Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

Pasternack, Alexander; Bhend, Jonas; Liniger, Mark A.; Rust, Henning W.; Müller, Wolfgang A.; Ulbrich, Uwe

doi:10.5194/gmd-11-351-2018

Cited by 23 publications

(75 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several recent efforts have explored weighted multimodel calibration methods to combine ensembles from different models in order to improve probabilistic seasonal forecasts for temperature and precipitation anomalies as well as forecasts of extremes (Becker 2017). Calibration methods have also been developed for ensemble decadal hindcasts to adjust both the bias and ensemble spread with a parametric dependency on lead time and initialization time (Pasternack et al 2018). Such methods are found to improve both the conditional bias and probabilistic skill of decadal hindcasts.…”

Section: Seasonal To Decadalmentioning

confidence: 99%

Current and Emerging Developments in Subseasonal to Decadal Prediction

Merryfield

Baehr

Batté

et al. 2020

Bulletin of the American Meteorological Society

Self Cite

173

113

View full text Add to dashboard Cite

Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correction, calibration, and forecast quality assessment; model resolution; atmosphere–ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.

show abstract

Section: Seasonal To Decadalmentioning

confidence: 99%

Current and Emerging Developments in Subseasonal to Decadal Prediction

Merryfield

Baehr

Batté

et al. 2020

Bulletin of the American Meteorological Society

Self Cite

173

113

View full text Add to dashboard Cite

show abstract

“…There is a potential to improve the decadal prediction by post-processing. Within MiKlip Pasternack et al (2018) developed the Decadal Climate Forecast Recalibration Strategy (DeFoReSt) to recalibrate the raw simulation data against observation. DeFoReSt accounts for a leadand start-time dependent unconditional bias, conditional bias and ensemble dispersion.…”

Section: Recalibrationmentioning

confidence: 99%

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

Feldmann¹,

Pinto²,

Laube³

et al. 2019

Tellus A: Dynamic Meteorology and Oceanography

Self Cite

View full text Add to dashboard Cite

In recent years, several decadal prediction systems have been developed to provide multi-year predictions of the climate for the next 5-10 years. On the global scale, high decadal predictability has been identified for the North Atlantic sector, often extending over Europe. The first full regional hindcast ensemble, derived from dynamical downscaling, was produced within the German MiKlip project ('decadal predictions'). The ensemble features annual starting dates from 1960 to 2017, with 10 decadal hindcasts per starting year. The global component of the prediction system uses the MPI-ESM-LR and the downscaling is performed with the regional climate model COSMO-CLM (CCLM). The present study focusses on a range of aspects dealing with the skill and added value of regional decadal temperature predictions over Europe. The results substantiate the added value of the regional hindcasts compared to the forcing global model as well as to uninitialized simulations. The results show that the hindcasts are skilful both for annual and seasonal means, and that the scores are comparable for different observational reference data sets. The predictive skill increases from earlier to more recent start-years. A recalibration of the simulation data generally improves the skill further, which can also be transferred to more user-relevant variables and extreme values like daily maximum temperatures and heating degree-days. These results provide evidence of the potential for the regional climate predictions to provide valuable climate information on the decadal timescale to users.

show abstract

“…Previous studies have examined how similar calibration methods can improve multi-year forecasts (e.g. Sansom et al, 2016;Pasternack et al, 2018). It would be of particular interest to examine how calibrated decadal predictions could be combined or merged with these calibrated projections.…”

Section: Discussionmentioning

confidence: 99%

“…Calibration techniques have previously been applied to output from initialised seasonal forecasts (as well as shorter range forecasts), and have been demonstrated to reduce the forecast error and, perhaps more crucially, improve the reliability of the probabilistic forecasts (Kharin and Zwiers, 2003;Doblas-Reyes et al, 2005;Manzanas et al, 2019). In addition to seasonal timescales, calibration techniques have also been shown to be effective on the output from decadal prediction systems (Sansom et al, 2016;Pasternack et al, 2018). However, these types of ensemble calibration techniques have not previously been applied to ensemble climate model projections.…”

Section: Introductionmentioning

confidence: 99%

Calibrating large-ensemble European climate projections using observational data

O’Reilly¹,

Befort²,

Weisheimer³

2020

Preprint

View full text Add to dashboard Cite

Abstract. This study examines methods of calibrating projections of future regional climate using large single model ensembles (the CESM Large Ensemble and MPI Grand Ensemble), applied over Europe. The three calibration methods tested here are more commonly used for initialised forecasts from weeks up to seasonal timescales. The calibration techniques are applied to ensemble climate projections, fitting seasonal ensemble data to observations over a reference period (1920–2016). The calibration methods were tested and verified using an imperfect model approach using the historical/RCP 8.5 simulations from the CMIP5 archive. All the calibration methods exhibit a similar performance, generally improving the out-of-sample projections in comparison to the uncalibrated (bias-corrected) ensemble. The calibration methods give results that are largely indistinguishable from one another, so the simplest of these methods, namely Homogeneous Gaussian Regression, is used for the subsequent analysis. An extension to this method – applying it to dynamically decomposed data (in which the underlying data is separated into dynamical and residual components) – is also tested. The verification indicates that this calibration method produces more reliable and accurate projections than the uncalibrated ensemble for future climate over Europe. The calibrated projections for temperature demonstrate a particular improvement, whereas the projections for changes in precipitation generally remain fairly unreliable. When the two large ensembles are calibrated using observational data, the climate projections for Europe are far more consistent between the two ensembles, with both projecting a reduction in warming but a general increase in the uncertainty of the projected changes.

show abstract

Parametric decadal climate forecast recalibration (DeFoReSt 1.0)

Cited by 23 publications

References 43 publications

Current and Emerging Developments in Subseasonal to Decadal Prediction

Current and Emerging Developments in Subseasonal to Decadal Prediction

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

Calibrating large-ensemble European climate projections using observational data

Contact Info

Product

Resources

About