Articles | Volume 32, issue 4
https://doi.org/10.5194/npg-32-439-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/npg-32-439-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Oct 2025
Research article |
| 24 Oct 2025
| 24 Oct 2025
Exploring the influence of spatio-temporal scale differences in coupled data assimilation
Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Bergen, Norway
Current affiliation: Barcelona Supercomputing Center, Earth Sciences Department, Barcelona, Spain
Alberto Carrassi
Department of Physics and Astronomy “Augusto Righi”, University of Bologna, Bologna, Italy
François Counillon
Geophysical Institute, University of Bergen and Bjerknes Centre for Climate Research, Bergen, Norway
Nansen Environmental and Remote Sensing Center and Bjerknes Centre for Climate Research, Bergen, Norway
Related authors
No articles found.
Yiguo Wang, François Counillon, Lea Svendsen, Ping-Gin Chiu, Noel Keenlyside, Patrick Laloyaux, Mariko Koseki, and Eric de Boisseson
Earth Syst. Sci. Data, 17, 4185–4211, https://doi.org/10.5194/essd-17-4185-2025, https://doi.org/10.5194/essd-17-4185-2025, 2025
Short summary
Short summary
CoRea1860+ is a new climate dataset that reconstructs past climate conditions from 1860 to today. By using advanced modelling techniques and incorporating sea surface temperature observations, it provides a consistent picture of long-term climate variability. The dataset captures key ocean, sea ice, and atmosphere changes, helping scientists understand past climate changes and variability.
Ieuan Higgs, Ross Bannister, Jozef Skákala, Alberto Carrassi, and Stefano Ciavatta
EGUsphere, https://doi.org/10.48550/arXiv.2504.05218, https://doi.org/10.48550/arXiv.2504.05218, 2025
Short summary
Short summary
We explored how machine learning can improve computer models that simulate ocean ecosystems. These models help us understand how the ocean works, but they often struggle due to limited observations and complex processes. Our approach uses machine learning to better connect the parts of the system we can observe with those we cannot. This leads to more accurate and efficient predictions, offering a promising way to improve future ocean monitoring and forecasting tools.
Nicholas Williams, Yiguo Wang, and François Counillon
EGUsphere, https://doi.org/10.5194/egusphere-2025-104, https://doi.org/10.5194/egusphere-2025-104, 2025
Short summary
Short summary
We assimilate satellite observations of Arctic sea ice thickness to create a skillful initial sea ice state, assimilating ENVISAT-derived sea ice thickness for the first time. We produce a reanalysis and seasonal hindcasts showing that sea ice thickness and volume estimates are significantly improved in both reanalysis and prediction. Predictions of summer sea ice extent in our model are also substantially improved by reducing the high sea ice thickness bias.
Simon Driscoll, Alberto Carrassi, Julien Brajard, Laurent Bertino, Einar Ólason, Marc Bocquet, and Amos Lawless
EGUsphere, https://doi.org/10.5194/egusphere-2024-2476, https://doi.org/10.5194/egusphere-2024-2476, 2024
Short summary
Short summary
The formation and evolution of sea ice melt ponds (ponds of melted water) are complex, insufficiently understood and represented in models with considerable uncertainty. These uncertain representations are not traditionally included in climate models potentially causing the known underestimation of sea ice loss in climate models. Our work creates the first observationally based machine learning model of melt ponds that is also a ready and viable candidate to be included in climate models.
Nil Irvalı, Ulysses S. Ninnemann, Are Olsen, Neil L. Rose, David J. R. Thornalley, Tor L. Mjell, and François Counillon
Geochronology, 6, 449–463, https://doi.org/10.5194/gchron-6-449-2024, https://doi.org/10.5194/gchron-6-449-2024, 2024
Short summary
Short summary
Marine sediments are excellent archives for reconstructing past changes in climate and ocean circulation. Yet, dating uncertainties, particularly during the 20th century, pose major challenges. 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 over the 20th century.
Yumeng Chen, Polly Smith, Alberto Carrassi, Ivo Pasmans, Laurent Bertino, Marc Bocquet, Tobias Sebastian Finn, Pierre Rampal, and Véronique Dansereau
The Cryosphere, 18, 2381–2406, https://doi.org/10.5194/tc-18-2381-2024, https://doi.org/10.5194/tc-18-2381-2024, 2024
Short summary
Short summary
We explore multivariate state and parameter estimation using a data assimilation approach through idealised simulations in a dynamics-only sea-ice model based on novel rheology. We identify various potential issues that can arise in complex operational sea-ice models when model parameters are estimated. Even though further investigation will be needed for such complex sea-ice models, we show possibilities of improving the observed and the unobserved model state forecast and parameter accuracy.
Ieuan Higgs, Jozef Skákala, Ross Bannister, Alberto Carrassi, and Stefano Ciavatta
Biogeosciences, 21, 731–746, https://doi.org/10.5194/bg-21-731-2024, https://doi.org/10.5194/bg-21-731-2024, 2024
Short summary
Short summary
A complex network is a way of representing which parts of a system are connected to other parts. We have constructed a complex network based on an ecosystem–ocean model. From this, we can identify patterns in the structure and areas of similar behaviour. This can help to understand how natural, or human-made, changes will affect the shelf sea ecosystem, and it can be used in multiple future applications such as improving modelling, data assimilation, or machine learning.
Akhilesh Sivaraman Nair, François Counillon, and Noel Keenlyside
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2023-217, https://doi.org/10.5194/gmd-2023-217, 2024
Publication in GMD not foreseen
Short summary
Short summary
This study demonstrates the importance of soil moisture (SM) in subseasonal-to-seasonal predictions. To addess this, we introduce the Norwegian Climate Prediction Model Land (NorCPM-Land), a land data assimilation system developed for the NorCPM. NorCPM-Land reduces error in SM by 10.5 % by assimilating satellite SM products. Enhanced land initialisation improves predictions up to a 3.5-month lead time for SM and a 1.5-month lead time for temperature and precipitation.
Tobias Sebastian Finn, Charlotte Durand, Alban Farchi, Marc Bocquet, Yumeng Chen, Alberto Carrassi, and Véronique Dansereau
The Cryosphere, 17, 2965–2991, https://doi.org/10.5194/tc-17-2965-2023, https://doi.org/10.5194/tc-17-2965-2023, 2023
Short summary
Short summary
We combine deep learning with a regional sea-ice model to correct model errors in the sea-ice dynamics of low-resolution forecasts towards high-resolution simulations. The combined model improves the forecast by up to 75 % and thereby surpasses the performance of persistence. As the error connection can additionally be used to analyse the shortcomings of the forecasts, this study highlights the potential of combined modelling for short-term sea-ice forecasting.
Sukun Cheng, Yumeng Chen, Ali Aydoğdu, Laurent Bertino, Alberto Carrassi, Pierre Rampal, and Christopher K. R. T. Jones
The Cryosphere, 17, 1735–1754, https://doi.org/10.5194/tc-17-1735-2023, https://doi.org/10.5194/tc-17-1735-2023, 2023
Short summary
Short summary
This work studies a novel application of combining a Lagrangian sea ice model, neXtSIM, and data assimilation. It uses a deterministic ensemble Kalman filter to incorporate satellite-observed ice concentration and thickness in simulations. The neXtSIM Lagrangian nature is handled using a remapping strategy on a common homogeneous mesh. The ensemble is formed by perturbing air–ocean boundary conditions and ice cohesion. Thanks to data assimilation, winter Arctic sea ice forecasting is enhanced.
Francine Schevenhoven and Alberto Carrassi
Geosci. Model Dev., 15, 3831–3844, https://doi.org/10.5194/gmd-15-3831-2022, https://doi.org/10.5194/gmd-15-3831-2022, 2022
Short summary
Short summary
In this study, we present a novel formulation to build a dynamical combination of models, the so-called supermodel, which needs to be trained based on data. Previously, we assumed complete and noise-free observations. Here, we move towards a realistic scenario and develop adaptations to the training methods in order to cope with sparse and noisy observations. The results are very promising and shed light on how to apply the method with state of the art general circulation models.
Yumeng Chen, Alberto Carrassi, and Valerio Lucarini
Nonlin. Processes Geophys., 28, 633–649, https://doi.org/10.5194/npg-28-633-2021, https://doi.org/10.5194/npg-28-633-2021, 2021
Short summary
Short summary
Chaotic dynamical systems are sensitive to the initial conditions, which are crucial for climate forecast. These properties are often used to inform the design of data assimilation (DA), a method used to estimate the exact initial conditions. However, obtaining the instability properties is burdensome for complex problems, both numerically and analytically. Here, we suggest a different viewpoint. We show that the skill of DA can be used to infer the instability properties of a dynamical system.
Ingo Bethke, Yiguo Wang, François Counillon, Noel Keenlyside, Madlen Kimmritz, Filippa Fransner, Annette Samuelsen, Helene Langehaug, Lea Svendsen, Ping-Gin Chiu, Leilane Passos, Mats Bentsen, Chuncheng Guo, Alok Gupta, Jerry Tjiputra, Alf Kirkevåg, Dirk Olivié, Øyvind Seland, Julie Solsvik Vågane, Yuanchao Fan, and Tor Eldevik
Geosci. Model Dev., 14, 7073–7116, https://doi.org/10.5194/gmd-14-7073-2021, https://doi.org/10.5194/gmd-14-7073-2021, 2021
Short summary
Short 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 adds data assimilation capability to the Norwegian Earth System Model version 1 (NorESM1) and has contributed output to the Decadal Climate Prediction Project (DCPP) as part of the sixth Coupled Model Intercomparison Project (CMIP6). We describe the system and evaluate its baseline, reanalysis and prediction performance.
Cited articles
Anderson, J. L.: An Ensemble Adjustment Kalman Filter for Data Assimilation, Mon. Weather Rev., 129, 2884–2903, https://doi.org/10.1175/1520-0493(2001)129<2884:AEAKFF>2.0.CO;2, 2001. a
Arnold, C. P. and Dey, C. H.: Observing-Systems Simulation Experiments: Past, Present, and Future, B. Am. Meteorol. Soc., 67, 687–695, https://doi.org/10.1175/1520-0477(1986)067<0687:OSSEPP>2.0.CO;2, 1986. a
Ayers, D., Lau, J., Amezcua, J., Carrassi, A., and Ojha, V.: Supervised machine learning to estimate instabilities in chaotic systems: Estimation of local Lyapunov exponents, Q. J. Roy. Meteorol. Soc., 149, 1236–1262, https://doi.org/10.1002/QJ.4450, 2023. a
Barthelemy, S., Counillon, F., and Wang, Y.: Adaptive covariance hybridization for the assimilation of SST observations within a coupled Earth system reanalysis, Journal of Advances in Modeling Earth Systems, 16, https://doi.org/10.1029/2023MS003888, 2024. a
Benettin, G., Galgani, L., Giorgilli, A., and Strelcyn, J.-M.: Lyapunov Characteristic Exponents for smooth dynamical systems and for hamiltonian systems; a method for computing all of them. Part 1: Theory, Meccanica, 15, 9–20, https://doi.org/10.1007/BF02128236, 1980. a
Bocquet, M. and Carrassi, A.: Four-dimensional ensemble variational data assimilation and the unstable subspace, Tellus A, https://doi.org/10.1080/16000870.2017.1304504, 2017. a
Bocquet, M. and Sakov, P.: Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems, Nonlin. Processes Geophys., 19, 383–399, https://doi.org/10.5194/npg-19-383-2012, 2012. a
Bocquet, M. and Sakov, P.: An iterative ensemble Kalman smoother, Q. J. Roy. Meteorol. Soc., 140, 1521–1535, https://doi.org/10.1002/qj.2236, 2014. a
Browne, P. A., de Rosnay, P., Zuo, H., Bennett, A., and Dawson, A.: Weakly Coupled Ocean–Atmosphere Data Assimilation in the ECMWF NWP System, Remote Sens., 11, https://doi.org/10.3390/rs11030234, 2019. a, b
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in the geosciences: An overview of methods, issues, and perspectives, Wiley Interdisciplin. Rev.: Clim. Change, 9, e535, https://doi.org/10.1002/wcc.535, 2018. a
Carrassi, A., Bocquet, M., Demaeyer, J., Grudzien, C., Raanes, P., and Vannitsem, S.: Data Assimilation for Chaotic Dynamics, Springer International Publishing, 1–42, https://doi.org/10.1007/978-3-030-77722-7_1, 2022. a
Clement, A., Bellomo, K., Murphy, L. N., Cane, M. A., Mauritsen, T., Rädel, G., and Stevens, B.: The Atlantic Multidecadal Oscillation without a role for ocean circulation, Science, 350, 320–324, 2015. a
Cruz, L. D., Demaeyer, J., and Vannitsem, S.: The Modular Arbitrary-Order Ocean-Atmosphere Model: MAOOAM v1.0, Geosci. Model Dev., 9, 2793–2808, https://doi.org/10.5194/gmd-9-2793-2016, 2016. a
Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of observation, background and analysis-error statistics in observation space, Q. J. Roy. Meteorol. Soc., 131, 3385–3396, https://doi.org/10.1256/QJ.05.108, 2006. a, b
Evensen, G.: The Ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, https://doi.org/10.1007/S10236-003-0036-9, 2003. a, b, c, d
Evensen, G., Vossepoel, F. C., and van Leeuwen, P. J.: Iterative ensemble smoothers for data assimilation in coupled nonlinear multiscale models, Mon. Weather Rev., https://doi.org/10.1175/MWR-D-23-0239.1, 2024. a, b, c, d
Frolov, S., Bishop, C. H., Holt, T., Cummings, J., and Kuhl, D.: Facilitating strongly coupled ocean–atmosphere data assimilation with an interface solver, Mon. Weather Rev., 144, 3–20, 2016. a
Han, G., Wu, X., Zhang, S., Liu, Z., and Li, W.: Error Covariance Estimation for Coupled Data Assimilation Using a Lorenz Atmosphere and a Simple Pycnocline Ocean Model, J. Climate, 26, 10218–10231, https://doi.org/10.1175/JCLI-D-13-00236.1, 2013. a, b, c
Hewitt, H. T., Bell, M. J., Chassignet, E. P., Czaja, A., Ferreira, D., Griffies, S. M., Hyder, P., McClean, J. L., New, A. L., and Roberts, M. J.: Will high-resolution global ocean models benefit coupled predictions on short-range to climate timescales?, Ocean Model., 120, 120–136, 2017. a
Kalnay, E., Sluka, T., Yoshida, T., Da, C., and Mote, S.: Review article: Towards strongly coupled ensemble data assimilation with additional improvements from machine learning, Nonlin. Processes Geophys., 30, 217–236, https://doi.org/10.5194/npg-30-217-2023, 2023. a
Kim, W. M., Yeager, S. G., Danabasoglu, G., and Chang, P.: Exceptional multi-year prediction skill of the Kuroshio Extension in the CESM high-resolution decadal prediction system, npj Clim. Atmos. Sci., 6, 118, https://doi.org/10.1038/s41612-023-00444-w, 2023. a
Kotsuki, S., Ota, Y., and Miyoshi, T.: Adaptive covariance relaxation methods for ensemble data assimilation: experiments in the real atmosphere, Q. J. Roy. Meteorol. Soc., 143, 2001–2015, https://doi.org/10.1002/QJ.3060, 2017. a
Kurosawa, K., Kotsuki, S., and Miyoshi, T.: Comparative study of strongly and weakly coupled data assimilation with a global land–atmosphere coupled model, Nonlin. Processes Geophys., 30, 457–479, https://doi.org/10.5194/npg-30-457-2023, 2023. a
Laloyaux, P., Balmaseda, M., Dee, D., Mogensen, K., and Janssen, P.: A coupled data assimilation system for climate reanalysis, Q. J. Roy. Meteorol. Soc., 142, 65–78, https://doi.org/10.1002/qj.2629, 2016. a, b
Laloyaux, P., de Boisseson, E., Balmaseda, M., Bidlot, J.-R., Broennimann, S., Buizza, R., Dalhgren, P., Dee, D., Haimberger, L., Hersbach, H., Kosaka, Y., Martin, M., Poli, P., Rayner, N., Rustemeier, E., and Schepers, D.: CERA-20C: A coupled reanalysis of the twentieth century, J. Adv. Model. Earth Syst., 10, 1172–1195, 2018. a
Li, H., Kalnay, E., and Miyoshi, T.: Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter, Q. J. Roy. Meteorol. Soc., 135, 523–533, https://doi.org/10.1002/QJ.371, 2009. a, b
Liu, Z., Wu, S., Zhang, S., Liu, Y., Xinyao, R., Liu, C., Wu, S., Zhang, S. Q., Liu, Y., and Rong, X. Y.: Ensemble Data Assimilation in a Simple Coupled Climate Model: The Role of Ocean-Atmosphere Interaction: Ensemble data assimilation in a simple coupled climate model: The role of ocean-atmosphere interaction, Adv. Atmos. Sci., 30, 1235–1248, https://doi.org/10.1007/s00376-013-2268-z, 2013. a
Lorenz, E. N.: Deterministic Nonperiodic Flow, J. Atmos. Sci., 20, 130–141, https://doi.org/10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2, 1963. a, b, c
Lu, F., Liu, Z., Zhang, S., Liu, Y., and Jacob, R.: Strongly Coupled Data Assimilation Using Leading Averaged Coupled Covariance (LACC). Part I: Simple Model Study, Mon. Weather Rev., 143, 3823–3837, https://doi.org/10.1175/MWR-D-14-00322.1, 2015a. a, b, c
Lu, F., Liu, Z., Zhang, S., Liu, Y., and Jacob, R.: Strongly coupled data assimilation using leading averaged coupled covariance (LACC). Part II: CGCM experiments, Mon. Weather Rev., 143, 4645–4659, 2015b. a
Meehl, G. A., Richter, J. H., Teng, H., Capotondi, A., Cobb, K., Doblas-Reyes, F., Donat, M. G., England, M. H., Fyfe, J. C., Han, W., Kim, H., Kirtman, B. P., Kushnir, Y., Lovenduski, N. S., Mann, M. E., Merryfield, W. J., Nieves, V., Pegion, K., Rosenbloom, N., Sanchez, S. C., Scaife, A. A., Smith, D., Subramanian, A. C., Sun, L., Thompson, D., Ummenhofer, C. C., and Xie, S.-P.: Initialized Earth System prediction from subseasonal to decadal timescales, Nat. Rev. Earth Environ., 2, 340–357, 2021. a, b
Miwa, N. and Sawada, Y.: Strongly Versus Weakly Coupled Data Assimilation in Coupled Systems With Various Inter-Compartment Interactions, J. Adv. Model. Earth Syst., 16, e2022MS003113, https://doi.org/10.1029/2022MS003113, 2024. a
Miyoshi, T.: The Gaussian approach to adaptive covariance inflation and its implementation with the local ensemble transform Kalman filter, Mon. Weather Rev., 139, 1519–1535, 2011. a
Morrow, R., Fu, L.-L., Ardhuin, F., Benkiran, M., Chapron, B., Cosme, E., d'Ovidio, F., Farrar, J. T., Gille, S. T., Lapeyre, G., Le Traon, P.-Y., Pascual, A., Ponte, A., Qiu, B., Rascle, N., Ubelmann, C., Wang, J., and Zaron, E. D.: Global observations of fine-scale ocean surface topography with the surface water and ocean topography (SWOT) mission, Front. Mar. Sci., 6, 232, https://doi.org/10.3389/fmars.2019.00232, 2019. a
Newman, M., Alexander, M. A., Ault, T. R., Cobb, K. M., Deser, C., Di Lorenzo, E., Mantua, N. J., Miller, A. J., Minobe, S., Nakamura, H., Schneider, N., Vimont, D. J., Phillips, A. S., Scott, J. D., and Smit, C. A.: The Pacific decadal oscillation, revisited, J. Climate, 29, 4399–4427, https://doi.org/10.1175/JCLI-D-15-0508.1, 2016. a
O'kane, T. J., Sandery, P. A., Monselesan, D. P., Sakov, P., Chamberlain, M. A., Matear, R. J., Collier, M. A., Squire, D. T., and Stevens, L.: Coupled data assimilation and ensemble initialization with application to multiyear ENSO prediction, J. Climate, 32, 997–1024, https://doi.org/10.1175/JCLI-D-18-0189.1, 2019. a
Palmer, T. and Stevens, B.: The scientific challenge of understanding and estimating climate change, P. Natl. Acad. Sci. USA, 116, 24390–24395, 2019. a
Penny, S. G. and Hamill, T. M.: Coupled Data Assimilation for Integrated Earth System Analysis and Prediction, B. Am. Meteorol. Soc., 98, ES169–ES172, https://doi.org/10.1175/BAMS-D-17-0036.1, 2017. a, b, c, d
Penny, S. G., Bach, E., Bhargava, K., Chang, C. C., Da, C., Sun, L., and Yoshida, T.: Strongly Coupled Data Assimilation in Multiscale Media: Experiments Using a Quasi-Geostrophic Coupled Model, J. Adv. Model. Earth Syst., 11, 1803–1829, https://doi.org/10.1029/2019MS001652, 2019. a, b, c, d
Quinn, C., O'kane, T. J., and Kitsios, V.: Application of a local attractor dimension to reduced space strongly coupled data assimilation for chaotic multiscale systems, Nonlin. Processes Geophys., 27, 51–74, https://doi.org/10.5194/npg-27-51-2020, 2020. a, b, c, d
Raanes, P. N., Bocquet, M., and Carrassi, A.: Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures, Q. J. Roy. Meteorol. Soc., 145, 53–75, https://doi.org/10.1002/QJ.3386, 2019. a
Richter, I., Xie, S.-P., Behera, S. K., Doi, T., and Masumoto, Y.: Equatorial Atlantic variability and its relation to mean state biases in CMIP5, Clim. Dynam., 42, 171–188, 2014. a
Sakov, P., Oliver, D. S., and Bertino, L.: An Iterative EnKF for Strongly Nonlinear Systems, Mon. Weather Rev., 140, 1988–2004, https://doi.org/10.1175/MWR-D-11-00176.1, 2012. a
Sandery, P. A., O’Kane, T. J., Kitsios, V., Sakov, P., O'Kane, T. J., Kitsios, V., and Sakov, P.: Climate Model State Estimation Using Variants of EnKF Coupled Data Assimilation, Mon. Weather Rev., 148, 2411–2431, https://doi.org/10.1175/MWR-D-18-0443.1, 2020. a, b, c, d
Shukla, J.: Seamless prediction of weather and climate: A new paradigm for modeling and prediction research, in: Climate Test Bed Joint Seminar Series, Citeseer, p. 8, https://www.slideserve.com/delila/seamless-weather-and-climate-prediction-powerpoint-ppt-presentation (last access: 21 October 2025), 2009. a
Skachko, S., Buehner, M., Laroche, S., Lapalme, E., Smith, G., Roy, F., Surcel-Colan, D., Bélanger, J.-M., and Garand, L.: Weakly coupled atmosphere–ocean data assimilation in the Canadian global prediction system (v1), Geosci. Model Dev., 12, 5097–5112, https://doi.org/10.5194/gmd-12-5097-2019, 2019. a
Sluka, T.: Strongly coupled ocean‐atmosphere data assimilation with the local ensemble transform Kalman filter, Phd thesis, College Park: University of Maryland, Example City, CA, https://www.proquest.com/openview/ (last access: 8 May 2024), 2018. a
Sluka, T. C., Penny, S. G., Kalnay, E., and Miyoshi, T.: Assimilating atmospheric observations into the ocean using strongly coupled ensemble data assimilation, Geophys. Res. Lett., 43, 752–759, https://doi.org/10.1002/2015GL067238, 2016. a, b, c
Stanley, Z. C., Draper, C., Frolov, S., Slivinski, L. C., Huang, W., and Winterbottom, H. R.: Vertical Localization for Strongly Coupled Data Assimilation: Experiments in a Global Coupled Atmosphere-Ocean Model, J. Adv. Model. Earth Syst., 16, e2023MS003783, https://doi.org/10.1029/2023MS003783, 2024. a, b
Tang, Q., Mu, L., Goessling, H. F., Semmler, T., and Nerger, L.: Strongly Coupled Data Assimilation of Ocean Observations Into an Ocean-Atmosphere Model, Geophys. Res. Lett., 48, e2021GL094941, https://doi.org/10.1029/2021GL094941, 2021. a
Tardif, R., Hakim, G. J., and Snyder, C.: Coupled atmosphere-ocean data assimilation experiments with a low-order climate model, Clim. Dynam., 43, 1631–1643, https://doi.org/10.1007/s00382-013-1989-0, 2014. a
Tian, B. and Dong, X.: The double-ITCZ bias in CMIP3, CMIP5, and CMIP6 models based on annual mean precipitation, Geophys. Res. Lett., 47, e2020GL087232, https://doi.org/10.1029/2020GL087232, 2020. a
Yang, S. C., Baker, D., Li, H., Cordes, K., Huff, M., Nagpal, G., Okereke, E., Villafañe, J., Kalnay, E., and Duane, G. S.: Data assimilation as synchronization of truth and model: Experiments with the three-variable Lorenz system, J. Atmos. Sci., 63, 2340–2354, https://doi.org/10.1175/JAS3739.1, 2006. a
Yang, S. C., Kalnay, E., and Hunt, B.: Handling Nonlinearity in an Ensemble Kalman Filter: Experiments with the Three-Variable Lorenz Model, Mon. Weather Rev., 140, 2628–2646, https://doi.org/10.1175/MWR-D-11-00313.1, 2012. a
Yoshida, T. and Kalnay, E.: Correlation-Cutoff Method for Covariance Localization in Strongly Coupled Data Assimilation, Mon. Weather Rev., 146, 2881–2889, https://doi.org/10.1175/MWR-D-17-0365.1, 2018. a, b, c
Zhang, R., Sutton, R., Danabasoglu, G., Kwon, Y.-O., Marsh, R., Yeager, S. G., Amrhein, D. E., and Little, C. M.: A review of the role of the Atlantic meridional overturning circulation in Atlantic multidecadal variability and associated climate impacts, Rev. Geophys., 57, 316–375, 2019. a
Zhang, S., Liu, Z., Zhang, X., Wu, X., Han, G., Zhao, Y., Yu, X., Liu, C., Liu, Y., Wu, S., Lu, F., Li, M., and Deng, X.: Coupled data assimilation and parameter estimation in coupled ocean–atmosphere models: a review, Clim. Dynam., 54, 5127–5144, https://doi.org/10.1007/S00382-020-05275-6, 2020. a, b
Short summary
We used a simple coupled model and a data assimilation method to find the correct initialisation for climate predictions. We aim to clarify when weakly or strongly coupled data assimilation (WCDA or SCDA) is best, depending on the system's dynamical characteristics (spatio-temporal) and data coverage. We found that WCDA is better in full data coverage. When we have a partially observed system, SCDA is better. This result depends on the temporal and spatial scale of the observed quantity.
We used a simple coupled model and a data assimilation method to find the correct initialisation...
