Tag: passos

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.

Impact of initialization methods on the predictive skill in NorCPM: an Arctic–Atlantic case study

Passos, L., Langehaug, HR., Årthun, M., Eldevik, T., Bethke, I., Kimmritz, M. 2022: Impact of initialization methods on the predictive skill in NorCPM: an Arctic–Atlantic case study. Clim Dyn. https://doi.org/10.1007/s00382-022-06437-4

Summary: The skilful prediction of climatic conditions on a forecast horizon of months to decades into the future remains a main scientific challenge of large societal benefit. Here we assess the hindcast skill of the Norwegian Climate Prediction Model (NorCPM) for sea surface temperature (SST) and sea surface salinity (SSS) in the Arctic–Atlantic region focusing on the impact of different initialization methods. We find the skill to be distinctly larger for the Subpolar North Atlantic than for the Norwegian Sea, and generally for all lead years analyzed. For the Subpolar North Atlantic, there is furthermore consistent benefit in increasing the amount of data assimilated, and also in updating the sea ice based on SST with strongly coupled data assimilation. The predictive skill is furthermore significant for at least two model versions up to 8–10 lead years with the exception for SSS at the longer lead years. For the Norwegian Sea, significant predictive skill is more rare; there is relatively higher skill with respect to SSS than for SST. A systematic benefit from more complex data assimilation approach can not be identified for this region. Somewhat surprisingly, skill deteriorates quite consistently for both the Subpolar North Atlantic and the Norwegian Sea when going from CMIP5 to corresponding CMIP6 versions. We find this to relate to change in the regional performance of the underlying physical model that dominates the benefit from initialization.

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

NorCPM1 and its contribution to CMIP6 DCPP

Bethke, I., Wang, Y., Counillon, F., Keenlyside, N., Kimmritz, M., Fransner, F., Samuelsen, A., Langehaug, H., Svendsen, L., Chiu, P.-G., Passos, L., Bentsen, M., Guo, C., Gupta, A., Tjiputra, J., Kirkevåg, A., Olivié, D., Seland, Ø., Solsvik Vågane, J., Fan, Y., Eldevik, T. 2021: NorCPM1 and its contribution to CMIP6 DCPP. Geosci Model Dev. https://doi.org/10.5194/gmd-14-7073-2021 .

For an easy-to-understand overview, we recommend starting with this neat article written by the Climate Futures team, a project connected to BCPU: “New Study: Decadal Climate Forecasts From The Norwegian Climate Prediction Model” (les heller på norsk).

Summary: The Norwegian Climate Prediction Model version 1 (NorCPM1) is a new research tool for performing climate reanalyses and seasonal-to-decadal climate predictions. It combines the Norwegian Earth System Model version 1 (NorESM1) – which features interactive aerosol–cloud schemes and an isopycnic-coordinate ocean component with biogeochemistry – with anomaly assimilation of sea surface temperature (SST) and -profile observations using the ensemble Kalman filter (EnKF).

We describe the Earth system component and the data assimilation (DA) scheme, highlighting implementation of new forcings, bug fixes, retuning and DA innovations. Notably, NorCPM1 uses two anomaly assimilation variants to assess the impact of sea ice initialization and climatological reference period: the first (i1) uses a 1980–2010 reference climatology for computing anomalies and the DA only updates the physical ocean state; the second (i2) uses a 1950–2010 reference climatology and additionally updates the sea ice state via strongly coupled DA of ocean observations.

We assess the baseline, reanalysis and prediction performance with output contributed to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). The NorESM1 simulations exhibit a moderate historical global surface temperature evolution and tropical climate variability characteristics that compare favourably with observations. The climate biases of NorESM1 using CMIP6 external forcings are comparable to, or slightly larger than those of, the original NorESM1 CMIP5 model, with positive biases in Atlantic meridional overturning circulation (AMOC) strength and Arctic sea ice thickness, too-cold subtropical oceans and northern continents, and a too-warm North Atlantic and Southern Ocean. The biases in the assimilation experiments are mostly unchanged, except for a reduced sea ice thickness bias in i2 caused by the assimilation update of sea ice, generally confirming that the anomaly assimilation synchronizes variability without changing the climatology. The i1 and i2 reanalysis/hindcast products overall show comparable performance. The benefits of DA-assisted initialization are seen globally in the first year of the prediction over a range of variables, also in the atmosphere and over land. External forcings are the primary source of multiyear skills, while added benefit from initialization is demonstrated for the subpolar North Atlantic (SPNA) and its extension to the Arctic, and also for temperature over land if the forced signal is removed. Both products show limited success in constraining and predicting unforced surface ocean biogeochemistry variability. However, observational uncertainties and short temporal coverage make biogeochemistry evaluation uncertain, and potential predictability is found to be high. For physical climate prediction, i2 performs marginally better than i1 for a range of variables, especially in the SPNA and in the vicinity of sea ice, with notably improved sea level variability of the Southern Ocean. Despite similar skills, i1 and i2 feature very different drift behaviours, mainly due to their use of different climatologies in DA; i2 exhibits an anomalously strong AMOC that leads to forecast drift with unrealistic warming in the SPNA, whereas i1 exhibits a weaker AMOC that leads to unrealistic cooling. In polar regions, the reduction in climatological ice thickness in i2 causes additional forecast drift as the ice grows back. Posteriori lead-dependent drift correction removes most hindcast differences; applications should therefore benefit from combining the two products.

The results confirm that the large-scale ocean circulation exerts strong control on North Atlantic temperature variability, implying predictive potential from better synchronization of circulation variability. Future development will therefore focus on improving the representation of mean state and variability of AMOC and its initialization, in addition to upgrades of the atmospheric component. Other efforts will be directed to refining the anomaly assimilation scheme – to better separate internal and forced signals, to include land and atmosphere initialization and new observational types – and improving biogeochemistry prediction capability. Combined with other systems, NorCPM1 may already contribute to skilful multiyear climate prediction that benefits society.

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