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The article discusses the First International Noah-MP Annual Users' Workshop, which brought together over 200 participants from 16 countries to discuss advancements in the Noah-MP land surface model. The workshop focused on enhancing the model's capabilities, applicability, and interoperability in Earth system applications. The article highlights the various applications of the Noah-MP model, including weather prediction, climate projection, hydrology, agriculture, and urban heat island studies. It also identifies current challenges and limitations of the model, such as uncertainties in certain processes and a lack of communication and coordination within the Noah-MP community. The article concludes with recommendations for future model development and the establishment of a Noah-MP Academia Collaboratory to support community collaborations and enhance research-to-operation activities. [Extracted from the article]

The Earth and life sciences are replete with portmanteau (blended) words and neologisms. Researchers at the interfaces between the traditional disciplines within the Earth and life sciences have coined dual titles for ‘new’ disciplines, such as geobiology/biogeology and ecohydrology/hydroecology. An upsurge in such coinage over the last few decades reflects a healthy willingness of many researchers to cross traditional disciplinary boundaries and to include the human sphere within their domains. However, some of the titles cause confusion because their meanings are not self-evident; rather, they sometimes demand extra research to reveal their meanings, thus rendering science less ‘open’ at a time when Open Science is being vigorously encouraged. After discussing the question of disciplinary neologisms, the paper probes the interchangeability of 15 dual ‘new’ discipline titles and concludes that six are more-or-less synonymous, three are nonsynonymous, and six are indeterminate for varying reasons. We question the usefulness of some, but by no means all, disciplinary neologisms, given that their meanings are not immediately apparent. Looking beyond portmanteau titles and other neologisms, the paper discusses the rise of interdisciplinary and transdisciplinary research, as seen in the concepts of the Critical Zone, multifunctional landscapes, and Earth System Science, the last of which supports a conceptual model of the planet as an interdependent set of spheres from global to local scales that has the advantages of combining the biotic, abiotic, and human spheres. [ABSTRACT FROM AUTHOR]

Continental- to global-scale hydrologic and land surface models increasingly include representations of the groundwater system. Such large-scale models are essential for examining, communicating, and understanding the dynamic interactions between the Earth system above and below the land surface as well as the opportunities and limits of groundwater resources. We argue that both large-scale and regional-scale groundwater models have utility, strengths, and limitations, so continued modeling at both scales is essential and mutually beneficial. A crucial quest is how to evaluate the realism, capabilities, and performance of large-scale groundwater models given their modeling purpose of addressing large-scale science or sustainability questions as well as limitations in data availability and commensurability. Evaluation should identify if, when, or where large-scale models achieve their purpose or where opportunities for improvements exist so that such models better achieve their purpose. We suggest that reproducing the spatiotemporal details of regional-scale models and matching local data are not relevant goals. Instead, it is important to decide on reasonable model expectations regarding when a large-scale model is performing "well enough" in the context of its specific purpose. The decision of reasonable expectations is necessarily subjective even if the evaluation criteria are quantitative. Our objective is to provide recommendations for improving the evaluation of groundwater representation in continental- to global-scale models. We describe current modeling strategies and evaluation practices, and we subsequently discuss the value of three evaluation strategies: (1) comparing model outputs with available observations of groundwater levels or other state or flux variables (observation-based evaluation), (2) comparing several models with each other with or without reference to actual observations (model-based evaluation), and (3) comparing model behavior with expert expectations of hydrologic behaviors in particular regions or at particular times (expert-based evaluation). Based on evolving practices in model evaluation as well as innovations in observations, machine learning, and expert elicitation, we argue that combining observation-, model-, and expert-based model evaluation approaches, while accounting for commensurability issues, may significantly improve the realism of groundwater representation in large-scale models, thus advancing our ability for quantification, understanding, and prediction of crucial Earth science and sustainability problems. We encourage greater community-level communication and cooperation on this quest, including among global hydrology and land surface modelers, local to regional hydrogeologists, and hydrologists focused on model development and evaluation. [ABSTRACT FROM AUTHOR]

In recent years, Earth system sciences are urgently calling for innovation on improving accuracy, enhancing model intelligence level, scaling up operation, and reducing costs in many subdomains amid the exponentially accumulated datasets and the promising artificial intelligence (AI) revolution in computer science. This paper presents work led by the NASA Earth Science Data Systems Working Groups and ESIP machine learning cluster to give a comprehensive overview of AI in Earth sciences. It holistically introduces the current status, technology, use cases, challenges, and opportunities, and provides all the levels of AI practitioners in geosciences with an overall big picture and to "blow away the fog to get a clearer vision" about the future development of Earth AI. The paper covers all the majorspheres in the Earth system and investigates representative AI research in each domain. Widely used AI algorithms and computing cyberinfrastructure are briefly introduced. The mandatory steps in a typical workflow of specializing AI to solve Earth scientific problems are decomposed and analyzed. Eventually, it concludes with the grand challenges and reveals the opportunities to give some guidance and pre-warnings on allocating resources wisely to achieve the ambitious Earth AI goals in the future. • A bird's eye view of the AI application in all spectrum of geosciences is provided. • The mandatory modular steps of typical Earth AI workflows are summarized. • Twelve grand challenges in Earth AI and potential opportunities are introduced. [ABSTRACT FROM AUTHOR]

ESA's Next Generation Gravity Mission (NGGM) is a candidate Mission of Opportunity for ESA–NASA cooperation in the frame of the Mass Change and Geosciences International Constellation (MAGIC). The mission aims at enabling long-term monitoring of the temporal variations of Earth's gravity field at relatively high temporal (down to 3 days) and increased spatial resolutions (up to 100 km) at longer time intervals. This implies also that time series of GRACE and GRACE-FO can be extended towards a climate series. Such variations carry information about mass change induced by the water cycle and the related mass exchange among atmosphere, oceans, cryosphere, land and solid Earth and will complete our picture of global and climate change. The main observable is the variation of the distance between two satellites measured by a ranging instrument. This is complemented by accelerometers that measure the nongravitational accelerations, which need to be reduced from ranging measurements to obtain the gravity signal. The preferred satellite constellation comprises one satellite pair in a near-polar and another in an inclined circular orbit. The paper focuses on the orbit selection methods for optimizing the spatial sampling for multiple temporal resolutions and then on the methodology for deriving the engineering requirements for the space segment, together with a discussion on the main mission parameters. [ABSTRACT FROM AUTHOR]

Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL), have created tremendous excitement and opportunities in the earth and environmental sciences communities. To leverage these new 'data-driven' technologies, however, one needs to understand the fundamental concepts that give rise to DL and how they differ from 'process-based', mechanistic modelling. This paper revisits those fundamentals and addresses 10 questions that might be posed by earth and environmental scientists, and with the aid of a real-world modelling experiment, it explains some critical, but often ignored, issues DL may face in practice. The overarching objective is to contribute to a future of AI-assisted earth and environmental sciences where AI models can (1) embrace the typically ignored knowledge base available, (2) function credibly in 'true' out-of-sample prediction, and (3) handle non-stationarity in earth and environmental systems. Comparing and contrasting earth and environmental problems with prominent AI applications, such as playing chess and trading in stock markets, provides critical insights for better directing future research in this field. • DL is rooted in connectionism, hyper-flexibility, and vigorous optimization , which are alien to process-based modelling. • A knowledge base is essential to enable predictions of complex, open , partially observable , and non-stationary systems. • Bridging DL and process-based modelling is embryonic but has great potential in an age of big data and computational power. [ABSTRACT FROM AUTHOR]