In the rapidly evolving field of climate science, one of the most pressing challenges lies in accurately forecasting temperature variability for future decades. While global warming trends have been broadly understood and modeled with increasing confidence, fine-scale projections of temperature fluctuations—particularly on regional and seasonal scales—continue to elude definitive consensus. Recent research led by Suarez-Gutierrez and Maher, published in Nature Communications (2025), delves deeply into this conundrum. Their work rigorously examines how temperature variability projections respond when constrained by the best-performing large ensembles of individual climate models, unveiling persistent uncertainties that could reshape our understanding of climate predictability.
At the heart of this research is the concept of constraining climate model projections using large ensembles. Large Ensemble simulations are a critical tool in climate modeling, where multiple model runs with slightly varied initial conditions are performed to capture the spectrum of possible future climate states. This approach helps scientists identify robust patterns amid the inherent chaotic variability of the climate system. Suarez-Gutierrez and Maher selected the best-performing individual climate models based on historical performance metrics to produce ensembles that should, theoretically, yield more reliable estimates by filtering out poorly behaving simulations.
However, their startling conclusion is that even after applying these constraints, the projections of temperature variability remain deeply uncertain. This runs counter to the optimistic expectation that model selection and ensemble constraining inherently improve the predictive capability for all aspects of climate, including variability. The research implicates fundamental complexities in climate system processes—particularly those influencing temperature fluctuations—that are not easily resolved by simply favoring “best-performing” models.
One notable source of uncertainty lies in the representation of internal climate variability. Internal variability includes naturally occurring fluctuations such as the El Niño-Southern Oscillation (ENSO) or the Atlantic Multidecadal Oscillation (AMO), which strongly influence temperature patterns on annual to decadal timescales. Climate models struggle to faithfully simulate these phenomena because of their nonlinear interactions across atmospheric, oceanic, and terrestrial systems. Suarez-Gutierrez and Maher found that divergences in how individual models represent these processes contribute significantly to the spread in temperature variability projections, even within large ensembles filtered for historical consistency.
The impact of external forcing uncertainty also remains significant. External forcings involve anthropogenic greenhouse gas emissions, aerosols, solar radiation variations, and volcanic activity, which modulate the energy balance of the Earth system. Despite strong constraints on greenhouse gas emission scenarios, uncertainties in aerosol-cloud interactions and volcanic forcing add layers of complexity that challenge models’ ability to project temperature variability precisely. The study underscores that assumptions about future aerosol emissions, in particular, are a considerable source of divergence among models even after constraining exercises.
Moreover, regional disparities in projection accuracy were highlighted. Temperature variability is strongly modulated by geographic factors such as topography, land-water contrasts, and regional atmospheric circulation patterns. The constrained ensembles showed better coherence in projecting large-scale warming trends globally but failed to produce reliable consensus on variability at smaller scales—like within continental interiors or coastal regions. This has profound implications for climate adaptation strategies, which often rely on local or regional climate variability forecasts for risk assessments and resource management.
The interplay between mean climate state changes and variability is another focal point of the research. Emerging evidence suggests that shifts in the Earth’s mean temperature and circulation patterns can amplify or dampen internal variability components, but these interactions are highly model-dependent. Suarez-Gutierrez and Maher’s analysis reveals that even “best-performing” models demonstrate wide-ranging projections on how variability might evolve alongside warming, further complicating efforts to translate mean state projections into actionable risk metrics based on variability.
This research also engages a deeper methodological discussion about ensemble design and model evaluation criteria. Conventional performance metrics typically emphasize models’ ability to recreate observed climatologies or long-term trends. Yet, Suarez-Gutierrez and Maher argue that these criteria may insufficiently capture models’ fidelity in simulating variability modes critical for temperature fluctuation projections. As such, they call for the development of targeted performance metrics that specifically evaluate variability characteristics, to better identify models capable of reliable temperature variability projections.
Another striking finding concerns the limits of emergent constraint approaches. Emergent constraints leverage observable relationships in model outputs to reduce uncertainty in projections. Although promising, these approaches depend heavily on the robustness of the statistical relationships employed. Suarez-Gutierrez and Maher demonstrate, through comprehensive testing, that emergent constraints aimed at variability indices often fail to tighten the projection spread meaningfully, exposing a gap that must be addressed by the climate modeling community.
The consequences of these uncertainties extend beyond academic debates, directly impacting society’s capacity to prepare for climate extremes. Temperature variability underpins many extreme weather phenomena, including heatwaves, cold spells, and agricultural frost events. Reliable projections of variability trends are critical for infrastructure planning, public health responses, and ecosystem management. The inability to narrow uncertainties in variability forecasts hence poses significant challenges for decision-makers aiming to anticipate and mitigate climate risks.
Reflecting on their findings, Suarez-Gutierrez and Maher emphasize that progress requires a multipronged approach combining improved physical process representation in models, enhanced observational datasets for more rigorous model validation, and refined methods for ensemble construction. The researchers advocate for focused investments in process-level studies, especially in areas such as cloud microphysics, ocean-atmosphere coupling dynamics, and land surface feedback mechanisms that drive temperature variability.
The study also highlights the importance of interdisciplinary collaboration between modelers, statisticians, and observational scientists to innovate evaluation frameworks and translate complex climate variability signals into usable information. Engaging with stakeholders early in the projection development process can help tailor scientific outputs to practical needs, ensuring that even within prevailing uncertainties, projections inform adaptive planning effectively.
In essence, Suarez-Gutierrez and Maher’s work serves both as a cautionary tale and a call to action. It tempers expectations around the precision of temperature variability projections while illuminating paths forward to enhance climate prediction science. Their analysis underscores the inherent complexity of Earth’s climate system and the ongoing quest to unravel its nuances, especially concerning the variability that defines so much of its immediate impacts.
As climate change accelerates, understanding not just how much Earth warms but how its temperature fluctuates remains critical. This study crystallizes that the road to mastering temperature variability predictions will be long and challenging, demanding innovation in models, methods, and integration. It reminds the scientific community and broader public that uncertainty is not a failure but a vital boundary condition guiding future exploration.
Ultimately, this research deepens the dialogue about climate model reliability and variability projections, shaping ongoing efforts to improve the science that underpins global climate response strategies. While an elusive target today, narrowing uncertainty in temperature variability remains an achievable goal through concerted research efforts spanning observational advances, modeling refinements, and systemic innovation. The findings of Suarez-Gutierrez and Maher thus represent a significant milestone informing future directions in climate variability science.
Subject of Research: Temperature variability projections and their uncertainties in climate models.
Article Title: Temperature variability projections remain uncertain after constraining them to best performing Large Ensembles of individual Climate Models.
Article References:
Suarez-Gutierrez, L., Maher, N. Temperature variability projections remain uncertain after constraining them to best performing Large Ensembles of individual Climate Models. Nat Commun (2025). https://doi.org/10.1038/s41467-025-67005-y
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