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.

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.

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 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.

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

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.

Managing vast volumes of climate data, often reaching into terabytes and petabytes, presents significant challenges in terms of storage, accessibility, efficient analysis, and on-the-fly interactive visualization. Traditional data handling techniques are increasingly inadequate for the massive atmospheric and oceanic data generated by modern climate research. We tackled these challenges by reorganizing the native data layout to optimize access and processing, implementing advanced visualization algorithms like OpenVisus for real-time interactive exploration, and extracting comprehensive metadata for all available fields to improve data discoverability and usability. Our work utilized extensive datasets, including downscaled projections of various climate variables and high-resolution ocean simulations from NEX GDDP CMIP6 and NASA DYAMOND datasets. By transforming the data into progressive, streaming-capable formats and incorporating ARCO (Analysis Ready, Cloud Optimized) features before moving them to the cloud, we ensured that the data is highly accessible and efficient for analysis, while allowing direct access to data subsets in the cloud. The direct integration of the Python library called Xarray allows efficient and easy access to the data, leveraging the familiarity most climate scientists have with it. This approach, combined with the progressive streaming format, not only enhances the findability, shareability and reusability of the data but also facilitates sophisticated analyses and visualizations from commodity hardware like personal cell phones and computers without the need for large computational resources. By collaborating with climate scientists and domain experts from NASA Jet Propulsion Lab and NASA Ames Research Center, we published more than 2 petabytes of climate data via our interactive dashboards for climate scientists and the general public. Ultimately, our solution fosters quicker decision-making, greater collaboration, and innovation in the global climate science community by breaking down barriers imposed by hardware limitations and geographical constraints and allowing access to sophisticated visualization tools via publicly available dashboards.

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.

This study explores integrating reinforcement learning (RL) with idealised climate models to address key parameterisation challenges in climate science. Current climate models rely on complex mathematical parameterisations to represent sub-grid scale processes, which can introduce substantial uncertainties. RL offers capabilities to enhance these parameterisation schemes, including direct interaction, handling sparse or delayed feedback, continuous online learning, and long-term optimisation. We evaluate the performance of eight RL algorithms on two idealised environments: one for temperature bias correction, another for radiative-convective equilibrium (RCE) imitating real-world computational constraints. Results show different RL approaches excel in different climate scenarios with exploration algorithms performing better in bias correction, while exploitation algorithms proving more effective for RCE. These findings support the potential of RL-based parameterisation schemes to be integrated into global climate models, improving accuracy and efficiency in capturing complex climate dynamics. Overall, this work represents an important first step towards leveraging RL to enhance climate model accuracy, critical for improving climate understanding and predictions. Code accessible at https://github.com/p3jitnath/climate-rl.