Climate feedbacks determine how much surface temperatures will eventually warm to balance anthropogenic radiative forcing, but remain difficult to constrain. The climate feedback due to some process X is defined as the partial derivative of outgoing radiation at the top of the atmosphere with respect to surface temperature following a change in X, λX=-∂Rout/TS|X, with total climate feedback a summation from all processes, λtotal=∑λX. Standard approaches evaluate climate feedbacks from finite temporal changes in surface temperatures and outgoing radiation, following observed or simulated perturbations to climate state. However, this introduces significant linear combination error (λtotal≠∑λX) when the applied perturbation is large enough to achieve a good signal-to-noise ratio. This study presents a new semi-empirical evaluation of non-cloud climate feedbacks, constrained instead by spatial variation in outgoing radiation and climate state. First, we observationally constrain functional relations for outgoing radiation over ocean and land in terms of surface temperature, pressure, relative humidity, the height of the tropopause, fractional clound amount and latitude. Then, these functional relations are differentiated with respect to surface temperature to calculate the climate feedbacks for infinitesimal perturbation, eliminating linear combination error at high signal-to-noise ratio. We find, when combined with a recent cloud feedback estimate, a present-day total climate feedback of -0.99 (-0.75 to -1.22 at 66% range) Wm-2K-1. Our method is independent of temporal variation approaches to evaluate climate feedback allowing Bayesian combination to further reduce uncertainty.

Sub-Antarctic Mode Waters (SAMWs) form to the north of the Antarctic Circumpolar Current (ACC) in the Indo-Pacific Ocean, whence they ventilate the ocean’s lower pycnocline and play an important role in the climate system. With a backward Lagrangian particle-tracking experiment in a data-assimilative model of the Southern Ocean (B-SOSE), we address the long-standing question of whether SAMWs originate from densification of southward-flowing subtropical waters, or lightening of northward-flowing Antarctic waters sourced by Circumpolar Deep Water (CDW) upwelling. Our analysis evidences the co-occurrence of both sources of SAMWs in all formation areas, and strong inter-basin contrasts in their relative contributions. Subtropical waters are the main precursor of Indian Ocean SAMWs (70-75% of particles) but contribute a smaller amount ($<$40%) to Pacific SAMWs, which are mainly sourced by CDW. By tracking property changes along particle trajectories, we show that SAMW formation from northern and southern sources involves contrasting drivers: subtropical source waters are cooled and densified by surface heat fluxes, and freshened by ocean mixing. Southern source waters are warmed and lightened by surface heat and freshwater fluxes, and they are made either saltier by mixing in the case of Indian SAMWs, or fresher by surface fluxes in the case of Pacific SAMWs. Our results underscore the distinct climatic impact of Indian and Pacific SAMWs, as net sources of atmospheric heat and net sinks of freshwater, respectively; a role that is conferred by the relative contributions of subtropical and Antarctic sources to their formation.

The dispersal of dissolved iron (DFe) from hydrothermal vents is poorly constrained. Combining field observations and a hierarchy of models, we show that the dispersal of DFe from the Trans-Atlantic-Geotraverse vent site occurs predominantly in the colloidal phase and is controlled by multiple physical processes. Enhanced mixing near the seafloor and transport through fracture zones at fine-scales interacts with the wider ocean circulation to drive predominant westward DFe dispersal away from the Mid-Atlantic ridge at the 100km scale. In contrast, diapycnal mixing predominantly drives northward DFe transport within the ridge axial valley. The observed DFe dispersal is not reproduced by the coarse resolution ocean models typically used to assess ocean iron cycling due to their omission of local topography and mixing. Unless biogeochemical models include high-resolution nested grids, they will inaccurately represent DFe dispersal from axial valley ridge systems, which make up half of the global ocean ridge crest.