Tsuyoshi Yamaura

Decadal change in the boreal summer intraseasonal oscillation

A decadal change in activity of the boreal summer intraseasonal oscillation (BSISO) was identified at a broad scale. The change was more prominent during August–October in the boreal summer. The BSISO activity during 1999–2008 (P2) was significantly greater than that during 1984–1998 (P1). Compared to P1, convection in the BSISO was enhanced and the phase speed of northward-propagating convection was reduced in P2. Under background conditions, warm sea surface temperature (SST) anomalies in P2 were apparent over the tropical Indian Ocean and the western tropical Pacific. The former supplied favorable conditions for the active convection of the BSISO, whereas the latter led to a strengthened Walker circulation through enhanced convection. This induced descending anomalies over the tropical Indian Ocean. Thermal convection tends to be suppressed by descending anomalies, whereas once an active BSISO signal enters the Indian Ocean, convection is enhanced through convective instability by positive SST anomalies. After P2, the BSISO activity was weakened during 2009–2014 (P3). Compared to P2, convective activity in the BSISO tended to be inactive over the southern tropical Indian Ocean in P3. The phase speed of the northward-propagating convection was accelerated. Under background conditions during P3, warmer SST anomalies over the maritime continent enhance convection, which strengthened the local Hadley circulation between the western tropical Pacific and the southern tropical Indian Ocean. Hence, the convection in the BSISO over the southern tropical Indian Ocean was suppressed. The decadal change in BSISO activity correlates with the variability in seasonal mean SST over the tropical Asian monsoon region, which suggests that it is possible to predict the decadal change.

CONeP: A cost-effective online nesting procedure for regional atmospheric models

Abstract We propose a cost-effective online nesting procedure (CONeP) for regional atmospheric models to improve computational efficiency. The conventional procedure of online nesting is ineffective because computations are executed sequentially for each domain, and it does not enable users freely to determine the number of computational nodes. However, CONeP can completely avoid this limitation through three actions: 1) splitting the processes into multiple subgroups; 2) making each subgroup manage just one domain; and 3) executing the computations for each domain simultaneously. Since users can assign an optimal number of nodes to each domain, the model with CONeP is computationally efficient. We demonstrate the computational advantage of CONeP over the conventional procedure, comparing the elapsed times with both procedures on a supercomputer. The elapsed time with CONeP is markedly shorter than that observed with the conventional procedure using the same number of computational nodes. This advantage becomes more significant as the number of nesting domains increases.

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.