Category: Publications2024

Publications that published in 2024

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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Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation

Nair, A.S., Counillon, F., Keenlyside, N. 2024: Improving subseasonal forecast skill in the Norwegian Climate Prediction Model using soil moisture data assimilation. Clim Dyn. https://doi.org/10.1007/s00382-024-07444-3

Summary: This study shows the importance of soil moisture (SM) in subseasonal-to-seasonal (S2S) predictions at mid-latitudes. We do this through introducing the Norwegian Climate Prediction Model Land (NorCPM-Land), a land reanalysis framework tailored for integration with the Norwegian Climate Prediction Model (NorCPM). NorCPM-Land assimilates blended SM data from the European Space Agency’s Climate Change Initiative into a 30-member offline simulation of the Community Land Model with fluxes from the coupled model. The assimilation of SM data reduces error in SM by 10.5 % when validated against independent SM observations. It also improves latent heat flux estimates, illustrating that the adjustment of underlying SM significantly augments the capacity to model land surface dynamics. We evaluate the added value of land initialisation for subseasonal predictions, by comparing the performance of hindcasts (retrospective prediction) using the standard NorCPM with a version where the land initial condition is taken from NorCPM-Land reanalysis. The hindcast covers the period 2000 to 2019 with four start dates per year. Land initialisation enhances SM predictions, reducing error by up to 2.5-month lead time. Likewise, the error for precipitation and temperature shows improvement up to a lead time of 1.5-month. The largest improvements are observed in regions with significant land-atmospheric coupling, such as the Central United States, the Sahel, and Central India. This method further enhances the prediction of extreme temperature variations, both high and low, with the most notable improvements seen in regions at mid and high latitudes, including parts of Europe, the United States, and Asia. Overall, our study provides further evidence for the significant role of SM content in enhancing the accuracy of subseasonal predictions. This study introduces a technique for improved land initialisation, utilising the same model employed in climate predictions.

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Coupled data assimilation for climate prediction: a focus on ocean-atmosphere coupling (PhD thesis)

Lilian Carolina Garcia Oliva (2024-10-17): Coupled data assimilation for climate prediction: a focus on ocean-atmosphere coupling. PhD thesis, University of Bergen, Bergen, Norway. https://hdl.handle.net/11250/3157446

Summary: Seasonal-to-Decadal (S2D) climate predictions can provide decision-making information for diverse sectors, such as food security, energy and climate adaptation. The initial condition of the ocean is fundamental for providing skilful S2D predictions. A method to estimate the ocean’s initial condition is by merging the model and observations through a process called Coupled Data Assimilation (CDA). Ocean observations have demonstrated their potential to achieve skilful prediction. The Norwegian Climate Prediction Model (NorCPM) features an advanced Ocean Data Assimilation (ODA) scheme based on an ensemble method. This thesis outlines our efforts to improve S2D predictions within the NorCPM using atmospheric observations.

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Hybrid covariance super-resolution data assimilation

Barthélémy, S., Counillon, F., Brajard, J., Bertino, L. 2024: Hybrid covariance super-resolution data assimilation. Ocean Dynamics. https://doi.org/10.1007/s10236-024-01643-6

Summary: The super-resolution data assimilation (SRDA) enhances a low-resolution (LR) model with a Neural Network (NN) that has learned the differences between high and low-resolution models offline and performs data assimilation in high-resolution (HR). The method enhances the accuracy of the EnKF-LR system for a minor computational overhead. However, performance quickly saturates when the ensemble size is increased due to the error introduced by the NN. We therefore combine the SRDA with the mixed-resolution data assimilation method (MRDA) into a method called “Hybrid covariance super-resolution data assimilation” (Hybrid SRDA). The forecast step runs an ensemble at two resolutions (high and low). The assimilation is done in the HR space by performing super-resolution on the LR members with the NN. The assimilation uses the hybrid covariance that combines the emulated and dynamical HR members. The scheme is extensively tested with a quasi-geostrophic model in twin experiments, with the LR grid being twice coarser than the HR. The Hybrid SRDA outperforms the SRDA, the MRDA, and the EnKF-HR at a given computational cost. The benefit is the largest compared to the EnKF-HR for small ensembles. However, even with larger computational resources, using a mix of high and low-resolution members is worth it. Besides, the Hybrid SRDA, the EnKF-HR, and the SRDA, unlike the MRDA, prevent the smoothing of dynamical structures of the background error covariance matrix. The Hybrid SRDA method is also attractive because it is customizable to available resources.

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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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Earth System Reanalysis in Support of Climate Model Improvements

Stammer, D., Amrhein, D.E., Alonso Balmaseda, M., Bertino, L., Bonavita, M., Buontempo, C., Caltabiano, N., Counillon, F., Fenty, I., Ferrari, R., Fujii, Y., et al. 2024: Earth System Reanalysis in Support of Climate Model Improvements. Bull. Amer. Meteor. Soc.. https://doi.org/10.1175/BAMS-D-24-0110.1

Summary: A 3-day workshop took place from 12 to 14 June 2023, at the Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, focusing on data assimilation (DA) and machine learning (ML) in the context of Earth system reanalysis and climate model improvements.
The workshop, organized 25 years after the inception of the Estimating the Circulation and Climate of the Ocean (ECCO), was an effort to lay out the roadmap for future development of DA in support of climate modeling and climate knowledge improvements, or “climate DA.” The following is a summary of the workshop outcomes and recommendations arising to move the field of DA forward in the context of climate modeling.
Recent climate model developments, established through increased model resolution, have led to substantial improvements in model simulations of the time-evolving, coupled Earth system and its subcomponents. However, regardless of resolution, climate models will always produce climate features and variability that differ from the real world and will be prone to biases. This is due to many remaining uncertainties, such as in parametric and structural model uncertainty, in the initial conditions prescribed, and in the prescribed (scenario) forcing which varies on decadal to centennial time scales.
Further model improvements are expected to arise specifically from the improved representation of physical processes realized through model–data fusion. This will create an unprecedented opportunity to better exploit a large array of Earth observations, from in situ measurements to weather radars and satellite observations, as the resolved scales of the models approach those of the observations. For this, climate DA will be the central tool to bring models and observations into consistency, by improving initial conditions, inferring uncertain model parameters and structure, and quantifying uncertainty. Generally, there will be advantages and complementarities of adjoint-based smoother approaches, ensemble-based filter approaches, or new ML-inspired approaches. Yet the ever-increasing model resolution will present growing challenges arising from computational cost, calling for new ways of performing data assimilation and model optimization. Using the complementarity in a hybrid approach, blending tools and concepts from variational, ensemble, and ML methods might be what is required in the future. In this context, ML could be important to handle nonlinear responses and to better approximate non-Gaussian distributions.

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Revising chronological uncertainties in marine archives using global anthropogenic signals: a case study on the oceanic 13C Suess effect

Irvalı, N., Ninnemann, U.S., Olsen, A., Rose, N.L., Thornalley, D.J., Mjell, T.L., Counillon, F. 2024: Revising chronological uncertainties in marine archives using global anthropogenic signals: a case study on the oceanic 13C Suess effect. Geochronology. https://doi.org/10.5194/gchron-6-449-2024

Summary: Marine sediments are excellent archives for reconstructing past changes in climate and ocean circulation. Overlapping with instrumental records, they hold the potential to elucidate natural variability and contextualize current changes. Yet, dating uncertainties of traditional approaches (e.g., up to ± 30–50 years for the last 2 centuries) pose major challenges for integrating the shorter instrumental records with these extended marine archives. Hence, robust sediment chronologies are crucial, and most existing age model constraints do not provide sufficient age control, particularly for the 20th century, which is the most critical period for comparing proxy records to historical changes. 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. As a test, we apply this new approach to a marine sediment core located at the Gardar Drift, in the subpolar North Atlantic, and revise the previously published age model for this site. We further provide a refined estimate of regional reservoir corrections and uncertainties for Gardar Drift.

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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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Warm Advection as a Cause for Extreme Heat Event in North China

Wang, X., Zhang, Z., Yu, E., Guo, C., Otterå, O. H., Counillon, F. 2024: Warm Advection as a Cause for Extreme Heat Event in North China. Geophysical Research Letters. https://doi.org/10.1029/2024GL108995

Summary: Nowadays, heat waves have a significant impact on our society and result in substantial economic losses. Climate projections indicate that extreme heat events (EHEs) will become more frequent. However, heat waves have also often occurred in the past 300 years during periods with much lower anthropogenic forcing. One notable example is the severe heat event in the summer of 1743, which killed more than 10 thousand people in North China. The mechanism behind such events remains uncertain, making it exciting and valuable to investigate such heat waves in the past. In this study, we use a global model, a nested regional model, and tree-ring records to explore the mechanisms driving EHEs. The statistical robustness of the connection between EHEs in North China and Northeast China Vortexes is supported by modern observations. Notably, from 1950 to 2021, 63.6% of EHEs in North China coincide with active Northeast China Vortexes.

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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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