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Communication

Simulation Study on the Effect of Elevated CO2 on Regional Temperature Change on the Loess Plateau

by
Zhifang Shi
1,2,3,4,
Yaoping Cui
1,2,3,4,*,
Liyang Wu
2,
Yan Zhou
2,
Mengdi Li
1,2,3,4 and
Shenghui Zhou
2
1
Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Henan University, Ministry of Education, Kaifeng 475000, China
2
School of Geography and Environmental Science, Henan University, Kaifeng 475000, China
3
Henan Dabieshan National Field Observation and Research Station of Forest Ecosystem, Zhengzhou 450046, China
4
Xinyang Academy of Ecological Research, Xinyang 464000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(10), 2607; https://doi.org/10.3390/rs15102607
Submission received: 5 March 2023 / Revised: 10 May 2023 / Accepted: 15 May 2023 / Published: 17 May 2023
(This article belongs to the Special Issue Advancement of Remote Sensing in Regional Climate Modeling: Observations, Mechanisms, and Projections)
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Abstract

:
CO2 undisputedly affects global temperature change, but the specific impact of change in atmospheric CO2 concentration on regional warming remains to be quantified, especially in different climatic backgrounds. Taking the Loess Plateau as the research area, this study quantified the effect of CO2 elevation on regional temperature change based on a single-factor sensitivity experiment of the regional Weather Research and Forecasting (WRF) climatic model, and the results revealed the following: (i) The correlation coefficient between monthly mean values of temperature simulated by the WRF model and the observed values reached 0.96 (p < 0.01), and the overall spatial trends of simulated and observed temperatures increased from the northwest to the southeast. (ii) CO2 concentration increased from 370.70 ppm in 2000 to 414.54 ppm in 2020, and the Loess Plateau region warmed by 0.04 and 0.06 °C under the MODIS land cover of 2000 and 2020, respectively. This indicates that increase in CO2 concentration over the Loess Plateau has greater impact than land cover change on regional temperature change. (iii) As CO2 concentration increased, the maximum fluctuation of temperature in summer exceeded 2.0 °C, while the fluctuations in spring (0.72 °C), autumn (0.77 °C), and winter (0.15 °C) were relatively small, indicating that summer temperature is most sensitive to CO2 concentration change. By emphasizing the marked temperature difference associated with the same CO2 change in different seasons, this study provides an important basis for extending the understanding of the differences in the effect of CO2 on regional temperatures.

1. Introduction

The rapid increase in greenhouse gas (GHG) concentration is closely related to human activities and, as a direct result, the global temperature has increased by approximately 1 °C relative to that of the preindustrial period [1], with CO2 (an important GHG component) contributing 76% to the total global warming [2]. The Grain to Green (GTG) program was a large-scale ecological restoration program in China in the late 1990s. Following implementation of this program, vegetation coverage on the Loess Plateau increased from 29% to 46% [3,4,5]. Afforestation modifies the characteristics of the land surface and thus influences the physical processes of land–atmosphere interactions [6,7]. Vegetation coverage on the Loess Plateau has improved substantially, and the quality of the ecological environment has been enhanced [8]. Therefore, studying the effect of change in GHG concentration on the temperature of the region is an important indicator of regional ecological development.
Numerous studies have demonstrated the impact of change in atmospheric CO2 concentration on temperature at global and regional scales [9,10,11]. For example, de Noblet-Ducoudré and Pitman used two different land cover types, CO2 levels, and corresponding sea surface temperatures to compare the effect of increasing GHG concentration on global temperature [12,13]. Benjamin conducted simulations to compare the contribution to temperature change of variation in CO2 concentration associated with land use change, based on CO2 concentration levels and land cover types in different periods [14]. Not only does CO2 affect climate change on the global scale but it also has a microclimatic effect on the regional scale [15,16,17]. Some studies have shown that similar land use changes can cause different climatic responses in different climatic contexts, leading to differences in the change in near-surface temperature [18,19]. Through numerical simulations, Hu Zuheng found that the effects of land use change on near-surface temperature in regions of Europe differed under different climatic contexts (e.g., CO2 concentration levels in 1850 and 2000) [20]. The most recent studies have focused on the relationship between GHG concentration and climate change at global and large regional scales, and they have found that the adoption of GHG concentrations representative of different periods can directly affect regional climate change [21,22,23]. Such studies have mainly used CO2 as a background climatic field to explore its influence on climate change.
Numerical modeling allows sensitivity simulations to be performed with parameter control to discern the possible climatic effects of changes in CO2 concentration. Although studies based on observational data emphasize the importance of the climatic effects of GHGs, such an approach has limitations. For example, not only is it problematic to exclude the effects of other radiative forcing factors such as change in vegetation and aerosols, and to eliminate the effects of long-term climate fluctuations, but also the causal relationships that exist in land–air interactions are difficult to interpret [24,25,26]. In contrast, numerical modeling based on climate models can compensate for the shortcomings of observational methods, both by quantitatively characterizing the spatial and temporal characteristics of regional climate change caused by GHG change, and by providing insight into the processes and mechanisms involved [27,28,29]. Numerical experiments on background changes in GHGs have shown that increases in GHGs largely control the net effect of regional temperature change, which also demonstrates the importance of the background climatic field (CO2) in influencing regional temperatures [19]. Numerical simulations play an important role in climate research and can be used to reveal the effects of temperature change due to CO2 change [30].
This study used the Weather Research and Forecasting (WRF) model to quantify the effect of changes in CO2 on regional near-surface temperature (2-m air temperature) on the Loess Plateau. Sensitivity experiments were set up by varying land use cover and CO2 concentration to evaluate the effects of the same regional CO2 increase on two sets of MODIS land cover. The objective was to provide new scientific evidence to extend the understanding of the differences in the effects of change in CO2 concentration on regional temperature in different climatic contexts and at different times.

2. Materials and Methods

2.1. Study Area

The Loess Plateau lies between 33°43′–41°16′N and 100°54′–114°33′E, covering an area of approximately 6.4 × 105 km2 (Figure 1). The Loess Plateau has a special geographical position, with undulating terrain from northwest to southeast, and an elevation difference of over 5000 m. It bridges the areas of plains and plateaus in China and is an important climatic transition zone. It has a continental monsoon climate, with an average annual temperature ranging from 4 to 12 °C and average annual precipitation of approximately 600 mm; the temperature and precipitation are distributed extremely unevenly, both temporally and spatially [31]. The Loess Plateau is the main source of sand production for the Yellow River, and it is also one of the areas with the most serious soil erosion in China and even in the world. Indeed, the soil of the Loess Plateau is loose and easily scoured by heavy rainfall. Consequently, more than 60% of the land area has varying degrees of soil erosion, with an average annual sediment loss of 2000–2500 t/km2, and nearly 90% of the Yellow River’s incoming cement sand originates from this area [32].

2.2. Data Resource and Preprocessing

The 2000 and 2020 remote sensing image data used in this study were acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the US Earth Observation Program EOS/TERRA satellite and made available by the LAADS DAAC of NASA (https://ladsweb.modaps.eosdis.nasa.gov/ (accessed on 7 July 2022)). The MODIS data are directly available as data products that include the subsurface product MCD12Q1, leaf area index product MOD15A2H, surface albedo product MCD43A3, and surface specific emissivity product MOD11A2.
The land use transfer matrix is the main method for the quantitative study of the quantity and direction of interconversion between land use types, which can specifically reflect the structural characteristics of land use change and the direction of transfer be-tween types. This paper uses the land use transfer matrix to analyze the land use transfer directions in different zones in the Loess Plateau from 2000 to 2020 (Table 1). The table shows the area of each category transferred from 2000 to 2020 from top to bottom, where the last column is the area of each category in 2000 and the last row is the area of each category in 2020. The areas of forest and cropland on the Loess Plateau increased substantially; the areas of built-up land and water bodies gradually expanded, and the areas of grassland and barren land decreased markedly. The areas of forest and cropland on the Loess Plateau increased substantially; the areas of built-up land and water bodies gradually expanded, and the areas of grassland and barren land decreased markedly.
The National Centers for Environmental Prediction (NCEP) FNL reanalysis is characterized by its high resolution and full assimilation of a large number of observations and satellite inversions. The data, updated from July 1999 to the present, have spatial resolution of 1° × 1° and 6 h temporal resolution, and are available from the NCEP/National Center for Atmospheric Research (NCAR) website (https://rda.ucar.edu/ (accessed on 15 August 2022)).
The temperature data were derived from the Chinese 1-km-resolution monthly mean temperature dataset, available from the National Tibetan Plateau Scientific Data Center (https://data.tpdc.ac.cn/zh-hans/ (accessed on 10 November 2022)) [33]. The temperature dataset is based on the global 0.5° climate dataset published by the Climatic Research Unit (CRU) of the University of East Anglia (UK), using the Delta spatial downscaling scheme, and it is stored in the common format used for meteorological data, i.e., NETCDF. This regional high-resolution meteorological dataset for China, which has been validated by more than 400 meteorological stations with good fitting results, is considered suitable for use in regional climate studies in China [34,35,36].
The CO2 concentration data were obtained from NOAA station data (https://gml.noaa.gov/ccgg/ (accessed on 20 August 2022)). The annual average CO2 concentration data from the Mt. Waliguan, Tae-ahn Peninsula, and Anmyeon-do stations, located at the same latitude as that of the Loess Plateau region, were selected and averaged to obtain a CO2 concentration of 370.70 ppm in 2000 and 414.54 ppm in 2020.

2.3. Model Setup and Experiments

The WRF mesoscale numerical weather model was developed by NCEP, NCAR, and other operational departments and research institutions. The WRF model has been used widely in regional climatic simulations, land–air interactions, and other scientific research fields [37,38]. In this study, the WRF simulation area comprised two nested layers (D01 and D02) with a central latitude and longitude position of 38.00°N, 107.50°E. Domain 2 (D02) consisted of 411 × 331 grid points with a 3 km horizontal grid resolution. The projection adopted was the Lambert equirectangular conical projection suitable for mid-latitude regions, with two standard latitude lines: one at 30°N and the other at 60°N (Table 2). The NCEP FNL reanalysis information provided the meteorological forcing fields for the WRF model. The modeling system was configured with 45 vertical levels from the surface to the 50 hPA level. The parameterization schemes chosen included the Thompson microphysical parameterization scheme [39], Rapid Radiative Transfer Model longwave radiation parameterization scheme [40], Dudhia shortwave radiation parameterization scheme [41], MYJ boundary layer parameterization scheme [42], and Noah land surface parameterization scheme [43].
Land cover type information and CO2 concentration data for 2000 and 2020 were used to provide a more realistic representation of the impact of increasing CO2 concentration on the regional climate of the Loess Plateau in 2000 and 2020. Four sets of simulations were designed for this study, and all four sets of experiments used the same combination of parameterization schemes and NCEP FNL reanalysis data (Table 3). In the L1 experiment, the MODIS land subsurface data for 2000 were used, and the GHG concentration was taken as the value for 2000 (i.e., 370.70 ppm) to represent the temperature scenario of the Loess Plateau in 2000. The L2 experiment used the MODIS land subsurface data for 2000 and the GHG concentration in 2020 (i.e., 414.54 ppm). The L3 experiment used the MODIS land subsurface data for 2020 and the GHG concentration in 2000. The L4 experiment used the MODIS land subsurface data for 2020 and the GHG concentration in 2020. L2−L1 revealed the effects of increasing CO2 concentration on the regional temperature of the Loess Plateau with 2000 land cover; L4−L3 revealed the effects of increasing CO2 concentration on the regional temperature of the Loess Plateau with 2020 land cover. To analyze the seasonal pattern of temperature change on the Loess Plateau, the study period was divided into four seasons: spring (March–May), summer (June–August), autumn (September–November), and winter (December–February)

3. Results

3.1. Verification of Simulation Accuracy

In this study, to verify the simulation capability of the WRF model, the results of the WRF simulation (L4) were compared with the observation temperature data for 2020 (Figure 2). The results show that the WRF model can effectively simulate the spatial distribution of annual mean temperature on the Loess Plateau, which shows a trend of increase from the northwest to the southeast, with temperature contours rising from 6 °C to 8 °C and 12 °C. The WRF simulated temperature correlates with site elevation, with lower temperatures simulated in the northwestern region at higher elevations (lowest temperature; −11.17 °C) and higher temperatures simulated on the plains (temperature: >12 °C). In comparison with the simulated results, the observed data reflect lower temperatures in the northwest of the Loess Plateau, with a minimum temperature of −12.2 °C and higher temperatures in the low-elevation regions in the southeast, with a maximum temperature of 16.0 °C (all temperatures in this part of the region were >12 °C). Although there is a difference of 1–3 °C between the simulated and observed maximum temperatures, the spatial pattern of temperature is consistent from the highlands and mountains in the northwest to the plains in the southeast. This demonstrates that the WRF model can simulate near-surface temperatures in the study area reasonably well.
The temperatures simulated for the study period fit the observed data well temporally, with the R2 value of 0.99 (p < 0.01) (Figure 3). The mean temperature difference between observation and WRF simulation was 1.21 °C. The temperature bias mostly appears around 0.0 °C, and 76.56% of temperature bias was within the interval (−3, 3) °C. The WRF-simulated temperatures were overestimated, which might reflect the uncertainties of the model’s dynamics framework, inaccurate input topography, and the biases of boundary field driven data [44,45]. Comparison reveals that the WRF model can reproduce the spatial and temporal variations in temperature well, and that its application in this study has both reasonableness and reliability.

3.2. Effect of CO2 Concentration Variation on Annual Variation of Temperature

The increase in CO2 concentration from 370.70 ppm in 2000 to 414.54 ppm in 2020 caused the Loess Plateau to warm by 0.04 °C in 2000 and by 0.06 °C in 2020 (Figure 4). in According to 2000 land cover, comparing L2 to L1, central and northern areas of the Loess Plateau became warmed, which accounted for 76.67% of the total area, and approximately 23.33% of the Loess Plateau became cooled. Comparing L4 to L3, the increase in CO2 concentration caused 88.54% of the Loess Plateau to be warmed, representing an increase in area of 11.87% in comparison with that from 2000 land cover. Moreover, the main area of warming shifted from central and northern regions toward the south. Finally, the results suggested that the increase of CO2 concentration over the Loess plateau has a greater impact on regional temperature change than land cover change.
In comparison with the 2000 land cover, the warming intensity on the Loess Plateau with 2020 land cover due to the increase in CO2 concentration was higher. Comparison of L2 to L1 reveals that the warming of 0.12 °C or more was mainly concentrated in northern parts of the region, and there was also a scattered distribution in central parts, representing an area of 1.78% with a maximum temperature difference of 0.21 °C. Comparison of L4 to L3 reveals that the warming on the Loess Plateau due to the increase of CO2 concentration was even stronger, with 8.00% of the area warming by up to 0.12 °C or more, including 0.34% of the area in southeastern parts with warming of up to 0.20 °C or more, and the maximum warming difference was 0.28 °C. The results show that, compared with the land cover in 2000, the land cover in 2020 is more sensitive to the increase of CO2.

3.3. Impact of CO2 Concentration on Intra-Annual Temperature

The temperature variation due to elevated levels of atmospheric CO2 concentration on the Loess Plateau varies seasonally, with significant temperature variation in summer and insignificant temperature variation in spring, autumn, and winter (Figure 5). Comparison of L2 to L1 reveals that the main feature of the temperature changes in spring was the warming in central and southern parts of the Loess Plateau, and the area of the warming was approximately 85.54%. In summer, approximately 41.15% of the area of the Loess Plateau experienced significant cooling, among which 0.43% had cooling of 0.40 °C or more, while all other areas had different degrees of warming. In autumn, approximately 72.08% of the region experienced warming, with only a small change in temperature. On the Loess Plateau, 83.82% of the region experienced warming in winter, among which there was significant warming in northern parts, with warming of more than 0.30 °C. Comparison of L4 to L3 reveals that the area of spring warming on the Loess Plateau expanded with 2020 land cover, and the area of warming increased by 3.38%. There was significant change in the summer temperature distribution pattern, with significant warming in central and southeastern parts of the Loess Plateau, and the area of warming increased from 58.85% to 74.11%. The areas of warming in autumn and winter decreased. However, warming increased in some areas in the southeast in autumn, and the areas of warming in winter were mainly concentrated in northern regions.
The daily temperature difference on the Loess Plateau fluctuated significantly in summer, fluctuated moderately in spring and autumn, and did not change significantly in winter (Figure 6). In summer, there was significant fluctuation in the daily temperature difference on the Loess Plateau of 1.91 °C. In autumn, the daily variation of temperature difference was 0.77 °C, and the temperature difference had some fluctuation. In winter, the mean change in temperature difference was 0.045 °C, with a daily variation of 0.15 °C. The daily variation in temperature difference due to the increase in atmospheric CO2 concentration with 2020 land cover was similar to that with 2000 land cover in terms of seasonal performance. In spring, the daily temperature variation on the Loess Plateau increased in magnitude (0.72 °C). In summer, the daily temperature difference continued to show significant changes, with variation of 1.90 °C. In autumn, the daily temperature variation on the Loess Plateau decreased (0.54 °C). The daily temperature difference in winter still did not show significant change (0.12 °C). This indicates that the Loess Plateau is most sensitive to change in atmospheric CO2 concentration in summer, followed by spring and autumn, while winter temperatures are less sensitive to change in atmospheric CO2 concentration.

4. Discussion

In comparison with the results and observations of other studies, the results of this study are within a reasonable range. Liu Pan analyzed observations from 79 meteorological stations on the Loess Plateau and found that the warming rate in eastern parts was higher than the overall warming rate, which is consistent with the changes in warming patterns found in this study [46]. Tian used regional climate model simulations to find that the annual temperature on the Loess Plateau increased annually, further demonstrating the reliability of the results of this study [47]. The results of this study showed that summer temperature was most sensitive to change in CO2 concentration, and that the response in some areas was cooling. This might be related to the fact that summer is the growing season of vegetation. The greening of vegetation could lead to a marked reduction in albedo, which would increase the net shortwave radiation and thus increase the sensible heat flux [48]. Additionally, the latent heat flux would also be increased substantially and the cooling effect due to evapotranspiration would reduce the temperature in some areas. However, the change of sensible heat flux was stronger than the energy change caused by evapotranspiration at the inter-annual scale. Overall, the Loess Plateau showed a warming effect on the annual scale [47]. Some discrepancies exist between the simulated results of this study and the observed data, which is a common problem in relation to numerical models [49]. This might be related to the spatial resolution of the setting. The resolution of the WRF simulation in this study was 3 × 3 km, but the input data was 1 × 1 km, which might have caused errors in the simulation results [50]. In comparison with analyses based on station data and reanalysis information, simulation experiments have the advantage of wide spatial coverage and good data coincidence [51]. In this study, the climatic effects of elevated levels of atmospheric CO2 concentration were studied by comparing and analyzing the differences between the simulations; consequently, systematic errors do not affect the validity of the results. The approach adopted in this study had some limitations Soil erosion, aerosols, vegetation type, soil moisture, and air pollution are directly related to land cover; however, they were not considered in this study. In addition, other soil-related GHGs were not included in the study. The impact of such factors on regional temperature will be considered in our future research.
Land cover changes have different sensitivity to the increase in CO2. From 2000 to 2020, the areas of forest and cropland on the Loess Plateau increased significantly, the areas of built-up land and water bodies gradually expanded, and the areas of grassland and barren land decreased markedly (Figure 7). Through comparative analysis of the differences between simulation experiments, we found that the increase of CO2 concentration has a greater impact on regional temperature than land cover change. By analyzing the difference of regional temperature caused by CO2 concentration increase with land use in 2020 and land use in 2000, it is found that in the process of CO2 concentration increase and the average temperature difference of most land use transfer is positive, showing a warming phenomenon (Table 4). Only the average values of Barren land–Grassland, Barren land–Barren Land, Water Body–Grassland and Grassland–Barren Land temperature differences were negative. The minimum and maximum values of most land use transformation were negative and positive, indicating that the same land use transfers presented different temperature changes during the process of CO2 concentration increase. Owing to the marked variation in elevation fluctuation of the Loess Plateau, the importance of the albedo effect increases and that of the warming effect decreases with increasing elevation [52,53].
The rate of increase in temperature on the Loess Plateau is much smaller than the rate of change in temperature across China. The average annual temperature in China showed a trend of increase between 2000 and 2016, with a rate of increase of 0.0905 °C/10a [54]. Our results show that the rate of increase in temperature on the Loess Plateau was 0.018 and 0.028 °C/10a for the 2000 and 2020 scenarios, respectively, i.e., much smaller than the annual average rate of increase in temperature in China. Implementation of the Grain-to-Green program in China has made notable contribution to the recovery of vegetation on the Loess Plateau, with substantial increases in the area of woodland and grassland and vegetation coverage [55]. The average annual rate of change in the Normalized Difference Vegetation Index on the Loess Plateau between 2000 and 2015 reached 0.15%, and ecological restoration projects were the main drivers of vegetation change in the region [56,57]. Afforestation can mitigate the effect of warming in the Loess Plateau region via carbon sequestration and by providing higher efficiency in terms of heat dissipation and evapotranspiration [58,59].
Ecological restoration of the Loess Plateau has increased the contribution of local ecosystems to carbon storage, and the area of the Loess Plateau showing carbon sink properties has gradually increased. Carbon sequestration by the Loess Plateau ecosystem has increased significantly (19.2 gC m−2 a−1 on average), which has resulted in a total of 96.1 Tg C being fixed in the land, equivalent to 6.4% of China’s total fossil fuel CO2 emissions in 2006 [60]. The carbon sink area has expanded in all directions, centered on the grassland ecosystem in the central part of the Loess Plateau, which is the area of focus of the Loess Plateau Integrated Soil Erosion Management Project [61,62]. Generally, the Loess Plateau is mainly a carbon sink, and with implementation of reforestation projects, the forested areas of the Loess Plateau have increased, making them more stable than the grasslands and croplands that are more vulnerable to the effects of human activities [63,64,65]. The carbon source/sink effect of the Loess Plateau ecosystem also has an impact on the regional climate, and its role in regional temperature change should be considered in future studies.

5. Conclusions

Using the WRF climate model, this study investigated the impact of elevated levels of atmospheric CO2 concentration on regional temperature through four sets of sensitivity experiments, using MODIS remote sensing data products to evaluate the effects of increasing CO2 on land cover in the Loess Plateau in 2000 and 2020. The study demonstrated that the monthly mean temperature values simulated by the WRF model correlated strongly with the observed values (R2 = 0.99, p < 0.01), and that the overall spatial trend of temperature increase from the northwest to the southeast reflected good performance on spatial and temporal scales. The results suggested that the increase in CO2 concentration over the Loess Plateau has greater impact than land cover change on regional temperature change. The Loess Plateau is most sensitive to change in CO2 concentration in summer, followed by spring and autumn, while winter temperatures are least sensitive to changes in CO2 concentration. Our findings reveal the impact of CO2 variability on regional temperature in relation to different land cover and elucidate the differences in sensitivity on the seasonal scale. In addition, both model-driven data and model-physical process parameterization schemes have some uncertainties, thus the results of this study are still subject to some uncertainties and require more model simulations for comparison and ensemble simulations, or more ground observation data for validation.

Author Contributions

Conceptualization, Y.C. and Z.S.; methodology, Z.S., Y.C., L.W. and S.Z.; software, Z.S., S.Z. and L.W.; formal analysis, Y.C., Z.S., Y.Z. and M.L.; writing—original draft preparation, Z.S.; writing—review and editing, Y.C. and M.L.; supervision, Y.Z.; funding acquisition, Y.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Natural Science Foundation of China (42071415), the Outstanding Youth Foundation of Henan Natural Science Foundation (202300410049), the Social Science Planning Decision Consulting Project of Henan Province (2022JC45), the Postgraduate Education Reform and Quality Improvement Project of Henan Province (YJS2023JC22, SYLJC2022001), and the Xinyang Academy of Ecological Research Open Foundation (2023XYMS01).

Data Availability Statement

Data in this study are available upon request by contacting the corresponding author.

Acknowledgments

The authors acknowledge the National Tibetan Plateau Data Center and NASA’s Land Processes Distributed Active Archive Center (LP DAAC, https://modis.gsfc.nasa.gov/data/ (last accessed on 10 November 2022)) for providing temperature data, land use data, and MODIS products. We also thank James Buxton MSc for editing the English text of a draft of this manuscript.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Location map of the Loess Plateau: (a) the location of the WRF domain and (b) the spatial distribution of altitude within the Loess Plateau.
Figure 1. Location map of the Loess Plateau: (a) the location of the WRF domain and (b) the spatial distribution of altitude within the Loess Plateau.
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Figure 2. Comparison of spatial distribution of mean annual near-surface temperature in 2020: (a) gridded observation temperatures and (b) WRF simulated temperatures.
Figure 2. Comparison of spatial distribution of mean annual near-surface temperature in 2020: (a) gridded observation temperatures and (b) WRF simulated temperatures.
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Figure 3. Comparison of observation and WRF L4 simulation data: (a) scatter plot of monthly mean temperature of observation and WRF simulation for 2020, (b) spatial and statistical distributions of biases at the pixel level from the observation and WRF simulation for 2020.
Figure 3. Comparison of observation and WRF L4 simulation data: (a) scatter plot of monthly mean temperature of observation and WRF simulation for 2020, (b) spatial and statistical distributions of biases at the pixel level from the observation and WRF simulation for 2020.
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Figure 4. Effects of CO2 concentration changes on the temperature of Loess Plateau in 2000 and 2020. The left figure reveals the effects of increasing CO2 concentration on the regional temperature of the Loess Plateau with 2000 land cover, and the right figure reveals the effects of increasing CO2 concentration on the regional temperature of the Loess Plateau with 2020 land cover.
Figure 4. Effects of CO2 concentration changes on the temperature of Loess Plateau in 2000 and 2020. The left figure reveals the effects of increasing CO2 concentration on the regional temperature of the Loess Plateau with 2000 land cover, and the right figure reveals the effects of increasing CO2 concentration on the regional temperature of the Loess Plateau with 2020 land cover.
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Figure 5. Effects of CO2 concentration changes on the temperature of Loess Plateau in spring, summer, autumn and winter.
Figure 5. Effects of CO2 concentration changes on the temperature of Loess Plateau in spring, summer, autumn and winter.
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Figure 6. Effects of CO2 concentration changes on the daily temperature of the Loess Plateau in 2000 and 2020.
Figure 6. Effects of CO2 concentration changes on the daily temperature of the Loess Plateau in 2000 and 2020.
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Figure 7. The historical land cover changes over the Loess Plateau (LP): (a) the 2000 land cover, (b) the 2020 land cover, (c) areas where land cover has changed between 2000 and 2020.
Figure 7. The historical land cover changes over the Loess Plateau (LP): (a) the 2000 land cover, (b) the 2020 land cover, (c) areas where land cover has changed between 2000 and 2020.
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Table
Table 1. The land use transfer matrix of the Loess Plateau from 2000 to 2020 (km2).
Table 1. The land use transfer matrix of the Loess Plateau from 2000 to 2020 (km2).
GrasslandBuilt-Up LandCroplandForestWater BodyBarren LandTotal in 2000
Grassland371,904.39413.3230,864.0315,999.86237.58814.95420,234.13
Built-up Land89.7010,665.10265.5723.260.372.7511,046.76
Cropland6496.21930.23119,449.906754.0874.9763.25133,768.65
Forest3162.24167.007233.0252,330.64123.341.4563,017.70
Water Body46.830.703.6853.29503.7418.94627.19
Barren Land7631.1218.6527.06228.9868.8512,477.7620,452.42
Total in 2020389,330.5012,195.00157,843.2675,390.121008.8613,379.11649,146.85
Table
Table 2. WRF model configuration.
Table 2. WRF model configuration.
D01D02
ModelARW-WRFV4.3.1
Centre point coordinates38.00°N, 107.50°E
Simulated integration steps60s12s
Grid spacing15 × 15 km3 × 3 km
Number of grids212 × 181411 × 331
MicrophysicsThompson graupel schemeThompson graupel scheme
Long-wave radiationRapid Radiative Transfer Model (RRTM)Rapid Radiative Transfer Model (RRTM)
Short-wave radiationDudhia schemeDudhia scheme
Near-surface layer physicsRevised MM5 Monin-Obukhov schemeRevised MM5 Monin-Obukhov scheme
Land-surface surfaceNoah land-surface modelNoah land-surface model
Planetary boundary layerMellor–Yamada–Janjic (Eta) TKE schemeMellor–Yamada–Janjic (Eta) TKE scheme
Table
Table 3. Design of WRF experiments.
Table 3. Design of WRF experiments.
Experimental ProtocolCO2 Concentration SettingsLand Use Data
L1CO2 concentration in 2000MODIS land subsurface data for 2000
L2CO2 concentration in 2020MODIS land subsurface data for 2000
L3CO2 concentration in 2000MODIS land subsurface data for 2020
L4CO2 concentration in 2020MODIS land subsurface data for 2020
Table
Table 4. Statistical table of temperature change of land use transfer caused by increasing CO2 concentration.
Table 4. Statistical table of temperature change of land use transfer caused by increasing CO2 concentration.
Land Use Transition∆Tmin∆Tmax∆TmeanLand Use Transition∆Tmin∆Tmax∆Tmean
Forest–Forest−0.200.260.06Grassland–Forest−0.150.250.06
Forest–Grassland−0.160.240.09Grassland–Cropland−0.220.280.01
Forest–Water Body−0.110.220.09Grassland–Water Body−0.130.200.04
Forest–Cropland−0.150.290.08Grassland–Cropland−0.170.280.05
Forest–Built-up Land−0.080.210.11Grassland–Built-up Land−0.120.240.02
Forest–Barren Land0.080.080.08Grassland–Barren Land−0.130.17−0.02
Barren Land–Forest−0.050.090.03Cropland–Forest−0.170.280.02
Barren Land–Grassland−0.140.15−0.02Cropland–Grassland−0.150.260.03
Barren Land–Water Body−0.050.180.04Cropland–Water Body−0.040.210.06
Barren Land–Cropland−0.010.130.06Cropland–Cropland−0.200.300.04
Barren Land–Built-up Land−0.050.070.01Cropland–Built-up Land−0.160.260.06
Barren Land–Barren Land−0.170.21−0.03Cropland–Barren Land−0.030.260.06
Built-up Land–Forest0.020.150.10Water Body–Forest−0.050.060.01
Built-up Land–Grassland−0.080.210.00Water Body–Grassland−0.04−0.01−0.02
Built-up Land–Water Body0.190.190.19Water Body–Water Body−0.090.220.01
Built-up Land–Cropland−0.150.220.08Water Body–Barren Land0.000.000.00
Built-up Land–Built-up Land−0.170.250.05
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Shi, Z.; Cui, Y.; Wu, L.; Zhou, Y.; Li, M.; Zhou, S. Simulation Study on the Effect of Elevated CO2 on Regional Temperature Change on the Loess Plateau. Remote Sens. 2023, 15, 2607. https://doi.org/10.3390/rs15102607

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Shi Z, Cui Y, Wu L, Zhou Y, Li M, Zhou S. Simulation Study on the Effect of Elevated CO2 on Regional Temperature Change on the Loess Plateau. Remote Sensing. 2023; 15(10):2607. https://doi.org/10.3390/rs15102607

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Shi, Zhifang, Yaoping Cui, Liyang Wu, Yan Zhou, Mengdi Li, and Shenghui Zhou. 2023. "Simulation Study on the Effect of Elevated CO2 on Regional Temperature Change on the Loess Plateau" Remote Sensing 15, no. 10: 2607. https://doi.org/10.3390/rs15102607

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