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The origins of uncertainty in climate projections have major consequences for the scientific and policy decisions made in response to climate change. Internal climate variability, for example, is an inherent uncertainty in the climate system that is undersampled by the multimodel ensembles used in most climate impacts research. Because of this, decision makers are left with the question of whether the range of climate projections across models is due to structural model choices, thus requiring more scientific investment to constrain, or instead is a set of equally plausible outcomes consistent with the same warming world. Similarly, many questions faced by scientists require a clear separation of model uncertainty and that arising from internal variability. With this as motivation and the renewed attention to large ensembles given planning for Phase 7 of the Coupled Model Intercomparison Project (CMIP7), we illustrate the scientific and policy value of the attribution and quantification of uncertainty from initial condition large ensembles, particularly when analyzed in conjunction with multimodel ensembles. We focus on how large ensembles can support regional‐scale robust adaptation decision‐making in ways multimodel ensembles alone cannot. We also acknowledge several recently identified problems associated with large ensembles, namely, that they are (1) resource intensive, (2) redundant, and (3) biased. Despite these challenges, we show, using examples from hydroclimate, how large ensembles provide unique information for the scientific and policy communities and can be analyzed appropriately for regional‐scale climate impacts research to help inform risk management in a warming world. Plain Language Summary Estimating uncertainties in projections of climate change poses challenges but is crucial to focusing scientific and policy efforts. Initial condition large ensembles (the same model run many times with the same set of assumptions) has revealed that irreducible uncertainty arising from natural variations in the climate system—called internal variability—can be larger and more persistent than expected when compared to the set of models typically used in climate impacts assessments. Because of this, some argue that the large magnitude of internal variability presents a challenge to effective adaptations in response to climate change. Here we show using examples from water management that characterizing internal variability, even if it is large and irreducible, is the means to more effective decision‐making, pointing to the importance of initial condition large ensembles in this effort. We also discuss the criticisms of large ensembles: that they are costly, redundant, and biased. We show that despite these challenges, large ensembles provide unique information that is consistent with the insights from decision science about how to position effective decisions under conditions of deep uncertainty. Key Points Initial condition large ensembles of climate simulations have their scientific challenges, being expensive, possibly redundant, and biased Despite such challenges, large ensembles provide unique information to the scientific and policy communities Large ensembles are a valuable tool for robust decision‐making, which is a strategy for making difficult decisions under deep uncertainty

The authors discuss connections between the problems of two-stage stochastic programming, robust decision-making, robust statistical estimation, and machine learning. In the conditions of uncertainty, possible extreme events and outliers, these problems require quantile-based criteria, constraints, and “goodness-of-fit” indicators. The two-stage stochastic optimization (STO) problems with quantile-based criteria can be effectively solved with the iterative stochastic quasigradient (SQG) solution algorithms. The SQG methods provide a new type of machine learning algorithms that can be effectively used for general-type nonsmooth, possibly discontinuous, and nonconvex problems, including quantile regression and neural network training. In general problems of decision-making, feasible solutions and concepts of optimality and robustness are characterized from the context of decision-making situations. Robust machine learning (ML) approaches can be integrated with disciplinary or interdisciplinary decision-making models, e.g., land use, agricultural, energy, etc., for robust decision-making in the conditions of uncertainty, increasing systemic interdependencies, and “unknown risks.”

Climate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate‐related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling. Plain Language Summary Over the past few years, large power outage events in California and Texas have underscored the vulnerability of our power systems to extreme weather. By increasing the intensity and frequency of extreme weather, climate change could lead to more power outages. In response, power system planners are grappling with how to plan for extreme weather and climate change when making investment decisions, such as in wind and solar power. In our research, we build and apply a new analytical framework for making power system investment decisions under climate change. Our framework draws on a hundred realizations of future climate, and integrates weather in those realizations with power system models that make investment decisions and explore the risk of power outages. We find five alternative investment pathways all could suffer from moderate to significant power outages under possible climate realizations by 2040. But by identifying what realizations drive outage risk in these pathways, we construct a new pathway that does not exhibit outage risks to our future climate realizations. Overall, these insights demonstrate the value of our new analytical framework for making better investment decisions under uncertainty posed by climate change. Key Points We identify a decarbonization pathway for the power system that is robust to future climate realizations Our framework is extensible to long‐term planning by utilities, regions, and regulators Large climate ensembles expose significant resource adequacy vulnerabilities in alternative decarbonization pathways

Robust decision-making is being increasingly used to support environmental resources decisions and policy analysis under changing climate and society. In this context, a robust decision is a decision that is as much as possible insensitive to a large degree of uncertainty and ensures certain performance across multiple plausible futures. Yet, the concept of robustness is neither unique nor static. Multiple robustness metrics, such as maximin, optimism-pessimism, max regret, have been proposed in the literature, reflecting diverse optimistic/pessimistic attitudes by the decision maker. Further, these attitudes can evolve in time as a response to sequences of favorable (or adverse) events, inducing possible dynamic changes in the robustness metrics. In this paper, we explore the impact of alternative definitions of robustness and their evolution in time for a case of water resources system management under changing climate. We study the decisions of the Lake Como operator, who is called to regulate the lake by balancing irrigation supply and flood control, under an ensemble of climate change scenarios. Results show a considerable variability in the system performance across multiple robustness metrics. In fact, the mis-definition of the actual decision maker’s attitude biases the simulation of its future decisions and produces a general underestimation of the system performance. The analysis of the dynamic evolution of the decision maker’s preferences further confirms the potentially strong impact of changing robustness definition on the decision-making outcomes. Climate change impact assessment studies should therefore include the definition of robustness among the uncertain parameters of the problem in order to analyze future human decisions under uncertainty.

Robustness is a key criterion for evaluating alternative decisions under conditions of deep uncertainty. However, no systematic, general approach exists for finding robust strategies using the broad range of models and data often available to decision makers. This study demonstrates robust decision making (RDM), an analytic method that helps design robust strategies through an iterative process that first suggests candidate robust strategies, identifies clusters of future states of the world to which they are vulnerable, and then evaluates the trade-offs in hedging against these vulnerabilities. This approach can help decision makers design robust strategies while also systematically generating clusters of key futures interpretable as narrative scenarios. Our study demonstrates the approach by identifying robust, adaptive, near-term pollution-control strategies to help ensure economic growth and environmental quality throughout the 21st century.

When less water is available, hydropower turbines are less efficient, or have to stop altogether. This reality is often neglected in recent work on the planning and operations of hydropower systems, despite widespread expected increases in drought intensity, frequency and duration. This paper is the first to integrate variable‐efficiency turbines into a hydropower plant design framework that accounts for design optimization as well as deep uncertainty in climatic and socio‐economic variables. Specifically, this framework focuses on leveraging multi‐objective robust decision making for the financially robust design of run‐of‐river hydropower plants, whose output is highly sensitive to flow variability. Application to five plants in Türkiye challenges two key design assumptions, use of net present value as a design objective and use of identical turbines. Instead, maximizing the benefit‐cost ratio yields plants with better financial viability over a range of plausible futures. They tend to have smaller capacity, and feature a small turbine that is well‐adapted to low‐flow periods. Another key insight is that socio‐economic uncertainties have as much or even more impact on robustness than climate conditions. In fact, these uncertainties have the potential to make many small hydropower projects too risky to build. Our findings are of considerable practical relevance at a time where 140 GW of unexploited small hydropower potential could help power the energy transition. They also highlight the need for similar research in reservoir‐based plants, considering over 3,000 such plants planned or in construction worldwide. Key Points Traditional approaches to hydropower planning need to be revisited to account for the impact of a variable climate on turbine efficiency Maximizing the benefit cost ratio rather than the net present value promotes smaller designs, better adapted to a drought‐prone world Socio‐economic and climatic factors are both crucial to design financial robustness

Despite widespread efforts to implement climate services, there is almost no literature that systematically analyzes users' needs. This paper addresses this gap by applying a decision analysis perspective to identify what kind of mean sea level rise (SLR) information is needed for local coastal adaptation decisions. We first characterize these decisions, then identify suitable decision analysis approaches and the sea level information required, and finally discuss if and how these information needs can be met given the state of the art of sea level science. We find that four types of information are needed: (i) probabilistic predictions for short‐term decisions when users are uncertainty tolerant; (ii) high‐end and low‐end SLR scenarios chosen for different levels of uncertainty tolerance; (iii) upper bounds of SLR for users with a low uncertainty tolerance; and (iv) learning scenarios derived from estimating what knowledge will plausibly emerge about SLR over time. Probabilistic predictions can only be attained for the near term (i.e., 2030–2050) before SLR significantly diverges between low and high emission scenarios, for locations for which modes of climate variability are well understood and the vertical land movement contribution to local sea levels is small. Meaningful SLR upper bounds cannot be defined unambiguously from a physical perspective. Low‐ to high‐end scenarios for different levels of uncertainty tolerance and learning scenarios can be produced, but this involves both expert and user judgments. The decision analysis procedure elaborated here can be applied to other types of climate information that are required for mitigation and adaptation purposes. Plain Language Summary Information on future sea‐level rise (SLR) is needed for diverse coastal adaptation decisions such as deciding on how much sand to apply for counteracting beach erosion, designing the height and strength of coastal protection infrastructure, and planing future developments in the coastal zone. Different kinds of decisions thereby require different kinds of SLR information and not all kinds of information required can be delivered by the state‐of‐the‐art of sea‐level rise science. This paper addresses this problem from the points of view of both decision science and sea‐level rise science. We find that three kinds of SLR information can be produced to inform coastal decision making. First, probabilistic predictions of mean SLR can be produced for short term decisions (i.e., 2030‐2050) and some locations. Second, high‐end sea‐level rise scenarios chosen for different levels of uncertainty tolerance of decision makers can be developed by SLR experts assigning confidence levels to available SLR studies. Third, learning scenarios estimating what will be known about SLR at given points in the future can further improve decision making. The procedure elaborated in this paper can be applied to other types of climate information such as temperature or precipitation. Key Points Different kinds of contexts require different kinds of sea level rise information to support coastal adaptation decision making Uncertainty intolerant users require high‐end and low‐end sea level rise scenarios produced for different levels of uncertainty intolerance Long‐term decisions can be improved through learning scenarios estimating what will be learned about sea level rise in the future

The network of smart cities is a typical complex network with big data in the theoretical level and practical level. How can we analysis the smart cities better under the complexity and uncertainty. It is a question. We try to give a new framework to this question, providing a chance to make the smart city smarter.

Allocation of water in the situation of climate change presents various uncertainties. Consequently, decisions must be made to ensure stability and functionality across different climatic scenarios. This study aims to examine the effectiveness of adaptation strategies in the agricultural sector, including a 5% increase in irrigation efficiency (S1) and a shift in irrigation method to Dry-DSR (direct seeded rice) under conditions of climatic uncertainty using a decision-making approach. The study focuses on the basin downstream of the Sefidroud dam, encompassing the Sefidroud irrigation and drainage network. Initially, basin modeling was conducted using the WEAP integrated management software for the period 2006–2020. Subsequently, the impact of climate change was assessed, considering RCP2.6, RCP4.5, and RCP8.5 emission scenarios on surface water resources from 2021 to 2050. Runoff and cultivated area, both subject to uncertainty, were identified as key parameters. To evaluate strategy performance under different uncertainties and determine the efficacy of each strategy, regret and satisfaction approaches were employed. Results indicate a projected decrease in future rainfall by 3.5–11.8% compared to the base period, accompanied by an increase in maximum and minimum temperatures (0.83–1.62 °C and 1.15–1.33 °C, respectively). Inflow to the Sefidroud dam is expected to decrease by 13–28%. Presently, the Sefidroud irrigation and drainage network faces an annual deficit of 505.4 MCM, and if current trends persist with the impact of climate change, this shortfall may increase to 932.7 MCM annually. Furthermore, satisfaction indices for strategy (S2) are 0.77 in an optimistic scenario and 0.70 in strategy (S1). In a pessimistic scenario, these indices are 0.67 and 0.56, respectively. Notably, changing the irrigation method with Dry-DSR is recommended as a robust strategy, demonstrating the ability to maintain basin stability under a broad range of uncertainties and climate change scenarios. It is crucial to note that the results solely highlight the effects of climate change on water sources entering the Sefidroud dam. Considering anthropogenic activities upstream of the Sefidroud basin, water resource shortages are expected to increase. Therefore, reallocating water resources and implementing practical and appropriate measures in this area are imperative.

This paper presents a new approach to the spatiotemporal design of groundwater quality monitoring networks for coastal aquifers. A fusion model combines the outputs of several developed simulation models to make estimates more accurate. A modified GALDIT method is used to incorporate the aquifer vulnerability to saltwater intrusion. The value of information (VOI) theory is applied to determine sufficient monitoring wells. The groundwater quality monitoring network is designed by employing a robust decision-making (RDM) approach under different management strategies and economic considerations. This approach incorporates the deep uncertainties of some critical variables, including water level and total dissolved solids (TDS) concentration at the coastline and pumping flow rates of agricultural wells. The new methodology is implemented in the coastal Qom-Kahak aquifer, Iran. The results illustrate that the combination model has significantly improved evaluation criteria compared to individual prediction models. The fusion model results indicate that thirty monitoring wells would be ideal. The RDM-based analyses in the Qom-Kahak aquifer showed that an optimal network with 30 monitoring wells outperforms the current network regarding various criteria, such as VOI and variance of estimation error. The new well configuration also demonstrates a suitable spatial distribution. Given that the current sampling frequencies are unsuitable for areas with varying vulnerabilities, we recommend sampling every 3 months in areas with moderate vulnerabilities and once every three seasons in areas with low vulnerabilities, based on the information transfer index. Finally, a management strategy in which the pumping rate should be less than 60% of the current rate is suggested to prevent saltwater intrusion into the aquifer.