Tag: wang

An ensemble-based coupled reanalysis of the climate from 1860 to the present (CoRea1860+)

Wang, Y., Counillon, F., Svendsen, L., Chiu, P.-G., Keenlyside, N., Laloyaux, P., Koseki, M., and de Boisseson, E. 2025: An ensemble-based coupled reanalysis of the climate from 1860 to the present (CoRea1860+). Earth Syst. Sci. Data. https://doi.org/10.5194/essd-17-4185-2025

Summary: Climate reanalyses are essential for studying climate variability, understanding climate processes, and initializing climate predictions. We present CoRea1860+ (Wang and Counillon, 2025, https://doi.org/10.11582/2025.00009), a 30-member coupled reanalysis spanning from 1860 to the present, produced using the Norwegian Climate Prediction Model (NorCPM) and assimilating sea surface temperature (SST) observations. NorCPM combines the Norwegian Earth System Model with the ensemble Kalman filter data assimilation method. SST, available throughout the entire period, serves as the primary source of instrumental oceanic measurements prior to the 1950s. CoRea1860+ belongs to the category of sparse-input reanalyses, designed to minimize artefacts arising from changes in the observation network over time. By exclusively assimilating oceanic data, this reanalysis offers valuable insights into the ocean’s role in driving climate system variability, including its influence on the atmosphere and sea ice. This study first describes the numerical model, the SST dataset, and the assimilation implementation used to produce CoRea1860+. It then provides a comprehensive evaluation of the reanalysis across four key aspects, namely reliability, ocean variability, sea ice variability, and atmospheric variability, benchmarked against more than 10 independent reanalyses and observational datasets. Overall, CoRea1860+ demonstrates strong reliability, particularly in observation-rich periods, and provides a reasonable representation of climate variability. It successfully captures key features such as multi-decadal variability and long-term trends in ocean heat content, the Atlantic meridional overturning circulation, and sea ice variability in both hemispheres. Furthermore, to some extent, CoRea1860+ agrees with the reference atmospheric datasets for surface air temperature, precipitation, sea level pressure, and 500 hPa geopotential height, especially in the tropics where air–sea interactions are most pronounced.

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Correcting errors in seasonal Arctic sea ice prediction of Earth system models with machine learning

He, Z, Wang, Y, Brajard, J, Wang, X, and Shen, Z 2025: Correcting errors in seasonal Arctic sea ice prediction of Earth system models with machine learning. The Cryosphere. https://doi.org/10.5194/tc-19-3279-2025

Summary: While Earth system models are essential for seasonal Arctic sea ice prediction, they often exhibit significant errors that are challenging to correct. In this study, we integrate a multilayer perceptron (MLP) machine learning (ML) model into the Norwegian Climate Prediction Model (NorCPM) to improve seasonal sea ice predictions. We compare the online and offline error correction approaches. In the online approach, ML corrects errors in the model’s instantaneous state during the model simulation, while in the offline approach, ML post-processes and calibrates predictions after the model simulation. Our results show that the ML models effectively learn and correct dynamical model errors in both approaches, leading to improved predictions of Arctic sea ice during the test period (i.e., 2003–2021). Both approaches yield the most significant improvements in the marginal ice zone, where error reductions in sea ice concentration exceed 20 %. These improvements vary seasonally, with the most substantial enhancements occurring in the Atlantic, Siberian, and Pacific regions from September to January. The offline error correction approach consistently outperforms the online error correction approach. This is primarily because the online approach targets only instantaneous model errors on the 15th of each month, while errors can grow during the subsequent 1-month model integration due to interactions among the model components, damping the error correction in monthly averages. Notably, in September, the online approach reduces the error of the pan-Arctic sea ice extent by 50 %, while the offline approach achieves a 75 % error reduction.

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Improving Ocean Reanalysis with the Offline Ensemble Kalman Smoother

Wang, Y., Counillon, F., Ying, Y., Barthélémy, S., Evensen, G. 2025: Improving Ocean Reanalysis with the Offline Ensemble Kalman Smoother. Tellus A. https://doi.org/10.16993/tellusa.4087

Summary: The Ensemble Kalman Smoother (EnKS), an extension of the Ensemble Kalman Filter, can improve the accuracy of the state estimate by assimilating ‘future’ observations. We propose to use the EnKS algorithm to enhance the accuracy and reliability of preexisting reanalyses produced with a fully coupled Earth system model. The offline EnKS is applied to two reanalyses of the Norwegian Climate Prediction Model (NorCPM) to update sea surface height, mixed layer depth, and temperature and salinity for all depth levels of the reanalyses. In an idealized framework, we tune temporal localization parameters and reveal that the optimal temporal localization parameter is 0.1, corresponding to a time delay of about 13 days. In a real framework, we find that observation error variance has to be inflated by a factor of four to account for the autocorrelation of the gridded observational product and avoid overfitting. In both frameworks, the offline EnKS improves the accuracy for the top 300 m temperature, sea surface height, and mixed layer depth, but yields limited improvements in the top 300 m salinity and the water properties below 300 m. Also, it enhances the reliability of the reanalysis. The improvement is notably lower in a real framework than in an idealized framework; this is mostly due to the lack of high quality and independent datasets for proper validation. Overall, this study demonstrates that the offline EnKS has the potential for efficiently improving pre-existing reanalyses.

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A novel ensemble-based parameter estimation for improving ocean biogeochemistry in an Earth system model

Singh, T., Counillon, F., Tjiputra, J., Wang, Y. 2025: A novel ensemble-based parameter estimation for improving ocean biogeochemistry in an Earth system model. JAMES. https://doi.org/10.1029/2024MS004237

Summary: Estimating ocean biogeochemistry (BGC) parameters in Earth System Models is challenging due to multiple error sources and interlinked parameter sensitivities. Reducing the temperature and salinity bias in the ocean physical component of the Norwegian Earth System Model (NorESM) diminishes the BGC state bias at intermediate depth but leads to a greater bias increase near the surface. This suggests that BGC parameters are tuned to compensate for the physical ocean model biases. We successfully apply the iterative ensemble smoother data assimilation technique to estimate BGC parameters in NorESM with reduced bias in its physical ocean component. We estimate BGC parameters based on the monthly climatological error of nitrate, phosphate, and oxygen in a coupled reanalysis of NorESM that assimilates observed monthly climatology of temperature and salinity. First, we compare the performance of globally uniform and spatially varying parameter estimations. Both approaches reduce BGC bias obtained with default parameters, even for variables not assimilated in the parameter estimation (e.g., CO2 fluxes and primary production). While spatial parameter estimation performs locally best, it also increases biases in areas with few observations, and overall performs poorer than global parameter estimation. A second iteration further reduces the bias in the near-surface BGC with global parameter estimation. Finally, we assess the performance of global estimated parameters in a 30-year coupled reanalysis produced by assimilating time-varying temperature and salinity observations. This reanalysis reduces error by 10%–20% for phosphate, nitrate, oxygen, and dissolved inorganic carbon compared to a reanalysis done with default parameters.

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Impact of Ocean, Sea Ice or Atmosphere Initialization on Seasonal Prediction of Regional Antarctic Sea Ice

Xiu, Y., Wang, Y., Luo, H., Garcia-Oliva, L., Yang, Q. 2025: Impact of ocean, sea ice or atmosphere initialization on seasonal prediction of regional Antarctic sea ice. JAMES. https://doi.org/10.1029/2024MS004382

Summary: This study investigates how the atmosphere, ocean, or sea ice observations affect the seasonal prediction of Antarctic sea ice. We analyze three sets of predictions from the Norwegian Climate Prediction Model, each integrating different data sets of the atmosphere, ocean, or sea ice. Initially, we assess the seasonal cycles, trends, and variability of Antarctic sea ice in these data sets. We found that including atmosphere observations gave the best seasonal cycle compared to the observed sea ice. However, the linear trend in sea ice when including atmospheric data is poorly reproduced in the western Southern Ocean. Regarding variability, including the combined ocean and sea ice data gave the best performance. Next, we assess the accuracy of regional Antarctic sea ice prediction. We found that the accuracy varies with region and season. Austral winter predictions in western Antarctic have some skill up to a year in advance, while those in the eastern Antarctic are less reliable. Predictions based on atmosphere data are generally more accurate than those based on ocean or ocean/sea-ice data, especially when predicting from July or October. Interestingly, once ocean data is used, involving additional sea ice data improves sea ice concentration in the reanalysis but not in the predictions.

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Ice-kNN-South: A Lightweight Machine Learning Model for Antarctic Sea Ice Prediction

Lin, Y., Yang, Q., Li, X., Dong, X., Luo, H., Nie, Y., et al. 2025: Ice-kNN-south: A lightweight machine learning model for Antarctic sea ice prediction. JAMES. https://doi.org/10.1029/2024JH000433

Summary: Antarctic sea ice has undergone a transition, with more frequent extreme minimum events observed since 2014, emphasizing the ongoing need for accurate predictions. We developed a lightweight machine learning model called Ice-k-nearest neighbor (kNN)-South to improve Antarctic sea ice prediction. Compared with commonly used benchmarks, such as anomaly persistence, climatology, and the European Centre for Medium-Range Weather Forecasts predictions, the Ice-kNN-South shows skillful predictions for almost 90 lead days, especially in summer. Even in years with extreme minimum sea ice areas, Ice-kNN-South shows strong skill in predicting Antarctic sea ice cover. Additionally, due to its minimal computational resource requirements, Ice-kNN-South shows promise for operational and real-time Antarctic sea ice prediction applications.

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Evaluation of the effects of Argo data quality control on global ocean data assimilation systems

Ishikawa I, Fujii Y, de Boisseson E, Wang Y and Zuo H 2024: Evaluation of the effects of Argo data quality control on global ocean data assimilation systems. Front Mar Sci. https://doi.org/10.3389/fmars.2024.1496409

Summary: A series of observing system experiments (OSEs) were conducted in order to evaluate the effects of Argo data quality control (QC), by using the three global ocean data assimilation systems. During the experimental period between 2015 and 2020, some Argo floats are affected by the abrupt salinity drifts, which caused spurious increasing trend of the global mean salinity in the reanalyses using the observations with only real-time QC applied. The spurious trend is mitigated by applying the gray list provided by the Argo Global Data Assembly Centres (GDAC), and further reduced by assimilating the delayed-mode Argo data of the Argo GDAC instead of the real-time Argo data. These impacts of the Argo QC are generally consistent among the three ocean data assimilation systems. Further investigations in the JMA’s system show that errors in the analyzed salinity with respect to the delayed-mode Argo data are smaller in the OSE with more rigorous QC, and the spatiotemporal variations in the sea-surface dynamic height are reproduced better. Additionally, QC impacts on the analyzed temperatures are shown not to directly reflect the difference in temperature observations among OSEs, and may be affected by difference in the salinity observations among OSEs through the cross-covariance relationship in the data-assimilation systems.

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Decadal prediction centers prepare for a major volcanic eruption

Sospedra-Alfonso, R., Merryfield, W.J., Toohey, M., Timmreck, C., Vernier, J-P., Bethke, I., Wang, Y., Bilbao, R., Donat, M.G., Ortega, P., Cole, J., Lee, W.-S., Delworth, T.L., Paynter, D., Zeng, F., Zhang, L., Khodri, M., Mignot, J., Swingedouw, D., Torres, O., Hu, S., Man, W., Zuo, M., Hermanson, L., Smith, D., Kataoka, T., Tatebe, H. 2024: Decadal prediction centers prepare for a major volcanic eruption. Bulletin of the American Meteorological Society. https://doi.org/10.1175/BAMS-D-23-0111.1

Summary: The World Meteorological Organization’s Lead Centre for Annual-to-Decadal Climate prediction issues operational forecasts annually as guidance for regional climate centers, climate outlook
forums and national meteorological and hydrological services. The occurrence of a large volcanic eruption such as that of Mount Pinatubo in 1991, however, would invalidate these forecasts and prompt producers to modify their predictions. To assist and prepare decadal prediction centers for this eventuality, the Volcanic Response activities under the World Climate Research Programme’s Stratosphere-troposphere Processes And their Role in Climate (SPARC) and the Decadal Climate Prediction Project (DCPP) organized a community exercise to respond to a hypothetical large eruption occurring in April 2022. As part of this exercise, the Easy Volcanic Aerosol forcing generator was used to provide stratospheric sulfate aerosol optical properties customized to the configurations of individual decadal prediction models. Participating centers then reran forecasts for 2022-2026 from their original initialization dates and in most cases also from just before the eruption at the beginning of April 2022, according to two candidate response protocols. This article describes various aspects of this SPARC/DCPP Volcanic Response Readiness Exercise (VolRes-RE), including the hypothesized volcanic event, the modified forecasts under the two protocols from the eight contributing centers, the lessons learned during the coordination and execution of this exercise, and the recommendations to the decadal prediction community for the response to an actual eruption.

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Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison

Bushuk, M., Ali, S., Bailey, D.A., Bao, Q., Batté, L., Bhatt, U.S., Blanchard-Wrigglesworth, E., Blockley, E., Cawley, G., Chi, J., Counillon, F., et al. 2024: Predicting September Arctic Sea Ice: A Multimodel Seasonal Skill Comparison. Bull. Amer. Meteor. Soc.. https://doi.org/10.1175/BAMS-D-23-0163.1

Summary: This study quantifies the state of the art in the rapidly growing field of seasonal Arctic sea ice prediction. A novel multimodel dataset of retrospective seasonal predictions of September Arctic sea ice is created and analyzed, consisting of community contributions from 17 statistical models and 17 dynamical models. Prediction skill is compared over the period 2001–20 for predictions of pan-Arctic sea ice extent (SIE), regional SIE, and local sea ice concentration (SIC) initialized on 1 June, 1 July, 1 August, and 1 September. This diverse set of statistical and dynamical models can individually predict linearly detrended pan-Arctic SIE anomalies with skill, and a multimodel median prediction has correlation coefficients of 0.79, 0.86, 0.92, and 0.99 at these respective initialization times. Regional SIE predictions have similar skill to pan-Arctic predictions in the Alaskan and Siberian regions, whereas regional skill is lower in the Canadian, Atlantic, and central Arctic sectors. The skill of dynamical and statistical models is generally comparable for pan-Arctic SIE, whereas dynamical models outperform their statistical counterparts for regional and local predictions. The prediction systems are found to provide the most value added relative to basic reference forecasts in the extreme SIE years of 1996, 2007, and 2012. SIE prediction errors do not show clear trends over time, suggesting that there has been minimal change in inherent sea ice predictability over the satellite era. Overall, this study demonstrates that there are bright prospects for skillful operational predictions of September sea ice at least 3 months in advance.

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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.

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