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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.


Near-surface air-temperature forecast quality.(a,b) RMSSS (multiplied by 100) of the ensemble mean of the Init multi-model for predictions averaged over the forecast years 2–5 (a) and 6–9 (b). A combination of temperatures from GHCN/CAMS47 air temperature over land, ERSST48 and GISTEMP 1200 (ref. 49) over the polar areas is used as a reference. Black dots correspond to the points where the skill score is statistically significant with 95% confidence using a one-sided F-test taking into account the autocorrelation of the observation minus prediction time series. (c,d) Ratio of RMSEs between the Init and NoInit multi-model experiments for predictions averaged over the forecast years 2–5 (c) and 6–9 (d). Contours are used for areas where the ratio of at least 75% of the individual forecast systems has a value above or below 1 in agreement with the multi-model ensemble-mean result. Dots are used for the points where the ratio is statistically significantly above or below 1 with 90% confidence using a two-sided F-test that takes into account the autocorrelation of the observation minus prediction time series. Poorly observationally sampled areas are masked in grey.
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f2: Near-surface air-temperature forecast quality.(a,b) RMSSS (multiplied by 100) of the ensemble mean of the Init multi-model for predictions averaged over the forecast years 2–5 (a) and 6–9 (b). A combination of temperatures from GHCN/CAMS47 air temperature over land, ERSST48 and GISTEMP 1200 (ref. 49) over the polar areas is used as a reference. Black dots correspond to the points where the skill score is statistically significant with 95% confidence using a one-sided F-test taking into account the autocorrelation of the observation minus prediction time series. (c,d) Ratio of RMSEs between the Init and NoInit multi-model experiments for predictions averaged over the forecast years 2–5 (c) and 6–9 (d). Contours are used for areas where the ratio of at least 75% of the individual forecast systems has a value above or below 1 in agreement with the multi-model ensemble-mean result. Dots are used for the points where the ratio is statistically significantly above or below 1 with 90% confidence using a two-sided F-test that takes into account the autocorrelation of the observation minus prediction time series. Poorly observationally sampled areas are masked in grey.

Mentions: Although simple indices help to characterize the behaviour of a system, the users of climate information also require spatial information. Near-term climate forecast systems have positive near-surface temperature skill, as measured with the root mean square skill score (RMSSS) (see Methods), over large regions, which is often statistically significantly different from zero as reflected in the large stippled areas found in Fig. 2 both over the ocean and the land341724. The regions with high skill agree in many cases with those where the relative importance of the linear trend with respect to the interannual variability is at its highest (Fig. 3), which again points at the important role of the specified variations in atmospheric composition that are responsible of the upward trend in the last 50 years.


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)

Near-surface air-temperature forecast quality.(a,b) RMSSS (multiplied by 100) of the ensemble mean of the Init multi-model for predictions averaged over the forecast years 2–5 (a) and 6–9 (b). A combination of temperatures from GHCN/CAMS47 air temperature over land, ERSST48 and GISTEMP 1200 (ref. 49) over the polar areas is used as a reference. Black dots correspond to the points where the skill score is statistically significant with 95% confidence using a one-sided F-test taking into account the autocorrelation of the observation minus prediction time series. (c,d) Ratio of RMSEs between the Init and NoInit multi-model experiments for predictions averaged over the forecast years 2–5 (c) and 6–9 (d). Contours are used for areas where the ratio of at least 75% of the individual forecast systems has a value above or below 1 in agreement with the multi-model ensemble-mean result. Dots are used for the points where the ratio is statistically significantly above or below 1 with 90% confidence using a two-sided F-test that takes into account the autocorrelation of the observation minus prediction time series. Poorly observationally sampled areas are masked in grey.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f2: Near-surface air-temperature forecast quality.(a,b) RMSSS (multiplied by 100) of the ensemble mean of the Init multi-model for predictions averaged over the forecast years 2–5 (a) and 6–9 (b). A combination of temperatures from GHCN/CAMS47 air temperature over land, ERSST48 and GISTEMP 1200 (ref. 49) over the polar areas is used as a reference. Black dots correspond to the points where the skill score is statistically significant with 95% confidence using a one-sided F-test taking into account the autocorrelation of the observation minus prediction time series. (c,d) Ratio of RMSEs between the Init and NoInit multi-model experiments for predictions averaged over the forecast years 2–5 (c) and 6–9 (d). Contours are used for areas where the ratio of at least 75% of the individual forecast systems has a value above or below 1 in agreement with the multi-model ensemble-mean result. Dots are used for the points where the ratio is statistically significantly above or below 1 with 90% confidence using a two-sided F-test that takes into account the autocorrelation of the observation minus prediction time series. Poorly observationally sampled areas are masked in grey.
Mentions: Although simple indices help to characterize the behaviour of a system, the users of climate information also require spatial information. Near-term climate forecast systems have positive near-surface temperature skill, as measured with the root mean square skill score (RMSSS) (see Methods), over large regions, which is often statistically significantly different from zero as reflected in the large stippled areas found in Fig. 2 both over the ocean and the land341724. The regions with high skill agree in many cases with those where the relative importance of the linear trend with respect to the interannual variability is at its highest (Fig. 3), which again points at the important role of the specified variations in atmospheric composition that are responsible of the upward trend in the last 50 years.

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.