3
Key Characteristics Needed for Next Generation Earth Systems Science at NSF
Integrated approaches are the foundation of next generation Earth Systems Science. The committee considered how advances in scientific knowledge and support structures have set the stage for NSF to undertake a truly comprehensive approach. 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.
3.1 CHARACTERISTIC 1: Advance both curiosity-driven and use-inspired basic research on the Earth’s systems across spatial, temporal, and social organization scales.
Earth Systems Science is a deeply interdisciplinary field in which scientific discoveries are made both through curiosity-driven research (driven by a quest for understanding) and use-inspired basic research (driven by the potential use to which the knowledge will be applied; Stokes, 1997). For example, one of the most urgent scientific concerns of our time is predicting how the Earth’s systems will respond to rising atmospheric greenhouse gas concentrations, understanding how rising greenhouse gas concentrations will affect coupled human-environmental systems (e.g., NASEM, 2021a), and informing decisions to mitigate climate
change and adapt to current and projected impacts. Curiosity-driven research is crucial to examine coupled perturbations of human activities and temperature rise, ice sheet stability, ocean circulation, atmospheric dynamics, sea level change, extreme precipitation and temperatures, and agricultural systems, as well as a range of environmental conditions such as land aridity, ocean acidification, and changes in biodiversity through time. 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
projections involve understanding of the relevant processes revealed through modern observations and hindsight on millions of years of climate-related linkages, processes, feedbacks, and thresholds (see Box 3.1).
Identifying both natural rhythms and divergent behavior in the Earth’s systems also entails an interdisciplinary approach of uncommon breadth and connectedness—one that values expansive expertise in physical, biological, and social sciences—and a suite of modern observing and modeling systems. Bringing together scholars from diverse fields to collaborate meaningfully has been and remains fraught with difficulty. The
start-up costs associated with building interdisciplinary research can be large because people from different fields must learn the different ways (habits of thought) that scholarship is viewed, a process that can take years (Strober, 2010). Challenges include the desire for a common vocabulary, the difficulty of sharing and building on knowledge across discipline lines, and minimizing the chance for conflicts when geographically dispersed members undertake interdependent tasks (NASEM, 2015). Lessons from the sports world suggests that resilient teams have four things in common: (1) they believe they can effectively complete tasks together, (2) they share a common mental model of teamwork, (3) they can improvise, and (4) they trust one another and feel safe (Kirkman et al., 2019; see also Defila et al., 2006; Stokols et al., 2008; Misra, 2011). Complementary studies from the business world show that diverse teams are better suited to complex problem solving (Page, 2019).
An example of use-inspired basic research is ecological forecasting, which aims to improve the ability to predict near-term ecological change at a scale and speed far beyond what is currently possible, and to provide actionable information on the future state of ecosystems and their services (see Box 3.2). Ecological forecasting is a key part of a broader goal of improving Earth systems predictability (OSTP, 2020). As outlined in a workshop on Earth system predictability research and development (NASEM, 2020a), improvements could be achieved through diverse stakeholder engagement, new computational and modeling methods and opportunities, and systems approaches. The diverse temporal and spatial scales over which ecological, physical, and social processes vary make forecasting challenging. The outstanding difficulties to be overcome include mismatches in the scales of different data sets, scarcity of some types of data and overload in others, sampling biases, process understanding, and model development and assessment (Bonan and Doney, 2018; Zipkin et al., 2021). Despite these and other limitations (e.g., Estes et al., 2018), much progress has been made. Daily to decadal projections of natural and managed ecosystems (e.g., energy, farming, ranching, forestry, fisheries, and reservoirs) now have the potential to improve resource management decisions at regional, state, and local levels as well as to enhance national security, economic vitality, and environmental quality (Dietze et al., 2018). 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).
3.2 CHARACTERISTIC 2: Facilitate convergence of social, natural, computational, and engineering sciences to advance science and inform solutions to Earth systems−related problems.
The production of scientific knowledge necessary to help address many of today’s critical problems involves embracing broad perspectives to conceptualize and understand complex Earth systems interactions. In particular, it is crucial that social systems, engineered systems, and technologies are recognized and accounted for as critical components of the Earth’s systems (see Figure 1.1). These disciplines take diverse approaches to formulating research questions and applying methods for investigation, requiring programs to facilitate conversation and collaboration among these disciplines’ convergent knowledge production. 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). Another important feature is a culture that welcomes a diversity of perspectives, including non-academic stakeholders who are seeking solutions to problems that will benefit society, in framing and conducting the research (NRC, 2014). The complex integration of disciplines, approaches, and perspectives is challenging given the diverse vocabularies, cultures, and power dynamics that exist throughout the realms of science and society. The complex mosaic of spatial and temporal units of analysis, epistemological norms, and vast methodological differences across disciplines involves new ways to integrate research endeavors, including providing ways for scientists, engineers, decision-makers, and other stakeholders to engage with one another.
Convergence concepts are being explored in a wide variety of Earth systems−related problems, including food security (e.g., Davila et al., 2018; Hubeau et al., 2018), mitigation and adaptation to climate change (e.g., de Jong et al., 2016; DeLorme et al., 2016), sustainable socio-ecological systems (e.g., Brandt et al., 2013; Clark and Harley, 2020; NASEM, 2021b), and resilience to natural hazards and disasters such as landslides, earthquakes, volcanoes, and tsunamis (e.g., Thompson et al., 2017). For example, research on the heterogeneous factors that influence the occurrence of disaster events and the complexity of interactions among natural, constructed, and human systems is involved in managing natural disasters (see Box 3.3).
3.3 CHARACTERISTIC 3: Ensure diverse, inclusive, equitable, and just approaches to Earth Systems Science.
The integration of a diversity of expertise and perspectives is key to all aspects of next generation Earth Systems Science, from identifying the research questions, to carrying out the research, to communicating the results to stakeholders (Hong and Page, 2004; Medin and Lee, 2012). Equally important is ensuring an inclusive healthy workplace culture. In particular, the following interrelated realities merit careful consideration when setting the research agenda:
- The people who are engaged in identifying the scientific priorities define the questions addressed.
- The people who make up the community of Earth systems scientists and engineers reflect and influence the values and norms of the community.
- The people who have access to the infrastructure and training central to carrying out the science influence the community composition.
- How this community welcomes (or not) diverse participation and insights, and conducts its research, as well as what the research foci are, helps determine scientific outcomes and societal impacts.
However, if the research agenda is developed and implemented without intentionally including an ethos of diversity, equity, inclusion, and justice, Earth Systems Science risks losing insights and opportunities and repeating past injustices. Therefore, such an ethos is a foundational component
of next generation Earth Systems Science.1 It invites new perspectives, fosters collaborations among multiple disciplines, and facilitates the development of new partnerships, including global perspectives and voices that have been excluded or not historically been part of the conversation.
Developing that ethos is hindered in part by the current demographics of the Earth Systems Science community, which is not representative of the broader U.S. society. The growing mandate for truly broadening participation in Earth Systems Science is apparent, for example, by looking at degree attainment in the geosciences (Earth, atmosphere, and ocean science), which are among the least diverse science disciplines (NSF, 2018). Although the fraction of women earning degrees in those disciplines has increased substantially, the proportion of marginalized racial groups earning PhDs has not changed in 40 years (Bernard and Cooperdock, 2018). The proportion of U.S. geoscience bachelor’s degree recipients from marginalized racial and ethnic groups has increased about threefold in the past 20 years, more than for graduate degrees. However, much of this progress currently is concentrated at a small number of universities (Beane et al., 2021).
Another example comes from a national survey of U.S. faculty in 17 environmental disciplines that revealed that only 11 percent are people from marginalized and excluded groups, with a majority of faculty reporting having either one or zero faculty of color in their department (Pearson and Schuldt, 2014). Black, Hispanic, and Native American students and
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1 For the purposes of this report, the committee presents the following definitions:
- Diversity is defined as the broad spectrum of experiences, cultures, and physical attributes 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).
- Equity is defined as allocating resources to ensure everyone has access to the same opportunities. Equity recognizes that individuals do not all start from the same place and must acknowledge and make adjustments to imbalances arising from bias or systemic structures. (Adapted from https://jedicollaborative.com and https://www.naceweb.org/about-us/equity-definition.)
- Inclusion is defined as a culture comprising a framework that allows an individual to effectively engage and thrive in a community. The framework includes social policies and processes that provide access to opportunities and information; capacity to influence accepted institutional norms and behaviors; security in the organization to fully express inherent skills and talent; and the ability to exercise one’s own informal or formal power (NRC Governing Board, 2021).
- Justice is defined as dismantling barriers to resources and opportunities so that all individuals and communities can benefit from and contribute to a system, organization, or group. (Adapted from https://jedicollaborative.com.)
- NSF-Division of Earth Sciences (EAR) uses the term “BAJEDI” meaning belonging, accessibility, justice, equity, diversity, and inclusion; see, for example, https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505906.
faculty are also largely underrepresented in environmental engineering programs in the United States (Montoya et al., 2020). Although African Americans comprise 13 percent of the U.S. 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) reported having a disability.6 Some differences exist within disciplines that make up Earth Systems Science: biological scientists self-report a 6 percent disability rate; Earth scientists, geologists, and oceanographers (together) report an 11 percent disability rate; and social scientists report a 16 percent disability rate.7
These data also fail to illuminate the experiences of individuals from marginalized and excluded groups who may hold more than one identity. In particular, it is important to recognize the mutually constructive and reciprocal relationships among race, ethnicity, sexuality, class, disability, and other social positions that influence one’s experiences, and therefore develop approaches to Earth Systems Science with an intersectional lens (Crenshaw, 1989, 1991, 2014; Collins, 2015).
The barriers to developing and engaging the full diversity of expertise and perspectives extend beyond academic institutions and their student or faculty populations. Fostering intentionally inclusive and equitable research collaborations and partnerships can also be a challenge. For example, in January 2021, 200 researchers wrote an open letter to leadership at NSF about its Navigating the New Arctic Program to “urge additional action to increase inclusivity by dismantling the barriers that
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2 See https://datausa.io/profile/cip/environmental-science#demographics.
3 See https://diverseeducation.com/article/166456.
4 The National Survey of College Graduates asks the degree of difficulty—none, slight, moderate, severe, or unable to do—an individual has in seeing (with glasses); hearing (with hearing aid); walking without assistance; lifting 10 pounds; or concentrating, remembering, or making decisions. Respondents who answered “moderate,” “severe,” or “unable to do” for any activity were classified as having a disability.
5 See https://ncses.nsf.gov/pubs/nsf19304/digest/employment.
6 See https://ncses.nsf.gov/pubs/nsf19304/digest/enrollment.
7 See Table 9-8 from NSF, 2017, National Center for Science and Engineering Statistics, National Survey of College Graduates, https://www.nsf.gov/statistics/2020/nsf20300.
prevent Indigenous participation within Arctic research.”8 Moreover, “[American Indian/Alaska Native] tribes are sovereign nations and determine their own governance structures, laws, and collaborations, including research and research priorities” (Lucero et al., 2020). Valuing lived experiences in Earth Systems Science approaches is critical not only to ensuring an ethos of diversity, equity, and inclusion but also to dismantling the barriers necessary to create a more just community (Behl et al., 2021). Achieving this diverse, equitable, inclusive, and just Earth Systems Science workforce would be a paradigm shift in science that would provide resources and create new opportunities for groundbreaking discoveries.
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. Stakeholders are broadly defined as the individuals, government agencies, private companies, and civil society groups and organizations that are involved in or affected by a research outcome, decision, or policy (Checkland, 1981; Newig et al., 2017; Sterling et al., 2017).
Interactions between scientists and stakeholders can occur at individual, community, national, and international scales and take many forms. Scientists and stakeholders can have different types of relationships (e.g., collegial, collaborative, consultative, and contractual) and structure those relationships differently, depending on the research objective (Meadow et al., 2015; see Table 3.1). In addition, stakeholders have different levels of urgency, legitimacy, and influence (Mitchell et al., 1997), which must be considered to ensure the engagement of key stakeholders, especially in policy and decision-making contexts (Mikalsen and Jentoft, 2001). It is easy to exclude stakeholders with little influence, but doing so may reduce the likelihood of beneficial outcomes (Reed et al., 2009). Stakeholder analysis methods (e.g., Reed et al., 2009) provide a means to identify stakeholders, differentiate or categorize stakeholder groups, and understand relationships between stakeholders. Effective engagement with stakeholders and co-production of knowledge are complex and dependent on intention, time, and labor allocations among other institutional and cultural
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8 See http://kotz.org/wp-content/uploads/2021/02/NNA-scientists-letter-to-NSF-January2021.pdf.
factors. Necessary skills include listening, respect and appreciation for stakeholder knowledge and experience, historical and cultural literacy, and mastery of methods and strategies for facilitation and team coordination. Working with stakeholders is complex, and effective co-production depends upon reducing barriers to diverse participation. Further, co-production should follow inclusive compensation and engagement practices, especially when this work is not part of stakeholders’ professional duties (e.g., salary or honorarium, travel and participation expenses, child care, scheduling). In the case of government agencies and other institutional partners, entity-specific constraints, guidelines, and other requirements should be considered.
Partnerships specify formal roles for stakeholders and differ somewhat from the types of engagement mentioned above. Examples include (1) research partnerships among organizations to speed discovery by expanding the pool of assets (e.g., knowledge, computation, observations, funding) and effort available for a problem; (2) partnerships between research and mission agencies to put Earth Systems Science into practice for the benefit of society; and (3) partnerships with local communities to study and address local problems. NSF has extensive experience with the first form: partnering with other U.S. federal agencies, industry, private foundations, nongovernmental organizations, and international organizations on research, education, logistics, and infrastructure (see NSF, 2020). Most of NSF’s efforts to support decision-making, research to operations or research to service, and community partnerships occur through grants that encourage or require collaboration. An example is Smart and Connected Communities,9 which requires researchers to engage meaningfully with either communities (i.e., geographically defined entities with a governance structure such as towns, cities, and incorporated rural areas), community partners (i.e., government departments, schools, libraries, health and social service providers, nonprofits, cultural organizations, and businesses), or both to identify and define challenges they are facing. Involving stakeholders in developing relevant scientific questions and how to address them is essential for supporting policy and decision-making and ultimately putting science into practice.
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). Relevant inputs may include qualitative and quantitative scientific data, as well as local and indigenous knowledge, observations, responses, and values (e.g.,
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9 See https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=505364.
TABLE 3.1 Generalized Types of Relationships Between Scientists and Stakeholders, Which Vary Across Contexts and Depend on Research Objectives
| Mode | Objective | Origin of Research Question | Type of Relationship | Stakeholder Involvement | Stakeholder Representation |
|---|---|---|---|---|---|
| Contractual | Test applicability of new technology or knowledge | Researchers | Unidirectional flow of information from researchers to stakeholders | Primarily as passive recipient of new knowledge or technology | Views and opinions of stakeholders are not emphasized |
| Consultative | Use research to solve real-world problems | Stakeholders or researchers | Researchers consult with stakeholders, diagnose the problem, and try to find a solution | At specific stages of research such as problem definition, research design, diffusion of findings | Stakeholder views primarily filtered through third party (e.g., social scientists) |
| Collaborative | Learn from stakeholders to guide applied research | Stakeholders | Stakeholders and researchers are partners | Continuous with emphasis on specific activities, depending on joint diagnosis of the problem | Stakeholders themselves, local representatives, trained research team members |
| Collegial | Understand and strengthen local research and development capacity | Stakeholders | Researchers actively encourage local research and development capacity | Variable, but ongoing | Stakeholders themselves |
SOURCE: Meadow et al., 2015. © American Meteorological Society. Used with permission.
Matson et al., 2021). The co-production process can produce novel, actionable science (Lubchenco et al., 2015; Clark et al., 2016); build networks; develop capacity (Norström et al., 2020); and improve the usability of data products (Kenney and Janetos, 2020; Gerst et al., 2021). It is critical to be intentional about the goals for the processes for stakeholder engagement and co-production (Meadow et al., 2015; Bremer and Meisch, 2017; Wyborn et al., 2019; Mach et al., 2020) and the evaluation of their effectiveness (Wall et al., 2017; Jagannathan et al., 2020; Norström et al., 2020).
In leading and participating in co-produced research, individuals must be cognizant that the research outcomes are indeed the reduction, rather than widening, of inequities (Rosentraub and Sharp, 1981; Turnhout et al., 2020). An example is partnerships between researchers and Arctic indigenous communities on the climate and environmental impacts of thawing permafrost (see Box 3.4).
3.5 CHARACTERISTIC 5: Use observational, computational, and modeling capabilities synergistically to accelerate discovery and convergence.
Convergence in Earth Systems Science relies on the integration of observational, computational, and modeling infrastructures. Observations and monitoring reveal changes in the Earth’s systems. Data from diverse sources are assimilated (using formal methods for combining observational data and models to improve estimates) into models that represent natural- and social-system processes and their interactions across the Earth’s systems. 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), and computational modeling within a rigorous, statistical framework (Schneider et al., 2021), such as that shown in Figure 3.4. This conceptual illustration highlights the importance of co-design among researchers and users to guide the generation of information as well as the delivery to users. Figure 3.4 also shows that partnership and co-design between researchers and users is key for delivering information products that are useful for decision-making. An example is the Polar Prediction Project (PPP) and the associated Year of Polar Prediction (2017−2019; YOPP), established by the World Weather Research Program, which promotes international cooperative research to enable improved weather and environmental predictions from hourly
>to seasonal timescales in polar regions. The goals of the PPP/YOPP are informed by engagement with users of weather, water, ice, and climate information in order to inform important areas of research, such as sea ice predictions that inform safe navigation (see Dawson et al., 2017). Polar predictions, such as sea ice prediction and verification, require observations and coupled models of the ocean, atmosphere, sea ice, snow, and land. Currently, observations in the polar regions are sparse, and models and data assimilation methods need further development (Jung et al., 2016; Zampieri et al., 2018).
Observational, computational, and modeling capabilities have undergone dramatic and transformational advances in the past decades. Large volumes of data are available from satellites (including constellations of SmallSats and CubeSats), major observing facilities, field stations, and targeted data collection campaigns in natural and social science contexts (e.g., Figure 3.5). 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. The collection, compilation, and maintenance of Earth systems data by entities outside
of NSF (e.g., the National Oceanic and Atmospheric Administration, the U.S. Geological Survey, and others) are essential to enabling knowledge discovery and innovation in Earth Systems Science in research supported by NSF. Similarly, social science data on human systems managed by entities other than NSF (e.g., the Census Bureau, the American Community Survey, the Bureau of Labor and Statistics, the Centers for Disease Control and Prevention, and other U.S. and international organizations) are essential for next generation Earth Systems Science at NSF, and sustained support, advancement, and expansion of open and integrated social science data on human systems are foundational for success. 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. The
end of Moore’s law and Dennard (frequency) scaling10 is driving changes and innovation in processor architecture, in which the state-of-the-art efforts are transitioning from the traditional architecture based on central processing units to so-called accelerators such as graphics processing units, tensor processing units, and field-programmable gate arrays (Schneider et al., 2021). Models are the backbone “scientific instruments” for understanding, exploring, and projecting changes in the Earth’s systems. Like all sophisticated scientific instruments, they are developed continually by assimilating the expanding and diverse data streams, including and improving the representations of critical processes, running on the most advanced platforms, and increasing the sophistication of underlying computational algorithms (e.g., Rude et al., 2018).
The challenges of “big data” in both observations and model output have led to an explosion of artificial intelligence and machine learning applications in Earth Systems Science. Advances at the human/technology interfaces may provide new capabilities for consideration, and the technology is changing rapidly. Machine learning is a powerful method when using very large amounts of data to create general functional representations that may serve as surrogate models to describe (interpolate) the data, and, potentially, to predict (extrapolate) behavior (Reichstein et al., 2019) (e.g., of a turbulent fluid) or make decisions (e.g., redistricting). Explainable neural networks can be used to advance understanding of the complex Earth system. In many cases, machine learning can be perceived as a way to create a mapping from a high-dimensional space of data to a much smaller set of behaviors or actions. When suitable training data are available (either from observations or synthetic data from simulations), 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. Hybrid machine learning approaches that incorporate process-based insights may address these limitations (Balaji, 2021). The revolution
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10 Moore’s law (really an observation) is about feature size; Dennard scaling explains why smaller features permit faster operation, until other effects (specifically, leakage current) begin to dominate. Individual computing cores became faster because of Dennard scaling, driven by Moore’s law, until Dennard scaling ended in 2006–2007. Since then, most increases in performance are due to parallelism and/or specialization of function.
11 There may also be equity implications of systems or models that feature the development and maintenance of large data sets that capture—and may further reify—existing societal biases that disadvantage specific groups. See, for example, Crawford (2021).
in observational, computational, and modeling capabilities has, for example, enabled the development of the European Union’s Destination Earth, a “Digital Twin”12 of the Earth with an Earth system modeling framework to explore scenarios of climate mitigation and adaptation (Bauer et al., 2021), and the development of the World Climate Research Programme “Digital Earths”13 aimed at creating digital and dynamic representations of climate in the Earth system that can be openly explored and accessed in new and innovative ways. Simulations of cloud cover from the Digital Twin at 1 km resolution are indistinguishable from satellite observations (see Figure 3.6). 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. For example, the large number of affordable air quality sensors (e.g., Purple Air) widely deployed in California has enabled citizens to adapt their activities during fires and has revealed the ever-changing micrometeorology that alters aerosol loadings in neighborhoods, especially in the vicinity of complex
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12 A Digital Twin of the Earth is an information system that exposes users to a digital replication of the state and temporal evolution of the Earth system constrained by available observations and the laws of physics (Bauer et al., 2021).
13 See https://www.wcrp-climate.org/lighthouse-activities/2107-science-plans/2107-SciencePlan-LHA-DE.pdf.
topography.14 Large ensembles of model projections, such as those carried out by the National Center for Atmospheric Research (NCAR) Large Ensemble Community Project, have been used to study the interactions among the inherently nonlinear processes in the Earth’s systems and have differentiated forced climate change response from internal climate variability (Kay et al., 2015; Deser, 2020; Deser et al., 2020). Numerical simulations of proposed climate intervention experiments using Earth system models indicate that solar radiation management not only affects surface temperatures, as intended, but also may alter precipitation patterns, the North Atlantic Oscillation, winter storm tracks (e.g., Jones et al., 2021; Kravitz et al., 2021), and the strength of the Atlantic Multi-decadal Overturning Circulation (e.g., Muthers et al., 2016).
Earth systems information at a resolution useful for planning and action entails not only the most up-to-date observations, cyberinfrastructure, data analytics, and representation of natural- and social-system processes and their interactions, but also cyberinfrastructure skills and expertise to enable interoperability and portability of data and codes across computing architectures and platforms. These skills, processes, and interactive capacities are not currently evenly distributed across the sciences relevant to understanding complex Earth systems (Cutcher-Gershenfeld et al., 2016), but examples exist of cutting-edge accomplishments in effective decision support systems (Street et al., 2018; Webber, 2019; Mach et al., 2020).
More importantly, convergence in Earth Systems Science demands a common culture, a sense of shared community with well-defined metrics for success and decision-making, recognized rewards, and an understanding that science skills and infrastructure skills are not the same, particularly in cyberinfrastructure. 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 sub-disciplines. The data are difficult to discover and utilize except by the disciplinary community, and the lack of standardized interfaces and training further inhibits integration across multiple sources and disciplines. Some mechanisms for addressing these problems are discussed in Chapter 4.
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14 See https://www.cnet.com/home/smart-home/california-fires-boost-interest-in-purpleairair-pollution-sensors.
3.6 CHARACTERISTIC 6: Educate and support a workforce with the skills and knowledge to effectively identify, conduct, and communicate Earth Systems Science.
The Earth Systems Science workforce builds on the intellectual capital of natural, social, computational, and data scientists; system and disciplinary engineers; and discipline-based educational researchers and professional development experts. A thriving next generation Earth Systems Science workforce can be composed of five major aspects (see Figure 3.7). 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.
Developing this robust workforce begins with targeted discipline-based education research (DBER) and scholarship on teaching and learning at the K–12, undergraduate, and graduate levels, and continues with training programs for professionals. In keeping with Task 5 (see Box 1.2), the following discussion focuses on higher education and on training for current scientists and engineers that can contribute aspects of the vision laid out in Figure 3.7.
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. At the undergraduate level, such research has informed teaching and learning (Viskupic et al., 2019), shed light on scientific thinking and practices (e.g., Kastens and Manduca, 2017), and sought to understand the barriers to equity, inclusion, and diversity (NASEM, 2016). In addition, recognition that experiences and challenges in education and training are often common across disciplines has resulted in cross-DBER research collaborative opportunities (e.g., Reinholz and Andrews, 2019) and research studies (e.g., Lombardi et al., 2021).
Coursework for undergraduate and graduate students is generally discipline based, reflecting the organization of academic institutions and programs. The disciplinary knowledge and skills that students gain in their education are an essential part of Earth Systems Science. For example, summer and course-based research experiences (e.g., NSF-supported Research Experiences for Undergraduates),15 which are typically discipline based, are one of the most effective avenues for preparing students for careers in the discipline (Corwin et al., 2015; NASEM, 2017).
However, helping undergraduate and graduate students identify and solve complex system-based problems, and thus become better prepared to enter the Earth Systems Science workforce, involves additional intentional approaches. Students benefit from curriculum and instruction that effectively engage them in interdisciplinary research and to build transdisciplinary skills in computational literacy, quantitative and qualitative competencies, spatial and temporal reasoning, team science and leadership skills, and the conduct of community- and stakeholder-engaged science (Huntington, 2000; Clark and Button, 2010; Huntington et al., 2019; St. John et al., 2020; Mosher and Keane, 2021). The latter two skills are especially important for convergence research. Finding effective ways to teach about the Earth in the context of societal problems, especially complex or ill-structured problems at the intersection between the natural and human systems, would be transformational (Aster et al., 2016; St. John
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15 See https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=5517.
et al., 2019). Students also benefit from considering ethical and cultural issues related to their research and learning (NASEM, 2018).
Although educating the future workforce is a key to next generation Earth Systems Science in the long term, the current workforce also has a duty to leverage skills and opportunities to engage in interdisciplinary and convergence research on the Earth’s systems now. Natural, social, and data scientists and engineers are encouraged to engage in systems science, work in teams, and communicate across disciplines and with a wide range of stakeholders. They also may work within broader Earth systems scientific and societal contexts. The development of these skills,
and the ability to integrate discipline-based research into a broader interdisciplinary and transdisciplinary Earth Systems Science context, has not been a focus of most Earth sciences graduate and post-graduate education programs in the past and, as such, these skills are largely lacking in the current workforce (e.g., Rapley and De Meyer, 2014; Weaver et al., 2014).
Equally important is training scientists and scholars to be culturally competent and help create a sense of belonging in the Earth Systems Science community, not only for the wide range of fields involved in convergence research (see Characteristic 2) but also for groups that have been excluded or not historically been well represented (Medin et al., 2017; Behl et al., 2021; see also Characteristic 3). An example of the latter is the NSF-funded program URGE (Unlearning Racism in the Geosciences),16 which uses community discussion and curriculum to unlearn racism and change the culture of the scientific community by improving accessibility, justice, equity, and inclusion. Effective mentorship practices can also help create supportive and inclusive environments (NASEM, 2019). In addition, recognizing the structural and systemic barriers to entering and remaining in the workforce, supporting the many pathways by which individuals enter and continue, and expanding mentoring that is proactive and responsive to needs are critical for next generation Earth Systems Science. 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.
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