scholarly journals Decadal Climate Prediction: An Update from the Trenches

2014 ◽  
Vol 95 (2) ◽  
pp. 243-267 ◽  
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.

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

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

<p>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.<br><br>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.<br><br>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.</p>

scholarly journals The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

2016 ◽  
Vol 9 (10) ◽  
pp. 3751-3777 ◽  
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.

scholarly journals Improved decadal climate prediction in the North Atlantic using EnOI-assimilated initial condition

2017 ◽  
Vol 62 (16) ◽  
pp. 1142-1147 ◽  
Author(s):  
Min Wei ◽  
Qingquan Li ◽  
Xiaoge Xin ◽  
Wei Zhou ◽  
Zhenyu Han ◽  
...  
Keyword(s):  
North Atlantic ◽  
Climate Prediction ◽  
Initial Condition ◽  
The North ◽  
The North Atlantic ◽  
Decadal Climate ◽  
Decadal Climate Prediction

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

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

<p>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.</p><p>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.</p><p>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.</p><p>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.</p>

scholarly journals Assessment of a full-field initialised decadal climate prediction system with the CMIP6 version of EC-Earth

2020 ◽  
Author(s):  
Roberto Bilbao ◽  
Simon Wild ◽  
Pablo Ortega ◽  
Juan Acosta-Navarro ◽  
Thomas Arsouze ◽  
...  
Keyword(s):  
Surface Temperature ◽  
North Atlantic ◽  
Climate Prediction ◽  
Tropical Pacific ◽  
Labrador Sea ◽  
Prediction System ◽  
Full Field ◽  
Predictive Skill ◽  
Decadal Climate ◽  
Decadal Climate Prediction

Abstract. In this paper we present and evaluate the skill of the EC-Earth3.3 decadal prediction system contributing to the Decadal Climate Prediction Project - Component A (DCPP-A). This prediction system is capable of skilfully simulating past global mean surface temperature variations at interannual and decadal forecast times as well as the local surface temperature in regions such as the Tropical Atlantic, the Indian Ocean and most of the continental areas, although most of the skill comes from the representation of the externally forced trends. A benefit of initialisation in the predictive skill is evident in some areas of the Tropical Pacific and North Atlantic Oceans in the first forecast years, an added value that gets mostly confined to the south-east Tropical Pacific and the eastern Subpolar North Atlantic at the longest forecast times (6–10 years). The central Subpolar North Atlantic shows poor predictive skill and a detrimental effect of the initialisation due to the occurrence of an initialisation shock, itself related to a collapse in Labrador Sea convection by the third forecast year that leads to a rapid weakening of the Atlantic Meridional Overturning Circulation (AMOC) and excessive local sea ice growth. The shutdown in Labrador Sea convection responds to a gradual increase in the local density stratification in the first years of the forecast, ultimately related to the different paces at which surface and subsurface temperature and salinity drift towards their preferred mean state. This transition happens rapidly in the surface and more slowly in the subsurface, where, by the tenth forecast year, the model is still far from the typical mean states in the corresponding ensemble of historical simulations with EC-Earth3. Our study thus highlights the importance of the Labrador Sea for initialisation, the relevance of reducing model bias by model tuning or, preferably, model improvement when using full-field initialisation, and the need to identify optimal initialisation strategies.

Exploring North Atlantic and North Pacific Decadal Climate Prediction Using Self-Organizing Maps

2020 ◽  
Author(s):  
Qinxue Gu ◽  
Melissa Gervais
Keyword(s):  
North Atlantic ◽  
North Pacific ◽  
Internal Variability ◽  
Climate Prediction ◽  
Self Organizing Maps ◽  
The North ◽  
Decadal Climate ◽  
The North Pacific ◽  
Decadal Climate Prediction ◽  
Self Organizing

<p>Decadal 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 with the ultimate goal of improving decadal climate prediction. SOM is applied to 11-year running mean winter Sea Surface Temperature (SST) in the North Pacific and North Atlantic within the Community Earth System Model 1850 pre-industrial 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 and lagged transition probabilities are conducted.</p>

scholarly journals Assessment of a full-field initialized decadal climate prediction system with the CMIP6 version of EC-Earth

2021 ◽  
Vol 12 (1) ◽  
pp. 173-196
Author(s):  
Roberto Bilbao ◽  
Simon Wild ◽  
Pablo Ortega ◽  
Juan Acosta-Navarro ◽  
Thomas Arsouze ◽  
...  
Keyword(s):  
Surface Temperature ◽  
North Atlantic ◽  
Climate Prediction ◽  
Tropical Pacific ◽  
Labrador Sea ◽  
Prediction System ◽  
Full Field ◽  
Predictive Skill ◽  
Decadal Climate ◽  
Decadal Climate Prediction

Abstract. In this paper, we present and evaluate the skill of an EC-Earth3.3 decadal prediction system contributing to the Decadal Climate Prediction Project – Component A (DCPP-A). This prediction system is capable of skilfully simulating past global mean surface temperature variations at interannual and decadal forecast times as well as the local surface temperature in regions such as the tropical Atlantic, the Indian Ocean and most of the continental areas, although most of the skill comes from the representation of the external radiative forcings. A benefit of initialization in the predictive skill is evident in some areas of the tropical Pacific and North Atlantic oceans in the first forecast years, an added value that is mostly confined to the south-east tropical Pacific and the eastern subpolar North Atlantic at the longest forecast times (6–10 years). The central subpolar North Atlantic shows poor predictive skill and a detrimental effect of initialization that leads to a quick collapse in Labrador Sea convection, followed by a weakening of the Atlantic Meridional Overturning Circulation (AMOC) and excessive local sea ice growth. The shutdown in Labrador Sea convection responds to a gradual increase in the local density stratification in the first years of the forecast, ultimately related to the different paces at which surface and subsurface temperature and salinity drift towards their preferred mean state. This transition happens rapidly at the surface and more slowly in the subsurface, where, by the 10th forecast year, the model is still far from the typical mean states in the corresponding ensemble of historical simulations with EC-Earth3. Thus, our study highlights the Labrador Sea as a region that can be sensitive to full-field initialization and hamper the final prediction skill, a problem that can be alleviated by improving the regional model biases through model development and by identifying more optimal initialization strategies.

scholarly journals The Decadal Climate Prediction Project

2016 ◽  
Author(s):  
George J. Boer ◽  
Douglas M . Smith ◽  
Christophe Cassou ◽  
Francisco Doblas-Reyes ◽  
Gokhan Danabasoglu ◽  
...  
Keyword(s):  
Decadal Variability ◽  
Climate Prediction ◽  
Decadal Prediction ◽  
Science And Society ◽  
Climate Data ◽  
Climate Shifts ◽  
Component C ◽  
Decadal Climate ◽  
Decadal Climate Prediction ◽  
Ensemble Generation

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.

scholarly journals Large-Scale Controls on Atlantic Tropical Cyclone Activity on Seasonal Time Scales

2016 ◽  
Vol 29 (18) ◽  
pp. 6727-6749 ◽  
Author(s):  
Young-Kwon Lim ◽  
Siegfried D. Schubert ◽  
Oreste Reale ◽  
Andrea M. Molod ◽  
Max J. Suarez ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
North Atlantic ◽  
General Circulation ◽  
Large Scale ◽  
Southern Oscillation ◽  
Vertical Wind Shear ◽  
Circulation Model ◽  
The North ◽  
Predictive Skill ◽  
The North Atlantic

Abstract Interannual variations in seasonal tropical cyclone (TC) activity (e.g., genesis frequency and location, track pattern, and landfall) over the Atlantic are explored by employing observationally constrained simulations with the NASA Goddard Earth Observing System, version 5 (GEOS-5), atmospheric general circulation model. The climate modes investigated are El Niño–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), and the Atlantic meridional mode (AMM). The results show that the NAO and AMM can strongly modify and even oppose the well-known ENSO impacts, like in 2005, when a strong positive AMM (associated with warm SSTs and a negative SLP anomaly over the western tropical Atlantic) led to a very active TC season with enhanced TC genesis over the Caribbean Sea and a number of landfalls over North America, under a neutral ENSO condition. On the other end, the weak TC activity during 2013 (characterized by weak negative Niño index) appears caused by a NAO-induced positive SLP anomaly with enhanced vertical wind shear over the tropical North Atlantic. During 2010, the combined impact of the three modes produced positive SST anomalies across the entire low-latitudinal Atlantic and a weaker subtropical high, leading to more early recurvers and thus fewer landfalls despite enhanced TC genesis. The study provides evidence that TC number and track are very sensitive to the relative phases and intensities of these three modes and not just to ENSO alone. Examination of seasonal predictability reveals that the predictive skill of the three modes is limited over tropics to subtropics, with the AMM having the highest predictability over the North Atlantic, followed by ENSO and NAO.

Trends, Variability and Predictive Skill of the Ocean Heat Content in North Atlantic: An Analysis with the EC-Earth3 Model

2021 ◽  
Author(s):  
Teresa Carmo-Costa ◽  
Roberto Bilbao ◽  
Pablo Ortega ◽  
Ana Teles-Machado ◽  
Emanuel Dutra
Keyword(s):  
North Atlantic ◽  
Climate Model ◽  
Decadal Variability ◽  
Heat Content ◽  
Internal Variability ◽  
Ocean Heat Content ◽  
Internal Climate Variability ◽  
The North ◽  
Predictive Skill ◽  
Modeling Uncertainties

Abstract This study investigates trends, variability and predictive skill of the upper ocean heat content (OHC) in the North Atlantic basin. This is a region where strong decadal variability superimposes the externally forced trends, introducing important differences in the local warming rates, and leading in the case of the Central Subpolar North Atlantic to an overall long-term cooling. Our analysis aims to better understand these regional differences, by investigating how internal and forced variability contribute to local trends, exploring also their role on the local prediction skill. The analysis combines the study of three ocean reanalyses to document the uncertainties related to observations, with two sets of CMIP6 experiments performed with the global coupled climate model EC-Earth3: a historical ensemble to characterise the forced signals; and a retrospective decadal prediction system, to additionally characterise the contributions from internal climate variability. Our results show that internal variability is essential to understand the spatial pattern of North Atlantic OHC trends, contributing decisively to the local trends and providing high levels of predictive skill in the Eastern Subpolar North Atlantic and the Irminger and Iceland Seas, and to a lesser extent in the Labrador Sea. Skill and trends in other areas like the Subtropical North Atlantic, or the Gulf Stream Extension are mostly externally forced. Large observational and modeling uncertainties affect the trends and interannual variability in the Central Subpolar North Atlantic, the only region exhibiting a cooling during the study period, uncertainties that might explain the very poor local predictive skill.

Sign in / Sign up

Export Citation Format

Share Document