Research interests
- Sea ice and ice shelves in the climate system
- Polar meteorology and oceanography
- Ice - ocean interactions in the climate system
- Improvement of the synergetic use of (climate) models and remote sensing products
- Neural networks as parameterisations
Melting at the base of Antarctic ice shelves
In my current work, I investigate 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 am currently working 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 am now looking at an application on circum-Antarctic scale and have submitted an article to JAMES (preprint here).
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 am currently working 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 am now looking at an application on circum-Antarctic scale and have submitted an article to JAMES (preprint 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.
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 understandwhether 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).
Read more: Burgard and Notz (2017).
Supervision
I supervised Benjamin Bouissou's master internship from February to June 2022. Title: "Parameterization of basal melting of an ice shelf with idealized geometry via a neural network"
Conferences and workshops
2024
2023
2022
2021
2020
2019
2018
2017
2016
2014
- OCCD Colloquium, GEOMAR, Kiel, Germany, January 2024 - Invited Presentation
2023
- Forum for Research into Ice Shelf Processes 2023, Stalheim, Norway, June 2023 - Poster
- EGU 2023, Vienna, Austria, April 2023 - Poster
- GISS Sea Level Rise Seminar, NASA GISS, online, February 2023 - Invited presentation
- Cryosphere BXL seminar, VUB and ULB, Brussels, Belgium, February 2023 - Invited presentation
- IGS Global Seminar Series, online, February 2023 - Presentation (link)
- Polar Oceans Seminar, British Antarctic Survey, Cambridge, UK, January 2023 - Presentation
2022
- H2020 PROTECT Fall Meeting, Grenoble, France, October 2022 - Keynote presentation
- Forum for Research into Ice Shelf Processes 2022, Northumbria, UK, September 2022 - Presentation
- ECCOMAS 2022, Oslo, Norway, June 2022 - Session convener and Presentation
- EGU 2022, Vienna, Austria, May 2022 - Presentation
- Ocean Sciences Meeting 2022, online, March 2022 - virtual presentation
2021
- H2020 PROTECT Fall Meeting, online, September 2021 - virtual poster
- H2020 PROTECT Spring Meeting, online, May 2021 - virtual poster
- vEGU 2021, online, April 2021 - vPICO presentation
2020
- shareEGU 2020, online, May 2020 - PICO presentation (highlight) and convener of a webinar short course (link)
2019
- GeoScience Communication School, Trieste, Italy, September 2019
- IGS Symposium "Sea Ice at the Interface", Winnipeg, Canada, August 2019 - Presentation
- Snow Winter School, Hailuoto, Finland, February 2019
- European Security Seminar - North, George C. Marshall Center, Garmisch-Partenkirchen, February 2019 - Invited Presentation
2018
- 3 Cluster Conference, Berlin, Germany, September 2018 - Invited Presentation
- POLAR2018, Davos, Switzerland, June 2018 - Presentation
- Polar Prediction School, Abisko, Sweden, April 2018
- Arctic System Change Workshop, NCAR, Boulder, USA, April 2018- Poster
- Cryospheric and Polar Processes Seminar, NSIDC, Boulder, USA, February 2018 - Presentation
2017
- Workshop on improved satellite retrievals of sea-ice concentration and sea-ice thickness for climate applications, Hamburg, Germany, October 2017 - Invited Presentation
- Max Planck Visions in Science Conference, Berlin, Germany, September 2017 - Organisation Team
- Workshop on Multi-scale modelling of ice characteristics and behavior, Cambridge, UK, September 2017 - Poster
- Summer School on Earth System Modelling, Hamburg, Germany, September 2017
- EGU 2017, Vienna, Austria, April 2017 - Poster and co-convener of two short courses
- Polar Prediction Workshop & 2nd Sea Ice MIP Meeting, Bremerhaven, Germany, March 2017 - Presentation
2016
- NERC Advanced Training Course - Earth Observations for Weather and Climate Studies, Reading, UK, September 2016 - Poster
- EGU 2016, Vienna, Austria, April 2016 - Poster
2014
- Arctic Climate Change, Economy and Society and Arctic Resilience Report Summer School, Stockholm, Sweden, September 2014