skip to main content

Search for: All records

Creators/Authors contains: "Yang, Yan"

Note: When clicking on a Digital Object Identifier (DOI) number, you will be taken to an external site maintained by the publisher. Some full text articles may not yet be available without a charge during the embargo (administrative interval).
What is a DOI Number?

Some links on this page may take you to non-federal websites. Their policies may differ from this site.

  1. Reaction of the complexes [Fe 2 (μ 2 -NP(pip) 3 ) 2 (NP(pip) 3 ) 2 ] ( 1-Fe ) and [Co 2 (μ 2 -NP(pip) 3 ) 2 (NP(pip) 3 ) 2 ] ( 1-Co ), where [NP(pip) 3 ] 1− is tris(piperidinyl)imidophosphorane, with nitrous oxide, S 8 , or Se 0 results in divergent reactivity. With nitrous oxide, 1-Fe forms [Fe 2 (μ 2 -O)(μ 2 -NP(pip) 3 ) 2 (NP(pip) 3 ) 2 ] ( 2-Fe ), with a very short Fe 3+ –Fe 3+ distance. Reactions of 1-Fe with S 8 or Se 0 result in the bridging, side-on coordination (μ-κ 1 :κ 1 -E 2 2− ) of the heavy chalcogens in complexes [Fe 2 (μ-κ 1 :κ 1 -E 2 )(μ 2 -NP(pip) 3 ) 2 (NP(pip) 3 ) 2 ] (E = S, 3-Fe , or Se, 4-Fe ). In all cases, the complex 1-Co is inert.
  2. Abstract. The terrestrial carbon cycle plays a critical role in modulating the interactions of climate with the Earth system, but different models often make vastly different predictions of its behavior. Efforts to reduce model uncertainty have commonly focused on model structure, namely by introducing additional processes and increasing structural complexity. However, the extent to which increased structural complexity can directly improve predictive skill is unclear. While adding processes may improve realism, the resulting models are often encumbered by a greater number of poorly determined or over-generalized parameters. To guide efficient model development, here we map the theoretical relationship between model complexity and predictive skill. To do so, we developed 16 structurally distinct carbon cycle models spanning an axis of complexity and incorporated them into a model–data fusion system. We calibrated each model at six globally distributed eddy covariance sites with long observation time series and under 42 data scenarios that resulted in different degrees of parameter uncertainty. For each combination of site, data scenario, and model, we then predicted net ecosystem exchange (NEE) and leaf area index (LAI) for validation against independent local site data. Though the maximum model complexity we evaluated is lower than most traditional terrestrial biosphere models, themore »complexity range we explored provides universal insight into the inter-relationship between structural uncertainty, parametric uncertainty, and model forecast skill. Specifically, increased complexity only improves forecast skill if parameters are adequately informed (e.g., when NEE observations are used for calibration). Otherwise, increased complexity can degrade skill and an intermediate-complexity model is optimal. This finding remains consistent regardless of whether NEE or LAI is predicted. Our COMPLexity EXperiment (COMPLEX) highlights the importance of robust observation-based parameterization for land surface modeling and suggests that data characterizing net carbon fluxes will be key to improving decadal predictions of high-dimensional terrestrial biosphere models.« less