Isolating and Reconstructing Key Components of North Atlantic Ocean Variability From a Sclerochronological Spatial Network

This is the final version. Available from AGU via the DOI in this record.Our understanding of North Atlantic Ocean variability within the coupled climate system is limited by the brevity of instrumental records and a deficiency of absolutely dated marine proxies. Here we demonstrate that a spatial network of marine stable oxygen isotope series derived from molluscan sclerochronologies (δ18Oshell) can provide skillful annually resolved reconstructions of key components of North Atlantic Ocean variability with absolute dating precision. Analyses of the common δ18Oshell variability, using principal component analysis, highlight strong connections with tropical North Atlantic and subpolar gyre (SPG) sea surface temperatures and sea surface salinity in the North Atlantic Current (NAC) region. These analyses suggest that low-frequency variability is dominated by the tropical Atlantic signal while decadal variability is dominated by variability in the SPG and salinity transport in the NAC. Split calibration and verification statistics indicate that the composite series produced using the principal component analysis can provide skillful quantitative reconstructions of tropical North Atlantic and SPG sea surface temperatures and NAC sea surface salinities over the industrial period (1864–2000). The application of these techniques with extended individual δ18Oshell series provides powerful baseline records of past North Atlantic variability into the unobserved preindustrial period. Such records are essential for developing our understanding of natural climate variability in the North Atlantic Ocean and the role it plays in the wider climate system, especially on multidecadal to centennial time scales, potentially enabling reduction of uncertainties in future climate predictions

Model derived uncertainties in deep ocean temperature trends between 1990-2010 (dataset)

Dataset for plotting the figures in the Garry et al. (2019) article "Model derived uncertainties in deep ocean temperature trends between 1990-2010" published in the Journal of Geophysical Research: Oceans. Requires Python 2.7.The article associated with this dataset is located in ORE at: http://hdl.handle.net/10871/35491We construct a novel framework to investigate the uncertainties and biases associated with estimates of deep ocean temperature change from hydrographic sections, and demonstrate this framework in an eddy-permitting ocean model. Biases in estimates from observations arise due to sparse spatial coverage (few sections in a basin), low frequency of occupations (typically 5-10 years apart), mismatches between the time period of interest and span of occupations, and from seasonal biases relating to the practicalities of sampling during certain times of year. Between the years 1990 and 2010, the modeled global abyssal ocean biases are small, although regionally some biases (expressed as a heat flux into the 4000 - 6000 m layer) can be up to 0.05 W/m². In this model, biases in the heat flux into the deep 2000 - 4000 m layer, due to either temporal or spatial sampling uncertainties, are typically much larger and can be over 0.1 W/m² across an ocean. Overall, 82% of the warming trend deeper than 2000 m is captured by hydrographic section-style sampling in the model. At 2000 m, only half the model global warming trend is obtained from observational-style sampling, with large biases in the Atlantic, Southern and Indian Oceans. Biases due to different sources of uncertainty can have opposing signs and differ in relative importance both regionally and with depth, revealing the importance of reducing temporal and spatial uncertainties in future deep ocean observing design.Natural Environment Research CouncilEuropean Research Counci

Model derived uncertainties in deep ocean temperature trends between 1990-2010 (dataset)

Dataset for plotting the figures in the Garry et al. (2019) article "Model derived uncertainties in deep ocean temperature trends between 1990-2010" published in the Journal of Geophysical Research: Oceans. Requires Python 2.7.The article associated with this dataset is located in ORE at: http://hdl.handle.net/10871/35491We construct a novel framework to investigate the uncertainties and biases associated with estimates of deep ocean temperature change from hydrographic sections, and demonstrate this framework in an eddy-permitting ocean model. Biases in estimates from observations arise due to sparse spatial coverage (few sections in a basin), low frequency of occupations (typically 5-10 years apart), mismatches between the time period of interest and span of occupations, and from seasonal biases relating to the practicalities of sampling during certain times of year. Between the years 1990 and 2010, the modeled global abyssal ocean biases are small, although regionally some biases (expressed as a heat flux into the 4000 - 6000 m layer) can be up to 0.05 W/m². In this model, biases in the heat flux into the deep 2000 - 4000 m layer, due to either temporal or spatial sampling uncertainties, are typically much larger and can be over 0.1 W/m² across an ocean. Overall, 82% of the warming trend deeper than 2000 m is captured by hydrographic section-style sampling in the model. At 2000 m, only half the model global warming trend is obtained from observational-style sampling, with large biases in the Atlantic, Southern and Indian Oceans. Biases due to different sources of uncertainty can have opposing signs and differ in relative importance both regionally and with depth, revealing the importance of reducing temporal and spatial uncertainties in future deep ocean observing design.Natural Environment Research CouncilEuropean Research Counci

Model derived uncertainties in deep ocean temperature trends between 1990-2010 (article)

This is the final version. Available from Wiley via the DOI in this record.The research data supporting this publication are available as a supplement to this article and are openly available from the University of Exeter’s institutional repository at: https://doi.org/10.24378/exe.1104We construct a novel framework to investigate the uncertainties and biases associated with estimates of deep ocean temperature change from hydrographic sections, and demonstrate this framework in an eddy-permitting ocean model. Biases in estimates from observations arise due to sparse spatial coverage (few sections in a basin), low frequency of occupations (typically 5-10 years apart), mismatches between the time period of interest and span of occupations, and from seasonal biases relating to the practicalities of sampling during certain times of year. Between the years 1990 and 2010, the modeled global abyssal ocean biases are small, although regionally some biases (expressed as a heat flux into the 4000 - 6000 m layer) can be up to 0.05 W/m². In this model, biases in the heat flux into the deep 2000 - 4000 m layer, due to either temporal or spatial sampling uncertainties, are typically much larger and can be over 0.1 W/m² across an ocean. Overall, 82% of the warming trend deeper than 2000 m is captured by hydrographic section-style sampling in the model. At 2000 m, only half the model global warming trend is obtained from observational-style sampling, with large biases in the Atlantic, Southern and Indian Oceans. Biases due to different sources of uncertainty can have opposing signs and differ in relative importance both regionally and with depth, revealing the importance of reducing temporal and spatial uncertainties in future deep ocean observing design.Marine Physics and Ocean Climate group at the National Oceanography CentreMet Office Hadley CentreUniversity of Southampton Vice Chancellor’s Awar