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Earth system models (ESMs) are important tools for assessing the historical characteristics and predicting the future characteristics of precipitation, yet the quantitative understanding of how these land–atmospheric coupling models perform in simulating precipitation characteristics remains limited. This study conducts a comprehensive evaluation of precipitation changes simulated by 43 ESMs in CMIP5 and 32 ESMs in CMIP6 in Arid Central Asia (ALL) and its two sub-regions for 1959–2005 with reference to Climate Research Unit (CRU) data, and predicts precipitation changes for 2054–2100. Our analyses suggest the following: (a) no single model consistently outperformed the others in all aspects of simulated precipitation variability (annual averages, long-term trends, and climatological monthly patterns); (b) the CMIP5 and CMIP6 model simulations tended to overestimate average annual precipitation for most of the ALL region, especially in the Xinjiang Uygur Autonomous Region of China (XJ); (c) most model simulations projected a stronger increasing trend in average annual precipitation; (d) although all the model simulations reasonably captured the climatological monthly precipitation, there was an underestimation; (e) compared to CMIP5, most CMIP6 model simulations exhibited an enhanced capacity to simulate precipitation across all aspects, although discrepancies persisted in individual sub-regions; (f) it was confirmed that the multi-model ensemble mean (MME) provides a more accurate representation of the three aspects of precipitation compared to the majority of single-model simulations. Lastly, the values of precipitation predicted by the more efficient models across the ALL region and its sub-regions under the different scenarios showed an increasing trend in most seasons. Notably, the strongest increasing trend in precipitation was seen under the high-emission scenarios. [ABSTRACT FROM AUTHOR]

Forest canopy height is an indispensable forest vertical structure parameter for understanding the carbon cycle and forest ecosystem services. A variety of studies based on spaceborne Lidar, such as ICESat, ICESat-2 and airborne Lidar, were conducted to estimate forest canopy height at multiple scales. However, while a few studies have been conducted based on ICESat-2 simulated data from airborne Lidar data, few studies have analyzed ATL08 and ATL03 products derived from the ATLAS sensor onboard ICESat-2 for regional vegetation canopy height mapping. It is necessary and promising to explore how data obtained by ICESat-2 can be applied to estimate forest canopy height. This study proposes a new means to estimate forest canopy height, defined as the mean height of trees within a given forest area, using a combination of ICESat-2 ATL08 and ATL03 data and ZY-3 satellite stereo images. Five procedures were used to estimate the forest canopy height of the city of Nanning in China: (1) Processing ground photons in a 30 m × 30 m grid; (2) Extracting a digital surface model (DSM) using ZY-3 stereo images; (3) Calculating a discontinuous canopy height model (CHM) dataset; (4) Validating the DSM and ground photon height using GEDI data; (5) Estimating the regional wall-to-wall forest canopy height product based on the backpropagation artificial neural network (BP-ANN) model and Landsat 8 vegetation indices and independent accuracy assessments with field measured plots. The validation shows a root mean square error (RMSE) of 3.34 m to 3.47 m and a coefficient of determination R2 = 0.51. The new method shows promise and can be used for large-scale forest canopy height mapping at various resolutions or in combination with other data, such as SAR images. Finally, this study analyzes resolutions and how to filter effective data when ATL08 data are directly used to generate regional or global vegetation height products, which will be the focus of future research. [ABSTRACT FROM AUTHOR]