The effects of bias, drift, and trends in calculating anomalies for evaluating skill of seasonal-to-decadal initialized climate predictions

Meehl, Gerald A.; Teng, Haiyan; Smith, Doug; Yeager, Stephen; Merryfield, William J.; Doblas-Reyes, Francisco; Glanville, Anne A.

doi:10.1007/s00382-022-06272-7

Cited by 14 publications

(22 citation statements)

References 28 publications

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“…Despite numerous advances and constant development of climate modeling, errors are intrinsic to the process. However, several studies point to different causes for the deviations, such as deficiency in SST simulation, errors in the initialization of soil moisture conditions, and inappropriate physical parameterization [3,[34][35][36]78]. In addition, improved extreme events prediction requires a deep understanding of drought and flood mechanisms, refined observations from data assimilation, better parameterizing techniques, efficient ensemble methodologies, and proper uncertainty quantification [17].…”

Section: Discussionmentioning

confidence: 99%

“…Seasonal climate forecasts start from an observed state of all Earth system components and then evolve over a few months. Thus, errors present at the beginning of the forecast persist or grow during the integration of the model, reaching magnitudes comparable to the forecast signals [34][35][36]. In this scenario, the coupled general circulation model components must be consistent with each other at the initial time of the forecasts to avoid the influence of initialization shock, which is associated with the departure of the model climatology from the observed [37].…”

Section: Ecmwf-seas5 Datamentioning

confidence: 99%

“…Furthermore, other errors in climate predictions are systematic, and by comparing retrospective forecasts (hindcasts) and observations, it is possible to correct these systematic flaws [34][35][36]. These systematic errors, also known as biases, arise from the difference between the atmospheric and oceanic states simulated by the model and those of the observed climate and can vary by season, region, and forecast lead time.…”

Section: Ecmwf-seas5 Datamentioning

confidence: 99%

“…In this context, we note that evaluating the performance of forecasts and hindcasts is essential since the latter has a more extensive set of predictions, allowing the model's assessment with more robust statistics. On the other hand, the retrospective predictions present more idealized conditions than the prognostic forecasts since the hindcasts are better calibrated and submitted to complete data ingestion for the initial conditions [34][35][36].…”

Section: Precipitationmentioning

confidence: 99%

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Evaluation of ECMWF-SEAS5 Seasonal Temperature and Precipitation Predictions over South America

Ferreira

Reboita

Drumond

2022

Climate

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Nowadays, a challenge in Climate Science is the seasonal forecast and knowledge of the model’s performance in different regions. The challenge in South America reflects its huge territory; some models present a good performance, and others do not. Nevertheless, reliable seasonal climate forecasts can benefit numerous decision-making processes related to agriculture, energy generation, and extreme events mitigation. Thus, given the few works assessing the ECMWF-SEAS5 performance in South America, this study investigated the quality of its seasonal temperature and precipitation predictions over the continent. For this purpose, predictions from all members of the hindcasts (1993–2016) and forecasts (2017–2021) ensemble were used, considering the four yearly seasons. The analyses included seasonal mean fields, bias correction, anomaly correlations, statistical indicators, and seasonality index. The best system’s performance occurred in regions strongly influenced by teleconnection effects, such as northern South America and northeastern Brazil, in which ECMWF-SEAS5 even reproduced the extreme precipitation anomalies that happened in recent decades. Moreover, the system indicated a moderate capability of seasonal predictions in medium and low predictability regions. In summary, the results show that ECMWF-SEAS5 climate forecasts are potentially helpful and should be considered to plan various strategic activities better.

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Section: Discussionmentioning

confidence: 99%

Section: Ecmwf-seas5 Datamentioning

confidence: 99%

Section: Ecmwf-seas5 Datamentioning

confidence: 99%

Section: Precipitationmentioning

confidence: 99%

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Evaluation of ECMWF-SEAS5 Seasonal Temperature and Precipitation Predictions over South America

Ferreira

Reboita

Drumond

2022

Climate

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“…Meehl et al, 2016). A third is a compromise between the rst two whereby a mean drift is calculated in the hindcasts for the 15 model years prior to the initial year, and use that as a reference to compute anomalies, with a similar calculation for the observations (Meehl et al, 2022). It has been noted that this third method is likely the most "fair" of the three methods in that computing a model climatology over the entire reference period is not what occurs in a real-time prediction exercise where you only have information prior to the initial year (Risbey et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Evaluating skill in predicting the Interdecadal Pacific Oscillation in initialized decadal climate prediction hindcasts in E3SMv1 and CESM1 using two different initialization methods and a small set of start years

Meehl

Kirtman

Glanville

et al. 2023

Preprint

Self Cite

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Having to run a large set of initialized hindcasts with many ensemble members for enough samples of start years to form a drifted climatology from which to compute anomalies to compare to observations (to quantify skill of the hindcasts) is a daunting computational challenge. This limits the ability to experiment with case studies and other applications where only a few initial years are needed. Here we run a set of hindcasts with CESM1 and E3SMv1 using two different initialization methods for a limited set of start years and use the respective uninitialized free-running historical simulations to form the model climatologies. Since the drifts from the observed initial states in the hindcasts toward the uninitialized model state are large and rapid, after a few years the drifted initialized models approach the uninitialized model climatological errors. Therefore, hindcasts from the limited start years can use the uninitialized climatology to represent the drifted model states after about lead year 3, providing a means to compute forecast anomalies in the absence of a large hindcast sample. There is comparable skill for predicting the Interdecadal Pacific Oscillation using this method compared to the conventional methodology with a large hindcast data set. The drifts are somewhat different in the two initialization methods, but the drifts are so large and consistent that by about lead year 3 the two methods are roughly comparable, though there is a model dependence to the drifts in the two initialization methods.

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Evaluating skill in predicting the Interdecadal Pacific Oscillation in initialized decadal climate prediction hindcasts in E3SMv1 and CESM1 using two different initialization methods and a small set of start years

Meehl,

Kirtman,

Glanville

et al. 2023

Clim Dyn

View full text Add to dashboard Cite

It is a daunting challenge to conduct initialized hindcasts with enough ensemble members and associated start years to form a drifted climatology from which to compute the anomalies necessary to quantify the skill of the hindcasts when compared to observations. This limits the ability to experiment with case studies and other applications where only a few initial years are needed. Here we run a set of hindcasts with CESM1 and E3SMv1 using two different initialization methods for a limited set of start years and use the respective uninitialized free-running historical simulations to form the model climatologies. Since the drifts from the observed initial states in the hindcasts toward the uninitialized model state are large and rapid, after a few years the drifted initialized models approach the uninitialized model climatological errors. Therefore, hindcasts from the limited start years can use the uninitialized climatology to represent the drifted model states after about lead year 3, providing a means to compute forecast anomalies in the absence of a large hindcast sample. There is comparable skill for predicting spatial patterns of multi-year Pacific sea surface temperature anomalies in the domain of the Interdecadal Pacific Oscillation using this method compared to the conventional methodology with a large hindcast data set, though there is a model dependence to the drifts in the two initialization methods.

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The effects of bias, drift, and trends in calculating anomalies for evaluating skill of seasonal-to-decadal initialized climate predictions

Cited by 14 publications

References 28 publications

Evaluation of ECMWF-SEAS5 Seasonal Temperature and Precipitation Predictions over South America

Evaluation of ECMWF-SEAS5 Seasonal Temperature and Precipitation Predictions over South America

Evaluating skill in predicting the Interdecadal Pacific Oscillation in initialized decadal climate prediction hindcasts in E3SMv1 and CESM1 using two different initialization methods and a small set of start years

Evaluating skill in predicting the Interdecadal Pacific Oscillation in initialized decadal climate prediction hindcasts in E3SMv1 and CESM1 using two different initialization methods and a small set of start years

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