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Climate experiments using global cloud-system resolving models (GCRMs) are expected to realistically simulate precipitation diurnal cycle (PDC) in the tropics, which is important for better representation of influences of cumulus convection on the climate system. This study examines how three series of decade-long climate experiments with Nonhydrostatic ICosahedral Atmospheric Model (NICAM), one of the GCRMs, realistically simulate the PDC over tropical coastal regions. Analyses reveal that it is more difficult to reproduce the PDC over coastal waters than that over coastal land, the former of which is characterized by nighttime offshore migration of precipitation areas. A comparison with in situ shipborne observations further reveals that biases in the offshore migration feature are associated with poor representation of convective cold pools; experiments with poor reproducibility of the offshore migration underestimate overall intensity of cold pools. The underestimation of the intensity may be associated with overestimation of environmental moisture in the lower free troposphere. As reproducing the environmental field is a difficult task particularly for climate experiments with global models, it seems more challenging for the climate experiments to realistically simulate the PDC over the coastal waters than for short-term experiments and regional climate experiments.

The observation of wind over oceans remains challenging. This results in difficulty in predicting the wind speed and direction. In this study, we examined the accuracy of upper-tropospheric wind speed forecasts along the flights between Tokyo International Airport and Los Angeles International Airport. We compared the Global Spectral Model data from the Japan Meteorological Agency (as forecast data) with the observation data from the aircraft's Quick Access Recorder (as true values). The forecast errors are highest over the North Pacific Ocean, not at the end of flight when the elapsed hours are longest, with meridional winds having a larger forecast error than zonal winds. Analysis of the meteorological field where a large meridional wind forecast error occurred using ERA5 indicates that the convergence-divergence of the jet stream or the blocking of westerly winds by the upper trough may have affected the large forecast error.

Afternoon thunderstorms, mesoscale convective systems, and other short-duration rainfall events threaten property and transportation. Recent deep learning techniques have been proven effective in nowcasting for rainfall accumulation (rain maps), but predicting occurrences of intense convective cells can add additional value to decision-making procedures. This study develops a deep-learning model that predicts the locations of cell occurrences in the next 60 minutes. The training data include reflectivities from the Taiwanese radar network and convective cell trajectories from the System for Convection Analysis and Nowcasting (SCAN). The label is the SCAN cell occurrence (1 or 0) within a 7.5 × 7.5 km² area in the next hour. In addition to providing occurrence probabilities, the post-analysis procedure deploys a threshold mask to convert the probabilistic forecast into deterministic forecasts; it achieves a ∼40% improvement in the critical success index compared with the baseline method. Furthermore, the new model informs users about the risks under the chosen threshold selected based on their risk tolerance. This study provides proof of concept that replacing the predicting objectives (“cell occurrence” instead of “rainfall”) of the model may help forecasters' decisions and the integration of deep learning into operational forecasting.

Temperature trends in Japanese cities were analyzed using data at 433 stations on the AMeDAS network from April 1979 to March 2023. It was found that urban warming, defined by a temperature increase at an urban station relative to the surrounding non-urban stations, had slowed down in the latter part of the analysis period. The deceleration of urban warming was commonly found for northern, eastern, and western Japan, and not only for stations in densely inhabited areas but also those at weakly urbanized sites where the surrounding population density was 100-300 km⁻². The deceleration was observed in all seasons and time of the day, although it tended to be more conspicuous in winter than in other seasons, and in the nighttime than in the daytime.

Climate change impact modelling studies often require not only mean temperature and precipitation but also other climate variables (e.g., solar radiation and wind speed) and extreme indices as input data. However, studies on observational constraints (emergent constraints) about these variables and indices are limited. Based on linearities of future climate change as functions of global warming levels and biases in recent global mean temperature trends in the simulations of 40 Earth system models (ESMs), the upper bounds of uncertainties in future changes of various variables (annual mean temperature, annual maximum daily maximum temperature, mean specific humidity, mean downward longwave radiation and specific humidity on days when annual maximum daily precipitation (Rx1day) events occur) are successfully lowered in most regions of the world. We can also reduce inter-model variances of regional changes in mean precipitation, Rx1day, mean downward shortwave radiation, mean sea level pressure and mean surface wind speed in some areas. These results would be useful for climate change impact studies to consider whether they should weight ESMs or exclude some ESMs to prevent possible biases in impact assessments.

An extreme rainfall event with 48-h accumulated precipitation amounts exceeding 500 mm on the north (Japan Sea) side of western Japan occurred when Typhoon Lan (2023) approached and passed over Japan in a weak baroclinic environment. The rainfall event included two local heavy precipitation peaks. In the present study, we perform numerical simulations with a cloud-system-resolving model to investigate the potential roles of two factors in the first event peak: (1) an abnormally high sea surface temperature (AHSST) anomaly (∼ +4°C) and (2) a mesoscale low formed over the Sea of Japan. The results of sensitivity experiments showed that the AHSST increased the total rainfall amount by about 100 mm. The mesoscale low, which was generated by southeasterly flows over the mountain ranges of central Japan, determined the location of the heavy rainfall by controlling the direction and intensity of low-level flows. The role of this terrain-induced mesoscale low provided new insight into the mechanisms producing heavy rainfall in association with typhoons approaching Japan in a weak baroclinic environment.

Field observations were conducted at Ibaraki Airport, Japan, to determine the vertical development speed of shallow radiation fog. The development speed in shallow (less than several meters) fog was 3-16 cm min⁻¹, and was slower at higher wind speeds. The speed decreased when the air above the fog layer became drier, possibly due to the mixing of the fog layer with the dry air above it. The speed data presented here can be used in nowcasting fog development for aviation decision making.

In this study, we introduce a new seasonal prediction system using an atmosphere–ocean-coupled general circulation model called CFES (hereafter referred to as CFES ESPreSSO). We compare its prediction skill of the interannual variability of the surface air temperature (SAT) and precipitation anomalies with that of the SINTEX-F2 seasonal prediction system. We find that CFES ESPreSSO has a higher skill in predicting the SAT variability in January-February-March over East Asia and northeastern North America than SINTEX-F2, while the following season (April-May-June), SINTEX-F2 provides better predictions of the SAT variability over the Maritime Continent and subtropical North Pacific. Meanwhile, CFES better predicts the SAT variability in July-August-September over Eurasia and Arctic, and it continues to be so over the following season (October-November-December) over Eurasia. However, the prediction skill of SINTEX-F2 is generally better in the tropics (e.g., SAT in the subtropical North Pacific, SAT and precipitation in the Maritime Continent). Regarding climate indices, CFES shows a better prediction skill for the Atlantic Niño and Ningaloo Niño indices, whereas SINTEX-F2 is generally better for El Niño and the Indian Ocean dipole mode. These results suggest that for improved seasonal forecasting, it is beneficial to consider a multi-model approach, leveraging the respective strengths of each model.

Typhoon HAISHEN, Typhoon No. 10 in 2020, was weaker than forecasts as it moved north over the western coast of Kyushu. The typhoon intensity forecasting scheme called TIFS operated at the Japan Meteorological Agency (JMA) tended to predict HAISHEN's intensity more strongly than the observed one, resulting in large errors in JMA's operational forecasts. One possible reason for the large errors is that TIFS does not include the effect of ocean cooling associated with tropical cyclones. Here, we investigated whether the accuracy of the typhoon intensity predictions can be improved by replacing static sea surface temperature and ocean heat content used in the conventional TIFS by those predicted by an ocean model. The results of prediction experiments using the pseudo-ocean-coupled TIFS show that the over-intensification of HAISHEN was suppressed and that the prediction errors were significantly reduced. We also extended the evaluation to all typhoons in 2020 and found that the pseudo-ocean-coupled TIFS reduced the prediction errors by about 10% compared to the conventional TIFS for prediction times of 3 to 5 days. This indicates that pseudo-ocean coupling of the conventional TIFS can improve the accuracy of typhoon intensity forecasts.

Drylands, which occupy 41% of Earth's land area, have large effects on Earth's climate via land-atmosphere interactions, and simulations of future climate indicate that drylands will be very sensitive to climate changes associated with global warming. Monitoring of drylands is therefore necessary to help guide sustainable development in drylands and to protect the global environment. This study examined changes of the global distribution of the aridity index from 2000 to 2020 and compared them to changes from 1951 to 1980. The regions with relatively wet climates, that is, semi-arid and dry sub-humid regions, became drier from 2000 to 2020. The largest use of land in drylands was grassland, followed by open shrubland, cropland, savanna, and woody savanna. More than 50% of dry land was accounted for by grasslands (18,651,109 km²) and dryland forests including shrubland and savanna (13,331,231 km²). The relationship between the aridity index and the normalized difference vegetation index indicated that the value of the aridity index of dryland forests and grasslands equaled the threshold for climatically stable existence, although the range of the aridity index was wide in both cases. We also made rough assessments of soil organic carbon sequestration in dryland forests and grasslands.