The Futures of Climate Modeling: Charting New Paths for a Warming World

Seychelles Warbler

16-09-2025

Kingfisher is unsure if he is too worried, but every time he counts the fish in the pond, the number of fish seems to decrease. The hot and stressful weather also makes his feathers molt and grow slower. The situation seems life-threatening!

In “GHG Emissions”; Wild Wise Weird [1]

© Neda Astani

Climate models are the backbone of our understanding of global warming. Since the 1960s, when they first confirmed that greenhouse gases warm the Earth, models have evolved from simple atmospheric simulations into Earth System Models (ESMs) that represent the coupled dynamics of atmosphere, oceans, ice, and biosphere [2,3]. These tools have been central to the Coupled Model Intercomparison Project (CMIP) and to international climate assessments, guiding policy and adaptation strategies worldwide.

Yet climate modeling now stands at a crossroads. Despite decades of progress, current models still struggle with regional accuracy, coarse resolution, and discrepancies such as mismatches in tropical Pacific sea surface temperature trends [3]. At the same time, new technologies—from kilometer-scale models to artificial intelligence—are transforming the landscape.

In their perspective, Bordoni and colleagues [4] outline three promising, but imperfect, paths forward. High-resolution kilometer-scale models can explicitly simulate processes like convection and ocean eddies but are computationally expensive [5]. Advanced parameter calibration techniques refine existing models by tuning key variables, though they risk compensating for structural errors [6]. Hybrid physics–AI approaches promise faster and more detailed parameterizations, but concerns remain about stability, interpretability, and applicability to future climates [7].

The authors argue that no single approach offers a “silver bullet.” Instead, progress will require pluralism—combining diverse tools, cross-validating methods, and learning from past successes. Indeed, history shows that breakthroughs, such as the first El Niño forecasts or Manabe’s Nobel-winning climate sensitivity work, emerged from layering theory, models, and observations step by step [8,9].

Climate models are not just scientific predictions—they are mirrors of our relationship with Earth, helping societies anticipate risks and weigh choices [10]. Strengthening the Nature Quotient (NQ)—the intelligence to recognize and act upon ecological interdependence for survival and development—means investing in climate modeling as a public good [11]. Modern societies must advance modeling to foresee hazards, manage uncertainty, and safeguard planetary stability.

References

[1] Vuong QH. (2024). Wild Wise Weird. https://books.google.com/books?id=N10jEQAAQBAJ

[2] Randall DA, et al. (2019). 100 years of earth system model development. Meteorological Monographs, 59, 12.1-12.66. https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0018.1 

[3] Shaw TA, et al. (2024). Regional climate change: consensus, discrepancies, and ways forward. Frontiers in Climate, 6, 1391634. https://doi.org/10.3389/fclim.2024.1391634 

[4] Bordoni S, et al. (2025). The futures of climate modeling. npj Climate and Atmospheric Science, 8, 99. https://doi.org/10.1038/s41612-025-00955-8 

[5] Bauer P, Stevens B, Hazeleger W. (2021). A digital twin of Earth for the green transition. Nature Climate Change, 11, 80-83. https://doi.org/10.1038/s41558-021-00986-y 

[6] Schmidt GA, et al. (2017). Practice and philosophy of climate model tuning across six US modeling centers. Geoscientific Model Development, 10, 3207-3223. https://doi.org/10.5194/gmd-10-3207-2017 

[7] Rasp S, Pritchard MS, Gentine P. (2018). Deep learning to represent subgrid processes in climate models. PNAS, 115, 9684-9689. https://doi.org/10.1073/pnas.1810286115 

[8] Cane MA, Zebiak SE, Dolan SC. (1986). Experimental forecasts of EI Nino. Nature, 321, 827-832. https://doi.org/10.1038/321827a0 

[9] Manabe S, Wetherald RT. (1967). Thermal equilibrium of the atmosphere with a given distribution of relative humidity. Journal of the Atmospheric Sciences, 24, 241-259. https://doi.org/10.1175/1520-0469(1967)024%3C0241:TEOTAW%3E2.0.CO;2 

[10] Nguyen MH. (2024). How can satirical fables offer us a vision for sustainability? Visions for Sustainability, 23(11267), 323-328. https://doi.org/10.13135/2384-8677/11267

[11] Vuong QH, Nguyen MH. (2025). On Nature Quotient. Pacific Conservation Biology, 31, PC25028. https://doi.org/10.1071/PC25028