Seasonal prediction of equatorial Atlantic sea surface temperature using simple initialization and bias correction techniques

Dippe, Tina; Greatbatch, Richard John; Ding, Hui

doi:10.1002/asl.898

Cited by 25 publications

(13 citation statements)

References 34 publications

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“…Here we investigate this issue for the tropical Atlantic, because this is a region where current models exhibit large biases compared to the amplitude of interannual variability and have demonstrated low prediction skill. Furthermore, several previous studies have reported an influence of model biases on the simulated variability and prediction skill in this region (Ding et al 2015a;Dippe et al 2018Dippe et al , 2019.…”

Section: Discussionmentioning

confidence: 90%

“…These biases cause an underestimation of the thermocline feedback (Deppenmeier et al 2016) and an overestimation of thermodynamic ocean-atmosphere interaction (Jouanno et al 2017); and reducing them enhances the simulation of dynamical ocean-atmosphere interaction and Atlantic Niño variability (Ding et al 2015a, b;Dippe et al 2018;Harlaß et al 2018). Furthermore, Dippe et al (2019) show that reducing this bias improves the prediction of Atlantic Niño variability, although prediction skill remains poor-i.e. only beating marginally persistence for May start and beating persistence by 0.1 for August start up to 4 lead month.…”

Section: Supplementary Informationmentioning

confidence: 99%

“…In flux correction a fixed numerical correction is added to the exchanged fluxes (Sausen et al 1988), while in anomaly coupling the variables used to compute air-sea fluxes are corrected (Kirtman et al 1997). Although neither approach guarantees an improved simulation of variability, anomaly coupling has been shown effective in improving the simulation and prediction of equatorial Atlantic interannual variability (Ding et al 2015a;Dippe et al 2018Dippe et al , 2019. A drawback with both approaches is that estimating effective numerical corrections is difficult, because coupled feedbacks in the tropics can strongly amplify errors (Neelin and Dijkstra 1995).…”

Section: Supplementary Informationmentioning

confidence: 99%

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Relating model bias and prediction skill in the equatorial Atlantic

et al. 2021

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We investigate the impact of large climatological biases in the tropical Atlantic on reanalysis and seasonal prediction performance using the Norwegian Climate Prediction Model (NorCPM) in a standard and an anomaly coupled configuration. Anomaly coupling corrects the climatological surface wind and sea surface temperature (SST) fields exchanged between oceanic and atmospheric models, and thereby significantly reduces the climatological model biases of precipitation and SST. NorCPM combines the Norwegian Earth system model with the ensemble Kalman filter and assimilates SST and hydrographic profiles. We perform a reanalysis for the period 1980–2010 and a set of seasonal predictions for the period 1985–2010 with both model configurations. Anomaly coupling improves the accuracy and the reliability of the reanalysis in the tropical Atlantic, because the corrected model enables a dynamical reconstruction that satisfies better the observations and their uncertainty. Anomaly coupling also enhances seasonal prediction skill in the equatorial Atlantic to the level of the best models of the North American multi-model ensemble, while the standard model is among the worst. However, anomaly coupling slightly damps the amplitude of Atlantic Niño and Niña events. The skill enhancements achieved by anomaly coupling are largest for forecast started from August and February. There is strong spring predictability barrier, with little skill in predicting conditions in June. The anomaly coupled system show some skill in predicting the secondary Atlantic Niño-II SST variability that peaks in November–December from August 1st.

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Section: Discussionmentioning

confidence: 90%

Section: Supplementary Informationmentioning

confidence: 99%

Section: Supplementary Informationmentioning

confidence: 99%

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Relating model bias and prediction skill in the equatorial Atlantic

et al. 2021

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“…(2012): higher potential predictive skill in the tropical Pacific SST anomalies is found in the west in our ATL run (Table 2 and Figure S3) but in the east in the pacemaker experiment (Figure 4 in their paper). This discrepancy provides another perspective on the predictability that involves ENSO diversity (Capotondi et al., 2015), which might be modulated by Atlantic mean state biases, model systematic errors, and assimilation methods (Ding, Keenlyside, et al., 2015; Ding, Greatbatch, et al., 2015; Dippe et al., 2019; Johnson et al., 2020). According to previous studies (Ham, Kug, Park, & Jin, 2013; Ham, Kug, & Park, 2013), the boreal summer Atlantic Niño enhances occurrences in the eastern Pacific type of ENSO in the subsequent winter, whereas the spring North Atlantic SST anomalies contribute to an increase in the central Pacific type of ENSO events.…”

Section: Discussionmentioning

confidence: 99%

El Niño–Southern Oscillation Evolution Modulated by Atlantic Forcing

et al. 2020

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The El Niño–Southern Oscillation (ENSO) exerts a strong influence on tropical Atlantic variability, but it is also affected by Atlantic forcing. Previous research has proposed three Atlantic precursors for ENSO: the North tropical Atlantic, the equatorial Atlantic, and the entire tropical Atlantic. However, the relative importance of these Atlantic precursors for ENSO remains unclear. Here, we present evidence from a set of multimodel partial ocean assimilation experiments that equatorial Atlantic cooling is the main contributor for weakening equatorial zonal winds in the Indo‐Pacific sector and subsequent ocean warming in the tropical Pacific. Opposite tendencies occur for a warmer equatorial Atlantic. The equatorial Atlantic affects the interbasin climate seesaw between the Atlantic and Pacific through an atmospheric zonal Wavenumber 1 pattern. However, model mean state biases and systematic errors prevent a precise assessment of the response times for the equatorial Pacific trade winds to Atlantic forcing.

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“…• Skill of monthly ENSO hindcasts Smith et al, 2013;Vecchi et al, 2014;Ding et al, 2015;Kim et al, 2017;Scaife et al, 2019;Dippe et al, 2019). For example, Manganello and Huang (2009) found that a CGCM warm SST bias off the South American coast degrades ENSO forecast skill.…”

Section: 1029/2019gl086765mentioning

confidence: 99%

Relating CMIP5 Model Biases to Seasonal Forecast Skill in the Tropical Pacific

Ding

Newman

Alexander

et al. 2020

Geophysical Research Letters

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We examine links between tropical Pacific mean state biases and El Niño/Southern Oscillation forecast skill, using model‐analog hindcasts of sea surface temperature (SST; 1961–2015) and precipitation (1979–2015) at leads of 0–12 months, generated by 28 different models from the fifth phase of the Coupled Model Intercomparison Project (CMIP5). Model‐analog forecast skill has been demonstrated to match or even exceed traditional assimilation‐initialized forecast skill in a given model. Models with the most realistic mean states and interannual variability for SST, precipitation, and 10‐m zonal winds in the equatorial Pacific also generate the most skillful precipitation forecasts in the central equatorial Pacific and the best SST forecasts at 6‐month or longer leads. These results show direct links between model climatological biases and seasonal forecast errors, demonstrating that model‐analog hindcast skill—that is, how well a model can capture the observed evolution of tropical Pacific anomalies—is an informative El Niño/Southern Oscillation metric for climate simulations.

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Seasonal prediction of equatorial Atlantic sea surface temperature using simple initialization and bias correction techniques

Cited by 25 publications

References 34 publications

Relating model bias and prediction skill in the equatorial Atlantic

Relating model bias and prediction skill in the equatorial Atlantic

El Niño–Southern Oscillation Evolution Modulated by Atlantic Forcing

Relating CMIP5 Model Biases to Seasonal Forecast Skill in the Tropical Pacific

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