1. Land–Atmosphere Interactions
Land behaves quite differently from oceans in the way it stores and exchanges heat and water, and unlike oceans, it dries out and is limited by water availability. These aspects of land are intricately linked to near surface atmospheric variables like 2m air-temperatures and humidity, making land–atmosphere exchange a key control on regional climates.
Key questions are
Q. How can we improve the modelling of land-atmosphere exchange?
Q. What role do they play in shaping variability in temperatures as well as extremes like heatwaves, droughts and floods?
Q. Are there physical limits that govern these exchanges?
My work has shown that the heat and moisture exchange between land and atmosphere are subject to strong thermodynamic constraints (Ghausi et al., 2023, PNAS). These constraints primarily arise from how much work atmosphere can perform to generate motion. Building on this, I have developed energetically-constrained models of the atmospheric boundary layer that reproduce diurnal air temperatures and their variations across diverse climates (Ghausi et al., 2025, GRL).
2. Temperature Extremes and Heatwaves
Temperature extremes have detrimental impacts on terrestrial ecosystems. However, such events can be caused by diverse processes, generating heat waves with different characteristics that have distinct and even contrasting impacts and trends under warming. As a result, it becomes increasingly important to understand their drivers in order to reliably project them in future, understand intermodal uncertainties as well as enhance their short-term forecasting ability.
I have worked on developing analytical models that links radiative and surface evaporative conditions to changes in daily maximum temperatures requiring no free parameters. These models essentially use energetic constraints like surface-energy balance, heat storage variations in lower atmosphere and thermodynamic constraints on turbulent fluxes. These models than provide strong physical frameworks to identify different drivers of temperature variability and extremes.
At regional scales like Tibet, we found that changes in solar and longwave radiation drive both hot and cold extremes that are linked to large-scale circulation patterns (Tian et al., 2023, ERL). Extending this framework globally, we showed that global warming increases the proportion of sunny-dry and advective heatwaves, with important implications for terrestrial ecosystems (Tian et al., 2024, Nature Communications Earth & Environment).
3. Extreme Rainfall and Floods
There exists strong physical basis to suggest that rainfall extremes should intensify with warming as the warmer atmosphere holds more moisture, yet observations often show weaker or even negative relationships between precipitation and temperature, particularly in the tropics. Reconciling these discrepancies is critical for understanding future flood risks.
My research has focused on addressing this gap across regional to global scales. Over South Asia, I showed that rainfall–temperature scaling becomes negative at high temperatures, in contrast to the positive streamflow–temperature scaling (Ghausi et al., 2020, GRL). I hypothesized that this negative relationship arises from cloud radiative effects, which influence surface temperatures and introduce an additional covariation between rainfall and temperature beyond the thermodynamic effect of temperature on atmospheric moisture holding capacity. I first tested this idea over India, where incorporating cloud effects into a thermodynamically constrained surface energy balance model revealed that rainfall extremes intensify in line with climate model projections (Ghausi et al., 2022, HESS). Extending this framework globally, I showed that cloud radiative effects explain much of the negative scaling observed in the tropics and help reconcile the long-standing mismatch between observations and models (Ghausi et al., 2024, Nature Communications).
A critical open question is how global warming alters the dynamics of moist convection and vertical motion, and how these changes interact with thermodynamic drivers to shape future rainfall extremes?
4. AI and Hybrid Modelling
Physical models provide mechanistic understanding but often miss local detail, while AI methods capture patterns but lack interpretability. Combining these two approaches offers a way to improve climate predictions while retaining physical meaning.
In our group, we are focused on developing hybrid approaches that embed machine learning within process-based models. The long-term goal is to scale these hybrid frameworks to provide hyper-local forecasts and early warning systems relevant for actionable climate information.