IF 7.3 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Earths Future Pub Date : 2024-12-11 DOI:10.1029/2023EF004204
P. Gooya, N. C. Swart, P. Landschützer
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引用次数: 0

摘要

要实现商定的气候目标和政策,一个必不可少的步骤是能够了解和预测全球碳循环的近期变化,重要的是海洋碳吸收。事实证明,初始化的气候模型模拟能够在短期内预测关键的物理气候变量,如温度、降水等。相比之下,对海洋碳通量等生物地球化学领域的预测仍处于起步阶段。初步研究表明,一些 CMIP6 模式可以在全球范围内进行长达 6 年的熟练预测。然而,与核心物理变量不同,生物地球化学变量在现有的十年期预测系统中没有直接初始化,地球系统模式中海洋生物地球化学的大量经验参数化带来了很大的不确定性。在此,我们提出了一种新的方法,利用观测约束统计模型来替代海洋生物地球化学模型,从而提高十年海洋碳通量预测的技能。我们利用观测数据训练多线性和神经网络模型来预测海洋碳通量。为了考虑观测的不确定性,我们使用六种不同的通量观测估计值进行训练。然后,我们使用来自加拿大地球系统模式(CanESM5)十年期预测系统的输入预测因子来应用这些训练有素的统计模型,从而得出新的十年期预测结果。与原始 CanESM5 后期预测相比,我们的混合 GCM 统计方法显著提高了 1990-2019 年的预测技能。我们的混合模型技能也高于任何可用的 CMIP6 模型。利用经过偏差校正的 CanESM5 预测因子,我们对 2020-2029 年的海洋碳通量进行了预测。两种统计模式预测的海洋碳通量的增加都大于 CanESM5 预测的变化。我们的工作强调了通过使用观测训练的统计模型和基于 GCM 十年期预测的稳健输入预测因子来改进十年期海洋碳通量预测的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Improving GCM-Based Decadal Ocean Carbon Flux Predictions Using Observationally-Constrained Statistical Models

Improving GCM-Based Decadal Ocean Carbon Flux Predictions Using Observationally-Constrained Statistical Models

An essential step toward meeting agreed climate targets and policies is the ability to understand and predict near-term changes in global carbon cycle, and importantly, ocean carbon uptake. Initialized climate model simulations have proven skillful for near-term predictability of the key physical climate variables, for example, temperature, precipitation, etc. By comparison, predictions of biogeochemical fields like ocean carbon flux, are still emerging. Initial studies indicate skillful predictions are possible for lead-times up to 6 years at global scale for some CMIP6 models. However, unlike core physical variables, biogeochemical variables are not directly initialized in existing decadal prediction systems, and extensive empirical parametrization of ocean-biogeochemistry in Earth System Models introduces a significant source of uncertainty. Here we propose a new approach for improving the skill of decadal ocean carbon flux predictions using observationally-constrained statistical models, as alternatives to the ocean-biogeochemistry models. We use observations to train multi-linear and neural-network models to predict the ocean carbon flux. To account for observational uncertainties, we train using six different observational estimates of the flux. We then apply these trained statistical models using input predictors from the Canadian Earth System Model (CanESM5) decadal prediction system to produce new decadal predictions. Our hybrid GCM-statistical approach significantly improves prediction skill, relative to the raw CanESM5 hindcast predictions over 1990–2019. Our hybrid-model skill is also larger than that obtained by any available CMIP6 model. Using bias-corrected CanESM5 predictors, we make forecasts for ocean carbon flux over 2020–2029. Both statistical models predict increases in the ocean carbon flux larger than the changes predicted from CanESM5 forecasts. Our work highlights the ability to improve decadal ocean carbon flux predictions by using observationally-trained statistical models together with robust input predictors from GCM-based decadal predictions.

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来源期刊
Earths Future
Earths Future ENVIRONMENTAL SCIENCESGEOSCIENCES, MULTIDI-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
11.00
自引率
7.30%
发文量
260
审稿时长
16 weeks
期刊介绍: Earth’s Future: A transdisciplinary open access journal, Earth’s Future focuses on the state of the Earth and the prediction of the planet’s future. By publishing peer-reviewed articles as well as editorials, essays, reviews, and commentaries, this journal will be the preeminent scholarly resource on the Anthropocene. It will also help assess the risks and opportunities associated with environmental changes and challenges.
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