Local and Remote Forcing Factors of Heatwave in India -A Reanalysis and Adjoint model based study

Abhirup Banerjee; Armin Koehl; Frank Lunkeit; Detlef Stammer

Home/Archive/arXiv

arXiv · 2410.17083

Local and Remote Forcing Factors of Heatwave in India -A Reanalysis and Adjoint model based study

Abhirup Banerjee·Armin Koehl·Frank Lunkeit·Detlef Stammer

View original source ↗Read source document ↗Export metadata ↓

Abstract

Continental heatwaves can dramatically impact ecosystems and societies, e.g., by leading to excess mortality, wildfires, and harvest failures. With a warming climate, their impacts potentially intensify globally, but the Indian subcontinent appears to be particularly vulnerable to such extreme events. In this study, we use reanalysis and the adjoint of the atmospheric model, PlaSim, to identify drivers of heatwaves occurring April and May over north-central India. Reanalysis results suggest that the existence of high temperatures in the study region is highly sensitive to the low local soil moisture which is observed weeks before a heatwave commences. Soil moisture variability in northern India is influenced by moisture transport from the west during winter--spring. Preceding dry soil moisture conditions can be associated with a `persistent jet' conditions linked to atmospheric dynamical changes in the North Atlantic region. An associated northward shift in the upper tropospheric zonal wind occurs approximately a month prior to the heatwaves, influencing the area and intensity of western disturbances embedded in the jet stream. This weakens the moisture flow from the north of the Arabian Sea, further reducing soil moisture levels and creating conditions conducive to heatwaves. An adjoint sensitivity analysis and forward model perturbation experiments confirm the causal relationships for the proposed heatwave development mechanism over north-central India, identifying the remote influence of North Atlantic sea surface temperature variability on extreme temperatures in India. Our findings highlight the complex interplay of local and remote factors in heatwave development over India.

Explore related subjects

physics.ao-ph

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Abhirup Banerjee, Armin Koehl, Frank Lunkeit, Detlef Stammer. 2024-10-22. Local and Remote Forcing Factors of Heatwave in India -A Reanalysis and Adjoint model based study. https://arxiv.org/abs/2410.17083

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

The impact of two-dimensional filtering on SWOT observations with white noise

The Surface Water and Ocean Topography (SWOT) mission provides two-dimensional observations of sea surface height (SSH) at unprecedented spatial resolution, enabling exploration of ocean variability down to scales of $O(10~\mathrm{ km})$. At these scales, however, interpreting SSH variability is challenging because ocean dynamical signals overlap with measurement noise, and their respective spectral signatures are not yet fully understood. Recent analyses of SWOT 2-km posting observations have shown that along-track spectra transition to flatter but still red spectra at scales around 30 km. These flatter portions of the spectra have a power-law-like behavior and spectral slopes of approximately $-1$ or steeper, and their magnitudes and slopes are correlated with SWOT measurement noise magnitude. Here, we investigate the hypothesis that these flatter but still red along-track small-scale spectra can arise from two-dimensional filtering and aliasing of spatially uncorrelated (white) noise. Using synthetic experiments, we show that the resulting one-dimensional along-track spectra exhibit a similar transition to red, power-law-like behavior at scales of 15--50 km, qualitatively similar to spectral behavior reported in SWOT observations. The transition scale and apparent spectral slopes depend on the noise level, its cross-track variability, and the background ocean signal. This finding highlights the importance of carefully accounting for measurement noise and processing effects when interpreting SWOT spectra, and suggests that a white noise model should serve as a baseline null hypothesis for small-scale spectral analyses.

physics.ao-ph↗

arXiv2026

Improving global precipitation forecasts with an AI weather model trained on satellite observations

Precipitation forecasts shape decision-making across the global economy, particularly in sectors such as agriculture. However, unlike variables such as temperature, precipitation is highly intermittent and localized, making it difficult to forecast. While recent advances in AI weather prediction systems have enabled them to surpass physical models on globally averaged metrics, improvements in mean error rarely translate to actionable forecasts of severe flooding or dry crop fields. Furthermore, most of these models are trained and evaluated against a reanalysis data product, ERA5, which has well-known biases. Here we retrain AIFS, ECMWF's widely-used, open-source operational 0.25° probabilistic graph-transformer weather model, on satellite-based precipitation observations. Our model, Laxmi, improves global probabilistic accuracy by 19% and resolves systematic distributional biases in ERA5. Specifically, Laxmi reduces drizzle overprediction by 33% for amounts less than 3 mm per day. It also mitigates extreme rainfall underprediction, improving the global 95th percentile Brier skill score by 57%. Across a case study of 10 Indian tropical storms, Laxmi delivered the most accurate forecast of 150 mm event-total precipitation in 7 events, compared to 1 for AIFS and 2 for the leading physical model, IFS. Our results demonstrate that incorporating observation-based precipitation data directly into training can substantially improve forecasts.

physics.ao-ph↗

arXiv2026

HClimRep-Ocean: A Global Ocean Emulator on an Unstructured Mesh

Machine-learning (ML) emulators for atmospheric processes have advanced rapidly in recent years, transforming weather forecasting. Although early ML ocean forecasting models now exist, they remain less developed than their atmospheric counterparts. Unlike the atmosphere, much of the ocean's kinetic energy resides in mesoscale eddies whose characteristic spatial scales are approximately an order of magnitude smaller than those of comparable atmospheric features. Moreover, complex coastlines, narrow straits, and ice-covered seas make boundary representation a central challenge that atmospheric models do not face. Consequently, numerical ocean simulations commonly use locally refined or even completely unstructured meshes. However, their data-driven counterparts have so far been built around latitude-longitude grids. We present HClimRep-Ocean, an ocean emulator that operates directly on the native unstructured mesh of FESOM2. The emulator is trained on a 209-year AWI-CM3 control integration and is run without atmospheric forcing, receiving the atmospheric state only at initialisation time, which isolates the predictability carried by the ocean state itself. Skill is strongly field-dependent: for currents, HClimRep-Ocean outperforms every reference at 30 day forecast, whereas for temperature and salinity a damped-anomaly persistence forecast remains the more accurate estimator. This behaviour is physically interpretable: current variability is largely geostrophic and internally generated, whereas sea-surface temperature and salinity fluctuations are driven by atmospheric forcing through weather state. Evaluated independently on the OceanBench benchmark, a reanalysis-trained variant of HClimRep-Ocean achieves the lowest RMSE against GLORYS reanalysis among all assessed systems, confirming the competitiveness of the native-mesh approach.

physics.ao-ph↗