Author: mariko

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.

Probabilistic models for harmful algae: application to the Norwegian coast

Silva, E., Brajard, J., Counillon, F., Pettersson, L.H., Naustvoll, L. 2024: Probabilistic models for harmful algae: application to the Norwegian coast. Environmental Data Science. https://doi.org/10.1017/eds.2024.11

Summary: We have developed probabilistic models to estimate the likelihood of harmful algae presence and outbreaks along the Norwegian coast, which can help optimization of the national monitoring program and the planning of mitigation actions. We employ support vector machines to calibrate probabilistic models for estimating the presence and harmful abundance (HA) of eight toxic algae found along the Norwegian coast, including Alexandrium spp., Alexandrium tamarense, Dinophysis acuta, Dinophysis acuminata, Dinophysis norvegica, Pseudo-nitzschia spp., Protoceratium reticulatum, and Azadinium spinosum. The inputs are sea surface temperature, photosynthetically active radiation, mixed layer depth, and sea surface salinity. The probabilistic models are trained with data from 2006 to 2013 and tested with data from 2014 to 2019. The presence models demonstrate good statistical performance across all taxa, with R (observed presence frequency vs. predicted probability) ranging from 0.69 to 0.98 and root mean squared error ranging from 0.84% to 7.84%. Predicting the probability of HA is more challenging, and the HA models only reach skill with four taxa (Alexandrium spp., A. tamarense, D. acuta, and A. spinosum). There are large differences in seasonal and geographical variability and sensitivity to the model input of different taxa, which are presented and discussed. The models estimate geographical regions and periods with relatively higher risk of toxic species presence and HA, and might optimize the harmful algae monitoring. The method can be extended to other regions as it relies only on remote sensing and model data as input and running national programs of toxic algae monitoring.

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

Causal links between sea-ice variability in the Barents-Kara Seas and oceanic and atmospheric drivers

Dörr, J., Årthun, M., Docquier, D., Li, C., Eldevik, T. 2024: Causal links between sea-ice variability in the Barents-Kara Seas and oceanic and atmospheric drivers. Geophysical Research Letters. https://doi.org/10.1029/2024GL108195

Summary: The sea ice in the Barents and Kara Seas (BKS) is melting due to Arctic warming, but this is overlaid by large natural variability. This variability is caused by variations in the ocean and the atmosphere, but it is not clear which is more important in which parts of the region. We use a relatively new method that allows us to quantify cause-effect relationships between sea ice and atmospheric and oceanic drivers. We find that in the north of the BKS, the atmosphere has the biggest impact, in the central and northeastern parts, it is the heat from the ocean, and in the south, it is the local sea temperature. We also find that wind patterns over the Nordic Seas affect how much oceanic heat comes into the Barents Sea, and that, in turn, affects the sea ice. Looking ahead, as the ice is expected to melt more in the future, the atmosphere is likely to become more important in driving sea ice variability in the BKS. This study helps us better understand how the ocean and atmosphere work together to influence the yearly changes in sea ice in this region.

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

The Role of Ocean Heat Content on the Madden–Julian Oscillation (PhD thesis)

Ashneel Chandra (2024-03-19): The Role of Ocean Heat Content on the Madden–Julian Oscillation. PhD thesis, University of Bergen, Bergen, Norway. https://hdl.handle.net/11250/3124162

Summary: The overall goal of this dissertation is to understand the role of upper ocean heat content (OHC) and equatorial ocean dynamics on the Madden-Julian Oscillation (MJO). While the response of the ocean to atmospheric forcing on intraseasonal timescales has been studied extensively, the feedback of OHC on the MJO has not been systematically investigated. Recently, a new line of research has emerged that highlights the interaction between ocean dynamics, OHC, and the MJO in the Indian Ocean (IO) basin. In the IO, synchronization between oceanic equatorial waves and the MJO is possible because of the basin-scale, the propagation speed of oceanic equatorial waves, and the timescale of MJO variability. In a series of three papers, this thesis aims to contribute to understanding the variability and interactions between the MJO, equatorial ocean dynamics, and OHC in the IO basin.

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

Arctic-Atlantic Climate Variability and Predictability in Observations and in a Dynamical Prediction System (PhD thesis)

Goncalves Dos Passos, Leilane (2023-11-03): Arctic-Atlantic Climate Variability and Predictability in Observations and in a Dynamical Prediction System. PhD thesis, University of Bergen, Bergen, Norway. https://bora.uib.no/bora-xmlui/handle/11250/3099594

Summary: The major focus of this thesis is on understanding decadal climate predictability to improve climate models and their predictions. Climate predictions show promising results but are still facing challenges, especially in connecting the ocean and atmosphere. The ocean is the main source of predictability. The ocean’s capacity to store and release heat over long periods of time makes it a thermal memory of the climate system. In the Arctic-Atlantic region, ocean currents transport heat to polar areas, and along this path, the ocean releases the heat to the atmosphere through surface fluxes. From this interaction, both the ocean and the atmosphere change. On the one hand, as the ocean releases heat into the atmosphere, it cools down, increasing its density. The denser water eventually flows southward as part of the Atlantic Meridional Overturning Circulation (AMOC). On the other hand, the atmosphere being warmed by the ocean affects nearby land areas through the winds, influencing the climate variability of Western Europe.
This dynamic ocean-atmosphere interaction is a source of predictability in the Arctic-Atlantic region and is investigated here using observations and a dynamical prediction system, the Norwegian Climate Prediction Model (NorCPM). Dynamical prediction systems are useful tools for investigating and predicting climate variability on decadal timescales. Beginning their development in the early 2000s, these systems are currently the focus of significant efforts by the scientific community to provide operational decadal forecasts with reliable and accurate information. The research of this thesis is aligned with the development of NorCPM while also focusing on investigating key mechanisms that give rise to predictability in the Arctic-Atlantic region.
Climate predictions are initialized in different ways, which affects their performance. The first study of the thesis investigates the best initialization method for the Arctic-Atlantic region using NorCPM. Paper I finds that employing a more complex data assimilation method leads to the improved predictive skill of temperature and salinity in the Subpolar North Atlantic (SPNA) but not in the Norwegian Sea. The loss of skill in the Norwegian Sea is found in regions characterized by intense surface heat fluxes and eddy activity, such as the Norwegian and Lofoten Basins. The warm Atlantic water moving northwards from the SPNA to the Norwegian Sea carries thermohaline anomalies, and it is transformed from light-to-dense waters by surface forcing along the path. These two mechanisms are investigated in observation-based data in Paper II. Their relationship is analyzed, focusing on the decadal timescale in the eastern SPNA. Paper II finds that warm anomalies are associated with surface-forced water mass transformation in the light-density classes, while during cold anomalies, more transformation happens in denser classes. This relationship was disrupted during the Great Salinity Anomaly events of the 70s and 90s. Furthermore, the study highlights a faster propagation of thermohaline anomalies in the SPNA compared to the Norwegian Sea, particularly regarding temperature.
The influence of the ocean on the climate of Europe is investigated in Paper III. This study advances the understanding of how constrained ocean variability impacts the atmosphere of NorCPM. The results show a more realistic thermodynamic component of surface air temperature (SAT) over the ocean and some European regions. Paper III shows that there is potential to improve multi-annual to decadal predictions over Europe, which is currently challenging in prediction systems. The research presented in this Thesis enhances the understanding of climate predictability in the Arctic-Atlantic region. It provides insights into the interactions between the atmosphere and ocean and adds to the development of the Norwegian Climate Prediction Model, contributing to making this prediction system operational in the coming years. Following similar approaches as presented in this thesis for other dynamical prediction systems would be highly recommended.

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

Editorial: Past Reconstruction of the Physical and Biogeochemical Ocean State

Masina, S., Counillon, F., Grégoire, M., Storto, A., Tsujino, H. 2022: Editorial: Past Reconstruction of the Physical and Biogeochemical Ocean State. Front Earth Sci. https://doi.org/10.3389/feart.2022.890370

Summary: Knowledge of the ocean’s physical, biogeochemical and ecosystem state and variability is crucial for understanding the evolution of our climate system and better predicting its future. However, the sparseness and inhomogeneous distribution of observations hinder the creation of sound 4-dimensional reconstructions of the past (for an overview of ocean observing systems see the Research Topic Oceanobs’19: An Ocean of Opportunity). Instead, we must rely on a combination of ocean modeling and data analysis to infer past changes. Over the last decade the quality of ocean reanalyses has improved mainly thanks to advances in data assimilation methods and more quality-controlled observation data sets. Reanalyses provide the best-possible state estimate by assimilating observations into a dynamical model (Balmaseda et al., 2015; Masina and Storto, 2017; Storto et al., 2019). In addition, advanced statistical mapping methods (e.g., objective or variational analysis) provide observation-based gridded fields whose resolution depends on the amount of available data (among many Cheng et al., 2017, Ishii et al., 2017; Boyer et al., 2018). For many variables, particularly biogeochemical, the lack of observations more strongly limits the spatial and temporal resolution of these gridded products (Fennel et al., 2019).

The Research Topic gathers contributions aiming at reconstructing the past physical, sea ice and biogeochemical state of the ocean using models in combination with data. Ocean reanalyses and observation-mapping are proposed to further our knowledge, to demonstrate their use in supporting various applications, and to increase confidence in these reconstructions within the scientific community. The products and applications described in this topic provide a foundation for their use in ecosystem-based management, policy advice to support mitigation and adaptation strategies, and in the identification of pathways towards a sustainable ocean.

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