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1. Ideas and perspectives: Strengthening the biogeosciences in environmental research networks

   Richter, Daniel D; Billings, Billings; Groffman, Peter M. (August 2018, Biogeosciences)

   Long-term environmental research networks are one approach to advancing local, regional, and global environmental science and education. A remarkable number and wide variety of environmental research networks operate around the world today. These are diverse in funding, infrastructure, motivating questions, scientific strengths, and the sciences that birthed and maintain the networks. Some networks have individual sites that were selected because they had produced invaluable long-term data, while other networks have new sites selected to span ecological gradients. However, all long-term environmental networks share two challenges. Networks must keep pace with scientific advances and interact with both the scientific community and society at large. If networks fall short of successfully addressing these challenges, they risk becoming irrelevant. The objective of this paper is to assert that the biogeosciences offer environmental research networks a number of opportunities to expand scientific impact and public engagement. We explore some of these opportunities with four networks: the International Long-Term Ecological Research Network programs (ILTERs), critical zone observatories (CZOs), Earth and ecological observatory networks (EONs), and the FLUXNET program of eddy flux sites. While these networks were founded and expanded by interdisciplinary scientists, the preponderance of expertise and funding has gravitated activities of ILTERs and EONs toward ecology and biology, CZOs toward the Earth sciences and geology, and FLUXNET toward ecophysiology and micrometeorology. Our point is not to homogenize networks, nor to diminish disciplinary science. Rather, we argue that by more fully incorporating the integration of biology and geology in long-term environmental research networks, scientists can better leverage network assets, keep pace with the ever-changing science of the environment, and engage with larger scientific and public audiences. 

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2. Ideas and perspectives: Strengthening the biogeosciences in environmental research networks

   https://doi.org/10.5194/bg-15-4815-2018  

   Richter, Daniel D.; Billings, Sharon A.; Groffman, Peter M.; Kelly, Eugene F.; Lohse, Kathleen A.; McDowell, William H.; White, Timothy S.; Anderson, Suzanne; Baldocchi, Dennis D.; Banwart, Steve; et al (January 2018, Biogeosciences)

   Abstract. Long-term environmental research networks are one approach toadvancing local, regional, and global environmental science and education. Aremarkable number and wide variety of environmental research networks operatearound the world today. These are diverse in funding, infrastructure,motivating questions, scientific strengths, and the sciences that birthed andmaintain the networks. Some networks have individual sites that wereselected because they had produced invaluable long-term data, while othernetworks have new sites selected to span ecological gradients. However, alllong-term environmental networks share two challenges. Networks must keeppace with scientific advances and interact with both the scientific communityand society at large. If networks fall short of successfully addressing thesechallenges, they risk becoming irrelevant. The objective of this paper is toassert that the biogeosciences offer environmental research networks a numberof opportunities to expand scientific impact and public engagement. Weexplore some of these opportunities with four networks: the InternationalLong-Term Ecological Research Network programs (ILTERs), critical zoneobservatories (CZOs), Earth and ecological observatory networks (EONs),and the FLUXNET program of eddy flux sites. While these networks were foundedand expanded by interdisciplinary scientists, the preponderance of expertise andfunding has gravitated activities of ILTERs and EONs toward ecology andbiology, CZOs toward the Earth sciences and geology, and FLUXNET towardecophysiology and micrometeorology. Our point is not to homogenize networks,nor to diminish disciplinary science. Rather, we argue that by more fullyincorporating the integration of biology and geology in long-termenvironmental research networks, scientists can better leverage networkassets, keep pace with the ever-changing science of the environment, andengage with larger scientific and public audiences. 

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3. Cultivating Macroalgae for Biofuels: Visualizing the Chemistry

   SAMSEL, F; ABRAM, G; ZELLER, S; CAO, Z; WOLFRAM, P; HERMAN, B; KEEFE, D. (November 2022, International Conference for High Performance Computing Networking Storage and Analysis)

   Wylie, B. (Ed.)

   Marine macroalgae in the Gulf of Mexico is an important potential source for biofuel. However, identifying locations with the correct biogeochemical and hydrodynamic conditions for cultivation on a large enough scale to meet the needs of the U.S. private energy sector is impossible from purely observational studies. Large-scale, HPC modeling of earth systems processes enables researchers to study complex physical relationships with high fidelity. Here, we present novel visualization techniques showing the results of a global run of the E3SM’s MPAS-Ocean model data with biogeochemistry extensions to improve ongoing research in macroalgae cultivation. 

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4. Machine Learning for Robust Identification of Complex Nonlinear Dynamical Systems: Applications to Earth Systems Modeling

   Yadav, Nishant; Ravela, Sai; Ganguly, Auroop R. (August 2020, ArXivorg)

   null (Ed.)

   Systems exhibiting nonlinear dynamics, including but not limited to chaos, are ubiquitous across Earth Sciences such as Meteorology, Hydrology, Climate and Ecology, as well as Biology such as neural and cardiac processes. However, System Identification remains a challenge. In climate and earth systems models, while governing equations follow from first principles and understanding of key processes has steadily improved, the largest uncertainties are often caused by parameterizations such as cloud physics, which in turn have witnessed limited improvements over the last several decades. Climate scientists have pointed to Machine Learning enhanced parameter estimation as a possible solution, with proof-of-concept methodological adaptations being examined on idealized systems. While climate science has been highlighted as a "Big Data" challenge owing to the volume and complexity of archived model-simulations and observations from remote and in-situ sensors, the parameter estimation process is often relatively a "small data" problem. A crucial question for data scientists in this context is the relevance of state-of-the-art data-driven approaches including those based on deep neural networks or kernel-based processes. Here we consider a chaotic system - two-level Lorenz-96 - used as a benchmark model in the climate science literature, adopt a methodology based on Gaussian Processes for parameter estimation and compare the gains in predictive understanding with a suite of Deep Learning and strawman Linear Regression methods. Our results show that adaptations of kernel-based Gaussian Processes can outperform other approaches under small data constraints along with uncertainty quantification; and needs to be considered as a viable approach in climate science and earth system modeling. 

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5. Steering operational synergies in terrestrial observation networks: opportunity for advancing Earth system dynamics modelling

   https://doi.org/10.5194/esd-9-593-2018  

   Baatz, Roland; Sullivan, Pamela L.; Li, Li; Weintraub, Samantha R.; Loescher, Henry W.; Mirtl, Michael; Groffman, Peter M.; Wall, Diana H.; Young, Michael; White, Tim; et al (January 2018, Earth System Dynamics)

   Abstract. Advancing our understanding of Earth system dynamics (ESD) depends on thedevelopment of models and other analytical tools that apply physical,biological, and chemical data. This ambition to increase understanding anddevelop models of ESD based on site observations was the stimulus forcreating the networks of Long-Term Ecological Research (LTER), Critical ZoneObservatories (CZOs), and others. We organized a survey, the results of whichidentified pressing gaps in data availability from these networks, inparticular for the future development and evaluation of models that representESD processes, and provide insights for improvement in both data collectionand model integration. From this survey overview of data applications in the context of LTER andCZO research, we identified three challenges: (1) widen application ofterrestrial observation network data in Earth system modelling,(2) develop integrated Earth system models that incorporate processrepresentation and data of multiple disciplines, and (3) identifycomplementarity in measured variables and spatial extent, and promotingsynergies in the existing observational networks. These challenges lead toperspectives and recommendations for an improved dialogue between theobservation networks and the ESD modelling community, including co-locationof sites in the existing networks and further formalizing theserecommendations among these communities. Developing these synergies willenable cross-site and cross-network comparison and synthesis studies, whichwill help produce insights around organizing principles, classifications,and general rules of coupling processes with environmental conditions. 

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