FY 2019 Performance Metrics

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



Evaluate the Effects of Uncertainty in Biogeochemistry Methodology in the Land Model  

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.


Implement and Evaluate the Effects of Air Temperature Change and Water Management on Stream Temperature 

Product Definition

Stream temperature is a key water quality measure for water management, thermoelectric power production, and conservation activities. Currently, 85% of electricity generation in the United States comes from power plants that require cooling. Changes in stream temperature and water availability directly affect thermoelectric power generation capacity. High water temperature and low streamflow can increase cooling water requirements and restrict cooling water availability, which constrains the usable capacity of thermoelectric power plants. This constraint may become more acute in the future, as droughts are projected to be more widespread and prolonged in a warmer climate. These motivate the need to model stream temperature and understand the relative impacts of air temperature and water management on stream temperature.

Stream temperature is mainly controlled by the heat exchanges between river water body and air and river banks, and heat transportation along the river networks. Notably, 98% of the rivers and streams in the United States have been dammed, diverted, or developed. Reservoirs regulate flows for various purposes, such as flood control, irrigation, hydropower production, and navigation. This alters the flow regime by storing water during high-flow periods and enhancing the low flows during the dry season. Changes in the flow regime have important impacts on stream temperature, as they alter the heat exchanges between the rivers and air and river banks.

Through an effort supported by the MultiSector Dynamics activity within the Earth and Environmental Systems Modeling program, a stream temperature module has been developed as part of the Model for Scale Adaptive River Transport (MOSART) and coupled with a water management model (WM). This metric report describes (1) the implementation of the stream temperature module and its global testing and evaluation within the Department of Energy’s Energy Exascale Earth System Model (E3SM) and (2) analysis of simulations to understand the relative impacts of air temperature and water management on stream temperature in basins around the world. Simulations with and without water management show that water management has large impacts on the seasonal cycle of stream temperature in arid and semi-arid regions (western U.S., central Asia, northeastern China) where water management alters the flow regime to provide irrigation water supply and in India where groundwater pumping for irrigation is prominent.

Product Documentation

MOSART-heat is a stream temperature module (Li et al. 2015a) developed on top of MOSART (Li et al. 2013; 2015b), which is the river component of E3SM v1. MOSART-heat has been implemented in E3SM through coupling with the E3SM Land Model (ELM). MOSART-heat mainly represents natural thermodynamic processes that control stream temperature, including the lateral heat fluxes from hillslope and soils (along with surface and subsurface runoff) into tributary channels, heat balance in tributary channels and main channels respectively. The surface runoff temperature is estimated as the average soil temperature of the top three soil layers in ELM, and the subsurface runoff temperature is estimated as the average soil temperature within the saturated soil layers, which vary with time due to changes in the water table level.


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