Bangladesh, the eighth-most populated country with around 180 million people, is highly vulnerable to flooding due to its location in the Bengal Delta and its low-lying, flat topography. The country faces two main types of flooding: flash floods during the pre-monsoon season (March to mid-June), and seasonal flooding during the South Asian monsoon (June to October). In August 2024, an unprecedented flash flood hit Bangladesh’s northeastern and southeastern regions, diverging from typical flood patterns. The flood, occurring between August 21 and 28, killed 71 people, affected six million, and displaced half a million residents who took shelter in over 3,400 emergency locations. More than 7,000 schools closed, interrupting education for 1.7 million students. Initial estimates place the economic impact at around $1.2 billion USD.Despite these impacts, the Bangladesh Meteorological Department and flood forecasting agencies failed to predict the flood, leading to a severe humanitarian and public health crisis across 11 districts. This study aims to investigate the meteorological factors that contributed to this catastrophic flood. Using satellite, radar, and ERA5 reanalysis data, we identified favorable local, regional, and large-scale conditions that led to extreme rainfall over eastern Bangladesh and India in the third week of August. From August 17 to 22, a low-pressure system was positioned over Bangladesh and the Indian states of Tripura, Assam, and West Bengal due to downstream atmospheric blocking. This coincided with a sea surface temperature anomaly exceeding 2°C in the northeastern Bay of Bengal, an MJO amplitude over 2 in Phases 2 and 3, a westerly wind burst near 70°E, and a strong extratropical jet over the Indian subcontinent. These factors together produced around 700 mm of rain in six days, with 24-hour totals over 300 mm, leading to widespread flooding. Uncoordinated water releases from upstream dams in India’s Tripura state further intensified transboundary river flooding. The August 2024 flood underscores the urgent need for improved flood forecasting and cross-border water management protocols. Enhanced regional cooperation, particularly regarding dam water release notifications, and investment in predictive technology could help reduce the impact of future floods.

Purpose of ReviewForests are integral to global ecological stability, climate regulation, and economic resilience. They function as major carbon sinks, act as biodiversity reservoirs, and provide ecosystem services. Accurately modeling forest growth is essential to predict ecosystem responses to climate change and optimize ecosystem services. However, predicting forest growth remains challenging due to complex interactions between ecological processes, external drivers like climate change, and intrinsic dynamics, such as legacy effects and emergent properties, that influence forest responses over time.This work offers a detailed examination of theories in forest growth modeling, with a focus on emergent approaches as implemented in 18 forest growth models, which vary in their approaches and goals. Recent FindingsForest modeling requires a deep understanding of forest growth theories driven by multiple, often interacting, processes. Our findings reveal distinct model clusters with varying process integrations and complexity, ranging from stand-level to terrestrial ecosystem models. Additionally, we highlight the trade-offs between model detail and scalability.SummaryOur review showcases multiple theories, such as Functional Balance, Local Determination of Growth, and Optimality Principles of forest growth, thus providing a synthetic overview of the main frameworks for resource allocation in plants. As multiple studies emphasize the importance of integrating different and recent theories to better capture growth dynamics, we build on a state-of-the-art multi-modelling comparison to discuss what the implications of different theories might be at different temporal and spatial resolutions. Finally, we explore how emerging technologies, such as machine learning, can enhance predictive accuracy and help address current modeling limitations.

A document by PJ Tuckman. Click on the document to view its contents.

A document by Hing Ong. Click on the document to view its contents.

A document by Xinchuan Huang. Click on the document to view its contents.

Clouds of various kinds play a substantial role in a wide variety of atmospheric processes. They are directly linked to the formation of precipitation, and significantly affect the atmospheric energy budget via radiative effects and latent heat. Moreover, knowledge of currently occurring cloud types allows the observer to draw conclusions about the short-term evolution of the state of the atmosphere and the weather. Therefore, a consistent cloud classification scheme has already been introduced almost 100 years ago. In this work, we train an ensemble of identically initialized multi-label residual neural network architectures from scratch with ground-based RGB pictures. Operational human observations, consisting of up to three out of 30 cloud classes per instance, are used as ground truth. To the best of our knowledge, we are the first to classify clouds with this methodology into 30 different classes. Class-specific resampling is used to reduce prediction biases due to a highly imbalanced ground truth class distribution. Results indicate that the ensemble mean outperforms the best single member in each cloud class. Still, each single member clearly outperforms both random and climatological predictions. Attributes diagrams indicate underconfidence in heavily augmented classes and very good calibration in all other classes. Autonomy and output consistency are the main advantages of such a trained classifier, hence we consider operational cloud monitoring as main application. Either for consistent cloud class observations or to observe the current state of the weather and its short time evolution with high temporal resolution, e.g. in proximity of solar power plants.

Enhanced mass loss of the Antarctic ice sheet yields ocean surface freshening, cooling and sea ice expansion, which ought to drive change in the atmosphere. Using the Southern Ocean Freshwater Input from Antarctica (SOFIA) multi-model ensemble, we study the atmospheric response to a 100-year idealized freshwater release of 0.1 Sv. All models simulate consistently surface-intensified tropospheric cooling and lower-stratospheric warming south of 35˚S. Tropospheric cooling is attributed to sea ice expansion and hence albedo increase in winter and a colder sea surface in summer. This cooling yields a downward displacement of the tropopause, reduced stratospheric water vapor content and ultimately warming around 200 hPa. An enhanced southward eddy heat flux explains warming at 10-100 hPa during austral winter. Despite a temporally (and spatially) uniform prescribed freshwater flux, a prominent sea ice seasonal cycle and atmosphere dynamics result in a distinct seasonal pattern in the occurrence and magnitude of the responses.

Despite its high tropical cyclone (TC) density, the Eastern North Pacific (ENP) basin has received relatively little research attention on landfall variability. This study investigates the climatological seasonal cycle and interannual variability of TC landfalls in the ENP. We find that the basin is characterized by a bimodal distribution of landfalls, with peaks in June and September–October. Using a composite analysis of high and low landfall years, we show that this distribution is primarily driven by landfall probability rather than genesis. The absence of landfalls during July is due to enhanced easterlies from the Caribbean Low-Level Jet entering the ENP through gaps in the Americas Cordillera. High landfall years feature enhanced easterly wind reversals from a northward-shifted Intertropical Convergence Zone. These additional steering winds drive hurricanes ashore in the vulnerable region of southwest Mexico. This study provides valuable insights for improving TC landfall forecasts and preparedness in the region.

Science centers and museums are increasingly exploring a range of approaches for encouraging their visitors and communities to not only learn about climate solutions, but to discover their own pathways to sustained, meaningful participation in action. These place-based institutions have unique opportunities to cultivate a culture of hope and action thanks to their local expertise, diverse programming (e.g., field trips, special events, and community science projects), and wide reach (including both in-person and online interactions). These assets enable science centers and museums to communicate about climate solutions in ways that are visible, relatable, and accessible, all attributes that make them more likely to be taken up. One approach that several museums and other groups have piloted is the use of a GIS-based tool to invite members of their community to share the climate actions they’re undertaking and explore those of others. The tool produces open-access climate action maps that showcase real actions taking place in a particular community and provide an opportunity to leverage several evidence-based practices for communicating about climate solutions. By featuring everyday people who live in one’s community, doing actions that are broadly accessible, the maps build a sense of agency, provide inspiration, convey action-oriented norms, and build a sense of belonging in climate solutions. We will describe the ways that this flexible tool and resulting map can be embedded in a range of museum contexts and will discuss the ways that contributing to and exploring the map benefits museums, their visitors, and communities. We will also discuss opportunities for increasing impact through connections with local media, decision-makers, and K-12 school systems. This work demonstrates the role that science museums can play as hubs for collecting and coordinating climate stories and actionable resource centers for increasing community-led participation in climate action.

Science centers and museums are increasingly exploring a range of approaches for encouraging their visitors and communities to not only learn about climate solutions but also to discover their own pathways to sustained, meaningful participation in action. These institutions have unique opportunities to cultivate a culture of hope and action thanks to their local expertise, diverse programming (e.g., field trips, special events, and community science projects), and wide reach (including both in-person and online interactions). These assets enable science centers and museums to communicate about climate solutions in ways that are visible, relatable, and accessible, all attributes that make them more likely to be taken up. One approach that several museums and other groups have piloted is the use of a GIS-based tool to invite members of their community to share the climate actions they're undertaking and explore those of others. The tool produces open-access climate action maps that showcase real actions taking place in a particular community and provide an opportunity to leverage several evidence-based practices for communicating about climate solutions. By featuring everyday people who live in one's community, doing actions that are broadly accessible, the maps build a sense of agency, provide inspiration, convey action-oriented norms, and build a sense of belonging in climate solutions. We will describe (a) the ways that this flexible tool and resulting maps can be embedded in a range of museum contexts, and (b), the ways in which contributing to and exploring the map can benefit museums, their visitors, and their communities. We will also discuss opportunities for increasing impact through connections with local media, decision-makers, and K-12 school systems. This work demonstrates the role that science museums can play as hubs for collecting and coordinating climate stories and resources, and promoting community-led participation in climate action.

As sea ice decreases, navigation in the Arctic is becoming more feasible, and new routes are likely to emerge. However, the impact of these potential routes on sea ice remains uncertain. In this study, we compare the regional impacts of two major Arctic routes: the Northern Sea Route (NSR) and the Transpolar Sea Route (TSR). Using the Community Earth System Model (CESM2), we simulate black carbon (BC) emissions until 2050 along these routes and assess their effects on Arctic sea ice (ASI). We focus on regional changes in net shortwave (SW) radiation, sea ice extent, and surface temperature. While our study does not account for other pollutants that could counteract BC effects, our results reveal significant differences in ASI's response between routes. The TSR, in particular, exerts a stronger and more widespread influence on ASI than the NSR across all seasons, especially in increasing net SW radiation over the ice.

This paper represents the components of the Smart Grid, issues regarding the implementation of Smart Grid concept and how battery energy storage systems can be used in the Smart Grid. The relationship between climate change and smart grid, distributed generation and microgrids have been discussed. There is an overview of smart meter, smart tariff, electricity generation, and demand. The battery's potential for Demand Side Management (DSM) has been explained. An extensive review of batteries has been given in terms of their use in the transmission, distribution, and residential sectors. In several segments, the electrical grids of New South Wales (Australia) and California has been discussed as case studies. Finally, some real-world Smart Grid examples have been included.

A document by Fucent Hsuan Wei Hsu. Click on the document to view its contents.

To discuss slip behaviors in shallow slow earthquake regions, we investigate source characteristics of shallow very low frequency earthquakes (VLFEs) southeast off the Kii Peninsula in the Nankai subduction zone. VLFEs are a kind of slow earthquakes and are clearly observed at frequencies below 0.1 Hz. A non-linear inversion technique for moment rate function estimation and the permanent ocean-bottom seismometer network provided us with precise locations and detailed kinematic source characteristics of shallow VLFEs. The high activity of shallow VLFEs around the western edge of the subducted Paleo-Zenisu ridge is similar to previous studies. A notable trend change in the along-dip dependency of shallow VLFE moment rates was found. Along the profile west side of the Paleo-Zenisu ridge, moment rates of shallow VLFEs increase with reaching the megathrust zone. Small-scale topographic fluctuations of the subducted oceanic plate exist along this profile, but large-scale seamount subduction has not been identified even from dense seismic surveys. Similar tendencies have been reported in tectonic tremors in the Nankai and Cascadia subduction zones. On the other hand, the opposite trend appeared along the profile with the Paleo-Zenisu ridge. Small shallow VLFEs were dominant near the summit of the Paleo-Zenisu ridge. Fracture networks or stress fields due to seamount subduction possibly impede large shallow VLFEs around the subducted seamount. Our results suggest that the large-scale heterogeneity of the upper surface of the subducted oceanic plate could control source characteristics of shallow slow earthquakes.

As extreme precipitation events become more frequent and intense, local-scale climate services are increasingly needed to help communities adapt. We here evaluate two fully-coupled convection-permitting Earth System Models for their ability to resolve mesoscale extreme weather events. Using the Integrated Forecasting System (IFS) and Icosahedral Nonhydrostatic Weather and Climate Model (ICON) within the Next Generation Earth Modelling Systems (nextGEMS) project, we evaluate their depiction of extreme precipitation with a focus on the Mediterranean region through a comparison with high resolution reanalysis, gridded observations, a regional climate model, and a large-ensemble lower-resolution climate model. The results are then compared at a coarser resolution globally with CESM2. For dry extremes, we find that the higher resolution and hybrid/explicit representation of convection of the nextGEMS models improve the representation of dry hour frequency and alleviates the drizzle bias observed in CMIP6 models. The explicit representation of convection in ICON helps create realistic dry spell lengths over land, but also generates overly intense convective precipitation. Generally, the nextGEMS models concentrate dry spells into limited frequency yet overly long periods. For wet extremes, the nextGEMS models properly high intensities of heavy precipitation, aside from overestimation in ICON over mountainous terrain. The models appear capable of resolving extreme weather systems like medicanes and tropical cyclones making them useful for extreme weather climatology studies. Overall, the depiction of wet and dry precipitation extremes in the Mediterranean region are representative of the nextGEMS’ models performance across the global mid-latitudes demonstrating the models’ value in simulating extreme weather systems.

This study unveils the ability of 200-m near to Large-Eddy Simulation (LES) to resolve smaller scales in urban areas of a coastal megacity Chennai in India, which are crucial for accurately modelling Urban Induced Turbulence (UIT) and Urban Heat Island (UHI) effects. Unlike physics-based ensemble (PBE) models in kilometre-scale simulations, LES captures fine-scale atmospheric dynamics, offering enhanced resolution to simulate localized turbulence and heat flux variations. With a resolution of 200 m, Weather Research Forecasting (WRF) LES simulation can convincingly resolve a higher urban heat flux, friction velocity, surface wind speeds, and moisture transport over urban areas. These factors are found to play a critical role in intensifying extreme weather events such as heavy rainfall, especially in the downwind regions of coastal megacities like Chennai. This phenomenon is driven by LES-resolved interaction between the urban heat island effect and the prevailing winds.

A document by Alice Puppin. Click on the document to view its contents.

Clear air turbulence (CAT) is a safety threat within the aviation sector and is projected to worsen under global warming. Stratospheric aerosol injection (SAI) is a climate intervention strategy that aims to ameliorate climate change by artificially cooling Earth. Climate model simulations have found a side-effect of SAI would be a strengthening of the positive phase of the North Atlantic Oscillation (NAO). This links to a stronger North Atlantic jet stream and suggests enhanced CAT in the region. Here, we analyse simulations from the UKESM1 climate model to evaluate the impact of a realistic SAI application on winter-time trans-Atlantic CAT. We find a 23 % decrease in severe CAT frequency under SAI when compared to a baseline high-end global warming scenario. Our results indicate that the amelioration of global warming under SAI has a more dominant impact on CAT over the North Atlantic than residual impacts to the NAO.