Did the ECMWF Seasonal Forecast Model Outperform Statistical ENSO Forecast Models over the Last 15 Years?

2005 ◽  
Vol 18 (16) ◽  
pp. 3240-3249 ◽  
Author(s):  
Geert Jan van Oldenborgh ◽  
Magdalena A. Balmaseda ◽  
Laura Ferranti ◽  
Timothy N. Stockdale ◽  
David L. T. Anderson
Keyword(s):  
El Niño ◽  
Statistical Models ◽  
El Nino ◽  
Southern Oscillation ◽  
Forecast Model ◽  
Seasonal Forecasts ◽  
Weather Forecasts ◽  
Forecast Models ◽  
System 2 ◽  
System 1

Abstract The European Centre for Medium-Range Weather Forecasts (ECMWF) has made seasonal forecasts since 1997 with ensembles of a coupled ocean–atmosphere model, System-1 (S1). In January 2002, a new version, System-2 (S2), was introduced. For the calibration of these models, hindcasts have been performed starting in 1987, so that 15 yr of hindcasts and forecasts are now available for verification. Seasonal predictability is to a large extent due to the El Niño–Southern Oscillation (ENSO) climate oscillations. ENSO predictions of the ECMWF models are compared with those of statistical models, some of which are used operationally. The relative skill depends strongly on the season. The dynamical models are better at forecasting the onset of El Niño or La Niña in boreal spring to summer. The statistical models are comparable at predicting the evolution of an event in boreal fall and winter.

Towards onset: shades of ENSO skill

2020 ◽  
Author(s):  
Dougal Squire ◽  
James Risbey
Keyword(s):  
El Niño ◽  
Statistical Models ◽  
El Nino ◽  
Southern Oscillation ◽  
Skill Score ◽  
Forecast Skill ◽  
The North ◽  
Forecast Models ◽  
The Impact ◽  
Better Than

<p>Climate forecast skill for the El Nino-Southern Oscillation (ENSO) is better than chance, but has increased little in recent decades. Further, the relative skill of dynamical and statistical models varies in skill assessments, depending on choices made about how to evaluate the forecasts. Using a suite of models from the North American Multi-Model Ensemble (NMME) archive we outline the consequences for skill of how the bias corrections and forecast anomalies are formed. We show that the method for computing forecast anomalies is such a critical part of the provenance of a skill score that any score for forecast anomalies lacking clarity about the method is open to wide interpretation. Many assessments of hindcast skill are likely to be overestimates of attainable forecast skill because the hindcast anomalies are informed by observations over the period assessed that would not be available to a real forecast. The relative skill rankings of forecast models can change between hindcast and forecast systems because the impact of model bias on skill is sensitive to the ways in which forecast anomalies are formed. Dynamical models are found to be more skillful than simple statistical models for forecasting the onset of El Nino events.</p>

scholarly journals Lagged Oceanic Effects on the East African Short Rains

2021 ◽  
Author(s):  
Erik W Kolstad ◽  
David MacLeod
Keyword(s):  
El Niño ◽  
Statistical Models ◽  
El Nino ◽  
Southern Oscillation ◽  
Forecast Errors ◽  
Seasonal Forecasts ◽  
East African ◽  
Forecast Performance ◽  
Dry Conditions ◽  
Short Rains

Abstract The East African ‘short rains’ in October–December (OND) exhibit large interannual variability. Drought and flooding are not unusual, and long-range rainfall forecasts can guide planning and preparedness. Although seasonal forecasts based on dynamical models are making inroads, statistical models based on sea surface temperature (SST) precursors are still widely used. It is important to better understand the sources of skill of statistical models and why they sometimes fail. Here, we define a linear regression model, where the August states of El Niño-Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) predict about 40% of the short rains variability in 1950–2020. The forecast errors are traced back to the initial SSTs: toowet (too-dry) forecasts are linked linearly to positive (negative) initial ENSO and IOD states in August. The link to the initial IOD state is mediated by IOD between August and OND, highlighting a physical mechanism for prediction busts. We also identify asymmetry and nonlinearity: when ENSO and/or the IOD are positive in August, the range and variance of OND forecast errors are larger than when the SST indices are negative. Upfront adjustments of predictions conditional on initial SSTs would have helped in some years with large forecast busts, such as the dry 1987 season during a major El Niño, for which the model erroneously predicts copious rainfall, but it would have exacerbated the forecast performance in the wet 2019 season, when the IOD was strongly positive and the model predicts too-dry conditions.

scholarly journals Three Putative Types of El Niño Revealed by Spatial Variability in Impact on Australian Wheat Yield

2005 ◽  
Vol 18 (10) ◽  
pp. 1566-1574 ◽  
Author(s):  
A. B. Potgieter ◽  
G. L. Hammer ◽  
H. Meinke ◽  
R. C. Stone ◽  
L. Goddard
Keyword(s):  
Spatial Patterns ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
Wheat Yield ◽  
Severe Drought ◽  
Seasonal Forecasts ◽  
El Niño Events ◽  
Australian Wheat ◽  
El Nino Events

Abstract The El Niño–Southern Oscillation (ENSO) phenomenon significantly impacts rainfall and ensuing crop yields in many parts of the world. In Australia, El Niño events are often associated with severe drought conditions. However, El Niño events differ spatially and temporally in their manifestations and impacts, reducing the relevance of ENSO-based seasonal forecasts. In this analysis, three putative types of El Niño are identified among the 24 occurrences since the beginning of the twentieth century. The three types are based on coherent spatial patterns (“footprints”) found in the El Niño impact on Australian wheat yield. This bioindicator reveals aligned spatial patterns in rainfall anomalies, indicating linkage to atmospheric drivers. Analysis of the associated ocean–atmosphere dynamics identifies three types of El Niño differing in the timing of onset and location of major ocean temperature and atmospheric pressure anomalies. Potential causal mechanisms associated with these differences in anomaly patterns need to be investigated further using the increasing capabilities of general circulation models. Any improved predictability would be extremely valuable in forecasting effects of individual El Niño events on agricultural systems.

scholarly journals Seasonal Forecasting in the Pacific Using the Coupled Model POAMA-2

2013 ◽  
Vol 28 (3) ◽  
pp. 668-680 ◽  
Author(s):  
Andrew Cottrill ◽  
Harry H. Hendon ◽  
Eun-Pa Lim ◽  
Sally Langford ◽  
Kay Shelton ◽  
...  
Keyword(s):  
El Niño ◽  
El Nino ◽  
Regional Climate ◽  
Coupled Model ◽  
Forecast Model ◽  
Seasonal Prediction ◽  
La Niña ◽  
Spatial And Temporal Variability ◽  
Seasonal Forecasts ◽  
La Nina

Abstract The development of a dynamical model seasonal prediction service for island nations in the tropical South Pacific is described. The forecast model is the Australian Bureau of Meteorology's Predictive Ocean–Atmosphere Model for Australia (POAMA), a dynamical seasonal forecast system. Using a hindcast set for the period 1982–2006, POAMA is shown to provide skillful forecasts of El Niño and La Niña many months in advance and, because the model faithfully simulates the spatial and temporal variability of rainfall associated with displacements of the southern Pacific convergence zone (SPCZ) and ITCZ during La Niña and El Niño, it also provides good predictions of rainfall throughout the tropical Pacific region. The availability of seasonal forecasts from POAMA should be beneficial to Pacific island countries for the production of regional climate outlooks across the region.

Global Seasonal Precipitation Anomalies Robustly Associated with El Niño and La Niña Events—An OLR Perspective*,+

2015 ◽  
Vol 28 (15) ◽  
pp. 6133-6159 ◽  
Author(s):  
Andrew M. Chiodi ◽  
D. E. Harrison
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
La Niña ◽  
Seasonal Precipitation ◽  
Seasonal Forecasts ◽  
Enso Events ◽  
La Nina ◽  
Precipitation Anomalies ◽  
Weather Anomalies

Abstract El Niño–Southern Oscillation (ENSO) events are associated with particular seasonal weather anomalies in many regions around the planet. When the statistical links are sufficiently strong, ENSO state information can provide useful seasonal forecasts with varying lead times. However, using conventional sea surface temperature or sea level pressure indices to characterize ENSO state leads to many instances of limited forecast skill (e.g., years identified as El Niño or La Niña with weather anomalies unlike the average), even in regions where there is considerable ENSO-associated anomaly, on average. Using outgoing longwave radiation (OLR) conditions to characterize ENSO state identifies a subset of the conventional ENSO years, called OLR El Niño and OLR La Niña years herein. Treating the OLR-identified subset of years differently can both usefully strengthen the level of statistical significance in the average (composite) and also greatly reduce the year-to-year deviations in the composite precipitation anomalies. On average, over most of the planet, the non-OLR El Niño and non-OLR La Niña years have much more limited statistical utility for precipitation. The OLR El Niño and OLR La Niña indices typically identify years in time to be of use to boreal wintertime and later seasonal forecasting efforts, meaning that paying attention to tropical Pacific OLR conditions may offer more than just a diagnostic tool. Understanding better how large-scale environmental conditions during ENSO events determine OLR behavior (and deep atmospheric convection) will lead to improved seasonal precipitation forecasts for many areas.

scholarly journals Analisa Pola Temperatur Udara Permukaan di Sumatera Barat Tahun 1980 - 2017

2019 ◽  
Vol 8 (1) ◽  
pp. 91-97
Author(s):  
Hanifah Azzaura Musyayyadah ◽  
Mutya Vonnisa
Keyword(s):  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Model Data ◽  
Weather Forecasts ◽  
European Centre ◽  
Medium Range

Temperatur udara permukaan di Sumatera Barat telah diteliti menggunakan data stasiun Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) untuk 11 tahun pengamatan (2007 – 2017) di empat lokasi, yaitu Teluk Bayur (Padang), Minang Kabau (batas kota Padang – Padang Pariaman), Sincincin (Padang Pariaman) dan Padang Panjang. Selain itu digunakan juga data re-analisis dari European Centre for Medium-Range Weather Forecasts Re-Analysis interim model data (ECMWF ERA-Interim) untuk 38 tahun pengamatan (1980 – 2017). Osilasi internal temperatur udara permukaan di Sumatera Barat diamati menggunakan transformasi wavelet dengan mother Maxican Hat. Hasil penelitian menunjukkan bahwa temperatur udara permukaan rata – rata di Sumatera Barat meningkat sekitar 0,007⁰C-0,01⁰C/tahun. Temperatur maksimum harian meningkat sekitar 0,058⁰C-0,066⁰C/tahun, sedangkan temperature udara minimum harian meningkat sekitar 0,028⁰C-0,045⁰C/tahun. Periode ulang temperatur udara permukaan di Sumatera Barat yang paling dominan adalah satu tahun, atau biasa disebut osilasi tahunan yang disebabkan oleh monsun. Selain itu, terdapat osilasi 4 dan 8 tahun yang bersesuaian dengan siklus El-Nino Southern Oscillation (ENSO). Dengan demikian dapat dikatakan bahwa temperatur udara permukaan di Sumatera Barat dipengaruhi oleh monsun dan ENSO. Kata kunci: Temperatur udara permukaan, Sumatera Barat, ERA-Interim, monsun, ENSO

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