ENSO asymmetry: the search for extreme El Niño events in HadGEM

2021 ◽  
Author(s):  
Sarah Ineson ◽  
Nick Dunstone ◽  
Adam Scaife ◽  
Kuniko Yamazaki
Keyword(s):  
Coupled Model ◽  
Control Model ◽  
Decadal Prediction ◽  
Enso Asymmetry ◽  
Prediction Systems ◽  
Hadley Centre ◽  
Control Behaviour

<p>Analysis of a long control run of the Hadley Centre coupled model shows that ENSO asymmetry is weak. We use the same model in our seasonal and decadal prediction systems, and while on seasonal timescales the initialised prediction realistically captures the amplitude of extreme El Niño events, on longer timescales the predictions revert to the control behaviour i.e. there are no very large El Niño events. This may impact on our ability evaluate the risk of extreme regional events. Here we show results exploring asymmetry in both the control model, and also from a number of perturbed parameter experiments, each a plausible realisation of the control.</p>

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.

scholarly journals Skillful decadal prediction of unforced southern European summer temperature variations

2021 ◽  
Author(s):  
Leonard Borchert ◽  
Vimal Koul ◽  
Matthew Menary ◽  
Daniel Befort ◽  
Didier Swingedouw ◽  
...  
Keyword(s):  
Statistical Model ◽  
Coupled Model ◽  
Response Prediction ◽  
Forced Response ◽  
Summer Temperature ◽  
Prediction Skill ◽  
Decadal Prediction ◽  
Temperature Variations ◽  
Eastern North Atlantic ◽  
Prediction Systems

We assess the capability of decadal prediction simulations from the Coupled Model Intercomparison Project phase 6 (CMIP6) archive to predict European summer temperature during the period 1970-2014. Using a multi-model ensemble average from 8 decadal prediction systems, we show that European summer temperatures are highly predictable for up to 10 years in CMIP6. Much of this predictive capability, or skill, is related to the externally forced response. Prediction skill for the unforced signal of European summer temperature is low. A link between unforced Southern European summer temperature and preceding spring Eastern North Atlantic - Mediterranean sea surface temperature (SST) observed during the period 1900-1969 motivates the application of a dynamical-statistical model to overcome the low summer temperature skill over Europe. This dynamical-statistical model uses dynamical spring SST predictions to predict European summer temperature. Our model significantly increases decadal prediction skill of unforced European summer temperature variations: Unlike purely dynamical predictions, the dynamical-statistical model shows significant prediction skill for unforced Southern European summer temperature 2-9 years ahead. Our results highlight that dynamical-statistical models can serve to benefit the decadal prediction of variables with initially limited skill beyond the forcing, such as summer temperature over Europe.

scholarly journals The Interpretation and Use of Biases in Decadal Climate Predictions

2014 ◽  
Vol 27 (8) ◽  
pp. 2931-2947 ◽  
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.

scholarly journals Pan-spectral observing system simulation experiments of shortwave reflectance and long-wave radiance for climate model evaluation

2015 ◽  
Vol 8 (7) ◽  
pp. 1943-1954 ◽  
Author(s):  
D. R. Feldman ◽  
W. D. Collins ◽  
J. L. Paige
Keyword(s):  
Climate Change ◽  
Climate Model ◽  
Coupled Model ◽  
Climate System ◽  
Spectral Measurements ◽  
Intergovernmental Panel ◽  
Climate System Model ◽  
Detection And Attribution ◽  
Long Wave ◽  
Hadley Centre

Abstract. Top-of-atmosphere (TOA) spectrally resolved shortwave reflectances and long-wave radiances describe the response of the Earth's surface and atmosphere to feedback processes and human-induced forcings. In order to evaluate proposed long-duration spectral measurements, we have projected 21st Century changes from the Community Climate System Model (CCSM3.0) conducted for the Intergovernmental Panel on Climate Change (IPCC) A2 Emissions Scenario onto shortwave reflectance spectra from 300 to 2500 nm and long-wave radiance spectra from 2000 to 200 cm−1 at 8 nm and 1 cm−1 resolution, respectively. The radiative transfer calculations have been rigorously validated against published standards and produce complementary signals describing the climate system forcings and feedbacks. Additional demonstration experiments were performed with the Model for Interdisciplinary Research on Climate (MIROC5) and Hadley Centre Global Environment Model version 2 Earth System (HadGEM2-ES) models for the Representative Concentration Pathway 8.5 (RCP8.5) scenario. The calculations contain readily distinguishable signatures of low clouds, snow/ice, aerosols, temperature gradients, and water vapour distributions. The goal of this effort is to understand both how climate change alters reflected solar and emitted infrared spectra of the Earth and determine whether spectral measurements enhance our detection and attribution of climate change. This effort also presents a path forward to understand the characteristics of hyperspectral observational records needed to confront models and inline instrument simulation. Such simulation will enable a diverse set of comparisons between model results from coupled model intercomparisons and existing and proposed satellite instrument measurement systems.

On Coastal Ocean Systems, Coupled Model Architectures, Products and Services: Morphing from Observations to Operations and Applications

2007 ◽  
Vol 41 (1) ◽  
pp. 44-52
Author(s):  
L.J. Pietrafesa ◽  
E. B. Buckley ◽  
M. Peng ◽  
S. Bao ◽  
H. Liu ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Coupled Model ◽  
Coastal Ocean ◽  
Federal Agencies ◽  
Coastal Zones ◽  
Societal Needs ◽  
Prediction Systems ◽  
Ocean Observing ◽  
Regional Effort ◽  
Observing Systems

The national build-up of “coastal ocean observing systems” (COOSs) to establish the coastal observing component of the national component of the Integrated Ocean Observing System (IOOS) network must be well organized and must acknowledge, understand and address the needs of the principal clients, the federal, and in some cases state as well, agencies that provide financial support if it is to have substantive value. The funds being spent in support of COOS should be invested in pursuit of the establishment of the National Backbone (NB) that is needed: to greatly improve atmospheric, oceanic and coastal “weather” forecasting, broadly defined; for ecosystem management; and to document climate variability and change in coastal zones. However, this process has not occurred in a well conceived, orderly, well integrated manner due to historical and cultural bases and because of local priorities. A sub-regional effort that is designed to meet federal agency needs and mission responsibilities with an emphasis on meeting societal needs is presented by way of example to show that university and industry partners with federal agencies have an important role to play in the future of building out ocean and coastal observing and prediction systems and networks.

Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model

2013 ◽  
Vol 114 (3-4) ◽  
pp. 673-690 ◽  
Author(s):  
S. Samadi ◽  
Catherine A. M. E. Wilson ◽  
Hamid Moradkhani
Keyword(s):  
Uncertainty Analysis ◽  
Statistical Downscaling ◽  
Coupled Model ◽  
Hadley Centre

scholarly journals The Decadal Climate Prediction Project (DCPP) contribution to CMIP6

2016 ◽  
Vol 9 (10) ◽  
pp. 3751-3777 ◽  
Author(s):  
George J. Boer ◽  
Douglas M. Smith ◽  
Christophe Cassou ◽  
Francisco Doblas-Reyes ◽  
Gokhan Danabasoglu ◽  
...  
Keyword(s):  
Decadal Variability ◽  
Coupled Model ◽  
Climate Prediction ◽  
Grand Challenge ◽  
Decadal Prediction ◽  
Climate Data ◽  
Intercomparison Project ◽  
Near Term ◽  
Decadal Climate ◽  
Decadal Climate Prediction

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 the fifth Coupled Model Intercomparison Project (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 a contribution to CMIP6 (Eyring et al., 2016) and to the WCRP Grand Challenge on Near Term Climate Prediction (Kushnir et al., 2016). 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, analysis and dissemination 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.

Evaluation of FIO-ESM v1.0 Seasonal Prediction Skills Over the North Pacific

2021 ◽  
Author(s):  
Yajuan Song ◽  
Xunqiang Yin
Keyword(s):  
Data Assimilation ◽  
North Pacific ◽  
Climate Model ◽  
Initial Conditions ◽  
Coupled Model ◽  
Seasonal Prediction ◽  
Optimal Interpolation ◽  
The North ◽  
Prediction Systems ◽  
The North Pacific

<p>Accurate prediction over the North Pacific, especially for the key parameter of sea<br>surface temperature (SST), remains a challenge for short-term climate prediction. In<br>this study, seasonal predicted skills of the First Institute of Oceanography Earth System<br>Model version 1.0 (FIO-ESM v1.0) over the North Pacific were assessed. Ensemble<br>adjustment Kalman filter (EAKF) and Projection Optimal Interpolation (Projection-OI) data<br>assimilation schemes were used to provide initial conditions for FIO-ESM v1.0 hindcasts<br>that were started from the first day of each month between 1993 and 2017. Evolution<br>and spacial distribution of SST anomalies over the North Pacific were reasonably<br>reproduced in EAKF and Projection-OI assimilation output. Two hindcast experiments<br>show that the skill of FIO-ESM v1.0 with the EAKF data assimilation scheme to predict<br>SST over the North Pacific is considerably higher than that with Projection-OI data<br>assimilation for all lead times of 1–6 months, especially in the central North Pacific where<br>the subsurface ocean temperature in the initial conditions is significantly improved by<br>EAKF data assimilation. For the Kuroshio–Oyashio extension (KOE) region, the errors<br>in the initial conditions have more rapid propagation resulting in large discrepancies<br>between simulated and observed values, which are reduced by inducing surface<br>waves into the climate model. Incorporation of realistic initial conditions and reasonable<br>physical processes into the coupled model is essential to improving seasonal prediction<br>skill. These results provide a solid basis for the development of operational seasonal<br>prediction systems for the North Pacific.</p>

scholarly journals Development and evaluation of an Earth-System model – HadGEM2

2011 ◽  
Vol 4 (4) ◽  
pp. 1051-1075 ◽  
Author(s):  
W. J. Collins ◽  
N. Bellouin ◽  
M. Doutriaux-Boucher ◽  
N. Gedney ◽  
P. Halloran ◽  
...  
Keyword(s):  
Climate Model ◽  
Coupled Model ◽  
Earth System Model ◽  
Tropospheric Chemistry ◽  
System Model ◽  
Earth System ◽  
Evaluation Phase ◽  
System Components ◽  
The Individual ◽  
Hadley Centre

Abstract. We describe here the development and evaluation of an Earth system model suitable for centennial-scale climate prediction. The principal new components added to the physical climate model are the terrestrial and ocean ecosystems and gas-phase tropospheric chemistry, along with their coupled interactions. The individual Earth system components are described briefly and the relevant interactions between the components are explained. Because the multiple interactions could lead to unstable feedbacks, we go through a careful process of model spin up to ensure that all components are stable and the interactions balanced. This spun-up configuration is evaluated against observed data for the Earth system components and is generally found to perform very satisfactorily. The reason for the evaluation phase is that the model is to be used for the core climate simulations carried out by the Met Office Hadley Centre for the Coupled Model Intercomparison Project (CMIP5), so it is essential that addition of the extra complexity does not detract substantially from its climate performance. Localised changes in some specific meteorological variables can be identified, but the impacts on the overall simulation of present day climate are slight. This model is proving valuable both for climate predictions, and for investigating the strengths of biogeochemical feedbacks.

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