Evaluation of Land Surface Subprocesses and Their Impacts on Model Performance With Global Flux Data

Abstract

Study on the uncertainties in land surface models (LSMs) helps us understand the differences and errors in climate models. Meanwhile, uncertainty in model structure, derived from the many possible parameterization schemes for the same physical subprocess, is a primary source of land model uncertainties. To attribute structural errors and model parameterization scheme uncertainties, it is critical to identify the key subprocesses involved and investigate the interactions of these subprocesses on LSM behavior, which will ultimately help us identify the “optimal” parameterization schemes for various plant functional types, soil types, and different locations. Here, we conduct physical ensemble simulations for multiple sites from FLUXNET and then apply a variance‐based sensitivity analysis method to quantitatively assess the impacts of uncertainties in the parameterization schemes of subprocesses in the Noah with multiparameterization (Noah‐MP) LSM on model performance. The results show that three subprocesses—surface exchange coefficient, runoff and groundwater, and surface resistance to evaporation—have the most significant impacts on the performance of the simulated sensible heat flux, latent heat flux, and net absorbed radiation in the Noah‐MP LSM. The interaction between two subprocesses could contribute up to 50% of the variation in model performance for some sites, which highlights the need for taking into consideration the interactions of subprocesses to improve LSMs. Finally, a statistical optimal combination of the parameterization schemes is recommended for global land modeling, although it is noticed that the optimal schemes vary with regions and can be different even for neighboring sites.

Quantifying Contributions of Uncertainties in Physical Parameterization Schemes and Model Parameters to Overall Errors in Noah‐MP Dynamic Vegetation Modeling

“…In contrast to Li et al (2019) focusing on the reasons for model differences, this study stressed the errors in the Noah-MP LSM with dynamic vegetation, but generally, the results of the parameterization sensitivities were consistent with previous studies. For example, both this study and Li et al (2019) agreed that the uncertainty in SFC greatly impacted the energy and water balance. The same conclusion can be found in Niu et al (2011), Yang et al (2011), Zhang et al (2016), and Zheng et al (2019), although the quantitative sensitivity of SFC varied with region due to diverse climatic conditions.…”

Implementation and Evaluation of a Unified Turbulence Parameterization Throughout the Canopy and Roughness Sublayer in Noah‐MP Snow Simulations

“…Further details of Noah-MP snowpack treatment can be found in Niu et al (2011) and Chen et al (2014). Multiple physics options within Noah-MP provide the unique ability to run a multi-model ensemble using a single LSM (Hong et al, 2014;Li et al, 2019Li et al, , 2020Yang et al, 2011;You et al, 2020;Zhang et al, 2016). In experiments conducted herein, model-physics options are selected to match the WRF/Noah-MP options used in the continental-scale convection-permitting regional climate simulations (He et al, 2019;Liu et al, 2017 (Yang et al, 2011).…”

“…In experiments conducted herein, model-physics options are selected to match the WRF/Noah-MP options used in the continental-scale convection-permitting regional climate simulations (He et al, 2019;Liu et al, 2017 (Yang et al, 2011). Quantitative results presented in this manuscript may change depending on different model-physics options and parameters; however, comprehensively assessing model sensitivity to various combinations of physics options available within Noah-MP such as in Zhang et al (2016), Li et al (2019) and Zhang et al (2020) is beyond the scope of this study.…”

Ensemble Skill Gains Obtained From the Multi‐Physics Versus Multi‐Model Approaches for Continental‐Scale Hydrological Simulations

“…The labels are r1 for SIMGM, r2 for SIMTOP, r3 for NOAHR, and r4 for BATS; b1 for NOAHB, b2 for CLM, b3 for SSiB; t1 for M-O, t2 for Chen97; c1 for Ball-Berry, c2 for Jarvis. (Li et al, 2019;Zheng et al, 2019), different parameterizations interplay with each other. With the SIMGM runoff scheme (r1), the Ball-Berry-type scheme (c1) of the stomatal conductance parameterization outperforms the Jarvis-type scheme (c2) in most RFCs except SE, NW, WG, and CN; whereas with the NOAHR runoff scheme (r3), the Jarvis scheme (c2) outperforms the Ball-Berry scheme (c1) in SE, NW, MB, WG, CN, and CB.…”