Predictions of Climate Several Years Ahead Using an Improved Decadal Prediction System

Abstract Decadal climate predictions are now established as a source of information on future climate alongside longer-term climate projections. This information has the potential to provide key evidence for decisions on climate change adaptation, especially at regional scales. Its importance implies that following the creation of an initial generation of decadal prediction systems, a process of continual development is needed to produce successive versions with better predictive skill. Here, a new version of the Met Office Hadley Centre Decadal Prediction System (DePreSys 2) is introduced, which builds upon the success of the original DePreSys. DePreSys 2 benefits from inclusion of a newer and more realistic climate model, the Hadley Centre Global Environmental Model version 3 (HadGEM3), but shares a very similar approach to initialization with its predecessor. By performing a large suite of reforecasts, it is shown that DePreSys 2 offers improved skill in predicting climate several years ahead. Differences in skill between the two systems are likely due to a multitude of differences between the underlying climate models, but it is demonstrated herein that improved simulation of tropical Pacific variability is a key source of the improved skill in DePreSys 2. While DePreSys 2 is clearly more skilful than DePreSys in a global sense, it is shown that decreases in skill in some high-latitude regions are related to errors in representing long-term trends. Detrending the results focuses on the prediction of decadal time-scale variability, and shows that the improvement in skill in DePreSys 2 is even more marked.

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Improvement of climate predictions and reduction of their uncertainties using learning algorithms

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-7707-2015 ◽

2015 ◽

Vol 15 (5) ◽

pp. 7707-7734 ◽

Cited By ~ 1

Author(s):

E. Strobach ◽

G. Bel

Keyword(s):

Climate Models ◽

Climate Model ◽

Learning Algorithm ◽

Learning Algorithms ◽

Climate Dynamics ◽

Past Performance ◽

Decadal Time Scale ◽

Simulated Climate ◽

Climate Model Simulations ◽

Decadal Climate

Abstract. Simulated climate dynamics, initialized with observed conditions is expected to be synchronized, for several years, with the actual dynamics. However, the predictions of climate models are not sufficiently accurate. Moreover, there is a large variance between simulations initialized at different times and between different models. One way to improve climate predictions and to reduce the associated uncertainties is to use an ensemble of climate model predictions, weighted according to their past performance. Here, we show that skillful predictions, for a decadal time scale, of the 2 m-temperature can be achieved by applying a sequential learning algorithm to an ensemble of decadal climate model simulations. The predictions generated by the learning algorithm are shown to be better than those of each of the models in the ensemble, the better performing simple average and a reference climatology. In addition, the uncertainties associated with the predictions are shown to be reduced relative to those derived from equally weighted ensemble of bias corrected predictions. The results show that learning algorithms can help to better assess future climate dynamics.

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

10.5194/ems2021-150 ◽

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

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.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.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.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.

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Decadal climate prediction (project GCEP)

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2008.0178 ◽

2008 ◽

Vol 367 (1890) ◽

pp. 925-937 ◽

Cited By ~ 9

Author(s):

Keith Haines ◽

Leon Hermanson ◽

Chunlei Liu ◽

Debbie Putt ◽

Rowan Sutton ◽

...

Keyword(s):

Climate Models ◽

Heat Content ◽

Academic Community ◽

Ensemble Prediction ◽

Initial Condition ◽

Decadal Prediction ◽

Predictive Skill ◽

International Panel ◽

Early Results ◽

Decadal Climate

Decadal prediction uses climate models forced by changing greenhouse gases, as in the International Panel for Climate Change, but unlike longer range predictions they also require initialization with observations of the current climate. In particular, the upper-ocean heat content and circulation have a critical influence. Decadal prediction is still in its infancy and there is an urgent need to understand the important processes that determine predictability on these timescales. We have taken the first Hadley Centre Decadal Prediction System (DePreSys) and implemented it on several NERC institute compute clusters in order to study a wider range of initial condition impacts on decadal forecasting, eventually including the state of the land and cryosphere. The eScience methods are used to manage submission and output from the many ensemble model runs required to assess predictive skill. Early results suggest initial condition skill may extend for several years, even over land areas, but this depends sensitively on the definition used to measure skill, and alternatives are presented. The Grid for Coupled Ensemble Prediction (GCEP) system will allow the UK academic community to contribute to international experiments being planned to explore decadal climate predictability.

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Improvement of climate predictions and reduction of their uncertainties using learning algorithms

Atmospheric Chemistry and Physics ◽

10.5194/acp-15-8631-2015 ◽

2015 ◽

Vol 15 (15) ◽

pp. 8631-8641 ◽

Cited By ~ 10

Author(s):

E. Strobach ◽

G. Bel

Keyword(s):

Climate Models ◽

Climate Model ◽

Learning Algorithm ◽

Learning Algorithms ◽

Climate Dynamics ◽

Decadal Time Scale ◽

Simulated Climate ◽

Climate Model Simulations ◽

Decadal Climate ◽

Better Than

Abstract. Simulated climate dynamics, initialized with observed conditions, is expected to be synchronized, for several years, with the actual dynamics. However, the predictions of climate models are not sufficiently accurate. Moreover, there is a large variance between simulations initialized at different times and between different models. One way to improve climate predictions and to reduce the associated uncertainties is to use an ensemble of climate model predictions, weighted according to their past performances. Here, we show that skillful predictions, for a decadal time scale, of the 2 m temperature can be achieved by applying a sequential learning algorithm to an ensemble of decadal climate model simulations. The predictions generated by the learning algorithm are shown to be better than those of each of the models in the ensemble, the better performing simple average and a reference climatology. In addition, the uncertainties associated with the predictions are shown to be reduced relative to those derived from an equally weighted ensemble of bias-corrected predictions. The results show that learning algorithms can help to better assess future climate dynamics.

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Sensitivity of projected climate impacts to climate model weighting: multi-sector analysis in eastern Africa

Climatic Change ◽

10.1007/s10584-021-02991-8 ◽

2021 ◽

Vol 164 (3-4) ◽

Author(s):

Seshagiri Rao Kolusu ◽

Christian Siderius ◽

Martin C. Todd ◽

Ajay Bhave ◽

Declan Conway ◽

...

Keyword(s):

Climate Models ◽

Climate Model ◽

Investment Decisions ◽

Climate Impacts ◽

Future Climate ◽

Eastern Africa ◽

Climate Projections ◽

Systematic Effect ◽

Risk Profiles ◽

Model Weighting

AbstractUncertainty in long-term projections of future climate can be substantial and presents a major challenge to climate change adaptation planning. This is especially so for projections of future precipitation in most tropical regions, at the spatial scale of many adaptation decisions in water-related sectors. Attempts have been made to constrain the uncertainty in climate projections, based on the recognised premise that not all of the climate models openly available perform equally well. However, there is no agreed ‘good practice’ on how to weight climate models. Nor is it clear to what extent model weighting can constrain uncertainty in decision-relevant climate quantities. We address this challenge, for climate projection information relevant to ‘high stakes’ investment decisions across the ‘water-energy-food’ sectors, using two case-study river basins in Tanzania and Malawi. We compare future climate risk profiles of simple decision-relevant indicators for water-related sectors, derived using hydrological and water resources models, which are driven by an ensemble of future climate model projections. In generating these ensembles, we implement a range of climate model weighting approaches, based on context-relevant climate model performance metrics and assessment. Our case-specific results show the various model weighting approaches have limited systematic effect on the spread of risk profiles. Sensitivity to climate model weighting is lower than overall uncertainty and is considerably less than the uncertainty resulting from bias correction methodologies. However, some of the more subtle effects on sectoral risk profiles from the more ‘aggressive’ model weighting approaches could be important to investment decisions depending on the decision context. For application, model weighting is justified in principle, but a credible approach should be very carefully designed and rooted in robust understanding of relevant physical processes to formulate appropriate metrics.

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The Interpretation and Use of Biases in Decadal Climate Predictions

Journal of Climate ◽

10.1175/jcli-d-13-00473.1 ◽

2014 ◽

Vol 27 (8) ◽

pp. 2931-2947 ◽

Cited By ~ 19

Author(s):

Ed Hawkins ◽

Buwen Dong ◽

Jon Robson ◽

Rowan Sutton ◽

Doug Smith

Keyword(s):

Climate Model ◽

Coupled Model ◽

Internal Variability ◽

Time Dependent ◽

Start Time ◽

Transient Climate Response ◽

Global Mean Temperature ◽

Decadal Climate ◽

Highly Correlated ◽

Hadley Centre

Abstract Decadal climate predictions exhibit large biases, which are often subtracted and forgotten. However, understanding the causes of bias is essential to guide efforts to improve prediction systems, and may offer additional benefits. Here the origins of biases in decadal predictions are investigated, including whether analysis of these biases might provide useful information. The focus is especially on the lead-time-dependent bias tendency. A “toy” model of a prediction system is initially developed and used to show that there are several distinct contributions to bias tendency. Contributions from sampling of internal variability and a start-time-dependent forcing bias can be estimated and removed to obtain a much improved estimate of the true bias tendency, which can provide information about errors in the underlying model and/or errors in the specification of forcings. It is argued that the true bias tendency, not the total bias tendency, should be used to adjust decadal forecasts. The methods developed are applied to decadal hindcasts of global mean temperature made using the Hadley Centre Coupled Model, version 3 (HadCM3), climate model, and it is found that this model exhibits a small positive bias tendency in the ensemble mean. When considering different model versions, it is shown that the true bias tendency is very highly correlated with both the transient climate response (TCR) and non–greenhouse gas forcing trends, and can therefore be used to obtain observationally constrained estimates of these relevant physical quantities.

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NAO predictability from external forcing in the late 20th century

npj Climate and Atmospheric Science ◽

10.1038/s41612-021-00177-8 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Jeremy M. Klavans ◽

Mark A. Cane ◽

Amy C. Clement ◽

Lisa N. Murphy

Keyword(s):

North Atlantic ◽

Radiative Forcing ◽

Climate Models ◽

North Atlantic Ocean ◽

Signal To Noise Ratio ◽

Prediction System ◽

Signal To Noise ◽

Late 20Th Century ◽

The North ◽

Predictive Skill

AbstractThe North Atlantic Oscillation (NAO) is predictable in climate models at near-decadal timescales. Predictive skill derives from ocean initialization, which can capture variability internal to the climate system, and from external radiative forcing. Herein, we show that predictive skill for the NAO in a very large uninitialized multi-model ensemble is commensurate with previously reported skill from a state-of-the-art initialized prediction system. The uninitialized ensemble and initialized prediction system produce similar levels of skill for northern European precipitation and North Atlantic SSTs. Identifying these predictable components becomes possible in a very large ensemble, confirming the erroneously low signal-to-noise ratio previously identified in both initialized and uninitialized climate models. Though the results here imply that external radiative forcing is a major source of predictive skill for the NAO, they also indicate that ocean initialization may be important for particular NAO events (the mid-1990s strong positive NAO), and, as previously suggested, in certain ocean regions such as the subpolar North Atlantic ocean. Overall, we suggest that improving climate models’ response to external radiative forcing may help resolve the known signal-to-noise error in climate models.

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Projecting malaria hazard from climate change in eastern Africa using large ensembles to estimate uncertainty

Geospatial health ◽

10.4081/gh.2016.393 ◽

2016 ◽

Vol 11 (1s) ◽

Cited By ~ 8

Author(s):

Joseph Leedale ◽

Adrian M. Tompkins ◽

Cyril Caminade ◽

Anne E. Jones ◽

Grigory Nikulin ◽

...

Keyword(s):

Climate Change ◽

Malaria Transmission ◽

Climate Models ◽

Climate Model ◽

Eastern Africa ◽

Future Climate Change ◽

Climate Projections ◽

Model Ensemble ◽

East African Community ◽

Recent Climate

The effect of climate change on the spatiotemporal dynamics of malaria transmission is studied using an unprecedented ensemble of climate projections, employing three diverse bias correction and downscaling techniques, in order to partially account for uncertainty in climate- driven malaria projections. These large climate ensembles drive two dynamical and spatially explicit epidemiological malaria models to provide future hazard projections for the focus region of eastern Africa. While the two malaria models produce very distinct transmission patterns for the recent climate, their response to future climate change is similar in terms of sign and spatial distribution, with malaria transmission moving to higher altitudes in the East African Community (EAC) region, while transmission reduces in lowland, marginal transmission zones such as South Sudan. The climate model ensemble generally projects warmer and wetter conditions over EAC. The simulated malaria response appears to be driven by temperature rather than precipitation effects. This reduces the uncertainty due to the climate models, as precipitation trends in tropical regions are very diverse, projecting both drier and wetter conditions with the current state-of-the-art climate model ensemble. The magnitude of the projected changes differed considerably between the two dynamical malaria models, with one much more sensitive to climate change, highlighting that uncertainty in the malaria projections is also associated with the disease modelling approach.

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Future humidity extremes in an urban-rural context - using regional climate model projections down to convection permitting scales for Berlin

10.5194/egusphere-egu21-12054 ◽

2021 ◽

Author(s):

Gaby S. Langendijk ◽

Diana Rechid ◽

Daniela Jacob

Keyword(s):

Climate Change ◽

Urban Areas ◽

Climate Models ◽

Climate Model ◽

Regional Climate ◽

Climate Change Impacts ◽

Regional Climate Models ◽

Climate Projections ◽

Climate Data ◽

Urban Rural

Urban areas are prone to climate change impacts. A transition towards sustainable and climate-resilient urban areas is relying heavily on useful, evidence-based climate information on urban scales. However, current climate data and information produced by urban or climate models are either not scale compliant for cities, or do not cover essential parameters and/or urban-rural interactions under climate change conditions. Furthermore, although e.g. the urban heat island may be better understood, other phenomena, such as moisture change, are little researched. Our research shows the potential of regional climate models, within the EURO-CORDEX framework, to provide climate projections and information on urban scales for 11km and 3km grid size. The city of Berlin is taken as a case-study. The results on the 11km spatial scale show that the regional climate models simulate a distinct difference between Berlin and its surroundings for temperature and humidity related variables. There is an increase in urban dry island conditions in Berlin towards the end of the 21st century. To gain a more detailed understanding of climate change impacts, extreme weather conditions were investigated under a 2&#176;C global warming and further downscaled to the 3km scale. This enables the exploration of differences of the meteorological processes between the 11km and 3km scales, and the implications for urban areas and its surroundings. The overall study shows the potential of regional climate models to provide climate change information on urban scales.

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Comparison of different infiltration schemes in the regional climate model REMO

10.5194/egusphere-egu21-9874 ◽

2021 ◽

Author(s):

Daniel Abel ◽

Katrin Ziegler ◽

Felix Pollinger ◽

Heiko Paeth

Keyword(s):

Regional Climate Model ◽

Climate Models ◽

Climate Model ◽

Root Zone ◽

Regional Climate ◽

Climate Projections ◽

Soil Hydrology ◽

Spatiotemporal Resolution ◽

Development Fund ◽

Near Surface

The European Regional Development Fund-Project BigData@Geo aims to create highly resolved climate projections for the model region of Lower Franconia in Bavaria, Germany. These projections are analyzed and made available to local stakeholders of agriculture, forestry, and viniculture as well as general public. Since regional climate models&#8217; spatiotemporal resolution often is too coarse to deal with such local issues, the regional climate model REMO is improved within the frame of the project in cooperation with the Climate Service Center Germany (GERICS).Accurate and highly resolved climate projections require realistic modeling of soil hydrology. Thus, REMO&#8217;s original bucket scheme is replaced by a 5-layer soil scheme. It allows for the representation of water below the root zone. Evaporation is possible solely from the top layer instead of the entire bucket and water can flow vertically between the layers. Consequently, the properties and processes change significantly compared to the bucket scheme. Both, the bucket and the 5-layer scheme, use the improved Arno scheme to separate throughfall into infiltration and surface runoff.In this study, we examine if this scheme is suitable for use with the improved soil hydrology or if other schemes lead to better results. For this, we (1) modify the improved Arno scheme and further introduce the infiltration equations of (2) Philip as well as (3) Green and Ampt. First results of the comparison of these four different schemes and their influence on soil moisture and near-surface atmospheric variables are presented.

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