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Journal articles on the topic 'Decadal climate prediction'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Haines, Keith, Leon Hermanson, Chunlei Liu, et al. "Decadal climate prediction (project GCEP)." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 367, no. 1890 (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 instit

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2

Keenlyside, Noel S., and Jin Ba. "Prospects for decadal climate prediction." Wiley Interdisciplinary Reviews: Climate Change 1, no. 5 (2010): 627–35. http://dx.doi.org/10.1002/wcc.69.

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Meehl, Gerald A., Lisa Goddard, George Boer, et al. "Decadal Climate Prediction: An Update from the Trenches." Bulletin of the American Meteorological Society 95, no. 2 (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 sk

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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 (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 appr

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Polkova, Iuliia, Armin Köhl, and Detlef Stammer. "Climate-mode initialization for decadal climate predictions." Climate Dynamics 53, no. 11 (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

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6

Boer, George J., Douglas M. Smith, Christophe Cassou, et al. "The Decadal Climate Prediction Project (DCPP) contribution to CMIP6." Geoscientific Model Development 9, no. 10 (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 a

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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 (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 fro

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Dunstone, Nick, Julia Lockwood, Balakrishnan Solaraju-Murali, et al. "Towards Useful Decadal Climate Services." Bulletin of the American Meteorological Society 103, no. 7 (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 s

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Hurrell, James. "Decadal climate prediction: Challenges and opportunities." IOP Conference Series: Earth and Environmental Science 6, no. 2 (2009): 022001. http://dx.doi.org/10.1088/1755-1307/6/2/022001.

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10

Knight, Jeff R., Martin B. Andrews, Doug M. Smith, et al. "Predictions of Climate Several Years Ahead Using an Improved Decadal Prediction System." Journal of Climate 27, no. 20 (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, wh

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Marotzke, Jochem, Wolfgang A. Müller, Freja S. E. Vamborg, et al. "MiKlip: A National Research Project on Decadal Climate Prediction." Bulletin of the American Meteorological Society 97, no. 12 (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; t

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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 (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

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13

Krueger, Oliver, and Jin-Song Von Storch. "A Simple Empirical Model for Decadal Climate Prediction." Journal of Climate 24, no. 4 (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 usin

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Mahmood, Rashed, Markus G. Donat, Pablo Ortega, et al. "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 (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

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Hermanson, Leon, Doug Smith, Melissa Seabrook, et al. "WMO Global Annual to Decadal Climate Update: A Prediction for 2021–25." Bulletin of the American Meteorological Society 103, no. 4 (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

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16

Strobach, Ehud, and Golan Bel. "Decadal Climate Predictions Using Sequential Learning Algorithms." Journal of Climate 29, no. 10 (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.

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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 (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

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18

Bilbao, Roberto, Pablo Ortega, Didier Swingedouw, et al. "Impact of volcanic eruptions on CMIP6 decadal predictions: a multi-model analysis." Earth System Dynamics 15, no. 2 (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

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19

Hoerling, Martin, James Hurrell, Arun Kumar, et al. "On North American Decadal Climate for 2011–20." Journal of Climate 24, no. 16 (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 diagnos

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20

Gaetani, Marco, and Elsa Mohino. "Decadal Prediction of the Sahelian Precipitation in CMIP5 Simulations." Journal of Climate 26, no. 19 (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 d

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21

Nicolì, Dario, Alessio Bellucci, Paolo Ruggieri, et al. "The Euro-Mediterranean Center on Climate Change (CMCC) decadal prediction system." Geoscientific Model Development 16, no. 1 (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-C

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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 (2018): 391–99. http://dx.doi.org/10.1127/metz/2018/0872.

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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 (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. Contribut

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Murphy, J., V. Kattsov, N. Keenlyside, et al. "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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Mehta, Vikram, Gerald Meehl, Lisa Goddard, et al. "Decadal Climate Predictability and Prediction: Where Are We?" Bulletin of the American Meteorological Society 92, no. 5 (2011): 637–40. http://dx.doi.org/10.1175/2010bams3025.1.

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Robson, Jon, Rowan Sutton, and Doug Smith. "Predictable Climate Impacts of the Decadal Changes in the Ocean in the 1990s." Journal of Climate 26, no. 17 (2013): 6329–39. http://dx.doi.org/10.1175/jcli-d-12-00827.1.

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Abstract During the 1990s there was a major change in the state of the world's oceans. In particular, the North Atlantic underwent a rapid warming, with sea surface temperatures (SSTs) in the subpolar gyre region increasing by 1°C in just a few years. Associated with the changes in SST patterns were changes in the surface climate, in particular, a tendency for warm and dry conditions over areas of North America in all seasons, and warm springs and wet summers over areas of Europe. Here, the extent to which a climate prediction system initialized using observations of the ocean state is able to

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Yasunaka, Sayaka, Masayoshi Ishii, Masahide Kimoto, Takashi Mochizuki, and Hideo Shiogama. "Influence of XBT Temperature Bias on Decadal Climate Prediction with a Coupled Climate Model." Journal of Climate 24, no. 20 (2011): 5303–8. http://dx.doi.org/10.1175/2011jcli4230.1.

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Abstract The influence of the expendable bathythermograph (XBT) depth bias correction on decadal climate prediction is presented by using a coupled atmosphere–ocean general circulation model called the Model for Interdisciplinary Research on Climate 3 (MIROC3). The global mean subsurface ocean temperatures that were simulated by the model with the prescribed anthropogenic and natural forcing are consistent with bias-corrected observations from the mid-1960s onward, but not with uncorrected observations. The latter is reflected by biases in subsurface ocean temperatures, particularly along ther

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Kadow, Christopher, Sebastian Illing, Oliver Kunst, et al. "Evaluation of forecasts by accuracy and spread in the MiKlip decadal climate prediction system." Meteorologische Zeitschrift 25, no. 6 (2016): 631–43. http://dx.doi.org/10.1127/metz/2015/0639.

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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 (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 Plan

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Merryfield, William J., Johanna Baehr, Lauriane Batté, et al. "Subseasonal to Decadal Prediction: Filling the Weather–Climate Gap." Bulletin of the American Meteorological Society 101, no. 9 (2020): 767–70. http://dx.doi.org/10.1175/bams-d-19-0037.a.

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Merryfield, William J., Johanna Baehr, Lauriane Batté, et al. "Subseasonal to Decadal Prediction: Filling the Weather–Climate Gap." Bulletin of the American Meteorological Society 101, no. 9 (2020): 767–70. http://dx.doi.org/10.1175/bams-d-19-0037.a.

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Hunt, A. G., and A. A. Tsonis. "The Pacific decadal oscillation and long-term climate prediction." Eos, Transactions American Geophysical Union 81, no. 48 (2000): 581. http://dx.doi.org/10.1029/eo081i048p00581.

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Guemas, Virginie, Javier García-Serrano, Annarita Mariotti, Francisco Doblas-Reyes, and Louis-Philippe Caron. "Prospects for decadal climate prediction in the Mediterranean region." Quarterly Journal of the Royal Meteorological Society 141, no. 687 (2014): 580–97. http://dx.doi.org/10.1002/qj.2379.

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Kröger, Jürgen, Wolfgang A. Müller, and Jin-Song von Storch. "Impact of different ocean reanalyses on decadal climate prediction." Climate Dynamics 39, no. 3-4 (2012): 795–810. http://dx.doi.org/10.1007/s00382-012-1310-7.

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Volpi, Danila, Virginie Guemas, Francisco J. Doblas-Reyes, Ed Hawkins, and Nancy K. Nichols. "Decadal climate prediction with a refined anomaly initialisation approach." Climate Dynamics 48, no. 5-6 (2016): 1841–53. http://dx.doi.org/10.1007/s00382-016-3176-6.

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Monerie, Paul-Arthur, Laure Coquart, Éric Maisonnave, Marie-Pierre Moine, Laurent Terray, and Sophie Valcke. "Decadal prediction skill using a high-resolution climate model." Climate Dynamics 49, no. 9-10 (2017): 3527–50. http://dx.doi.org/10.1007/s00382-017-3528-x.

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Nobre, P., J. A. Marengo, I. F. A. Cavalcanti, et al. "Seasonal-to-Decadal Predictability and Prediction of South American Climate." Journal of Climate 19, no. 23 (2006): 5988–6004. http://dx.doi.org/10.1175/jcli3946.1.

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Abstract The dynamical basis for seasonal to decadal climate predictions and predictability over South America is reviewed. It is shown that, while global tropical SSTs affect both predictability and predictions over South America, the current lack of SST predictability over the tropical Atlantic represents a limiting factor to seasonal climate predictions over some parts of the continent. The model’s skill varies with the continental region: the highest skill is found in the “Nordeste” region and the lowest skill over southeastern Brazil. It is also suggested that current two-tier approaches

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Sospedra-Alfonso, Reinel, William J. Merryfield, George J. Boer, et al. "Decadal climate predictions with the Canadian Earth System Model version 5 (CanESM5)." Geoscientific Model Development 14, no. 11 (2021): 6863–91. http://dx.doi.org/10.5194/gmd-14-6863-2021.

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Abstract. The Canadian Earth System Model version 5 (CanESM5) developed at Environment and Climate Change Canada's Canadian Centre for Climate Modelling and Analysis (CCCma) is participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). A 40-member ensemble of CanESM5 retrospective decadal forecasts (or hindcasts) is integrated for 10 years from realistic initial states once a year during 1961 to the present using prescribed external forcing. The results are part of CCCma's contribution to the Decadal Climate Prediction Project (DCPP) component of CMIP6. This paper evaluates

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Illing, Sebastian, Christopher Kadow, Holger Pohlmann, and Claudia Timmreck. "Assessing the impact of a future volcanic eruption on decadal predictions." Earth System Dynamics 9, no. 2 (2018): 701–15. http://dx.doi.org/10.5194/esd-9-701-2018.

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Abstract. The likelihood of a large volcanic eruption in the future provides the largest uncertainty concerning the evolution of the climate system on the timescale of a few years, but also an excellent opportunity to learn about the behavior of the climate system, and our models thereof. So the following question emerges: how predictable is the response of the climate system to future eruptions? By this we mean to what extent will the volcanic perturbation affect decadal climate predictions and how does the pre-eruption climate state influence the impact of the volcanic signal on the predicti

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Mochizuki, Takashi, Masayoshi Ishii, Masahide Kimoto, et al. "Pacific decadal oscillation hindcasts relevant to near-term climate prediction." Proceedings of the National Academy of Sciences 107, no. 5 (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

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Shiogama, Hideo, Seita Emori, Takashi Mochizuki, et al. "Possible Influence of Volcanic Activity on the Decadal Potential Predictability of the Natural Variability in Near-Term Climate Predictions." Advances in Meteorology 2010 (2010): 1–7. http://dx.doi.org/10.1155/2010/657318.

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Initialization based on data assimilations using historical observations possibly improves near-term climate predictions. Significant volcanic activity in the future is unpredictable and not assumed in future climate predictions. To examine the possible influence of unpredictable future volcanic activity on the decadal potential predictability of the natural variability, we performed a 2006–2035 climate prediction experiment with the assumption that the 1991 Mt. Pinatubo eruption would take place again in 2010. The Pinatubo forcing induced not only significant cooling responses but also consid

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Meehl, Gerald A., Aixue Hu, and Claudia Tebaldi. "Decadal Prediction in the Pacific Region." Journal of Climate 23, no. 11 (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 patte

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Weimer, Michael, Sebastian Mieruch, Gerd Schädler, and Christoph Kottmeier. "A new estimator of heat periods for decadal climate predictions – a complex network approach." Nonlinear Processes in Geophysics 23, no. 4 (2016): 307–17. http://dx.doi.org/10.5194/npg-23-307-2016.

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Abstract. Regional decadal predictions have emerged in the past few years as a research field with high application potential, especially for extremes like heat and drought periods. However, up to now the prediction skill of decadal hindcasts, as evaluated with standard methods, is moderate and for extreme values even rarely investigated. In this study, we use hindcast data from a regional climate model (CCLM) for eight regions in Europe and quantify the skill of the model alternatively by constructing time-evolving climate networks and use the network correlation threshold (link strength) as

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Tan, Jing, Fei Zheng, Tingwei Cao, Yongyong Huang, and Haiyan Wang. "A New Perspective of the Spring Predictability Barrier Based on the Zonal Sea Level Pressure Gradient." Journal of Marine Science and Engineering 12, no. 9 (2024): 1463. http://dx.doi.org/10.3390/jmse12091463.

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Currently, the “spring predictability barrier” (SPB) is still a controversial problem in many atmosphere–ocean coupled models and has significant impacts on degrading the El Niño–Southern Oscillation (ENSO) predictions across the boreal spring. In this study, unlike previous studies that viewed the SPB issue from the perspective of sea surface temperature (SST), based on the Bjerknes feedback theory and the decadal variations in Walker circulation over the tropical Pacific, a new perspective of the SPB is revealed by the seasonal variations in the observed zonal sea level pressure (SLP) gradie

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Weimer, M., S. Mieruch, G. Schädler, and C. Kottmeier. "Predicting climate extremes – a complex network approach." Nonlinear Processes in Geophysics Discussions 2, no. 5 (2015): 1481–505. http://dx.doi.org/10.5194/npgd-2-1481-2015.

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Abstract. Regional decadal predictions have emerged in the past few years as a research field with high application potential, especially for extremes like heat and drought periods. However, up to now the prediction skill of decadal hindcasts, as evaluated with standard methods is moderate, and for extreme values even rarely investigated. In this study, we use hindcast data from a regional climate model (CCLM) for 8 regions in Europe to construct time evolving climate networks and use the network correlation threshold (link strength) as a predictor for heat periods. We show that the skill of t

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Cassou, Christophe, Yochanan Kushnir, Ed Hawkins, et al. "Decadal Climate Variability and Predictability: Challenges and Opportunities." Bulletin of the American Meteorological Society 99, no. 3 (2018): 479–90. http://dx.doi.org/10.1175/bams-d-16-0286.1.

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Abstract The study of Decadal Climate Variability (DCV) and Predictability is the interdisciplinary endeavor to characterize, understand, attribute, simulate, and predict the slow, multiyear variations of climate at global (e.g., the recent slowdown of global mean temperature rise in the early 2000s) and regional (e.g., decadal modulation of hurricane activity in the Atlantic, ongoing drought in California or in the Sahel in the 1970s–80s, etc.) scales. This study remains very challenging despite decades of research, extensive progress in climate system modeling, and improvements in the availa

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Yu, Hang, Junbo Lei, Pengfei Lin, et al. "Improved Pacific Decadal Oscillation Prediction by an Optimizing Model Combined Bidirectional Long Short-Term Memory and Multiple Modal Decomposition." Remote Sensing 17, no. 15 (2025): 2537. https://doi.org/10.3390/rs17152537.

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The Pacific Decadal Oscillation (PDO), as the dominant mode of decadal sea surface temperature variability in the North Pacific, exhibits both interannual and decadal fluctuations that significantly influence global climate. The complexity associated with PDO changes poses challenges for accurate predictions. This study develops a BiLSTM-WOA-MMD (BWM) model, which integrates a bidirectional long short-term memory network with a whale optimization algorithm (WOA) and multiple modal decomposition (MMD), to forecast PDO at both interannual and decadal time scales. The model successfully predicts

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Dewitte, Steven, Jan P. Cornelis, Richard Müller, and Adrian Munteanu. "Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction." Remote Sensing 13, no. 16 (2021): 3209. http://dx.doi.org/10.3390/rs13163209.

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Artificial Intelligence (AI) is an explosively growing field of computer technology, which is expected to transform many aspects of our society in a profound way. AI techniques are used to analyse large amounts of unstructured and heterogeneous data and discover and exploit complex and intricate relations among these data, without recourse to an explicit analytical treatment of those relations. These AI techniques are unavoidable to make sense of the rapidly increasing data deluge and to respond to the challenging new demands in Weather Forecast (WF), Climate Monitoring (CM) and Decadal Predic

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Keenlyside, N. S., M. Latif, J. Jungclaus, L. Kornblueh, and E. Roeckner. "Advancing decadal-scale climate prediction in the North Atlantic sector." Nature 453, no. 7191 (2008): 84–88. http://dx.doi.org/10.1038/nature06921.

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Choi, Jung, Seok-Woo Son, Yoo-Geun Ham, June-Yi Lee, and Hye-Mi Kim. "Seasonal-to-Interannual Prediction Skills of Near-Surface Air Temperature in the CMIP5 Decadal Hindcast Experiments." Journal of Climate 29, no. 4 (2016): 1511–27. http://dx.doi.org/10.1175/jcli-d-15-0182.1.

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Abstract This study explores the seasonal-to-interannual near-surface air temperature (TAS) prediction skills of state-of-the-art climate models that were involved in phase 5 of the Coupled Model Intercomparison Project (CMIP5) decadal hindcast/forecast experiments. The experiments are initialized in either November or January of each year and integrated for up to 10 years, providing a good opportunity for filling the gap between seasonal and decadal climate predictions. The long-lead multimodel ensemble (MME) prediction is evaluated for 1981–2007 in terms of the anomaly correlation coefficien

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