Research
I am interested in the dynamical response of the ocean to anthropogenic climate change. I use novel statistical and data science tools, alongside state-of-the-art climate models and observations, to quantify thermodynamic and circulation changes in the global ocean.
T_S_anim.mp4A volume census of the global ocean in T-S space from 1970 - 2014, from monthly-averaged 1-degree gridded observations (EN4).
Global ocean heat content
I focus on applying novel water mass transformation frameworks to understand the role of mixing and surface fluxes on global ocean heat content changes. I have developed a novel percentile-based framework to a combination of gridded hydrographic ocean observations and state-of-the-art CMIP6 climate models to undrstand changes in the global ocean.
Future directions of this work include developing new inverse models which infer heat content changes due to mixing and surface fluxes purely from observations.
Ensemble-mean salinity change in the historical simulation of a suite of CMIP6 models, from Sohail et al., 2022
Global water cycle change
Using the ocean's salinity as a 'dynamic rain gauge', I infer changes to the water cycle in our earth system from a combination of CMIP6 historical simulations and oceanic observations. Previously, I used this method to quantify the total earth-system fresh water transport from warm to cold regions, a crucial limb of the global water cycle.
Currently, I am expanding on this work by working with others to infill missing salinity observations and develop a gridded hydropgraphic dataset that can minimise the uncertainty of salinity observations.
Gaussian Mixture Modelling-based clusters obtained from the temperature and salinity distribution in the Antarctic shelf region, from the ACCESS-OM2-01 ocean model.
Unsupervised machine learning and classification
I work on applying unsupervised clustering methods to geophysical applications. I use Binary Space Partitioning to objectively identify water masses based on a pre-determined theshold, and Gaussian Mixture Modelling to explore changes in key oceanic regimes in the Antarctic and beyond.
I hope to extend this work to incoprorate Neural Networks and gain constrained observational estimates of oceanic change in response to climate change.
Average aerosol optical depth across the AA-only experiment for CSIRO-Mk3-6-0 model (CMIP5)
Detection and attribution of climate change signals
I use a suite of simulations from the Detection and Attribution Model Intercomparison Project (DAMIP) from CMIP6 to understand the relative impact of greenhouse gases (GHGs), anthropogenic aerosols (AAs) and natural forcings on ocean heat content.
Drawing on a novel temperature-percentile framework, I quantify the relative impact of greenhouse gases, anthropogenic aerosols and natural forcings on the global heat budget, and frame this impact in terms of an ocean heat uptake efficiency.
Final_Deep_Convection.mp4A turbulence-resolving Direct Numerical Simulation (DNS) of open ocean convection illustrating the key stages of deep convection in a stratified medium. From T. Sohail et. al., Journal of Physical Oceanography, 2020.
Southern Ocean circulation dynamics
I am interested in the role of small-scale mixing and convection on large-scale circulation in the Southern Ocean. My particular focus is on convective mixing in the Southern Ocean and its impact of key features in the region - the Antarctic Circumpolar Current, the Antarctic Bottom Water and Intermediate Water, and the broader Meridional Overturning Circulation. My research has also focussed on novel parameterisations of convection in large-scale ocean models. I have worked with Direct Numerical Simulations and GFDL-MOM6 in developing these parameterisations.
Future directions of this work include quantifying the impact of future ice melting on transport and circulation dynamics in the Southern Ocean.