Selecting Future Climate Projections of Surface Solar Radiation in Japan

Shiogama, Hideo; Ito, Rui; Imada, Yukiko; Nakaegawa, Toshiyuki; Hirota, Noriyuki; Ishizaki, Noriko N.; Toko, Kiyoshi; Takayabu, Izuru; Emori, Seita

doi:10.2151/sola.2020-013

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…In general, the high-resolution MRI simulations indicated that the topographic pattern of the region would determine the extent of future changes and that, among the investigated variables, these variations remained coherent. Overall, the general patterns agreed with those of previous studies based on ensemble CMIP5 models showing future increases in solar radiation due to circulation trends causing cloud cover decreases and reduced wind speeds around Japan 35 .…”

Section: Resultssupporting

confidence: 88%

“…Overall, our analysis of PV power outputs confirmed the results of previous studies that suggested generally increased solar power output in Japan 22 , 35 . Therefore, our results confirm the reliability of the CNN method for producing super-resolution climate scenarios and will enable energy planners to anticipate the effects of future weather variability.…”

Section: Discussionsupporting

confidence: 90%

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Exploring super-resolution spatial downscaling of several meteorological variables and potential applications for photovoltaic power

Damiani,

Ishizaki,

Sasaki

et al. 2024

Sci Rep

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We applied a perfect prognosis approach to downscale four meteorological variables that affect photovoltaic (PV) power output using four machine learning (ML) algorithms. In addition to commonly investigated variables, such as air temperature and precipitation, we also focused on wind speed and surface solar radiation, which are not frequently examined. The downscaling performance of the four variables followed the order of: temperature > surface solar radiation > wind speed > precipitation. Having assessed the dependence of the downscaling accuracy on the scaling factor, we focused on a super-resolution downscaling. We found that the convolutional neural network (CNN) generally outperformed the other linear and non-linear algorithms. The CNN was further able to reproduce extremes. With the rapid transition from coal to renewables, the need to evaluate low solar output conditions at a regional scale is expected to benefit from CNNs. Because weather affects PV power output in multiple ways, and future climate change will modify meteorological conditions, we focused on obtaining exemplary super-resolution application by evaluating future changes in PV power outputs using climate simulations. Our results confirmed the reliability of the CNN method for producing super-resolution climate scenarios and will enable energy planners to anticipate the effects of future weather variability.

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Section: Resultssupporting

confidence: 88%

Section: Discussionsupporting

confidence: 90%

Exploring super-resolution spatial downscaling of several meteorological variables and potential applications for photovoltaic power

Damiani,

Ishizaki,

Sasaki

et al. 2024

Sci Rep

Self Cite

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“…A significant decreasing trend in cloud cover was projected by the end of the 21st century in eastern China (Yang et al., 2018; Zou et al., 2019). Meanwhile, over China as a whole, cloud cover was projected to increase in the future period compared with the past (Dutta et al., 2022; Yu et al., 2019), while other studies have shown opposite changes (Feron et al., 2021; Shiogama et al., 2020). Thus, it is vital to select optimal CMIP6 models and make reliable future projections of winter cloud cover and CDs in China.…”

Section: Introductionmentioning

confidence: 99%

Winter Cloudy Days Will Diminish Over China Under Global Warming

Tan,

Zhu,

Miao

et al. 2023

Earth's Future

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Changes in winter cloudy days (CDs) exert substantial impacts on agricultural production, public transport, and the solar photovoltaic (PV) power industry. However, the physical mechanism behind the variability of winter CDs over China remains largely unclear, making it challenging to predict the future dynamics of winter CDs. In this study, we explored the spatiotemporal features of winter CDs over China from 1961 to 2013 and identified two independent formation mechanisms associated with its leading mode. Then, we projected the future changes (2021–2099) in winter CDs under global warming constrained by the identified formation mechanisms. Results revealed that: (a) The leading mode of winter CDs from 1961 to 2013 presents a homogeneous pattern. The positive winter CDs anomaly is connected to the anomalous southerlies related to the Eurasian Rossby wave train triggered by the Barents Sea thermal forcing, as well as the Asia‐Pacific zonal geopotential height gradient induced by the convection over the Maritime Continent; (b) Selected state‐of‐the‐art climate models project a notable decrease in winter CDs in the Tibetan Plateau and southern China regions, and almost unchanged winter CDs over northern China, under global warming; (c) In contrast to present‐day mechanisms, in the future, the shift of the Eurasian mean flow will terminate the role of the Barents Sea thermal forcing. Instead, the enhanced convection over the Maritime Continent emerges as the primary contributor to the decline in winter CDs. Our findings provide valuable insights for policymakers in the solar PV industry, particularly in relation to the challenges faced under global warming.

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“…Another coordinated multi-sector impact assessment project in Japan called S-8 also used only four CMIP5 GCMs (Hanasaki et al 2014. Shiogama et al (2020 suggested that the selected GCMs of S-8 do not cover the CMIP5 range of shortwave radiation changes in Japan. Shi2020 also developed a method to select a better subset of GCMs that are more widely distributed and are not biased regarding shortwave radiation.…”

Section: Introductionmentioning

confidence: 99%

Selecting CMIP6-Based Future Climate Scenarios for Impact and Adaptation Studies

Shiogama

Ishizaki

Hanasaki

et al. 2021

SOLA

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Climate change impact assessment studies often use future projections of only a few global climate models (GCMs) due to limited research resources. Here we develop a novel method to select a small subset of GCMs that widely capture the uncertainty range of large ensemble. By applying this method, we select a subset of five GCM projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) ensemble for impact and adaptation studies in Japan. At first, we omit GCMs whose global warming projections have been evaluated to be overestimated in the recent literature. Then, we select a subset of five GCMs that widely captures the uncertainty ranges for 8 climate variables and have good performances in present-climate simulations. These selected GCM simulations will be used to provide better climate scenarios for impact and adaptation studies than those in the previous impact assessment project.

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Selecting Future Climate Projections of Surface Solar Radiation in Japan

Cited by 8 publications

References 19 publications

Exploring super-resolution spatial downscaling of several meteorological variables and potential applications for photovoltaic power

Exploring super-resolution spatial downscaling of several meteorological variables and potential applications for photovoltaic power

Winter Cloudy Days Will Diminish Over China Under Global Warming

Selecting CMIP6-Based Future Climate Scenarios for Impact and Adaptation Studies

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