Researchers define a new sensitivity index to quantify the uncertainty contribution from each process under model structural uncertainty.
Earth system models consist of multiple processes, each of them being a submodel in the integrated system model. A research team, including scientists at Florida State University, Pacific Northwest National Laboratory, and Oak Ridge National Laboratory, derived a new process sensitivity index to rank the importance of each process in a system model with multiple choices of each process model.
The new process sensitivity index tackles the model uncertainty in a rigorous mathematical way, which has not been dealt with in conventional sensitivity analyses. Accounting for model structural uncertainty in complex multiphysics, multiprocess models has been a long-recognized need in the modeling community.
Most of the processes in a multiprocess model could be conceptualized in multiple ways, leading to multiple alternative models of a system. One question often asked is which process contributed to the most variability or uncertainty in the system model outputs. Global sensitivity analysis methods are an important and often used venue for quantifying such contributions and identifying the targets for efficient uncertainty reduction. However, existing methods of global sensitivity analysis only consider variability in the model parameters and are not capable of handling variability that arises from conceptualization of one or more processes. This research developed a new method to isolate the contribution of each process to the overall variability in model outputs by integrating model averaging concepts with a variance-based global sensitivity analysis. The researchers derived a process sensitivity index as a measure of relative process importance, which accounts for variability caused by both process models and their parameters. They demonstrated the new method with a hypothetical groundwater reactive transport modeling case that considers alternative physical heterogeneity and surface recharge submodels. However, the new process sensitivity index is generally applicable to a wide range of problems in hydrologic and biogeochemical problems in Earth system models. This research offers an advanced systematic approach to prioritizing model inspired experiments.
Contacts (BER PM)
Subsurface Biogeochemical Research Program
Terrestrial Ecosystem Science Program
Ming Ye, Florida State University, email@example.com
Xingyuan Chen, Pacific Northwest National Laboratory (PNNL), Xingyuan.Chen@pnnl.gov
Anthony P. Walker, Oak Ridge National Laboratory (ORNL), firstname.lastname@example.org
This work was supported by the U.S. Department of Energy, Office of Science, Office of Biological Research, Early Career Award and PNNL Subsurface Science Research Scientific Focus Area and ORNL Terrestrial Ecosystem Science Scientific Focus Area.
Dai, H., M. Ye, A. P. Walker, and X. Chen. 2017. “A New Process Sensitivity Index to Identify Important System Processes Under Process Model Uncertainty and Parametric Uncertainty,” Water Resources Research 53(4), 3746-90. [DOI: 10.1002/2016WR019715]. (Reference link)
SC-23.1 Climate and Environmental Sciences Division, BER
BER supports basic research and scientific user facilities to advance DOE missions in energy and environment. More about BER
May 10, 2019
Quantifying Decision Uncertainty in Water Management via a Coupled Agent-Based Model
Considering risk perception can improve the representation of human decision-making processes in age [more...]
May 09, 2019
Projecting Global Urban Area Growth Through 2100 Based on Historical Time Series Data and Future Scenarios
Study provides country-specific urban area growth models and the first dataset on country-level urba [more...]
May 05, 2019
Calibrating Building Energy Demand Models to Refine Long-Term Energy Planning
A new, flexible calibration approach improved model accuracy in capturing year-to-year changes in bu [more...]
May 03, 2019
Calibration and Uncertainty Analysis of Demeter for Better Downscaling of Global Land Use and Land Cover Projections
Researchers improved the Demeter model’s performance by calibrating key parameters and establi [more...]
Apr 22, 2019
Representation of U.S. Warm Temperature Extremes in Global Climate Model Ensembles
Representation of warm temperature events varies considerably among global climate models, which has [more...]
List all highlights (possible long download time)