The Decadal Climate Prediction Project

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 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 part of CMIP6. 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, dissemination and analysis 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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The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

Geoscientific Model Development ◽

10.5194/gmd-9-3751-2016 ◽

2016 ◽

Vol 9 (10) ◽

pp. 3751-3777 ◽

Cited By ~ 105

Author(s):

George J. Boer ◽

Douglas M. Smith ◽

Christophe Cassou ◽

Francisco Doblas-Reyes ◽

Gokhan Danabasoglu ◽

...

Keyword(s):

Decadal Variability ◽

Coupled Model ◽

Climate Prediction ◽

Grand Challenge ◽

Decadal Prediction ◽

Climate Data ◽

Intercomparison Project ◽

Near Term ◽

Decadal Climate ◽

Decadal Climate Prediction

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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Predicting Climate Change over the multi-annual range: a perspective from CMCC Decadal Prediction System

10.5194/ems2021-150 ◽

2021 ◽

Author(s):

Dario Nicolì ◽

Alessio Bellucci ◽

Paolo Ruggieri ◽

Panos Athanasiadis ◽

Giusy Fedele ◽

...

Keyword(s):

Climate Change ◽

North Atlantic ◽

Decadal Variability ◽

Climate Prediction ◽

Added Value ◽

Prediction System ◽

Decadal Prediction ◽

Predictive Skill ◽

Annual Range ◽

Decadal Climate

After the early pioneering studies during the 2000s, and the first coordinated multi-model effort within the framework of the 5th Coupled Model Inter-comparison Project (CMIP5) in early 2010s, decadal climate predictions are now entering a more mature phase of their historical development. Near-term climate prediction activities have been recently endorsed by the World Climate Research Programme (WCRP) as one of the Grand Challenges in climate science research, and the Lead Centre for Annual-to-Decadal Climate Prediction, collecting hindcasts and forecasts from several contributing centres worldwide has been established by the WMO.Here we present results from the CMIP6 DCPP-A decadal hindcasts produced with the CMCC decadal prediction system (CMCC DPS), based on the fully-coupled CMCC-CM2-SR5 dynamical model. A 10-member suite of 10-year retrospective forecasts, initialized every year from 1960 to 2019, is performed using a full-field initialization strategy.The predictive skill for key quantities is assessed and compared with a non-initialized historical simulation, so as to verify the added value of initialization. In particular, the CMCC DPS is capable to skilfully reproduce past-climate surface temperature over the North Atlantic ocean, the Indian ocean and the Western Pacific ocean, as well as over most part of the continents. Beyond the contribution of the climate change, predictive skill emerges, among other regions, for the subpolar North Atlantic sea-surface temperatures, resembling the imprint of the extra-tropical part of the Atlantic Multidecadal Variability.In terms of precipitation, CMCC DPS is able to capture most of the decadal variability over the Northern part of the Eurasian continent. Indeed, a set of regional diagnostics is aimed to investigate the process at stake behind this high predictive skill.

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Decadal Climate Prediction: An Update from the Trenches

Bulletin of the American Meteorological Society ◽

10.1175/bams-d-12-00241.1 ◽

2014 ◽

Vol 95 (2) ◽

pp. 243-267 ◽

Cited By ~ 296

Author(s):

Gerald A. Meehl ◽

Lisa Goddard ◽

George Boer ◽

Robert Burgman ◽

Grant Branstator ◽

...

Keyword(s):

North Atlantic ◽

Decadal Variability ◽

Southern Oscillation ◽

Climate Prediction ◽

Western Pacific ◽

The North ◽

Predictive Skill ◽

Near Term ◽

Decadal Climate ◽

Decadal Climate Prediction

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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The regional MiKlip decadal forecast ensemble for Europe: the added value of downscaling

Geoscientific Model Development ◽

10.5194/gmd-7-2983-2014 ◽

2014 ◽

Vol 7 (6) ◽

pp. 2983-2999 ◽

Cited By ~ 13

Author(s):

S. Mieruch ◽

H. Feldmann ◽

G. Schädler ◽

C.-J. Lenz ◽

S. Kothe ◽

...

Keyword(s):

Decadal Variability ◽

Climate Prediction ◽

Skill Assessment ◽

Added Value ◽

Lead Times ◽

Data Set ◽

Regional Downscaling ◽

Decadal Climate ◽

Decadal Climate Prediction

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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A comparison of full-field and anomaly initialization for seasonal to decadal climate prediction

Climate Dynamics ◽

10.1007/s00382-013-1683-2 ◽

2013 ◽

Vol 41 (11-12) ◽

pp. 3325-3338 ◽

Cited By ~ 92

Author(s):

Doug M. Smith ◽

Rosie Eade ◽

Holger Pohlmann

Keyword(s):

Climate Prediction ◽

Full Field ◽

Decadal Climate ◽

Decadal Climate Prediction ◽

Anomaly Initialization

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Developing prototype decadal climate prediction services

10.5194/egusphere-egu21-5437 ◽

2021 ◽

Author(s):

Nick Dunstone ◽

Panos Athanasiadis ◽

Louis-Philippe Caron ◽

Francisco Doblas-Reyes ◽

Barbara Frueh ◽

...

Keyword(s):

Large Scale ◽

Rapid Development ◽

Coupled Model ◽

Climate Prediction ◽

Lessons Learned ◽

Decadal Climate Variability ◽

Climate Services ◽

Essential Information ◽

Decadal Climate ◽

Decadal Climate Prediction

Here we present an overview of results emerging from a project to develop prototype decadal climate prediction services, funded by the EU Copernicus Climate Change Service (C3S). The field of interannual to decadal climate prediction has matured rapidly over the last ~15 years, becoming an established part of the Coupled Model Intercomparison Project (CMIP) process with multi-model decadal climate predictions made in CMIP5 and CMIP6 (DCPP MIP). It has further been highlighted by the recent creation of the WMO Lead Centre for Annual-to-Decadal Climate Prediction. Whilst these activities have led to rapid development in our understanding of decadal climate predictability and mechanisms driving global and regional annual to decadal climate variability, the creation of useful climate services on this timescale is still in its infancy.This EU funded project was designed to start to address decadal climate services and brings together many of the key European institutions involved in decadal climate predictions from four different countries: Germany (DWD), Italy (CMCC), Spain (BSC) and the UK (Met Office). Each partner is working with a different sector: infrastructure, energy, agriculture and insurance where they have been developing a prototype decadal climate service in partnership with a user in that sector. Here we report on the progress made so far and highlight a number of key lessons learned along the way. These include the use of both large multi-model ensembles and more predictable large-scale circulation indicators in order to give skilful regional predictions of user relevant variables. We also describe the development of a common product format to present forecast information to users, this contains essential information about the current probabilistic forecast, retrospective forecast skill and reliability.

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A Drift-Free Decadal Climate Prediction System for the Community Earth System Model

Journal of Climate ◽

10.1175/jcli-d-18-0788.1 ◽

2019 ◽

Vol 32 (18) ◽

pp. 5967-5995 ◽

Cited By ~ 6

Author(s):

Yoshimitsu Chikamoto ◽

Axel Timmermann ◽

Matthew J. Widlansky ◽

Shaoqing Zhang ◽

Magdalena A. Balmaseda

Keyword(s):

Three Dimensional ◽

Climate Prediction ◽

System Model ◽

Prediction System ◽

Ocean Temperature ◽

Perfect Model ◽

Community Earth System Model ◽

Decadal Climate ◽

Decadal Climate Prediction

Abstract Performance of a newly developed decadal climate prediction system is examined using the low-resolution Community Earth System Model (CESM). To identify key sources of predictability and determine the role of upper and deeper ocean data assimilation, we first conduct a series of perfect model experiments. These experiments reveal the importance of upper ocean temperature and salinity assimilation in reducing sea surface temperature biases. However, to reduce biases in the sea surface height, data assimilation below 300 m in the ocean is necessary, in particular for high-latitude regions. The perfect model experiments clearly emphasize the key role of combined three-dimensional ocean temperature and salinity assimilation in reproducing mean state and model trajectories. Applying this knowledge to the realistic decadal climate prediction system, we conducted an ensemble of ocean assimilation simulations with the fully coupled CESM covering the period 1960–2014. In this system, we assimilate three-dimensional ocean temperature and salinity data into the ocean component of CESM. Instead of assimilating direct observations, we assimilate temperature and salinity anomalies obtained from the ECMWF Ocean Reanalysis version 4 (ORA-S4). Anomalies are calculated relative to the sum of the ORA-S4 climatology and an estimate of the externally forced signal. As a result of applying the balanced ocean conditions to the model, our hindcasts show only very little drift and initialization shocks. This new prediction system exhibits multiyear predictive skills for decadal climate variations of the Atlantic meridional overturning circulation (AMOC) and North Pacific decadal variability.

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The decadal climate prediction skill with focus on the North Atlantic region

10.5194/egusphere-egu2020-11243 ◽

2020 ◽

Author(s):

Shuting Yang ◽

Bo Christiansen

Keyword(s):

North Atlantic ◽

Large Degree ◽

Climate Prediction ◽

Prediction Skill ◽

Lead Times ◽

The North ◽

Ensemble Mean ◽

The North Atlantic ◽

Decadal Climate ◽

Decadal Climate Prediction

The skill of the decadal climate prediction is analyzed based on recent ensemble experiments from the CMIP5 and CMIP6 decadal climate prediction projects (DCPP) and the Community Earth System Model (CESM) Large Ensemble (LENS) Project. The experiments are initialized every year at November 1 for the period of 1960-2005 in the CMIP5 DCPP experiments and 1960-2016 for the CMIP6 DCPP models as well as the CESM LENS decadal prediction. The CMIP5/6 ensemble has 10 members for each model and the CESM ensemble has 40 members. For the considered models un-initialized (historical) ensembles with the same forcings exist. The advantage of initialization is analyzed by comparing these two sets of experiments. We find that the models agree that for lead-times between 4-10 years little effect of initialization is found except in the North Atlantic sub-polar gyre region (NASPG). This well-known result is found for all the models and is robust to temporal and spatial smoothing. In the sub-polar gyre region the ensemble mean of the forecast explains 30-40 % more of the observed variance than the ensemble mean of the historical non-initialized experiments even for lead-times of 10 years. However, the skill in the NASPG seems to a large degree to be related to the shift towards warmer temperatures around 1996. Weak or no skill is found when the sub-periods before and after 1996 are considered. We further analyze the characteristics of other climate indicators than surface temperature as well as the NAO to understand the cause and implication of the prediction skill.

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