Mechanisms Controlling Global Mean Sea Surface Temperature Determined From a State Estimate

Abstract

Global mean sea surface temperature ( falseT¯) is a variable of primary interest in studies of climate variability and change. The temporal evolution of falseT¯ can be influenced by surface heat fluxes ( falsescriptF¯) and by diffusion ( falsescriptD¯) and advection ( falsescriptA¯) processes internal to the ocean, but quantifying the contribution of these different factors from data alone is prone to substantial uncertainties. Here we derive a closed falseT¯ budget for the period 1993–2015 based on a global ocean state estimate, which is an exact solution of a general circulation model constrained to most extant ocean observations through advanced optimization methods. The estimated average temperature of the top (10‐m thick) level in the model, taken to represent falseT¯, shows relatively small variability at most time scales compared to falsescriptF¯, falsescriptD¯, or falsescriptA¯, reflecting the tendency for largely balancing effects from all the latter terms. The seasonal cycle in falseT¯ is mostly determined by small imbalances between falsescriptF¯ and falsescriptD¯, with negligible contributions from falsescriptA¯. While falsescriptD¯ seems to simply damp falsescriptF¯ at the annual period, a different dynamical role for falsescriptD¯ at semiannual period is suggested by it being larger than falsescriptF¯. At periods longer than annual, falsescriptA¯ contributes importantly to falseT¯ variability, pointing to the direct influence of the variable ocean circulation on falseT¯ and mean surface climate.

Local and Remote Forcing of Interannual Sea‐Level Variability at Nantucket Island

“…They help the numerical model accurately account for the mass and density contributions to sea level by material exchanges through the ocean surface (Campin et al., 2008 ; Forget et al., 2015 ). Numerous studies have used various ECCO products to investigate changes in sea level (e.g., Forget & Ponte, 2015 ; Schloesser et al., 2021 ), temperature and salinity (e.g., Liu et al., 2019 ; Ponte et al., 2021 ; Ponte & Piecuch, 2018 ), and other oceanic physical properties. A comprehensive list of ECCO‐related publications can be found at https://ecco-group.org/publications.htm .…”

Putting It All Together: Adding Value to the Global Ocean and Climate Observing Systems With Complete Self-Consistent Ocean State and Parameter Estimates

“…As experience is gained, application of state estimation is expanding from drawing inferences from sampling the estimates akin to observations to quantitatively analyzing processes by utilizing the complete physics embodied in the model. Examples of such include analyses of property budgets that are closed without unresolved components (e.g., Buckley et al, 2015;Piecuch et al, 2017;Ponte and Piecuch, 2018), tracing origins and fate of ocean water masses (e.g., Fukumori et al, 2004;Gao et al, 2011;Qu et al, 2013) and quantifying causal mechanisms controlling the ocean (e.g., Fukumori et al, 2015;Pillar et al, 2016Pillar et al, , 2018Jones et al, 2018;Smith and Heimbach, 2019). The model's adjoint offers a unique tool in such efforts by providing an efficient means to evaluate physical dependencies among different quantities of interest.…”