The social cost of carbon, often abbreviated SC-CO2, attempts to answer a deceptively simple question: how much economic damage, in dollars, does one additional tonne of carbon dioxide emitted today inflict across the entire future? The concept traces back to Pigouvian welfare economics, and in recent years it has moved from academic obscurity to the centre of regulatory policy, informing everything from power plant standards to fuel economy rules. In 2022, a landmark analysis in Nature by Rennert and colleagues pushed central estimates sharply upward, and the United States Environmental Protection Agency subsequently adopted estimates incorporating recent scientific advances. Yet the damage functions underpinning these figures have remained incomplete, and one of the most glaring omissions has been the effect of heat on the human capacity to work.

The physiological mechanism is well understood. As wet bulb globe temperature rises, the human body must divert more blood flow to the skin for cooling, heart rate climbs, and workers instinctively take more breaks or reduce their working intensity to avoid dangerous heat strain. Occupational health standards, including the widely used wet bulb globe temperature index maintained by the International Organization for Standardization, codify exactly how much work time is lost at given heat levels. Decades of field studies, from Indian rice harvesters to West Bengal brick workers and Hong Kong construction crews, have documented these losses in practice, and economic research has confirmed measurable impacts on output in manufacturing and on cognitive performance as well. What has been missing is a rigorous translation of this physiological and empirical evidence into the global, sector-resolved economic accounting that the social cost of carbon requires.

The new study closes that gap with an unusually detailed modelling chain. Qinqin Kong and Matthew Huber produced bias-corrected projections of wet bulb globe temperature under the CMIP6 climate model ensemble, correcting known model biases and computing heat stress metrics explicitly rather than through crude approximations, which earlier work has shown can materially misestimate labour losses. These projections cover three levels of work intensity and both indoor and outdoor conditions. The team then applied two distinct labour response functions, one based on the ISO occupational standard and another drawn from a separate empirical framework, to convert heat exposure into losses of effective labour capacity by job type, economic sector and region.

Crucially, the researchers did not simply multiply lost labour hours by wages. Instead, they fed the labour productivity shocks into a general equilibrium model built on the Global Trade Analysis Project database, allowing prices, trade flows, sectoral reallocation and other economic adaptations to buffer or amplify the initial shock. This is a key distinction, because heat stress does not hit the world economy uniformly. It concentrates in already hot, labour-intensive economies, propagates through global supply chains as the prices of agricultural and manufactured goods shift, and triggers substitutions that general equilibrium modelling can capture but simpler accounting cannot. The resulting damages were then expressed as regional damage functions, relating warming to welfare losses as a percentage of initial income, and incorporated into the GIVE integrated assessment framework used in recent official estimates of the social cost of carbon.

The headline result for labour is striking: heat-related labour productivity damages amount to US$41 per tonne of carbon dioxide emitted in 2025, with a 90 percent confidence interval running from US$1 to US$108. Losses are heavily concentrated in South, East and Southeast Asia and in Africa, regions where outdoor and physically demanding work remains a large share of employment and where cooling infrastructure is least widespread. Under an illustrative warming level of 1.7 degrees Celsius, the maps of projected labour capacity loss reveal a world of profound inequality, with tropical and subtropical working populations bearing damages that temperate, wealthy economies largely escape. This geographic concentration matters not only for equity but also for policy design, since it identifies where adaptation investments such as shaded worksites, adjusted working hours, mechanisation and expanded access to cooling would deliver the greatest returns.

The second major contribution of the study is a downward revision of agricultural damages. Previous estimates, including the authors’ own earlier work, had translated the findings of crop-yield meta-analyses into damage functions that implied agricultural losses of US$95 per tonne of carbon dioxide. The Sixth Assessment Report of the Intergovernmental Panel on Climate Change, drawing on a much larger body of evidence including process-based crop models and studies accounting for adaptation, carbon dioxide fertilisation and changing growing regions, supports substantially smaller aggregate impacts. Incorporating that assessment reduces the agricultural damage component to US$29 per tonne. The revision is a reminder that damage estimates are only as good as the underlying impact literature, and that as climate impact science matures, policy-relevant numbers must be updated rather than fossilised.

Netted together, the two revisions lower the expected 2025 social cost of carbon dioxide from US$204 to US$179 per tonne, using a 2 percent near-term discount rate in 2020 dollars. But the authors emphasise that the more important change may be the treatment of uncertainty. By building labour damages from explicit physiological data, bias-corrected climate projections and structural economic modelling, and by grounding agricultural damages in an authoritative assessment synthesis, the study substantially narrows the confidence interval around the social cost of carbon. For regulators, who must defend these figures in courtrooms and rulemaking dockets, a central estimate backed by a transparent, reproducible evidence chain is arguably worth as much as the point value itself.

The findings land at a politically charged moment, as governments weigh how heavily carbon damages should weigh in cost-benefit analysis and as the scientific community continues to expand the catalogue of climate impacts, from mortality and morbidity to energy demand and coastal inundation. This study demonstrates both directions of that expansion: adding a previously missing damage category centred on the world’s most vulnerable workers, while trimming another that had likely been overstated. The complete methodological chain, from gridded heat stress datasets and damage module code to the revised integrated assessment calculations, has been made openly available, allowing other researchers to scrutinise and extend the work. As the evidence base grows, the social cost of carbon is becoming less of a contested abstraction and more of a measurable summary of what each tonne of carbon dioxide truly costs the human economy, and the newest answer is that it costs most dearly in the sweat of those who work under the sun.

Subject of Research: Estimating the social cost of carbon dioxide by incorporating heat-related labour productivity damages and updated agricultural damage functions

Article Title: New labour and agricultural damages improve climate cost estimates

Article References: Moore, F. C., Haqiqi, I., Kong, Q., Rennels, L., Baldos, U., Ganapathi, H., Huber, M., & Hertel, T. (2026). New labour and agricultural damages improve climate cost estimates. Nature Climate Change. https://doi.org/10.1038/s41558-026-02749-z

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DOI: 10.1038/s41558-026-02749-z

Keywords: social cost of carbon, heat stress, labour productivity, climate change damages, agriculture, integrated assessment modelling, CMIP6, wet bulb globe temperature, general equilibrium, Nature Climate Change, climate economics, adaptation

The research team, led by climatologists Chuan He, Andrew C. Clement, and Mark A. Cane, meticulously analyzed multiple state-of-the-art climate simulation models alongside comprehensive observational datasets spanning the past several decades. Their analysis revealed a pattern of systematic exaggeration in how these models represent temperature differences between the Northern and Southern Hemispheres as well as tropical climate responses attributed to anthropogenic greenhouse gas emissions. This nuanced discrepancy points to complex interactions and feedback mechanisms in climate dynamics that existing models may inadequately capture.

Central to modern climate science is the ability to simulate the Earth’s response to increasing concentrations of greenhouse gases, especially carbon dioxide and methane, which trap infrared radiation and contribute to global warming. Models typically predict distinct patterns of warming across latitude bands, with the Northern Hemisphere often expected to warm more intensely due to its larger landmass and human activity concentration. However, the new findings suggest that the models amplify this hemispheric contrast beyond what is observed in reality, indicating potential over-sensitivity or missing physical processes in the models.

This overestimation could stem from inadequate representation of oceanic and atmospheric circulations that mediate heat distribution between hemispheres. Ocean currents such as the Atlantic Meridional Overturning Circulation and atmospheric phenomena like the Intertropical Convergence Zone are critical regulators of temperature gradients, and any mischaracterization could skew model outputs. The study’s multidisciplinary approach integrated oceanographic data, satellite measurements, and paleoclimate reconstructions to identify where model predictions diverged from natural variability and observed trends.

One intriguing aspect of the research is its focus on the tropical climate system, which historically has been challenging to simulate due to its complex interplay of convection, cloud formation, and radiation dynamics. The authors found that while climate models correctly capture the general warming trend in tropical regions, they tend to exaggerate regional temperature variations and precipitation anomalies, potentially overshooting the intensity of climate impacts such as droughts and storms. This has critical ramifications for vulnerable tropical populations and ecosystems, for whom climate adaptation planning depends on reliable forecasts.

The implications of these findings extend beyond academic curiosity. Policy decisions surrounding emission reductions, climate adaptation investments, and international agreements are often informed by projections generated from these models. If the magnitude of greenhouse gas impacts is systematically overstated, there is a risk of misallocating resources or misunderstanding the urgency and nature of certain climate threats. Conversely, recognizing the limitations of current models opens avenues to refine models and incorporate additional processes such as aerosol-cloud interactions, natural variability modes like the Pacific Decadal Oscillation, and biogeochemical feedbacks.

In their methodological framework, the researchers utilized ensemble simulations, which combine multiple model runs to assess uncertainty and variability. These ensembles allowed them to compare how different models respond to the same greenhouse gas forcing and to isolate consistent biases. By aligning simulated data with observed temperature records, they could identify a persistent pattern of inflated interhemispheric temperature gradients that is not reflected in empirical measurements. Their statistical rigor ensures confidence in the robustness of these conclusions.

Moreover, the study discusses the importance of temporal and spatial resolution in climate modeling. Many global models operate at coarse scales, potentially smoothing out fine-scale processes that are critical to accurate regional climate representations. This limitation may partly explain why models struggle to replicate observed tropical climate patterns with complete fidelity. Advances in high-resolution modeling and increased computational power offer promising routes to overcome such obstacles, enabling better integration of mesoscale dynamics and localized feedback effects.

The potential causes of the exaggerated greenhouse gas impact could also be linked to how models treat radiative forcing components. For example, some models may insufficiently account for compensating effects of natural aerosols or underestimate land-atmosphere interactions that buffer temperature changes. Additionally, uncertainties in cloud microphysics and the representation of convective processes introduce further complexity, often leading to greater variability across models in simulating tropical climates.

Beyond theoretical advancements, this research underscores the vital role of comprehensive observational networks. Satellite missions, ocean buoys, and ground-based stations provide essential ground-truth data that enable continual model validation and adjustment. The disparity between models and observations revealed in this study calls for enhancing observational coverage, particularly in under-monitored Southern Hemisphere and tropical regions, to better constrain model development and calibration.

The broader climate science community has received these findings with keen interest, recognizing both the challenges they present and the opportunities they afford. Recalibrating model sensitivity to greenhouse gases is not a rejection of climate change science but a refinement that enhances scientific accuracy and predictive confidence. It also exemplifies the iterative nature of scientific progress—models evolve alongside growing data inputs and deepening understanding of Earth’s climate complexities.

Finally, this study encourages a balanced narrative when communicating climate risks to the public and policymakers. While it confirms that greenhouse gases remain a dominant driver of recent climate change, it suggests that the severity and patterns of some impacts might differ from earlier projections. Clear, transparent communication about model uncertainties and strengths is paramount to maintaining trust and fostering informed decision-making.

Moving forward, the authors advocate for intensified collaboration between observational scientists, modelers, and theoreticians to address identified gaps. By integrating more comprehensive physical processes and improving model parameterizations, the climate science community can develop more precise tools for forecasting future climate scenarios. This progress is essential for formulating effective mitigation and adaptation strategies that are resilient and responsive to the true dynamics of the Earth’s climate system.

In conclusion, the study by He, Clement, Cane, and colleagues represents a pivotal contribution to climate science, highlighting critical nuances in how climate models simulate greenhouse gas impacts across hemispheres and the tropics. It offers a sophisticated perspective on climate model performance, combining rigorous statistical analysis with physically grounded interpretations. As climate science continues to advance, such research not only deepens our understanding but also reinforces the call for ongoing refinement of predictive models in service of humanity’s enduring challenge to navigate a changing climate.

Subject of Research:
The study investigates the discrepancies between climate model simulations and observed temperature patterns between the Northern and Southern Hemispheres, as well as tropical climate responses, focusing on the impact of greenhouse gases.

Article Title:
Climate models exaggerate greenhouse gas impact on recent interhemispheric temperature patterns and tropical climate.

Article References:
He, C., Clement, A.C., Cane, M.A. et al. Climate models exaggerate greenhouse gas impact on recent interhemispheric temperature patterns and tropical climate. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69783-5

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Prevailing climate models have long grappled with uncertainties surrounding the quantitative estimates of extreme precipitation under global warming scenarios. These inconsistencies stem from the complex interplay of atmospheric dynamics, thermodynamics, and feedback mechanisms that influence local and regional rainfall intensities. The work spearheaded by Shiogama and co-authors addresses these uncertainties head-on, employing a novel methodology that harnesses diverse lines of evidence, thereby narrowing the uncertainty bounds that have historically hampered policymaking and disaster preparedness.

Central to their approach is the concept of emergent constraints, where present-day climatological variables serve as fingerprints that correlate robustly with future climate responses simulated by Earth system models. Unlike traditional model intercomparisons that weight each model equally, this technique uses observed climate system characteristics to statistically constrain projections. This study elevates this concept by combining multiple emergent constraints focused on different facets of precipitation and atmospheric behavior, opening a new frontier in predictive climatology.

The researchers first examined satellite and ground-based observations of current extreme precipitation distributions alongside atmospheric moisture dynamics, which have a direct influence on convective rainfall intensity. Through exhaustive analysis, they identified measurable indicators that reliably predict how extreme precipitation extremes are likely to evolve as global mean surface temperatures climb. These indicators included parameters such as moisture convergence rates, atmospheric stability indices, and precipitation frequency-intensity relationships, which are instrumental in constraining future scenarios.

Furthermore, the study leveraged state-of-the-art climate models from the latest Coupled Model Intercomparison Project (CMIP6) ensemble, selecting models that exhibited the highest fidelity in replicating present-day precipitation extremes. This selective process was crucial, enabling the research team to assign appropriate weights to each model based on its performance rather than treating all projections equally. Incorporating these weighted projections yielded significantly sharpened projections, with approximately 30-50% reductions in uncertainty ranges for future extreme rainfall intensity.

One of the most striking revelations from this exhaustive analysis is the anticipated amplification of heavy rainfall events even under moderate warming scenarios. The synthesized emergent constraints suggest that extreme precipitation could intensify far more rapidly than previously estimated, particularly in mid-latitude and tropical regions. This intensification is tied intrinsically to the Clausius-Clapeyron relationship, which governs the exponential increase of atmospheric moisture holding capacity with temperature escalation, thus fueling heavier downpours during convective storms.

However, the study also highlights a more nuanced spatial heterogeneity, revealing that some regions might experience more pronounced increases in extreme precipitation frequencies, while others may face alterations primarily in rainfall intensity without corresponding frequency changes. Such regional variability underscores the importance of localized climate adaptation strategies and infrastructure planning that account for divergent future scenarios rather than one-size-fits-all solutions.

Crucially, the combined emergent constraint approach also tackled the vexing problem of model biases related to tropical convection and storm dynamics, which have historically undermined confidence in precipitation projections. By correlating observed convection characteristics with model-simulated extreme rainfall narratives, the team corrected systemic biases and achieved heightened consistency between models and reality. This advancement paves the way for more reliable forecasts of extreme hydrological phenomena crucial for disaster risk reduction.

In addition to improving the quantitative estimates, the study elucidates the underlying physical mechanisms driving the shifts in extreme precipitation. It clarifies the prominent role of thermodynamic factors, such as increased moisture availability, and dynamic factors, including changes in large-scale atmospheric circulation patterns that modulate storm tracks and intensities. Delineating these distinct influences is vital for advancing our mechanistic understanding and for fine-tuning climate models that must encapsulate these processes accurately.

Moreover, the integration of observational constraints facilitates a more robust affirmation of the physical realism of climate models. This synergy between models and observations not only increases projection confidence but also equips policymakers and planners with actionable intelligence. It informs flood risk assessments, urban drainage designs, and agricultural water management by quantifying potential shifts in precipitation extremes with greater precision.

Anticipating future changes in extreme precipitation is more than a scientific curiosity; it is a societal imperative. Flooding triggered by extreme rainfall ranks among the costliest and deadliest natural disasters globally, with escalating trends linked to climate change. The findings of Shiogama and colleagues arm stakeholders with a more dependable scientific foundation to strategize mitigation efforts, emergency preparedness, and infrastructure resilience, especially in vulnerable coastal and riverine megacities where population exposure is highest.

From a methodological perspective, the study’s emphasis on combining multiple emergent constraints rather than singular indicators exemplifies a paradigm shift in climate projection science. This multidimensional synthesis decorrelates confounding uncertainties and cross-validates emergent patterns, creating a cumulative constraint effect that incrementally sharpens the predictive lens. Such integrative techniques can serve as templates for tackling uncertainties in other climate change impact domains, including heatwaves, droughts, and tropical cyclone intensities.

Importantly, this research also opens avenues for future observational campaigns and satellite missions targeted at refining critical emergent variables. Enhanced measurements of atmospheric moisture fluxes, cloud microphysics, and precipitation isotopic compositions would feed into the emergent constraint machinery, further elevating the accuracy and regional specificity of future projections. The iterative interplay between observation, model development, and emergent constraint application symbolizes a dynamic trajectory for climate sciences.

The study, while comprehensive, also acknowledges inherent limitations. Some uncertainties remain related to cloud-aerosol interactions and microscale convective dynamics that elude current climate models’ resolution. Likewise, internal climate variability and potential tipping elements in the climate system could modify precipitation extremes in unexpected ways. Nevertheless, the combined emergent constraint framework offers a pragmatic pathway to incrementally reduce these uncertainties over successive model generations.

The implications of this intensified understanding ripple across sectors – from urban planners designing stormwater systems to insurance companies recalibrating risk models, and from agricultural stakeholders adjusting cropping calendars to international climate policy negotiations centered on adaptation funding allocations. In many respects, the study calls for urgent international cooperation to integrate improved climate hazard projections with sustainable development goals.

As society confronts accelerating climate impacts, the ability to foresee changes in extreme precipitation with higher fidelity equips humanity with critical foresight. Shiogama and the team’s landmark study exemplifies how cutting-edge statistical techniques married with robust observational datasets can propel climate science beyond traditional modeling confines. Consequently, it marks a hopeful stride toward building more resilient and adaptive societies prepared for the storms ahead.

In sum, this comprehensive investigation into future extreme precipitation changes using combined emergent constraints sets a new benchmark for projection reliability. It conveys a dual message of caution and preparedness: extreme rainfall events are poised to escalate significantly under warming scenarios, but through advanced science and informed policy, their societal impacts can be mitigated. As climate risk dialogues intensify globally, such studies embody the scientific rigor and innovation necessary to safeguard future generations.

Subject of Research: Future changes in extreme precipitation patterns and their projection uncertainties.

Article Title: Combined emergent constraints on future extreme precipitation changes.

Article References:
Shiogama, H., Hayashi, M., Hirota, N. et al. Combined emergent constraints on future extreme precipitation changes. Nat Commun 16, 5293 (2025). https://doi.org/10.1038/s41467-025-60385-1

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