FY 2019 Performance Metrics

Demonstrate in the Coupled DOE-E3SM Model, the Importance of Environmental Factors
in Affecting Ecosystem Productivity and Surface Energy Exchanges


1ST QUARTER METRIC: Evaluate the Effects of Uncertainty in Biogeochemistry Methodology in the Land Model – COMPLETED 

Product Definition

Soils contain the largest terrestrial pool of organic carbon (C), storing at least twice as much C as earth’s atmosphere (Köchy et al., 2015; Scharlemann et al., 2014). Uncertainties surrounding the response of soils to climatic and other changes contribute substantial uncertainty to C cycle and climate projections in the Earth system (Arora et al., 2013; Friedlingstein et al., 2014; Todd-Brown et al., 2013): the magnitude of their uncertainty is comparable to that of cloud feedbacks, traditionally regarded as the most significant unknown in climate modeling (Gregory et al., 2009). For example, Jones and Falloon (2009) reported a strong relationship between changes in soil organic C (SOC) and the strength of simulated C‐climate feedbacks within ESMs, while Riley et al. (2018) and Gaudio et al. (2015) found that model representation of nitrogen biogeochemistry and uptake patterns had significant climatic effects at larger spatial scales. At the same time, models’ structural uncertainty (the uncertainty deriving from how various models represent particular processes differently) is an unknown factor (Tebaldi and Knutti, 2007); there have been few attempts to examine how structural uncertainty within a single model–as opposed to model-to-model variability in, e.g., CMIP5 (Friedlingstein et al., 2014; Knutti and Sedláček, 2012)–affects model behavior and performance (Ricciuto et al., 2008). The investigation here indicates that the structural uncertainty deriving from models’ biogeochemical process representation is significant, although not as large as other sources such as parametric uncertainty (uncertainty deriving from the model inputs such as field-based data).

Product Documentation

The U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM) is unusual among ESMs in that it has two approaches to terrestrial biogeochemistry in its land model, the E3SM Land Model (ELM): the primary approach ELMv1-CTC-CNP (led by a team at Oak Ridge National Laboratory) and the alternative ELMv1-ECA-CNP (led by Lawrence Berkeley National Laboratory group). These differ in three key aspects of biogeochemistry–stoichiometry, allocation, and nutrient competition–and represent distinct approaches to the overall problem, as described below. To evaluate the effects of uncertainty in biogeochemistry methodology, we performed a series of site- and global-scale uncoupled simulations using both CTC and ECA. The models’ outputs were compared against a variety of observational reference datasets. This work will allow the model structural uncertainty in this area to be assessed, for the first time, against other sources of uncertainty, e.g. parametric and ensemble sources.


2ND QUARTER METRIC: Implement and Evaluate the Effects of Air Temperature Change and Water Management on Stream Temperature


3RD QUARTER METRIC: Evaluate the Effects of Including Phosphorous Limitations on the Carbon Cycle


4TH QUARTER METRIC: Evaluate the Effects of Including Vegetation Dynamics on Productivity and Surface Energy in the E3SM Model