Quais são os desafios em escalar modelos climáticos para uso global?

As a Chemical/Environmental student in Norway, and originally from Luanda, Angola, I've observed stark contrasts in environmental monitoring between developed and developing countries. In Angola, the lack of robust monitoring infrastructure often results in poorly documented industrial impacts on ecosystems. In contrast, Norway employs advanced technologies like drones and AI for data collection, significantly enhancing monitoring accuracy. I participated in a project to develop a data integration tool that standardizes diverse environmental datasets, addressing global challenges in climate modeling. This blend of experiences highlights the need for innovative solutions tailored to different geographical and economic contexts.

Communication gaps Lots of technical information exists including climate models but the understanding and transfer-ability of such to the ed users ad communities where the causes ad impacts hit worst at the lowest level remains largely minimal at least in developing countries. At some point not so long ago l was baffled when a friend of mine who works in the media insisted that climate change was a scam!

Computational Demands: High-Resolution Requirements: To accurately simulate small-scale processes, models need high spatial and temporal resolution, which increases computational demands. Data Storage and Management: The vast amounts of data generated require significant storage capacity and efficient data management systems.

As a carbon/LCA specialist, I have developed several country, regional, and global-level climate models/tools. One of the key challenges in scaling these models for global use is obtaining country or region-specific data and emission factors. The lack of data is a significant problem in the developing world. Additionally, global-level climate models are often very lengthy and complicated, making them difficult for government officials, who are the primary users, to understand and use effectively for assessing and quantifying climate impacts.

Una gran demanda siempre requiere de una gran oferta, desafortunadamente para el caso en concreto esa oferta que en este caso serían los datos deben cumplir con estrictos requisitos que permitan una lectura uniforme en los modelos climáticos que se utilicen. El gran desafió se encamina al crecimiento y formación de la mano de obra calificada que procese, filtre y valide los datos.

In addition, one would need to add the reality of raw energy required to power the required computing systems. Further, there are perhaps two “universal” climate models that need to be concurrently run, so twice the computational power and twice the energy consumption. The first is the “digital twin” of the existing “earth”, and the second is the climate forecasting model, which would, if perfectly accurate, be able to predict the “digital twin”. The absolute vastness of the computational power that the model requires makes one want to give up, and to contemplate the possibility that perhaps some radical new approach to science/computer language and computer architecture is needed to be able to resolve this.

The pursuit of increasingly detailed global climate models poses computational challenges. Each simulation demands immense computing power, often requiring supercomputers to operate for extended periods. This scale of computation not only escalates costs but also limits the number of simulations that can be run simultaneously. Engineers face the critical task of balancing the need for high-resolution models with the available computing resources. This often results in trade-offs, where decisions must be made between model detail and simulation speed. Innovating to enhance computational efficiency is crucial, perhaps by incorporating AI to streamline data processing, thereby optimizing both the speed and quality of climate simulations.

We need to input the solar and batteries storage system in many places especially in Africa for more efficiency reduction of gases

We have the capabilities to do this and do it successfully. What we need is the will of our global leaders to prioritize their commitment to achieve these goals through their shared stakeholder position and their actions by way of policy development and commitment of resources.

This statement is valid in that the complexities of climate models require vast amounts of data and compromise is guaranteed if time and resources are limited due to political constraints such as election cycles and philosophical differences in climate policy at the individual country scale. Improve collaboration between countries on a global scale can help reduce these issues by allowing climate models to be more flexible by way of being a living models that are constantly evolving.

High-resolution climate models are crucial for accurate predictions, yet they present significant scaling challenges. These models need to capture intricate processes like cloud formation and land surface interactions, which can greatly increase computational demands. This creates a dilemma for environmental engineers, who must balance the level of detail with the feasibility of running these models on a global scale. Achieving this balance requires innovative solutions to manage the complexity without overwhelming resources, ensuring that the models are both detailed enough to be useful and practical enough to be implemented globally.

We should now add the climate training of our school program to allow the coming generation being aware about the challenges and to edify them more that will allow them to include in their culture

Os modelos climáticos globais enfrentam desafios significativos quando aplicados regionalmente, dado que a precisão para fenômenos locais pode diminuir. É crucial que os especialistas climáticos integrem conhecimentos e dados específicos de cada região sem prejudicar a eficácia global desses modelos. A solução passa por desenvolver métodos que permitam aos modelos se ajustarem e responderem às variáveis condições de diferentes locais, mantendo sua integridade em escala global. Este desafio requer inovação constante e abordagens adaptativas no design de modelos climáticos, para que possam ser verdadeiramente eficazes tanto em nível local quanto global.

quelques solutions pour palier à la différence d'échelle étudiée : - Modèles Hybrides : Utilisation de modèles hybrides qui combinent des modèles globaux avec des modèles régionaux détaillés. - Partenariats Locaux : Collaboration avec des institutions locales pour améliorer la collecte de données et l’intégration des connaissances spécifiques à chaque région. - Techniques d’Apprentissage Automatique : Application de techniques d’apprentissage automatique pour améliorer la capacité des modèles à prédire les phénomènes locaux. En fonction de la puissance de calcul, la possibilité d'application de systèmes neuronaux, deep ou encore machine learning, semble la solution capable de la plus large échelle d'application.

As models expand to a global scope, their ability to predict localized phenomena often diminishes. Engineersare challenged to incorporate local data and insights into these models without losing their global utility. Innovative model design that adapts to different scales is crucial. Modular designs allow for flexibility, with components that can be tailored to regional specifics using high-resolution local data, such as detailed topographies or specific weather patterns. Additionally, advancements like machine learning improve the handling and integration of complex datasets, enabling models to maintain accuracy both regionally and globally. This balance ensures that climate models are both precise on a local scale and broadly applicable.

Challenges in scaling climate models for global use with respect to regional specificity include capturing fine-scale local climate phenomena accurately, representing diverse regional geographies and ecosystems, integrating regional data with global models, resolving disparities in data availability and quality across regions, and addressing the varied impacts of climate change that differ significantly from one region to another.

Challenges in scaling climate models for global use with respect to regional specificity include capturing fine-scale local climate phenomena accurately, representing diverse regional geographies and ecosystems, integrating regional data with global models, resolving disparities in data availability and quality across regions, and addressing the varied impacts of climate change that differ significantly from one region to another.

Uncertainty in climate modeling stems from the unpredictable aspects of climate processes and gaps in our understanding. As models are scaled globally, managing this uncertainty becomes increasingly complex. Engineers need to develop methods to quantify and minimize these uncertainties, crucial for creating reliable climate projections. Strategies include refining the parameterizations of climate processes and employing ensemble modeling techniques, which use multiple model runs to represent a range of possible outcomes. These approaches help provide more accurate and trustworthy projections for policymakers and stakeholders, ensuring that the models can be effectively used in decision-making.

Uncertainty management in scaling climate models for global use involves quantifying and reducing uncertainties arising from model assumptions, parameterizations, and input data. It requires robust methods to account for variability in natural systems, incorporating diverse scenarios to capture potential future conditions. Additionally, improving the representation of complex processes and integrating observational data can help refine models. Effective communication of uncertainties to policymakers and stakeholders is also crucial for informed decision-making

Desde la experiencia con diferentes sectores, la incertidumbre siempre es un tema complejo de abordar al presentar los resultados de los modelos climáticos bajo diferentes escenarios, principalmente porque la expectativa general es la de obtener un pronóstico similar al del tiempo. Cuantificarla y reducirla es el principal reto para proporcionar información útil para la toma de decisiones, aunado al fortalecimiento de la cultura general sobre el entendimiento del sistema climático y su interrelación con otros factores.

Proper modelling should be done with specific data and natural conditions should also be considered properly. Ground water problem is another basic problem which should be considered proper ly.Arsenic mitigation and it's problem related with its mitigation.

How do modelers propose to describe error propagation for inputs and compound calculations before characterizing calibration statistics and R2 residual error?

Scaling climate models for global use poses challenges: computational power, data availability, model complexity, and resolution trade-offs. Uncertainty quantification, coupling multiple components, and validation are also difficult. Scalability, data management, and international collaboration are essential. Innovative solutions, high-performance computing, and data standardization can overcome these challenges, enabling reliable global climate models to inform decision-making and mitigate climate change impacts.

Iniciando com a frase do estimado pesquisador George Box: "All models are wrong, but some are useful". Os pesquisadores do mundo todo enfrentam desafios significativos relacionados a ausência de dados. Ao abordar o caso de implementar uma modelagem climática em nível global precisamos considerar alguns pontos: 1°- Sem um organismo global formado por todas as nações do mundo destinadas a esse fim, sempre encontraremos dificuldades na compatibilização de dados. 2°- Questões geopolíticas serão desafios reais para a cooperação global nesta construção. 3°- Nem todos os países possuem tecnologia compatível com medições capazes de alimentar um modelo de previsão robusto. 4°- A capacidade de processamento atual limita as iterações destes modelos.

The choices you offer are all structural. What does global use even mean? Models are built for purpose. So first a specific problem needs to be posed, then the model designed and evaluated for that purpose. Plus a portfolio of models is required to describe uncertainty.

Recently my one book is published titled Ancient methods to search the ground water by Dr.D.S.Pandey.Available on Amazon and Flipkart and also from Notion press Chennai.This book should be circulated to all relevant users and it's significant value should be taught to primary level to students for future use.

I think there must be a somewhat tight limit to scaling up climate models for global usage. Depending on the target area considered, it may not be viable at all. For instance, wind patterns and water currents should be highly dependent upon the region/area considered.

I think we are still far away from establishing a bank of efficient interrelationships between different factors of the global climate and their simultaneous relationships with a hundred different components of the environment. Before we embark on establishing successful and efficient climate modelling, we need to develop a very comprehensive bank of such interrelationships to feed into such climate models. I also think that designing these relationships in a way that can be handled efficiently with available computational resources will solve one of the problems discussed above.