An unprecedentedly large ensemble of climate simulations with a 60-km atmospheric general circulation model and dynamical downscaling with a 20-km regional climate model has been performed to obtain probabilistic future projections of low-frequency local-scale events. The climate of the latter half of the twentieth century, the climate 4 K warmer than the preindustrial climate, and the climate of the latter half of the twentieth century without historical trends associated with the anthropogenic effect are each simulated for more than 5,000 years. From large ensemble simulations, probabilistic future changes in extreme events are available directly without using any statistical models. The atmospheric models are highly skillful in representing localized extreme events, such as heavy precipitation and tropical cyclones. Moreover, mean climate changes in the models are consistent with those in phase 5 of the Coupled Model Intercomparison Project (CMIP5) ensembles. Therefore, the results enable the assessment of probabilistic change in localized severe events that have large uncertainty from internal variability. The simulation outputs are open to the public as a database called “Database for Policy Decision Making for Future Climate Change” (d4PDF), which is intended to be utilized for impact assessment studies and adaptation planning for global warming.

The high temperature event in July 2018 caused record-breaking human damage throughout Japan. Large-ensemble historical simulations with a high-resolution atmospheric general circulation model showed that the occurrence rate of this event under the condition of external forcings in July 2018 was approximately 20%. This high probability was a result of the high-pressure systems both in the upper and lower troposphere in July 2018. The event attribution approach based on the large-ensemble simulations with and without human-induced climate change indicated the following: (1) The event would never have happened without anthropogenic global warming. (2) The strength of the two-tiered high-pressure systems was also at an extreme level and at least doubled the level of event probability, which was independent of global warming. Moreover, a set of the large-ensemble dynamically downscaled outputs revealed that the mean annual occurrence of extremely hot days in Japan will be expected to increase by 1.8 times under a global warming level of 2°C above pre-industrial levels.

Cancers adversely affect organismal physiology. To date, the genes within a patient responsible for systemically spreading cancer-induced physiological disruption remain elusive. To identify host genes responsible for transmitting disruptive, cancer-driven signals, we thoroughly analyzed the transcriptome of a suite of host organs from mice bearing 4T1 breast cancer, and discovered complexly rewired patterns of circadian gene expression in the liver. Our data revealed that 7 core clock transcription factors, represented by Rev-erba and Rorg, exhibited abnormal daily expression rhythm in the liver of 4T1-bearing mice. Accordingly, expression patterns of specific set of downstream circadian genes were compromised. Osgin1, a marker for oxidative stress, was an example. Specific downstream genes, including E2f8, a transcriptional repressor that controls cellular polyploidy, displayed a striking pattern of disruption, “day-night reversal.” Meanwhile, we found that the liver of 4T1-bearing mice suffered from increased oxidative stress. The tetraploid hepatocytes population was concomitantly increased in 4T1-bearing mice, which has not been previously appreciated as a cancer-induced phenotype. In summary, the current study provides a comprehensive characterization of the 4T1-affected hepatic circadian transcriptome that possibly underlies cancer-induced physiological alteration in the liver.