Silicate rock weathering maintains a stable and long‐term absorption of CO₂. However, the magnitude, spatial pattern, and evolution characteristics of global silicate rock weathering carbon sink (SCS) remain unclear. To solve this problem, based on high‐precision hydrometeorological data (1996–2017) and CMIP5 data (2041–2060), using the Celine model, we calculated the global silicate rock weathering carbon sink flux (SCSF) magnitude and spatio‐temporal distribution for 1996–2017. We also predicted the SCSF under two future greenhouse gas emission scenarios (RCP 4.5 and RCP 8.5). Then, we produced a spatial data set (0.5 × 0.5) of global SCSF from 1996 to 2017 and found that the global average annual SCSF was 1.67 t/km²/yr, and the SCS was 127.11 Tg/yr. In particular, Brazil's silicate rock contribution accounts for nearly a quarter of the global SCS (24.41%). Although the GEM‐CO₂ model is now widely used, the SCSF, without considering the temperature, may be overestimated by 5.4%, and the maximum contribution of temperature to it can reach 240 kg/km²/yr. Moreover, the global SCS is now showing a downward trend, but the global emission of greenhouse gases in the future (2041–2060) will continue to increase the carbon sink capacity (23.8%) due to temperature changes. In summary, we have produced a set of high‐resolution spatiotemporal data of the past and the future. The above results fill up the large‐scale data gap of SCSF and provide a scientific basis for quantitatively assessing the impact of climate change on SCS.

中文翻译：

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硅酸盐岩化学风化的全球二氧化碳消费量：过去和未来

硅酸盐岩的风化作用保持了CO ₂的长期稳定吸收。但是，全球硅酸盐岩风化碳汇（SCS）的大小，空间格局和演化特征仍不清楚。为了解决这个问题，基于高精度的水文气象数据（1996–2017）和CMIP5数据（2041–2060），我们使用Celine模型，计算了全球硅酸盐岩风化碳汇通量（SCSF）的大小和时空分布1996-2017年。我们还预测了未来两种温室气体排放情景（RCP 4.5和RCP 8.5）下的SCSF。然后，我们制作了1996年至2017年全球SCSF的空间数据集（0.5×0.5），发现全球年平均SCSF为1.67 t / km ²/年，而SCS为127.11 Tg /年。特别是，巴西的硅酸盐岩贡献量占全球SCS的近四分之一（24.41％）。尽管现在已广泛使用GEM-CO ₂模型，但不考虑温度的情况下，SCSF可能被高估了5.4％，温度对其的最大贡献可以达到240 kg / km ²/年。此外，目前全球SCS呈下降趋势，但由于温度变化，未来（2041-2060年）全球温室气体排放量将继续增加碳汇能力（23.8％）。总而言之，我们生成了一组过去和将来的高分辨率时空数据。以上结果填补了SCSF的大规模数据空白，为定量评估气候变化对SCS的影响提供了科学依据。