Improving the treatment of subgrid cloud variability in warm rain simulation in CESM2

<p>Representing subgrid variability of cloud properties has always been a challenge in global climate models (GCMs). In microphysics schemes, the effects of subgrid cloud variability on warm rain process rates calculated based on mean cloud properties are usually accounted for by scaling process rates by an enhancement factor (EF) that is derived from the subgrid variance of cloud water. In our study, we find that the EF derived from Cloud Layers Unified by Binormals (CLUBB) in Community Earth System Model Version 2 (CESM2) is severely overestimated in most of the oceanic areas, which leads to the strong overestimation in the autoconversion rate. Through an EF formula based on empirical fitting of MODIS, we improve the EF in the liquid phase clouds. Results show that the model has a more reasonable relationship between autoconversion rate, cloud liquid water content (LWC), and droplet number concentration (CDNC) in warm rain simulation. The annual mean liquid cloud fraction (LCF), liquid water path (LWP), and CDNC show obvious increases for marine stratocumulus, where the probability of precipitation (POP) shows an obvious decrease. The annual mean LCF, cloud optical thickness (COT), and shortwave cloud forcing (SWCF) match better with observation. The sensitivity of LWP to aerosol decreases obviously. The sensitivities of LCF, LWP, cloud top droplet effective radius (CER), and COT to aerosol are in better agreement with MODIS, but the model still underestimates the response of cloud albedo to aerosol. These results indicate the importance of representing reasonable subgrid cloud variabilities in the simulation of cloud properties and aerosol-cloud interaction in climate models.</p>

Vegetative characteristics of soybean (Glycine max L.) as bioindicator parameter of herbicide in the soil

The objective of the present study was to evaluate the presence of the herbicide 2,4-D in the Neosol. We conducted the experiment in a greenhouse using the soybean crop as a bioindicator. A randomized block design with 5 x 3 factorial scheme composed of five application periods before sowing (0, 3, 6, 9, and 12 days) and three simulated rain (0 mm, 20 mm, and 30 mm), with four repetitions was conducted. The herbicide dose was 1500 g a.i. ha-1, the rainfall was simulated one hour after pulverization. Twelve hours after the last rain simulation, Cv. TMG® ANTA 82 RR was sown, and pot moisture remained at 80% of pot capacity throughout the experiment. Herbicide in the soil was evaluated by visual plant phyto-intoxication, plant height, shoot fresh mass and root fresh mass, and shoot dry mass and root dry mass at 26 days after sowing. Statistical analysis was performed according to the polynomial regression model. The application of herbicides in dry soils that remained without rain during the first hours resulted in greater residual effect on the soil (0 mm of rain). The occurrence of higher humidity accelerated the degradation of the herbicide in the soil (30 mm of rain). Longer periods between application and sowing provided more significant increments. The herbicide’s toxic effects reduced linearly as started from 12 days before sowing. The 2,4-D showed low persistence in the soil, and 12 days was observed to represent a safe time length between spraying and sowing, regardless of the occurrence of rainfall. The soybean was a good indicator of 2,4-D.

Visual effects of colors on façades during rain

In this study, rain would be regarded as an important factor in landscape color planning, and the visual effects and impressions of colors on façades during rainfall were focused. Verification experiments in which the authors produced a rain simulation device that can control rainfall and illuminance were conducted. Experiments were conducted based on an experimental design with four factors of hue (5R / 5Y / 5G / 5PB), Chroma, lightness, and rainfall. In the rainfall simulation experiment, the visual effects of the color based on visual colorimetry were evaluated, and impression evaluations were extracted. As results, this study presented our findings about the relationship between rainfall and the visual effects and impressions of color, and determined that there are differences in the evaluation depending on the Hue and the amount of rainfall. In landscape color planning, considering the influence of rainfall is important for a fascinating landscape. This study will show basic important references of such new landscape color planning.