ESS-DIVE: Environmental Systems Science Data Infrastructure for a Virtual Ecosystem
ESS-DIVE is a data repository for Earth and environmental science data. The platform enables contributors to archive and share data with supporting information in consistent formats that can be cited and tracked. Users, in turn, can efficiently find and obtain data that are easier to interpret, integrate, and analyze. ESS-DIVE is funded by the Data Management program within BER’s Earth and Environmental Systems Sciences Division.
AmeriFlux Network Data
The AmeriFlux Network gathers and shares long-term carbon, water, and energy flux measurements and site metadata collected by a cohort of sites spanning different climates and ecosystems across the Americas. AmeriFlux ensures the availability of these continuous, long-term ecosystem measurements (which are necessary to build effective models and multisite syntheses), while maximizing insights through robust, site-specific, independent research programs.
FRED: Fine Root Ecology Database
FRED gathers observations of root traits from across the globe into a common framework, freely available to ecologists and modelers alike. FRED facilitates the quantification of fine-root trait variation within and among species and across environments, as well as the improved representation and parameterization of fine-root processes in terrestrial biosphere models.
LeafWeb is a global database of biochemical, physiological, and biophysical properties of single leaves to support studies of plant functions and terrestrial carbon cycle modeling. LeafWeb’s automated online tool for analyzing leaf photosynthesis and fluorescence applies the principle of SErvices in Exchange for Data Sharing (SEEDS). With the approval of the user, the data that LeafWeb receives are preserved, captured, and shared with the broader scientific community, along with proper credits and acknowledgement of data contributions.
ESGF: Earth System Grid Federation
ESGF is an international collaboration for the software that powers most global climate change research, notably assessments by the Intergovernmental Panel on Climate Change. ESGF manages the first-ever decentralized database for handling climate science data, with multiple petabytes of data at dozens of federated sites worldwide. The ESGF community holds the premier collection of simulations and observational and reanalysis data for climate change research. ESGF is funded by the Data Management program within BER’s Earth and Environmental Systems Sciences Division.
ARM: Atmospheric Radiation Measurement User Facility
ARM provides the research community with strategically located in situ and remote-sensing observatories for atmospheric and climate science. These observatories are designed to 1) advance the understanding of clouds and aerosols and their interactions and coupling with the Earth’s surface and 2) improve their representation in climate and Earth system models. ARM data are available at no charge through the ARM Data Center. The ARM user facility is supported by BER’s Earth and Environmental Systems Sciences Division.
LLNL Surface Complexation Database Converter
The Lawrence Livermore National Laboratory (LLNL) Surface Complexation Database Converter (SCDC) is a R-based script that creates a unified dataset of surface complexation experimental data with respective parameters and results.
Artificial Intelligence for Earth System Predictability (AI4ESP)
A multilaboratory initiative working with the Earth and Environmental Systems Science Division (EESSD) of the Office of Biological and Environmental Research (BER) to develop a new paradigm for Earth system predictability focused on enabling artificial intelligence across field, lab, modeling, and analysis activities.
IDEAS: Interoperable Design of Extreme-scale Application Software
The IDEAS family of projects provides community-based approaches to software development. Within this family, the IDEAS–Watersheds project is accelerating watershed science through the development and support of a sustainable community-driven software ecosystem of interoperable components and libraries. This flexible modeling capacity is an important part of the growing ESS community cyberinfrastructure that supports the integration of diverse and complex environmental datasets with multiscale, multiphysics models.
The ExaSheds project advances the understanding of watershed systems through exascale simulation and data-driven machine learning (ML). To improve predictive capabilities for watershed and river-basin function, the project explores synergies between ML approaches and process-based hydro-biogeochemical high-performance computing simulations. ExaSheds is funded by the Data Management program within BER’s Earth and Environmental Systems Sciences Division.
ILAMB: International Land Model Benchmarking Project
ILAMB is a model-data intercomparison and integration project designed to improve the performance of land models and, in parallel, improve the design of new measurement campaigns to reduce uncertainties associated with key land surface processes. The project provides new analysis approaches and benchmarking tools that facilitate evaluation of land models, and ILAMB is expected to be a primary analysis tool for the Coupled Model Intercomparison Project (CMIP6). The project is sponsored by the Regional and Global Climate Modeling program within BER’s Earth and Environmental Systems Sciences Division.
PCMDI: Program for Climate Model Diagnosis and Intercomparison
PCMDI develops improved methods and tools for diagnosing and evaluating climate models, provides leadership and infrastructure in support of internationally coordinated model intercomparison activities, and conducts research related largely to the multimodel ensemble of results hosted by PCMDI’s infrastructure. PCMDI also is encouraging the development of observational datasets needed for model evaluation through a relatively new obs4MIPs initiative. The project is sponsored by the Regional and Global Climate Modeling program within BER’s Earth and Environmental Systems Sciences Division.
Software – Productivity and Sustainability Improvement Plan (S-PSIP)
As part of the DOE Office of Science intent to promote openness in federally supported scientific research, a Software – Productivity and Sustainability Improvement Plan (S-PSIP) is a document that contains information about a numerical code (software) that is used by the ESS community to perform process, system or multi-scale modeling and/or prediction of future states. The Productivity and Sustainability Improvement Planning Tools link consists of a repository of documents that support an iterative planning process for improving the productivity of scientific software developers and the sustainability of the software through improved software practices, processes, and tools.