Journal articles on the topic 'Decadal prediction'
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Meehl, Gerald A., Lisa Goddard, James Murphy, Ronald J. Stouffer, George Boer, Gokhan Danabasoglu, Keith Dixon, et al. "Decadal Prediction." Bulletin of the American Meteorological Society 90, no. 10 (October 2009): 1467–86. http://dx.doi.org/10.1175/2009bams2778.1.
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Haines, Keith, Leon Hermanson, Chunlei Liu, Debbie Putt, Rowan Sutton, Alan Iwi, and Doug Smith. "Decadal climate prediction (project GCEP)." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1890 (December 16, 2008): 925–37. http://dx.doi.org/10.1098/rsta.2008.0178.
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Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.
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Meehl, Gerald A., Lisa Goddard, George Boer, Robert Burgman, Grant Branstator, Christophe Cassou, Susanna Corti, et al. "Decadal Climate Prediction: An Update from the Trenches." Bulletin of the American Meteorological Society 95, no. 2 (February 1, 2014): 243–67. http://dx.doi.org/10.1175/bams-d-12-00241.1.
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This paper provides an update on research in the relatively new and fast-moving field of decadal climate prediction, and addresses the use of decadal climate predictions not only for potential users of such information but also for improving our understanding of processes in the climate system. External forcing influences the predictions throughout, but their contributions to predictive skill become dominant after most of the improved skill from initialization with observations vanishes after about 6–9 years. Recent multimodel results suggest that there is relatively more decadal predictive skill in the North Atlantic, western Pacific, and Indian Oceans than in other regions of the world oceans. Aspects of decadal variability of SSTs, like the mid-1970s shift in the Pacific, the mid-1990s shift in the northern North Atlantic and western Pacific, and the early-2000s hiatus, are better represented in initialized hindcasts compared to uninitialized simulations. There is evidence of higher skill in initialized multimodel ensemble decadal hindcasts than in single model results, with multimodel initialized predictions for near-term climate showing somewhat less global warming than uninitialized simulations. Some decadal hindcasts have shown statistically reliable predictions of surface temperature over various land and ocean regions for lead times of up to 6–9 years, but this needs to be investigated in a wider set of models. As in the early days of El Niño–Southern Oscillation (ENSO) prediction, improvements to models will reduce the need for bias adjustment, and increase the reliability, and thus usefulness, of decadal climate predictions in the future.
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Mieruch, S., H. Feldmann, G. Schädler, C. J. Lenz, S. Kothe, and C. Kottmeier. "The regional MiKlip decadal forecast ensemble for Europe: the added value of downscaling." Geoscientific Model Development 7, no. 6 (December 17, 2014): 2983–99. http://dx.doi.org/10.5194/gmd-7-2983-2014.
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Abstract. The prediction of climate on time scales of years to decades is attracting the interest of both climate researchers and stakeholders. The German Ministry for Education and Research (BMBF) has launched a major research programme on decadal climate prediction called MiKlip (Mittelfristige Klimaprognosen, Decadal Climate Prediction) in order to investigate the prediction potential of global and regional climate models (RCMs). In this paper we describe a regional predictive hindcast ensemble, its validation, and the added value of regional downscaling. Global predictions are obtained from an ensemble of simulations by the MPI-ESM-LR model (baseline 0 runs), which were downscaled for Europe using the COSMO-CLM regional model. Decadal hindcasts were produced for the 5 decades starting in 1961 until 2001. Observations were taken from the E-OBS data set. To identify decadal variability and predictability, we removed the long-term mean, as well as the long-term linear trend from the data. We split the resulting anomaly time series into two parts, the first including lead times of 1–5 years, reflecting the skill which originates mainly from the initialisation, and the second including lead times from 6–10 years, which are more related to the representation of low frequency climate variability and the effects of external forcing. We investigated temperature averages and precipitation sums for the summer and winter half-year. Skill assessment was based on correlation coefficient and reliability. We found that regional downscaling preserves, but mostly does not improve the skill and the reliability of the global predictions for summer half-year temperature anomalies. In contrast, regionalisation improves global decadal predictions of half-year precipitation sums in most parts of Europe. The added value results from an increased predictive skill on grid-point basis together with an improvement of the ensemble spread, i.e. the reliability.
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Mieruch, S., H. Feldmann, G. Schädler, C. J. Lenz, S. Kothe, and C. Kottmeier. "The regional MiKlip decadal forecast ensemble for Europe." Geoscientific Model Development Discussions 6, no. 4 (November 22, 2013): 5711–45. http://dx.doi.org/10.5194/gmdd-6-5711-2013.
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Abstract. Funded by the German Ministry for Education and Research (BMBF) a major research project called MiKlip (Mittelfristige Klimaprognose, Decadal Climate Prediction) was launched and global as well as regional predictive ensemble hindcasts have been generated. The aim of the project is to demonstrate for past climate change whether predictive models have the capability of predicting climate on time scales of decades. This includes the development of a decadal forecast system, on the one hand to support decision making for economy, politics and society for decadal time spans. On the other hand, the scientific aspect is to explore the feasibility and prospects of global and regional forecasts on decadal time scales. The focus of this paper lies on the description of the regional hindcast ensemble for Europe generated by COSMO-CLM and on the assessment of the decadal variability and predictability against observations. To measure decadal variability we remove the long term bias as well as the long term linear trend from the data. Further, we applied low pass filters to the original data to separate the decadal climate signal from high frequency noise. The decadal variability and predictability assessment is applied to temperature and precipitation data for the summer and winter half-year averages/sums. The best results have been found for the prediction of decadal temperature anomalies, i.e. we have detected a distinct predictive skill and reasonable reliability. Hence it is possible to predict regional temperature variability on decadal timescales, However, the situation is less satisfactory for precipitation. Here we have found regions showing good predictability, but also regions without any predictive skill.
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Meehl, Gerald A., Aixue Hu, and Claudia Tebaldi. "Decadal Prediction in the Pacific Region." Journal of Climate 23, no. 11 (June 1, 2010): 2959–73. http://dx.doi.org/10.1175/2010jcli3296.1.
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Abstract A “perfect model” configuration with a global coupled climate model 30-member ensemble is used to address decadal prediction of Pacific SSTs. All model data are low-pass filtered to focus on the low-frequency decadal component. The first three EOFs in the twentieth-century simulation, representing nearly 80% of the total variance, are used as the basis for early twenty-first-century predictions. The first two EOFs represent the forced trend and the interdecadal Pacific oscillation (IPO), respectively, as noted in previous studies, and the third has elements of both trend and IPO patterns. The perfect model reference simulation, the target for the prediction, is taken as the experiment that ran continuously from the twentieth to twenty-first century using anthropogenic and natural forcings for the twentieth century and the A1B scenario for the twenty-first century. The other 29 members use a perturbation in the atmosphere at year 2000 and are run until 2061. Since the IPO has been recognized as a dominant contributor to decadal variability in the Pacific, information late in the twentieth century and early in the twenty-first century is used to select a subset of ensemble members that are more skillful in tracking the time evolution of the IPO (EOF2) in relation to a notional start date of 2010. Predictions for the 19-yr period centered on the year 2020 use that subset of ensemble members to construct Pacific SST patterns based on the predicted evolution of the first three EOFs. Compared to the perfect model reference simulation, the predictions show some skill for Pacific SST predictions with anomaly pattern correlations greater than +0.5. An application of the Pacific SST prediction is made to precipitation over North America and Australia. Even though there are additional far-field influences on Pacific SSTs and North American and Australian precipitation involving the Atlantic multidecadal oscillation (AMO) in the Atlantic, and Indian Ocean and South Asian monsoon variability, there is qualitative skill for the pattern of predicted precipitation over North America and Australia using predicted Pacific SSTs. This exercise shows that, in the presence of a large forced trend like that in the large ensemble, much of Pacific region decadal predictability about 20 years into the future arises from increasing greenhouse gases.
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Knight, Jeff R., Martin B. Andrews, Doug M. Smith, Alberto Arribas, Andrew W. Colman, Nick J. Dunstone, Rosie Eade, et al. "Predictions of Climate Several Years Ahead Using an Improved Decadal Prediction System." Journal of Climate 27, no. 20 (October 7, 2014): 7550–67. http://dx.doi.org/10.1175/jcli-d-14-00069.1.
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Abstract Decadal climate predictions are now established as a source of information on future climate alongside longer-term climate projections. This information has the potential to provide key evidence for decisions on climate change adaptation, especially at regional scales. Its importance implies that following the creation of an initial generation of decadal prediction systems, a process of continual development is needed to produce successive versions with better predictive skill. Here, a new version of the Met Office Hadley Centre Decadal Prediction System (DePreSys 2) is introduced, which builds upon the success of the original DePreSys. DePreSys 2 benefits from inclusion of a newer and more realistic climate model, the Hadley Centre Global Environmental Model version 3 (HadGEM3), but shares a very similar approach to initialization with its predecessor. By performing a large suite of reforecasts, it is shown that DePreSys 2 offers improved skill in predicting climate several years ahead. Differences in skill between the two systems are likely due to a multitude of differences between the underlying climate models, but it is demonstrated herein that improved simulation of tropical Pacific variability is a key source of the improved skill in DePreSys 2. While DePreSys 2 is clearly more skilful than DePreSys in a global sense, it is shown that decreases in skill in some high-latitude regions are related to errors in representing long-term trends. Detrending the results focuses on the prediction of decadal time-scale variability, and shows that the improvement in skill in DePreSys 2 is even more marked.
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Towler, Erin, Debasish PaiMazumder, and James Done. "Toward the Application of Decadal Climate Predictions." Journal of Applied Meteorology and Climatology 57, no. 3 (March 2018): 555–68. http://dx.doi.org/10.1175/jamc-d-17-0113.1.
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AbstractDecadal prediction is a relatively new branch of climate science that bridges the gap between seasonal climate forecasts and multidecadal-to-century projections of climate change. This paper develops a three-step framework toward the potential application of decadal temperature predictions using the Community Climate System Model, version 4 (CCSM4). In step 1, the predictions are evaluated and it is found that the temperature hindcasts show skill over some regions of the United States and Canada. In step 2, the predictions are manipulated using two methods: a deterministic-anomaly approach (like climate change projections) and a probabilistic tercile-based approach (like seasonal forecasts). In step 3, the predictions are translated by adding a delta (for the anomaly manipulation) and conducting a weighted resample (for the probabilistic manipulation), as well as using a new hybrid method. Using the 2010 initialized hindcast, the framework is demonstrated for predicting 2011–15 over two case-study watersheds [Ottawa (Canada) and Colorado]. For the Colorado watershed, there was a noticeable shift toward higher temperatures, and the delta, weighted resample, and hybrid translations all were better at capturing the observed temperatures than was an approach that used climatological values. For the Ottawa watershed, the observed temperatures over the period of prediction were only subtly different than the climatological values; therefore, the difference between the translation methods was less noticeable. The advantages and disadvantages of the manipulation and translation approaches are discussed, as well as how their use will depend on the user context. The authors emphasize that skill evaluations should be tailored to particular applications and identify additional steps that are needed before the decadal temperature predictions can be readily incorporated into applications.
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Li, Hongmei, Tatiana Ilyina, Tammas Loughran, Aaron Spring, and Julia Pongratz. "Reconstructions and predictions of the global carbon budget with an emission-driven Earth system model." Earth System Dynamics 14, no. 1 (February 1, 2023): 101–19. http://dx.doi.org/10.5194/esd-14-101-2023.
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Abstract. The global carbon budget (GCB) – including fluxes of CO2 between the atmosphere, land, and ocean and its atmospheric growth rate – show large interannual to decadal variations. Reconstructing and predicting the variable GCB is essential for tracing the fate of carbon and understanding the global carbon cycle in a changing climate. We use a novel approach to reconstruct and predict the variations in GCB in the next few years based on our decadal prediction system enhanced with an interactive carbon cycle. By assimilating physical atmospheric and oceanic data products into the Max Planck Institute Earth System Model (MPI-ESM), we are able to reproduce the annual mean historical GCB variations from 1970–2018, with high correlations of 0.75, 0.75, and 0.97 for atmospheric CO2 growth, air–land CO2 fluxes, and air–sea CO2 fluxes, respectively, relative to the assessments from the Global Carbon Project (GCP). Such a fully coupled decadal prediction system, with an interactive carbon cycle, enables the representation of the GCB within a closed Earth system and therefore provides an additional line of evidence for the ongoing assessments of the anthropogenic GCB. Retrospective predictions initialized from the simulation in which physical atmospheric and oceanic data products are assimilated show high confidence in predicting the following year's GCB. The predictive skill is up to 5 years for the air–sea CO2 fluxes, and 2 years for the air–land CO2 fluxes and atmospheric carbon growth rate. This is the first study investigating the GCB variations and predictions with an emission-driven prediction system. Such a system also enables the reconstruction of the past and prediction of the evolution of near-future atmospheric CO2 concentration changes. The Earth system predictions in this study provide valuable inputs for understanding the global carbon cycle and informing climate-relevant policy.
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Gaetani, Marco, and Elsa Mohino. "Decadal Prediction of the Sahelian Precipitation in CMIP5 Simulations." Journal of Climate 26, no. 19 (September 24, 2013): 7708–19. http://dx.doi.org/10.1175/jcli-d-12-00635.1.
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Abstract In this study the capability of eight state-of-the-art ocean–atmosphere coupled models in predicting the monsoonal precipitation in the Sahel on a decadal time scale is assessed. To estimate the importance of the initialization, the predictive skills of two different CMIP5 experiments are compared, a set of 10 decadal hindcasts initialized every 5 years in the period 1961–2009 and the historical simulations in the period 1961–2005. Results indicate that predictive skills are highly model dependent: the Fourth Generation Canadian Coupled Global Climate Model (CanCM4), Centre National de Recherches Météorologiques Coupled Global Climate Model, version 5 (CNRM-CM5), and Max Planck Institute Earth System Model, low resolution (MPI-ESM-LR) models show improved skill in the decadal hindcasts, while the Model for Interdisciplinary Research on Climate, version 5 (MIROC5) is skillful in both the decadal and historical experiments. The Beijing Climate Center, Climate System Model, version 1.1 (BCC-CSM1.1), Hadley Centre Coupled Model, version 3 (HadCM3), L'Institut Pierre-Simon Laplace Coupled Model, version 5, coupled with NEMO, low resolution (IPSL-CM5A-LR), and Meteorological Research Institute Coupled Atmosphere–Ocean General Circulation Model, version 3 (MRI-CGCM3) models show insignificant or no skill in predicting the Sahelian precipitation. Skillful predictions are produced by models properly describing the SST multidecadal variability and the initialization appears to play an important role in this respect.
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Hossain, Md Monowar, A. H. M. Faisal Anwar, Nikhil Garg, Mahesh Prakash, and Mohammed Bari. "Monthly Rainfall Prediction at Catchment Level with the Facebook Prophet Model Using Observed and CMIP5 Decadal Data." Hydrology 9, no. 6 (June 17, 2022): 111. http://dx.doi.org/10.3390/hydrology9060111.
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Early prediction of rainfall is important for the planning of agriculture, water infrastructure, and other socio-economic developments. The near-term prediction (e.g., 10 years) of hydrologic data is a recent development in GCM (General Circulation Model) simulations, e.g., the CMIP5 (Coupled Modelled Intercomparison Project Phase 5) decadal experiments. The prediction of monthly rainfall on a decadal time scale is an important step for catchment management. Previous studies have considered stochastic models using observed time series data only for rainfall prediction, but no studies have used GCM decadal data together with observed data at the catchment level. This study used the Facebook Prophet (FBP) model and six machine learning (ML) regression algorithms for the prediction of monthly rainfall on a decadal time scale for the Brisbane River catchment in Queensland, Australia. Monthly hindcast decadal precipitation data of eight GCMs (EC-EARTH MIROC4h, MRI-CGCM3, MPI-ESM-LR, MPI-ESM-MR, MIROC5, CanCM4, and CMCC-CM) were downloaded from the CMIP5 data portal, and the observed data were collected from the Australian Bureau of Meteorology. At first, the FBP model was used for predictions based on: (i) the observed data only; and (ii) a combination of observed and CMIP5 decadal data. In the next step, predictions were performed through ML regressions where CMIP5 decadal data were used as features and corresponding observed data were used as target variables. The prediction skills were assessed through several skill tests, including Pearson Correlation Coefficient (PCC), Anomaly Correlation Coefficient (ACC), Index of Agreement (IA), and Mean Absolute Error (MAE). Upon comparing the skills, this study found that predictions based on a combination of observed and CMIP5 decadal data through the FBP model provided better skills than the predictions based on the observed data only. The optimal performance of the FBP model, especially for the dry periods, was mainly due to its multiplicative seasonality function.
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Keenlyside, Noel S., and Jin Ba. "Prospects for decadal climate prediction." Wiley Interdisciplinary Reviews: Climate Change 1, no. 5 (August 2, 2010): 627–35. http://dx.doi.org/10.1002/wcc.69.
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Boer, George J., Douglas M. Smith, Christophe Cassou, Francisco Doblas-Reyes, Gokhan Danabasoglu, Ben Kirtman, Yochanan Kushnir, et al. "The Decadal Climate Prediction Project (DCPP) contribution to CMIP6." Geoscientific Model Development 9, no. 10 (October 25, 2016): 3751–77. http://dx.doi.org/10.5194/gmd-9-3751-2016.
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Abstract. The Decadal Climate Prediction Project (DCPP) is a coordinated multi-model investigation into decadal climate prediction, predictability, and variability. The DCPP makes use of past experience in simulating and predicting decadal variability and forced climate change gained from the fifth Coupled Model Intercomparison Project (CMIP5) and elsewhere. It builds on recent improvements in models, in the reanalysis of climate data, in methods of initialization and ensemble generation, and in data treatment and analysis to propose an extended comprehensive decadal prediction investigation as a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Prediction (Kushnir et al., 2016). The DCPP consists of three components. Component A comprises the production and analysis of an extensive archive of retrospective forecasts to be used to assess and understand historical decadal prediction skill, as a basis for improvements in all aspects of end-to-end decadal prediction, and as a basis for forecasting on annual to decadal timescales. Component B undertakes ongoing production, analysis and dissemination of experimental quasi-real-time multi-model forecasts as a basis for potential operational forecast production. Component C involves the organization and coordination of case studies of particular climate shifts and variations, both natural and naturally forced (e.g. the “hiatus”, volcanoes), including the study of the mechanisms that determine these behaviours. Groups are invited to participate in as many or as few of the components of the DCPP, each of which are separately prioritized, as are of interest to them.The Decadal Climate Prediction Project addresses a range of scientific issues involving the ability of the climate system to be predicted on annual to decadal timescales, the skill that is currently and potentially available, the mechanisms involved in long timescale variability, and the production of forecasts of benefit to both science and society.
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Yang, Chao-Yuan, Jiping Liu, Yongyun Hu, Radley M. Horton, Liqi Chen, and Xiao Cheng. "Assessment of Arctic and Antarctic sea ice predictability in CMIP5 decadal hindcasts." Cryosphere 10, no. 5 (October 21, 2016): 2429–52. http://dx.doi.org/10.5194/tc-10-2429-2016.
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Abstract. This paper examines the ability of coupled global climate models to predict decadal variability of Arctic and Antarctic sea ice. We analyze decadal hindcasts/predictions of 11 Coupled Model Intercomparison Project Phase 5 (CMIP5) models. Decadal hindcasts exhibit a large multi-model spread in the simulated sea ice extent, with some models deviating significantly from the observations as the predicted ice extent quickly drifts away from the initial constraint. The anomaly correlation analysis between the decadal hindcast and observed sea ice suggests that in the Arctic, for most models, the areas showing significant predictive skill become broader associated with increasing lead times. This area expansion is largely because nearly all the models are capable of predicting the observed decreasing Arctic sea ice cover. Sea ice extent in the North Pacific has better predictive skill than that in the North Atlantic (particularly at a lead time of 3–7 years), but there is a re-emerging predictive skill in the North Atlantic at a lead time of 6–8 years. In contrast to the Arctic, Antarctic sea ice decadal hindcasts do not show broad predictive skill at any timescales, and there is no obvious improvement linking the areal extent of significant predictive skill to lead time increase. This might be because nearly all the models predict a retreating Antarctic sea ice cover, opposite to the observations. For the Arctic, the predictive skill of the multi-model ensemble mean outperforms most models and the persistence prediction at longer timescales, which is not the case for the Antarctic. Overall, for the Arctic, initialized decadal hindcasts show improved predictive skill compared to uninitialized simulations, although this improvement is not present in the Antarctic.
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Krieger, Daniel, Sebastian Brune, Patrick Pieper, Ralf Weisse, and Johanna Baehr. "Skillful decadal prediction of German Bight storm activity." Natural Hazards and Earth System Sciences 22, no. 12 (December 14, 2022): 3993–4009. http://dx.doi.org/10.5194/nhess-22-3993-2022.
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Abstract. We evaluate the prediction skill of the Max Planck Institute Earth System Model (MPI-ESM) decadal hindcast system for German Bight storm activity (GBSA) on a multiannual to decadal scale. We define GBSA every year via the most extreme 3-hourly geostrophic wind speeds, which are derived from mean sea-level pressure (MSLP) data. Our 64-member ensemble of annually initialized hindcast simulations spans the time period 1960–2018. For this period, we compare deterministically and probabilistically predicted winter MSLP anomalies and annual GBSA with a lead time of up to 10 years against observations. The model produces poor deterministic predictions of GBSA and winter MSLP anomalies for individual years but fair predictions for longer averaging periods. A similar but smaller skill difference between short and long averaging periods also emerges for probabilistic predictions of high storm activity. At long averaging periods (longer than 5 years), the model is more skillful than persistence- and climatology-based predictions. For short aggregation periods (4 years and less), probabilistic predictions are more skillful than persistence but insignificantly differ from climatological predictions. We therefore conclude that, for the German Bight, probabilistic decadal predictions (based on a large ensemble) of high storm activity are skillful for averaging periods longer than 5 years. Notably, a differentiation between low, moderate, and high storm activity is necessary to expose this skill.
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Marotzke, Jochem, Wolfgang A. Müller, Freja S. E. Vamborg, Paul Becker, Ulrich Cubasch, Hendrik Feldmann, Frank Kaspar, et al. "MiKlip: A National Research Project on Decadal Climate Prediction." Bulletin of the American Meteorological Society 97, no. 12 (December 1, 2016): 2379–94. http://dx.doi.org/10.1175/bams-d-15-00184.1.
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Abstract Mittelfristige Klimaprognose (MiKlip), an 8-yr German national research project on decadal climate prediction, is organized around a global prediction system comprising the Max Planck Institute Earth System Model (MPI-ESM) together with an initialization procedure and a model evaluation system. This paper summarizes the lessons learned from MiKlip so far; some are purely scientific, others concern strategies and structures of research that target future operational use. Three prediction system generations have been constructed, characterized by alternative initialization strategies; the later generations show a marked improvement in hindcast skill for surface temperature. Hindcast skill is also identified for multiyear-mean European summer surface temperatures, extratropical cyclone tracks, the quasi-biennial oscillation, and ocean carbon uptake, among others. Regionalization maintains or slightly enhances the skill in European surface temperature inherited from the global model and also displays hindcast skill for wind energy output. A new volcano code package permits rapid modification of the predictions in response to a future eruption. MiKlip has demonstrated the efficacy of subjecting a single global prediction system to a major research effort. The benefits of this strategy include the rapid cycling through the prediction system generations, the development of a sophisticated evaluation package usable by all MiKlip researchers, and regional applications of the global predictions. Open research questions include the optimal balance between model resolution and ensemble size, the appropriate method for constructing a prediction ensemble, and the decision between full-field and anomaly initialization. Operational use of the MiKlip system is targeted for the end of the current decade, with a recommended generational cycle of 2–3 years.
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Gu, Qinxue, and Melissa Gervais. "Exploring North Atlantic and North Pacific Decadal Climate Prediction Using Self-Organizing Maps." Journal of Climate 34, no. 1 (January 2021): 123–41. http://dx.doi.org/10.1175/jcli-d-20-0017.1.
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AbstractDecadal climate prediction can provide invaluable information for decisions made by government agencies and industry. Modes of internal variability of the ocean play an important role in determining the climate on decadal time scales. This study explores the possibility of using self-organizing maps (SOMs) to identify decadal climate variability, measure theoretical decadal predictability, and conduct decadal predictions of internal climate variability within a long control simulation. SOM is applied to an 11-yr running-mean winter sea surface temperature (SST) in the North Pacific and North Atlantic Oceans within the Community Earth System Model 1850 preindustrial simulation to identify patterns of internal variability in SSTs. Transition probability tables are calculated to identify preferred paths through the SOM with time. Results show both persistence and preferred evolutions of SST depending on the initial SST pattern. This method also provides a measure of the predictability of these SST patterns, with the North Atlantic being predictable at longer lead times than the North Pacific. In addition, decadal SST predictions using persistence, a first-order Markov chain, and lagged transition probabilities are conducted. The lagged transition probability predictions have a reemergence of prediction skill around lag 15 for both domains. Although the prediction skill is very low, it does imply that the SOM has the ability to predict some aspects of the internal variability of the system beyond 10 years.
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Nicolì, Dario, Alessio Bellucci, Paolo Ruggieri, Panos J. Athanasiadis, Stefano Materia, Daniele Peano, Giusy Fedele, Riccardo Hénin, and Silvio Gualdi. "The Euro-Mediterranean Center on Climate Change (CMCC) decadal prediction system." Geoscientific Model Development 16, no. 1 (January 5, 2023): 179–97. http://dx.doi.org/10.5194/gmd-16-179-2023.
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Abstract. Decadal climate predictions, obtained by constraining the initial condition of a dynamical model through a truthful estimate of the observed climate state, provide an accurate assessment of near-term climate change and are a useful tool to inform decision-makers on future climate-related risks. Here we present results from the CMIP6 (Coupled Model Intercomparison Project Phase 6) Decadal Climate Prediction Project (DCPP) decadal hindcasts produced with the operational CMCC (Euro-Mediterranean Center on Climate Change) decadal prediction system (DPS), based on the fully coupled CMCC-CM2-SR5 dynamical model. A 20-member suite of 10-year retrospective forecasts, initialized every year from 1960 to 2020, is performed using a full-field initialization strategy. The predictive skill for key variables is assessed and compared with the skill of an ensemble of non-initialized historical simulations so as to quantify the added value of the initialization. In particular, the CMCC DPS is able to skillfully reproduce past climate surface and subsurface temperature fluctuations over large parts of the globe. The North Atlantic Ocean is the region that benefits the most from initialization, with the largest skill enhancement occurring over the subpolar region compared to historical simulations. On the other hand, the predictive skill over the Pacific Ocean rapidly decays with forecast time, especially over the North Pacific. In terms of precipitation, the skill of the CMCC DPS is significantly higher than that of the historical simulations over a few specific regions, including the Sahel, northern Eurasia, and over western and central Europe. The Atlantic multidecadal variability is also skillfully predicted, and this likely contributes to the skill found over remote areas through downstream influence, circulation changes, and teleconnections. Considering the relatively small ensemble size, a remarkable prediction skill is also found for the North Atlantic Oscillation, with maximum correlations obtained in the 1–9 lead year range. Systematic errors also affect the forecast quality of the CMCC DPS, featuring a prominent cold bias over the Northern Hemisphere, which is not found in the historical runs, suggesting that, in some areas, the adopted full-field initialization strategy likely perturbs the equilibrium state of the model climate quite significantly. The encouraging results obtained in this study indicate that climate variability over land can be predictable over a multiyear range, and they demonstrate that the CMCC DPS is a valuable addition to the current generation of DPSs. This stresses the need to further explore the potential of the near-term predictions, further improving future decadal systems and initialization methods, with the aim to provide a reliable tool to inform decision-makers on how regional climate will evolve in the next decade.
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Hurrell, James. "Decadal climate prediction: Challenges and opportunities." IOP Conference Series: Earth and Environmental Science 6, no. 2 (January 1, 2009): 022001. http://dx.doi.org/10.1088/1755-1307/6/2/022001.
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Kaspar, Frank, Henning W. Rust, Uwe Ulbrich, and Paul Becker. "Verification and process oriented validation of the MiKlip decadal prediction system." Meteorologische Zeitschrift 25, no. 6 (December 21, 2016): 629–30. http://dx.doi.org/10.1127/metz/2016/0831.
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Kruschke, Tim, Henning W. Rust, Christopher Kadow, Wolfgang A. Müller, Holger Pohlmann, Gregor C. Leckebusch, and Uwe Ulbrich. "Probabilistic evaluation of decadal prediction skill regarding Northern Hemisphere winter storms." Meteorologische Zeitschrift 25, no. 6 (December 21, 2016): 721–38. http://dx.doi.org/10.1127/metz/2015/0641.
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Huang, Yanyan, Huijun Wang, and Ke Fan. "Improving the Prediction of the Summer Asian–Pacific Oscillation Using the Interannual Increment Approach." Journal of Climate 27, no. 21 (October 24, 2014): 8126–34. http://dx.doi.org/10.1175/jcli-d-14-00209.1.
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Abstract The summer Asian–Pacific oscillation (APO) is a dominant teleconnection pattern over the extratropical Northern Hemisphere that links the large-scale atmospheric circulation anomalies over the Asian–North Pacific Ocean sector. In this study, the direct Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER) model outputs from 1960 to 2001, which are limited in predicting the interannual variability of the summer Asian upper-tropospheric temperature and the decadal variations, are applied using the interannual increment approach to improve the predictions of the summer APO. By treating the year-to-year increment as the predictand, the interannual increment scheme is shown to significantly improve the predictive ability for the interannual variability of the summer Asian upper-tropospheric temperature and the decadal variations. The improvements for the interannual and interdecadal summer APO variability predictions in the interannual increment scheme relative to the original scheme are clear and significant. Compared with the DEMETER direct outputs, the statistical model with two predictors of APO and sea surface temperature anomaly over the Atlantic shows a significantly improved ability to predict the interannual variability of the summer rainfall over the middle and lower reaches of the Yangtze River valley (SRYR). This study therefore describes a more efficient approach for predicting the APO and the SRYR.
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Krueger, Oliver, and Jin-Song Von Storch. "A Simple Empirical Model for Decadal Climate Prediction." Journal of Climate 24, no. 4 (February 15, 2011): 1276–83. http://dx.doi.org/10.1175/2010jcli3726.1.
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Abstract Decadal climate prediction is a challenging aspect of climate research. It has been and will be tackled by various modeling groups. This study proposes a simple empirical forecasting system for the near-surface temperature that can be used as a benchmark for climate predictions obtained from atmosphere–ocean GCMs (AOGCMs). It is assumed that the temperature time series can be decomposed into components related to external forcing and internal variability. The considered external forcing consists of the atmospheric CO2 concentration. Separation of the two components is achieved by using the Intergovernmental Panel on Climate Change Fourth Assessment Report (IPCC AR4) twentieth-century integrations. Temperature anomalies due to changing external forcing are described by a linear regression onto the forcing. The future evolution of the external forcing that is needed for predictions is approximated by a linear extrapolation of the forcing prior to the initial time. Temperature anomalies owing to the internal variability are described by an autoregressive model. An evaluation of hindcast experiments shows that the empirical model has a cross-validated correlation skill of 0.84 and a cross-validated rms error of 0.12 K in hindcasting global-mean temperature anomalies 10 years ahead.
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Mahmood, Rashed, Markus G. Donat, Pablo Ortega, Francisco J. Doblas-Reyes, Carlos Delgado-Torres, Margarida Samsó, and Pierre-Antoine Bretonnière. "Constraining low-frequency variability in climate projections to predict climate on decadal to multi-decadal timescales – a poor man's initialized prediction system." Earth System Dynamics 13, no. 4 (October 19, 2022): 1437–50. http://dx.doi.org/10.5194/esd-13-1437-2022.
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Abstract. Near-term projections of climate change are subject to substantial uncertainty from internal climate variability. Here we present an approach to reduce this uncertainty by sub-selecting those ensemble members that more closely resemble observed patterns of ocean temperature variability immediately prior to a certain start date. This constraint aligns the observed and simulated variability phases and is conceptually similar to initialization in seasonal to decadal climate predictions. We apply this variability constraint to large multi-model projection ensembles from the Coupled Model Intercomparison Project phase 6 (CMIP6), consisting of more than 200 ensemble members, and evaluate the skill of the constrained ensemble in predicting the observed near-surface temperature, sea-level pressure, and precipitation on decadal to multi-decadal timescales. We find that the constrained projections show significant skill in predicting the climate of the following 10 to 20 years, and added value over the ensemble of unconstrained projections. For the first decade after applying the constraint, the global patterns of skill are very similar and can even outperform those of the multi-model ensemble mean of initialized decadal hindcasts from the CMIP6 Decadal Climate Prediction Project (DCPP). In particular for temperature, larger areas show added skill in the constrained projections compared to DCPP, mainly in the Pacific and some neighboring land regions. Temperature and sea-level pressure in several regions are predictable multiple decades ahead, and show significant added value over the unconstrained projections for forecasting the first 2 decades and the 20-year averages. We further demonstrate the suitability of regional constraints to attribute predictability to certain ocean regions. On the example of global average temperature changes, we confirm the role of Pacific variability in modulating the reduced rate of global warming in the early 2000s, and demonstrate the predictability of reduced global warming rates over the following 15 years based on the climate conditions leading up to 1998. Our results illustrate that constraining internal variability can significantly improve the accuracy of near-term climate change estimates for the next few decades.
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Wu, Bo, Xiaolong Chen, Fengfei Song, Yong Sun, and Tianjun Zhou. "Initialized Decadal Predictions by LASG/IAP Climate System Model FGOALS-s2: Evaluations of Strengths and Weaknesses." Advances in Meteorology 2015 (2015): 1–12. http://dx.doi.org/10.1155/2015/904826.
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Decadal prediction experiments are conducted by using the coupled global climate model FGOALS-s2, following the CMIP 5 protocol. The paper documents the initialization procedures for the decadal prediction experiments and summarizes the predictive skills of the experiments, which are assessed through indicators adopted by the IPCC AR5. The observational anomalies of surface and subsurface ocean temperature and salinity are assimilated through a modified incremental analysis update (IAU) scheme. Three sets of 10-year-long hindcast and forecast runs were started every five years in the period of 1960–2005, with the initial conditions taken from the assimilation runs. The decadal prediction experiment by FGOALS-s2 shows significant high predictive skills in the Indian Ocean, tropical western Pacific, and Atlantic, similar to the results of the CMIP5 multimodel ensemble. The predictive skills in the Indian Ocean and tropical western Pacific are primarily attributed to the model response to the external radiative forcing associated with the change of atmospheric compositions. In contrast, the high skills in the Atlantic are attributed, at least partly, to the improvements in the prediction of the Atlantic multidecadal variability coming from the initialization.
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Dunstone, Nick, Julia Lockwood, Balakrishnan Solaraju-Murali, Katja Reinhardt, Eirini E. Tsartsali, Panos J. Athanasiadis, Alessio Bellucci, et al. "Towards Useful Decadal Climate Services." Bulletin of the American Meteorological Society 103, no. 7 (July 2022): E1705—E1719. http://dx.doi.org/10.1175/bams-d-21-0190.1.
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Abstract The decadal time scale (∼1–10 years) bridges the gap between seasonal predictions and longer-term climate projections. It is a key planning time scale for users in many sectors as they seek to adapt to our rapidly changing climate. While significant advances in using initialized climate models to make skillful decadal predictions have been made in the last decades, including coordinated international experiments and multimodel forecast exchanges, few user-focused decadal climate services have been developed. Here we highlight the potential of decadal climate services using four case studies from a project led by four institutions that produce real-time decadal climate predictions. Working in co-development with users in agriculture, energy, infrastructure, and insurance sectors, four prototype climate service products were developed. This study describes the challenge of trying to match user needs with the current scientific capability. For example, the use of large ensembles (achieved via a multisystem approach) and skillfully predicted large-scale environmental conditions, are found to improve regional predictions, particularly in midlatitudes. For each climate service, a two-page “product sheet” template was developed that provides users with both a concise probabilistic forecast and information on retrospective performance. We describe the development cycle, where valuable feedback was obtained from a “showcase event” where a wider group of sector users were engaged. We conclude that for society to take full and rapid advantage of useful decadal climate services, easier and more timely access to decadal climate prediction data are required, along with building wider community expertise in their use.
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Spangehl, Thomas, Marc Schröder, Sophie Stolzenberger, Rita Glowienka-Hense, Alex Mazurkiewicz, and Andreas Hense. "Evaluation of the MiKlip decadal prediction system using satellite based cloud products." Meteorologische Zeitschrift 25, no. 6 (December 21, 2016): 695–707. http://dx.doi.org/10.1127/metz/2015/0602.
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Bilbao, Roberto, Pablo Ortega, Didier Swingedouw, Leon Hermanson, Panos Athanasiadis, Rosie Eade, Marion Devilliers, et al. "Impact of volcanic eruptions on CMIP6 decadal predictions: a multi-model analysis." Earth System Dynamics 15, no. 2 (April 26, 2024): 501–25. http://dx.doi.org/10.5194/esd-15-501-2024.
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Abstract. In recent decades, three major volcanic eruptions of different intensity have occurred (Mount Agung in 1963, El Chichón in 1982 and Mount Pinatubo in 1991), with reported climate impacts on seasonal to decadal timescales that could have been potentially predicted with accurate and timely estimates of the associated stratospheric aerosol loads. The Decadal Climate Prediction Project component C (DCPP-C) includes a protocol to investigate the impact of volcanic aerosols on the climate experienced during the years that followed those eruptions through the use of decadal predictions. The interest of conducting this exercise with climate predictions is that, thanks to the initialisation, they start from the observed climate conditions at the time of the eruptions, which helps to disentangle the climatic changes due to the initial conditions and internal variability from the volcanic forcing. The protocol consists of repeating the retrospective predictions that are initialised just before the last three major volcanic eruptions but without the inclusion of their volcanic forcing, which are then compared with the baseline predictions to disentangle the simulated volcanic effects upon climate. We present the results from six Coupled Model Intercomparison Project Phase 6 (CMIP6) decadal prediction systems. These systems show strong agreement in predicting the well-known post-volcanic radiative effects following the three eruptions, which induce a long-lasting cooling in the ocean. Furthermore, the multi-model multi-eruption composite is consistent with previous work reporting an acceleration of the Northern Hemisphere polar vortex and the development of El Niño conditions the first year after the eruption, followed by a strengthening of the Atlantic Meridional Overturning Circulation the subsequent years. Our analysis reveals that all these dynamical responses are both model- and eruption-dependent. A novel aspect of this study is that we also assess whether the volcanic forcing improves the realism of the predictions. Comparing the predicted surface temperature anomalies in the two sets of hindcasts (with and without volcanic forcing) with observations we show that, overall, including the volcanic forcing results in better predictions. The volcanic forcing is found to be particularly relevant for reproducing the observed sea surface temperature (SST) variability in the North Atlantic Ocean following the 1991 eruption of Pinatubo.
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Teng, Haiyan, Gerald A. Meehl, Grant Branstator, Stephen Yeager, and Alicia Karspeck. "Initialization Shock in CCSM4 Decadal Prediction Experiments." Past Global Changes Magazine 25, no. 1 (July 2017): 41–46. http://dx.doi.org/10.22498/pages.25.1.41.
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Kirtman, Ben P., and Paul S. Schopf. "Decadal Variability in ENSO Predictability and Prediction." Journal of Climate 11, no. 11 (November 1998): 2804–22. http://dx.doi.org/10.1175/1520-0442(1998)011<2804:dviepa>2.0.co;2.
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Sandgathe, Scott, Bonnie R. Brown, Jessie C. Carman, Johnna M. Infanti, Bradford Johnson, David McCarren, and Eileen McIlvain. "Exploring the Need for Reliable Decadal Prediction." Bulletin of the American Meteorological Society 101, no. 2 (February 1, 2020): E141—E145. http://dx.doi.org/10.1175/bams-d-19-0248.1.
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Matei, Daniela, Holger Pohlmann, Johann Jungclaus, Wolfgang Müller, Helmuth Haak, and Jochem Marotzke. "Two Tales of Initializing Decadal Climate Prediction Experiments with the ECHAM5/MPI-OM Model." Journal of Climate 25, no. 24 (December 15, 2012): 8502–23. http://dx.doi.org/10.1175/jcli-d-11-00633.1.
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Abstract This paper investigates the impact of different ocean initialization strategies on the forecast skill of decadal prediction experiments performed with the ECHAM5/Max Planck Institute Ocean Model (MPI-OM) coupled model. The ocean initializations assimilate three-dimensional temperature and salinity anomalies from two different ocean state estimates, the ocean reanalysis of the German contribution to Estimating the Circulation and Climate of the Ocean (GECCO) and an ensemble of MPI-OM ocean experiments forced with the NCEP–NCAR atmospheric reanalysis. The results show that North Atlantic and Mediterranean sea surface temperature (SST) variations can be skillfully predicted up to a decade ahead and with greater skill than by both uninitialized simulations and persistence forecasts. The regional distribution of SST predictive skill is similar in both initialization approaches; however, higher skill is found for the NCEP hindcasts than for the GECCO hindcasts when a combination of predictive skill measures is used. Skillful predictions of surface air temperature are obtained over northwestern Europe, northern Africa, and central-eastern Asia. The North Atlantic subpolar gyre region stands out as the region with the highest predictive skill beyond the warming trend, in both SST and upper-ocean heat-content predictions. Here the NCEP hindcasts deliver the best results due to a more accurate initialization of the observed variability. The dominant mechanism for North Atlantic climate predictability is of dynamical origin and can be attributed to the initialization of the Atlantic meridional overturning circulation, thus explaining the reoccurrence of high predictive skill within the second pentad of the hindcasts experiments. The results herein demonstrate that ocean experiments forced with the observed history of the atmospheric state constitute a simple but successful alternative strategy for the initialization of skillful climate predictions over the next decade.
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Wang, Bin, Juan Li, Mark A. Cane, Jian Liu, Peter J. Webster, Baoqiang Xiang, Hye-Mi Kim, Jian Cao, and Kyung-Ja Ha. "Toward Predicting Changes in the Land Monsoon Rainfall a Decade in Advance." Journal of Climate 31, no. 7 (April 2018): 2699–714. http://dx.doi.org/10.1175/jcli-d-17-0521.1.
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Predictions of changes of the land monsoon rainfall (LMR) in the coming decades are of vital importance for successful sustainable economic development. Current dynamic models, though, have shown little skill in the decadal prediction of the Northern Hemisphere (NH) LMR (NHLMR). The physical basis and predictability for such predictions remain largely unexplored. Decadal change of the NHLMR reflects changes in the total NH continental precipitation, tropical general circulation, and regional land monsoon rainfall over northern Africa, India, East Asia, and North America. Using observations from 1901 to 2014 and numerical experiments, it is shown that the decadal variability of the NHLMR is rooted primarily in (i) the north–south hemispheric thermal contrast in the Atlantic–Indian Ocean sector measured by the North Atlantic–south Indian Ocean dipole (NAID) sea surface temperature (SST) index and (ii) an east–west thermal contrast in the Pacific measured by an extended El Niño–Southern Oscillation (XEN) index. Results from a 500-yr preindustrial control experiment demonstrate that the leading mode of decadal NHLMR and the associated NAID and XEN SST anomalies may be largely an internal mode of Earth’s climate system, although possibly modified by natural and anthropogenic external forcing. A 51-yr, independent forward-rolling decadal hindcast was made with a hybrid dynamic conceptual model and using the NAID index predicted by a multiclimate model ensemble. The results demonstrate that the decadal changes in the NHLMR can be predicted approximately a decade in advance with significant skills, opening a promising way forward for decadal predictions of regional land monsoon rainfall worldwide.
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Mochizuki, Takashi, Masayoshi Ishii, Masahide Kimoto, Yoshimitsu Chikamoto, Masahiro Watanabe, Toru Nozawa, Takashi T. Sakamoto, et al. "Pacific decadal oscillation hindcasts relevant to near-term climate prediction." Proceedings of the National Academy of Sciences 107, no. 5 (January 11, 2010): 1833–37. http://dx.doi.org/10.1073/pnas.0906531107.
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Decadal-scale climate variations over the Pacific Ocean and its surroundings are strongly related to the so-called Pacific decadal oscillation (PDO) which is coherent with wintertime climate over North America and Asian monsoon, and have important impacts on marine ecosystems and fisheries. In a near-term climate prediction covering the period up to 2030, we require knowledge of the future state of internal variations in the climate system such as the PDO as well as the global warming signal. We perform sets of ensemble hindcast and forecast experiments using a coupled atmosphere-ocean climate model to examine the predictability of internal variations on decadal timescales, in addition to the response to external forcing due to changes in concentrations of greenhouse gases and aerosols, volcanic activity, and solar cycle variations. Our results highlight that an initialization of the upper-ocean state using historical observations is effective for successful hindcasts of the PDO and has a great impact on future predictions. Ensemble hindcasts for the 20th century demonstrate a predictive skill in the upper-ocean temperature over almost a decade, particularly around the Kuroshio-Oyashio extension (KOE) and subtropical oceanic frontal regions where the PDO signals are observed strongest. A negative tendency of the predicted PDO phase in the coming decade will enhance the rising trend in surface air-temperature (SAT) over east Asia and over the KOE region, and suppress it along the west coasts of North and South America and over the equatorial Pacific. This suppression will contribute to a slowing down of the global-mean SAT rise.
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Wang, Huijie, Yanyan Huang, Dapeng Zhang, and Huijun Wang. "Decadal Prediction of the Summer Extreme Precipitation over Southern China." Atmosphere 14, no. 3 (March 21, 2023): 595. http://dx.doi.org/10.3390/atmos14030595.
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The decadal variability of the summer extreme precipitation over southern China (EPSC) is remarkable, especially for the significant decadal enhancement after the 1990s. The study documented that the summer sea surface temperature (SST) over the North Atlantic and spring sea ice concentration (SIC) over the East Siberian Sea can significantly affect the EPSC. The summer SST over the North Atlantic influences the low-pressure cyclone in the western Pacific by modulating the SST over the tropical Pacific, thus affecting EPSC. A decrease in the SIC of the East Siberian Sea induces a negative Arctic Oscillation, which induces the increased SST over northwest Pacific and the anomalous cyclone over there, in turn, affecting EPSC. Both predictors have a quasi-period of 10–14 years, which provides useful predictive signals for EPSC. The leading 7-year SST and the leading 5-year SIC are chosen to establish the prediction model based on the decadal increment method, which can well predict the EPSC, especially for the shift in the early 1990s. These results provide a clue to the limited predictability of decadal-scale extreme climate events.
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Hawkins, Ed, Buwen Dong, Jon Robson, Rowan Sutton, and Doug Smith. "The Interpretation and Use of Biases in Decadal Climate Predictions." Journal of Climate 27, no. 8 (April 10, 2014): 2931–47. http://dx.doi.org/10.1175/jcli-d-13-00473.1.
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Abstract Decadal climate predictions exhibit large biases, which are often subtracted and forgotten. However, understanding the causes of bias is essential to guide efforts to improve prediction systems, and may offer additional benefits. Here the origins of biases in decadal predictions are investigated, including whether analysis of these biases might provide useful information. The focus is especially on the lead-time-dependent bias tendency. A “toy” model of a prediction system is initially developed and used to show that there are several distinct contributions to bias tendency. Contributions from sampling of internal variability and a start-time-dependent forcing bias can be estimated and removed to obtain a much improved estimate of the true bias tendency, which can provide information about errors in the underlying model and/or errors in the specification of forcings. It is argued that the true bias tendency, not the total bias tendency, should be used to adjust decadal forecasts. The methods developed are applied to decadal hindcasts of global mean temperature made using the Hadley Centre Coupled Model, version 3 (HadCM3), climate model, and it is found that this model exhibits a small positive bias tendency in the ensemble mean. When considering different model versions, it is shown that the true bias tendency is very highly correlated with both the transient climate response (TCR) and non–greenhouse gas forcing trends, and can therefore be used to obtain observationally constrained estimates of these relevant physical quantities.
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Troccoli, Alberto, and T. N. Palmer. "Ensemble decadal predictions from analysed initial conditions." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, no. 1857 (June 14, 2007): 2179–91. http://dx.doi.org/10.1098/rsta.2007.2079.
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Sensitivity experiments using a coupled model initialized from analysed atmospheric and oceanic observations are used to investigate the potential for interannual-to-decadal predictability. The potential for extending seasonal predictions to longer time scales is explored using the same coupled model configuration and initialization procedure as used for seasonal prediction. It is found that, despite model drift, climatic signals on interannual-to-decadal time scales appear to be detectable. Two climatic states have been chosen: one starting in 1965, i.e. ahead of a period of global cooling, and the other in 1994, ahead of a period of global warming. The impact of initial conditions and of the different levels of greenhouse gases are isolated in order to gain insights into the source of predictability.
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Polkova, Iuliia, Armin Köhl, and Detlef Stammer. "Climate-mode initialization for decadal climate predictions." Climate Dynamics 53, no. 11 (September 16, 2019): 7097–111. http://dx.doi.org/10.1007/s00382-019-04975-y.
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Abstract In the context of decadal climate predictions, a climate-mode initialization method is being tested by which ocean ORAS4 reanalysis is projected onto dominant modes of variability of the Earth System Model from the Max Planck Institute for Meteorology (MPI-ESM). The method aims to improve the prediction skill of the model by filtering out dynamically unbalanced noise during the initialization step. Used climate modes are calculated as statistical 3-D modes based on the bivariate empirical orthogonal function (EOF) analysis applied to temperature and salinity anomalies from an ensemble of historical simulations from the MPI-ESM. The climate-mode initialization method shows improved surface temperature skill, particularly over the tropical Pacific Ocean at seasonal-to-interannual timescales associated with the improved zonal momentum balance. There, the new initialization somewhat outperforms the surface temperature skill of the anomaly initialization also for lead years 2–5. In other parts of the world ocean, both initialization methods currently are equivalent in skill. However, only 44% of variance in the original ORAS4 reconstruction remains after the projection on model modes, suggesting that the ORAS4 modes are not fully compatible with the model modes. Moreover, we cannot dismiss the possibility that model modes are not sufficiently sampled with the data set underlying the EOF analysis. The full potential of the climate-mode initialization method for future decadal prediction systems therefore still needs to be quantified based on improved modal representation.
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Pham, Trang Van, Jennifer Brauch, Barbara Früh, and Bodo Ahrens. "Added decadal prediction skill with the coupled regional climate model COSMO-CLM/NEMO." Meteorologische Zeitschrift 27, no. 5 (December 7, 2018): 391–99. http://dx.doi.org/10.1127/metz/2018/0872.
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Stolzenberger, Sophie, Rita Glowienka-Hense, Thomas Spangehl, Marc Schröder, Alex Mazurkiewicz, and Andreas Hense. "Revealing skill of the MiKlip decadal prediction system by three-dimensional probabilistic evaluation." Meteorologische Zeitschrift 25, no. 6 (December 21, 2016): 657–71. http://dx.doi.org/10.1127/metz/2015/0606.
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Hoerling, Martin, James Hurrell, Arun Kumar, Laurent Terray, Jon Eischeid, Philip Pegion, Tao Zhang, Xiaowei Quan, and TaiYi Xu. "On North American Decadal Climate for 2011–20." Journal of Climate 24, no. 16 (August 15, 2011): 4519–28. http://dx.doi.org/10.1175/2011jcli4137.1.
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Abstract The predictability of North American climate is diagnosed by taking into account both forced climate change and natural decadal-scale climate variability over the next decade. In particular, the “signal” in North American surface air temperature and precipitation over 2011–20 associated with the expected change in boundary conditions related to future anthropogenic greenhouse gas (GHG) forcing, as well as the “noise” around that signal due to internally generated ocean–atmosphere variability, is estimated. The structural uncertainty in the estimate of decadal predictability is diagnosed by examining the sensitivity to plausible scenarios for the GHG-induced change in boundary forcing, the model dependency of the forced signals, and the dependency on methods for estimating internal decadal noise. The signal-to-noise analysis by the authors is thus different from other published decadal prediction studies, in that this study does not follow a trajectory from a particular initial state but rather considers the statistics of internal variability in comparison with the GHG signal. The 2011–20 decadal signal is characterized by surface warming over the entire North American continent, precipitation decreases over the contiguous United States, and precipitation increases over Canada relative to 1971–2000 climatological conditions. The signs of these forced responses are robust across different sea surface temperature (SST) scenarios and the different models employed, though the amplitude of the response differs. The North American decadal noise is considerably smaller than the signal associated with boundary forcing, implying a potential for high forecast skill for 2011–20 North American climate even for prediction methods that do not attempt to initialize climate models. However, the results do suggest that initialized decadal predictions, which seek to forecast externally forced signals and also constrain the internal variability, could potentially improve upon uninitialized methods in regions where the external signal is small relative to internal variability.
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Hermanson, Leon, Doug Smith, Melissa Seabrook, Roberto Bilbao, Francisco Doblas-Reyes, Etienne Tourigny, Vladimir Lapin, et al. "WMO Global Annual to Decadal Climate Update: A Prediction for 2021–25." Bulletin of the American Meteorological Society 103, no. 4 (April 2022): E1117—E1129. http://dx.doi.org/10.1175/bams-d-20-0311.1.
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Abstract 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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Boer, George J., and Reinel Sospedra-Alfonso. "Assessing the skill of the Pacific Decadal Oscillation (PDO) in a decadal prediction experiment." Climate Dynamics 53, no. 9-10 (July 22, 2019): 5763–75. http://dx.doi.org/10.1007/s00382-019-04896-w.
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Strobach, Ehud, and Golan Bel. "Decadal Climate Predictions Using Sequential Learning Algorithms." Journal of Climate 29, no. 10 (May 6, 2016): 3787–809. http://dx.doi.org/10.1175/jcli-d-15-0648.1.
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Abstract Ensembles of climate models are commonly used to improve decadal climate predictions and assess the uncertainties associated with them. Weighting the models according to their performances holds the promise of further improving their predictions. Here, an ensemble of decadal climate predictions is used to demonstrate the ability of sequential learning algorithms (SLAs) to reduce the forecast errors and reduce the uncertainties. Three different SLAs are considered, and their performances are compared with those of an equally weighted ensemble, a linear regression, and the climatology. Predictions of four different variables—the surface temperature, the zonal and meridional wind, and pressure—are considered. The spatial distributions of the performances are presented, and the statistical significance of the improvements achieved by the SLAs is tested. The reliability of the SLAs is also tested, and the advantages and limitations of the different measures of the performance are discussed. It was found that the best performances of the SLAs are achieved when the learning period is comparable to the prediction period. The spatial distribution of the SLAs performance showed that they are skillful and better than the other forecasting methods over large continuous regions. This finding suggests that, despite the fact that each of the ensemble models is not skillful, they were able to capture some physical processes that resulted in deviations from the climatology and that the SLAs enabled the extraction of this additional information.
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Rubinetti, Sara, Carla Taricco, Silvia Alessio, Angelo Rubino, Ilaria Bizzarri, and Davide Zanchettin. "Robust Decadal Hydroclimate Predictions for Northern Italy Based on a Twofold Statistical Approach." Atmosphere 11, no. 6 (June 25, 2020): 671. http://dx.doi.org/10.3390/atmos11060671.
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The Mediterranean area belongs to the regions most exposed to hydroclimatic changes, with a likely increase in frequency and duration of droughts in the last decades. However, many climate records like, e.g., North Italian precipitation and river discharge records, indicate that significant decadal variability is often superposed or even dominates long-term hydrological trends. The capability to accurately predict such decadal changes is, therefore, of utmost environmental and social importance. Here, we present a twofold decadal forecast of Po River (Northern Italy) discharge obtained with a statistical approach consisting of the separate application and cross-validation of autoregressive models and neural networks. Both methods are applied to each significant variability component extracted from the raw discharge time series using Singular Spectrum Analysis, and the final forecast is obtained by merging the predictions of the individual components. The obtained 25-year forecasts robustly indicate a prominent dry period in the late 2020s/early 2030s. Our prediction provides information of great value for hydrological management, and a target for current and future near-term numerical hydrological predictions.
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Balmaseda, Magdalena A., Michael K. Davey, and David L. T. Anderson. "Decadal and Seasonal Dependence of ENSO Prediction Skill." Journal of Climate 8, no. 11 (November 1995): 2705–15. http://dx.doi.org/10.1175/1520-0442(1995)008<2705:dasdoe>2.0.co;2.
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Murphy, J., V. Kattsov, N. Keenlyside, M. Kimoto, G. Meehl, V. Mehta, H. Pohlmann, A. Scaife, and D. Smith. "Towards Prediction of Decadal Climate Variability and Change." Procedia Environmental Sciences 1 (2010): 287–304. http://dx.doi.org/10.1016/j.proenv.2010.09.018.
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DelSole, Timothy. "Decadal Prediction of Temperature: Achievements and Future Prospects." Current Climate Change Reports 3, no. 2 (May 11, 2017): 99–111. http://dx.doi.org/10.1007/s40641-017-0066-x.
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Mehta, Vikram, Gerald Meehl, Lisa Goddard, Jeff Knight, Arun Kumar, Mojib Latif, Tong Lee, Anthony Rosati, and Detlef Stammer. "Decadal Climate Predictability and Prediction: Where Are We?" Bulletin of the American Meteorological Society 92, no. 5 (May 1, 2011): 637–40. http://dx.doi.org/10.1175/2010bams3025.1.
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Redolat, Dario, Robert Monjo, Cesar Paradinas, Javier Pórtoles, Emma Gaitán, Carlos Prado‐Lopez, and Jaime Ribalaygua. "Local decadal prediction according to statistical/dynamical approaches." International Journal of Climatology 40, no. 13 (March 20, 2020): 5671–87. http://dx.doi.org/10.1002/joc.6543.
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