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Regional climate classification aids the investigation of the causes of changes in natural vegetation distribution and allows the selection of appropriate crops under climate fluctuations. In this study, the Japanese climate was classified using a simple convolutional network (CNN) into nine regional areas based on meteorological factors (channels). One dataset of each channel was processed by an arrangement into two dimensions of 12 months and 10 years. Combinations of five channels were used by the CNN to search for the best combination for climate classification. A combination of four channels, excluding snow depth data, showed the best test accuracy. Regional climate change was analyzed by comparing the different patterns between the latest and former decades. The climate in most regions tended to shift towards the north. However, the number of regions that shifted towards north decreased in the most recent decade compared with those in previous decades, indicating that Japanese climate is generally oriented southward. The simple convolutional network based on the processed two-dimensional data from the meteorological time-series dataset enabled recent climate change evaluation and predicted regional climate change, which could help decision makers for choosing crops and formulating disaster management strategies in the near future.

Known as the heat-mitigation effect, irrigated rice-paddy fields distribute a large fraction of their received energy to the latent heat during the growing season. The present hypothesis is that increased atmospheric CO2 concentration decreases the stomatal conductance of rice plants and increases the air temperature by means of an increased sensible heat flux. To test this hypothesis, a coupled regional atmospheric and crop energy-balance model is developed and applied to a 300 × 300 km2 region in Japan. Downscaling meteorological variables from grid-mean values of mixed land use (3 × 3 km2) generates realistic typical diurnal cycles of air temperature in rice paddies and adjacent residential areas. The model simulation shows that, on a typical sunny day in summer, doubling the CO2 concentration increases the daily maximum grid-mean air temperature, particularly where rice paddies are present, by up to 0.7 °C. This CO2 effect on the grid-mean air temperature is approximately half the effect of the reduction in rice-paddy area that is postulated to occur on a time scale similar to that of the atmospheric CO2 change. However, within the internal atmospheric boundary layer of the rice paddies, the CO2 effect on the air temperature (+ 0.44 °C) still exceeds the effects of the land-use change (+ 0.11 °C). These results show a potentially important interplay of plant physiological responses regarding atmospheric CO2 in the heat-mitigation effect of rice-paddy fields under a changing climate.

To evaluate the long-term response of soil organic carbon decomposition to global warming in Asian monsoon forests, we established a multichannel automated chamber system combined with infrared carbon-filament heat lamps in a 70-year-old cool-temperate broad-leaved deciduous forest in northern Japan in September 2011. We designed control plots to measure total soil respiration (Rs), root exclusion (trenched) plots to measure heterotrophic respiration (Rh), and warmed trenched plots to measure Rh under warmed conditions (+2.5 °C soil temperature at 5 cm depth, Rhw). Summed effluxes during the non-snowing season from 3 June to 10 November ranged from 9.33 to 11.12 tC ha-1 in Rs, 6.14 to 9.13 tC ha-1 in Rh and 7.02 to 11.91 tC ha-1 in Rhw over 5 years (2012–2016). During the summer season (between July and September), the daily warming effect was negatively related to soil temperature. In comparison, the relationship between soil moisture and the daily warming effect varied in each year depending on soil moisture levels. The annual warming effect exhibited large inter-annual variation, ranging from 6.2 to 17.7% °C-1; however, the 5-year average (10.9% °C-1) was close to the estimated value (10.2% °C-1) based on the annual Q10 of Rh (2.66). Inter-annual variation was positively related to the number of rainy days (p = 0.013). Our results indicate that existing global terrestrial models underestimate the strength of the feedback of Rh to global warming in Asian monsoon forests, because most global terrestrial models used relatively low Q10 values (usually below 2.0).

A water temperature simulation model was developed to estimate the effects of water management on the thermal environment in rice paddies. The model was based on two energy balance equations: for the ground and for the vegetation, and considered the water layer and changes in the aerodynamic properties of its surface with water depth. The model was examined with field experiments for water depths of 0 mm (drained conditions) and 100 mm (flooded condition) at two locations. Daily mean water temperatures in the flooded condition were mostly higher than in the drained condition in both locations, and the maximum difference reached 2.6°C. This difference was mainly caused by the difference in surface roughness of the ground. Heat exchange by free convection played an important role in determining water temperature. From the model simulation, the temperature difference between drained and flooded conditions was more apparent under low air temperature and small leaf area index conditions; the maximum difference reached 3°C. Most of this difference occurred when the range of water depth was lower than 50 mm. The season-long variation in modeled water temperature showed good agreement with an observation data set from rice paddies with various rice-growing seasons, for a diverse range of water depths (root mean square error of 0.8–1.0°C). The proposed model can estimate water temperature for a given water depth, irrigation, and drainage conditions, which will improve our understanding of the effect of water management on plant growth and greenhouse gas emissions through the thermal environment of rice paddies.

The role of the Brazilian Plateau (BP) in maintaining the South Atlantic convergence zone (SACZ) has been examined by statistical analysis and numerical experiments. Statistical analysis using 27 years of data showed that the SACZ is most intense when it is over the BP. In this case, low-level cyclonic circulation appears over the southwestern part of the BP and forms westerly flow, which intensifies low-level convergence along the SACZ with northeasterly flow from the Amazon and northerly flow along the western edge of the South Atlantic subtropical high. A vorticity budget analysis indicates that precipitation over the BP that accompanies stretching maintains the cyclonic circulation. Sensitivity experiments using a regional atmospheric model for two different cases indicate that precipitation over the BP plays a dominant role as an atmospheric heat source in maintaining the cyclonic circulation and the SACZ. In model experiments in which rain was stopped around the BP but the topography was kept, the cyclonic circulation disappeared, and the SACZ shifted southward away from its original position. In comparison with a control run, precipitation over the BP was weakened and the SACZ shifted southward in experiments in which the BP was removed or its complex, multiple-valley terrain was smoothed out. The results of this study support the ideas suggested in previous studies (i.e., that the BP has an anchor effect on the SACZ): Precipitation is intensified over the complex terrain of the BP, and mechanisms of conditional instability of the second kind occur between the precipitation over the BP and the cyclonic circulation to its southwest, which intensifies moisture convergence over the plateau.

This study presents ocean heat budget analysis on seas surface temperature (SST) anomalies during strong-weak Asian summer monsoon (southwest monsoon). As discussed by previous studies, there was close relationship between variations of Asian summer monsoon and SST anomaly in western Indian Ocean. In this study we utilized ocean heat budget analysis to elucidate the dominant mechanism that is responsible for generating SST anomaly during weak-strong boreal summer monsoon. Our results showed ocean advection plays more important role to initate SST anomaly than the atmospheric prcess (surface heat flux). Scatterplot analysis showed that vertical advection initiated SST anomaly in western Arabian Sea and southwestern Indian Ocean, while zonal advection initiated SST anomaly in western equatorial Indian Ocean.

Prior to future climate assessment of the 5th Coupled Model Intercomparison (CMIP5) experiments, how well CMIP5 models simulates present climate should be examined. Sea surface salinity (sss) play important role in ocean stratification and indirectly affects air sea interaction. However, few studies have been carried out to evaluate sss in CMIP5 models. In this study, performance of CMIP5 models in simulating sss in Indian Ocean was examined with respect to the observation. Our results showed that multi model ensemble (MME) mean of CMIP5 models displayed annual and seasonal salinity bias in three regions i.e. Western Indian Ocean (WIO), Bay of Bengal (BOB) and Southeastern Indian Ocean (SEIO). CMIP5 models overestimate sss in BOB about 1.5 psu and underestimated sss in WIO and SEIO about 0.4 psu. Biases in WIO and BOB were mainly attributed to bias in precipitation. CMIP5 models overestimated (underestimated) precipitation in WIO (BOB) with greater bias found during Boreal summer to winter. Meanwhile, advection process was responsible for negative SSS bias in SEIO.

A value for the exchange speed of sensible heat CHU under natural convective conditions was determined by both indoor and field experiments. Regardless of the type of experiment, the relationships for the CHU were obtained as CHU = b(TS − T)1/3. For a wet surface, ΔTv should be substituted for (TS − T). Here, TS is the ground surface temperature, T the air temperature, and ΔTv the virtual temperature difference. In addition, b is a coefficient having a value of 0.0011 m s−1 K−1/3 for a smooth surface and 0.0038 m s−1 K−1/3 over a rough surface. From the field observation data, it was concluded that under strongly unstable conditions (−1 > ζ > −477) the best pair of stability profile functions was proposed.