Tag: tjiputra

Key physical processes and their model representation for projecting climate impacts on subarctic Atlantic net primary production: A synthesis

Myksvoll, M. S., Sandø, A. B., Tjiputra, J., Samuelsen, A., Çağlar Yumruktepe, V., Li, C., Mousing, E. A., Bettencourt, J.P.H., Ottersen, G. 2023: Key physical processes and their model representation for projecting climate impacts on subarctic atlantic net primary production: A synthesis. Progress in Oceanography. https://doi.org/10.1016/j.pocean.2023.103084

Summary: Oceanic net primary production forms the foundation of marine ecosystems. Understanding the impact of climate change on primary production is therefore critical and we rely on Earth System Models to project future changes. Stemming from their use of different physical dynamics and biogeochemical processes, these models yield a large spread in long-term projections of change on both the global and regional scale. Here we review the key physical processes and biogeochemical parameterizations that influence the estimation of primary production in Earth System Models and synthesize the available projections of productivity in the subarctic regions of the North Atlantic. The key processes and modelling issues we focus on are mixed layer depth dynamics, model resolution and the complexity and parameterization of biogeochemistry. From the model mean of five CMIP6 models, we found a large increase in PP in areas where the sea ice retreats throughout the 21st century. Stronger stratification and declining MLD in the Nordic Seas, caused by sea ice loss and regional freshening, reduce the vertical flux of nutrients into the photic zone. Following the synthesis of the primary production among the CMIP6 models, we recommend a number of measures: constraining model hindcasts through the assimilation of high-quality long-term observational records to improve physical and biogeochemical parameterizations in models, developing better parameterizations for the sub-grid scale processes, enhancing the model resolution, downscaling and multi-model comparison exercises for improved regional projections of primary production.

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

Phytoplankton abundance in the Barents Sea is predictable up to five years in advance

Fransner, F., Olsen, A., Årthun, M., Counillon, F., Tjiputra, J., Samuelsen, A., Keenlyside, N. 2023: Phytoplankton abundance in the Barents Sea is predictable up to five years in advance. Commun Earth Environ. https://doi.org/10.1038/s43247-023-00791-9

Summary: The Barents Sea is a highly biologically productive Arctic shelf sea with several commercially important fish stocks. Interannual-to-decadal predictions of its ecosystem would therefore be valuable for marine resource management. Here, we demonstrate that the abundance of phytoplankton, the base of the marine food web, can be predicted up to five years in advance in the Barents Sea with the Norwegian Climate Prediction Model. We identify two different mechanisms giving rise to this predictability; 1) in the southern ice-free Atlantic Domain, skillful prediction is a result of the advection of waters with anomalous nitrate concentrations from the Subpolar North Atlantic; 2) in the northern Polar Domain, phytoplankton predictability is a result of the skillful prediction of the summer ice concentration, which influences the light availability. The skillful prediction of the phytoplankton abundance is an important step forward in the development of numerical ecosystem predictions of the Barents Sea.

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

Estimation of Ocean Biogeochemical Parameters in an Earth System Model Using the Dual One Step Ahead Smoother: A Twin Experiment

Singh, T., Counillon, F., Tjiputra, J., Wang Y., El Gharamti, M. 2022: Estimation of Ocean Biogeochemical Parameters in an Earth System Model Using the Dual One Step Ahead Smoother: A Twin Experiment. Front. Mar. Sci. https://doi.org/10.3389/fmars.2022.775394 .

For an easy-to-understand overview of this publication, produced in collaboration with the TRIATLAS project, we recommend starting with this neat article written by Henrike Wilborn, at NERSC: “Making climate models more accurate by improving their tuning“.

Summary: Ocean biogeochemical (BGC) models utilise a large number of poorly-constrained global parameters to mimic unresolved processes and reproduce the observed complex spatio-temporal patterns. Large model errors stem primarily from inaccuracies in these parameters whose optimal values can vary both in space and time. This study aims to demonstrate the ability of ensemble data assimilation (DA) methods to provide high-quality and improved BGC parameters within an Earth system model in an idealized perfect twin experiment framework. We use the Norwegian Climate Prediction Model (NorCPM), which combines the Norwegian Earth System Model with the Dual-One-Step ahead smoothing-based Ensemble Kalman Filter (DOSA-EnKF). We aim to estimate five spatially varying BGC parameters by assimilating salinity and temperature profiles and surface BGC (Phytoplankton, Nitrate, Phosphate, Silicate, and Oxygen) observations in a strongly coupled DA framework—i.e., jointly updating ocean and BGC state-parameters during the assimilation. We show how BGC observations can effectively constrain error in the ocean physics and vice versa. The method converges quickly (less than a year) and largely reduces the errors in the BGC parameters. Some parameter error remains, but the resulting state variable error using the estimated parameters for a free ensemble run and for a reanalysis performs nearly as well as with true parameter values. Optimal parameter values can also be recovered by assimilating climatological BGC observations or sparse observational networks. The findings of this study demonstrate the applicability of the DA approach for tuning the system in a real framework.

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.

Ocean Biogeochemical Predictions—Initialization and Limits of Predictability

Fransner, F., Counillon, F., Bethke, I., Tjiputra, J., Samuelsen, A., Nummelin, A., Olsen, A. 2020: Ocean Biogeochemical Predictions—Initialization and Limits of Predictability. Front Mar Sci. https://doi.org/10.3389/fmars.2020.00386 .

Summary: Predictions of ocean biogeochemistry, such as primary productivity and CO2 uptake, would help to understand the changing marine environment and the global climate. There is an emerging number of studies where initialization of ocean physics has led to successful predictions of ocean biogeochemistry. It is, however, unclear how much these predictions could be improved by also assimilating biogeochemical data to reduce uncertainties of the initial conditions. Further, the mechanisms that lead to biogeochemical predictability are poorly understood. Here we perform a suite of idealized twin experiments with an Earth System Model (ESM) with the aim to (i) investigate the role of biogeochemical tracers’ initial conditions on their predictability, and (ii) understand the physical processes that give rise to, or limit, predictability of ocean carbon uptake and export production. Our results suggest that initialization of the biogeochemical state does not significantly improve interannual-to-decadal predictions, which we relate to the strong control ocean physics exerts on the biogeochemical variability on these time scales. The predictability of ocean carbon uptake generally agrees well with the predictability of the mixed layer depth (MLD), suggesting that the predictable signal comes from the exchange of dissolved inorganic carbon (DIC) with deep-waters. The longest predictability is found in winter in at high latitudes, as for sea surface temperature and salinity, but the predictability of the MLD and carbon exchange is lower as it is more directly influenced by the atmospheric variability, e.g., the wind. The predictability of the annual mean export production is, on the contrary, nearly non-existing at high latitudes, despite the strong predictive skill for annual mean nutrient concentrations in these regions. This is related to the low predictability of the physical state of the summer surface ocean. Due to the shallow mixed layer it is decoupled from the ocean below and therefore strongly influenced by the chaotic atmosphere. Our results show that future studies need to target the predictability of the mixed layer to get a better understanding of the real-world predictability of ocean biogeochemistry.

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