How Climate Scientists Saw the Future Before It Arrived – Quanta Magazine

 

Report on the State and Future of Climate Modeling in the Context of Sustainable Development Goals

Understanding the Earth’s interconnected climate system is fundamental to achieving the United Nations Sustainable Development Goals (SDGs). The journey of a single dust particle from the Sahara—impacting glaciers, fertilizing rainforests (SDG 15: Life on Land), and feeding ocean phytoplankton that absorb carbon (SDG 14: Life Below Water)—illustrates the complex, global-scale processes that climate models aim to replicate. The development, refinement, and application of these models are therefore critical for informing strategies related to SDG 13: Climate Action and ensuring a sustainable future for all.

The Evolution of Climate Modeling: Building the Foundation for Climate Action (SDG 13)

Historical Development of Climate Simulation

The capacity to model Earth’s climate has evolved over a century, providing the scientific bedrock for global climate policy and action.

1. Early Theoretical Work: Lewis Fry Richardson pioneered the use of physics to model weather systems in the 1920s, laying the conceptual groundwork for numerical forecasting.
2. The Dawn of Computing: In the 1950s, John von Neumann utilized the first digital computers to run simple atmospheric models, nearly achieving real-time weather forecasting.
3. General Circulation Models: Norman Phillips and Joseph Smagorinsky developed models that captured gross features of atmospheric circulation, leading to the establishment of the Geophysical Fluid Dynamics Laboratory (GFDL).
4. Pioneering Climate Science: In the 1960s, Syukuro Manabe developed a 3D atmospheric model that successfully simulated the greenhouse effect, providing the first robust, computer-based evidence linking carbon dioxide to global warming.

Key Scientific Breakthroughs Informing the SDGs

Generations of scientists have built upon these foundations, creating increasingly sophisticated planetary replicas that underpin our understanding of climate change.

• Quantifying Global Warming: A landmark 1967 paper by Manabe and Wetherald estimated that a doubling of atmospheric CO2 would warm the Earth by approximately 2.3°C, a figure remarkably close to modern estimates and central to the urgency of SDG 13.
• Coupled Ocean-Atmosphere Models: In 1969, Manabe and Kirk Bryan created the first model to integrate oceanic and atmospheric systems, a critical step toward simulating the entire Earth system and its impact on SDG 14 and SDG 15.
• Attribution Science: Klaus Hasselmann developed statistical methods to distinguish the “fingerprints” of human activities, such as burning fossil fuels, from natural climate variability, providing undeniable evidence of anthropogenic climate change.

Climate Models in Practice: From Prediction to Policy

Informing Global Action and Partnerships (SDG 17)

Climate models transitioned from academic tools to essential instruments for public policy following catastrophic weather events and growing scientific consensus. The 1988 testimony of scientists like James Hansen, confirming that human-driven global warming was underway, led to the formation of the Intergovernmental Panel on Climate Change (IPCC). The IPCC institutionalizes the role of climate modeling in global governance, fostering international collaboration essential for SDG 17: Partnerships for the Goals. Current models, compiled by the UN, project a warming trajectory of 2.6-3.1°C this century, signaling severe threats to global health (SDG 3), sustainable communities (SDG 11), and terrestrial and marine ecosystems (SDG 14, 15).

Current Challenges and the Need for Refined Regional Models

Despite their success in predicting global trends, current models face challenges in accurately forecasting regional phenomena, which is crucial for local adaptation strategies under SDG 11: Sustainable Cities and Communities.

• Discrepancies in tropical Pacific Ocean warming patterns, which affect global drought conditions.
• Unexpected drops in humidity in regions like the southwestern United States.
• Faster-than-anticipated increases in heat extremes in Western Europe.
• Failure to predict the record-breaking global temperature spike in 2023.

These shortcomings highlight a need to revisit the fundamental assumptions of climate modeling to provide more precise guidance for SDG implementation.

The Future of Climate Science: Innovation for Sustainable Development (SDG 9)

Technological and Methodological Advancements

The scientific community is pursuing new approaches to enhance model accuracy and predictive power, representing a significant investment in scientific innovation (SDG 9: Industry, Innovation, and Infrastructure).

• High-Resolution Modeling: Efforts are underway to decrease model grid boxes to a one-kilometer scale, allowing for the direct simulation of crucial “mesoscale” processes like thunderclouds and ocean eddies.
• Artificial Intelligence (AI): AI is being explored to make existing models more efficient and to develop entirely new, AI-driven climate emulators.
• Hierarchical Modeling: This technique involves systematically simplifying models to isolate and understand the root causes of predictive errors.

Enhancing Partnerships and Accessibility

Initiatives like the European Union’s Destination Earth aim to bridge the gap between scientific research and practical application. By creating interactive “digital twins” of the planet, this project empowers policymakers, city planners, and industry leaders to directly use climate data for strategic planning, fostering the multi-stakeholder partnerships vital for SDG 17.

Existential Threats to Climate Research and SDG Progress

The Dismantling of Scientific Infrastructure

Progress in climate science is under severe threat from political actions that dismantle the research ecosystem. In the United States, recent administrative actions have resulted in a systemic assault on climate research capabilities.

• Massive Funding Cuts: The proposed fiscal 2026 budget calls for eliminating NOAA’s scientific research arm, terminating over 1,000 employees, and cutting NSF and NASA science budgets by more than half.
• Termination of Research: Key climate reports have been cancelled, and foundational research institutions like GFDL and NASA’s Goddard Institute for Space Studies face closure or eviction.
• Loss of Talent: Thousands of federal scientists have been fired, creating a collapse of support for the next generation of researchers and a significant brain drain.

Global Implications for Sustainable Development

The degradation of the world’s leading climate research ecosystem represents a profound setback for the global community. It undermines the international collaboration (SDG 17) required to monitor the planet and handicaps the collective ability to respond to the climate crisis. This deliberate destruction of scientific capacity directly threatens the achievement of SDG 13 and jeopardizes progress across the entire 2030 Agenda for Sustainable Development.

Conclusion: The Imperative of Continued Research

The ongoing discovery of previously overlooked climate variables, such as the significant atmospheric impact of large Saharan dust particles, underscores the critical need for continued research. Uncertainty over whether such dust has a net cooling or warming effect can only be resolved through sustained observation and modeling. However, the very satellite systems needed for this work are slated for cancellation. Despite these existential challenges, the scientific community remains dedicated to advancing our understanding of the Earth system. This commitment is indispensable for providing the knowledge required to navigate the climate crisis and secure a sustainable future in line with the SDGs.

SDGs Addressed in the Article

The following Sustainable Development Goals (SDGs) are connected to the issues discussed in the article:

• SDG 13: Climate Action: The entire article is centered on climate change, the scientific modeling used to predict its impacts, and the urgent need for action. It discusses rising global temperatures, the role of CO2 emissions, and the prediction of calamities like heat waves, superstorms, and desertification.
• SDG 9: Industry, Innovation, and Infrastructure: The article extensively covers the scientific research and technological infrastructure behind climate modeling. It highlights the importance of innovation in this field, from early computer simulations to modern high-resolution models and AI. The discussion of funding cuts and the dismantling of research institutions like GFDL directly relates to the goal of enhancing scientific research and technological capabilities.
• SDG 14: Life Below Water: The article connects climate processes to marine ecosystems. It describes how atmospheric dust provides iron to phytoplankton in the Atlantic, which in turn absorbs carbon dioxide. It also mentions the ocean’s role in the climate system through coupled ocean-atmosphere models.
• SDG 15: Life on Land: The article touches upon terrestrial ecosystems by explaining how dust from the Sahara fertilizes the Amazon rainforest. It also warns that climate change could lead to “desertification and ecosystem collapse” and references past events like fires ravaging the Arctic during warmer periods. Furthermore, it notes that land-use changes and agriculture have doubled dust emissions since the Industrial Revolution.
• SDG 16: Peace, Justice and Strong Institutions: The article details a significant threat to key scientific institutions. The “whole-scale destruction” of the U.S. research ecosystem, including the gutting of agencies like NOAA, the firing of federal scientists, and the burying of datasets, represents an attack on effective, accountable, and transparent institutions that are crucial for evidence-based policymaking.
• SDG 17: Partnerships for the Goals: The collaborative nature of climate science is a key theme. The article mentions the Intergovernmental Panel on Climate Change (IPCC) as a global body, the use of “community models” by scientists worldwide, and warns that the loss of U.S. contributions would “handicap the global collaborative effort to monitor the planet,” highlighting the importance of international partnerships in science and technology.

Specific SDG Targets Identified

Based on the article’s content, the following specific SDG targets can be identified:

1. SDG 13: Climate Action

   • Target 13.2: Integrate climate change measures into national policies, strategies and planning. The article discusses the disconnect between the “dire warnings blared by their models and the restrained policies enacted by world leaders.” It also mentions the development of “digital twins” of the planet to bring climate models “into the hands of policymakers and the public” to help them strategize.
   • Target 13.3: Improve education, awareness-raising and human and institutional capacity on climate change mitigation, adaptation, impact reduction and early warning. The article’s focus on climate models as tools to forecast long-term trends and warn of calamities directly relates to improving early warning capacity. The political assault on research institutions like GFDL and NOAA is described as a direct threat to this institutional capacity. The virtual rally organized by scientists to present research to the public is an example of awareness-raising.

2. SDG 9: Industry, Innovation and Infrastructure

   • Target 9.5: Enhance scientific research, upgrade the technological capabilities…substantially increasing the number of research and development workers…and public and private research and development spending. The article details the evolution of climate modeling as a history of enhanced scientific research. It explicitly discusses the threat to this target by describing how the Trump administration “strips funding,” “guts agencies,” has “fired thousands of federal scientists,” and proposed budget cuts that would eliminate NOAA’s scientific research arm and slash NSF and NASA science budgets.

3. SDG 14: Life Below Water & SDG 15: Life on Land

   • Target 14.3: Minimize and address the impacts of ocean acidification… While not mentioning acidification directly, the article describes a core related process: how phytoplankton “soak up carbon dioxide emitted by factories and power plants,” which is the primary driver of ocean acidification.
   • Target 15.3: By 2030, combat desertification, restore degraded land and soil… The article lists “desertification” as a potential calamity triggered by global warming. It also links rising dust emissions to “agriculture and other land-use changes,” which are related to land degradation.

4. SDG 16: Peace, Justice and Strong Institutions

   • Target 16.6: Develop effective, accountable and transparent institutions at all levels. The article describes the “dismantling of the birthplace of climate modeling” (GFDL) and the “conscious effort to destroy institutions which are in some ways the foundation of modern society” as a direct attack on the effectiveness of these scientific bodies.

5. SDG 17: Partnerships for the Goals

   • Target 17.6: Enhance…international cooperation on and access to science, technology and innovation… The article highlights the global, collaborative nature of climate modeling through the IPCC and community models. It warns that the U.S. “taking itself off the map” would be a “setback for everyone,” undermining international cooperation.
   • Target 17.16: Enhance the Global Partnership for Sustainable Development…that mobilize and share knowledge, expertise, technology and financial resources… The global climate modeling effort described in the article is a prime example of such a partnership. The threats of buried datasets, cancelled satellite missions, and defunded research directly threaten the sharing of knowledge and resources within this partnership.

Indicators for Measuring Progress

The article mentions or implies several indicators that can be used to measure progress towards the identified targets:

1. For SDG 13 (Climate Action):

   • Global Temperature Increase: The article explicitly states that “Earth is on track to warm between 2.6 and 3.1 degrees Celsius over the course of this century” and that 1988 was on track to be the “warmest year on record.” This is a direct indicator of climate change.
   • Atmospheric Carbon Dioxide Concentration: The article references the landmark 1967 paper that calculated the warming effect of doubling the amount of carbon dioxide in the atmosphere. CO2 concentration is a primary indicator for tracking the driver of climate change.
   • Frequency and Intensity of Extreme Weather Events: The article mentions that models predict calamities like “heat waves and superstorms” and that global warming “spurred extreme weather events.” Tracking these events serves as an indicator of climate impacts.

2. For SDG 9 (Industry, Innovation and Infrastructure):

   • Public Expenditure on Research & Development (R&D): The proposed budget cuts for the National Science Foundation, NASA, and NOAA are a direct, quantifiable indicator of R&D spending. The article states the budget “called for cutting National Science Foundation and NASA science budgets by more than half.”
   • Number of Researchers: The article provides a clear indicator of a decline in research capacity by stating that “nearly 800 employees were fired” at NOAA and that the proposed budget would terminate “over 1,000 additional employees.”

3. For SDG 15 (Life on Land):

   • Atmospheric Dust Emission Levels: The article states that “dust emission is only rising, having roughly doubled since the Industrial Revolution” due to land-use changes. This serves as an indicator of land degradation and its atmospheric impact.

4. For SDG 16 (Peace, Justice and Strong Institutions) & SDG 17 (Partnerships for the Goals):

   • Funding and operational status of scientific institutions: The proposed elimination of NOAA’s research arm and the shuttering of “around a dozen institutes, including GFDL” are direct indicators of institutional strength and capacity.
   • Availability of Publicly Accessible Scientific Data: The article mentions the administration “buries datasets” and that NASA’s satellites that “track properties of dust in the atmosphere…are slated for cancellation.” The availability of data from such sources is an indicator of transparency and the health of global scientific partnerships.

Table of SDGs, Targets, and Indicators

SDGs	Targets	Indicators (Mentioned or Implied in the Article)
SDG 13: Climate Action	13.2: Integrate climate change measures into national policies. 13.3: Improve education, awareness-raising, and institutional capacity for early warning.	Global mean temperature increase (e.g., “on track to warm between 2.6 and 3.1 degrees Celsius”). Atmospheric CO2 concentration (e.g., “doubling its concentration would warm Earth”). Frequency of extreme weather events (e.g., “heat waves and superstorms”).
SDG 9: Industry, Innovation and Infrastructure	9.5: Enhance scientific research and increase R&D spending and personnel.	Public expenditure on R&D (e.g., proposed cuts to NASA, NSF, NOAA budgets). Number of researchers (e.g., “fired thousands of federal scientists”).
SDG 14: Life Below Water	14.3: Minimize and address the impacts of ocean acidification.	Rate of CO2 absorption by marine life (e.g., phytoplankton “soak up carbon dioxide”).
SDG 15: Life on Land	15.3: Combat desertification and restore degraded land.	Extent of desertification (mentioned as a predicted “calamity”). Atmospheric dust emission levels (e.g., “roughly doubled since the Industrial Revolution”).
SDG 16: Peace, Justice and Strong Institutions	16.6: Develop effective, accountable and transparent institutions.	Funding and operational status of scientific institutions (e.g., “shuttering around a dozen institutes, including GFDL”). Public access to scientific information (e.g., administration “buries datasets”).
SDG 17: Partnerships for the Goals	17.6: Enhance international cooperation on science and technology. 17.16: Enhance the Global Partnership for Sustainable Development.	Number and stability of international scientific collaborations (e.g., IPCC, community models). Availability of global datasets from shared infrastructure (e.g., NASA satellites “slated for cancellation”).

Source: quantamagazine.org