The social cost of carbon dioxide (SC-CO₂) measures the monetized value of the damages to society caused by an incremental metric tonne of CO₂ emissions and is a key metric informing climate policy. Used by governments and other decision-makers in benefit–cost analysis for over a decade, SC-CO₂ estimates draw on climate science, economics, demography and other disciplines. However, a 2017 report by the US National Academies of Sciences, Engineering, and Medicine¹ (NASEM) highlighted that current SC-CO₂ estimates no longer reflect the latest research. The report provided a series of recommendations for improving the scientific basis, transparency and uncertainty characterization of SC-CO₂ estimates. Here we show that improved probabilistic socioeconomic projections, climate models, damage functions, and discounting methods that collectively reflect theoretically consistent valuation of risk, substantially increase estimates of the SC-CO₂. Our preferred mean SC-CO₂ estimate is $185 per tonne of CO₂ ($44–$413 per tCO₂: 5%–95% range, 2020 US dollars) at a near-term risk-free discount rate of 2%, a value 3.6 times higher than the US government’s current value of $51 per tCO₂. Our estimates incorporate updated scientific understanding throughout all components of SC-CO₂ estimation in the new open-source Greenhouse Gas Impact Value Estimator (GIVE) model, in a manner fully responsive to the near-term NASEM recommendations. Our higher SC-CO₂ values, compared with estimates currently used in policy evaluation, substantially increase the estimated benefits of greenhouse gas mitigation and thereby increase the expected net benefits of more stringent climate policies.

All physical process models and field observations are inherently imperfect, so there is a need to both (1) obtain measurements capable of constraining quantities of interest and (2) develop frameworks for assessment in which the desired processes and their uncertainties may be characterized. Incorporation of stable water isotopes into land surface schemes offers a complimentary approach to constrain hydrological processes such as evapotranspiration, and yields acute insight into the hydrological and biogeochemical behaviors of the domain. Here a stable water isotopic scheme in the National Center for Atmospheric Research's version 4 of the Community Land Model (CLM4) is presented. An overview of the isotopic methods is given. Isotopic model results are compared to available data sets on site‐level and global scales for validation. Comparisons of site‐level soil moisture and isotope ratios reveal that surface water does not percolate as deeply into the soil as observed in field measurements. The broad success of the new model provides confidence in its use for a range of climate and hydrological studies, while the sensitivity of simulation results to kinetic processes stands as a reminder that new theoretical development and refinement of kinetic effect parameterizations is needed to achieve further improvements.

Simulation models of multi‐sector systems are increasingly used to understand societal resilience to climate and economic shocks and change. However, multi‐sector systems are also subject to numerous uncertainties that prevent the direct application of simulation models for prediction and planning, particularly when extrapolating past behavior to a nonstationary future. Recent studies have developed a combination of methods to characterize, attribute, and quantify these uncertainties for both single‐ and multi‐sector systems. Here, we review challenges and complications to the idealized goal of fully quantifying all uncertainties in a multi‐sector model and their interactions with policy design as they emerge at different stages of analysis: (a) inference and model calibration; (b) projecting future outcomes; and (c) scenario discovery and identification of risk regimes. We also identify potential methods and research opportunities to help navigate the tradeoffs inherent in uncertainty analyses for complex systems. During this discussion, we provide a classification of uncertainty types and discuss model coupling frameworks to support interdisciplinary collaboration on multi‐sector dynamics (MSD) research. Finally, we conclude with recommendations for best practices to ensure that MSD research can be properly contextualized with respect to the underlying uncertainties.

Simple models can play pivotal roles in the quantification and framing of uncertainties surrounding climate change and sea-level rise. They are computationally efficient, transparent, and easy to reproduce. These qualities also make simple models useful for the characterization of risk. Simple model codes are increasingly distributed as open source, as well as actively shared and guided. Alas, computer codes used in the geosciences can often be hard to access, run, modify (e.g., with regards to assumptions and model components), and review. Here, we describe the simple model framework BRICK (Building blocks for Relevant Ice and Climate Knowledge) v0.2 and its underlying design principles. The paper adds detail to an earlier published model setup and discusses the inclusion of a land water storage component. The framework largely builds on existing models and allows for projections of global mean temperature as well as regional sea levels and coastal flood risk. BRICK is written in R and Fortran. BRICK gives special attention to the model values of transparency, accessibility, and flexibility in order to mitigate the above-mentioned issues while maintaining a high degree of computational efficiency. We demonstrate the flexibility of this framework through simple model intercomparison experiments. Furthermore, we demonstrate that BRICK is suitable for risk assessment applications by using a didactic example in local flood risk management.

Key Points: We characterize key deep uncertainties surrounding flood risk projections for a levee ring in New Orleans using 18 probabilistic scenarios The levee system alone may provide flood protection between the 100‐ and 500‐year return period Uncertainty in the storm surge distribution shape parameter is the primary driver of flood risk variability