What are the main remaining uncertainties in regional climate projections and how do they affect policymaking?
This fact-check may be outdated. Consider refreshing it to get the most current information.
Executive summary
Regional climate projections remain indispensable for adaptation and planning but are bounded by three families of uncertainty—scenario (what humans do), model (how the climate system is represented) and internal variability (natural fluctuations)—and by gaps in representing land use, extreme-event variability, and poorly modelled regions; these uncertainties shape, delay, or distort policymaking from local planning to national mitigation commitments [1] [2] [3]. Policymakers must therefore balance the narrowing of some uncertainties with persistent regional unknowns by using multi-model ensembles, targeted observations, and decision frameworks that are robust to a range of plausible futures [4] [5].
1. Major structural uncertainties: emission scenarios and socio-economic pathways
A primary remaining uncertainty is scenario-driven: future regional climate depends critically on which emissions pathway societies follow, and different socio-economic trajectories produce materially different regional outcomes, making projections contingent on policy choices rather than purely physical limits [1] [5]. International assessments and trackers underscore that even recent pledges change global temperature projections only modestly—UNEP’s 2025 analysis shows NDCs shifting century-scale warming by tenths of a degree—so regional planning must treat scenario uncertainty as policy-dependent risk rather than a settled input [6].
2. Model uncertainty: formulations, missing physics, and downscaling biases
Regional projections inherit structural differences between Global Climate Models and Regional Climate Models, including incomplete or missing physics and diverse responses to forcings such as land-use change, and downscaling methods themselves introduce biases that vary by region and variable of interest [7] [1] [8]. The literature warns that models “tend to produce diverse physical responses to land use change” and that regional prediction is subject to “known-unknown” physics absent from many models, directly limiting confidence in localized projections [7].
3. Internal variability and extremes: the stubborn wildcard
Natural variability and changes in temperature variability remain large sources of uncertainty for regional extremes; even when constrained to best-performing model ensembles, projections of temperature variability and the sign and intensity of change remain large in poorly modelled regions, which complicates planning for extremes such as heatwaves, droughts and floods [3]. Short-term (decadal) regional trends can be dominated by natural variability, meaning that in some places projected trends may be indistinguishable from noise over policy-relevant horizons [2].
4. Where progress narrows uncertainty — and where it doesn’t
Progress in climate science and policy has measurably narrowed uncertainty in global mean warming over the past decade, which benefits long-lived infrastructure planning, and combining socio-economic constraints with physical model performance can further reduce regional uncertainty [9] [5]. Yet the narrowing is uneven: some regions and variables, particularly variability and extremes or areas affected by complex local forcings, retain large uncertainties despite improved ensembles and data accessibility through platforms like Copernicus [3] [4].
5. Practical impacts on policymaking: timing, scale and risk allocation
These scientific uncertainties translate into concrete policy effects: they complicate the timing and prioritization of adaptation investments, increase insurance and capital costs for infrastructure with long lifetimes, and create political cover for both action and inaction depending on interpretation of model spreads [9] [10]. Decision-makers therefore face trade-offs—wait for higher confidence on localized impacts, which risks lock-in and greater damages, or act on broader robust signals (e.g., global warming levels) while accepting residual regional uncertainty [2] [10].
6. Governance, agendas and solutions: ensembles, observatories and decision frameworks
Addressing remaining uncertainties requires transparent multi-model ensembles, targeted observation networks to improve poorly modelled regions, hybrid downscaling approaches, and decision frameworks that prioritize robustness and flexibility [4] [8] [5]. Hidden agendas appear when stakeholders cherry-pick model subsets to justify narrow policy choices; the scientific response is explicit: use ensembles and constrained projections to expose robustness and uncertainty rather than obscure it [4] [5].
7. Bottom line — uncertainty is a management problem, not a refusal of action
The core remaining uncertainties are about magnitude, timing and regional distribution of climate impacts rather than the reality of anthropogenic warming, and they should be handled as management challenges—deploying ensembles, scenario planning, adaptive policymaking and targeted research—so that policy is resilient across plausible futures instead of paralyzed by endemic model spread [11] [1] [5].