No abstract

Despite knowledge of the presence of the Tibetan Plateau (TP) in reorganizing large-scale atmospheric circulation, it remains unclear how surface albedo darkening over TP will impact local glaciers and remote Asian monsoon systems. Here, we use a coupled land-atmosphere global climate model and a glacier model to address these questions. Under a high-emission scenario, TP surface albedo darkening will increase local temperature by 0.24 K by the end of this century. This warming will strengthen the elevated heat pump of TP, increasing South Asian monsoon precipitation while exacerbating the current “South Flood-North Drought” pattern over East Asia. The albedo darkening-induced climate change also leads to an accompanying TP glacier volume loss of 6.9%, which further increases to 25.2% at the equilibrium, with a notable loss in western TP. Our findings emphasize the importance of land-surface change responses in projecting future water resource availability, with important implications for water management policies.

Lake surface water temperature (LSWT) is sensitive to climate change; however, simulated LSWT and its response to climate change remain uncertain. In this study, FLake, a onedimensional freshwater lake model, is optimized to simulate the LSWTs of 94 large lakes with surface areas greater than 100 km 2 in China. While most of these lakes are seasonally icecovered over the Tibetan Plateau, FLake with default parameters significantly underestimated LSWT in spring and winter and slightly overestimated LSWT in summer and autumn in seasonally ice-covered lakes. We performed sensitivity experiments and calibration in the trial lake (Qinghai Lake). Then, parameter calibrations of three lake-specific properties (albedo, lake mean depth and light extinction coefficient) were performed in all the studied lakes. The optimized FLake substantially improved the simulations of seasonal and interannual variations in LSWT. The root mean square error decreased from 3.64 ± 1.54 ℃ to 1.97 ± 0.72 ℃, and the mean bias of 96% of the lakes decreased to less than 1 ℃. Our study showed that the optimized FLake can reproduce the temporal variations in LSWT across China with optimized parameters, providing the possibility to simulate and project the response of LSWT to rapid climate change.

Accurate quantification of vegetation carbon turnover time (τveg) is critical for reducing uncertainties in terrestrial vegetation response to future climate change. However, in the absence of global information of litter production, τveg could only be estimated based on net primary productivity under the steady‐state assumption. Here, we applied a machine‐learning approach to derive a global dataset of litter production by linking 2401 field observations and global environmental drivers. Results suggested that the observation‐based estimate of global natural ecosystem litter production was 44.3 ± 0.4 Pg C year−1. By contrast, land‐surface models (LSMs) overestimated the global litter production by about 27%. With this new global litter production dataset, we estimated global τveg (mean value 10.3 ± 1.4 years) and its spatial distribution. Compared to our observation‐based τveg, modelled τveg tended to underestimate τveg at high latitudes. Our empirically derived gridded datasets of litter production and τveg will help constrain global vegetation models and improve the prediction of global carbon cycle.

Light use efficiency (LUE) is a critical parameter, on which we rely to quantify the vegetation's capability to harvest solar energy and thus the photosynthesis carbon uptake. However, our understanding on its magnitude, global pattern, and climatic drivers remains uncertain. In this study, we investigated global mean annual LUE during 1980-2013 based on state-of-the-art estimates from scaled-up eddy covariance measurements (data-driven models) and 10 process-based models used in Global Carbon Project. We found comparable global LUE estimates from data-driven models (0.90 ± 0.25 g C MJ −1) and process-based models (0.73 ± 0.22 g C MJ −1), with individual model estimates ranging from 0.49 to 0.98 g C MJ −1. While both types of models agreed on highest LUE over the tropical evergreen forests, discrepancies remained over middle and high latitudes of the Northern Hemisphere, which probably resulted from different response of LUE to climatic factors. Spatial analyses revealed that LUE variations were more driven by temperature and precipitation than by solar radiation. While the response of LUE to precipitation was almost always positive globally, the response of LUE to temperature was only positive in part of the nontropical regions. Process-based models showed that precipitation strongly regulated the response of LUE to temperature, which shifted from negative to positive when mean annual precipitation was larger than 1,000 mm yr −1. Our results provide converging global LUE estimates and highlight the need to take fully account of interactive effects of climatic factors in regulating LUE and thus the response of photosynthesis to climate change.