Expanding the use of plant trait observations and ecological theory in Earth system models: DOE Workshop Report.
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Koven, Charles D.↗
Engineering topics
Thornton, Peter E
Publications and source records attributed to Thornton, Peter E.
Substrate and environmental controls on microbial assimilation of soil organic carbon: a framework for Earth System Models
Microbial assimilation of soil organic carbon is one of the fundamental processes of global carbon cycling and it determines the magnitude of microbial biomass in soils. Mechanistic understanding of microbial assimilation of soil organic carbon and its controls is important for to improve Earth system models ability to simulate carbon-climate feedbacks. Although microbial assimilation of soil organic carbon is broadly considered to be an important parameter, it really comprises two separate physiological processes: one-time assimilation efficiency and time-dependent microbial maintenance energy. Representing of these two mechanisms is crucial to more accurately simulate carbon cycling in soils. In this study, a simple modeling framework was developed to evaluate the substrate and environmental controls on microbial assimilation of soil organic carbon using a new term: microbial annual active period (the length of microbes remaining active in one year). Substrate quality has a positive effect on microbial assimilation of soil organic carbon: higher substrate quality (lower C:N ratio) leads to higher ratio of microbial carbon to soil organic carbon and vice versa. Increases in microbial annual active period from zero stimulate microbial assimilation of soil organic carbon; however, when microbial annual active period is longer than an optimal threshold, increasing this period decreases microbial biomass. The simulated ratios of soil microbial biomass to soil organic carbon are reasonably consistent with a recently compiled global dataset at the biome-level. The modeling framework of microbial assimilation of soil organic carbon and its controls developed in this study offers an applicable ways to incorporate microbial contributions to the carbon cycling into Earth system models for simulating carbon-climate feedbacks and to explain global patterns of microbial biomass.
Xu, Xiaofeng↗
Evaluation of eleven terrestrial carbon-nitrogen cycle models against observations from two temperate Free-Air CO2 Enrichment Studies
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Zaehle, S↗
Where does the carbon go? A model-data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites.
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DeKauwe, Martin G↗
A Functional Test Platform for the Community Land Model
A functional test platform is presented to create direct linkages between site measurements and the process-based ecosystem model within the Community Earth System Models (CESM). The platform consists of three major parts: 1) interactive user interfaces, 2) functional test model and 3) observational datasets. It provides much needed integration interfaces for both field experimentalists and ecosystem modelers to improve the model s representation of ecosystem processes within the CESM framework without large software overhead.
Xu, Yang↗
Comprehensive ecosystem model-experiment synthesis using multiple datasets at two temperate forest free-air CO2 enrichment experiments: model performance and compensating biases
Free Air CO2 Enrichment (FACE) experiments provide a remarkable wealth of data to test the sensitivities of terrestrial ecosystem models (TEMs). In this study, a broad set of 11 TEMs were compared to 22 years of data from two contrasting FACE experiments in temperate forests of the south eastern US the evergreen Duke Forest and the deciduous Oak Ridge forest. We evaluated the models' ability to reproduce observed net primary productivity (NPP), transpiration and Leaf Area index (LAI) in ambient CO2 treatments. Encouragingly, many models simulated annual NPP and transpiration within observed uncertainty. Daily transpiration model errors were often related to errors in leaf area phenology and peak LAI. Our analysis demonstrates that the simulation of LAI often drives the simulation of transpiration and hence there is a need to adopt the most appropriate of hypothesis driven methods to simulate and predict LAI. Of the three competing hypotheses determining peak LAI (1) optimisation to maximise carbon export, (2) increasing SLA with canopy depth and (3) the pipe model the pipe model produced LAI closest to the observations. Modelled phenology was either prescribed or based on broader empirical calibrations to climate. In some cases, simulation accuracy was achieved through compensating biases in component variables. For example, NPP accuracy was sometimes achieved with counter-balancing biases in nitrogen use efficiency and nitrogen uptake. Combined analysis of parallel measurements aides the identification of offsetting biases; without which over-confidence in model abilities to predict ecosystem function may emerge, potentially leading to erroneous predictions of change under future climates.
Walker, Anthony P↗
Big data for ecologists: Highlighting the ORNL DAAC
Ecologists are increasingly confronted by questions that can be addressed only by integrating data from numerous sources, often across large geographic areas and broad time periods. The supply of ecological big data is increasing at a rapid pace as researchers are publishing their data sets and large, public science and data infrastructures (such as NEON, DataONE, LTER, & NCEAS) are producing and curating extensive volumes of complex data and metadata. While supply of, and demand for, ecological data is on the rise, many ecologists now face a new challenge in locating and synthesizing the data relevant for their particular question. Here we highlight selected popular big data products applicable to ecological research available from the NASA Distributed Active Archive Center (DAAC) located at Oak Ridge National Laboratory (ORNL).
Boyer, Alison G↗
Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2.
More information: http://daymet.ornl.gov Presenter: Ranjeet Devarakonda Environmental Sciences Division Oak Ridge National Laboratory (ORNL) Daymet: Daily Surface Weather Data and Climatological Summaries provides gridded estimates of daily weather parameters for North America, including daily continuous surfaces of minimum and maximum temperature, precipitation occurrence and amount, humidity, shortwave radiation, snow water equivalent, and day length. The current data product (Version 2) covers the period January 1, 1980 to December 31, 2013 [1]. The prior product (Version 1) only covered from 1980-2008. Data are available on a daily time step at a 1-km x 1-km spatial resolution in Lambert Conformal Conic projection with a spatial extent that covers the conterminous United States, Mexico, and Southern Canada as meteorological station density allows. Daymet data can be downloaded from 1) the ORNL Distributed Active Archive Center (DAAC) search and order tools (http://daac.ornl.gov/cgi-bin/cart/add2cart.pl?add=1219) or directly from the DAAC FTP site (http://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1219) and 2) the Single Pixel Tool [2] and THREDDS (Thematic Real-time Environmental Data Services) Data Server [3]. The Single Pixel Data Extraction Tool allows users to enter a single geographic point by latitude and longitude in decimal degrees. A routine is executed that translates the (lon, lat) coordinates into projected Daymet (x,y) coordinates. These coordinates are used to access the Daymet database of daily-interpolated surface weather variables. Daily data from the nearest 1 km x 1 km Daymet grid cell are extracted from the database and formatted as a table with one column for each Daymet variable and one row for each day. All daily data for selected years are returned as a single (long) table, formatted for display in the browser window. At the top of this table is a link to the same data in a simple comma-separated text format, suitable for import into a spreadsheet or other data analysis software. The Single Pixel Data Extraction Tool also provides the option to download multiple coordinates programmatically. A multiple extractor script is freely available to download at http://daymet.ornl.gov/files/daymet.zip. The ORNL DAAC s THREDDS data server (TDS) provides customized visualization and access to Daymet time series of North American mosaics. Users can subset and download Daymet data via a variety of community standards, including OPeNDAP, NetCDF Subset service, and Open Geospatial Consortium (OGC) Web Map/Coverage Service. The ORNL DAAC TDS also exposes Daymet metadata through its ncISO service to facilitate harvesting Daymet metadata records into 3rd party catalogs. References: [1] Thornton, P.E., M.M. Thornton, B.W. Mayer, N. Wilhelmi, Y. Wei, R. Devarakonda, and R.B. Cook. 2014. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 2. Data set. Available on-line [http://daac.ornl.gov] from Oak Ridge National Laboratory Distributed Active Archive Center, Oak Ridge, Tennessee, USA. [2] Devarakonda R., et al. 2012. Daymet: Single Pixel Data Extraction Tool. Available on-line [http://daymet.ornl.go/singlepixel.html]. [3] Wei Y., et al. 2014. Daymet: Thematic Real-time Environmental Data Services. Available on-line [http://daymet.ornl.gov/thredds_tiles.html].
Thornton, Peter E↗
Web-based Visual Analytics for Extreme Scale Climate Science
In this paper, we introduce a Web-based visual analytics framework for democratizing advanced visualization and analysis capabilities pertinent to large-scale earth system simulations. We address significant limitations of present climate data analysis tools such as tightly coupled dependencies, ineffi- cient data movements, complex user interfaces, and static visualizations. Our Web-based visual analytics framework removes critical barriers to the widespread accessibility and adoption of advanced scientific techniques. Using distributed connections to back-end diagnostics, we minimize data movements and leverage HPC platforms. We also mitigate system dependency issues by employing a RESTful interface. Our framework embraces the visual analytics paradigm via new visual navigation techniques for hierarchical parameter spaces, multi-scale representations, and interactive spatio-temporal data mining methods that retain details. Although generalizable to other science domains, the current work focuses on improving exploratory analysis of large-scale Community Land Model (CLM) and Community Atmosphere Model (CAM) simulations.
climate science↗