Surrogate modeling uses cheap “surrogates” to represent the response surface of simulation models. It involves several steps, including initial sampling, regression and adaptive sampling. This study evaluates an adaptive surrogate modeling based optimization (ASMO) method on two benchmark problems: the Hartman function and calibration of the SAC-SMA hydrologic model. Our results show that: 1) Gaussian Processes are the best surrogate model construction method. A minimum Interpolation Surface method is the best adaptive sampling method. Low discrepancy Quasi Monte Carlo methods are the most suitable initial sampling designs. Some 15–20 times the dimension of the problem may be the proper initial sample size; 2) The ASMO method is much more efficient than the widely used Shuffled Complex Evolution global optimization method. However, ASMO can provide only approximate optimal solutions, whose precision is limited by surrogate modeling methods and problem-specific features; and 3) The identifiability of model parameters is correlated with parameter sensitivity

Land surface processes and their coupling to the atmosphere over the Tibetan Plateau (TP) play an important role in modulating the regional and global climate. Therefore, identifying and quantifying uncertainty in these land surface model (LSM) processes are essential for improving climate models. The specifications of land cover and soil texture types, intertwined with the uncertainties in associated vegetation and soil parameters in LSMs, are significant sources of uncertainty due to the lack of detailed land survey in the TP. To differentiate the effects of land cover or soil texture specifications in the Noah with Multiple Parameterizations (Noah‐MP) LSM from the effects of uncertainties in the model parameters, this study first identified the most sensitive vegetation and soil parameters through global sensitivity analysis and then conducted parametric ensemble simulations using two land cover data sets and two soil texture data sets over the central TP to estimate their corresponding impacts on the overall model responses. The distinction level and the Kolmogorov‐Smirnov test were then applied to assess the differences between the results from parametric ensemble simulations using different land cover or soil texture data sets. The results show that the simulated energy and water fluxes over the central TP are dominated by soil parameters. The canopy height is the most sensitive vegetation parameter, and the Clapp‐Hornberger b parameter (the exponent in the function that relates soil water potential and water content) is the most sensitive soil parameter. Relative to the background parametric uncertainties, the Noah‐MP LSM could not sufficiently distinguish the effects of changes between forested types or soil texture types, which highlight the need for further quantifying and reducing the parametric uncertainties in LSMs. Further analysis shows significant sensitivities of the distinction level and changes in model response to annual precipitation and vegetation fraction. This work provides a scientific reference for assessing the impacts of land cover or soil texture changes on Noah‐MP simulations under future climate change conditions.

Field observations show that subtropical forests can accumulate soil organic carbon (SOC) at high rate; however, the key mechanisms of SOC accumulation in subtropical forests remain unclear. Here we selected three typical subtropical forests with soil originated from same parent material under same climate condition in Dinghushan Biosphere Reserve, southern China: coniferous forest, mixed forest, and broadleaved forest with very different rates of SOC accumulation over the last three decades. To quantify the relative importance of physical versus chemical process on SOC stabilization in these forests, we used regression analysis to evaluate the relationship between the soil physical (clay, silt, and sand) and chemical (pH, Fe, and Al oxides) properties and SOC, and explored their respective contributions to SOC stabilization. Results showed that SOC stabilization was more strongly influenced by soil clay fraction through sorptive protection or microaggregate formation with SOC in the coniferous forest (r² = 0.89, p < 0.05), and by chemical protection through forming organo‐mineral complexes in the mixed or broadleaved forest. Increasing carbon input was likely the main cause for SOC accumulation in the coniferous forest, but was not in the broadleaved forest. The strong linear relationships between SOC and Fe/Al oxides indicate that both the mixed (r² = 0.63 and 0.57 with Fe_o and Al_o, p < 0.05) and broadleaved forests (r² = 0.96 and 0.78 with Fe_d and Al_o, p < 0.05) on acid soils in southern China still have a great potential for further carbon sequestration in the future.

Uncertainty quantification (UQ) refers to quantitative characterization and reduction of uncertainties present in computer model simulations. It is widely used in engineering and geophysics fields to assess and predict the likelihood of various outcomes. This paper describes a UQ platform called UQ-PyL (Uncertainty Quantification Python Laboratory), a flexible software platform designed to quantify uncertainty of complex dynamical models. UQ-PyL integrates different kinds of UQ methods, including experimental design, statistical analysis, sensitivity analysis, surrogate modeling and parameter optimization. It is written in Python language and runs on all common operating systems. UQ-PyL has a graphical user interface that allows users to enter commands via pull-down menus. It is equipped with a model driver generator that allows any computer model to be linked with the software. We illustrate the different functions of UQ-PyL by applying it to the uncertainty analysis of the Sacramento Soil Moisture Accounting Model. We will also demonstrate that UQ-PyL can be applied to a wide range of applications

Structural and parametric problems associated with physical parameterizations are often tied together in weather and climate models. This study examines the sensitivities of turbine‐height wind speeds to structural and parametric uncertainties associated with the planetary boundary layer (PBL) parameterizations in the Weather Research and Forecasting model over an area of complex terrain. The sensitivity analysis is based on experiments from two perturbed parameter ensembles using the Mellor‐Yamada‐Nakanishi‐Niino (MYNN) and Yonsei University (YSU) PBL schemes, respectively. In each scheme, most of the intermember variances can be explained by a few parameters. Compared to the YSU parameters, the MYNN parameters induce relatively weaker (stronger) impacts on wind speeds during daytime (nighttime). The two schemes can overall reproduce the observed diurnal features of turbine‐height wind speeds. Differences in the daytime wind speeds are evident between the two ensembles. The daytime biases exist even with well‐tuned parameter values in MYNN, indicating the structural error. The YSU scheme better matches monthly mean daytime observations, partly due to the compensation among the biases in different wind strengths. Compared to YSU, MYNN generally better agrees with observations in both weak and strong wind conditions. However, the improvements accomplished for one condition by parameter tuning may degrade model performances for others, suggesting the relationships that link different conditions are not accurately represented in the parameterizations. Simulated biases due to structural errors are further identified by evaluating them for different time of day and locations. Ultimately, this study improves understanding of structural limitations in the PBL schemes and provides insights on further parameterization development.