near-surface wind speed, (b) temperature, (c) precipitation, and (d) downward surface solar radiation over land for the 16 CMIP5 models initially selected.“REF” is a reference level equaling 1.0

      Fig.2 Ensemble-simulated the historical period (1986–2005).Boxes denote regions chosen for further analysis in this study

      4 Projection of global wind resources over land in the 21st century

      The distributions of relative changes in wind resources in the 21st century showed great spatial discrepancies (Fig.3).During the EP, under RCP2.6, wind resources increased in South America and central Africa, but decreased in most of North America, Australia, and northern Asian; under RCP8.5, wind resources increased in South America,central North America, and central Africa, but decreased in northern Asia and northeastern Australia.During the MP, under RCP2.6, wind resources over most of the world increased compared to the EP, but decreased in some parts of northern Asia; under RCP8.5, wind resources decreased in northern Asia.During the LP, under RCP2.6, wind resources decreased in most of the world except South America, central and southern Africa, and southeastern Asia; under RCP8.5, wind resources increased in South America, most of Africa, and southern North America, but decreased in central and northern Asia and central North America.

      Further comparison of the time series of relative changes in wind resources over land in Asia, the Americas and Europe between the 21st century and the historical period(not shown) indicated that wind resources would decrease in Asia and Europe during the entire 21st century, with the largest decreasing trend occurring under RCP8.5 in the late 21st century; wind resources decreased more significantly in Asia than in Europe.In the Americas, wind resources during the 21st century had a decreasing trend under RCP4.5 but an increasing trend under RCP2.6 and RCP8.5.

      Fig.3 Relative changes in global annual mean wind resources (%) and distribution over land during the (a-b)EP, (c-d) MP, and (e-f) LP of the 21st century under (a,c,e) RCP2.6 and (b,d,f) RCP8.5, compared to 1986–2005

      From a seasonal perspective, the decrease and increase in wind resources in the Americas were most significant in January and July, respectively, under both RCP scenarios(Table 2).In Asia and Europe, the overall wind resources generally presented a decreasing trend, with the largest drops occurring in January and October, respectively, under both RCP scenarios.

      Table 2 Relative change in seasonal mean wind resources (%)over land by region in the 21st century, compared to 1986–2005

      Region RCPs Jan Apr Jul Oct The Americas RCP2.6 -8.3 -5.4 12.3 -0.9 RCP8.5 -13.1 -3.5 17.6 0.2 Asia RCP2.6 -10.8 -7.0 2.9 -2.9 RCP8.5 -10.6 -4.0 -0.6 -7.0 Europe RCP2.6 -4.3 -7.4 3.3 -10.8 RCP8.5 -4.4 -8.8 -1.1 -8.6

      5 Projection of global solar resources over land in the 21st century

      Global solar resources over land increased during the whole 21st century under both RCP scenarios, especially in Europe, eastern Asia, and eastern North America (Fig.4);this increase was more evident under RCP8.5 than under RCP2.6.Solar resources tended to increase more as the century progressed from EP to MP to LP.

      From a seasonal perspective, the greatest increases in solar resources in the Americas, Asia, and Europe occurred under both RCP scenarios in October, July, and July,respectively, while all three regions had a lower increase trend in January.

      Table 3 Relative change in seasonal mean solar resources (%)over land by region in the 21st century, compared to 1986–2005

      Region RCPs Jan Apr Jul Oct The Americas RCP2.6 10.2 11.7 12.1 13.3 RCP8.5 10.0 11.1 12.1 13.3 Asia RCP2.6 10.9 11.0 12.4 11.4 RCP8.5 10.7 10.6 12.3 11.0 Europe RCP2.6 10.1 10.8 12.9 10.9 RCP8.5 9.8 10.1 12.8 10.3

      6 Discussion and conclusions

      Accurate projections of wind and solar resources under future climate change scenarios are important to the development of renewable energy.This study used a CIMP5 model ensemble to project changes in the spatiotemporal distribution of global wind and solar resources in the 21st century (compared to the 1986–2005 baseline historical period), reaching the following conclusions:

      Fig.4 Relative changes in global solar resources (%) and distribution over land during the (a-b) EP, (c-d)MP, and (e-f) LP of the 21st century under (a,c,e) RCP2.6 and (b,d,f) RCP8.5, compared to 1986–2005

      (1) Global near-surface wind speeds and downward surface solar radiation over land during the historical period were accurately reproduced by the selected models,indicating that the multiple-model ensemble could reliably project global wind and solar resources in the 21st century.

      (2) Compared to the historical period, the distribution of relative changes in global wind and solar resources had greater spatial discrepancies over land, especially for wind resources.Under both RCP2.6 and RCP8.5, projected wind resources century generally presented a decreasing trend in Asia and Europe and an increasing trend in the Americas.However, projected global solar resources over land generally has an increasing trend throughout the 21st century, especially in Europe, eastern Asia, and eastern North America.The increase in future solar resources over land was more evident under RCP8.5 than under RCP2.6.In addition, solar resources under both scenarios were projected to become more abundant in the late 21st century.

      (3) Compared to the historical period, the distribution of relative changes in global wind and solar resources had greater seasonal discrepancies.Under both RCP scenarios,decreases and increases in wind resources in the Americas were most significant in January and July, respectively.Wind resources in Asia and Europe had the largest decreasing trends in January and October, respectively.The most significant increases in solar resources in the Americas, Asia, and Europe occurred in October, July, and July, respectively.

      Although the simulation performances of climate models have improved over the years, projections of future climate based on mean ensemble results still have great uncertainties and discrepancies.This is particularly true for near-surface wind speed and downward surface solar radiation over complex terrain, which is mainly limited by uncertainties in climate forcing data and the physics-based parameterizations of the models.For instance, a previous study found that model results are inconsistent with reanalysis datasets over higher-terrain regions, especially for land regions at latitudes greater than 30°N [26].As such uncertainties regarding the future projection of renewable resources remain inevitable,this deserves to be further investigated.

      Moreover, the climate change mechanisms influencing changes in wind and solar resources are complex.For instance, previous studies have indicated that atmospheric circulation, the El Niño-Southern Oscillation (ENSO), and dynamic and thermal effects from the underlying terrain and human activities all have important impacts on changes in global near-surface wind speed [28].However, these factors still cannot be fully addressed by numerical models as the current mechanisms remain controversial.This could be clearly seen in the current study, where the relative changes of future wind and solar resources did not increase or decrease consistently with increasing or decreasing RCP scenarios, implying the presence of complex and nonlinear responses of wind and solar resources to radiation forcing(climate change) in the future.Thus, possible factors influencing these future resources and their associated interconnections should be studied further.

      Acknowledgements

      This work was supported by the Fundamental Research Funds of the Central Universities (NO.lzujbky-2017-71),the National Key Research and Development Program of China(No.2018YFB0904000), the foundation of the State Grid Energy Research Institute (NO.B3680116048700ZS000000),and the State Grid Science and Technology Project.

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      Fund Information

      supported by the Fundamental Research Funds of the Central Universities(NO.lzujbky-2017-71); the National Key Research and Development Program of China(No.2018YFB0904000); the foundation of the State Grid Energy Research Institute(NO.B3680116048700ZS000000); the State Grid Science and Technology Project;

      supported by the Fundamental Research Funds of the Central Universities(NO.lzujbky-2017-71); the National Key Research and Development Program of China(No.2018YFB0904000); the foundation of the State Grid Energy Research Institute(NO.B3680116048700ZS000000); the State Grid Science and Technology Project;

      Author

      • Feimin Zhang

        Feimin Zhang received his Ph.D.degree from Lanzhou University, China, in 2017.He is now working at Lanzhou University, China.His research interests include numerical weather prediction, data assimilation, wind power prediction, and land-air interactions.

      • Chenghai Wang

        Chenghai Wang works at the College of Atmospheric Sciences, Lanzhou University,China.His research interests include landatmosphere interactions in cold and arid regions, climate dynamics and prediction,modeling climate and weather, and wind and solar power predictions.

      • Guohui Xie

        Guohui Xie received his Ph.D.degree from North China Electricity Power University,Beijing, China, in 2010.He now works at the State Grid Energy Research Institute.His research interests include power systems,power market operations, energy development strategies and plan, and new energy integration and consumption.

      • Weizheng Kong

        Weizheng Kong received his master degree from Tsinghua University in 2010.He now works at the State Grid Energy Research Institute.His research interests include renewable energy development and integration policy.

      • Shuanglong Jin

        Shuanglong Jin received his master and Ph.D.degrees from Lanzhou University, Lanzhou,from 2008–2013.He now works at the China Electric Power Research Institute, Beijing.His research interests include renewable energy assessment and forecasting and electric power meteorology research and applications.

      • Ju Hu

        Ju Hu received her master degree from Lanzhou University in 2012.She now works at the China Electric Power Research Institute.Her research interests include power meteorology and new energy resources assessment.

      • Xi Chen

        Xi Chen received his bachelor degree from Beijing Technology and Business University,Beijing, China, master degree from University of London, U.K., and Ph.D.degree from the Hong Kong Polytechnic University in 2003,2005 and 2009, respectively.He currently is the Chief Information Officer at GEIRI North America.His research interests include the internet of things, smart grid, electric vehicle charging infrastructure,and complex networks analysis and its applications.

      Publish Info

      Received:2018-08-03

      Accepted:2018-08-27

      Pubulished:2018-10-25

      Reference: Feimin Zhang,Chenghai Wang,Guohui Xie,et al.(2018) Projection of global wind and solar resources over land in the 21st century.Global Energy Interconnection,1(4):443-451.

      (Editor Chenyang Liu)
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