Dr Muhammad Adnan Abid

Abstract:

Early winter (October-December; OND) precipitation is critical for Pakistan’s water resources, agriculture and socio-economic stability, yet the large-scale drivers and physical mechanism driving its interannual variability remains poorly understood. Using in-situ observations and ERA5 reanalysis for 1981–2023, we have investigated the dominant mode of early winter precipitation variability over Pakistan and its associated atmospheric teleconnections. The leading Empirical Orthogonal Function (EOF1) explains approximately 60% of the total variance, with the maximum interannual variability centered over northern Pakistan, including the Upper Indus Basin. A Pakistan Precipitation Index (PRI) is developed over this core precipitation region. Regression analysis demonstrates that positive PRI anomalies are associated with anomalous upper-level divergence over central-eastern equatorial Pacific and western Indian Ocean, compensated by convergence over Maritime Continent. A quasi-stationary Rossby wave train excites from tropical pacific and propagates eastward along the subtropical westerly jet into Eurasia producing an anomalous upper-level trough over southwest Asia including Pakistan. This favors enhanced ascent and cyclonic circulation anomalies along with moisture transport from western Indian Ocean and Bay of Bengal, resulting in above normal precipitation over Pakistan. The PRI is significantly correlated (r = 0.5) with the warm central-eastern equatorial Sea Surface Temperature (SST) anomalies (i.e., Niño3.4 index), indicating the El Niño-Southern Oscillation (ENSO) as a dominant mode of early winter precipitation variability. During El Niño, the warm SST anomalies in central-eastern equatorial Pacific enhance upper-level divergence, excite stationary Rossby wave and shift the subtropical westerly jet southward. At same time, the enhanced moisture transport and wave activity from the western Indian Ocean, favors convergence over Pakistan. In contrast, La Niña produces the opposite response. This shows ENSO along with the Indian Ocean strongly influences Pakistan precipitation variability in early winter. These findings provide valuable basis for seasonal prediction and support water resource management, agriculture and climate resilient planning across Pakistan.

Abstract:

Nudging still is a cost-effective data assimilation technique in coupled climate models, but conventional schemes apply fixed spatial strengths and are less effective in representing heterogeneous ocean processes. An adaptive nudging framework based on a spatially varying gain matrix is proposed to dynamically balance model and observational errors. The method not only preserves the merits of the latitude-dependent nudging approach but also provides a more physically consistent determination of the spatial distribution of nudging coefficients. Implemented in the SPEEDY-NEMO coupled model, the framework is systematically evaluated against the traditional latitude-dependent scheme. Results show that the adaptive approach substantially improves subsurface temperature assimilation, particularly in the Niño3.4 region, the tropical Indian Ocean, North Pacific, North Atlantic, and the northeastern Pacific. In the tropics, the improvement is mainly achieved above and within the thermocline (roughly 100--200 m), where strong vertical stratification and sharp gradients make fixed nudging strengths inadequate:the RMSE decreases by 20% and the correlation with observations increases by 30% compared with the traditional latitude-dependent scheme. By dynamically adjusting the assimilation strength, the adaptive scheme better constrains the thermocline variability and surface-subsurface interactions. In mid- to high-latitude regions, the improvement extends to greater depths, consistent with a deeper thermocline, where oceanic processes dominated by the mixed layer dynamics and convection exhibit large regional biases that require spatially adaptive correction. In addition, compared with the latitude-dependent nudging scheme, the adaptive approach achieves simultaneous corrections of both the systematic bias term and the variance term of temperature deviations, thereby enhancing not only the mean state but also the model’s ability to capture variability. Generally, the root-mean-square errors decrease by 20-30% and the correlation with observations increases around 30-50% by the adaptive scheme. Beyond temperature, improvements are also evident in salinity, currents, and sea surface height anomalies, indicating the broader benefits of the adaptive scheme. These results indicate that spatially adaptive nudging provides a more effective and practical alternative to fixed schemes, offering a solid basis for improving ocean state estimation in coupled models.

Abstract:

Nudging remains a cost-effective data assimilation technique in coupled climate models, yet conventional schemes with fixed spatial strengths struggle to represent heterogeneous ocean processes. This study introduces an adaptive nudging framework in which a spatially varying gain matrix dynamically balances model and observational errors, providing a more physically consistent determination of nudging coefficients. Implemented in the SPEEDY-NEMO coupled model, the method is systematically evaluated against a traditional latitude-dependent scheme. Results show substantial improvements in subsurface temperature assimilation across key regions, including the Niño3.4, tropical Indian Ocean, North Pacific, North Atlantic, and northeastern Pacific. The most pronounced gains occur above and within the thermocline, where strong stratification renders fixed nudging strengths inadequate, yielding a 20–30% reduction in RMSE and a 30–50% increase in correlation. In mid- to high-latitude regions, improvements extend to greater depths, consistent with deeper thermocline structures. The adaptive framework corrects both systematic bias and variance, enhancing not only the mean state but also variability representation. Additional benefits are found in salinity, currents, and sea surface height, demonstrating that spatially adaptive nudging provides a more effective and practical alternative for improving ocean state estimation in coupled models.

Abstract:

Plain Language Summary: We investigate how the variability in the Indian Ocean Sea Surface Temperature in autumn, known as the Indian Ocean Dipole (IOD), influences the precipitation regime and storm activity in the North Atlantic, Europe, and Mediterranean regions during the winter season. Our results indicate that IOD variability triggers December shifts in atmospheric pressure over these regions and alters precipitation patterns, influencing the frequency and intensity of precipitation events. The strongest impacts are observed at mid‐latitudes, with storm activity decreasing over the Eastern Atlantic and Western Mediterranean. These storm changes are tied to stronger temperature contrasts between the north and south part of the domain, which produce significant changes in the vertical wind shear. Our study further supports the idea that Indian Ocean variability may influence the early winter weather in Europe and the Mediterranean—an important insight for improving sub‐seasonal to seasonal forecasts.