“共享社会经济路径（SSP）”是什么？
What are Shared Socioeconomic Pathways (SSPs)?
SSP 是刻画未来人口、经济、能源与技术走向的几条叙事情景（如 SSP1 可持续发展、SSP3 分裂世界），与排放情景组合，供气候模型评估不同发展道路下的气候结果。
SSPs are narrative scenarios of future population, economy, energy and technology (e.g., SSP1 sustainable, SSP3 fragmented), combined with emissions to test climate outcomes under different development paths.
难度 专业 · 适合 高中(15-18)
CC BY 4.0 · 已核验 · Verified 2026-08-23
来源 · Source
IPCC AR6 第一工作组报告,Cambridge University Press,2021
IPCC AR6 WG1, Climate Change 2021: The Physical Science Basis, CUP, 2021
#SSP #情景 #气候模式
气候模型“分辨率”的限制会带来什么不确定性？
What uncertainty comes from limited resolution in climate models?
网格太粗会平滑掉云、山地与城市等小尺度过程，而这些过程对能量平衡影响很大，只能用“参数化”近似，是模式间差异的重要来源。
Coarse grids smooth out small-scale processes like clouds, mountains and cities that strongly affect energy balance; they are approximated by 'parameterization', a major source of inter-model spread.
难度 专业 · 适合 高中(15-18)
CC BY 4.0 · 已核验 · Verified 2026-08-23
来源 · Source
IPCC AR6 第一工作组报告,Cambridge University Press,2021
IPCC AR6 WG1, Climate Change 2021: The Physical Science Basis, CUP, 2021
#分辨率 #参数化 #云
CMIP6情景标签SSP1-2.6由哪两部分构成？面对同一情景，不同全球气候模式的预估结果为何存在明显差异？
In CMIP6, how is a scenario label such as SSP1-2.6 constructed, and why do different global climate models produce clearly divergent projections under the same scenario?
标签前半是共享社会经济路径编号，SSP1代表侧重可持续发展的世界；后半为该路径下2100年前后达到的辐射强迫值，单位瓦/平方米，2.6表示先升后降的低强迫情景。各模式差异主要源于云等次网格过程的参数化方案不同，加之分辨率、气溶胶处理与内部变率位相各异，故采用多模式集合均值与区间表达不确定性，而非依赖单一模式。
The first half names the shared socio-economic pathway — SSP1, a world prioritising sustainability; the second half fixes the radiative forcing reached around 2100 in watts per square metre, with 2.6 marking a low-forcing peak-and-decline case. Models diverge chiefly because sub-grid processes, above all clouds, are parameterised differently, compounded by varying resolutions, aerosol treatments and phases of internal variability; hence projections rely on multi-model ensemble means and ranges rather than any single run.
难度 专业 · 适合 高中(15-18)
CC BY 4.0 · 已核验 · Verified 2026-11-03
来源 · Source
Eyring 等，Geoscientific Model Development，2016年，doi:10.5194/gmd-9-1937-2016
O'Neill et al., The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geoscientific Model Development 9, 2016, doi:10.5194/gmd-9-3461-2016
独立核验依据 · Independent verification: 核验依据 1 CROSSREF 标识 10.5194/gmd-9-3461-2016：The Scenario Model I
#气候模式 #情景预估 #辐射强迫 #集合不确定性
冰芯等古气候数据如何帮助约束“气候敏感度”？
How do paleoclimate data like ice cores help constrain climate sensitivity?
冰芯记录过去 CO2 与温度变化、年轮等代用指标给出“已发生”的强迫-响应关系，用来检验模型在古今不同气候态下的表现，从而收紧敏感度估计范围。
Ice cores record past CO2 and temperature; proxies like tree rings give observed forcing-response, testing models across climate states and tightening the estimated sensitivity range.
难度 进阶 · 适合 大众
CC BY 4.0 · 来源待核验 · Source pending review
来源 · Source
出处备注：此条未指向带版次的可引用著作（通识性内容、机构文件或未展开的英文书名），本库不提供独立书目出处；如需精确引用，需由编辑补全具体篇目与版次。
Paleoclimate 研究
paleoclimate
#冰芯 #古气候 #敏感度
为什么不同气候模型对同一排放情景给出不同的升温幅度？
Why do different climate models give different warming for the same emission scenario?
各模式对云、海洋混合、碳循环等过程的参数化不同，导致“气候敏感度”有差异；把多模式集合平均可缩小但无法消除这种不确定性。
Models differ in how they parameterize clouds, ocean mixing and carbon cycle, giving a spread in 'climate sensitivity'; multi-model ensembles narrow but don't erase the uncertainty.
难度 进阶 · 适合 大众
CC BY 4.0 · 已核验 · Verified 2026-08-23
来源 · Source
IPCC AR6 第一工作组报告,Cambridge University Press,2021
IPCC AR6 WG1, Climate Change 2021: The Physical Science Basis, CUP, 2021
#模式集合 #气候敏感度 #不确定性