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3 Key Characteristics Needed for Next Generation Earth Systems Science at NSF
Pages 47-80

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From page 47...
... Through multiple meetings, responses to questionnaires, several workshops on selected topics, and presentations from a number of people with various experiences organizing and conducting interdisciplinary and convergent research (see Appendix A) , the committee developed a set of key characteristics for a next generation Earth Systems Science program at NSF.
From page 48...
... This effort is challenged by the immense range of space, time, and social organization scales at play and the continuum of nonlinear interactions of processes active between and across each of them. Accurate BOX 3.1 Curiosity-Driven Research Across Time and Space: Using the Geologic Record to Probe Climate Sensitivity in a Warming World The geologic record is a rich and diverse archive of the Earth's response to and recovery from global warming events.
From page 49...
... NOTES: Scientific ocean drilling reveals the range, rates, and conditions of past climate changes. Paleoclimate data established that it has been at least 3 million years since atmospheric CO2 exceeded 410 ppm.
From page 50...
... . The improving skill of ecological forecasting is built on testing predictions with new data, timely availability of data, improving data interoperability, quantifying uncertainty, building the cyberinfrastructure necessary for automated workflows, iteratively improving system representations of the models, and ultimately operationalizing forecasts for decision support (Dietze et al., 2018)
From page 51...
... Understanding the interactions of these components with natural system components implies a systems thinking perspective and approach that relies on convergent approaches. A primary feature of convergence research is that it addresses complex problems by fusing multiple disciplinary approaches and methodologies (e.g., Wickson et al., 2006)
From page 52...
... The field also focuses on advancing co-production with partners; environmental data science education and training; and justice, diversity, equity, and inclusion within and beyond the ecological forecasting community. The community aims to increase the number and transparency of short-term forecasts, improve forecast skill through iterative learning (Dietze et al., 2018; see Figure 3.2)
From page 53...
... . Analysis and partitioning of forecast uncertainties facilitates targeted data collection (dotted line)
From page 54...
... CONVERGE promotes ethical, rigorous, and coordinated extreme events research; provides leadership and workforce development; is community-driven; and connects researchers across disciplines and research teams. Science and technology provide situation-specific decision support information for disaster mitigation, response, and recovery (Science for Disaster Reduction Interagency Coordinating Group, 2021)
From page 55...
... . Black, Hispanic, and Native American students and 1 Forthe purposes of this report, the committee presents the following definitions: • Diversity is defined as the broad spectrum of experiences, cultures, and physical at tributes within a community including, but not limited to, race or ancestry, national origin, religion, age, ability, gender, gender identity or expression, sexual orientation, socioeconomic status, and perspective (NRC Governing Board, 2021)
From page 56...
... population, they received only 2.8 percent of the nation's total environmental science degrees in 2016, according to DATAUSA,2 making environmental science also among the least diverse fields of scientific study.3 Gender and racial or ethnic identity are not the only significant axis of consideration. For example, according to a report from the National Center for Science and Engineering Statistics, approximately 10 percent of scientists and engineers who were employed in 2017 had one or more disabilities, including mobility, hearing, vision, and cognitive challenges.4,5 This is almost 10 percent less than the 19.5 percent of undergraduates who (in 2016)
From page 57...
... 3.4 CHARACTERISTIC 4: Prioritize engagement and partnerships with diverse stakeholders to benefit society and address Earth systems−related problems at community, state, national, and international scales. Many of the curiosity-driven, use-inspired, and convergent research approaches described above extend beyond the research domain and call for engagement with stakeholders to advance the fundamental understanding of the Earth's systems or to use those advances to support policy and decision-making.
From page 58...
... Meaningful stakeholder interactions can be established at all stages of the research process, such as collaborating to co-produce knowledge, co-develop approaches to analyze data, and evaluate the implications of findings (Lemos and Morehouse, 2005; Meadow et al., 2015; Lemos et al., 2018; Arnott et al., 2020; Mach et al., 2020; Gewin, 2021)
From page 59...
... © American Meteorological Society. Used with permission.
From page 60...
... Computation provides the framework for putting together the complex pieces of Earth Systems Science, supporting data collection and analysis, generation of forecasts for scenarios of natural and anthropogenic forcing, and interpretation of model results. The most promising path forward in many Earth systems applications involves combining theory, observations (data)
From page 61...
... . Polar predictions, such as sea ice prediction and verification, require observations and coupled models of the ocean, atmosphere, sea ice, snow, and land.
From page 62...
... . Figure 3.5 illustrates the complex array of social science data types that may need to be managed when integrating social and hydrologic science in order to understand water systems.
From page 63...
... SOURCE: Ruti et al., 2020. © American Meteorological Society.
From page 64...
... In addition, engineering advances in robotic systems, sensors, and communication technologies are enabling widespread observations and creating new data streams from cheap sensor technology and real-time communication.
From page 65...
... , these methods can be extremely effective, particularly in extracting and recognizing complex patterns from data. However, their utility can be limited when presented with input data not well represented by their training data, much as conventional extrapolation is less reliable than interpolation.11 For climate research, no data on future climate exist to train algorithms, and pure data-driven machine learning methods are not well suited to the vast degrees of freedom that characterize the Earth's climate.
From page 66...
... . The development of sophisticated as well as cheap sensor technology and real-time communication has enabled ubiquitous observations for research as well as for citizen science.
From page 67...
... Delivery and accessibility of data and model results to users as well as interaction with users involves a cultural change, wherein social organization and disciplinary cultural issues are tackled together with the technological issues. Currently Earth systems data and model output are scattered in thousands of data repositories, with management, data formatting, and sharing protocols specific to subdisciplines.
From page 68...
... . While maintaining strong disciplinary knowledge and skills, the next generation Earth Systems Science workforce will develop a shared set of transdisciplinary skills and practices, and will promote a work culture that values and encourages diverse perspectives and contributions.
From page 69...
... DBER marries social science theories, models, and methods with science, technology, engineering, and mathematics (STEM) disciplinary knowledge to address questions important to disciplinary education and training.
From page 70...
... The development of these skills, FIGURE 3.8  Workforce career development model using a braided river analogy that incorporates varied entry pathways and points into the geoscience workforce. NOTES: "A braided river is a wide, shallow system composed of numerous interwoven and changeable channels separated by small islands.
From page 71...
... These pathways are illustrated in Figure 3.8. The following chapter discusses strategies for implementing these key characteristics into next generation Earth Systems Science at NSF.
From page 72...
... 2021. Advancing lake and reservoir water quality management with near-term, iterative ecological forecasting.
From page 73...
... 2017. Ecological Forecasting.
From page 74...
... Regional Environmental Change 19:1217–1223. https://doi.org/10.1007/ s10113-019-01478-8.
From page 75...
... Large Ensemble Project: A commu nity resource for studying climate change in the presence of internal climate variability. Bulletin of the American Meteorological Society 96(8)
From page 76...
... 2015. Moving toward the deliberate coproduction of climate science knowledge.
From page 77...
... 2014. Climate science reconsidered.
From page 78...
... 2020. A community framework for geoscience education research: Summary and rec ommendations for future research priorities, Journal of Geoscience Education 69(1)
From page 79...
... 2017. Developing evaluation indicators to im prove the process of coproducing usable climate science.


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