The incremental dynamical downscaling and analysis system (InDDAS) which has been developed from the pseudo-global-warming method by appending partial functions was applied for a probabilistic regional scale climate change projection with the target regions of Kanto and Japan Alps. In InDDAS, the most reliable future state was projected by a regional climate model (RCM) simulation with an ensemble mean among the climatological increments of multiple general circulation model (GCM) simulations. In addition, the uncertainty of the future projections is estimated by RCM simulations with the multi-modal statistical increments calculated by the singular vector decomposition of the multiple GCMs. An increase of rainfall with the change ratio of 7-16 % was projected in Kanto region, where the most reliable value was 10 %. The change ratios of the vicinity quantiles of extreme rainfall was projected to be larger than that of rainfall and was almost the same as the value explained by the Clausius—Clapeyron effect.

We investigated the impact of using high‐resolution sea surface temperature (SST) data and a sophisticated urban model on the simulation of surface air temperature (SAT) in the Nagoya metropolitan area using a regional climate model. The spatially detailed structure of SST, expressed in high‐resolution SST data, had relatively little impact on SAT. On the other hand, the difference in areal mean value of SST strongly affected SAT across a wide range of land surfaces. When a spatially inhomogeneous distribution was used for the urban fraction and anthropogenic heat, and appropriate physical properties for building materials were given according to the specific urban categories, we achieved significant improvements in both the diurnal range of SAT and its daily mean. Based on a comparison with an additional sensitivity experiment for building albedo, the sophistication of urban fraction and thermal parameters related to building materials had a comparable impact on SAT as presumable building albedo in the daytime, while they indicated a larger impact on the nighttime SAT. We conclude that (1) the areal mean SST is critical rather than its resolution for the climatological average of SAT over the land; (2) the simultaneous refinement of the urban fraction and building material parameters, as well as an appropriate building albedo setting, greatly improves the representation of SAT; and (3) the refinement of areal mean SST and the urban data have the same degree of importance for a better representation of the SAT.

We investigated climate change in the annual snow cover period in mountainous areas of central Japan by downscaling simulations of four climate change projections based on a Coupled Model Intercomparison Project phase 3 (CMIP3) Special Report on Emission Scenarios (SRES) A1B emission scenario using a regional climate model. Our numerical simulation reproduced well the observed snow depths and areas of snow cover. The projected snow disappearance date in all areas occurred earlier in the future climate due to global warming and were substantially earlier in areas of both light and heavy snowpacks in the present climate. The time shift was smaller at areas where the present-day maximum snow depth is around 100 cm and the snow disappearance date is in mid-April. These projected changes in the duration of snow cover were associated with decreasing snowfall and accelerated snowmelt due to increasing surface air temperatures. The effect was interpreted using an idealized model of temporal variation in surface air temperature. Earlier snowmelt causes local enhancement of surface air temperature increases that will have considerable impact on mountain ecosystems.

This chapter contains sections titled: Introduction FSW + MAG Welding Process Experiment and Discussion Conclusions

This study used a 4-km resolution regional climate model to examine the sensitivity of surface air temperature on the Pacific coast of Japan to sea surface temperature (SST) south of the Pacific coast of Japan during summer. The authors performed a control simulation (CTL) driven by reanalysis and observational SST datasets. A series of sensitivity experiments using climatological values from the CTL SST datasets over a 31-yr period was conducted. The interannual variation in surface air temperature over the Pacific coast was well simulated in CTL. The interannual variation in SST over the Kuroshio region amplified the interannual variation in surface air temperature over the Pacific coast. It was found that 30% of the total variance of interannual variation in surface air temperature can be controlled by interannual variation in SST. The calculated surface air temperature on the Pacific coast increased by 0.4 K per 1-K SST warming in the Kuroshio region. Note that this sensitivity was considerably greater during nighttime than during daytime. Concurrent with the warming in surface air temperature, downward longwave radiation at the surface was also increased. In summer, the increase in latent heat flux was considerably larger than that in sensible heat flux over the ocean because of SST warming, according to the temperature dependence of the Bowen ratio. This implies that the primary factor for the increase in surface air temperature in summer is increased moisture in the lower troposphere, indicating that the regional warming was caused by an increase in H2O greenhouse gas.

This study focuses on the main factor of regional difference in Altitudinal Dependency of Snow Depth (ADSD) and discusses the applicable range of snow cover estimation method with ADSD. We use the high-density surface observational data and a regional climate model in Niigata Prefecture. The estimation method with ADSD produces significant estimation error if the method is applied to broad areas. The high-density observational data show the regional difference of ADSD between windward and leeward areas with a coastal mountain. Numerical simulation reproduces these observational results. We evaluate mountain effects on the regional difference of ADSD using sensitivity experiments. In the sensitivity experiment, the altitude is changed from a mountain to a flat plain. The sensitivity experiment shows that the regional differences of ADSD are not simulated. The results indicate that the applicable range of the estimation method with ADSD is limited to a single slope and mountain. It should be noted that this method has a high probability of increasing the estimation error in complex mountainous areas, particularly in the coastal area of the Japan Sea.

We revealed long-term variation of winter precipitation at the northwest coast of Japan and its link to decadal variation of autumn-to-winter sea-surface fluxes from the Japan/East Sea with observational data of sea-surface fluxes, sea-surface temperature (SST), and ocean heat content (OHC) data. Much precipitation was observed after 1995 because both SST and OHC were significantly higher in the Japan/East Sea. In a numerical experiment forced by the climatological SST that excluded the decadal change, the decadal variation of the land precipitation did not appear, suggesting that the decadal variation of the coastal precipitation is sensitive to SST.

Reliable assessment of Climate Change (CC) impacts on water resources of the Tone river basin is critically important due to its key role on Japan’s socio-economic systems. Hence, to increase the confidence level in CC projections, this study performed high-resolution Pseudo Global Warming Downscaling (PGW-DS) experiments on cautiously selected Coupled Ocean-Atmosphere General Circulation Models (CGCMs) outputs. Value added future climate datasets were developed at the basin scale by removing systematic biases from the PGW-DS outputs using gauged precipitations and a validated statistical bias correction method over this basin. Results showed that annual and monthly climatology of precipitation and corresponding discharges will be increased in the future climate, especially during the Baiu and typhoon periods. Flood-prone areas will be extended and peak discharges will be amplified during warming climate. Recurrence analysis revealed that peak discharges will be increased with increasing return periods, however, larger differences exist in rate of increments between a sub-basin outlet and the main basin outlet, indicating that future investigations should be focused at sub-basins including the effects of dams on discharges to quantify the CC impacts in detail.

The peaks of the appearance frequency of the surface air temperature during precipitation are clearly observed near the melting point of water on the Toyama Plain during the winter monsoon. The peaks could be explained by the hypothesis that the melting of snowfall is the primary cause of the cooling on the Toyama Plain. To verify this hypothesis, we investigated the relation of temperature between the inland and the coast using observed data in January from 1990 to 2009 and applied a simple estimation method of the cooling due to the melting of snowfall. The temperature on the Toyama Plain tends to remain around the melting point when the surface air temperature on the coast is higher than 273.15 K and lower than 277.15 K, which almost corresponds to the changeover from snowfall to rainfall. The relation is unclear when hardly any precipitation is observed. The simply estimated cooling by the melting of snowfall using the observed precipitation can also represents the cooling on the Toyama Plain. Accordingly, the local climatic temperature could be greatly influenced by advection of the air mass cooled by the melting of snowfall until the air mass reaches the Toyama Plain during the winter monsoon.

The Pseudo-Global-Warming Downscaling (PGWDS) method is a simple way to downscale future climate change using the reanalysis data added by the long-term mean difference between present and future climate data projected by a general circulation model (GCM). The PGWDS method has three substantial advantages as compared to a conventional dynamical downscaling method, (1) removing GCM biases, (2) reducing the amount of required GCM output, and (3) shortening the simulation duration. We have focused on the third advantage and applied the PGWDS method to a local area in Japan in January. The future changes in monthly mean precipitation, snowfall, and surface air temperature estimated by the 10-year averages can be regarded as the future climatic changes estimated by the 30-year averages because most of the 10-year averages are within the standard error of the 30-year averages. Meanwhile, the future frequencies in the 10 years are often larger than the error range of those in the 30 years in the extreme events. Short sampling duration seems to be a primary cause. It is necessary to check the possibility of shortening the simulation duration because the estimation of future change is not appropriate in some cases.

In this study, a regional climate model (WRF-ARW; the Advanced Research Weather Research and Forecasting model) having a resolution of 4.5 kmwas used to examine the sensitivity of precipitation on the Japan Sea side of Japan to the sea surface temperature (SST) in the Japan Sea during winter. We performed a control simulation (CTL) driven by reanalysis and observational SST datasets. Three sensitivity experiments in which SSTs over the entire domain were 1K, 2K, and − 1K different from the CTL SST were conducted to examine the sensitivity of precipitation to SST. The calculated precipitation on the Japan Sea side increased by 6̶12%K −1 of SST warming. Concurrent with the precipitation changes, latent heat flux over the Japan Sea increased by 11̶14% K −1 of SST warming. Because the changes in surface relative humidity were very small, the increase can be explained by the Clausius̶Clapeyron equation. The deviation from the 7% increase in latent heat flux calculated from this equation can be quantitatively explained by the development of the planetary boundary layer over the Japan Sea, which was related to an increase in sensible heat flux due to the SST warming. This result also implies that the 1 K uncertainty in simulated and projected SST over the Japan Sea among multiple atmosphere̶ocean global climate models corresponds to an approximately 10% uncertainty in precipitation on the Japan Sea side of Japan.

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.

A five-year research project of high performance regional numerical weather prediction is underway as one of the five research fields of the Strategic Programs for Innovative Research (SPIRE). The ultimate goal of the project is to demonstrate feasibility of precise prediction of severe weather phenomena using the K-computer. Three sub-themes of the project are shown with achievements at the present and developments in the near future.

Snowfall amounts have fallen sharply along the eastern coast of the Sea of Japan since the mid-1980s. Toyama Prefecture, located approximately in the center of the Japan Sea region, includes high mountains of the northern Japanese Alps on three of its sides. The scarcity of meteorological observation points in mountainous areas limits the accuracy of hydrological analysis. With the development of computing technology, a dynamical downscaling method is widely applied into hydrological analysis. In this study, we numerically modeled river discharge using runoff data derived by a regional climate model (4.5-km spatial resolution) as input data to river networks (30-arcseconds resolution) for the Toyama Prefecture. The five main rivers in Toyama (the Oyabe, Sho, Jinzu, Joganji, and Kurobe rivers) were selected in this study. The river basins range in area from 368 to 2720 km2. A numerical experiment using climate comparable to that at present was conducted for the 1980s and 1990s. The results showed that seasonal river discharge could be represented and that discharge was generally overestimated compared with measurements, except for Oyabe River discharge, which was always underestimated. The average correlation coefficient for 10-year average monthly mean discharge was 0.8, with correlation coefficients ranging from 0.56 to 0.88 for all five rivers, whereas the Nash-Sutcliffe efficiency coefficient indicated that the simulation accuracy was insufficient. From the water budget analysis, it was possible to speculate that the lack of accuracy of river discharge may be caused by insufficient accuracy of precipitation simulation.

The performance of the pseudo-global-warming downscaling (PGWDS) method is tested by comparison with the assumed true climate (ATC), which is a downscaling using a general circulation model (GCM) output data directly. The PGWDS is a simple way to downscale for a future climate using current weather data of a GCM added by the long-term mean difference between the present and the future climate projected by a GCM. The verification focuses on the East Asia during the rainy season of June. A significant change in the 30-year averaged monthly precipitation is found around the rain band in the future in both downscaling methods. Between the experiments of the PGWDS and the ATC, no significant differences in temperature and precipitation can be seen except for limited small areas. The findings indicate that the PGWDS has a highly potential to the reliable downscaling of the future climate. In smaller downscaling domains, however, the differences in precipitation increase remarkably near the upstream side of the lateral boundaries. The choice of the downscaling area is a critical issue for accuracy.