Abstract. The spatio-temporal variability of Indian SummerMonsoon is well studied based on different types of rainfall data. However, very few attempts have been made to study the underlying role of clouds and its hydrometeors on Monsoon Intraseasonal Oscillations. The northward propagating Monsoon Intraseasonal Oscillations and its characteristics remain a challenge for the numerical modelers even today. In view of this, we have set out to analyze the role of cloud hydrometeors and their linkage with northward propagating Monsoon Intraseasonal Oscillations. The science question that we intend to address here is whether the different phases of the cloud hydrometeors show similar propagation characteristics as that of rainfall, and what are the relations of their phases with the convection centre using Tropical Rainfall Measuring Mission data. In answering the question, we have analyzed ten years of Tropical Rainfall Measuring Mission 2A12 hydrometeor data over Indian region. Our analyses show that the cloud water and cloud ice do show a large scale organization during the Indian Summer Monsoon regime of June-September, and systematically progress northward getting initiated over equatorial Indian Ocean. On further analyses, we found that cloud water actually leads the rainfall and cloud ice lags the rainfall. We have further demonstrated the process by analyzing dynamical parameters from Modern Era-Retrospective Analysis for Research and Applications. The presence of cloud water in the lower troposphere in the leading edge of rainfall indicates the lower level moistening and preconditioning of the convective instability due to enhanced moisture convergence. Subsequently, deep convection is triggered, which generates hydrometeor above freezing level and cloud ice in the upper troposphere. To quantify objectively the relation among cloud liquid water, cloud ice and rainfall, the lag correlation is computed with respect to convection center, where the above hypothesis is established that cloud liquid water leads the rainfall and cloud ice lag. This relation among hydrometeors may help the numerical modelers to incorporate such processes for capturing the characteristics of Monsoon Intraseasonal Oscillations.
The present study examines the ability of Coupled Model Inter‐comparison Project phase 6 (CMIP6) models in representing the dominant modes of tropical Indian Ocean (TIO) sea surface temperature (SST) variability on the interannual and decadal time scale. Historical simulations from 27 CMIP6 models are assessed against Extended Reconstructed SST over the period of 1854 to 2014. Spectrum analysis reveals that many models reproduce interannual and decadal variability of TIO SST but underestimate the amplitude of variability with some disparity in the periodicity. All models can reproduce the dominant basin‐wide mode of interannual and decadal variability of TIO SST reasonably well. Skill score analysis of TIO SST variability reveals that KACE‐1‐0‐G has highest skill, followed by FGOALS‐f3‐L, EC‐Earth3‐Veg‐LR, ACCESS‐ESM1‐5, CanESM5‐CanOE on the interannual timescale and FGOALS‐f3‐L, CanESM5‐CanOE, KACE‐1‐0‐G and CanESM5, respectively, showed highest skills for decadal variability. It is found that variations in radiation and latent heat flux are primarily responsible for interannual variability in TIO SST, the basin‐wide warming, in the observations. Taylor diagram analysis reveals that all the models exhibit better skill for the radiative flux; however, skill for the latent heat and momentum flux varies from model to model. It is important to note that the models in which the latent heat flux and zonal wind are better represented have produced better TIO SST variability compared to other models. A higher discrepancy in latent heat and zonal momentum flux leads to improper wind‐evaporation‐SST and wind‐circulation‐SST feedback, which in turn restricts the model skill. Besides, model that has realistic central and eastern Pacific SST variability show better skill for TIO SST variability in both interannual and decadal time scales. The present study advocates that better representation of latent heat flux and zonal wind in coupled models is important for the accurate simulation of interannual and decadal variability in TIO SST.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.