Impact and Sensitivity Analysis of Soil Water and Heat Transfer Parameterizations in Community Land Surface Model on the Tibetan Plateau

Abstract

Soil water and heat transfer is especially complicated during the freezing and thawing processes over the high‐altitude cold regions. In this study, four sensitivity tests of soil water and heat transfer parameterizations including replacing soil property data (SP1), soil resistance scheme modification (SP2), soil thermal conductivity scheme (SP3) and virtual temperature scheme (SP4), and four combination experiments (SP1+SP2+SP3/SP5, SP1+SP2+SP4/SP6, SP1+SP3+SP4/SP7, and SP1+SP2+SP3+SP4/SP8) were done using Community Land Model (CLM5.0) to examine its performances for soil water and heat transfer modeling on the Tibetan Plateau (TP) both in single‐point and regional simulations. The observed data from five eddy covariance sites, four soil moisture and temperature networks and 60 sites of soil temperature observations on the TP were used to evaluate the results. Single‐point simulations show that SP2 experiment reduced the wet biases of soil moisture in semiarid area, but enhanced the error of soil temperature. SP6 shows the best performances in simulating soil moisture, and SP3 in soil temperature. Regional simulations show that the SP7 experiment had the best performances for soil water and heat transfer simulation on the TP, and improved the simulation of soil freezing‐thawing processes. Compared to CLM5.0 default simulation, SP7 shows the best performances. For soil moisture, it reduced average Bias by 23%, Root Mean Square Error (RMSE) by 18%, and increased the Correlation Coefficient (Corr) by2%. For soil temperature, it reduced the Bias by 9%, 10%, 23%, and 13% at four soil depths on the TP, respectively.

How Well Does the ERA5 Reanalysis Capture the Extreme Climate Events Over China? Part II: Extreme Temperature

“…Table 4 demonstrates the evaluation criteria used to quantitatively assess the applicability of ERA5, comprising the NRMSE, NMAE, RB, and CC. In addition, this study adopted a new comprehensive indicator DISO, with the advantage of evaluating the simulation accuracy of data more comprehensively than other indicators, instead of a single indicator limited to describing unilateral performance (Hu et al, 2019;Deng et al, 2021). The DISO had been confirmed more flexible to express the quality of models or datasets than the Taylor diagram (Xu and Han, 2020;Zhou et al, 2021).…”

“…Accordingly, the distance between indices of simulation and observation (DISO) combined with the CC, NRMSE, NMAE, and RB was used to evaluate the applicability of ERA5. The best advantage of DISO is that it can represent the performance of simulation at different stations after normalizing the reanalysis data and observational data (Hu et al, 2019;Deng et al, 2021). In addition, further research is required to determine the credibility of the latest ERA5 reanalysis dataset in capturing extreme temperature events at different timescales over various Chinese regions, which is not limited to the analysis of heatwave event trends or spatial distribution characteristics (Albergel et al, 2018;Kozubek et al, 2020;Awasthi 2021).…”

CCHZ‐DISO: A Timely New Assessment System for Data Quality or Model Performance From Da Dao Zhi Jian

“…In fact, CCHZ‐DISO provide a new framework about the evaluation system, which is a reconstruction of the present system. The most important contribution of our CCHZ‐DISO is that it can comprehensively qualify the overall performances of different datasets or models when the number of statistical metrics is larger than 1, which is highly recommend by large literature (Deng et al., 2021; Kalmar et al., 2021).…”

The Response of Vegetation to Regional Climate Change on the Tibetan Plateau Based on Remote Sensing Products and the Dynamic Global Vegetation Model

“…To investigate the performance of CLM5.0-DGVM in simulating ecosystem productivity and vegetation distribution over the TP, we conducted the following set of regional simulations on the TP using CLM5.0-DGVM. (1) Following Deng et al [24], we replaced the soil property data with the Beijing Normal University soil property data, the Balland and Arp, and the virtual temperature schemes were used to replace the original code in the CLM5.0-DGVM. In addition, we modified the establishment and survival code so that PFTs could establish on the TP, and we replaced the vegetation parameters with observations.…”

“…The accuracy of soil moisture and temperature simulations in the model is significant for improving the ability to simulate ecosystem productivity. Following the work of Deng et al [24], in which the soil moisture and temperature simulations in CLM5.0 were improved for the TP, the revised soil water and heat transfer schemes were used in all the simulations in this paper. Firstly, we modified the original code in the establishment and survival module, replaced the default vegetation parameters with observations (referred to as the BGCDV_CTL experiment), and evaluated the performance of the BGCDV_CTL experiment.…”