Li Tong, Zwiers Francis W, Zhang Xuebin
Pacific Climate Impacts Consortium, University of Victoria, Victoria, BC, Canada.
Key Laboratory of Meteorological Disaster, Ministry of Education, Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing, China.
Sci Adv. 2025 May 16;11(20):eadt6485. doi: 10.1126/sciadv.adt6485.
Empirical evidence indicates that the range of model-projected future warming can be successfully narrowed by conditioning the projected warming on past observed warming. We demonstrate that warming projections conditioned on the entire instrumental annual surface temperature record are of sufficiently high quality and should be considered as long-term predictions rather than merely as projections. We support this view by considering the skill of predicted 20- and 50-year lead temperature changes under the Shared Economic Pathway (SSP)1-2.6 and SSP5-8.5 emission scenarios in climates of different sensitivities. Using climate model simulations, we show that adjusting raw multimodel projections of future warming with the Kriging for Climate Change (KCC) method eliminates most biases and reduces the uncertainty of warming projections irrespective of the sensitivity of the climate being considered. Simpler methods, or using only the more recent part of the temperature record, provide less effective constraints. The high-skill future warming predictions obtained via KCC have a serious place in informing global climate policies.
实证证据表明，通过将未来变暖预测基于过去观测到的变暖情况，可以成功缩小模型预测的未来变暖范围。我们证明，基于整个仪器记录的年度地表温度记录的变暖预测具有足够高的质量，应被视为长期预测，而不仅仅是预测。我们通过考虑在共享经济路径（SSP）1-2.6和SSP5-8.5排放情景下，不同敏感性气候中预测的20年和50年超前温度变化的技能来支持这一观点。使用气候模型模拟，我们表明，无论所考虑的气候敏感性如何，使用气候变化克里金法（KCC）调整未来变暖的原始多模型预测都能消除大多数偏差并降低变暖预测的不确定性。更简单的方法，或仅使用温度记录中较新的部分，提供的约束效果较差。通过KCC获得的高技能未来变暖预测在为全球气候政策提供信息方面具有重要地位。
