scholarly journals Comparison of full field and anomaly initialisation for decadal climate prediction: towards an optimal consistency between the ocean and sea-ice anomaly initialisation state

2016 ◽  
Vol 49 (4) ◽  
pp. 1181-1195 ◽  
Author(s):  
Danila Volpi ◽  
Virginie Guemas ◽  
Francisco J. Doblas-Reyes
Keyword(s):  
Sea Ice ◽  
Climate Prediction ◽  
Full Field ◽  
Decadal Climate ◽  
Decadal Climate Prediction

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.

Prediction skill of Arctic sea ice in decadal climate simulations of the EC-Earth3 model

2020 ◽  
Author(s):  
Pasha Karami ◽  
Tim Kruschke ◽  
Tian Tian ◽  
Torben Koenigk ◽  
Shuting Yang
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Lower Troposphere ◽  
Climate Prediction ◽  
Arctic Sea Ice ◽  
The Arctic ◽  
Arctic Sea ◽  
The Mean ◽  
Decadal Climate ◽  
Decadal Climate Prediction

<p>Arctic sea ice variability and long-term trend play a major role in affecting the climate of polar and lower latitudes via complex coupling with the polar atmospheric circulation and the North Atlantic Ocean circulation. Moreover, sea ice conditions in the Arctic have direct impacts on socio-economy (e.g. the key shipping regions) and on the ecosystem. Understanding and improving predictions of Arctic sea ice on seasonal to decadal time scales is therefore crucial. <span>We investigate the skill of decadal climate prediction simulations of the EC-Earth3 model (T255L91, ORCA1L75) with a focus on Arctic sea ice. In line with the protocol for the CMIP6 Decadal Climate Prediction Project (DCPP), we launched 59 hindcasts/forecasts from 1960 to 2018. Each hindcast/forecast has 15 ensemble members which were initialized on 1 November and integrated for 10 years (+ 2 months). Anomaly initialization approach for the ocean and sea-ice (based on data from the ORA-S5-reanalysis) and full-field initialization for the atmosphere/land surface (based on ERA-Interim/ERA-Land) were applied. We first present a comparison of our hindcasts to observations for global key parameters and provide quantitative estimates of hindcast skill by using </span><span>common deterministic metrics such as</span><span> correlation and the Mean Squared Error Skill Score. We focus particularly on the skill regarding sea ice concentration and area in the Arctic’s sub-basins and its relation to the temperature and circulation of lower troposphere as well as the mean state of the ocean </span><span>outside the Arctic</span><span>. We </span><span>also </span><span>explore relevant processes and how the ocean state and natural climate variability can </span><span>a</span><span>ffect our prediction skills to improve the prediction system.</span></p>

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.

Sign in / Sign up

Export Citation Format

Share Document