Advancements in Climate Modelling for Sustainable Development
By Andrew Gettelman1, Bayler Fox-Kemper2, Gregory Flato3, Daniel Klocke4, Detlef Stamer5, Bjorn Stevens4, and Pier Luigi Vidale6
Introduction
Global climate modelling has made significant progress over the past 50 years. From simple energy balance models to coarse resolution General Circulation Models, and now to coupled Earth System Models (ESMs), these models have provided broad outlines of future climate projections. However, the impacts of climate change at the local and regional levels, such as extreme weather events and sea-level rise, require more localized information. To address this need, a new class of regional and global ESMs with horizontal scales of less than 10 kilometers (< 10 km) is emerging. These "km-scale" models have the potential to simulate extreme weather dynamics and their driving factors over large domains. This article explores the advancements in km-scale models and their implications for sustainable development.
Advancements in Climate Modelling
Climate models and Numerical Weather Prediction (NWP) models share similarities in their dynamical core and key parameterizations. However, climate models require additional components, such as an active ocean circulation, evolving land biosphere, and balanced global energy and carbon budgets, to simulate long-term climate changes accurately. While ensembles of climate models have provided large-scale projections of global climate, regional and local impacts still have large uncertainties. Km-scale models offer fewer unrepresented processes and more accurate regional modelling, addressing these uncertainties.
Benefits of Km-Scale Models
Km-scale models have the potential to provide more localized information on climate risks and impacts. They can capture small-scale climate features, resolve weather systems that influence the tropics and extratropical summer storms, and simulate mesoscale instabilities in the ocean. These models explicitly represent important processes like vertical atmosphere motions, ocean submesoscale fronts and eddies, sea-ice leads, and ice shelves. While challenges remain in representing unresolved scales and processes, km-scale models allow for closer collaboration with observational communities working at similar scales.
Implications for Sustainable Development
Km-scale models have significant implications for sustainable development. They enable the integration of climate impacts at the regional scale, which can have global implications. For example, these models can address critical interactions between warming oceans and Antarctic ice shelves, providing insights into potential sea-level rise and meridional overturning shutdown. They also allow for the representation of phenomena like flooding, atmosphere-surface interactions, and sea ice at smaller scales. However, further development is required to improve these models and ensure their applicability beyond specific regions.
Challenges and Opportunities
Km-scale ESMs face challenges in procuring observational data, verifying slow components, and analyzing model output. However, advancements in computing resources, software, and computational efficiency offer opportunities for rapid development. The use of hardware accelerators and AI/ML methods can significantly speed up integration and analysis. New paradigms for data creation, archiving, and analysis are also emerging. Collaboration with stakeholders and the development of operational climate services are crucial for delivering climate information effectively.
Research and Development Efforts
The World Climate Research Programme (WCRP) plays a vital role in promoting and supporting research on km-scale models. Initiatives like the Digital Earths Lighthouse Activity and the Earth System Modelling and Observations (ESMO) core project facilitate model development, analysis, and research. Collaboration with stakeholders and the development of standardized approaches for delivering climate information are essential. Additionally, advancements in data management, AI/ML methods, and public access to climate data can enhance the usability and impact of km-scale models.
Conclusion
Km-scale ESMs offer new opportunities for understanding climate extremes, circulation changes, and their local impacts. These models have the potential to provide more accurate and localized information on climate risks and impacts. However, further development, collaboration, and standardization efforts are needed to maximize the benefits of km-scale models for sustainable development. By rising to these challenges and leveraging advancements in computing and data science, km-scale models can contribute to the delivery of actionable climate information and support decision-making for a more sustainable future.
SDGs, Targets, and Indicators
1. Which SDGs are addressed or connected to the issues highlighted in the article?
- SDG 13: Climate Action
- SDG 9: Industry, Innovation, and Infrastructure
- SDG 11: Sustainable Cities and Communities
2. What specific targets under those SDGs can be identified based on the article’s content?
- SDG 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters
- SDG 13.2: Integrate climate change measures into national policies, strategies, and planning
- SDG 9.5: Enhance scientific research, upgrade the technological capabilities of industrial sectors, and encourage innovation
- SDG 11.5: Reduce the impacts of natural disasters and improve urban resilience
3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?
Yes, the article mentions several indicators that can be used to measure progress towards the identified targets. These include:
- Computing advances enabling global km-scale simulations
- Resolution of small-scale climate features and processes
- Improved representation of physical processes in models
- Comparison of km-scale models with observations of weather phenomena
- Data assimilation methods for initializing slow components in models
- New paradigms for computation, data creation, archiving, and analysis
- Development of operational climate services and delivery of climate information
SDGs, Targets, and Indicators Table
| SDGs | Targets | Indicators |
|---|---|---|
| SDG 13: Climate Action | 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters | – Computing advances enabling global km-scale simulations – Improved representation of physical processes in models |
| SDG 13: Climate Action | 13.2: Integrate climate change measures into national policies, strategies, and planning | – Resolution of small-scale climate features and processes – Comparison of km-scale models with observations of weather phenomena |
| SDG 9: Industry, Innovation, and Infrastructure | – New paradigms for computation, data creation, archiving, and analysis – Development of operational climate services and delivery of climate information |
|
| SDG 11: Sustainable Cities and Communities | 11.5: Reduce the impacts of natural disasters and improve urban resilience | – Data assimilation methods for initializing slow components in models – Development of operational climate services and delivery of climate information |
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Fuente: wmo.int
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