ABSTRACT Reconstructing the North Atlantic Oscillation (NAO) and East Atlantic (EA) Pattern is crucial for understanding past climate variability and improving climate predictions. This study examines the impact of proxy network density and location on the reconstructability of these modes, using an idealised pseudo‐proxy approach based on ERA5 reanalysis temperature and precipitation. Results show that temperature proxies contribute more skill than precipitation and that the sign of the teleconnection matters. For NAO reconstruction from temperature, networks combining both dipole poles outperform single‐pole networks at small to moderate network sizes. EA reconstruction displays only small, variable‐dependent benefits from sampling both centres of action. A parallel analysis using the CESM1 Last Millennium Ensemble confirms these patterns hold over centennial timescales and under external forcing. Because our pseudo‐proxies are idealised and noise‐free, our results provide an upper ceiling on reconstruction skill and a lower bound on proxy‐network density for a given skill level, highlighting the importance of careful site selection.
ABSTRACT Deep convective clouds play a central role in the Earth water and energy cycles, yet their rapid vertical development remains difficult to characterize. The C2OMODO mission relies on a tandem of two passive microwave radiometers flying sequentially with a time separation ranging from 45 to 135 s. This configuration enables the estimation of short‐time brightness temperature differences that informs on convective dynamics. In this study, we introduce VARAND2, a one‐dimensional variational method that exploits multi‐temporal passive microwave observations like those provided by C2OMODO to retrieve key dynamical properties of deep convection. VARAND2 retrieves the vertical profile and short‐time evolution of the Ice Water Content (IWC), as well as a parametrized vertical profile of air vertical velocity (wair), characterized by its maximum amplitude (wmax) and the corresponding altitude (zmax). These quantities are estimated through a dynamic observation operator that links short‐time variations of IWC and brightness temperature via an embedded vertical advection scheme. Applied to simulated C2OMODO observations for a tropical convective case, VARAND2 provides consistent reconstructions of wair, with coefficients of determination (r2) of 0.71 and 0.72 for the retrieval of wmax and zmax, respectively. These results highlight the potential of multi‐temporal passive microwave observations to improve the characterization of deep convective updrafts and their representation in weather and climate models.
ABSTRACT Extreme convective events are posing increasing challenge to aviation. On December 13, 2024, an intense thunderstorm caused severe operational disruption at Rome–Fiumicino Airport. Here we analyze the dynamic and thermodynamic conditions associated with this event using a conditional analogue‐based framework applied to ERA5 reanalysis data. We identify synoptic and mesoscale patterns similar to the one observed during the event over the satellite era (1979 to present) and compare the associated environmental conditions between a counterfactual past period (1979–2000) and a factual present one (2002–2023). While the dynamical structure of the circulation patterns remains mostly unchanged, the thermodynamic background has evolved toward warmer conditions, with increased near‐surface air and sea surface temperatures over the Mediterranean basin. These changes are accompanied by increases in precipitation, convective instability, and wind shear. Observations at Rome–Fiumicino Airport further indicate stronger wind gusts, increased dew‐point temperature and reduced visibility during analogue situations in recent decades. Our results suggest that warming background conditions may amplify the convective impacts of otherwise similar synoptic and mesoscale patterns, increasing the potential for disruptive weather affecting Mediterranean aviation hubs.
ABSTRACT The rapid and unprecedented warming of the Indian Ocean intensifies climate risks across socio‐economically vulnerable regions, highlighting the need of accurate prediction over the coming decade. This study first assesses the decadal predictability of the Indian Ocean Dipole (IOD) using the Decadal Climate Prediction Project (DCPP) models of Coupled Model Intercomparison Project Phase 6 (CMIP6), then develops a deep learning model based on a bidirectional gated recurrent unit (BiGRU) to enhance its predictive skill. The BiGRU model is designed to process multisource sequential data and is trained on the IOD index derived from DCPP simulations. Results show the MME achieves moderate skill on detrended IOD with an anomaly correlation coefficient (ACC) and mean squared skill score (MSSS) of 0.51 and 0.20 during 1963–2020, respectively. The BiGRU model improved the skill with an anomaly correlation coefficient (ACC) of 0.88 and a mean squared skill score (MSSS) of 0.72 during the testing period of 2009–2020, and accurately captured extreme IOD events. Based on the enhanced IOD index, we further used it to improve the prediction skill of the IOD‐related Australian rainfall, achieving ACC and MSSS values of 0.90 and 0.70 during 2009–2020, compared to 0.29 and −1.62 from the MME, underscoring the reliability of the BiGRU model for both IOD and related precipitation forecasts.
ABSTRACT Satellite detections of hail signatures are uncertain in the tropics, where the deep troposphere and a warm, humid sub‐cloud environment increase the chances that ice detected aloft will melt before it reaches the surface. It is often suspected that satellite‐based detections of hail over the tropics are over‐estimations owing to this effect. Here, we used numerical weather simulations to examine the plausibility of damaging surface hail at times and locations when satellites detected hail signatures over Australia's tropics. Four microphysics schemes were used to simulate 61 hail signature detections with surface hailstone sizes estimated using a column model for hailstone growth and melting, and estimated by model microphysics. Using the column model, 65 of the 244 simulations were “damaging‐hail cases” that produced surface hail at least 20 mm in diameter. Although instability was not significantly different between the no‐hail and damaging‐hail cases, damaging‐hail cases had increased wind shear and mid‐ to upper‐level moisture, and produced storms with wider and stronger updrafts than no‐hail cases. These hail cases could therefore support the growth of larger hailstones that survived melting to reach the surface. The results are well explained by entrainment theory. These simulations show that satellite‐based hail detections in the tropics may plausibly contain a significant subset of damaging surface‐hail events.
