Understanding and mitigating uncertainties in the community Noah-MP land model simulations over the complex terrain Tibet region
The probability density distribution of flux simulations in ensemble experiment Ens1 (top panel), Ens2 (second panel), Ens3 (third panel) and Ens4 (bottom panel) in 2008. The x-axis: the ratio of simulated daytime accumulated sensible heat fluxes (H) to observed values (left panel) and the ratio of simulated accumulated latent heat fluxes (LE) to observed values(right panel) (daytime: 10am-15pm); The y-axis: probability distribution of these ratios; Ratio < 1: accumulated flux is underestimated; Ratio >1: accumulated flux is overestimated; Vertical dash line: ratio = 1, i.e., observed and simulated accumulated flux are equal.
Despite the widespread use of the new community Noah with multiparameterization (Noah-MP) land-surface model (LSM), it has not been rigorously evaluated over the complex Tibetan Plateau (TP). A new study in 2016 assessed uncertainties in Noah-MP simulations of a cropland site using observations from the 2008 Joint International Cooperation program (JICA) field campaign. This assessment was conducted in the context of performing a total number of 4,608 Noah-MP physics ensemble simulations using two analysis methods—the natural selection approach and Tukey's test—in which the impacts of uncertainties in atmospheric forcing conditions, vegetation parameters, and sub-processes on model simulations were identified. The control ensemble simulation (Ens 1 in Figure) significantly underestimated (overestimated) latent (sensible) heat fluxes. Uncertainty in precipitation data (Ens 2) exerts greater influence on the general behavior of Noah-MP ensemble simulations than that in the leaf area index (LAI) (Ens 3). However, using a more realistic seasonal LAI improves the seasonal variations of surface heat fluxes. Combining a better precipitation forcing dataset and MODIS monthly LAI (Ens 4) significantly reduces the uncertainty range of the ensemble mean of surface heat fluxes (Figure). The uncertainty analysis results using the natural selection method are largely similar to that from Tukey’s test, but show some subtle differences. Both methods reveal greater uncertainties in the following sub-process schemes: canopy resistance, soil moisture threshold for evaporation, runoff and groundwater, and surface-layer parameterization for this cropland site (Zhang et al. 2016). The uncertainty analysis identifies the parameterization schemes that demonstrably degrade model performance. The uncertainties in ensemble simulations were significantly reduced when those schemes were excluded, and it was possible to configure an optimal combination of parameterization schemes to obtain similar performance to the ensemble mean of the “best” ensemble experiment.
A similar study was conducted for the sparsely vegetated Amdo site located in the Central TP, using the Noah-MP ensemble simulations LSM to assess the relative importance of parameterizing vertical soil heterogeneity, organic matter, and soil rhizosphere, and their impacts on seasonal evolution of soil temperature, soil moisture, and surface energy and water budgets. Representing layered soil texture and organic matter does not improve low biases in topsoil moisture. Reducing the saturated conductivity from the mucilage in the rhizosphere produces better results. Surface sensible and latent heat fluxes are better simulated in the monsoon season as well. Adding layered soil texture and organic matter in Noah-MP retard the thawing in deep soil layers, and the rhizosphere effect delays thawing even more in the transient season. Uncertainties in soil initialization significantly affect deep-soil temperature and moisture, but uncertainties in atmospheric forcing conditions mainly affect topsoil variables and consequently the surface-energy fluxes. Differing land-surface physics cause 36% uncertainty in the accumulated evapotranspiration and subsurface runoff (Gao et al. 2015).
