scholarly journals Bias and Drift of the Medium-Range Decadal Climate Prediction System (MiKlip) validated by European Radiosonde Data

2016 ◽  
Vol 25 (6) ◽  
pp. 709-720 ◽  
Author(s):  
Margit Pattantyús-Ábrahám ◽  
Christopher Kadow ◽  
Sebastian Illing ◽  
Wolfgang A. Müller ◽  
Holger Pohlmann ◽  
...  
Keyword(s):  
Climate Prediction ◽  
Radiosonde Data ◽  
Prediction System ◽  
Medium Range ◽  
Decadal Climate ◽  
Decadal Climate Prediction

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.

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.

Multi-year predictability of Colorado River water supply using a drift-free decadal climate prediction system

2021 ◽  
Author(s):  
Yoshimitsu Chikamoto ◽  
Simon Wang ◽  
Matt Yost ◽  
Larissa Yocom ◽  
Robert Gillies
Keyword(s):  
Water Supply ◽  
River Basin ◽  
Colorado River ◽  
Climate Model ◽  
The United States ◽  
Climate Prediction ◽  
Colorado River Basin ◽  
Prediction System ◽  
Decadal Climate ◽  
Decadal Climate Prediction

<p>Skillful multi-year climate forecasts provide crucial information for decision-makers and resource managers to mitigate water scarcity. Yet, such forecasts remain challenging due to unpredictable weather noise and the lack of dynamical model capability. In this research, we demonstrate that the annual water supply of the Colorado River in the United States is predictable up to several years in advance by a drift-free decadal climate prediction system using a fully coupled climate model. Observational analyses and model experiments show that prolonged shortages of water supply in the Colorado River are significantly linked to sea surface temperature precursors, including tropical Pacific cooling, North Pacific warming, and southern tropical Atlantic warming. In the Colorado River basin, the water deficits can reduce crop yield and increase wildfire potential. Thus, a multi-year prediction of severe water shortages in the Colorado River basin could be useful as an early indicator of subsequent agricultural loss and wildfire risk.</p>

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