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

2013 ◽  
Vol 41 (11-12) ◽  
pp. 3325-3338 ◽  
Author(s):  
Doug M. Smith ◽  
Rosie Eade ◽  
Holger Pohlmann
Keyword(s):  
Climate Prediction ◽  
Full Field ◽  
Decadal Climate ◽  
Decadal Climate Prediction ◽  
Anomaly Initialization

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.

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.

Developing prototype decadal climate prediction services

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

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

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

2019 ◽  
Vol 32 (18) ◽  
pp. 5967-5995 ◽  
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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