Research Visit at The University Centre in Svalbard

Marianne Williams-Kerslake is a PhD student at the Nansen Center looking at marine heatwaves in the Arctic Ocean. Marine heatwaves are periods of extreme high sea surface temperatures relative to long-term trends. The annual intensity, frequency (number of events per year), and duration of marine heatwaves have increased in the Arctic Ocean in recent decades. In particular, a high frequency of marine heatwaves has been observed around the Svalbard archipelago, leading Marianne to focus on this area. Marianne has been using TOPAZ, a physical reanalysis for the Arctic Ocean, to characterise marine heatwaves around Svalbard. In the autumn of 2024, supported by the Bjerknes Climate Prediction Unit, she travelled to The University Centre in Svalbard (UNIS) for 2.5 months. She was there to validate and compare marine heatwaves in TOPAZ to observations (measurements) that have been gathered from multiple moorings around Svalbard.  This is an exciting opportunity and will enable us to determine how effectively TOPAZ can capture marine heatwave events in this region.

Using observations to assess the effectiveness of the TOPAZ model in portraying marine heatwave events contributes to the research aims of the Bjerknes Climate Prediction Unit, particularly, RA3 – assessing the limits of climate prediction. We will be able to quantify the impact of model errors/limitations on TOPAZ projections of marine heatwaves, increasing our understanding of systems such as TOPAZ.

“Working at UNIS and experiencing life and the nature in Svalbard was an amazing experience. I worked in the Arctic Geophysics group and was supervised by Ragnheid Skogseth and Frank Nilsen. It was fascinating and inspiring to learn about the wide range of research going on at UNIS; in the Arctic Geophysics group alone, there were a variety of projects from Aurora research and polar space missions to essential climate monitoring. During my visit, I obtained encouraging results regarding the accuracy of TOPAZ’s performance in the Svalbard region. I am now in the process of writing up these findings for my first paper. I  hope to return to Svalbard for the later studies in my PhD and continue collaboration between the Bjerknes Center and UNIS.” – Marianne Williams-Kerslake

 

Earth System Reanalysis in Support of Climate Model Improvements

Stammer, D., Amrhein, D.E., Alonso Balmaseda, M., Bertino, L., Bonavita, M., Buontempo, C., Caltabiano, N., Counillon, F., Fenty, I., Ferrari, R., Fujii, Y., et al. 2024: Earth System Reanalysis in Support of Climate Model Improvements. Bull. Amer. Meteor. Soc.. https://doi.org/10.1175/BAMS-D-24-0110.1

Summary: A 3-day workshop took place from 12 to 14 June 2023, at the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, focusing on data assimilation (DA) and machine learning (ML) in the context of Earth system reanalysis and climate model improvements.
The workshop, organized 25 years after the inception of the Estimating the Circulation and Climate of the Ocean (ECCO), was an effort to lay out the roadmap for future development of DA in support of climate modeling and climate knowledge improvements, or “climate DA.” The following is a summary of the workshop outcomes and recommendations arising to move the field of DA forward in the context of climate modeling.
Recent climate model developments, established through increased model resolution, have led to substantial improvements in model simulations of the time-evolving, coupled Earth system and its subcomponents. However, regardless of resolution, climate models will always produce climate features and variability that differ from the real world and will be prone to biases. This is due to many remaining uncertainties, such as in parametric and structural model uncertainty, in the initial conditions prescribed, and in the prescribed (scenario) forcing which varies on decadal to centennial time scales.
Further model improvements are expected to arise specifically from the improved representation of physical processes realized through model–data fusion. This will create an unprecedented opportunity to better exploit a large array of Earth observations, from in situ measurements to weather radars and satellite observations, as the resolved scales of the models approach those of the observations. For this, climate DA will be the central tool to bring models and observations into consistency, by improving initial conditions, inferring uncertain model parameters and structure, and quantifying uncertainty. Generally, there will be advantages and complementarities of adjoint-based smoother approaches, ensemble-based filter approaches, or new ML-inspired approaches. Yet the ever-increasing model resolution will present growing challenges arising from computational cost, calling for new ways of performing data assimilation and model optimization. Using the complementarity in a hybrid approach, blending tools and concepts from variational, ensemble, and ML methods might be what is required in the future. In this context, ML could be important to handle nonlinear responses and to better approximate non-Gaussian distributions.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Workshop: Chinese-Norwegian Collaboration Projects within Climate Systems, 5-8th August 2024 in Bergen, Norway, successfully completed!

From the 5th to the 8th August 2024, more than a hundred climate researchers meet to discuss research from a Norwegian-Chinese collaboration on climate research.

More than fifty participants come from the Institute of Atmospheric Physics at the Chinese Academy of Sciences, Nanjing University, Nanjing University of Information Science and Technology, and Fudan University, all Nansen-Zhu partners. Others come from China Ocean University, Sun Yat-Sen University, Danish Meteorological Institute, Norwegian Meteorological Institute and the University of Reading.

Read the article: Cooperation across Eurasia.
Read more about this workshop and the workshop programme: Chinese-Norwegian-climsys-2024workshop.

Revising chronological uncertainties in marine archives using global anthropogenic signals: a case study on the oceanic 13C Suess effect

Irvalı, N., Ninnemann, U.S., Olsen, A., Rose, N.L., Thornalley, D.J., Mjell, T.L., Counillon, F. 2024: Revising chronological uncertainties in marine archives using global anthropogenic signals: a case study on the oceanic 13C Suess effect. Geochronology. https://doi.org/10.5194/gchron-6-449-2024

Summary: Marine sediments are excellent archives for reconstructing past changes in climate and ocean circulation. Overlapping with instrumental records, they hold the potential to elucidate natural variability and contextualize current changes. Yet, dating uncertainties of traditional approaches (e.g., up to ± 30–50 years for the last 2 centuries) pose major challenges for integrating the shorter instrumental records with these extended marine archives. Hence, robust sediment chronologies are crucial, and most existing age model constraints do not provide sufficient age control, particularly for the 20th century, which is the most critical period for comparing proxy records to historical changes. Here we propose a novel chronostratigraphic approach that uses anthropogenic signals such as the oceanic 13C Suess effect and spheroidal carbonaceous fly-ash particles to reduce age model uncertainties in high-resolution marine archives. As a test, we apply this new approach to a marine sediment core located at the Gardar Drift, in the subpolar North Atlantic, and revise the previously published age model for this site. We further provide a refined estimate of regional reservoir corrections and uncertainties for Gardar Drift.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison

Bushuk, M., Ali, S., Bailey, D.A., Bao, Q., Batté, L., Bhatt, U.S., Blanchard-Wrigglesworth, E., Blockley, E., Cawley, G., Chi, J., Counillon, F., et al. 2024: Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison. Bull. Amer. Meteor. Soc.. https://doi.org/10.1175/BAMS-D-23-0163.1

Summary: This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least 3 months in advance.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Warm Advection as a Cause for Extreme Heat Event in North China

Wang, X., Zhang, Z., Yu, E., Guo, C., Otterå, O. H., Counillon, F. 2024: Warm Advection as a Cause for Extreme Heat Event in North China. Geophysical Research Letters. https://doi.org/10.1029/2024GL108995

Summary: Nowadays, heat waves have a significant impact on our society and result in substantial economic losses. Climate projections indicate that extreme heat events (EHEs) will become more frequent. However, heat waves have also often occurred in the past 300 years during periods with much lower anthropogenic forcing. One notable example is the severe heat event in the summer of 1743, which killed more than 10 thousand people in North China. The mechanism behind such events remains uncertain, making it exciting and valuable to investigate such heat waves in the past. In this study, we use a global model, a nested regional model, and tree-ring records to explore the mechanisms driving EHEs. The statistical robustness of the connection between EHEs in North China and Northeast China Vortexes is supported by modern observations. Notably, from 1950 to 2021, 63.6% of EHEs in North China coincide with active Northeast China Vortexes.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Adaptive Covariance Hybridization for the Assimilation of SST Observations Within a Coupled Earth System Reanalysis

Barthélémy, S., Counillon, F., Wang, Y. 2024: Adaptive Covariance Hybridization for the Assimilation of SST Observations Within a Coupled Earth System Reanalysis. JAMES. https://doi.org/10.1029/2023MS003888

Summary: Data assimilation is a statistical method that reduces uncertainty in a model, based on observations. Because of their ease of implementation, the ensemble data assimilation methods, that rely on the statistics of a finite ensemble of realizations of the model, are popular for climate reanalysis and prediction. However, observations are sparse—mostly near the surface—and the sampling error from data assimilation method introduces a deterioration in the deep ocean. We use a method that complements this ensemble with a pre-existing database of model states to reduce sampling error. We show that the approach substantially reduces error at the intermediate and deep ocean. The method typically requires the tunning of a parameter, but we show that it can be estimated online, achieving the best performance.

Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Workshop on Climate Prediction and Services over the Atlantic-Arctic region, 27-30th May 2024, successfully completed!

From the 27th to the 30th May 2024, 105 researchers gathered under the rain clouds in Bergen to discuss the science of climate prediction and services. Joined by an additional 60 colleagues online, the community gathered experts from 19 countries across 5 continents. The program included 6 keynote talks, 30 oral presentations, 5 break-out groups, 60 pitch presentations and a 2-hr “society meets science” side-event of 37 participants. Read more about this and consult the workshop programme and more here: https://bcpu.w.uib.no/workshop-may2024/.

Group photo of workshop participants taken on hotel staircase
Workshop participants in Bergen (not all are represented)