Research Projects

Over the past decade, advances in climate science have shown that accounting for the spatial pattern of surface warming, driven by heterogeneous ocean heat uptake, greenhouse gas and aerosol forcing, land surface changes, and internal variability—significantly improves the diagnosis of radiative feedbacks and long-term temperature projections (e.g., Andrews et al., 2018; Fueglistaler and Silvers, 2021; Modak and Mauritsen, 2023). This so-called “pattern-effect” arises because different SST anomaly patterns excite distinct radiative feedbacks, particularly cloud, water vapor, and lapse rate feedbacks which modulate the strength and evolution of surface warming (IPCC, 2021). Although pattern-effect has primarily informed our understanding of future temperature change, a burgeoning number of studies now link it to global hydrological sensitivity (e.g., Zhanget al., 2023). Yet, these efforts remain largely global, and the application of pattern-effect diagnostics to regional precipitation, especially in monsoon dominated region such as India, is conspicuously limited. Rainfall over India, dominated by the Indian summer monsoon (ISM) which supplies 80% of annual rainfall, is shaped by a confluence of internal variability (c.g. EINino Southern Oscillation (ENSO), Indian Ocean Dipole (IOD)), land-ocean thermal contrast, snow-albedo feedback, land-atmosphere interactions and aerosol forcing (e.g. Charney, 1975; Gadgil, 2003; Goswami et al., 2006; Turner and Annamalai, 2012; Krishnan et al., 2013; Bollasina et al., 2011). While extensive research has explored ISM’s dynamics, variability and trends (e.g., Rupa Kumar et al., 2006; Chen and Zhou, 2015; Roxy et al., 2015, 2017), a feedback-aware framework that connects SST pattern variations to radiative feedbacks and regional rainfall remains unexplored. This project pioneers a feedback-aware, SST pattern-effect framework to investigate the drivers of hydrological sensitivity and rainfall responses over India. Grounded in the PI’s recent work (Modak and Mauritsen, 2021, 2023), the study builds on novel diagnostics of historical pattern-effect and radiative feedbacks using observed SST-forced simulations with the MPI-ESM1.2-LR model. It will integrate multi-model ensembles, idealized experiments, and machine learning techniques to evaluate whether the relationship between SST pattern–radiative feedback–rainfall response can be used to reduce uncertainty in future monsoon projections. The proposed research will proceed through three interconnected stages. First, it will quantify how historical SST pattern effects have shaped hydrological sensitivity over India by analyzing radiative feedbacks and identifying the dominant ocean basins mediating this influence. Second, the study will use targeted SST-forced experiments to examine how internal variability modes, such as El Nifio, IPO, and Southern Ocean warming, modulate the feedback-precipitation linkage through distinct SST patterns. Finally, building on insights from historical and idealized simulations, the project will assess whether feedback-aware diagnostics can constrain future rainfall projections under warming scenarios. Key experiments include: (i) AMIP-style simulations forced with multiple observed SST datasets; (ii) targeted idealized SST-forcing experiments for variability modes; (iii) CMIP6 SSP-based simulations for emergent constraints. The PI will employ top-of-atmosphere radiative flux as well as radiative kernel decomposition, precipitation energetics, and machine learning methods for causal inference and uncertainty quantification. This will be the first systematic effort to apply a pattern-effect framework to precipitation energetics in a monsoonal context, and opens a new frontier for understanding and reducing uncertainty in rainfall projections over India.