Fyfe John

No Thumbnail Available
Last Name
Fyfe
First Name
John
ORCID

Filters

2019 - 2019 1 2020 - 2023 1
2
2
Reset filters

Settings

Search Results

Now showing 1 - 2 of 2
  • Article
    Ocean climate observing requirements in support of climate research and climate information
    (Frontiers Media, 2019-07-31) Stammer, Detlef ; Bracco, Annalisa ; AchutaRao, Krishna ; Beal, Lisa M. ; Bindoff, Nathaniel L. ; Braconnot, Pascale ; Cai, Wenju ; Chen, Dake ; Collins, Matthew ; Danabasoglu, Gokhan ; Dewitte, Boris ; Farneti, Riccardo ; Fox-Kemper, Baylor ; Fyfe, John ; Griffies, Stephen M. ; Jayne, Steven R. ; Lazar, Alban ; Lengaigne, Matthieu ; Lin, Xiaopei ; Marsland, Simon ; Minobe, Shoshiro ; Monteiro, Pedro M. S. ; Robinson, Walter ; Roxy, Mathew Koll ; Rykaczewski, Ryan R. ; Speich, Sabrina ; Smith, Inga J. ; Solomon, Amy ; Storto, Andrea ; Takahashi, Ken ; Toniazzo, Thomas ; Vialard, Jérôme
    Natural variability and change of the Earth’s climate have significant global societal impacts. With its large heat and carbon capacity and relatively slow dynamics, the ocean plays an integral role in climate, and provides an important source of predictability at seasonal and longer timescales. In addition, the ocean provides the slowly evolving lower boundary to the atmosphere, driving, and modifying atmospheric weather. Understanding and monitoring ocean climate variability and change, to constrain and initialize models as well as identify model biases for improved climate hindcasting and prediction, requires a scale-sensitive, and long-term observing system. A climate observing system has requirements that significantly differ from, and sometimes are orthogonal to, those of other applications. In general terms, they can be summarized by the simultaneous need for both large spatial and long temporal coverage, and by the accuracy and stability required for detecting the local climate signals. This paper reviews the requirements of a climate observing system in terms of space and time scales, and revisits the question of which parameters such a system should encompass to meet future strategic goals of the World Climate Research Program (WCRP), with emphasis on ocean and sea-ice covered areas. It considers global as well as regional aspects that should be accounted for in designing observing systems in individual basins. Furthermore, the paper discusses which data-driven products are required to meet WCRP research and modeling needs, and ways to obtain them through data synthesis and assimilation approaches. Finally, it addresses the need for scientific capacity building and international collaboration in support of the collection of high-quality measurements over the large spatial scales and long time-scales required for climate research, bridging the scientific rational to the required resources for implementation.
  • Article
    Robust human influence across the troposphere, surface, and ocean: a multivariate analysis
    (American Meteorological Society, 2023-10-20) Blackport, Russell ; Fyfe, John C. ; Santer, Benjamin D.
    Human influence has been robustly detected throughout many parts of the climate system. Pattern-based methods have been used extensively to estimate the strength of model-predicted “fingerprints,” both human and natural, in observational data. However, individual studies using different analysis methods and time periods yield inconsistent estimates of the magnitude of the influence of anthropogenic aerosols, depending on whether they examined the troposphere, surface, or ocean. Reducing the uncertainty of the impact of aerosols on the climate system is crucial for understanding past climate change and obtaining more reliable estimates of climate sensitivity. To reconcile divergent estimates of aerosol effects obtained in previous studies, we apply the same regression-based detection and attribution method to three different variables: mid-to-upper-tropospheric temperature, surface temperature, and ocean heat content. We find that quantitative estimates of human influence in observations are consistent across these three independently monitored components of the climate system. Combining the troposphere, surface, and ocean data into a single multivariate fingerprint results in a small (∼10%) reduction of uncertainty of the magnitude of the greenhouse gas fingerprint, but a large (∼40%) reduction for the anthropogenic aerosol fingerprint. This reduction in uncertainty results in a substantially earlier time of detection of the multivariate aerosol fingerprint when compared to aerosol fingerprint detection time in each of the three individual variables. Our results highlight the benefits of analyzing data across the troposphere, surface, and ocean in detection and attribution studies, and motivate future work to further constrain uncertainties in aerosol effects on climate.