Ke Zhang

Over the past half-century, environmental degradation and human disturbances have threatened the aboveground biomass carbon (AGC) in China’s karst ecosystems. However, recent ecological programs have led to environmental improvements, leaving it unclear whether China’s karst ecosystems act as an AGC sink or AGC source. In this study, we utilized L-band vegetation optical depth to quantify the dynamics of AGC across the karst regions of China from 2015 to 2021. We observed an increase in AGC density of 0.73 Mg C ha⁻¹ yr⁻¹, suggesting that karst ecosystems in China functioned as an AGC sink throughout the research period. The largest increase in AGC density, 1.29 Mg C ha⁻¹ yr⁻¹, was observed in Central China, indicating an AGC sink capacity stronger than that of other regions. Among the different land-use types, forests played a dominant role, exhibiting the largest net change in AGC density at 1.03 Mg C ha⁻¹ yr⁻¹. Furthermore, using the random forest model, temperature, soil clay content, and altitude were identified as the primary factors driving AGC changes. Our results enhance the understanding of the role of China’s karst terrestrial ecosystem in the global carbon cycle, emphasizing its contribution to the global carbon sink.

SM2RAIN is a well-established methodology for estimating precipitation from satellite or observed soil moisture and it has been applied as a complementary approach to conventional precipitation monitoring methods. However, satellite soil moisture retrievals are usually subject to various biases and limited number of retrievals (and therefore large intervals) in remote areas, such as the Tibetan Plateau (TP), and little is known about their potential impacts on precipitation estimation. This study seeks to quantify the uncertainties in Soil Moisture Active and Passive (SMAP) soil moisture estimated precipitation through the commonly used SM2RAIN by referring to in situ soil moisture observations from the central Tibetan Plateau soil moisture network. The estimated precipitation is evaluated against rain gauge observations. Additional attention is paid to different orbits of the SMAP retrievals. Results show that the original SM2RAIN algorithm tends to underestimate the precipitation amount in the central TP when using SMAP soil moisture retrievals as input. The retrieval accuracy and sampling interval of SMAP soil moisture from ascending (descending) orbits each count for 1.04 mm/5 d (−0.18 mm/5 d) and 1.67 mm/5 d (0.72 mm/5 d) of estimated precipitation uncertainties as represented by root mean square error. Besides, the descending product of SMAP with a relatively less sampling interval and higher retrieval accuracy outperforms the ascending one in estimating precipitation, and the combination of both two orbits does add value to the overall SM2RAIN estimation. This study is expected to provide guidance for future applications of SM2RAIN-derived precipitation. Meanwhile, more reliable SM2RAIN precipitation estimations are desired when using higher quality satellite soil moisture products with better retrieval accuracy and smaller intervals.

This article presents a comprehensive evaluation of the 2000–2018 Clouds and Earth's Radiant Energy System Synoptic 1° Ed4A (CERES SYN1deg Edition 4A) surface solar radiation (SSR) product. In particular, the global assessment is conducted over different temporal scales (i.e., hourly, daily, and monthly-average) with special attention given to the impact of clouds, and a regional evaluation is further implemented over the Mainland of China (MC) using SSR measurements from a denser observational network provided by the China Meteorological Administration. Evaluation across all valid station-grid pairs yields mixed performance with |MBE|≤2.8 (6.2) W m ⁻² , RMSE≤89.5 (31.6) W m ⁻² , and R ≥0.95 (0.93) over the globe (MC) for different temporal scales, and the monthly CERES SSR, with RMSE≤20 W m ⁻² , is found to hold promise for global numerical weather prediction and climate monitoring. In addition, CERES is found to generally underestimate and overestimate SSR over land and ocean, respectively. Comparison between year-round and cloudy-season suggests that the presence of clouds may potentially impact the SSR retrievals, especially at the hourly temporal scales, with an increase in RMSE values larger than 10 W m ⁻² for most stations. Further investigation of subgrid heterogeneity suggests that most in situ SSR measurements can reasonably represent the 1° grid average except for some stations with specific geographic deployments, which may raise significant spatial representativeness issues and, therefore, need to be used with great caution.