Yoshiyuki Kajikawa

Outcomes and challenges of global high-resolution non-hydrostatic atmospheric simulations using the K computer

This article reviews the major outcomes of a 5-year (2011–2016) project using the K computer to perform global numerical atmospheric simulations based on the non-hydrostatic icosahedral atmospheric model (NICAM). The K computer was made available to the public in September 2012 and was used as a primary resource for Japan’s Strategic Programs for Innovative Research (SPIRE), an initiative to investigate five strategic research areas; the NICAM project fell under the research area of climate and weather simulation sciences. Combining NICAM with high-performance computing has created new opportunities in three areas of research: (1) higher resolution global simulations that produce more realistic representations of convective systems, (2) multi-member ensemble simulations that are able to perform extended-range forecasts 10–30xa0days in advance, and (3) multi-decadal simulations for climatology and variability. Before the K computer era, NICAM was used to demonstrate realistic simulations of intra-seasonal oscillations including the Madden-Julian oscillation (MJO), merely as a case study approach. Thanks to the big leap in computational performance of the K computer, we could greatly increase the number of cases of MJO events for numerical simulations, in addition to integrating time and horizontal resolution. We conclude that the high-resolution global non-hydrostatic model, as used in this five-year project, improves the ability to forecast intra-seasonal oscillations and associated tropical cyclogenesis compared with that of the relatively coarser operational models currently in use. The impacts of the sub-kilometer resolution simulation and the multi-decadal simulations using NICAM are also reviewed.

Contributions of changes in climatology and perturbation and the resulting nonlinearity to regional climate change

Future changes in large-scale climatology and perturbation may have different impacts on regional climate change. It is important to understand the impacts of climatology and perturbation in terms of both thermodynamic and dynamic changes. Although many studies have investigated the influence of climatology changes on regional climate, the significance of perturbation changes is still debated. The nonlinear effect of these two changes is also unknown. We propose a systematic procedure that extracts the influences of three factors: changes in climatology, changes in perturbation and the resulting nonlinear effect. We then demonstrate the usefulness of the procedure, applying it to future changes in precipitation. All three factors have the same degree of influence, especially for extreme rainfall events. Thus, regional climate assessments should consider not only the climatology change but also the perturbation change and their nonlinearity. This procedure can advance interpretations of future regional climates.Changes in climatology and perturbation will lead to different impacts on regional climate change, but their effect remains a subject of debate. Here the authors develop a new downscaling procedure that reveals the importance of both changes on the regional climate and examines their nonlinear effect.

Precursors of deep moist convection in a subkilometer global simulation

Deepmoist convection in theatmosphereplays an important role in cloudyweatherdisturbances, such as hurricanes, and even in the global climate. The convection often causes disastrous heavy rainfall, and predicting such convection is therefore critical for both disaster prevention and climate projection. Although the key parameters for convection have been pointed out, understanding the preprocesses of convection is a challenging issue. Here we identified the precursors of convection by analyzing a global simulated data set with very high resolution in time and space. We found that the mass convergence near the Earth’s surface changed significantly several minutes before the initiation of early convection (the formation of cumulus clouds), which occurred with the increase in the convective available potential energy (CAPE). Decomposition of the statistical data revealed that a higher-CAPE environment resulted in stronger convection than in the stronger-convergence case. Furthermore, for the stronger-convergence case, the precursor was detected earlier than the total average (10–15min before the initiation), whereas the amplitude of maximum velocity was not so strong as the higher-CAPE case. This suggests that the strength of convection is connected with CAPE, and the predictability is sensitive to the convergence.

Decomposition of the large-scale atmospheric state driving downscaling: a perspective on dynamical downscaling for regional climate study

AbstractIn this study, we provide a perspective on dynamical downscaling that includes a comprehensive view of multiple downscaling methods and a strategy for achieving better assessment of future regional climates. A regional climate simulation is generally driven by a large-scale atmospheric state obtained by a global climate simulation. We conceptualize the large-scale state based on reconstruction by combining decomposed components of the states, such as climatology and perturbation, in different global simulations. The conceptualization provides a comprehensive view of the downscaling methods of previous studies. We propose a strategy for downscaling regional climate studies based on the concept of covering a wider range of possibilities of large-scale states to account for the uncertainty in global future predictions due to model errors. Furthermore, it also extracts the individual influences of the decomposed components on regional climate change, resulting in better understanding of the cause of the change. We demonstrate a downscaling experiment to highlight the importance of the simultaneous consideration of the individual influences of climatology and perturbation.n