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Initialized near-term regional climate change prediction.

Doblas-Reyes FJ, Andreu-Burillo I, Chikamoto Y, García-Serrano J, Guemas V, Kimoto M, Mochizuki T, Rodrigues LR, van Oldenborgh GJ - Nat Commun (2013)

Bottom Line: The Fifth Coupled Model Intercomparison Project set of co-ordinated climate-model experiments includes a set of near-term predictions in which several modelling groups participated and whose forecast quality we illustrate here.We show that climate forecast systems have skill in predicting the Earth's temperature at regional scales over the past 50 years and illustrate the trustworthiness of their predictions.Most of the skill can be attributed to changes in atmospheric composition, but also partly to the initialization of the predictions.

View Article: PubMed Central - PubMed

Affiliation: Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain. francisco.doblas-reyes@ic3.cat

ABSTRACT
Climate models are seen by many to be unverifiable. However, near-term climate predictions up to 10 years into the future carried out recently with these models can be rigorously verified against observations. Near-term climate prediction is a new information tool for the climate adaptation and service communities, which often make decisions on near-term time scales, and for which the most basic information is unfortunately very scarce. The Fifth Coupled Model Intercomparison Project set of co-ordinated climate-model experiments includes a set of near-term predictions in which several modelling groups participated and whose forecast quality we illustrate here. We show that climate forecast systems have skill in predicting the Earth's temperature at regional scales over the past 50 years and illustrate the trustworthiness of their predictions. Most of the skill can be attributed to changes in atmospheric composition, but also partly to the initialization of the predictions.

No MeSH data available.


Related in: MedlinePlus

Forecast quality of several climate indices.(a–c) Time series of the ensemble-mean forecast anomalies averaged over the forecast years 2–5 (solid, Init) and the accompanying non-initialized (dashed, NoInit) experiments of the global-mean near-surface air temperature (SAT) (a), the AMV (b) and IPO (c) indices. The observational time series, GISS49 global-mean near-surface air temperature and ERSST48 for the AMV and IPO, are represented with dark (positive anomalies) and light (negative anomalies) grey vertical bars, where a 4-year running mean has been applied for consistency with the time averaging of the predictions. The box-and-whisker represents the multi-model ensemble range (anomalies with respect to the multi-model ensemble mean) of Init (solid) and NoInit (dashed), where the whiskers correspond to the maximum and minimum, the box to the interquartile range and the horizontal bar to the median. The predictions have been initialized once every year over the period 1961–2006. (d–f): Correlation of the ensemble mean with the observational reference along the forecast time for 4-year averages. The one-sided 95% confidence level with a t-distribution is represented in grey, where the number of degrees of freedom has been computed taking into account the autocorrelation of the observational time series, which are different for each forecast time. A two-sided t-test (with the number of degrees of freedom computed taking into account the autocorrelation of the observational time series) for the differences between the Init and NoInit correlation found no significant results with confidence ≥90%. (g–i): RMSE of the ensemble mean along the forecast time for 4-year forecast averages. Squares are used where the Init skill is significantly better than the NoInit skill with 95% confidence using a two-sided F-test where the number of degrees of freedom takes into account the autocorrelation of the observation minus prediction time series. (j–l) Ensemble spread estimated as the s.d. of the anomalies around the multi-model ensemble mean.
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f1: Forecast quality of several climate indices.(a–c) Time series of the ensemble-mean forecast anomalies averaged over the forecast years 2–5 (solid, Init) and the accompanying non-initialized (dashed, NoInit) experiments of the global-mean near-surface air temperature (SAT) (a), the AMV (b) and IPO (c) indices. The observational time series, GISS49 global-mean near-surface air temperature and ERSST48 for the AMV and IPO, are represented with dark (positive anomalies) and light (negative anomalies) grey vertical bars, where a 4-year running mean has been applied for consistency with the time averaging of the predictions. The box-and-whisker represents the multi-model ensemble range (anomalies with respect to the multi-model ensemble mean) of Init (solid) and NoInit (dashed), where the whiskers correspond to the maximum and minimum, the box to the interquartile range and the horizontal bar to the median. The predictions have been initialized once every year over the period 1961–2006. (d–f): Correlation of the ensemble mean with the observational reference along the forecast time for 4-year averages. The one-sided 95% confidence level with a t-distribution is represented in grey, where the number of degrees of freedom has been computed taking into account the autocorrelation of the observational time series, which are different for each forecast time. A two-sided t-test (with the number of degrees of freedom computed taking into account the autocorrelation of the observational time series) for the differences between the Init and NoInit correlation found no significant results with confidence ≥90%. (g–i): RMSE of the ensemble mean along the forecast time for 4-year forecast averages. Squares are used where the Init skill is significantly better than the NoInit skill with 95% confidence using a two-sided F-test where the number of degrees of freedom takes into account the autocorrelation of the observation minus prediction time series. (j–l) Ensemble spread estimated as the s.d. of the anomalies around the multi-model ensemble mean.

Mentions: Global-mean near-surface air temperature and the Atlantic multi-decadal variability (AMV) and the interdecadal Pacific oscillation (IPO) indices are used as benchmarks to assess the ability to predict multi-annual variability425 (Fig. 1). The AMV and IPO are the dominant decadal ocean surface temperature variations over the North Atlantic26 and Pacific Oceans27, respectively, and have well-defined spatial characteristics4. Both indices have been estimated after removing the global-mean sea surface temperature (SST) to retain the differential cooling or warming of the corresponding basin with respect to the global behaviour. Apart from the multi-annual variability, these indices display either a long-term trend or low-frequency variability, which should be correctly predicted too.


Initialized near-term regional climate change prediction.

Doblas-Reyes FJ, Andreu-Burillo I, Chikamoto Y, García-Serrano J, Guemas V, Kimoto M, Mochizuki T, Rodrigues LR, van Oldenborgh GJ - Nat Commun (2013)

Forecast quality of several climate indices.(a–c) Time series of the ensemble-mean forecast anomalies averaged over the forecast years 2–5 (solid, Init) and the accompanying non-initialized (dashed, NoInit) experiments of the global-mean near-surface air temperature (SAT) (a), the AMV (b) and IPO (c) indices. The observational time series, GISS49 global-mean near-surface air temperature and ERSST48 for the AMV and IPO, are represented with dark (positive anomalies) and light (negative anomalies) grey vertical bars, where a 4-year running mean has been applied for consistency with the time averaging of the predictions. The box-and-whisker represents the multi-model ensemble range (anomalies with respect to the multi-model ensemble mean) of Init (solid) and NoInit (dashed), where the whiskers correspond to the maximum and minimum, the box to the interquartile range and the horizontal bar to the median. The predictions have been initialized once every year over the period 1961–2006. (d–f): Correlation of the ensemble mean with the observational reference along the forecast time for 4-year averages. The one-sided 95% confidence level with a t-distribution is represented in grey, where the number of degrees of freedom has been computed taking into account the autocorrelation of the observational time series, which are different for each forecast time. A two-sided t-test (with the number of degrees of freedom computed taking into account the autocorrelation of the observational time series) for the differences between the Init and NoInit correlation found no significant results with confidence ≥90%. (g–i): RMSE of the ensemble mean along the forecast time for 4-year forecast averages. Squares are used where the Init skill is significantly better than the NoInit skill with 95% confidence using a two-sided F-test where the number of degrees of freedom takes into account the autocorrelation of the observation minus prediction time series. (j–l) Ensemble spread estimated as the s.d. of the anomalies around the multi-model ensemble mean.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3644073&req=5

f1: Forecast quality of several climate indices.(a–c) Time series of the ensemble-mean forecast anomalies averaged over the forecast years 2–5 (solid, Init) and the accompanying non-initialized (dashed, NoInit) experiments of the global-mean near-surface air temperature (SAT) (a), the AMV (b) and IPO (c) indices. The observational time series, GISS49 global-mean near-surface air temperature and ERSST48 for the AMV and IPO, are represented with dark (positive anomalies) and light (negative anomalies) grey vertical bars, where a 4-year running mean has been applied for consistency with the time averaging of the predictions. The box-and-whisker represents the multi-model ensemble range (anomalies with respect to the multi-model ensemble mean) of Init (solid) and NoInit (dashed), where the whiskers correspond to the maximum and minimum, the box to the interquartile range and the horizontal bar to the median. The predictions have been initialized once every year over the period 1961–2006. (d–f): Correlation of the ensemble mean with the observational reference along the forecast time for 4-year averages. The one-sided 95% confidence level with a t-distribution is represented in grey, where the number of degrees of freedom has been computed taking into account the autocorrelation of the observational time series, which are different for each forecast time. A two-sided t-test (with the number of degrees of freedom computed taking into account the autocorrelation of the observational time series) for the differences between the Init and NoInit correlation found no significant results with confidence ≥90%. (g–i): RMSE of the ensemble mean along the forecast time for 4-year forecast averages. Squares are used where the Init skill is significantly better than the NoInit skill with 95% confidence using a two-sided F-test where the number of degrees of freedom takes into account the autocorrelation of the observation minus prediction time series. (j–l) Ensemble spread estimated as the s.d. of the anomalies around the multi-model ensemble mean.
Mentions: Global-mean near-surface air temperature and the Atlantic multi-decadal variability (AMV) and the interdecadal Pacific oscillation (IPO) indices are used as benchmarks to assess the ability to predict multi-annual variability425 (Fig. 1). The AMV and IPO are the dominant decadal ocean surface temperature variations over the North Atlantic26 and Pacific Oceans27, respectively, and have well-defined spatial characteristics4. Both indices have been estimated after removing the global-mean sea surface temperature (SST) to retain the differential cooling or warming of the corresponding basin with respect to the global behaviour. Apart from the multi-annual variability, these indices display either a long-term trend or low-frequency variability, which should be correctly predicted too.

Bottom Line: The Fifth Coupled Model Intercomparison Project set of co-ordinated climate-model experiments includes a set of near-term predictions in which several modelling groups participated and whose forecast quality we illustrate here.We show that climate forecast systems have skill in predicting the Earth's temperature at regional scales over the past 50 years and illustrate the trustworthiness of their predictions.Most of the skill can be attributed to changes in atmospheric composition, but also partly to the initialization of the predictions.

View Article: PubMed Central - PubMed

Affiliation: Institució Catalana de Recerca i Estudis Avançats (ICREA), Barcelona, Spain. francisco.doblas-reyes@ic3.cat

ABSTRACT
Climate models are seen by many to be unverifiable. However, near-term climate predictions up to 10 years into the future carried out recently with these models can be rigorously verified against observations. Near-term climate prediction is a new information tool for the climate adaptation and service communities, which often make decisions on near-term time scales, and for which the most basic information is unfortunately very scarce. The Fifth Coupled Model Intercomparison Project set of co-ordinated climate-model experiments includes a set of near-term predictions in which several modelling groups participated and whose forecast quality we illustrate here. We show that climate forecast systems have skill in predicting the Earth's temperature at regional scales over the past 50 years and illustrate the trustworthiness of their predictions. Most of the skill can be attributed to changes in atmospheric composition, but also partly to the initialization of the predictions.

No MeSH data available.


Related in: MedlinePlus