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欧洲海域经统计降尺度处理的CMIP6海洋变量。

Statistically downscaled CMIP6 ocean variables for European waters.

作者信息

Kristiansen Trond, Butenschön Momme, Peck Myron A

机构信息

Farallon Institute, Petaluma, CA, USA.

Actea Inc, San Francisco, CA, USA.

出版信息

Sci Rep. 2024 Jan 12;14(1):1209. doi: 10.1038/s41598-024-51160-1.

Abstract

Climate change impact studies need climate projections for different scenarios and at scales relevant to planning and management, preferably for a variety of models and realizations to capture the uncertainty in these models. To address current gaps, we statistically downscaled (SD) 3-7 CMIP6 models for five key indicators of marine habitat conditions: temperature, salinity, pH, oxygen, and chlorophyll across European waters for three climate scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5. Results provide ensemble averages and uncertainty estimates that can serve as input data for projecting the potential success of a range of Nature-based Solutions, including the restoration of habitat-forming species such as seagrass in the Mediterranean and kelp in coastal areas of Portugal and Norway. Evaluation of the ensemble with observations from four European regions (North Sea, Baltic Sea, Bay of Biscay, and Mediterranean Sea) indicates that the SD projections realistically capture the climatological conditions of the historical period 1993-2020. Model skill (Liu-mean efficiency, Pearson correlation) clearly improves for both surface temperature and oxygen across all regions with respect to the original ESMs demonstrating a higher skill for temperature compared to oxygen. Warming is evident across all areas and large differences among scenarios fully emerge from the background uncertainties related to internal variability and model differences in the second half of the century. Scenario-specific differences in acidification significantly emerge from model uncertainty and internal variability leading to distinct trajectories in surface pH starting before mid-century (in some cases starting from present day). Deoxygenation is also present across all domains, but the climate signal was significantly weaker compared to the other two indicators when compared to model uncertainty and internal variability, and the impact of different greenhouse gas trajectories is less distinct. The substantial regional and local heterogeneity in these three abiotic indicators underscores the need for highly spatially resolved physical and biogeochemical projections to understand how climate change may impact marine ecosystems.

摘要

气候变化影响研究需要针对不同情景以及与规划和管理相关的尺度进行气候预测,最好是针对多种模型和实现情况,以捕捉这些模型中的不确定性。为了填补当前的空白,我们对3 - 7个CMIP6模型进行了统计降尺度处理(SD),针对欧洲海域海洋栖息地条件的五个关键指标:温度、盐度、pH值、氧气和叶绿素,涵盖三种气候情景SSP1 - 2.6、SSP2 - 4.5和SSP5 - 8.5。结果提供了集合平均值和不确定性估计,可作为预测一系列基于自然的解决方案潜在成功性的输入数据,包括恢复栖息地形成物种,如地中海的海草以及葡萄牙和挪威沿海地区的海带。用来自欧洲四个地区(北海、波罗的海、比斯开湾和地中海)的观测数据对集合进行评估表明,统计降尺度预测真实地捕捉了1993 - 2020年历史时期的气候条件。与原始地球系统模型相比,所有地区的表面温度和氧气的模型技能(刘平均效率、皮尔逊相关性)都有明显提高,表明温度的技能高于氧气。所有区域都出现了变暖现象,到本世纪下半叶,情景之间的巨大差异完全从与内部变率和模型差异相关的背景不确定性中显现出来。酸化的情景特定差异从模型不确定性和内部变率中显著显现出来,导致表面pH值在本世纪中叶之前(在某些情况下从现在开始)出现不同的轨迹。所有区域也都存在脱氧现象,但与模型不确定性和内部变率相比,气候信号与其他两个指标相比明显较弱,不同温室气体轨迹的影响也不太明显。这三个非生物指标中存在的大量区域和局部异质性凸显了需要高空间分辨率的物理和生物地球化学预测,以了解气候变化如何影响海洋生态系统。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4943/10786869/7f3ec95c0a63/41598_2024_51160_Fig1_HTML.jpg

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