Tag: wang

Enhancing Seasonal Forecast Skills by Optimally Weighting the Ensemble from Fresh Data

Brajard, J., Counillon, F., Wang, Y., Kimmritz, M. 2023: Enhancing Seasonal Forecast Skills by Optimally Weighting the Ensemble from Fresh Data. Weather and Forecasting. https://doi.org/10.1175/WAF-D-22-0166.1

Summary: Dynamical climate predictions are produced by assimilating observations and running ensemble simulations of Earth system models. This process is time consuming and by the time the forecast is delivered, new observations are already available, making it obsolete from the release date. Moreover, producing such predictions is computationally demanding, and their production frequency is restricted. We tested the potential of a computationally cheap weighting average technique that can continuously adjust such probabilistic forecasts—in between production intervals—using newly available data. The method estimates local positive weights computed with a Bayesian framework, favoring members closer to observations. We tested the approach with the Norwegian Climate Prediction Model (NorCPM), which assimilates monthly sea surface temperature (SST) and hydrographic profiles with the ensemble Kalman filter. By the time the NorCPM forecast is delivered operationally, a week of unused SST data are available. We demonstrate the benefit of our weighting method on retrospective hindcasts. The weighting method greatly enhanced the NorCPM hindcast skill compared to the standard equal weight approach up to a 2-month lead time (global correlation of 0.71 vs 0.55 at a 1-month lead time and 0.51 vs 0.45 at a 2-month lead time). The skill at a 1-month lead time is comparable to the accuracy of the EnKF analysis. We also show that weights determined using SST data can be used to improve the skill of other quantities, such as the sea ice extent. Our approach can provide a continuous forecast between the intermittent forecast production cycle and be extended to other independent datasets.

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

Framework for an Ocean-Connected Supermodel of the Earth System

Counillon, F., Keenlyside, N., Wang, S., Devilliers, M., Gupta, A., Koseki, S., Shen, M.-L. 2023: Framework for an Ocean-Connected Supermodel of the Earth System. JAMES. https://doi.org/10.1029/2022MS003310

Summary: Observed and future winter Arctic sea ice loss is strongest in the Barents Sea. However, the anthropogenic signal of the sea ice decline is superimposed by pronounced internal variability that represents a large source of uncertainty in future climate projections. A notable manifestation of internal variability is rapid ice change events (RICEs) that greatly exceed the anthropogenic trend. These RICEs are associated with large displacements of the sea ice edge which could potentially have both local and remote impacts on the climate system. In this study we present the first investigation of the frequency and drivers of RICEs in the future Barents Sea, using multi-member ensemble simulations from CMIP5 and CMIP6. A majority of RICEs are triggered by trends in ocean heat transport or surface heat fluxes. Ice loss events are associated with increasing trends in ocean heat transport and decreasing trends in surface heat loss. RICEs are a common feature of the future Barents Sea until the region becomes close to ice-free. As their evolution over time is closely tied to the average sea ice conditions, rapid ice changes in the Barents Sea may serve as a precursor for future changes in adjacent seas.

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WMO Global Annual to Decadal Climate Update: A Prediction for 2021–25

Hermanson, L., Smith, D., Seabrook, M., Bilbao, R., Doblas-Reyes, F., Tourigny, E., Lapin, V., Kharin, V.V., Merryfield, W.J., Sospedra-Alfonso, R., Athanasiadis, P., Nicoli, D., Gualdi, S., Dunstone, N., Eade, R., Scaife, A., Collier, M., O’Kane, T., Kitsios, V., Sandery, P., Pankatz, K., Früh, B., Pohlmann, H., Müller, W., Kataoka, T., Tatebe, H., Ishii M., Imada, Y., Kruschke, T., Koenigk, T., Pasha Karami, M., Yang, S., Tian, T., Zhang, L., Delworth, T., Yang, X., Zeng, F., Wang, Y., Counillon, F., Keenlyside, N.S., Bethke, I., Lean, J., Luterbacher, J., Kumar Kolli, R., Kumar, A. 2022: WMO Global Annual to Decadal Climate Update: A Prediction for 2021–25. BAMS https://doi.org/10.1175/BAMS-D-20-0311.1 .

Summary: As climate change accelerates, societies and climate-sensitive socioeconomic sectors cannot continue to rely on the past as a guide to possible future climate hazards. Operational decadal predictions offer the potential to inform current adaptation and increase resilience by filling the important gap between seasonal forecasts and climate projections. The World Meteorological Organization (WMO) has recognized this and in 2017 established the WMO Lead Centre for Annual to Decadal Climate Predictions (shortened to “Lead Centre” below), which annually provides a large multimodel ensemble of predictions covering the next 5 years. This international collaboration produces a prediction that is more skillful and useful than any single center can achieve. One of the main outputs of the Lead Centre is the Global Annual to Decadal Climate Update (GADCU), a consensus forecast based on these predictions. This update includes maps showing key variables, discussion on forecast skill, and predictions of climate indices such as the global mean near-surface temperature and Atlantic multidecadal variability. it also estimates the probability of the global mean temperature exceeding 1.5°C above preindustrial levels for at least 1 year in the next 5 years, which helps policy-makers understand how closely the world is approaching this goal of the Paris Agreement. This paper, written by the authors of the GADCU, introduces the GADCU, presents its key outputs, and briefly discusses its role in providing vital climate information for society now and in the future..

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Propagation of Thermohaline Anomalies and Their Predictive Potential along the Atlantic Water Pathway

Langehaug, H. R., Ortega, P., Counillon, F., Matei, D., Maroon, E., Keenlyside, N., Mignot, J., Wang, Y., Swingedouw, D., Bethke, I., Yang, S., Danabasoglu, G., Bellucci, A., Ruggieri, P., Nicolì, D., Årthun, M. 2022: Propagation of Thermohaline Anomalies and Their Predictive Potential along the Atlantic Water Pathway. J Clim. https://doi.org/10.1007/s10236-022-01523-x

Summary: In this study, we find that dynamical prediction systems and their respective climate models struggle to realistically represent ocean surface temperature variability in the eastern subpolar North Atlantic and Nordic seas on interannual-to-decadal time scales. In previous studies, ocean advection is proposed as a key mechanism in propagating temperature anomalies along the Atlantic water pathway toward the Arctic Ocean. Our analysis suggests that the predicted temperature anomalies are not properly circulated to the north; this is a result of model errors that seems to be exacerbated by the effect of initialization shocks and forecast drift. Better climate predictions in the study region will thus require improving the initialization step, as well as enhancing process representation in the climate models.

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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.

Benefit of vertical localization for sea surface temperature assimilation in isopycnal coordinate model

Wang, Y., Counillon, F., Barthélémy, S., Barth, A. 2022: Benefit of vertical localization for sea surface temperature assimilation in isopycnal coordinate model. Front Clim. https://doi.org/10.3389/fclim.2022.918572

Summary: Sea surface temperature (SST) observations are a critical data set for long-term climate reconstruction. However, their assimilation with an ensemble-based data assimilation method can degrade performance in the ocean interior due to spurious covariances. Assimilation in isopycnal coordinates can delay the degradation, but it remains problematic for long reanalysis. We introduce vertical localization for SST assimilation in the isopycnal coordinate. The tapering functions are formulated empirically from a large pre-industrial ensemble. We propose three schemes: 1) a step function with a small localization radius that updates layers from the surface down to the first layer for which insignificant correlation with SST is found, 2) a step function with a large localization radius that updates layers down to the last layer for which significant correlation with SST is found, and 3) a flattop smooth tapering function. These tapering functions vary spatially and with the calendar month and are applied to isopycnal temperature and salinity. The impact of vertical localization on reanalysis performance is tested in identical twin experiments within the Norwegian Climate Prediction Model (NorCPM) with SST assimilation over the period 1980–2010. The SST assimilation without vertical localization greatly enhances performance in the whole water column but introduces a weak degradation at intermediate depths (e.g., 2,000–4,000 m). Vertical localization greatly reduces the degradation and improves the overall accuracy of the reanalysis, in particular in the North Pacific and the North Atlantic. A weak degradation remains in some regions below 2,000 m in the Southern Ocean. Among the three schemes, scheme 2) outperforms schemes 1) and 3) for temperature and salinity.

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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.

The Future Atlantic Ocean: Forecasting ecosystem functioning from microbiomes to fisheries

Side event at the All Atlantic Conference 2021, where climate forecasting on a broad level was discussed. BCPU has contributing members in the EU Horizon 2020 projects TRIATLAS and Blue Action, who were organising the event with projects AtlantECO and Mission Atlantic.

Watch the presentations and following discussion on Youtube:

Basin Interactions and Predictability. In: Interacting Climates of Ocean Basins: Observations, Mechanisms, Predictability, and Impacts

Keenlyside, N., Y. Kosaka, N. Vigaud, A. Robertson, Y. Wang, D. Dommenget, J.-J. Luo, and D. Matei. 2020: Basin Interactions and Predictability, In: Mechoso (Ed.). Interacting Climates of Ocean Basins Observations, Mechanisms, Predictability, and Impacts. Cambridge University Press, 2020, 258-292 .
Summary: The general public is familiar with weather forecasts and their utility, and the field of weather forecasting is well-established. Even the theoretical limit of the weather forecasting – two weeks – is known. In contrast, familiarity with climate prediction is low outside of the research field, the theoretical basis is not fully established, and we do not know the extent to which climate can be predicted. Variations in climate, however, can have large societal and economic consequences, as they can lead to droughts and floods, and spells of extreme hot and cold weather. Thus, improving our capabilities to predict climate is important and urgent, as it can enhance climate services and thereby contribute to the sustainable development of humans in this era of climate change.

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