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

Image Credits: AI Generated

The Pacific Meridional Mode is a climate pattern operational in the eastern Pacific Ocean, characterized by variations in sea surface temperatures and surface wind patterns. Unlike ENSO, which broadly oscillates between warm (El Niño) and cool (La Niña) phases on interannual timescales, the PMM’s fluctuations manifest prominently through changes in the strength of the northeast Pacific trade winds and corresponding surface water temperatures. Specifically, a positive PMM phase features weakened trade winds and anomalously warm sea surface temperatures extending from the central to the eastern Pacific. Conversely, a negative PMM phase is marked by strengthened trade winds and cooler surface waters. These states influence atmospheric circulation and, crucially, precipitation patterns far beyond their point of origin.

The University of Hawai‘i study, published in the Journal of Climate, employs a meticulously detailed diagnostic approach, combining observational data spanning atmospheric conditions and sea surface temperatures with advanced weather model simulations. This multifaceted analysis allowed the researchers to isolate and quantify the distinct influences of ENSO and the PMM on rainfall variability, dissecting seasonal nuances that had previously been conflated or overlooked. The team’s core insight reveals a seasonal dichotomy in the dominant climatic drivers of Hawaiian rainfall: ENSO exerts primary influence during the winter months, whereas the PMM emerges as a dominant force in the spring, especially impacting the islands of Maui and Hawai‘i.

During the spring season, the positive phase of the PMM triggers extensive rainfall across the Hawaiian archipelago. This precipitation increase correlates strongly with the passage of cold fronts, which deliver moisture to the islands’ ecosystems and communities. The linkage between PMM states and weather disturbances suggests that the positive PMM enhances atmospheric conditions conducive to frontal activity, effectively amplifying moisture transport. Consequently, this relationship elevates the frequency and intensity of rainfall events precisely when the region transitions toward drier months, carrying profound implications for regional hydrology and ecosystem resilience.

Moreover, the research highlights the impact of PMM phases on the spatial distribution of rainfall extremes. When the PMM is in a positive state during either winter or spring, the typically dry leeward sides of Hawaiian islands experience a marked increase in extreme rainfall events. The leeward side, usually sheltered from prevailing winds and relatively arid, becomes vulnerable to torrential downpours, raising the risk of flash floods and associated hazards such as landslides and infrastructure damage. In contrast, negative PMM phases correspond with reduced rainfall intensity on the windward sides, potentially exacerbating drought conditions commonly sustained by these regions, which depend on orographic precipitation generated by trade wind uplift.

This nuanced understanding of PMM’s modulation of rainfall variability enhances the predictive capacity for seasonal flooding and drought risks. Hawai‘i’s population is growing steadily, intensifying demands on freshwater resources for domestic consumption, agriculture, recreation, and industrial applications. Variability in regional precipitation directly affects water supply reliability, agricultural output, and urban planning. Water resource managers and disaster preparedness officials can leverage these scientific insights to refine forecasting models, optimize reservoir management, and develop proactive mitigation strategies that consider seasonal shifts driven by both ENSO and PMM.

Importantly, the disentanglement of ENSO and PMM effects challenges prior assumptions that heavily emphasized ENSO as the singular driver of rainfall variability. The researchers demonstrate that these two climate modes operate simultaneously yet differently across seasons, underscoring the intricacy of tropical Pacific climate dynamics. This dual influence necessitates a more sophisticated approach to climate modeling and hazard assessment that integrates multiple interacting climate drivers rather than attributing all variability to ENSO alone.

Beyond its direct relevance to Hawai‘i, the study’s findings resonate within the broader field of climate science by highlighting the substantial role of regional ocean-atmosphere coupling modes in shaping localized weather and climate phenomena. The PMM, long studied for its interactions with ENSO, is now recognized as a critical modulator of midlatitude and tropical Pacific climates, with implications for understanding climate teleconnections and variability patterns across the Americas and Oceania. This enhanced comprehension could inform global climate models and seasonal forecasting efforts more generally, improving preparedness for climate-related risks.

Technically, the research utilizes an array of data sources including satellite-based sea surface temperature records, surface wind measurements from buoys and meteorological stations, and outputs from numerical weather prediction models that simulate both atmospheric dynamics and oceanic responses. By integrating observational evidence with simulated climate scenarios, the researchers conducted a series of correlation and composite analyses that teased out the subtle but systematic fingerprints of the PMM on precipitation metrics across varying temporal windows and geographic locales within Hawai‘i.

The practical applications of this research extend to sectors far beyond hydrology. Ecosystem management, public health, agriculture, infrastructure development, and emergency response all stand to benefit from an improved understanding of precipitation drivers and their seasonal cycles. For instance, anticipating periods of increased flood risk on leeward coasts enables infrastructure planners to reinforce drainage systems and prepare emergency services for potential disasters. Conversely, recognizing drought-enhancing conditions associated with negative PMM phases on windward slopes informs irrigation planning and water conservation measures to safeguard crops and natural habitats.

Looking forward, the incorporation of PMM dynamics into regional climate prediction frameworks promises improved seasonal forecasting skill, particularly for spring rainfall variability that was previously harder to anticipate. The University of Hawai‘i team underscores the importance of sustained observational efforts and advanced model development to continue unraveling the complex interactions between the Pacific Ocean and atmosphere. As climate change proceeds to alter baseline conditions and variability patterns, understanding these natural modes of variability will be essential for adapting to new climate regimes and maintaining resilience in island communities.

The implications of this research extend to the cultural and societal fabric of Hawai‘i, where water is both a vital resource and a central element of heritage and livelihood. By empowering communities with richer, science-based climate information, the study supports efforts toward sustainable resource management and risk reduction in the face of climate uncertainty. It embodies the critical role of atmospheric science in connecting global climate processes with local impacts and human well-being.

In conclusion, the discovery of the Pacific Meridional Mode’s pivotal influence on Hawaiian rainfall variability marks a significant advance in climate science. It shifts long-held paradigms, illuminates seasonal rainfall drivers, and equips stakeholders with actionable knowledge. As agencies and communities strive to navigate the challenges of water security and extreme weather in the 21st century, this research lays a robust foundation for informed decision-making grounded in the interplay of oceanic and atmospheric forces that shape Hawai‘i’s unique environment.

Subject of Research: Not applicable

Article Title: Impact of the Pacific Meridional Mode on Hawaiian Rainfall Variability

News Publication Date: 16-Apr-2025

Web References:
https://journals.ametsoc.org/view/journals/clim/38/9/JCLI-D-24-0038.1.xml

References:
Lu, B.-Y., Chu, P.-S., et al. (2025). Impact of the Pacific Meridional Mode on Hawaiian Rainfall Variability. Journal of Climate. DOI: 10.1175/JCLI-D-24-0038.1

Image Credits: Heath Cajandig, licensed under CC BY 2.0.

Keywords: Pacific Meridional Mode, ENSO, Hawaiian rainfall variability, climate dynamics, sea surface temperature, trade winds, seasonal precipitation, flood risk, drought risk, atmospheric circulation, climate modeling, water resource management