Seasonal-to-Decadal Climate Forecasting

Suckling, Emma

doi:10.1007/978-3-319-68418-5_9

Cited by 8 publications

(3 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the Atlantic Ocean, the AMV is the dominant mode of decadal climate variability and has been linked to several climate impacts over Europe, North America and the Sahel (Zhang and Delwoth, 2006;Sutton and Dong, 2012;Ruprich-Robert et al, 2017 as well as to Atlantic tropical cyclones (e.g. Caron et al, 2015Caron et al, , 2018.…”

Section: Skill For the Main Ocean Modes Of Variabilitymentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Section: Skill For the Main Ocean Modes Of Variabilitymentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The emergence of climate science as an unavoidable prerequisite for the effective management of climate change risks has highlighted the imperative for climate knowledge development and adaptation learning as critical components of any effort to effectively management climate risks (Steynor and Pasquini, 2019). This has become even more important as advances continue to be made in seasonal climate forecasts, decadal climate predictions and multi-decadal climate change predications (Suckling, 2018).…”

Section: Climate Information Services As Adaptation Learningmentioning

confidence: 99%

Climate-Informed Decision-Making in Data-Poor Environments: Managing Climate Risk Through Citizen Science Networks

et al. 2022

View full text Add to dashboard Cite

Climate change impacts have become a verifiable reality in most communities in Africa and have already shown its ruthlessness in derailing modest gains made toward sustainable development. While evidence of climate change impacts abounds, especially in key climate-sensitive sectors, not many people living in affected communities have the requisite knowledge, understanding and capacity to respond to emerging impacts. Most communities in Ghana and Africa, broadly, lack the requisite climate change knowledge resources to inform adaptation choices. Adaptation decision-making, in most cases, is reactive, speculative, and based on flawed assumptions and understandings of the climate change phenomenon. This is essentially because most countries lack the capacity to make climate-informed decisions which is also a function of the pervasive lack of efficient climate information services regime across Africa. The paucity of climate change knowledge and associated climate information services is undoubtedly an issue of institutional capacity; however, it is also a function of an enduring culture—a poor attitude toward data collection and application—in decision-making processes. Data-poor environment, or data-poverty, as implied in this work, therefore, broadly describes the absence of a data management culture in decision-making processes; however, specifically to climate change, it describes the lack of functional climate information services regime in local communities in Africa and how such omissions impede the ability of countries to make climate-informed decisions to support adaptation and resilience building. Focusing on Ghana, the paper problematizes the lack of climate information in local communities. The paper argues that Africa's climate crisis is as much a knowledge and learning challenge which requires new and innovative learning approaches to build capacities to facilitate the making of data-driven and climate-informed adaptation decisions in local communities. The paper, therefore, foregrounds citizen-science networks as avenues for community-focused and community-based climate knowledge co-producing mechanisms.

show abstract

“…In the Atlantic Ocean, the AMV is the dominant mode of decadal climate variability, and has been linked to several climate impacts over Europe, North America and the Sahel (Zhang and Delwoth, 2006;Sutton and Dong, 2012;Ruprich-Robert et al, 2017, 2018 and to Atlantic tropical cyclones (e.g. Caron et al, 2015Caron et al, , 2018.…”

Section: Skill For the Main Ocean Modes Of Variabilitymentioning

confidence: 99%

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

Bilbao¹,

Wild²,

Ortega³

et al. 2020

Preprint

View full text Add to dashboard Cite

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.

show abstract

Seasonal-to-Decadal Climate Forecasting

Cited by 8 publications

References 47 publications

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

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

Climate-Informed Decision-Making in Data-Poor Environments: Managing Climate Risk Through Citizen Science Networks

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

Contact Info

Product

Resources

About