During the Asian Summer Monsoon Chemical and Climate Impact Project (ACCLIP), National Aeronautics and Space Administration (NASA) WB-57 high altitude research aircraft observed strong turbulence at an altitude of 15 to 18 km near the outflow of the Super Typhoon Hinnamnor in the Northwest Pacific. Our analyses based on the trial version of the “RRJ-ClimCORE” mesoscale reanalysis have revealed that a thin layer of strong vertical wind shear (VWS) extending just below the tropopause along the upper surface of the outflow from the typhoon eyewall provides favorable conditions for shear instability. Further analyses of the mesoscale background conditions based on RRJ-ClimCORE as well as the geostationary satellite Himawari-8 cloud imagery suggest that the convolution of such small-scale processes as convectively generated concentric gravity waves and shallow convection appearing as radially-banded cirrus clouds may have further increased the potential for turbulence generation. Absolute vorticity analysis suggests the occurrence of inertial instability to the north of the typhoon, which explains the development of radially-banded cirrus clouds as well as the layer of strong VWS around the flight altitudes. The present study thus demonstrates the advantages of the hourly output of RRJ-ClimCORE over 3-hourly output operational mesoscale analysis for investigating source mechanisms of turbulence in the upper-troposphere and lower-stratosphere.

Understanding the fate of microplastics (MPs) in soil is one of the most urgent environmental tasks we face. It is also a very challenging one, as there are numerous properties of both MPs and the soil, as well as hydrological and geochemical conditions, that interplay to affect MPs transport. In this study, we conduct laboratory experiments in which two of the most commonly used MPs types in agriculture (polyethylene terephthalate (PET) and polypropylene (PP)) are leached into an idealized soil analog (glass beads). We find that MPs inhibit water flow, delaying its passage through the sample and making it more tortuous, forcing the flow to occur through preferential pathways (fingers). These effects are more pronounced for PP, which is more hydrophobic, than for PET. The transport of PP is inhibited relative to that of PET, which is attributed to its both its impeding effect on water flow (the driving force), as well as its surface charge which increases its tendency to adsorb onto soil particles, and lower density which curbs downward transport.

Accurate prediction of the solar wind speed and arrival of solar transient events are among the most important but still open problems in space weather research. This article implements a Convolutional Neural Network (CNN) coupled Long-Short Term Memory cell (LSTM) deep-learning model for the prediction of solar wind speed at Sun-Earth L1 on a daily and 6-hourly basis. The model is trained and validated using Solar-wind speed observations at Sun-Earth L1. The proposed CNN-LSTM models are developed and trained using data for the period from 01 August 1996 to 31 December 2019 and tested with the data from 01 January 2020 to 30 June 2024. The daily prediction model can forecast solar wind speed with a RMSE of 52.12 km/s and an overall correlation coefficient of 0.79, while the 6-hourly model can forecast solar wind speed with a RMSE of 33.72 km/s and an overall correlation coefficient of 0.93. This shows that without explicitly building in physics based relationships, the Deep Learning based models are able to perform reasonably well compared to existing approaches.

Pacific decadal variability (PDV), low-frequency changes in Pacific sea surface temperatures (SSTs), significantly impacts global climate. However, disentangling anthropogenic effects upon PDV is challenging since both vary on similar time scales. Using single-forcing climate model large ensembles, we find that anthropogenic forcing primarily drives a spatially-varying pattern of mean-state change in North Pacific SST that project onto leading PDV patterns, principally the Pacific Decadal Oscillation (PDO) and North Pacific Gyre Oscillation (NPGO). In fact, when the trend is determined by the model ensemble mean, there is no forced change of the PDV modes. However, analysis of single model realizations, where the mean-state trend cannot be cleanly identified, suggests an apparent anthropogenic change in NPGO decadal variability. This suggests that observed PDV responses to anthropogenic forcing may be erroneously convolved with the background trend pattern. Therefore, correctly determining the mean-state trend is a necessary precursor for identifying forced changes to PDV.