scholarly journals Decadal climate prediction (project GCEP)

2008 ◽  
Vol 367 (1890) ◽  
pp. 925-937 ◽  
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.

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

2014 ◽  
Vol 27 (20) ◽  
pp. 7550-7567 ◽  
Author(s):  
Jeff R. Knight ◽  
Martin B. Andrews ◽  
Doug M. Smith ◽  
Alberto Arribas ◽  
Andrew W. Colman ◽  
...  
Keyword(s):  
Climate Models ◽  
Climate Model ◽  
Climate Projections ◽  
Prediction System ◽  
Decadal Prediction ◽  
Decadal Time Scale ◽  
Predictive Skill ◽  
Decadal Climate ◽  
Prediction Systems ◽  
Hadley Centre

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.

The challenge of combining initialised and uninitialised decadal projections

2020 ◽  
Author(s):  
James Murphy
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Climate Change Scenarios ◽  
Internal Climate Variability ◽  
Climate Risks ◽  
Early Results ◽  
Ensemble Mean ◽  
Decadal Climate

<p>The challenge of combining initialised and uninitialised decadal projections</p><p>James Murphy, Robin Clark, Nick Dunstone, Glen Harris, Leon Hermanson and Doug Smith</p><p>During the past 10 years or so, exploratory work in initialised decadal climate prediction, using global climate models started from recent analyses of observations, has grown into a coordinated international programme that contributes to IPCC assessments. At the same time, countries have continued to develop and update their national climate change scenarios.  These typically cover the full 21<sup>st</sup> century, including the initial decade that overlaps with the latest initialised forecasts. To date, however, national scenarios continue to be based exclusively on long-term (uninitialised) climate change simulations, with initialised information regarded as a separate stream of information.</p><p>We will use early results from the latest UK national scenarios (UKCP), and the latest CMIP6 initialised predictions, to illustrate the potential and challenges associated with the notion of combining both streams of information. This involves assessing the effects of initialisation on predictability and uncertainty (as indicated, for example, by the skill of ensemble-mean forecasts and the spread amongst constituent ensemble members). Here, a particular challenge involves interpretation of the “signal-to-noise” problem, in which ensemble-mean skill can sometimes be found which is larger than would be expected on the basis of the ensemble spread. In addition to initialisation, we will also emphasise the importance of understanding how the assessment of climate risks depends on other features of prediction system design, including the sampling of model uncertainties and the simulation of internal climate variability.</p>

Two Limits of Initial-Value Decadal Predictability in a CGCM

2010 ◽  
Vol 23 (23) ◽  
pp. 6292-6311 ◽  
Author(s):  
Grant Branstator ◽  
Haiyan Teng
Keyword(s):  
Initial Conditions ◽  
Heat Content ◽  
Forced Response ◽  
Climate System ◽  
Change Scenario ◽  
Model Errors ◽  
Initial Value ◽  
Climate System Model ◽  
Predictive Skill ◽  
Decadal Climate

Abstract When the climate system experiences time-dependent external forcing (e.g., from increases in greenhouse gas and aerosol concentrations), there are two inherent limits on the gain in skill of decadal climate predictions that can be attained from initializing with the observed ocean state. One is the classical initial-value predictability limit that is a consequence of the system being chaotic, and the other corresponds to the forecast range at which information from the initial conditions is overcome by the forced response. These limits are not caused by model errors; they correspond to limits on the range of useful forecasts that would exist even if nature behaved exactly as the model behaves. In this paper these two limits are quantified for the Community Climate System Model, version 3 (CCSM3), with several 40-member climate change scenario experiments. Predictability of the upper-300-m ocean temperature, on basin and global scales, is estimated by relative entropy from information theory. Despite some regional variations, overall, information from the ocean initial conditions exceeds that from the forced response for about 7 yr. After about a decade the classical initial-value predictability limit is reached, at which point the initial conditions have no remaining impact. Initial-value predictability receives a larger contribution from ensemble mean signals than from the distribution about the mean. Based on the two quantified limits, the conclusion is drawn that, to the extent that predictive skill relies solely on upper-ocean heat content, in CCSM3 decadal prediction beyond a range of about 10 yr is a boundary condition problem rather than an initial-value problem. Factors that the results of this study are sensitive and insensitive to are also discussed.

The decadal climate variations simulated in a coupled data assimilation system using only surface pressure observations

2020 ◽  
Author(s):  
Xiaosong Yang ◽  
Thomas Delworth ◽  
Fanrong Zeng ◽  
William Cooke ◽  
Liping Zhang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Surface Pressure ◽  
Climate Models ◽  
Coupled Model ◽  
Surface Fluxes ◽  
Decadal Prediction ◽  
Climate Variations ◽  
Decadal Climate ◽  
Data Assimilation System ◽  
Assimilation System

<p>Initializing climate models for decadal prediction is a major challenge, in part due to the lack of long-term subsurface ocean observations and the changing nature of observing systems. In order to overcome these limitations, we have developed a novel method for initializing a climate model for decadal prediction. Using GFDL’s next-generation prediction system, we developed a coupled ensemble data assimilation system, which assimilated only surface pressure observations, since the surface pressure measurements have been made since the late 1800s. Physically, by assimilating high-frequency surface pressure observations we constrain the model to experience a sequence of wind and storms, and thus surface fluxes, that is very similar to what is observed. The hypothesis is that by having the ocean component of the coupled model experience a very similar sequence of surface fluxes as observations, the ocean component of the coupled model will gradually reproduce the same variations as the observed system.</p><p>We assimilated the observed surface pressure station data used in the latest 20-century reanalysis. A coupled simulation during 1960 to 2016 has been completed. In this talk, we will review how well the observed decadal climate variations (e.g., PDO and AMO) can be reproduced solely from the surface pressure observations.  In addition, we will explore the multi-decadal variations of the Atlantic meridional overturning circulation (AMOC) and its connection with the North Atlantic sea surface temperature. The feasibility of using this method to initialize coupled climate models for realistic decadal predictions will be discussed in the talk.            </p>

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

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

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

scholarly journals A Decadal Prediction Case Study: Late Twentieth-Century North Atlantic Ocean Heat Content

2012 ◽  
Vol 25 (15) ◽  
pp. 5173-5189 ◽  
Author(s):  
Stephen Yeager ◽  
Alicia Karspeck ◽  
Gokhan Danabasoglu ◽  
Joe Tribbia ◽  
Haiyan Teng
Keyword(s):  
Twentieth Century ◽  
North Atlantic ◽  
Heat Content ◽  
Meridional Overturning Circulation ◽  
Ocean Heat Content ◽  
Decadal Prediction ◽  
The Core ◽  
Predictive Skill ◽  
Late Twentieth ◽  
Late Twentieth Century

Abstract An ensemble of initialized decadal prediction (DP) experiments using the Community Climate System Model, version 4 (CCSM4) shows considerable skill at forecasting changes in North Atlantic upper-ocean heat content and surface temperature up to a decade in advance. Coupled model ensembles were integrated forward from each of 10 different start dates spanning from 1961 to 2006 with ocean and sea ice initial conditions obtained from a forced historical experiment, a Coordinated Ocean-Ice Reference Experiment with Interannual forcing (CORE-IA), which exhibits good correspondence with late twentieth-century ocean observations from the North Atlantic subpolar gyre (SPG) region. North Atlantic heat content anomalies from the DP ensemble correlate highly with those from the CORE-IA simulation after correcting for a drift bias. In particular, the observed large, rapid rise in SPG heat content in the mid-1990s is successfully predicted in the ensemble initialized in January of 1991. A budget of SPG heat content from the CORE-IA experiment sheds light on the origins of the 1990s regime shift, and it demonstrates the extent to which low-frequency changes in ocean heat advection related to the Atlantic meridional overturning circulation dominate temperature tendencies in this region. Similar budgets from the DP ensembles reveal varying degrees of predictive skill in the individual heat budget terms, with large advective heat flux anomalies from the south exhibiting the highest correlation with CORE-IA. The skill of the DP in this region is thus tied to correct initialization of ocean circulation anomalies, while external forcing is found to contribute negligibly (and for incorrect reasons) to predictive skill in this region over this time period.

Quantifying the Predictive Skill in Long-Range Forecasting. Part II: Model Error in Coarse-Grained Markov Models with Application to Ocean-Circulation Regimes

2012 ◽  
Vol 25 (6) ◽  
pp. 1814-1826 ◽  
Author(s):  
Dimitrios Giannakis ◽  
Andrew J. Majda
Keyword(s):  
Long Range ◽  
Ocean Circulation ◽  
Climate Models ◽  
Markov Models ◽  
Low Frequency ◽  
Model Error ◽  
Ocean Model ◽  
Coarse Grained ◽  
Predictive Skill ◽  
Relaxation To Equilibrium

Abstract An information-theoretic framework is developed to assess the predictive skill and model error in imperfect climate models for long-range forecasting. Here, of key importance is a climate equilibrium consistency test for detecting false predictive skill, as well as an analogous criterion describing model error during relaxation to equilibrium. Climate equilibrium consistency enforces the requirement that long-range forecasting models should reproduce the climatology of prediction observables with high fidelity. If a model meets both climate consistency and the analogous criterion describing model error during relaxation to equilibrium, then relative entropy can be used as an unbiased superensemble measure of the model’s skill in long-range coarse-grained forecasts. As an application, the authors investigate the error in modeling regime transitions in a 1.5-layer ocean model as a Markov process and identify models that are strongly persistent but their predictive skill is false. The general techniques developed here are also useful for estimating predictive skill with model error for Markov models of low-frequency atmospheric regimes.

scholarly journals Evaluation of the Added Value of Regional Ensemble Forecasts on Global Ensemble Forecasts

2012 ◽  
Vol 27 (4) ◽  
pp. 972-987 ◽  
Author(s):  
Yong Wang ◽  
Simona Tascu ◽  
Florian Weidle ◽  
Karin Schmeisser
Keyword(s):  
Horizontal Resolution ◽  
Added Value ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Initial Condition ◽  
Weather Variables ◽  
Single Model ◽  
Ensemble Forecasts ◽  
Model Based ◽  
Surface Weather

Abstract The regional single-model-based Aire Limitée Adaptation Dynamique Développement International–Limited Area Ensemble Forecasting (ALADIN-LAEF) ensemble prediction system (EPS) is evaluated and compared with the global ECMWF-EPS to investigate the added value of regional to global EPS models. ALADIN-LAEF consists of 16 perturbed members at 18-km horizontal resolution, while ECMWF-EPS includes 50 perturbed members at 50-km horizontal resolution. In ALADIN-LAEF, the atmospheric initial condition uncertainty is quantified by using blending, which combines large-scale uncertainty generated by the ECMWF-EPS singular-vector approach with small-scale perturbations resolved by the ALADIN breeding technique. The surface initial condition perturbations are generated by use of the noncycling surface breeding (NCSB) technique, and different physics schemes are employed for different forecast members to account for model uncertainties. The verification and comparison have been carried out for a 2-month period during summer 2007 over central Europe. The results show a quite favorable level of performance for ALADIN-LAEF compared to ECMWF-EPS for surface weather variables. ALADIN-LAEF adds more value to precipitation forecasts and has greater skill for 10-m wind and mean sea level pressure results than does ECMWF-EPS. For 2-m temperature, ALADIN-LAEF forecasts have larger spread, are statistically more consistent, but also have less skill than ECMWF-EPS due to the strong cold bias in the ALADIN forecasts. For the upper-air weather parameters, the forecast of ALADIN-LAEF has a larger spread, but the forecast skill of ALADIN-LAEF is from neutral to slightly inferior compared to ECMWF-EPS. It may be concluded that a regional single-model-based EPS with fewer ensemble members could provide more added value in terms of greater skill for near-surface weather variables than the global EPS with larger ensemble size, whereas it may have limitations when applied to upper-air weather variables.

scholarly journals NAO predictability from external forcing in the late 20th century

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.

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.

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