A novel model independence methodology to improve multi-model seasonal forecasts combination

2020 ◽  
Author(s):  
Franco Catalano ◽  
Andrea Alessandri ◽  
Kristian Nielsen ◽  
Irene Cionni ◽  
Matteo De Felice
Keyword(s):  
Seasonal Prediction ◽  
Boreal Summer ◽  
Brier Score ◽  
Boreal Winter ◽  
Seasonal Forecasts ◽  
Surface Solar Radiation ◽  
Model Ensembles ◽  
Prediction Systems ◽  
June July ◽  
Forecasts Combination

<p align="justify">Multi-model ensembles (MMEs) are powerful tools in dynamical climate prediction as they account for the overconfidence and the uncertainties related to single model ensembles. The potential benefit that can be expected by using a MME amplifies with the increase of the independence of the contributing Seasonal Prediction Systems. To this aim, a novel methodology has been developed to assess the relative independence of the prediction systems in the probabilistic information they provide.</p><p align="justify"><span>We considered the Copernicus C3S seasonal forecasts product considering the one-month lead retrospective seasonal predictions for boreal summer and boreal winter seasons (1</span><sup><span>st</span></sup><span> May and 1</span><sup><span>st</span></sup><span> November start dates, i.e. June-July-August, JJA and December-January-February, DJF). We analysed the seasonal hindcasts in terms of deterministic and probabilistic scores with a particular focus on </span><span>continental areas</span><span>, since little evaluation has been performed so far over land domains that is where most of the applications of seasonal forecasts are based. The most relevant target variables of interest for the energy users have been considered and skill differences between the prediction systems have been analysed together with related possible sources of predictability. The analysis evidenced the importance of snow-albedo processes for temperature predictions in DJF and the effect of the atmospheric dynamics through moisture convergence for the prediction of surface solar radiation in JJA. </span><span>A</span><span> new metric, the Brier Score Covariance, designed to quantify the probabilistic independence among the models, has been </span><span>developed and </span><span>applied to optimize model selection and combination strategies with a particular focus on the most relevant variables for energy applications.</span></p>

Grand Multi-Model Seasonal Forecasts in the SECLI-FIRM project

2020 ◽  
Author(s):  
Andrea Alessandri ◽  
Franco Catalano ◽  
Matteo De Felice ◽  
Kristian Nielsen ◽  
Alberto Troccoli ◽  
...  
Keyword(s):  
Seasonal Prediction ◽  
Added Value ◽  
Japan Meteorological Agency ◽  
Brier Score ◽  
Seasonal Forecasts ◽  
Seasonal Climate ◽  
Climate Forecasts ◽  
Prediction Systems ◽  
Seasonal Climate Forecasts ◽  
Selection Of

<p>A key objective of the Added Value of Seasonal Climate Forecasts for Integrated Risk Management Decisions (SECLI-FIRM, www.secli-firm.eu) project is the optimisation of the performance of seasonal climate forecasts provided by many producing centers, in a Grand Multi-Model approach, for predictands relevant for the specific case studies considered in SECLI-FIRM.</p><p>The Grand Multi-Model Ensemble (MME) consists of the five Seasonal Prediction Systems (SPSs) provided by the European Copernicus C3S and a selection of other five SPSs independently developed by centres outside Europe, four by the North American (NMME) plus the SPS by the Japan Meteorological Agency (JMA).</p><p>All the possible multi-model combinations have been evaluated showing that, in general, only a limited number of SPSs is required to obtain the maximum attainable performance. Although the selection of models that perform better is usually different depending on the region/phenomenon under consideration, it is shown that the performance of the Grand-MME seasonal predictions is enhanced with the increase of the independence of the contributing SPSs, i.e. by mixing European SPSs with those from NMME-JMA.</p><p>Starting from the definition of the Brier score a novel metric has been developed, named the Brier score covariance (BScov), which estimates the relative independence of the prediction systems. BScov is used to quantify independence among the SPSs and, together with probabilistic skill metrics, used to develop a strategy for the identification of the combinations that optimize the probabilistic performance of seasonal predictions for the study cases.</p>

scholarly journals Seasonal prediction of the boreal winter stratosphere

2021 ◽  
Author(s):  
Alice Portal ◽  
Paolo Ruggieri ◽  
Froila M. Palmeiro ◽  
Javier García-Serrano ◽  
Daniela I. V. Domeisen ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Polar Vortex ◽  
Seasonal Prediction ◽  
Wave Activity ◽  
Boreal Winter ◽  
Quasi Biennial Oscillation ◽  
Stratospheric Polar Vortex ◽  
Eddy Heat Flux ◽  
Prediction Systems ◽  
Model Database

AbstractThe predictability of the Northern Hemisphere stratosphere and its underlying dynamics are investigated in five state-of-the-art seasonal prediction systems from the Copernicus Climate Change Service (C3S) multi-model database. Special attention is devoted to the connection between the stratospheric polar vortex (SPV) and lower-stratosphere wave activity (LSWA). We find that in winter (December to February) dynamical forecasts initialised on the first of November are considerably more skilful than empirical forecasts based on October anomalies. Moreover, the coupling of the SPV with mid-latitude LSWA (i.e., meridional eddy heat flux) is generally well reproduced by the forecast systems, allowing for the identification of a robust link between the predictability of wave activity above the tropopause and the SPV skill. Our results highlight the importance of November-to-February LSWA, in particular in the Eurasian sector, for forecasts of the winter stratosphere. Finally, the role of potential sources of seasonal stratospheric predictability is considered: we find that the C3S multi-model overestimates the stratospheric response to El Niño–Southern Oscillation (ENSO) and underestimates the influence of the Quasi–Biennial Oscillation (QBO).

scholarly journals Predictions of the boreal winter stratosphere with the C3S multi-model seasonal forecast system

2021 ◽  
Author(s):  
Alice Portal ◽  
Paolo Ruggieri ◽  
Froila M. Palmeiro ◽  
Javier Garcı́a-Serrano ◽  
Daniela I. V. Domeisen ◽  
...  
Keyword(s):  
Southern Oscillation ◽  
Polar Vortex ◽  
Seasonal Prediction ◽  
Seasonal Forecast ◽  
Wave Activity ◽  
Arctic Sea Ice ◽  
Mean Flow ◽  
Seasonal Forecasts ◽  
Stratospheric Polar Vortex ◽  
Prediction Systems

<p>As a result of the recent progress in the performance of seasonal prediction systems, forecasts of the mid-latitude weather at seasonal time scales are becoming increasingly important for societal decision making, as in risk estimate and management of meteorological extreme events. The predictability of the Northern-Hemisphere winter troposphere, especially in the Euro-Atlantic region, stems from the representation of a number of sources of predictability, notably El Nino Southern Oscillation, the stratospheric polar vortex, Arctic sea-ice extent, Eurasian snow cover. Among these, the stratospheric polar vortex is known to play a paramount role in seasonal forecasts of the winter tropospheric flow.</p><p>Here, we investigate the performance in the stratosphere of five seasonal prediction systems taking part in the Copernicus Climate Change Service (C3S), with a focus on the seasonal forecast skill and variability, and on the assessment of stratospheric processes. We show that dynamical forecasts of the stratosphere initialised at the beginning of November are considerably more skilful than empirical forecasts based on observed October or November anomalies. Advances in the representation of stratospheric seasonal variability and extremes, i.e. sudden stratospheric warming frequency, are identified with respect to previous generations of climate models running roughly a decade ago. Such results display, however, a large model dependence. Finally, we stress the importance of the relation between the stratospheric wave activity and the stratospheric polar vortex (i.e. the wave—mean-flow interaction), applied both to the variability and to the predictability of the stratospheric mean flow. Indeed, forecasts of the winter stratospheric polar vortex are closely connected to the prediction of November-to-February stratospheric wave activity, in particular in the Eurasian sector.</p>

scholarly journals Understanding Skill of Seasonal Mean Precipitation Prediction over California during Boreal Winter and Role of Predictability Limits

2020 ◽  
Vol 33 (14) ◽  
pp. 6141-6163
Author(s):  
Arun Kumar ◽  
Mingyue Chen
Keyword(s):  
Role Model ◽  
Systematic Approach ◽  
Seasonal Prediction ◽  
Prediction Skill ◽  
Boreal Winter ◽  
Southeast United States ◽  
The North ◽  
Precipitation Prediction ◽  
Prediction Systems

AbstractUsing extensive hindcasts from seasonal prediction systems participating in the North American Multi-Model Ensemble (NMME), possible causes for low skill in predicting seasonal mean precipitation over California during December–February (DJF) are investigated. The analysis focuses on investigating two possibilities for low prediction skill: role model biases or inherent predictability limits. The motivation for the analysis was the seasonal prediction during DJF 2015/16 that called for enhanced probability for above normal precipitation over southern California (which was consistent with expected conditions during an extreme El Niño) while the observed precipitation was below normal. Based on various analysis approaches and using hindcast datasets from multiple seasonal prediction systems, we build up the evidence that low skill in predicting seasonal mean precipitation over California is likely to be due to inherent predictability associated with a low signal-to-noise (SNR) regime. For the same set of seasonal prediction systems, the precipitation variability over California is contrasted with that over the southeast United States where prediction skill, as well as the SNR, is higher. The discussion also notes that building a knowledge base that goes beyond the well-known response to ENSO (based on the linear regression or composite techniques) has proven to be difficult and a systematic approach to reaching resolution to some of the overarching questions is required, and toward that end, a pathway is suggested.

scholarly journals Evaluation of Probabilistic Quality and Value of the ENSEMBLES Multimodel Seasonal Forecasts: Comparison with DEMETER

2011 ◽  
Vol 139 (2) ◽  
pp. 581-607 ◽  
Author(s):  
Andrea Alessandri ◽  
Andrea Borrelli ◽  
Antonio Navarra ◽  
Alberto Arribas ◽  
Michel Déqué ◽  
...  
Keyword(s):  
Seasonal Prediction ◽  
The Other ◽  
Seasonal Forecasts ◽  
Potential Predictability ◽  
Anomalous Surface ◽  
Discrimination Information ◽  
Time Variations ◽  
Prediction Systems ◽  
The Tropics ◽  
Start Dates

Abstract The performance of the new multimodel seasonal prediction system developed in the framework of the European Commission FP7 project called ENSEMBLE-based predictions of climate changes and their impacts (ENSEMBLES) is compared with the results from the previous project [i.e., Development of a European Multimodel Ensemble System for Seasonal-to-Interannual Prediction (DEMETER)]. The comparison is carried out over the five seasonal prediction systems (SPSs) that participated in both projects. Since DEMETER, the contributing SPSs have improved in all aspects with the main advancements including the increase in resolution, the better representation of subgrid physical processes, land, sea ice, and greenhouse gas boundary forcing, and the more widespread use of assimilation for ocean initialization. The ENSEMBLES results show an overall enhancement for the prediction of anomalous surface temperature conditions. However, the improvement is quite small and with considerable space–time variations. In the tropics, ENSEMBLES systematically improves the sharpness and the discrimination attributes of the forecasts. Enhancements of the ENSEMBLES resolution attribute are also reported in the tropics for the forecasts started 1 February, 1 May, and 1 November. Our results indicate that, in ENSEMBLES, an increased portion of prediction signal from the single-models effectively contributes to amplify the multimodel forecasts skill. On the other hand, a worsening is shown for the multimodel calibration over the tropics compared to DEMETER. Significant changes are also shown in northern midlatitudes, where the ENSEMBLES multimodel discrimination, resolution, and reliability improve for February, May, and November starting dates. However, the ENSEMBLES multimodel decreases the capability to amplify the performance with respect to the contributing single models for the forecasts started in February, May, and August. This is at least partly due to the reduced overconfidence of the ENSEMBLES single models with respect to the DEMETER counterparts. Provided that they are suitably calibrated beforehand, it is shown that the ENSEMBLES multimodel forecasts represent a step forward for the potential economical value they can supply. A warning for all potential users concerns the need for calibration due to the degraded tropical reliability compared to DEMETER. In addition, the superiority of recalibrating the ENSEMBLES predictions through the discrimination information is shown. Concerning the forecasts started in August, ENSEMBLES exhibits mixed results over both tropics and northern midlatitudes. In this case, the increased potential predictability compared to DEMETER appears to be balanced by the reduction in the independence of the SPSs contributing to ENSEMBLES. Consequently, for the August start dates no clear advantage of using one multimodel system instead of the other can be evidenced.

scholarly journals Wintertime Rossby Wave Breaking Persistence in Extended-range Seasonal Forecasts of Atlantic Tropical Cyclone Activity

2022 ◽  
pp. 1-49
Keyword(s):  
North Atlantic ◽  
Wave Breaking ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Seasonal Forecasts ◽  
North Atlantic Region ◽  
Atlantic Region ◽  
The North ◽  
Extended Range ◽  
The North Atlantic

Abstract In this study, we examine the wintertime environmental precursors of summer anticyclonic wave breaking (AWB) over the North Atlantic region and assess the applicability of these precursors in predicting AWB impacts on seasonal tropical cyclone (TC) activity. We show that predictors representing the environmental impacts of subtropical AWB on seasonal TC activity improve the skill of extended-range seasonal forecasts of TC activity. There is a significant correlation between boreal winter and boreal summer AWB activity via AWB-forced phases of the quasi-stationary North Atlantic Oscillation (NAO). Years with above-normal boreal summer AWB activity over the North Atlantic region also show above-normal AWB activity in the preceding boreal winter that tends to force a positive phase of the NAO that persists through the spring. These conditions are sustained by continued AWB throughout the year, particularly when El Niño-Southern Oscillation plays less of a role at forcing the large-scale circulation. While individual AWB events are synoptic and nonlinear with little predictability beyond 8-10 days, the strong dynamical connection between winter and summer wave breaking lends enough persistence to AWB activity to enable predictability of its potential impacts on TC activity. We find that the winter-summer relationship improves the skill of extended-range seasonal forecasts from as early as an April lead time, particularly for years when wave breaking has played a crucial role in suppressing TC development.

scholarly journals Evaluation of Submonthly Precipitation Forecast Skill from Global Ensemble Prediction Systems

2015 ◽  
Vol 143 (7) ◽  
pp. 2871-2889 ◽  
Author(s):  
Shuhua Li ◽  
Andrew W. Robertson
Keyword(s):  
Skill Score ◽  
Forecast Skill ◽  
Prediction Skill ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Boreal Summer ◽  
Seasonal Forecasts ◽  
Maritime Continent ◽  
Prediction Systems ◽  
The Tropics

Abstract The prediction skill of precipitation at submonthly time scales during the boreal summer season is investigated based on hindcasts from three global ensemble prediction systems (EPSs). The results, analyzed for lead times up to 4 weeks, indicate encouraging correlation skill over some regions, particularly over the Maritime Continent and the equatorial Pacific and Atlantic Oceans. The hindcasts from all three models correspond to high prediction skill over the first week compared to the following three weeks. The ECMWF forecast system tends to yield higher prediction skill than the other two systems, in terms of both correlation and mean squared skill score. However, all three systems are found to exhibit large conditional biases in the tropics, highlighted using the mean squared skill score. The sources of submonthly predictability are examined in the ECMWF hindcasts over the Maritime Continent in three typical years of contrasting ENSO phase, with a focus on the combined impact of the intraseasonal MJO and interannual ENSO. Rainfall variations over Borneo in the ENSO-neutral year are found to correspond well with the dominant MJO phase. The contribution of ENSO becomes substantial in the two ENSO years, but the MJO impact can become dominant when the MJO occurs in phases 2–3 during El Niño or in phases 5–6 during the La Niña year. These results support the concept that “windows of opportunity” of high forecast skill exist as a function of ENSO and the MJO in certain locations and seasons, which may lead to subseasonal-to-seasonal forecasts of substantial societal value in the future.

scholarly journals Seasonal Rainfall Forecasts for the Yangtze River Basin in the Extreme Summer of 2020

2021 ◽  
Author(s):  
Philip E. Bett ◽  
Gill M. Martin ◽  
Nick Dunstone ◽  
Adam A. Scaife ◽  
Hazel E. Thornton ◽  
...  
Keyword(s):  
Linear Regression ◽  
River Basin ◽  
Yangtze River ◽  
Asian Summer Monsoon ◽  
Yangtze River Basin ◽  
Early Summer ◽  
Lead Times ◽  
Seasonal Forecasts ◽  
June July ◽  
The Impact

AbstractSeasonal forecasts for Yangtze River basin rainfall in June, May–June–July (MJJ), and June–July–August (JJA) 2020 are presented, based on the Met Office GloSea5 system. The three-month forecasts are based on dynamical predictions of an East Asian Summer Monsoon (EASM) index, which is transformed into regional-mean rainfall through linear regression. The June rainfall forecasts for the middle/lower Yangtze River basin are based on linear regression of precipitation. The forecasts verify well in terms of giving strong, consistent predictions of above-average rainfall at lead times of at least three months. However, the Yangtze region was subject to exceptionally heavy rainfall throughout the summer period, leading to observed values that lie outside the 95% prediction intervals of the three-month forecasts. The forecasts presented here are consistent with other studies of the 2020 EASM rainfall, whereby the enhanced mei-yu front in early summer is skillfully forecast, but the impact of midlatitude drivers enhancing the rainfall in later summer is not captured. This case study demonstrates both the utility of probabilistic seasonal forecasts for the Yangtze region and the potential limitations in anticipating complex extreme events driven by a combination of coincident factors.

scholarly journals Atlantic Niño/Niña Prediction Skills in NMME Models

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 803
Author(s):  
Ran Wang ◽  
Lin Chen ◽  
Tim Li ◽  
Jing-Jia Luo
Keyword(s):  
Sea Surface ◽  
Prediction Skill ◽  
Boreal Summer ◽  
Boreal Winter ◽  
Equatorial Atlantic ◽  
Multimodel Ensemble ◽  
Spring Predictability Barrier ◽  
Anomaly Correlation Coefficient ◽  
Earth’S Climate ◽  
Atlantic Niño

The Atlantic Niño/Niña, one of the dominant interannual variability in the equatorial Atlantic, exerts prominent influence on the Earth’s climate, but its prediction skill shown previously was unsatisfactory and limited to two to three months. By diagnosing the recently released North American Multimodel Ensemble (NMME) models, we find that the Atlantic Niño/Niña prediction skills are improved, with the multi-model ensemble (MME) reaching five months. The prediction skills are season-dependent. Specifically, they show a marked dip in boreal spring, suggesting that the Atlantic Niño/Niña prediction suffers a “spring predictability barrier” like ENSO. The prediction skill is higher for Atlantic Niña than for Atlantic Niño, and better in the developing phase than in the decaying phase. The amplitude bias of the Atlantic Niño/Niña is primarily attributed to the amplitude bias in the annual cycle of the equatorial sea surface temperature (SST). The anomaly correlation coefficient scores of the Atlantic Niño/Niña, to a large extent, depend on the prediction skill of the Niño3.4 index in the preceding boreal winter, implying that the precedent ENSO may greatly affect the development of Atlantic Niño/Niña in the following boreal summer.

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