Despite the importance of seasonal snow in Japan, quantitative information on seasonal snow mass in the whole area is lacking. To understand the current mass balance of seasonal snow cover, we develop a method to quantify the mass balance of seasonal snow all over Japan using the Japan Meteorological Agency's operational regional atmospheric model, Local Forecast Model (LFM), as well as the snow physics model, Snow Metamorphism and Albedo Process (SMAP). Our model simulations using the LFM-SMAP model chain show that the seasonal snow water equivalent (SWE) evolution is mostly controlled by snowfall and runoff. It is estimated that the seasonal peak area-integrated SWE in Japan reaches 42.2 Gt on average during the 2017-2022 winters, and 60.1 Gt (+43% with respect to the average) and 22.0 Gt (−48%) during relatively heavy (2017-2018) and relatively light (2019-2020) snow years, respectively. The ratios between seasonal peak area-integrated SWE and winter-accumulated (November to February) snowfall amounts for the heavy, average, and light snow years are 83%, 74%, and 59%, respectively.

In this study, we investigated the remote effect of warm sea surface temperatures (SSTs) in the Kuroshio region of the East China Sea (ECS) on heavy rainfall that occurred in southern Kyushu, Japan on 3 July 2020 using the Weather Research and Forecasting model. To examine the effect, a simulation with realistic SSTs and two simulations with reduced SSTs in the ECS Kuroshio region were performed. Backward- and forward-trajectory analyses showed that low-level parcels in the simulation with warmer SSTs in the ECS Kuroshio region possessed larger amounts of water vapor than those in the reduced-SST experiments. The difference in the water-vapor amounts of low-level parcels between the simulations with warmer and colder SSTs remained until the parcels started to ascend in the heavy rainfall area. In addition, there were many more parcels with extremely large amounts of water vapor in the simulation with warmer SSTs. Such an increase in low-level water-vapor amounts under warmer SST conditions in the ECS Kuroshio region led not only to enhancement of the precipitation source but also to atmospheric destabilization, resulting in the production of a large amount of precipitation.

Extreme rainfall (ER) in Indonesia frequently leads to floods and landslides, disrupting economic activity and impacting human lives. Here, we investigate ER variability in association with climate teleconnection patterns (CTP) including the El Niño Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), and Madden-Julian Oscillation (MJO), using extreme value analysis based on daily rainfall data from 32 stations for 30 years (1985-2014). By fitting a generalized extreme value distribution, a significant association between the annual maximum rainfall (AMR) and CTP was found in 12 of 32 stations. The sensitivity test of location parameter showed that the AMR-CTP interconnection was spatially inhomogeneous. The positive (negative) significant association of ENSO and IOD to AMR was noticeable in south-western (eastern) Indonesia. Additionally, MJO positive (negative) association was detected at 4 (3) stations mostly located in Sumatra (Java) Island. Furthermore, the return level analysis shows that the 20-year ER intensity waiting time will be shorter and longer when CTP indexes strengthen and weaken, suggesting a potential increase and decrease in the likelihood of future ER occurrences, respectively. These results are relevant for understanding the relationship between ER and CTP that should be considered in the adaptation and mitigation plans to minimize the ER impacts.

This study investigated a recent tendency of interannual precipitation variations during the Baiu season (June–July) in southern Kyushu, Japan. Long-term satellite precipitation observations revealed a significant amplification of the interannual variability of Baiu precipitation after the beginning of this century and the appearance of a quasi-quadrennial variation (QQV). Composite analyses with respect to the unstable regime of Baiu activity when the QQV prevailed suggested a possible link between the Indo-western Pacific Ocean Capacitor (IPOC) mode and the QQV. Regression analyses with an IPOC index showed the dominance of an anomalous anticyclone in the lower troposphere centered over the South China Sea and the Philippine Sea and enhanced poleward moisture transport along its western periphery. The interdecadal shift in remote IPOC influence seen around the year 2000 featured the westward (northward) extension of the low-level anomalous anticyclone toward the Bay of Bengal (southern Japan); consequently, the significant moisture flux convergence area covered southern Kyushu during the unstable Baiu regime, consistent with the QQV appearance during the same period. It is also inferred that the IPOC mode modulation may come from the increased impact of central Pacific El Niño/Southern Oscillation on the IPOC in recent decades.

Anomalous coldness was observed over midlatitude Eurasia in December 2020 and over subpolar Eurasia in January 2021. The former was accompanied by the Warm Arctic and Cold Eurasia (WACE) pattern, while the latter by the negative phase of the Arctic Oscillation (AO). A set of large ensemble experiments with an atmospheric general circulation model suggests a contribution of reduced Arctic Sea ice to the midlatitude cooling and WACE pattern in December 2020. The tropical and extratropical sea surface temperature (SST) anomalies, however, contribute to warming over midlatitude Eurasia. In January 2021, neither the sea ice nor SST anomalies can explain the subpolar Eurasian cooling and the negative AO in our experiments.

This study performed 4-day numerical integration in 1-hour intervals using the Weather Research and Forecasting (WRF) model for four major cases of heavy snowfall that occurred from 2020 to 2021. The model-predicted snow depth data were compared with the ground-observed snow depth and the satellite-observed snow cover data and then were statistically verified. The scalar verification results for ground data from the four cases showed a root–mean–square error of 2.55-16.67 cm and a correlation coefficient of 0.48-0.80, whereas the verification results with satellite data showed the correlation coefficients of 0.38-0.60. For categorical verification, using a threshold value of a snow depth exceeding 5 cm, the proportion correct was 90% or higher for ground observations of each case. In addition, in the satellite categorical verification, when the threshold value of the Snow Cover Fraction (SCF) exceeds 0.5, the proportion correct was 50% or more. These results are meaningful because the model snow depth verification methods were devised strategically for the first time using both the snow depth data of the mesoscale ground observation networks and ultra-high-resolution Sentinel-2 satellite data currently available in Korea. The findings of this study will contribute to the development of a high-resolution numerical prediction model and its verification methodology for snowfalls in the Korean Peninsula, eventually leading to increased prediction accuracy and reduced snow damage.