"Environmental data science is an emerging research area, combining techniques from environmental science, computer science, and statistics, to make sense of the complex changes occurring in our natural environment. Alongside the ambiguities created from the collaboration of researchers with different disciplinary backgrounds, other types of uncertainty occur during scientific research – some inherent due to natural variability (aleatory), some due to limited knowledge (epistemic). Understanding the many sources of uncertainty, along the data to decision pipeline, will aid provision of robust scientific evidence to underpin decision-making. Grounded in data collected from interviews and focus groups, this work will discuss the uncertainties experienced by experts from environmental science, computer science, and statistics. A new typology of uncertainty for environmental data science will be presented. "

For wildlife stakeholders, conserving species in the face of climate change can be a complex endeavor. As conditions change on the ground, the landscapes that species use now may be different from the ones are available or they choose in the future. Exploratory analysis for habitat suitability in the future is essential to inform management and conservation strategies of wildlife species today. This study implemented the ecological niche modeling algorithm Maximum Entropy (Maxent) to examine present climatic niche and potential distribution of bighorn sheep (Ovis canadensis) populations in Mexico and climate change effects on its potential future habitat suitability and distribution. Maxent was built with 1,158 georeferenced occurrence records and uncertainty of climate change was addressed by exploring possible scenarios under four Representative Concentration Pathways (RCPs) (2.6, 4.5, 6.0 and 8.5) and four different General Circulation Models (GCM) in a time span of 20 years (2041-2060). The results showed that current protection strategies on public and private lands are inadequate for the conservation of target specie given projected climate impacts. Therefore, exploratory analysis may be most useful in supporting resilient management strategies by directing attention toward locations that may be suitable for wildlife management under future conditions. This analysis can be extended by considering multiple species, exploring strategies that employ an array of environmental policy options, and potential for learning that ease the reframing and refocusing of goals with which the wildlife management community and stakeholders may need to engage in the face of climate change.

"The evolving nature of climate risks represents new challenges for the world’s 500 million subsistence farmers.1,2 Farmers make livelihood decisions in deeply uncertain environments regarding weather conditions, economic markets, and the social-political systems in which they operate. In turn, uncertainty in how farmers make such decisions challenges the ability of policymakers at multiple governance scales to achieve key objectives such as maximizing economic growth, minimizing inequality, ensuring food security, and limiting rates of outmigration. Farming households and policymakers must therefore make climate adaptation decisions under exogenous sources of deep uncertainty, e.g. unknown probability distributions of climatic and economic states of the world, and endogenous deep uncertainty resulting from misalignment or misunderstanding of objectives among key stakeholders in a system. We create an integrated robust decision-making – agent-based model (RDM-ABM) framework to analyze how both types of deep uncertainty affect the robustness of potential climate adaptation policy interventions in the agricultural sector of Nepal. Nepal is a particularly important case study for this topic, owing to its high exposure to multiple climate risks,3–5 high dependence on subsistence agriculture and remittances from outmigration,6,7 and influx of international development financing.8 Previous work has typically applied robust decision making (RDM) and dynamic adaptive policy pathways (DAPP) frameworks to investigate “hard” infrastructure decisions under climate uncertainty.9–13 More recent work evaluates the robustness of farming strategies to deep climate and economic uncertainty,14,15 and assesses the prospects for reconciling resource tradeoffs between agriculture and other economic sectors under future climate scenarios.16,17 We contribute to this growing literature by analyzing (i) potential tradeoffs that emerge from differences between smallholder farmer objectives and those of policymakers at multiple scales regarding climate adaptation in Nepal's agricultural sector, (ii) the importance of different exogenous and endogenous sources of uncertainty in determining the likelihood of meeting these various objectives, and (iii) how different potential policy interventions - including investing in irrigation infrastructure, providing cash transfers, subsidizing weather-based crop insurance, and supporting migration remittances - compare in their robustness to these sources of uncertainty. Our work expands on a previous analysis of smallholder farmer livelihood decisions under climate risk,18 which used an ABM calibrated with survey data on Nepali farmers’ livelihood decisions and risk preferences. This model allows us to explore how farmers who are heterogeneous in their financial resources, risk preferences, and access to information make livelihood decisions under various climatic, economic, and policy states of the world. We also incorporate endogenous feedbacks between policymakers at multiple levels of government, international development agencies, and farming households. We apply this framework to investigate how real-world complexities of the policymaking process – including potentially competing objectives, misalignment between scales of policymaking, and lags in policy implementation – affects the robustness of proposed climate adaptation policies for subsistence agriculture in Nepal. References 1. Howden et al. (2007) 2. Lowder et al. (2016) 3. Karki & Gurung (2012) 4. MoALMC (2018) 5. Government of Nepal (2019) 6. Nepal Central Bureau of Statistics (2014) 7. Ghimire et al. (2020) 8. World Bank Group (2018) 9. Groves & Lempert (2007) 10. Keller et al. (2008) 11. Hallegatte & Lempert (2012) 12. Haasnoot et al. (2013) 13. Kwakkel et al. (2015) 14. González et al. (2020) 15. Jafino et al. (2021) 16. Knox et al. (2018) 17. Yoon et al. (2021) 18. Choquette-Levy et al. (2021)"

"Sea level rise is a growing threat to most coastal countries, but there is no systematic overview of how different countries plan for it and handle uncertainty in sea level rise. For example, some countries have national policies and sea level rise scenarios, while others leave coastal management to regional or even local planning. To develop an overview of how countries in Europe are preparing for sea level rise, an expert survey was conducted, covering topics such as time horizons and sea levels used in planning. Additionally, the study focused on how uncertainty in sea level rise is handled in the country and whether countries are considering high-end scenarios. The survey results indicate roughly half of countries in Europe use at least one approach for handling uncertainty in sea level rise planning guidance, including using multiple scenarios, planning cycles and adaptive plans. Respondents in many of these countries indicated that more than one approach is used. However, in almost half of countries respondents reported that there is no handling of uncertainty in SLR planning, and in almost every country. Results also suggest that uncertainty language and methods are not widely understood in the field of sea level rise planning and that approaches for uncertainty handling appear not to be formalized in many countries’ planning documents. Survey results on whether countries are preparing for or considering high-end sea level rise scenarios, indicate than less than half of countries are either using or exploring these scenarios in planning. Only a small minority of countries are considering sea levels above 1m or time horizons beyond 2100. Similar to planning under deep uncertainty, respondents from some countries appeared to conflate high-end sea level rise with RCP8.5, further suggesting that uncertainty may not be well understood in the field. This presentation will summarize the main findings of the research and share possible implications of disparate approaches to sea level rise planning and uncertainty handling."

Scenario Planning (SP) is designed to aid anticipation of surprises in the form of highly uncertain and extreme changes to an organisation’s external environment. Common forms of SP employ the concept of plausibility because probability is considered too constraining on the range of futures being considered. However, like probability, plausibility is founded on knowledge derived from past occurrences or presently available information. It may therefore also constrain the full potential extremity of the future. This is problematic because the extremity of a potential outcome can extend way beyond what was conceivable earlier in time based on information available at that point. We test a methodological adaptation to the common Intuitive Logics (IL) approach to SP. The proposed method is designed to allow extreme potential futures to come to mind by focusing participants’ attention on surprising extremes.

The changing climate conditions and extreme weather we are experiencing today expose our energy infrastructure's fragility. Deep uncertainties around climate change projections and future socioeconomic conditions increase the complexities of energy infrastructure planning. Electricity grid planners are confronted with competing objectives such as achieving cost-effectiveness, integrating low-carbon energy sources, maintaining reliability, and ensuring equitable access. The competing objectives layered with the high level of uncertainty pose a myriad of difficulties in making robust long-term planning decisions. In this exploratory study, we operationalize one main concept in the DMDU literature, robustness, to a simplified capacity expansion planning model, SWITCH 2.0 and illustrate challenges in climate-aware decision making. We simulate a simplified decision-making environment with multiple decision-makers with different priorities using a capacity expansion planning model. Using two different analysis approaches, regret-based and satisficing, we examine the dynamic nature of robustness, illuminate potential perplexities in implementing robustness to a climate-aware decision-making process, and demonstrate the applicability of the DMDU toolkit for practitioners. Finally, we offer recommendations for paving a pathway to better incorporate climate change in electricity grid planning.