Publication Abstracts

Kilbourne et al. 2017

Kilbourne, H., M. Klockmann, E. Moreno-Chamarro, P. Ortega, A. Romanou, M. Srokosz, Z. Szuts, K. Thirumalai, I. Hall, P. Heimback, D. Oppo, A. Schmittner, and R. R. Zhang, 2017: Connecting Paleo and Modern Oceanographic Data to Understand AMOC Over Decades to Centuries. CLIVAR Workshop Report 2017-3. US Climate Variability & Predictability Program, doi:10.5065/D6KP80KR.

Modeling is an important tool for understanding AMOC on all timescales. Mechanistic studies of modern AMOC variability have been hampered by a lack of consistency between free-running models and the sensitivity of AMOC to resolution and parameterization. Recent work within the framework of the phase two Coordinated Ocean- Reference Experiments (CORE-II) addresses this issue head on, looking at model differences of AMOC mean state and interannual variability. One consistent feature across the models is that AMOC mean transport is related to mixed layer depths and Labrador Sea salt content, whereas interannual variability is primarily associated with Labrador Sea temperature anomalies. This is consistent with the hypothesized importance of salt balance for AMOC variability on geological timescales. The simulated relationships between AMOC and subsurface temperature anomalies in fully coupled climate models reveal subsurface AMOC fingerprints that could be used to reconstruct historical AMOC variations at low frequency.

With the lack of long-term AMOC observations, models of ocean state that assimilate observational data have been explored as a way to reconstruct AMOC, but comparisons between models indicate they are quite variable in their AMOC representations. Karspeck et al. (2015) found that historical reconstructions of AMOC in such models are sensitive to the details of the data assimilation procedure. The ocean data assimilation community continues to address these issues through improved models and methods for estimating and representing error information.

Two objectives of paleoclimate modeling are 1) to provide mechanistic information for interpretation of paleoclimate observations, and 2) to test the ability of predictive models to simulate Earth's climate under different background forcing states. In a good example of the first objective, Schmittner and Lund (2015) and Menviel et al. (2014) provided key information about the proxy signals expected under freshwater disturbance of AMOC, which were used to support the paleoclimate observations made by Henry et al. (2016). In an example of the second objective, Muglia and Schmittner (2015) analyzed Third Paleoclimate Modeling Intercomparison Project (PMIP3) models of the Last Glacial Maximum (LGM) and found consistently more intense and deeper AMOC transports relative to preindustrial simulations, counter to the paleoclimate consensus of LGM conditions, indicating that some processes are not well represented in the PMIP3 models. One challenge is to find adequate paleo observations against which to test these models. PMIP is now in phase 4 (part of CMIP6), which includes experiments covering five periods in Earth's history: the last millennium, last glacial maximum, last interglacial, and the mid-Pliocene. Newly compiled paleoclimate datasets from the PAGES2k project, more transient simulations, and participation of isotope enabled models planned for CMIP6/PMIP4 will enable richer paleo data-model comparisons in the near future.

In order to further understand the variability of AMOC, we knew that the paleo and modern oceanographic communities needed to cooperate more to identify inadequacies in the instrumental record, where paleo data and modeling could be useful, and how best to merge results from both communities.

The overarching workshop goals were to combine forces of both paleo and modern communities to explore the state of knowledge of AMOC over decades to centuries and to identify promising and potentially synergistic research directions to better understand AMOC and its relationship to climate variability.

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BibTeX Citation

  author={Kilbourne, H. and Klockmann, M. and Moreno-Chamarro, E. and Ortega, P. and Romanou, A. and Srokosz, M. and Szuts, Z. and Thirumalai, K. and Hall, I. and Heimback, P. and Oppo, D. and Schmittner, A. and R. Zhang, R.},
  title={Connecting Paleo and Modern Oceanographic Data to Understand AMOC Over Decades to Centuries},
  publisher={US Climate Variability & Predictability Program},
  address={Washington, D.C.},

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RIS Citation

ID  - ki03200x
AU  - Kilbourne, H.
AU  - Klockmann, M.
AU  - Moreno-Chamarro, E.
AU  - Ortega, P.
AU  - Romanou, A.
AU  - Srokosz, M.
AU  - Szuts, Z.
AU  - Thirumalai, K.
AU  - Hall, I.
AU  - Heimback, P.
AU  - Oppo, D.
AU  - Schmittner, A.
AU  - R. Zhang, R.
PY  - 2017
BT  - Connecting Paleo and Modern Oceanographic Data to Understand AMOC Over Decades to Centuries
DO  - 10.5065/D6KP80KR
PB  - US Climate Variability & Predictability Program
CY  - Washington, D.C.
ER  -

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