Luke Gregor

TL;DR: Gregor et al. as mentioned in this paper presented a methodologically consistent global data set of all relevant surface ocean parameters, i.e., dissolved inorganic carbon (DIC), total alkalinity (TA), partial pressure of CO 2 ( pCO2 ), pH, and the saturation state with respect to mineral CaCO 3 ( Ω ) at a monthly resolution over the period 1985 through 2018 at a spatial resolution of 1 ∘ × 1 √.

TL;DR: In this article, the authors use hourly CO2 flux and driver observations collected by the combined deployment of ocean gliders to show that resolving the seasonal cycle is not sufficient to reduce the uncertainty of the flux of CO2 to below the threshold required to reveal climatic trends in CO 2 fluxes.

TL;DR: Gregor et al. as discussed by the authors presented an ensemble of global surface ocean CO 2 and air-sea carbon flux estimates using six global observation-based mapping products (CMEMS-FFNN, CSIR-ML6, JENA-MLS, JMA-MLR, MPI-SOMFFN, NIES-FNN).

TL;DR: In this article, a regional approach on empirical estimates of pCO2 to understand the role that seasonal variability has in long-term CO2 changes in the Southern Ocean was used, where an ensemble of three machine learning products: support vector regression (SVR), random forest regression (RFR), and self-organising-map feed-forward neural network (SOM-FFN) method from Landschutzer et al.

TL;DR: In this paper, the authors focus on the problem of sparse spatial and temporal coverage of observations in the Southern Ocean, which has led to a knowledge gap that increases uncertainty in atmosphere and ocean dynamics and boundary-layer thermodynamic processes, impeding improvements in weather and climate models.