Bjerknes Climate Prediction Unit

Sandø, A.B., Hjøllo, S.S., Hansen, C., Skogen, M.D., Hordoir, R., Sundby,S. 2024: A multi-scenario analysis of climate impacts on plankton and fish stocks in northern seas. https://doi.org/10.1111/faf.12834 Summary: Globally, impacts of climate change display an increasingly negative development of marine biomass, but there is large regional variability. In this analysis of future climate change on stock productivity proxies for the North Sea, the Norwegian Sea, and the Barents Sea, we have provided calculations of accumulated directional effects as a function of climate exposure and sensitivity attributes. Based on modelled changes in physical and biogeochemical variables from three scenarios and knowledge of 13 different stocks’ habitats and response to climate variations, climate exposures have been weighted, and corresponding directions these have on the stocks have been decided. SSP1-2.6 gives mostly a weak cooling in all regions with almost negligible impacts on all stocks. SSP2-4.5 and SSP5-8.5 both provide warmer conditions in the long term but are significantly different in the last 30 years of the century when the SSP5-8.5 warming is much stronger. The results show that it is the current stocks of cod and Calanus finmarchicusin the North Sea, and polar cod and capelin in the Barents Sea that will be most negatively affected by strong warming. Stocks that can migrate north into the northern seas such as hake in the Norwegian Sea, or stocks that are near the middle of the preferred temperature range such as mackerel and herring in the Norwegian Sea and cod and Calanus finmarchicus in the Barents Sea, are the winners in a warmer climate. The highly different impacts between the three scenarios show that multiple scenario studies of this kind matter. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

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.

Zheng, Y. X., S. L. Li, N. Keenlyside, S. P. He, Suo, L.L. 2024: Projecting spring consecutive rainfall events in the Three Gorges Reservoir based on triple-nested dynamical downscaling. Adv. Atmos. Sci. https://doi.org/10.1007/s00376-023-3118-2 Summary: Spring consecutive rainfall events (CREs) are key triggers of geological hazards in the Three Gorges Reservoir area (TGR), China. However, previous projections of CREs based on the direct outputs of global climate models (GCMs) are subject to considerable uncertainties, largely caused by their coarse resolution. This study applies a triple-nested WRF (Weather Research and Forecasting) model dynamical downscaling, driven by a GCM, MIROC6 (Model for Interdisciplinary Research on Climate, version 6), to improve the historical simulation and reduce the uncertainties in the future projection of CREs in the TGR. Results indicate that WRF has better performances in reproducing the observed rainfall in terms of the daily probability distribution, monthly evolution and duration of rainfall events, demonstrating the ability of WRF in simulating CREs. Thus, the triple-nested WRF is applied to project the future changes of CREs under the middle-of-the-road and fossil-fueled development scenarios. It is indicated that light and moderate rainfall and the duration of continuous rainfall spells will decrease in the TGR, leading to a decrease in the frequency of CREs. Meanwhile, the duration, rainfall amount, and intensity of CREs is projected to regional increase in the central-west TGR. These results are inconsistent with the raw projection of MIROC6. Observational diagnosis implies that CREs are mainly contributed by the vertical moisture advection. Such a synoptic contribution is captured well by WRF, which is not the case in MIROC6, indicating larger uncertainties in the CREs projected by MIROC6. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Ogilvie, A.E., King, L.A., Keenlyside, N., Counillon, F., Daviđsdóttir, B., Einarsson, N., Gulev, S., Fan, K., Koenigk, T., McGoodwin, J.R. and Rasmusson, M.H. 2024: Recent Ventures in Interdisciplinary Arctic Research: The ARCPATH Project. Adv. Atmos. Sci. https://doi.org/10.1007/s00376-023-3333-x Summary: This paper celebrates Professor Yongqi GAO’s significant achievement in the field of interdisciplinary studies within the context of his final research project Arctic Climate Predictions: Pathways to Resilient Sustainable Societies – ARCPATH (https://www.svs.is/en/projects/finished-projects/arcpath). The disciplines represented in the project are related to climatology, anthropology, marine biology, economics, and the broad spectrum of social-ecological studies. Team members were drawn from the Nordic countries, Russia, China, the United States, and Canada. The project was transdisciplinary as well as interdisciplinary as it included collaboration with local knowledge holders. ARCPATH made significant contributions to Arctic research through an improved understanding of the mechanisms that drive climate variability in the Arctic. In tandem with this research, a combination of historical investigations and social, economic, and marine biological fieldwork was carried out for the project study areas of Iceland, Greenland, Norway, and the surrounding seas, with a focus on the joint use of ocean and sea-ice data as well as social-ecological drivers. ARCPATH was able to provide an improved framework for predicting the near-term variation of Arctic climate on spatial scales relevant to society, as well as evaluating possible related changes in socioeconomic realms. In summary, through the integration of information from several different disciplines and research approaches, ARCPATH served to create new and valuable knowledge on crucial issues, thus providing new pathways to action for Arctic communities. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Silva, Edson (2023-11-30). Prediction of Harmful Algae Blooms Impacting Shellfish Farms in Norway. PhD thesis, University of Bergen, Norway. https://bora.uib.no/bora-xmlui/handle/11250/3104786 Summary: Harmful algae blooms (HABs) cause severe damage to the ecosystem and human health, and have significant economic impacts on shellfish farms. HAB prediction models have become increasingly popular because they can help stakeholder to take mitigation actions and reduce economic loss. Few studies have attempted to predict toxic algae species related to shellfish contamination because the time extent of data is limited and modeling the environmental response of specific taxa is complex. However, toxic algae monitoring programs have now been running for several years and have produced large datasets of toxic algae. Combined with long-time series observations by satellites and model reanalysis, we can now calibrate prediction models for toxic algae affecting shellfish farms. This thesis calibrates machine learning models to predict toxic algae impacting shellfish farms in Norwegian coastal waters for the first time. It is conducted by combining toxic algae data from the Norwegian Food Safety Authority with satellite observations of Chla concentration, Suspended Particulate Matter (SPM), Sea Surface Temperature (SST), Photosynthetically Active Radiation (PAR), and wind speed, as well as model reanalysis data of Mixed Layer Depth (MLD) and Sea Surface Salinity (SSS). Paper I demonstrates that the blooms phenology has a strong interannual variability in the North, Norwegian, and Barents Seas, which is related to the variability of the environmental ocean and atmospheric factors (SST, MLD, SPM, and winds). It implies that these variables are potential predictors for blooms in the region. Paper II exhibit that a Support Vector Machine (SVM) model can predict the presence probability of eight toxic algae on the Norwegian coast using SST, PAR, SSS, and MLD. The models can also predict the probability of harmful levels for Alexandrium spp., Alexandrium tamarense, Dinophysis acuta, and Azadinium spinosum. It can produce a climatological overview of the HABs along the Norwegian coast and provide monitoring and prediction applications. Paper III extends the SVM application to the prediction of D. acuminata abundance in a sub-seasonal range (7 -28 days) when fed with the current and past D. acuminata abundance, SST, PAR, and wind speed. The sub-seasonal forecast model is developed for the Lyngenfjord in northern Norway as a proof of concept. The probability estimates in Paper II and the sub-seasonal forecast of D. acuminata abundance in Paper III are two complementary approaches. The first is employable in the entire coast even where algae monitoring is unavailable, while the latter requires tuning to specific aquaculture farms and can achieve refined prediction. Since the SVM models are fed with data commonly available worldwide, they are portable to other regions where data from harmful algae monitoring programs are available. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Keenlyside, N., Ogilvie A., Yang, S. Koening, T., Counilon F. 2023: Climate and marine-ecosystem intelligence for a green and competitive Nordic region. Nordic Region Fast Track to Vision 2030, NordForsk Policy Brief. https://norden.diva-portal.org/smash/get/diva2:1789341/FULLTEXT03 Summary: Operational climate and marine ecosystem services are urgently needed at the Nordic level. These services are crucial for combating the climate and marine ecosystem emergencies currently threatening the region. They are also needed to manage climate risks and to increase resilience in transport, construction, and food sectors, as well as to develop a renewable energy sector to achieve carbon neutrality. They are important for managing human activities to ensure a healthy marine ecosystem and sustainable fisheries. We identify two priorities for developing climate and marine-ecosystem services that capitalise on world-leading Nordic research. First, fully integrated climate and marine ecosystems models need to be developed to predict changes on seasonal-to-decadal timescales. Second, services need to be co-developed with a fundamental understanding of societal needs. This requires trans-disciplinary collaboration among climate and ecosystem researchers, computational scientists, and social scientists, with the active participation of all users. Cooperation is needed at the Nordic level to address the common challenges that we face. Combining expertise and infrastructure will have major synergistic benefits. The shared cultural and societal values will facilitate the co-development of solutions to achieve a green and more competitive Nordic Region. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Byermoen, Emilie. 2023: Trends and internal variability in Brazilian hydropower catchments. Master’s thesis, University of Bergen, Norway. https://bora.uib.no/bora-xmlui/handle/11250/3071878 Summary: Hydropower is a major energy source in Brazil, and long-term hydropower production planning is crucial both for maintaining energy and water security in the country. The amount of water that is available to electricity production in the reservoirs have changed in the recent years, and there is an urgent need to understand the cause(s) of these changes, and whether observed stream flow trends will persist, reverse or amplify in the future. In this thesis, I therefore separate externally forced precipitation and evaporation trends and variability from internal variations originating in the ocean for three hydrographic catchments in Brazil: Óbidos catchment in Amazon, Propria catchment in São Francisco and Porto Murtinho catchment in Paraguay. I compare an ocean anomaly assimilation experiment of Norwegian Climate Prediction Model (NorCPM) to an externally forced historical experiment and observed stream flow, precipitation and evaporation in the catchments. The results indicate that the multi-decadal increasing stream flow trend in Amazon is (partly) externally forced, and might therefore persist, but that the SON stream flow is tightly connected to JJA precipitation variation which is shown to be driven by ocean variation, and may therefore reverse in the future. The long-term decrease of precipitation in São francisco is likely to be caused by internal variability, and is therefore likely to (partly) restore in the future, but results indicate that decadal stream flow variations in the basin is substantially impacted by other factors than precipitation as well. São Francisco catchment is found to be strongly connected to DJF precipitation variations that the model is unable to replicate. In Paraguay, I find that the austral summer stream flow is tightly connected to inter-annual precipitation variability that originates in the ocean in austral winter and spring. The steep significant decrease in stream flow over the last decades in Paraguay catchment is likely to have additional causes than precipitation, according to the results. All the results have implications for hydropower and water management planning in the three catchments in Brazil. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Silva, E., Counillon, F., Brajard, J., Pettersson, L.H., Naustvoll, L. 2023: Forecasting harmful algae blooms: Application to Dinophysis acuminata in northern Norway.  Harmful Algae. https://doi.org/10.1016/j.hal.2023.102442 Summary: Dinophysis acuminata produces Diarrhetic Shellfish Toxins (DST) that contaminate natural and farmed shellfish, leading to public health risks and economically impacting mussel farms. For this reason, there is a high interest in understanding and predicting D. acuminata blooms. This study assesses the environmental conditions and develops a sub-seasonal (7 – 28 days) forecast model to predict D. acuminata cells abundance in the Lyngen fjord located in northern Norway. A Support Vector Machine (SVM) model is trained to predict future D. acuminata cells abundance by using the past cell concentration, sea surface temperature (SST), Photosynthetic Active Radiation (PAR), and wind speed. Cells concentration of Dinophysis spp. are measured in-situ from 2006 to 2019, and SST, PAR, and surface wind speed are obtained by satellite remote sensing. D. acuminata only explains 40% of DST variability from 2006 to 2011, but it changes to 65% after 2011 when D. acuta prevalence reduced. The D. acuminata blooms can reach concentration up to 3954 cells l−1 and are restricted to the summer during warmer waters, varying from 7.8 to 12.7 °C. The forecast model predicts with fair accuracy the seasonal development of the blooms and the blooms amplitude, showing a coefficient of determination varying from 0.46 to 0.55. SST has been found to be a useful predictor for the seasonal development of the blooms, while the past cells abundance is needed for updating the current status and adjusting the blooms timing and amplitude. The calibrated model should be tested operationally in the future to provide an early warning of D. acuminata blooms in the Lyngen fjord. The approach can be generalized to other regions by recalibrating the model with local observations of D. acuminata blooms and remote sensing data. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Knudsen, Carina. 2023: Factors influencing interannual variability of Belg rain in Ethiopia. Master’s thesis, University of Bergen, Norway. https://bora.uib.no/bora-xmlui/handle/11250/3059081 Summary: The aim of this thesis is to investigate the factors affecting the interannual variability of the Belg rain in Ethiopia, in addition to see in which degree the NorESM can capture these factors. A significant connection was found between Belg rain and five ocean regions: Agulhas current, the northern and southern patch of the PMM, Benguela Niño, and the Indian Ocean. There was also found a connection between Belg rainfall in Ethiopia and a negative NAO index and La Niña events. The results showed that the wind pattern over the Indian Ocean is a large contributor, in addition to the Subtropical Westerly Jet. The weather in Ethiopia is highly variable, and capturing this variability has been a major challenge. Investigating the factors causing interannual variability is an important step in improving seasonal predictions and climate services. These predictions can contribute to warning systems in case of extreme events, which is important due to Ethiopia’s dependence on agriculture. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.

Kjesbu, O.S., Alix, M., Sandø, A.B., Strand, E., Wright, P.J., Johns, D.G., Thorsen, A., Marshall, C.T., Bakkeplass, K.G., Vikebø, F.B., Myksvoll, M.S., Ottersen, G., Allan, B.J.M., Fossheim, M., Stiansen, J.E., Huse, G., Sundby, S. 2023: Latitudinally distinct stocks of Atlantic cod face fundamentally different biophysical challenges under on-going climate change. Fish and Fisheries. https://doi.org/10.1111/faf.12728 Summary: Observed and future winter Arctic sea ice loss is strongest in the Barents Sea. However, the anthropogenic signal of the sea ice decline is superimposed by pronounced internal variability that represents a large source of uncertainty in future climate projections. A notable manifestation of internal variability is rapid ice change events (RICEs) that greatly exceed the anthropogenic trend. These RICEs are associated with large displacements of the sea ice edge which could potentially have both local and remote impacts on the climate system. In this study we present the first investigation of the frequency and drivers of RICEs in the future Barents Sea, using multi-member ensemble simulations from CMIP5 and CMIP6. A majority of RICEs are triggered by trends in ocean heat transport or surface heat fluxes. Ice loss events are associated with increasing trends in ocean heat transport and decreasing trends in surface heat loss. RICEs are a common feature of the future Barents Sea until the region becomes close to ice-free. As their evolution over time is closely tied to the average sea ice conditions, rapid ice changes in the Barents Sea may serve as a precursor for future changes in adjacent seas. Link to publication. You are most welcome to contact us or the corresponding author(s) directly, if you have questions.