Equilibrium climate sensitivity (ECS) has been directly estimated using reconstructions of past climates that are different than today's. A challenge to this approach is that temperature proxies integrate over the timescales of the fast feedback processes (e.g., changes in water vapor, snow, and clouds) that are captured in ECS as well as the slower feedback processes (e.g., changes in ice sheets and ocean circulation) that are not. A way around this issue is to treat the slow feedbacks as climate forcings and independently account for their impact on global temperature. Here we conduct a suite of Last Glacial Maximum (LGM) simulations using the Community Earth System Model version 1.2 (CESM1.2) to quantify the forcing and efficacy of land ice sheets (LISs) and greenhouse gases (GHGs) in order to estimate ECS. Our forcing and efficacy quantification adopts the effective radiative forcing (ERF) and adjustment framework and provides a complete accounting for the radiative, topographic, and dynamical impacts of LIS on surface temperatures. ERF and efficacy of LGM LIS are −3.2 W m⁻² and 1.1, respectively. The larger-than-unity efficacy is caused by the temperature changes over land and the Northern Hemisphere subtropical oceans which are relatively larger than those in response to a doubling of atmospheric CO₂. The subtropical sea-surface temperature (SST) response is linked to LIS-induced wind changes and feedbacks in ocean–atmosphere coupling and clouds. ERF and efficacy of LGM GHG are −2.8 W m⁻² and 0.9, respectively. The lower efficacy is primarily attributed to a smaller cloud feedback at colder temperatures. Our simulations further demonstrate that the direct ECS calculation using the forcing, efficacy, and temperature response in CESM1.2 overestimates the true value in the model by approximately 25 % due to the neglect of slow ocean dynamical feedback. This is supported by the greater cooling (6.8 ^∘C) in a fully coupled LGM simulation than that (5.3 ^∘C) in a slab ocean model simulation with ocean dynamics disabled. The majority (67 %) of the ocean dynamical feedback is attributed to dynamical changes in the Southern Ocean, where interactions between upper-ocean stratification, heat transport, and sea-ice cover are found to amplify the LGM cooling. Our study demonstrates the value of climate models in the quantification of climate forcings and the ocean dynamical feedback, which is necessary for an accurate direct ECS estimation.

中文翻译：

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最后冰川期（LGM）气候强迫和海洋动力反馈及其对估算气候敏感性的影响

平衡气候敏感性（ECS）是使用与今天不同的过去气候的重建直接估算的。这种方法的挑战在于，温度代理会在ECS中捕获的快速反馈过程（例如，水蒸气，雪和云的变化）和较慢的反馈过程（例如，冰盖的变化）的时间范围内集成和海洋环流）。解决此问题的一种方法是将缓慢的反馈视为气候强迫，并独立考虑它们对全球温度的影响。在这里，我们使用社区地球系统模型版本1.2（CESM1.2）进行了一系列的最后冰期最大值（LGM）模拟，以量化陆地冰原（LIS）和温室气体（GHG）的强迫和功效，以便估算ECS 。我们的强迫和功效量化采用有效的辐射强迫（ERF）和调整框架，并全面考虑了LIS对表面温度的辐射，地形和动力影响。LRF LIS的ERF和疗效为分别为-3.2  W m ^-2和1.1。大于一的功效是由于陆地和北半球亚热带海洋的温度变化所引起的，这些变化相对大于大气CO ₂倍增所引起的温度变化。副热带海面温度（SST）响应与LIS引起的风的变化以及海洋-大气耦合和云中的反馈有关。EGM和LGM GHG的功效为-2.8  W m ^-2和0.9。较低的功效主要是由于在较低的温度下云反馈较小。我们的模拟进一步表明，由于忽略了缓慢的海洋动力反馈，在CESM1.2中使用强迫，功效和温度响应进行的直接ECS计算高估了模型中的真实值约25％。在完全耦合的LGM模拟中，比（^5.3∘）更大的冷却（^6.8∘C） 支持了这一点。 C）在禁用海洋动力学的平板海洋模型模拟中。海洋动力反馈的大部分（67％）归因于南大洋的动力变化，在该动力变化中，上层海洋分层，热传输和海冰覆盖之间的相互作用被发现会放大LGM的冷却作用。我们的研究证明了气候模型在气候强迫和海洋动力反馈量化中的价值，这对于准确的直接ECS估算是必要的。