Articles | Volume 18, issue 4
06 Jul 2022
Research article | 06 Jul 2022
Causes of the 2015 North Atlantic cold anomaly in a global state estimate
Rachael N. C. Sanders et al.
No articles found.
Fouzia Fahrin, Daniel C. Jones, Yan Wu, James Keeble, and Alexander T. Archibald
Atmos. Chem. Phys. Discuss.,
Preprint under review for ACPShort summary
We use a machine learning technique called Gaussian Mixture Modeling (GMM) to classify vertical ozone profiles into groups based on how the ozone concentration changes with pressure. Even though the GMM algorithm was not provided with spatial information, the classes are geographically coherent. We also detect signatures of tropical broadening in UKESM1 future climate scenarios. GMM may be useful for understanding ozone structures in modeled and observed datasets.
Oscar Dimdore-Miles, Lesley Gray, Scott Osprey, Jon Robson, Rowan Sutton, and Bablu Sinha
Atmos. Chem. Phys., 22, 4867–4893,Short summary
This study examines interactions between variations in the strength of polar stratospheric winds and circulation in the North Atlantic in a climate model simulation. It finds that the Atlantic Meridional Overturning Circulation (AMOC) responds with oscillations to sets of consecutive Northern Hemisphere winters, which show all strong or all weak polar vortex conditions. The study also shows that a set of strong vortex winters in the 1990s contributed to the recent slowdown in the observed AMOC.
Ehud Strobach, Andrea Molod, Donifan Barahona, Atanas Trayanov, Dimitris Menemenlis, and Gael Forget
Geosci. Model Dev., 15, 2309–2324,Short summary
The Green's functions methodology offers a systematic, easy-to-implement, computationally cheap, scalable, and extendable method to tune uncertain parameters in models accounting for the dependent response of the model to a change in various parameters. Herein, we successfully show for the first time that long-term errors in earth system models can be considerably reduced using Green's functions methodology. The method can be easily applied to any model containing uncertain parameters.
Marilena Oltmanns, N. Penny Holliday, James Screen, D. Gwyn Evans, Simon A. Josey, Sheldon Bacon, and Ben I. Moat
Weather Clim. Dynam. Discuss.,
Revised manuscript under review for WCDShort summary
The Arctic is currently warming twice as fast as the global average. This results in enhanced melting and thus freshwater releases into the North Atlantic. Using a combination of observations and models, we show that atmosphere-ocean feedbacks initiated by freshwater releases into the North Atlantic lead to warmer and drier weather over Europe in subsequent summers. The existence of this dynamical link suggests that European summer weather can potentially be predicted months to years in advance.
Simon D. A. Thomas, Daniel C. Jones, Anita Faul, Erik Mackie, and Etienne Pauthenet
Ocean Sci., 17, 1545–1562,Short summary
We propose a probabilistic method and a new inter-class comparison metric for highlighting fronts in the Southern Ocean. We compare it with an image processing method that provides a more localised view of fronts that effectively highlights sharp jets. These two complementary approaches offer two views of Southern Ocean structure: the probabilistic method highlights boundaries between coherent thermohaline structures across the entire Southern Ocean, whereas edge detection highlights local jets.
Tillys Petit, M. Susan Lozier, Simon A. Josey, and Stuart A. Cunningham
Ocean Sci., 17, 1353–1365,Short summary
Recent work has highlighted the dominant role of the Irminger and Iceland basins in the production of North Atlantic Deep Water. From our analysis, we find that air–sea fluxes and the ocean surface density field are both key determinants of the buoyancy-driven transformation in the Iceland Basin. However, the spatial distribution of the subpolar mode water (SPMW) transformation is most sensitive to surface density changes as opposed to the direct influence of the air–sea fluxes.
Axel Peytavin, Bruno Sainte-Rose, Gael Forget, and Jean-Michel Campin
Geosci. Model Dev., 14, 4769–4780,Short summary
We present a new algorithm developed at The Ocean Cleanup to update ocean plastic models based on measurements from the field to improve future cleaning operations. Prepared in collaboration with MIT researchers, this initial study presents its use in several analytical and real test cases in which two observers in a flow field record regular observations to update a plastic forecast. We demonstrate this improves the prediction, even with inaccurate knowledge of the water flows driving plastic.
Rachel Furner, Peter Haynes, Dave Munday, Brooks Paige, Daniel C. Jones, and Emily Shuckburgh
Geosci. Model Dev. Discuss.,
Revised manuscript not acceptedShort summary
Traditional weather & climate models are built from physics-based equations, while data-driven models are built from patterns found in datasets using Machine Learning or statistics. There is growing interest in using data-driven models for weather & climate prediction, but confidence in their use depends on understanding the patterns they're finding. We look at this with a simple regression model of ocean temperature and see the patterns found by the regression model are similar to the physics.
Pablo Ortega, Jon I. Robson, Matthew Menary, Rowan T. Sutton, Adam Blaker, Agathe Germe, Jöel J.-M. Hirschi, Bablu Sinha, Leon Hermanson, and Stephen Yeager
Earth Syst. Dynam., 12, 419–438,Short summary
Deep Labrador Sea densities are receiving increasing attention because of their link to many of the processes that govern decadal climate oscillations in the North Atlantic and their potential use as a precursor of those changes. This article explores those links and how they are represented in global climate models, documenting the main differences across models. Models are finally compared with observational products to identify the ones that reproduce the links more realistically.
Adam T. Blaker, Manoj Joshi, Bablu Sinha, David P. Stevens, Robin S. Smith, and Joël J.-M. Hirschi
Geosci. Model Dev., 14, 275–293,Short summary
FORTE 2.0 is a flexible coupled atmosphere–ocean general circulation model that can be run on modest hardware. We present two 2000-year simulations which show that FORTE 2.0 is capable of producing a stable climate. Earlier versions of FORTE were used for a wide range of studies, ranging from aquaplanet configurations to investigating the cold European winters of 2009–2010. This paper introduces the updated model for which the code and configuration are now publicly available.
David Ian Duncan, Patrick Eriksson, Simon Pfreundschuh, Christian Klepp, and Daniel C. Jones
Atmos. Chem. Phys., 19, 6969–6984,Short summary
Raindrop size distributions have not been systematically studied over the oceans but are significant for remotely sensing, assimilating, and modeling rain. Here we investigate raindrop populations with new global in situ data, compare them against satellite estimates, and explore a new technique to classify the shapes of these distributions. The results indicate the inadequacy of a commonly assumed shape in some regions and the sizable impact of shape variability on satellite measurements.
David Storkey, Adam T. Blaker, Pierre Mathiot, Alex Megann, Yevgeny Aksenov, Edward W. Blockley, Daley Calvert, Tim Graham, Helene T. Hewitt, Patrick Hyder, Till Kuhlbrodt, Jamie G. L. Rae, and Bablu Sinha
Geosci. Model Dev., 11, 3187–3213,Short summary
We document the latest version of the shared UK global configuration of the NEMO ocean model. This configuration will be used as part of the climate models for the UK contribution to the IPCC 6th Assessment Report. 30-year integrations forced with atmospheric forcing show that the new configurations have an improved simulation in the Southern Ocean with the near-surface temperatures and salinities and the sea ice all matching the observations more closely.
Daniel B. Williamson, Adam T. Blaker, and Bablu Sinha
Geosci. Model Dev., 10, 1789–1816,Short summary
We present a method from the statistical science literature to assist in the tuning of global climate models submitted to CMIP. We apply the method to the NEMO ocean model and find choices of its free parameters that lead to improved representations of depth integrated global mean temperature and salinity. We argue against automatic tuning procedures that involve optimising certain outputs of a model and explain why our method avoids common difficulties with/arguments against automatic tuning.
Helene T. Hewitt, Malcolm J. Roberts, Pat Hyder, Tim Graham, Jamie Rae, Stephen E. Belcher, Romain Bourdallé-Badie, Dan Copsey, Andrew Coward, Catherine Guiavarch, Chris Harris, Richard Hill, Joël J.-M. Hirschi, Gurvan Madec, Matthew S. Mizielinski, Erica Neininger, Adrian L. New, Jean-Christophe Rioual, Bablu Sinha, David Storkey, Ann Shelly, Livia Thorpe, and Richard A. Wood
Geosci. Model Dev., 9, 3655–3670,Short summary
We examine the impact in a coupled model of increasing atmosphere and ocean horizontal resolution and the frequency of coupling between the atmosphere and ocean. We demonstrate that increasing the ocean resolution from 1/4 degree to 1/12 degree has a major impact on ocean circulation and global heat transports. The results add to the body of evidence suggesting that ocean resolution is an important consideration when developing coupled models for weather and climate applications.
Simona Aracri, Katrin Schroeder, Jacopo Chiggiato, Harry Bryden, Elaine McDonagh, Simon Josey, Yann Hello, and Mireno Borghini
Ocean Sci. Discuss.,
Preprint withdrawnShort summary
The abyssal velocity of the Northern Current, in the north-western Mediterranean has been estimated using for the first time MERMAIDs, i.e. submarine drifting instruments that record seismic waves. In this study the Northern Current shows an intense activity even in deep layers of the water column. Through pseudo-eulerian statistics different components of the observed variability are analysed and described, revealing the turbulent nature of the Liguro-Provençal basin abyssal circulation.
G. Forget, D. Ferreira, and X. Liang
Ocean Sci., 11, 839–853,Short summary
Results from the ECCO v4 ocean state estimate identify the constraint of fitting Argo profiles as an effective observational basis for inverse estimation of regional turbulent transport rates. The estimated parameters' geography is physically plausible and exhibits close connections with the observed upper-ocean stratification. They yield a clear reduction in the model drift away from observations over multi-century-long simulations, including for independent biochemistry variables.
G. Forget, J.-M. Campin, P. Heimbach, C. N. Hill, R. M. Ponte, and C. Wunsch
Geosci. Model Dev., 8, 3071–3104,Short summary
The ECCO v4 non-linear inverse modeling framework and its reference solution are made publicly available. The inverse estimate of ocean physics and atmospheric forcing yields a dynamically consistent and global state estimate without unidentified sources of heat and salt that closely fits in situ and satellite data. Any user can reproduce it accurately. Parametric and external model uncertainties are of comparable magnitudes and generally exceed structural model uncertainties.
A. Megann, D. Storkey, Y. Aksenov, S. Alderson, D. Calvert, T. Graham, P. Hyder, J. Siddorn, and B. Sinha
Geosci. Model Dev., 7, 1069–1092,
J. J.-M. Hirschi, A. T. Blaker, B. Sinha, A. Coward, B. de Cuevas, S. Alderson, and G. Madec
Ocean Sci., 9, 805–823,
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In 2015, record low temperatures were observed in the North Atlantic. Using an ocean model, we show that surface heat loss in December 2013 caused 75 % of the initial cooling before this "cold blob" was trapped below the surface. The following summer, the cold blob re-emerged due to a strong temperature difference between the surface ocean and below, driving vertical diffusion of heat. Lower than average surface warming then led to the coldest temperature anomalies in August 2015.
In 2015, record low temperatures were observed in the North Atlantic. Using an ocean model, we...