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.

The TanSat carbon satellite is to be launched at the end of 2016. In order to verify the performance of its instruments, a flight test of TanSat instruments was conducted in Jilin Province in September, 2015. The flight test area covered a total area of about 11,000 km² and the underlying surface cover included several lakes, forest land, grassland, wetland, farmland, a thermal power plant and numerous cities and villages. We modeled the column-average dry-air mole fraction of atmospheric carbon dioxide (XCO₂) surface based on flight test data which measured the near- and short-wave infrared (NIR) reflected solar radiation in the absorption bands at around 760 and 1610 nm. However, it is difficult to directly analyze the spatial distribution of XCO₂ in the flight area using the limited flight test data and the approximate surface of XCO₂, which was obtained by regression modeling, which is not very accurate either. We therefore used the high accuracy surface modeling (HASM) platform to fill the gaps where there is no information on XCO₂ in the flight test area, which takes the approximate surface of XCO₂ as its driving field and the XCO₂ observations retrieved from the flight test as its optimum control constraints. High accuracy surfaces of XCO₂ were constructed with HASM based on the flight’s observations. The results showed that the mean XCO₂ in the flight test area is about 400 ppm and that XCO₂ over urban areas is much higher than in other places. Compared with OCO-2’s XCO₂, the mean difference is 0.7 ppm and the standard deviation is 0.95 ppm. Therefore, the modelling of the XCO₂ surface based on the flight test of the TanSat instruments fell within an expected and acceptable range.

In the context of global warming, significant differences in temperature changes occur in different regions. Obtaining a more accurate temperature distribution is important for studying regional climate change. A high accuracy surface modeling (HASM) was introduced using the air temperature output from the weather research and forecasting (WRF) model as the driving field, and observation data from meteorological stations as the accuracy control conditions to obtain 30 years of high accuracy temperature fields in the Heihe River Basin. Verified by ground‐based observations, the WRF model has a limited ability to simulate temperature conditions and performs worse in low altitude areas and in winter. The fusion results of the HASM decrease the mean absolute error from 2.21 to 0.9°C and decrease the root mean square error from 2.58 to 1.16°C, thus significantly increasing the accuracy compared with the original simulation from the WRF model and resulting in a more reasonable temperature analysis. The Mann–Kendall test shows clear warming in the Heihe River Basin, but there is a mutation year of 1998 in winter and annual average. The space–time distributions of and variations in the temperature from the HASM results are also analysed by dividing the study period into three subperiods. The analysis shows consistent warming except for the winter 2001–2010 period compared with the winter 1991–2000 period, in which the northeast area of the lower reaches of the Heihe River Basin was cool. Temperature changes in the Heihe River Basin also show that the sparsely populated Gobi Desert in the north and the southern high mountains show extreme sensitivity to climate change.

The COVID-19 outbreak that began in 2020 has changed human activities and thus reduced anthropogenic carbon emissions in most parts of the world. To accurately study the impact of the COVID-19 pandemic on changes in atmospheric XCO₂ concentrations, a data fusion method called High Accuracy Surface Modeling (HASM) is applied using the CO₂ simulation from GEOS-Chem as the driving field and GOSAT XCO₂ observations as the accuracy control conditions to obtain continuous spatiotemporal global XCO₂ concentrations. Cross-validation shows that using High Accuracy Surface Modeling greatly improves the mean absolute error and root mean square error of the XCO₂ data compared with those for GEOS-Chem simulation data before fusion, and the R² is also increased from 0.54 to 0.79 after fusion. Moreover, OCO-2/OCO-3 XCO₂ observational data verify that the fused XCO₂ data achieve a lower MAE and RMSE. Spatiotemporal analysis shows that the global XCO₂ concentration exhibited no obvious trend before or after the COVID-19 outbreak, but the growth of global and terrestrial atmospheric XCO₂ in 2020 can reflect the impact of the COVID-19 pandemic; that is, the rapid growth in terrestrial atmospheric XCO₂ observed before 2019 slowed, and high-speed growth resumed in 2021. Finally, obvious differences in the pattern of XCO₂ growth are found on different continents.

In the context of global warming, the thermal conditions of the Tibetan Plateau have changed significantly in recent decades. In the present study, we analysed the spatiotemporal variation in T ≥ 0 °C accumulated temperature (AT0) on the Tibetan Plateau from 1980 to 2018 and its effect on normalized difference vegetation index (NDVI) changes by fusing climate model outputs and ground observations using the High Accuracy Surface Modeling (HASM). Cross-validation revealed that the root mean square error (RMSE) and R² of the fused data from HASM were 1.593 °C and 0.719, respectively, which were greater than the 5.864 °C and 0.385, respectively, before fusion, indicating that HASM fusion improved the accuracy and that a more accurate AT0 could be obtained. Over the past 39 years, AT0 on the Tibetan Plateau had increased at a rate of 7.53 °C/year. The growth period was extended by 0.46 days/year, while the start and end of the growth period were 0.27 days/year earlier and 0.18 days/year later, respectively. Analysis of the decadal change in AT0 revealed that the areas with AT0 < 500 °C decreased by 5 % and that the areas with AT0 > 2000 °C increased by 6.2 %. However, a slower warming trend appeared after 2010 because of the decreasing rate of the daily mean temperature increase during the growth period. Increasing AT0 also promoted vegetation growth, especially in parts of the southern and eastern plateau regions, with a Pearson's correlation coefficient of 0.46 on the entire plateau between AT0 and the average NDVI during the growth periods. However, there was a significant negative correlation with a coefficient lower than −0.4 in the Qaidam Basin, and partial correlation analysis showed that the extension of the growth period was the main factor influencing the decrease in the NDVI in the Qaidam Basin.

Temperature changes have a major impact on all aspects of human society and have attracted global attention. The scarcity of observation data and the inaccuracy of the models make obtaining accurate temperature distributions a challenge. This study introduces high-accuracy surface modeling (HASM) combined with temperature simulations from the Weather Research and Forecasting (WRF) model and temperature records from observation stations to investigate the spatiotemporal characteristics of temperature in the Beijing-Tianjin-Hebei region during the period of 1956–2005. Leave-one-out cross-validation is applied to verify the temperature fields before and after the fusion of the models. The results indicate that the WRF model has a limited ability to simulate temperature conditions, but the overall deviation across the region is relatively large. The fusion results of the HASM decrease the mean absolute error (MAE) and the root-mean-square error (RMSE) by half in most instances, and the correlation between the fusion data and observation data is approximately 0.01–0.03 higher than that with the WRF simulation data. Based on the fusion data, obvious warming trends are observed during 1976–2005. In general, temperatures in spring, summer, and autumn increase rapidly from 1996 to 2005 but from 1976 to 1995 in winter. The substantial fluctuations in the interannual temperature during 1996–2005 in summer, autumn, and winter may be related to the frequent occurrence of extreme weather. The spatial distribution of temperature change differs in each season during 1956–1995. A dramatic increase in temperature occurs in the western part of the study area during 1996–2005 but with no seasonal difference.