scholarly journals Development and prospects of the regional MiKlip decadal prediction system over Europe: predictive skill, added value of regionalization, and ensemble size dependency

2019 ◽  
Vol 10 (1) ◽  
pp. 171-187 ◽  
Author(s):  
Mark Reyers ◽  
Hendrik Feldmann ◽  
Sebastian Mieruch ◽  
Joaquim G. Pinto ◽  
Marianne Uhlig ◽  
...  
Keyword(s):  
Forecast Accuracy ◽  
Coupled Model ◽  
Horizontal Resolution ◽  
Added Value ◽  
Southern Europe ◽  
Prediction System ◽  
Decadal Prediction ◽  
Ensemble Size ◽  
Predictive Skill ◽  
General Potential

Abstract. The current state of development and the prospects of the regional MiKlip decadal prediction system for Europe are analysed. The MiKlip regional system consists of two 10-member hindcast ensembles computed with the global coupled model MPI-ESM-LR downscaled for the European region with COSMO-CLM to a horizontal resolution of 0.22∘ (∼25 km). Prediction skills are computed for temperature, precipitation, and wind speed using E-OBS and an ERA-Interim-driven COSMO-CLM simulation as verification datasets. Focus is given to the eight European PRUDENCE regions and to lead years 1–5 after initialization. Evidence of the general potential for regional decadal predictability for all three variables is provided. For example, the initialized hindcasts outperform the uninitialized historical runs for some key regions in Europe, particularly in southern Europe. However, forecast skill is not detected in all cases, but it depends on the variable, the region, and the hindcast generation. A comparison of the downscaled hindcasts with the global MPI-ESM-LR runs reveals that the MiKlip prediction system may distinctly benefit from regionalization, in particular for parts of southern Europe and for Scandinavia. The forecast accuracy of the MiKlip ensemble is systematically enhanced when the ensemble size is increased stepwise, and 10 members is found to be suitable for decadal predictions. This result is valid for all variables and European regions in both the global and regional MiKlip ensemble. The present results are encouraging for the development of a regional decadal prediction system.

scholarly journals Development and prospects of the regional MiKlip decadal prediction system over Europe: Predictive skill, added value of regionalization and ensemble size dependency

2017 ◽  
Author(s):  
Mark Reyers ◽  
Hendrik Feldmann ◽  
Sebastian Mieruch ◽  
Joaquim G. Pinto ◽  
Marianne Uhlig ◽  
...  
Keyword(s):  
Forecast Accuracy ◽  
Coupled Model ◽  
Horizontal Resolution ◽  
Added Value ◽  
Prediction System ◽  
Decadal Prediction ◽  
Ensemble Size ◽  
Predictive Skill ◽  
Current State ◽  
General Potential

Abstract. The current state of development and prospects of the regional MiKlip decadal prediction system for Europe are analysed. The Miklip regional system consists of two 10-member hindcast ensembles computed with the global coupled model MPI-ESM-LR downscaled for the European region with COSMO-CLM to a horizontal resolution of 0.22° (~ 25 km). Prediction skills are computed for temperature, precipitation, and wind speed using E-OBS and an ERA-Interim driven COSMO-CLM simulation as verification datasets. Focus is given to the eight European PRUDENCE regions and to lead 20 years 1–5 after initialization. Evidence of the general potential for regional decadal predictability for all three variables is provided. For example, the initialized hindcasts outperform the uninitialized historical runs for some key regions in Europe and for some variables both in terms of accuracy and reliability. However, forecast skill is not detected in all cases, but it depends on the variable, the region, and the hindcast generation. A comparison of the downscaled hindcasts with the global MPI-ESM-LR runs reveals that the MiKlip prediction system may distinctly benefit from regionalization, in particular for 25 parts of Southern Europe and for Scandinavia. The forecast accuracy and the reliability of the MiKlip ensemble is systematically enhanced when the ensemble size is stepwise increased, and a number of 10 members is found to be suitable for decadal predictions. This result is valid for all variables and European regions in both the global and regional MiKlip ensemble. The predictive skill improves distinctly, particularly for temperature, when retaining the long-term trend in the time series. The present results are encouraging towards the development of a regional decadal prediction system.

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

2019 ◽  
Vol 71 (1) ◽  
pp. 1618678 ◽  
Author(s):  
Hendrik Feldmann ◽  
Joaquim g. Pinto ◽  
Natalie Laube ◽  
Marianne Uhlig ◽  
Julia Moemken ◽  
...  
Keyword(s):  
Added Value ◽  
Prediction System ◽  
Decadal Prediction

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.

A large ensemble decadal prediction system with MPI-ESM

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>

Verification of the Storm surge Forecasting System for the Korean Coastal Area

2020 ◽  
Author(s):  
Nary La ◽  
Byoung Woong An ◽  
KiRyong Kang ◽  
Sang Myeong Oh ◽  
YoonJae Kim
Keyword(s):  
Storm Surge ◽  
Tide Gauge ◽  
Forecast Accuracy ◽  
Storm Surges ◽  
Shuttle Radar Topography Mission ◽  
Horizontal Resolution ◽  
Prediction System ◽  
Forecasting System ◽  
Reference Pressure ◽  
Height Predictions

<p><span>In recent years, coastal disasters have been frequently caused by typhoons and storm surges accompanied by high waves due to global warming and the changing marine environment. In addition, the development of coastal areas in Korea has also led to suffering great damage to society every year. </span></p><p><span>To cope with this issue, we have developed a new storm-surge prediction system based on the NEMO model for improving the predictability both the tide and the surge. This new regional tide-surge prediction system (RTSM) is constructed with a two-dimensional barotropic sigma coordinates and has a 1/12 degrees horizontal resolution. To find optimal coefficients of this model, several sensitivity experiments were conducted and verified with tide gauge measurements from the KHOA (Korea Hydrographic and Oceanographic Agency). Finally, we selected a bathymetry from SRTM (Shuttle Radar Topography Mission), Charnock coefficient as a constant value of 0.275 and the reference pressure for the inverse barometric effect as the domain mean. As the result of comparing surge-height predictions with the currently operating model (OPER-RTSM), the new system (RTSM) showed roughly 30% higher in forecast accuracy than the previous OPER-RTSM.</span></p>

scholarly journals The influence of the new ECMWF Ensemble Prediction System resolution on wind power forecast accuracy and uncertainty estimation

2012 ◽  
Vol 8 (1) ◽  
pp. 143-147 ◽  
Author(s):  
S. Alessandrini ◽  
S. Sperati ◽  
P. Pinson
Keyword(s):  
Wind Power ◽  
Prediction Accuracy ◽  
Wind Farm ◽  
Forecast Accuracy ◽  
Horizontal Resolution ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Meteorological Model ◽  
Ensemble Prediction System ◽  
Medium Range Weather Forecast

Abstract. The importance of wind power forecasting (WPF) is nowadays commonly recognized because it represents a useful tool to reduce problems of grid integration and to facilitate energy trading. If on one side the prediction accuracy is fundamental to these scopes, on the other it has become also clear that a reliable estimation about their uncertainty is paramount. In fact prediction accuracy is unfortunately not constant and can depend on the location of a particular wind farm, on the forecast time and on the atmospheric situation. Previous studies indicated that the spread of power forecasts derived from the Ensemble Prediction System (EPS) in use at the European Centre for Medium-Range Weather Forecast (ECMWF) could be used as indicator of a three-hourly, three days ahead, wind power forecast's accuracy. In this paper a new application of the EPS, whose horizontal resolution was increased on January 2010 from T399/T255 (60 km) to T639/T319 (32 km), shows an improvement in the results implying that the power spread has actually enough correlation with the error calculated on the deterministic forecast in order to be used as an accuracy predictor. The periods for this comparison are from January 2008 until October 2008 (T399/T255) and from January 2011 until October 2011 (T639/T319). Moreover we have focused our attention on the influence of the new EPS configuration on the performance of a deterministic WPF conducted with the ensemble mean: the results show that increasing the EPS resolution yields a single-valued WPF whose performance is comparable with that of the new ECMWF deterministic high-resolution meteorological model, whose spatial resolution increased from T799 (25 km) to T1279 (15 km).

scholarly journals Ensemble forecasting greatly expands the prediction horizon for ocean mesoscale variability

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Prasad G. Thoppil ◽  
Sergey Frolov ◽  
Clark D. Rowley ◽  
Carolyn A. Reynolds ◽  
Gregg A. Jacobs ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Horizontal Resolution ◽  
Ocean Model ◽  
Mesoscale Dynamics ◽  
Predictive Skill ◽  
Freshwater Transport ◽  
Prediction Horizon ◽  
Initial Location ◽  
Ocean Models ◽  
High Uncertainty

AbstractMesoscale eddies dominate energetics of the ocean, modify mass, heat and freshwater transport and primary production in the upper ocean. However, the forecast skill horizon for ocean mesoscales in current operational models is shorter than 10 days: eddy-resolving ocean models, with horizontal resolution finer than 10 km in mid-latitudes, represent mesoscale dynamics, but mesoscale initial conditions are hard to constrain with available observations. Here we analyze a suite of ocean model simulations at high (1/25°) and lower (1/12.5°) resolution and compare with an ensemble of lower-resolution simulations. We show that the ensemble forecast significantly extends the predictability of the ocean mesoscales to between 20 and 40 days. We find that the lack of predictive skill in data assimilative deterministic ocean models is due to high uncertainty in the initial location and forecast of mesoscale features. Ensemble simulations account for this uncertainty and filter-out unconstrained scales. We suggest that advancements in ensemble analysis and forecasting should complement the current focus on high-resolution modeling of the ocean.

Sign in / Sign up

Export Citation Format

Share Document