[1] The Sea of Japan side of Central Japan is one of the heaviest snowfall areas in the world. We investigate near-future snow cover changes on the Sea of Japan side using a regional climate model. We perform the pseudo global warming (PGW) downscaling based on the five global climate models (GCMs). The changes in snow cover strongly depend on the elevation; decrease in the ratios of snow cover is larger in the lower elevations. The decrease ratios of the maximum accumulated snowfall in the short term, such as 1 day, are smaller than those in the long term, such as 1 week. We conduct the PGW experiments focusing on specific periods when a 2 K warming at 850 hPa is projected by the individual GCMs (PGW-2K85). The PGW-2K85 experiments show different changes in precipitation, resulting in snow cover changes in spite of similar warming conditions. Simplified sensitivity experiments that assume homogenous warming of the atmosphere (2 K) and the sea surface show that the altitude dependency of snow cover changes is similar to that in the PGW-2K85 experiments, while the uncertainty of changes in the sea surface temperature influences the snow cover changes both in the lower and higher elevations. The decrease in snowfall is, however, underestimated in the simplified sensitivity experiments as compared with the PGW experiments. Most GCMs project an increase in dry static stability and some GCMs project an anticyclonic anomaly over Central Japan, indicating the inhibition of precipitation, including snowfall, in the PGW experiments.Citation: Kawase, H., M. Hara, T. Yoshikane, N. N. Ishizaki, F. Uno, H. Hatsushika, and F. Kimura (2013), Altitude dependency of future snow cover changes over Central Japan evaluated by a regional climate model,
This study assessed the sensitivity of the simulated future impact on forage yield over Japan to the precipitation change in growing season derived from the multiple downscaling models, taking the regional climate projection ensemble dataset for Japan as an example. Three regional climate models (RCMs: NHRCM, NRAMS, and TWRF), and one statistical model (CDFDM) provided the fine-resolution (20-km) climate data over Japan from the climate projection performed by a global climate model (GCM: MIROCHI) under A1B scenario. With the common boundaries for the RCMs (and predictor for the statistical model), there is a consistency for the increased summer precipitation. However, discrepancies were found for the degree of precipitation increase and change in the mean summer maximum number of consecutive dry days. These discrepancies caused the spread of the simulated future change in forage yield over Japan by 3.3-11.4% (2081-2100), relative to the present-day one (1981)(1982)(1983)(1984)(1985)(1986)(1987)(1988)(1989)(1990)(1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000). These results showed that the direction (increase or decrease) and amplitude of the simulated future impact di¤er between the climate scenarios from the downscaling models and those from the parent GCM, indicating that climate downscaling is a source of uncertainty in simulating future impact.
This study evaluated the performance of a regional weather prediction model. The horizontal resolution is increased to the sub-kilometer scale in a series of experiments over areas of Japan through the summer or winter seasons of 2015−2016. The performance improves less when increasing the horizontal resolution from 2 km to 1 km or 500 m than it does from 5 km to 2 km, especially when topography and ice microphysics are less relevant. Although the velocity and magnitude of updrafts, cloud size, and convection in the boundary layer indeed change with the horizontal resolution, these differences turn out to have little impact on the model performance.(Citation: Ito, J., S. Hayashi, A. Hashimoto, H. Ohtake, F. Uno, H. Yoshimura, T. Kato, and Y. Yamada, 2017: Stalled improvement in a numerical weather prediction model as horizontal resolution increases to the sub-kilometer scale. SOLA, 13, 151− 156,
A large number of photovoltaic (PV) power systems have been adopted in Japan after a feed‐in tariff was introduced in 2012. However, PV power generation data from residential rooftop and/or ground‐mounted PV systems, and larger MW‐size PV plants have not been measured accurately in real‐time. This is because PV power monitoring instruments (eg, smart meters) have not collected a sufficient amount of power generation data. In order to realize adequate safety control of electric power systems under high PV‐penetration conditions, it is important to fully understand the temporal and spatial variations associated with PV power generation. In this study, we estimated the PV power generation for a regional area (ie, prefecture or municipality) in terms of PV power installation capacity and satellite‐estimated solar irradiance using a Japanese geostationary satellite, Himawari‐8. The satellite‐derived regionally integrated PV power estimations were validated with reference data provided by electric power companies. The validation results showed that these estimations were comparable to the reference data, provided by the Kyushu Electric Power Company Inc. (Kyushu) and the Tokyo Electric Power Company Inc. (TEPCO). However, the results also identified slight overestimations of PV power in the spring and summer seasons. An advantage of the proposed method is that it does not require land‐based monitoring instruments, which can lead to increased operational cost savings for PV power systems. Furthermore, in consideration of future PV power penetration scenarios, it is suggested that PV power in excess of regional power demands could be generated under the same weather conditions.
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