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1. Two kinds of polar knowledge

   https://doi.org/10.1080/10899995.2020.1838849  

   Hamilton, Lawrence C. (November 2020, Journal of Geoscience Education)

   null (Ed.)

   Outreach and communication with the public have substantial value in polar research, in which studies often find changes of global importance that are happening far out of sight from the majority of people living at lower latitudes. Seeking evidence on the effectiveness of outreach programs, the U.S. National Science Foundation sponsored large-scale survey assessments before and after the International Polar Year in 2007/2008. Polar-knowledge questions have subsequently been tested and refined through other nationwide and regional surveys. More than a decade of such work has established that basic but fairly specific knowledge questions, with all answer choices sounding plausible but one being uniquely correct, can yield highly replicable results. Those results, however, paint a mixed picture of knowledge. Some factual questions seem to be interpreted by many respondents as if they had been asked for their personal beliefs about climate change, so their responses reflect sociopolitical identity rather than physical-world knowledge. Other factual questions, by design, do not link in obvious ways to climate-change beliefs—so responses have simpler interpretations in terms of knowledge gaps, and education needs. 

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2. Prospects and challenges of regional modeling frameworks to inform planning for food, energy, and water systems: Views of modelers and stakeholders

   https://doi.org/10.3389/fenvs.2023.1067559  

   Tuler, Seth P.; Webler, Thomas; Hansen, Robin; Vörösmarty, Charles J.; Melillo, Jerry M.; Wuebbles, Donald J. (March 2023, Frontiers in Environmental Science)

   Multisectoral models of regional bio-physical systems simulate policy responses to climate change and support climate mitigation and adaptation planning at multiple scales. Challenges facing these efforts include sometimes weak understandings of causal relationships, lack of integrated data streams, spatial and temporal incongruities with policy interests, and how to incorporate dynamics associated with human values, governance structures, and vulnerable populations. There are two general approaches to developing integrated models. The first involves stakeholder involvement in model design -- a participatory modeling approach. The second is to integrate existing models. This can be done in two ways: by integrating existing models or by a soft-linked confederation of existing models. A benefit of utilizing existing models is the leveraging of validated and familiar models that provide credibility. We report opportunities and challenges manifested in one effort to develop a regional food, energy, and water systems (FEWS) modeling framework using existing bio-physical models. The C-FEWS modeling framework (Climate-induced extremes on the linked food, energy, water system) is intended to identify and evaluate response options to extreme weather in the Midwest and Northeast United States thru the year 2100. We interviewed ten modelers associated with development of the C-FEWS framework and ten stakeholders from government agencies, planning agencies, and non-governmental organizations in New England. We inquired about their perspectives on the roles and challenges of regional FEWS modeling frameworks to inform planning and information needed to support planning in integrated food, energy, and water systems. We also analyzed discussions of meetings among modelers and among stakeholders and modelers. These sources reveal many agreements among modelers and stakeholders about the role of modeling frameworks, their benefits for policymakers, and the types of outputs they should produce. They also identify challenges to developing regional modeling frameworks that couple existing models and balancing model capabilities with stakeholder preferences for information. The results indicate the importance of modelers and stakeholders engaging in dialogue to craft modeling frameworks and scenarios that are credible and relevant for policymakers. We reflect on the implications for how FEWS modeling frameworks comprised of existing bio-physical models can be designed to better inform policy making at the regional scale. 

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3. Validity and Validation of Computer Simulations—A Methodological Inquiry with Application to Integrated Assessment Models

   https://doi.org/10.3390/knowledge3020018  

   Randall, Alan; Ogland-Hand, Jonathan (June 2023, Knowledge)

   Our purpose is to advance a reasoned perspective on the scientific validity of computer simulation, using an example—integrated assessment modeling of climate change and its projected impacts—that is itself of great and urgent interest to policy in the real world. The spirited and continuing debate on the scientific status of integrated assessment models (IAMs) of global climate change has been conducted mostly among climate change modelers and users seeking guidance for climate policy. However, it raises a number and variety of issues that have been addressed, with various degrees of success, in other literature. The literature on methodology of simulation was mostly skeptical at the outset but has become more nuanced, casting light on some key issues relating to the validity and evidentiary standing of climate change IAMs (CC-IAMs). We argue that the goal of validation is credence, i.e., confidence or justified belief in model projections, and that validation is a matter of degree: (perfect) validity is best viewed as aspirational and, other things equal, it makes sense to seek more rather than less validation. We offer several conclusions. The literature on computer simulation has become less skeptical and more inclined to recognize that simulations are capable of providing evidence, albeit a different kind of evidence than, say, observation and experiments. CC-IAMs model an enormously complex system of systems and must respond to several challenges that include building more transparent models and addressing deep uncertainty credibly. Drawing on the contributions of philosophers of science and introspective practitioners, we offer guidance for enhancing the credibility of CC-IAMs and computer simulation more generally. 

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4. Representation of indifference prices on a finite probability space

   https://doi.org/10.2140/involve.2025.18.495  

   Freitas, Jason; Huang, Joshua; Mostovyi, Oleksii (April 2025, Involve, a Journal of Mathematics)

   On a finite probability space, we consider the problem of indifference pricing of contingent claims, where the preferences of an economic agent are modeled by an Inada utility stochastic field — the interior of its effective domain being (a,∞) — for some a∈R∪{−∞}. This allows for including utilities on both R and R+. We consider arbitrary contingent claims and show that, for replicable ones, the indifference price equals the initial value of the replicating strategy and thus depends neither on the agent’s initial wealth, for which the indifference pricing problem is well-posed, nor the utility stochastic field. This, in particular, shows the consistency of the indifference and arbitrage-free pricing methodologies for complete models. For nonreplicable claims, we show that the indifference price is equal to the expectation of the discounted payoff under the dual-optimal measure, which is equivalent to the reference probability measure. In particular, we demonstrate that the indifference price is unique for every choice of a smooth Inada utility stochastic field and initial wealth in (a,∞). Our proofs rely on the change of numéraire technique and a reformulation of the indifference pricing problem. The advantages of the settings of this paper and the approach allow for bypassing the technicalities and issues related to choosing the notion of admissibility and for including a wide range of utilities, including stochastic ones. We augment the results with examples. 

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5. The Ongoing Need for High-Resolution Regional Climate Models: Process Understanding and Stakeholder Information

   https://doi.org/10.1175/BAMS-D-19-0113.1  

   Gutowski, W. J.; Ullrich, P. A.; Hall, A.; Leung, L. R.; O’Brien, T. A.; Patricola, C. M.; Arritt, R. W.; Bukovsky, M. S.; Calvin, K. V.; Feng, Z.; et al (May 2020, Bulletin of the American Meteorological Society)

   ABSTRACT Regional climate modeling addresses our need to understand and simulate climatic processes and phenomena unresolved in global models. This paper highlights examples of current approaches to and innovative uses of regional climate modeling that deepen understanding of the climate system. High-resolution models are generally more skillful in simulating extremes, such as heavy precipitation, strong winds, and severe storms. In addition, research has shown that fine-scale features such as mountains, coastlines, lakes, irrigation, land use, and urban heat islands can substantially influence a region’s climate and its response to changing forcings. Regional climate simulations explicitly simulating convection are now being performed, providing an opportunity to illuminate new physical behavior that previously was represented by parameterizations with large uncertainties. Regional and global models are both advancing toward higher resolution, as computational capacity increases. However, the resolution and ensemble size necessary to produce a sufficient statistical sample of these processes in global models has proven too costly for contemporary supercomputing systems. Regional climate models are thus indispensable tools that complement global models for understanding physical processes governing regional climate variability and change. The deeper understanding of regional climate processes also benefits stakeholders and policymakers who need physically robust, high-resolution climate information to guide societal responses to changing climate. Key scientific questions that will continue to require regional climate models, and opportunities are emerging for addressing those questions. 

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