Simulation and analysis of XCO2 in North China based on high accuracy surface modeling

Abstract

As an important cause of global warming, CO2 concentrations and their changes have aroused worldwide concern. Establishing explicit understanding of the spatial and temporal distributions of CO2 concentrations at regional scale is a crucial technical problem for climate change research. High accuracy surface modeling (HASM) is employed in this paper using the output of the CO2 concentrations from weather research and forecasting-chemistry (WRF-CHEM) as the driving fields, and the greenhouse gases observing satellite (GOSAT) retrieval XCO2 data as the accuracy control conditions to obtain high accuracy XCO2 fields. WRF-CHEM is an atmospheric chemical transport model designed for regional studies of CO2 concentrations. Verified by ground- and space-based observations, WRF-CHEM has a limited ability to simulate the conditions of CO2 concentrations. After conducting HASM, we obtain a higher accuracy distribution of the CO2 in North China than those calculated using the classical Kriging and inverse distance weighted (IDW) interpolation methods, which were often used in past studies. The cross-validation also shows that the averaging mean absolute error (MAE) of the results from HASM is 1.12 ppmv, and the averaging root mean square error (RMSE) is 1.41 ppmv, both of which are lower than those of the Kriging and IDW methods. This study also analyses the space-time distributions and variations of the XCO2 from the HASM results. This analysis shows that in February and March, there was the high value zone in the southern region of study area relating to heating in the winter and the dense population. The XCO2 concentration decreased by the end of the heating period and during the growing period of April and May, and only some relatively high value zones continued to exist.

Dynamical Downscaling of CO2 in 2016 Over the Contiguous United States Using WRF‐VPRM, a Weather‐Biosphere‐Online‐Coupled Model

“…Early studies of WRF‐VPRM development/application (Ahmadov et al, 2007; Ahmadov et al, 2009; Pillai et al, 2011) tested the system in a few case studies and with sparse tower measurements over small sub‐Europe domains. A few published studies (Diao et al, 2015; Feng et al, 2016; Liu et al, 2018; Park et al, 2018) have applied WRF‐VPRM to other continents. Diao et al (2015) evaluated WRF‐VPRM using surface CO 2 observations at three sites during a 5‐day period in 2010 over eastern China, and Liu et al (2018) evaluated WRF‐VPRM using the greenhouse gases observing satellite (GOSAT) retrieved column‐averaged CO 2 concentrations (XCO 2 ) over north China for 4 months in 2015; both Feng et al (2016) and Park et al (2018) applied WRF‐VPRM to the Southern California Air Basin, which is strongly affected by anthropogenic CO 2 emissions and less affected by biogenic CO 2 fluxes.…”

Terrestrial CO2 Fluxes, Concentrations, Sources and Budget in Northeast China: Observational and Modeling Studies

“…Nevertheless, the off‐line simulation cannot capture the real‐time feedback between synoptic weather and land‐surface dynamics and leads to large uncertainties in the simulated CO 2 fluxes/concentrations (Hu, 2008). Liu et al (2018) reported that surface CO 2 concentrations simulated with the WRF‐VPRM exhibited a similar trend with those observed at the Shangdianzi station in Beijing from February to May in 2015. In sum, long‐term simulations and observations of CO 2 fluxes/concentrations required to investigate and quantify CO 2 sources/sinks and budget in China are currently rarely reported (He et al, 2019; Thompson et al, 2016; Zhang et al, 2014).…”

Temperature changes in the Heihe River Basin based on high accuracy surface modelling

“…In previous research, the HASM has been successfully applied to simulate the distribution of soil properties (Shi et al, , ), carbon storage (Wang et al ., ; Yue et al, ) and XCO 2 (Zhang et al, , ; Liu et al, ) on global and regional scales. In a study on climate change, Zhao et al .…”

“…In previous research, the HASM has been successfully applied to simulate the distribution of soil properties (Shi et al, 2011(Shi et al, , 2016, carbon storage Yue et al, 2016) and XCO 2 Liu et al, 2018) on global and regional scales. In a study on climate change, Zhao et al (2017aZhao et al ( , 2017bZhao et al ( , 2018 introduced a HASM accompanied by GWR for temperature and precipitation simulations in China and in the Beijing-Tianjin-Heibei region, and great simulation results were achieved.…”