Abstract Uncertainties in estimates of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR) are influenced by observational temperature data sets. Variability exists not just among the data products, but also within the creation of each one. This includes significant variations among ensemble members within a single data product. Using the optimal fingerprint approach combined with Bayesian updating, we quantify the uncertainties in ECS and TCR estimates arising from both individual data sets and their various groupings. Our methodology, utilizing both spatial and temporal data, shows impacts on the estimates of ECS and TCR. As we assess different groupings of observational data products, we observe that using products sharing identical Sea Surface Temperatures (SST) introduce discernible biases. These results highlight that variations among ensemble members within a single data product are as influential as the disparities across multiple data products.

The Amazon rainforest is a region of global importance as it accounts for 10% of terrestrial biodiversity and stores at least 10 years’ worth of global anthropogenic carbon dioxide ( $$\hbox {CO}_2$$ ) emissions. However, the rainforest is currently under tremendous pressure from deforestation and the impacts of climate change, leading to rainforest degradation and perturbations of the regional carbon and water cycles. Using data sets from various sources, we produce spatial and temporal analyses of precipitation for the Amazon Basin from 1980 to 2022. Results demonstrate substantial seasonal and regional variations across the Amazon Basin, indicating that while some regions are experiencing increasing trends in precipitation, others are undergoing declines. These trends are not consistent among available datasets, with substantial differences between observational, reanalysis, and climate model data. For example, precipitation data from reanalyses for 1980–2022 reveal significant drying patterns in the southern and central Amazon during the dry season, which are not present in the observational datasets.

Climate change-related risk mitigation is typically addressed using cost-benefit analysis that evaluates mitigation strategies against a wide range of simulated scenarios and identifies a static policy to be implemented, without considering future observations. Due to the substantial uncertainties inherent in climate projections, this identified policy will likely be sub-optimal with respect to the actual climate trajectory that evolves in time. In this work, we thus formulate climate risk management as a dynamic decision-making problem based on Markov Decision Processes (MDPs) and Partially Observable MDPs (POMDPs), taking real-time data into account for evaluating the evolving conditions and related model uncertainties, in order to select the best possible life-cycle actions in time, with global optimality guarantees for the formulated optimization problem. The framework is developed for coastal adaptation applications, considering a wide variety of possible action types, including various forms of nature-based infrastructure. Related environmental impacts of carbon emissions and uptake are also incorporated, and social cost of carbon implications are discussed, together with several future directions and supported features. Climate change-related risk mitigation for infrastructure systems often requires adaptation. A computational framework for optimal decision-making under uncertainty based on dynamically changing conditions observed in time is developed in response.

Uncertainties in estimates of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR) are influenced by observational datasets. Variability exists not just among the data products, but also within the creation of individual ones. This includes significant variations among ensemble members within a single data product. Using the optimal fingerprint approach combined with Bayesian updating, we quantify the uncertainties in ECS and TCR estimates arising from both individual datasets and their various groupings. Our methodology, utilizing both spatial and temporal data, reveals impacts on the estimates of ECS and TCR. As we assess different groupings of observational data products, we observe that using products sharing identical Sea Surface Temperatures (SST) introduces a discernible biases. Furthermore, these results highlight that the variations among ensemble members within a single data product are as influential as the disparities across multiple data products.

Various climate change effects pose increasing risks to the nation's infrastructure. Available methodologies address the risk-management problem primarily through cost-benefit analysis frameworks, which evaluate a comprehensive set of protection strategies against a wide range of simulated possible future scenarios. However, due to the substantial climate model uncertainties present over the future planning horizon, such strategies can often lead to less informed policies that might be optimal in an average sense, over the mean of anticipated future scenarios, but cannot offer adaptive solutions based on the actual climate effects evolving in time. To address these limitations, in this research, climate risk mitigation is instead formulated as a decision-making problem within a closed-loop stochastic control-based framework using Markov decision processes (MDP), taking real-time data into account, for evaluating the evolving conditions, and selecting the best possible, most informed life-cycle actions in time. Although broadly applicable, the merit of the framework will be illustrated through coastal risk mitigation against storm surge and sea-level rise in an idealized coastal city setting.

After many years with little change in community views on equilibrium climate sensitivity (ECS), in 2021 the Intergovernmental Panel on Climate Change (IPCC) concluded that it was much better known than previously. This development underpinned increased confidence in long-term climate changes in that report. Here, we place this development in historical context, briefly assess progress since then, and discuss the challenges and opportunities for further improving our knowledge of this iconic concept. We argue that the probability distributions published in those assessments are still approximately valid; while various subsequent studies have claimed further narrowing, they have omitted important structural uncertainties associated with missing processes, imperfect relationships, or other factors that should be included. The distributions could nonetheless be narrowed in the future, particularly through better understanding of certain climate processes and paleoclimate proxies. Not all touted strategies are truly helpful, however. We also note that ECS does not address risks from the carbon cycle or possible tipping points, and as increasingly strong mitigation (i.e., “net-zero”) scenarios are considered, ECS becomes less informative about future climate change compared to other factors such as aerosol radiative forcing and influences on regional change such as ocean dynamics.

Decisions on how to manage future flood risks are frequently informed by both sophisticated and computationally expensive models. This complexity often limits the representation of uncertainties and the consideration of strategies. Here, we use an intermediate complexity model framework that enables us to analyze a rich set of strategies, objectives, and uncertainties. We find that allowing for more combinations of risk mitigation strategies can expand the solution set, help explain synergies and trade-offs, and point to strategies that can improve outcomes.

Convergence research is driven by specific and compelling problems and requires deep integration across disciplines. The potential of convergence research is widely recognized, but questions remain about how to design, facilitate, and assess such research. Here we analyze a seven-year, twelve-million-dollar convergence project on sustainable climate risk management to answer two questions. First, what is the impact of a project-level emphasis on the values that motivate and tie convergence research to the compelling problems? Second, how does participation in convergence projects shape the research of postdoctoral scholars who are still in the process of establishing themselves professionally? We use an interview-based approach to characterize what the project specifically enabled in each participant’s research. We find that (a) the project pushed participants’ research into better alignment with the motivating concept of convergence research and that this effect was stronger for postdoctoral scholars than for more senior faculty. (b) Postdocs’ self-assessed understanding of key project themes, however, appears unconnected to metrics of project participation, raising questions about training and integration. Regarding values, (c) the project enabled heightened attention to values in the research of a large minority of participants. (d) Participants strongly believe in the importance of explicitly reflecting on values that motivate and pervade scientific research, but they question their own understanding of how to put value-focused science into practice. This mismatch of perceived importance with poor understanding highlights an unmet need in the practice of convergence science.

CONTEXT: To meet the nutritional and environmental needs of a growing population, dairy producers must increase milk production while minimizing the farm-gate environmental impact and adapting to the effects of climate change. OBJECTIVE: Here we comprehensively assess the effects of climate change on the environmental performance and productivity of three typical US dairy farms, and evaluate the potential benefits of adaptation strategies and implementation of Beneficial Management Practices (BMPs) for mitigating these effects and the potential in-creases in environmental impact. METHODS: Using the Integrated Farm System Model (IFSM), we predicted the productivity and environmental impact of these baseline farms under current emission scenarios and climate projections of 6 general circulation models (GCM), for high and low emission scenarios. We simulated farm-specific BMPs for current and future climate conditions for both unadapted and 'adapted' field cultivation plans, based on experiences from other climate locations. Finally, the IFSM predictions were compared to those of two other process-based models to test result robustness. RESULTS AND CONCLUSIONS: We find that the environmental impact of the three northern US dairy farms (New York, Pennsylvania, and Wisconsin) generally increases by mid-century, if no mitigation measures are taken. Overall, feed production is maintained, as decreased corn grain yields are compensated by increased forage yields. Adoption of farm-specific Beneficial Management Practices can substantially reduce the GHG emissions and nutrient losses from dairy farms under current climate conditions and stabilize the environmental impact in future climate conditions, while maintaining farm productivity (milk and feed production). A comparison of three models corroborates the estimated reductions in methane and ammonia emissions associated with BMPs, as well as the relative trend in P-loss reduction. SIGNIFICANCE: This study provides a holistic assessment of the impacts of climate change on dairy production systems focusing on both feed production and environmental impacts. It demonstrates the interest of BMPs to both reduce GHG emissions and contribute to more resilient farming systems in a changing climate.

This study provides a holistic view of the coupled ocean-atmosphere-sea ice processes responsible for generating interannual variability in sea ice coverage in the Sea of Okhotsk as well as the atmospheric response to this variability. Simulations from the Community Earth System Model Large Ensemble project are analyzed, providing the ability to elucidate the time evolution of these relationships through weekly lead-lag composite analysis, while maintaining a large number of samples to provide robust conclusions. We find that thermodynamic processes involving anomalous ocean-atmosphere heat fluxes affect the timing of initial sea ice growth in the Sea of Okhotsk as early as November. Low-level wind anomalies in the winter affect the extent to which sea ice fully develops, both through advection of the sea ice itself and through changes in the transport of air masses over the Sea of Okhotsk. In this study, the results synthesize and support a diverse set of mechanisms identified in previous observational studies to be responsible for anomalous sea ice conditions, but in a coupled global climate model framework with a large sample size. We also find evidence that anomalous ocean-atmosphere heat fluxes in the winter can trigger an atmospheric response comprised of a local negative sea-level pressure anomaly and Rossby wave that extends over North America. The sign of the turbulent heat fluxes relative to the sea ice anomalies confirm that this is indeed a lagged response of the atmosphere forced by sea ice anomalies. This validates the Rossby wave train response identified in more idealized model simulations with prescribed sea ice and sea surface temperature by demonstrating that this process also occurs in a more realistic coupled model framework.

Addressing climate change is ultimately a challenge of risk management, which requires an understanding of the likelihood of potential outcomes. We provide integrated, probabilistic socio-economic and climate projections obtained using updated estimates of probability distributions for key parameters in both the human and Earth system components of the MIT Integrated Global System Model (IGSM). The Reference scenario results in median end-of-century warming of 3.5°C and a 90% range of 2.8–4.3°C, which is lower than the median of 5.7°C from a prior study using a previous version of the IGSM. About 0.5°C of the difference is due to updated estimates in the human system and the rest of the difference is explained by changes in Earth system estimates. Our results show that climate policy lowers the upper tail of temperature change distributions more than the median, and that even relatively modest policies can significantly reduce the likelihood of high global temperature outcomes. Human system uncertainties contribute more to uncertainty in projected CO2 concentrations and total radiative forcing, while Earth system uncertainties have the greatest influence on temperature and precipitation. Including additional uncertain inputs does not automatically increase the outcome range because uncertainties can offset one another. Results also show how policy costs can vary greatly among regions. As we improve understanding of underlying technology and economic factors as well as Earth system response to human forcing, further updating of these estimates of uncertainty can make an important contribution to decision-making about mitigation and adaptation.

G. Jeffrey Snyder is a Professor of Materials Science and Engineering at Northwestern University in Evanston Illinois. His interests are focused on engineering of electronic and thermal properties, and he is well known for his work on thermoelectric materials. He has developed new methods of electron band structure engineering and microstructure engineering of thermal and electrical properties of complex materials. His interdisciplinary approach stems from his background in solid state chemistry at Cornell University and the Max Planck Institute for solid state research, applied physics at Stanford University, and thermoelectric materials and device engineering at NASA/Jet Propulsion Laboratory and California Institute of Technology (Caltech).

The hazard of pluvial flooding is largely influenced by the spatial and temporal dependence characteristics of precipitation. When extreme precipitation possesses strong spatial dependence, the risk of flooding is amplified due to catchment factors such as topography that cause runoff accumulation. Temporal dependence can also increase flood risk as storm water drainage systems operating at capacity can be overwhelmed by heavy precipitation occurring over multiple days. While transformed Gaussian processes are common choices for modeling precipitation, their weak tail dependence may lead to underestimation of flood risk. Extreme value models such as the generalized Pareto processes for threshold exceedances and max-stable models are attractive alternatives, but are difficult to fit when the number of observation sites is large, and are of little use for modeling the bulk of the distribution, which may also be of interest to water management planners. While the atmospheric dynamics governing precipitation are complex and difficult to fully incorporate into a parsimonious statistical model, non-mechanistic analogue methods that approximate those dynamics have proven to be promising approaches to capturing the temporal dependence of precipitation. In this paper, we present a Bayesian analogue method that leverages large, synoptic-scale atmospheric patterns to make precipitation forecasts. Changing spatial dependence across varying intensities is modeled as a mixture of spatial Student-t processes that can accommodate both strong and weak tail dependence. The proposed model demonstrates improved performance at capturing the distribution of extreme precipitation over Community Atmosphere Model (CAM) 5.2 forecasts. Supplementary materials accompanying this paper appear online.

Retreat of the Antarctic ice sheet (AIS) is likely to be a major contributor to future sea-level rise (SLR). Current projections of SLR due to ice-sheet mass loss remain highly uncertain. Better understanding of how ice sheets respond to future climate forcing and variability is essential for assessing long-term risk of SLR. However, predictability of future climate is limited by uncertainties from emission scenarios, model structural differences, and internal climate variability (ICV) that is inherently generated within the fully coupled climate system. Among those uncertainties, the impact of ICV on the AIS changes has not been explicitly assessed. Here we quantify the effects of ICV on the AIS evolutions by employing climate fields from two large-ensemble experiments using the Community Earth System Model to force a three-dimensional ice-sheet model. We find that ICV of climate fields among ensemble members leads to significantly different AIS responses, and that most of the effect is due to atmospheric variability compared to oceanic. Our results show that ICV can cause about 0.08 m differences of AIS contribution to SLR by 2100 compared to the ensemble-mean AIS contribution of 0.38–0.45 m. Moreover, using ensemble-mean climate forcing fields as the forcing in an ice-sheet model significantly delays retreat of the West AIS for up to 20 years and significantly underestimates the AIS contribution to SLR by 0.07–0.11 m in 2100 and up to 0.34 m in the 2250’s. This study highlights the need to take internal climate variability into account in assessing uncertainty associated with the AIS contribution in sea-level rise projections.

Integrated assessment models (IAMs) are valuable tools that consider the interactions between socioeconomic systems and the climate system. Decision-makers and policy analysts employ IAMs to calculate the marginalized monetary cost of climate damages resulting from an incremental emission of a greenhouse gas. Used within the context of regulating anthropogenic methane emissions, this metric is called the social cost of methane (SC-CH$_4$). Because several key IAMs used for social cost estimation contain a simplified model structure that prevents the endogenous modeling of non-CO$_2$ greenhouse gases, very few estimates of the SC-CH$_4$ exist. For this reason, IAMs should be updated to better represent methane cycle dynamics that are consistent with comprehensive Earth System Models. We include feedbacks of climate change on the methane cycle to estimate the SC-CH$_4$. Our expected value for the SC-CH$_4$ is \$1163/t-CH$_4$ under a constant 3.0% discount rate. This represents a 44% increase relative to a mean estimate without feedbacks on the methane cycle.

Accurate representation of crop responses to climate is critically important to understand impacts of climate change and variability in food systems. We use Random Forest (RF), a diagnostic machine learning tool, to explore the dependence of yield on climate and technology for maize, sorghum and soybean in the US plains. We analyze the period from 1980 to 2016 and use a panel of county yields and climate variables for the crop-specific developmental phases: establishment, critical window (yield potential definition) and grain filling. The RF models accounted for between 71% to 86% of the yield variance. Technology, evaluated through the time variable, accounted for approximately 20% of the yield variance and indicates that yields have steadily increased. Responses to climate confirm prior findings revealing threshold-like responses to high temperature (yield decrease sharply when maximum temperature exceed 29 degrees C and 30 degrees C for maize and soybean), and reveal a higher temperature tolerance for sorghum, whose yield decreases gradually as maximum temperature exceeds 32.5 degrees C. We found that sorghum and soybean responded positively to increases in cool minimum temperatures. Maize yield exhibited a unique and negative response to low atmospheric humidity during the critical phase that encompasses flowering, as well as a strong sensitivity to extreme temperature exposure. Using maize as a benchmark, we estimate that if warming continues unabated through the first half of the 21st century, the best climatic conditions for rainfed maize and soybean production may shift from Iowa and Illinois to Minnesota and the Dakotas with possible modulation by soil productivity.

The North Atlantic Oscillation (NAO) and eddy-driven jet contain a forced component arising from sea surface temperature (SST) variations. Due to large amounts of internal variability, it is not trivial to determine where and to what extent SSTs force the NAO and jet. A linear statistical–dynamic method is employed with a large climate ensemble to compute the sensitivities of the winter and summer NAO and jet speed and latitude to the SSTs. Key regions of sensitivity are identified in the Indian and Pacific basins, and the North Atlantic tripole. Using the sensitivity maps and a long observational SST dataset, skillful reconstructions of the NAO and jet time series are made. The ability to skillfully forecast both the winter and summer NAO using only SST anomalies is also demonstrated. The linear approach used here allows precise attribution of model forecast signals to SSTs in particular regions. Skill comes from the Atlantic and Pacific basins on short lead times, while the Indian Ocean SSTs may contribute to the longer-term NAO trend. However, despite the region of high sensitivity in the Indian Ocean, SSTs here do not provide significant skill on interannual time scales, which highlights the limitations of the imposed SST approach. Given the impact of the NAO and jet on Northern Hemisphere weather and climate, these results provide useful information that could be used for improved attribution and forecasting.