Revealing skill of the MiKlip decadal prediction system by three-dimensional probabilistic evaluation

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.

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A Probabilistic Evaluation of the Least Squares Strain Tensor Derived from a Three-Dimensional Modular Strain Rosette Using the Monte-Carlo Technique

Strain ◽

10.1111/j.1475-1305.2006.00270.x ◽

2006 ◽

Vol 42 (3) ◽

pp. 207-216 ◽

Cited By ~ 6

Author(s):

E. G. Little ◽

D. Tocher ◽

D. Colgan ◽

P. O'Donnell

Keyword(s):

Monte Carlo ◽

Least Squares ◽

Strain Tensor ◽

Three Dimensional ◽

Monte Carlo Technique ◽

Probabilistic Evaluation ◽

Strain Rosette

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Skill and added value of the MiKlip regional decadal prediction system for temperature over Europe

Tellus A Dynamic Meteorology and Oceanography ◽

10.1080/16000870.2019.1618678 ◽

2019 ◽

Vol 71 (1) ◽

pp. 1618678 ◽

Cited By ~ 2

Author(s):

Hendrik Feldmann ◽

Joaquim g. Pinto ◽

Natalie Laube ◽

Marianne Uhlig ◽

Julia Moemken ◽

...

Keyword(s):

Added Value ◽

Prediction System ◽

Decadal Prediction

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

Journal of Climate ◽

10.1175/jcli-d-14-00069.1 ◽

2014 ◽

Vol 27 (20) ◽

pp. 7550-7567 ◽

Cited By ~ 16

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.

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Thermal-Mechanical Life Prediction System for Anisotropic Turbine Components

Volume 3: Turbo Expo 2005, Parts A and B ◽

10.1115/gt2005-68107 ◽

2005 ◽

Cited By ~ 2

Author(s):

F. J. Cunha ◽

M. T. Dahmer ◽

M. K. Chyu

Keyword(s):

High Pressure ◽

Life Prediction ◽

Mechanical Strain ◽

Three Dimensional ◽

Extreme Points ◽

Creep Damage ◽

Careful Consideration ◽

Shear Stresses ◽

Prediction System ◽

Prediction Approach

Modern gas turbine engines provide large amounts of thrust and withstand severe thermal-mechanical conditions during the load and mission operations characterized by cyclic transients and long dwell times. All these operational factors can be detrimental to the service life of turbine components and need careful consideration. Engine components subject to the harshest environments are turbine high-pressure vanes and rotating blades. Therefore, it is necessary to develop a turbine component three-dimensional life prediction system, which accounts for mission transients, anisotropic material properties, and multi-axial, thermal-mechanical, strain and stress fields. This paper presents a complete life prediction approach for either commercial missions or more complex military missions, which includes evaluation of component transient metal temperatures, resolved maximum shear stresses and strains, and subsequent component life capability for fatigue and creep damage. The procedure is based on considering all of the time steps in the mission profile by developing a series of extreme points that envelop every point in the mission. Creep damage is factored into the component capability by debiting thermal-mechanical accumulated cycles using the traditional Miner’s rule for accumulated fatigue and creep damage. Application of this methodology is illustrated to the design of the NASA Energy Efficient Engine (E3) high pressure turbine blade with operational load shakedown leading to stress relaxation on the external hot surfaces and potential state of overstress in the inner cold rib regions of the airfoil.

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Evaluation of the MiKlip decadal prediction system using satellite based cloud products

Meteorologische Zeitschrift ◽

10.1127/metz/2015/0602 ◽

2016 ◽

Vol 25 (6) ◽

pp. 695-707

Author(s):

Thomas Spangehl ◽

Marc Schröder ◽

Sophie Stolzenberger ◽

Rita Glowienka-Hense ◽

Alex Mazurkiewicz ◽

...

Keyword(s):

Prediction System ◽

Decadal Prediction

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A large ensemble decadal prediction system with MPI-ESM

10.5194/egusphere-egu21-9263 ◽

2021 ◽

Author(s):

Sebastian Brune ◽

Vimal Koul ◽

David Marcolino Nielsen ◽

Laura Hövel ◽

Holger Pohlmann ◽

...

Keyword(s):

North Atlantic ◽

Prediction Skill ◽

Ensemble Prediction ◽

Prediction System ◽

Decadal Prediction ◽

Ensemble Size ◽

Large Ensemble ◽

Ensemble Prediction System ◽

Atlantic Sector ◽

Ensemble Mean

<p>Current state-of-the-art decadal ensemble prediction systems are run with an ensemble size of 10 to 40 members, their retrospective forecasts of the past are used to assess the system's prediction skill. Here, we present an attempt for a large ensemble decadal prediction system for the time period 1960-today, with an ensemble size of 80 members, based on the low resolution version of the Max Planck Institute Earth system model (MPI-ESM-LR). The ensemble is forced with CMIP6 conditions and initialized every year in November through a weakly coupled assimilation using atmospheric reanalyses via nudging and observed oceanic temperature and salinity profiles via a 16-member ensemble Kalman filter. To generate ensemble members beyond 16, we use additional physical perturbations at stratospheric height. The analysis of our large ensemble prediction system presented here aims for answering two questions: (1) How does the ensemble mean deterministic prediction skill for global and North Atlantic key climate indices change with ensemble size? (2) How well may the 80-member ensemble serve as a basis for a robust statistical analysis of probabilities of extremes in the North Atlantic sector? Preliminary results for global and regional air surface temperature show that in terms of ensemble mean ACC and full ensemble CPRSS with reference data, the 80-member ensemble leads to similar prediction skill as the 16-member ensemble. This indicates that the additional ensemble members may lead to a better sampling of the distribution of model trajectories, paving the way for a more robust statistical probabilistic analysis.</p>

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