Climate models have consistently overestimated global warming since 1998, with most projections running about 2.2 times hotter than observed trends[4]. This section visualizes observed, multi-model mean, and high-end scenario projections.
Structural Model Uncertainty: Clouds, Feedbacks, and Tuning
Cloud processes remain the largest source of uncertainty in GCMs, with cloud radiative forcing uncertainty (±4.0 W/m²) exceeding the annual CO₂ forcing by over 100-fold[4]. Parameter tuning and subjective adjustments further complicate model objectivity.
Natural Variability and Non-CO₂ Drivers
Solar cycles, ocean-atmosphere oscillations (AMO, PDO, ENSO), and internal variability are systematically underrepresented in most models. This section compares observed indices with model outputs and highlights attribution challenges.
Regional Model Failures and Secondary Effect Risks
Persistent biases in regional rainfall, monsoon, and cyclone modeling limit the utility of even high-resolution forecasts. Secondary risks-ice sheet instability, carbon sink collapse-are becoming more impactful than primary modeled variables.
Forecast Errors and Scenario Misuse
High-profile failures (e.g., “ice-free Arctic by 2013,” Himalayan glacier collapse by 2035) and misuse of RCP 8.5 as a baseline have eroded trust and inflated risk estimates in policy and finance.
Consensus Distortion and Model Skepticism
The “97% consensus” is a rhetorical artifact, not a robust empirical metric. Less than half of surveyed climate scientists strongly trust model projections, especially for clouds, precipitation, and extremes.
Systemic Risk: Policy, Finance, and Governance Implications
Model overconfidence and scenario misuse drive regulatory and financial misalignment.
Secondary risks and natural variability are underweighted, increasing systemic fragility.
Consensus distortion and forecast failures erode public trust and policy legitimacy.
Research Priorities:
Empirical scenario validation
Modular, high-res models
Pluralistic, probabilistic frameworks
Transparent model disclosure
Cloud feedbacks remain the largest and most persistent source of uncertainty in climate sensitivity estimates. While recent research and successive IPCC assessments have increased confidence that the net global cloud feedback is positive (meaning clouds are likely amplifying rather than dampening warming), the magnitude and sign of individual cloud processes, especially those involving low clouds and cloud opacity, are still poorly constrained by observations and models. High clouds, particularly those increasing in altitude, are now assessed with high confidence as a positive feedback, but low clouds and their response to warming remain a major driver of disagreement among models and between models and observations.
The spread in climate sensitivity across current models is driven primarily by these inter-model differences in cloud feedbacks, with the largest discrepancies in regions dominated by low-level cloud cover, such as the tropics and subtropics. Experiments using high-resolution process models and new observational techniques (including satellite products and machine learning methods) are beginning to narrow this uncertainty, but substantial disagreement persists, especially regarding the response of boundary-layer clouds and the impact of aerosols on cloud properties.
Recent studies have shown that some of the latest climate models (CMIP6) estimate higher climate sensitivity largely because they simulate more positive cloud feedbacks than previous generations, but these estimates have been challenged by both observational data and paleoclimate evidence. In particular, the AR6 assessment settled on a likely cloud feedback value of +0.42 [-0.10 to 0.94] W m-2 per degree of warming, reflecting a synthesis of model and observational evidence, but also acknowledging that the uncertainty range remains wide and is the main reason why equilibrium climate sensitivity (ECS) estimates span such a broad range.
This persistent uncertainty in cloud feedbacks has direct implications for policy, scenario planning, and the credibility of climate projections. Until cloud processes (especially those involving low clouds and aerosol interactions) are better constrained, the range of possible future warming outcomes will remain wide, and the reliability of regional and decadal forecasts will be limited.