Dr. Clara Burgard

Dr. Clara Burgard

she/her

Project scientist in polar climate science

Research

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Research interests

Better including ice shelves in global climate models

In my current work, I explore ways to better include the effect of melting ice shelves on the Southern Ocean and beyond in global climate simulations. I am working with the ocean model NEMO and the climate model IPSL-CM.

Melting at the base of Antarctic ice shelves

In recent years, I worked extensively on investigating parameterisations describing the ocean-induced melt at the base of Antarctic ice shelves for ice sheet models.

Context

The melting of the ice shelves where they are in contact with the ocean is one of the largest uncertainty factors in the Antarctic contribution to future sea-level rise. Several parameterisations exist, linking oceanic properties in front of the ice shelf to melt at the base of the ice shelf, to force ice-sheet models.

Assessment of existing parameterisations

In our paper, we assess the potential of a range of these existing basal melt parameterisations to emulate basal melt rates simulated by a cavity-resolving ocean model on the circum-Antarctic scale. To do so, we perform two cross-validations, over time and over ice shelves respectively, and re-tune the parameterisations in a perfect model approach, to compare the melt rates produced by the newly tuned parameterisations to the melt rates simulated by the ocean model. We find that the quadratic dependence of melt to thermal forcing without dependency on the individual ice-shelf slope and the plume parameterisation yield the best compromise, in terms of integrated shelf melt and spatial patterns. The box parameterisation, which separates the sub-shelf circulation into boxes, the PICOP parameterisation, which combines the box and plume parameterisation, and quadratic parameterisations with dependency on the ice slope yield basal melt rates further from the model reference. The linear parameterisation cannot be recommended as the resulting integrated ice-shelf melt is comparably furthest from the reference. When using offshore hydrographic input fields in comparison to properties on the continental shelf, all parameterisations perform worse, however the box and the slope-dependent quadratic parameterisations yield the comparably best results. In addition to the new tuning, we provide uncertainty estimates for the tuned parameters. You can find the paper here: Burgard et al., 2022. A presentation I did to summarise the main messages of this paper can be found here (from 32:00 on).

If you have input temperature and salinity fields or profiles and want to play around with existing melt parameterisations, check out our python package MULTIMELT.

Development of new parameterisations using deep learning

I also worked on the development of a neural-network based parameterisations. With B. Bouissou, a master student (February to June 2022), we started exploring the use of a neural network in the idealised ISOMIP+ setup. The main results are summarised in this conference paper: Bouissou et al., 2022. I then worked on an application on circum-Antarctic scale and you can find the paper in JAMES here. The python package MULTIMELT now also includes this neural network parameterisation under the name “DEEPMELT”.

Understanding the response of basal melt to warming in the different parameterisations

The uncertain sensitivity of Antarctic ice shelf basal melt to ocean warming strongly contributes to uncertainties in sea level projections. Here, we explore the response of five basal melt models to an idealised sub-thermocline warming. The results show a large intermodel spread. For deep regions of presently fast-melting ice shelves, this spread can reach 2 orders of magnitude. Therefore, a consistent calibration to present-day conditions does not guarantee consistent melt sensitivities, and several basal melt forcings should be applied to prevent underestimating uncertainties in sea level projections. You can find the complete paper in The Cryosphere here.

Using the parameterisations and other advancements to explore the viability of Antarctic ice shelves

In a collaboration within the “platformist” team at the IGE in Grenoble, we explored the limit of viability of Antarctic ice shelves, i.e. we estimated when it becomes almost impossible for the ice shelves to maintain their present-day shape. We show that for a scenario in which global warming remains largely below 2 °C, only 1 out of 64 ice shelves will become likely non-viable by 2300. For a scenario in which global warming reaches nearly 12 °C by 2300, many ice shelves become non-viable once global warming exceeds 4.5 °C, loss that is mainly due to an increase in ocean-induced melt. By 2150 and 2300, 26 and 38 ice shelves, respectively, become likely non-viable. Loss of ice-sheet regions restrained by these 38 ice shelves represent a sea-level rise potential of 10 m. Our estimates are latest bounds for reaching non-viability, and ice-shelf collapse could occur even earlier, in particular owing to the synergy with hydrofracturing. You can find the complete paper in Nature here and the corresponding research briefing here.

ARC3O - The Arctic Ocean Observation Operator

The history behind ARC3O

The diversity in sea ice concentration observational estimates affects our understanding of past and future sea ice evolution as it inhibits reliable climate model evaluation [Notz et al., 2013] and initialization [Bunzel et al., 2016]. It also limits our ability to fully exploit relationships between the evolution of sea ice and other climate variables, such as global-mean surface temperature [Niederdrenk & Notz, 2018] and CO2 emissions [Notz & Stroeve, 2016].

To address these issues, during my PhD, we have constructed an observation operator for the Arctic Ocean at the frequency of 6.9 GHz. This operator provides an alternative approach for climate model evaluation and initialization with satellite observations.

The ARCtic Ocean Observation Operator (ARC3O) provides the possibility to simulate top-of-the-atmosphere brightness temperatures for the Arctic Ocean area at 6.9 GHz, vertical polarization, from climate model output. This simulated brightness temperature can be compared to brightness temperatures measured by satellites from space. You can check out the two publications explaining the method and evaluation of ARC3O: Burgard et al., 2020 (a) and Burgard et al., 2020 (b).

Can I use ARC3O?

Yes, please! You can download the source code on github or install it directly in python via pip or conda. The full documentation can be found here.

Arctic Ocean warming in CMIP5 models

As part of my master’s thesis and early PhD work, we investigated changes in the Arctic Ocean energy budget simulated by 26 general circulation models from the CMIP5 framework to understand whether the Arctic Ocean warming between 1961 and 2099 is primarily driven by changes in the net atmospheric surface flux or by changes in themeridional oceanic heat flux. We found that the models strongly disagree, due to different changes in the meridional oceanic heat flux. Read more: Burgard and Notz (2017).

Supervision

Conferences and workshops

2024

2023

2022

2021

2020

2019

2018

2017

2016

2014