Institute for Geosciences, University of Kiel, Ludewig-Meyn-Str. 10, 24118, Kiel, Germany.
IPP, Max-Planck-Institute for Plasma Physics, Garching, Germany.
Sci Rep. 2021 Oct 22;11(1):20949. doi: 10.1038/s41598-021-00334-2.
Pelagic biogeochemical models (BGCMs) have matured into generic components of Earth System Models. BGCMs mimic the effects of marine biota on oceanic nutrient, carbon and oxygen cycles. They rely on parameters that are adjusted to match observed conditions. Such parameters are key to determining the models' responses to changing environmental conditions. However, many of these parameters are difficult to constrain and constitute a major source of uncertainty in BGCM projections. Here we use, for the first time, variance-based sensitivity analyses to map BGCM parameter uncertainties onto their respective local manifestation in model entities (such as oceanic oxygen concentrations) for both contemporary climate and climate projections. The mapping effectively relates local uncertainties of projections to the uncertainty of specific parameters. Further, it identifies contemporary benchmarking regions, where the uncertainties of specific parameters manifest themselves, thereby facilitating an effective parameter refinement and a reduction of the associated uncertainty. Our results demonstrate that the parameters that are linked to uncertainties in projections may differ from those parameters that facilitate model conformity with present-day observations. In summary, we present a practical approach to the general question of where present-day model fidelity may be indicative for reliable projections.
海洋生物地球化学模式(BGCM)已经发展成为地球系统模式的通用组成部分。BGCM 模拟海洋生物对海洋营养、碳和氧循环的影响。它们依赖于经过调整以匹配观测条件的参数。这些参数是确定模型对环境变化响应的关键。然而,其中许多参数难以约束,是 BGCM 预测的主要不确定性来源。在这里,我们首次使用基于方差的敏感性分析,将 BGCM 参数不确定性映射到模型实体(如海洋氧气浓度)中,以反映当前气候和气候预测的情况。这种映射有效地将预测的局部不确定性与特定参数的不确定性联系起来。此外,它还确定了特定参数不确定性表现出来的当代基准区域,从而促进了有效的参数细化和相关不确定性的降低。我们的结果表明,与预测不确定性相关的参数可能与那些有助于模型与当前观测结果一致的参数不同。总之,我们提出了一种实用的方法来解决当前模型保真度是否可以指示可靠预测的一般性问题。