Limits...
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.


Precipitation forecast quality.(a,b) RMSSS (multiplied by 100) of the ensemble mean of the Init multi-model for predictions averaged over forecast years 2–5 (a) and 6–9 (b). GPCC50 precipitation 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 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. An inference tests at the grid point level was applied to assess if 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, but no point was found significant. Both predictions and the observational reference were smoothed to a 5° grid to reduce the spatial variability of the results.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3644073&req=5

f4: Precipitation forecast quality.(a,b) RMSSS (multiplied by 100) of the ensemble mean of the Init multi-model for predictions averaged over forecast years 2–5 (a) and 6–9 (b). GPCC50 precipitation 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 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. An inference tests at the grid point level was applied to assess if 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, but no point was found significant. Both predictions and the observational reference were smoothed to a 5° grid to reduce the spatial variability of the results.

Mentions: The skill for land precipitation (Fig. 4) is much lower than the skill for near-surface temperature, with several regions, especially in the Northern Hemisphere, displaying positive values. However, the existence of almost as many areas around the planet with negative as regions with positive skill suggests that near-term precipitation information should be used with great caution. The most that can be done at this early stage is to try to understand the sources of the positive precipitation skill. The skill in areas like Europe and Sahelian Africa might be linked to the positive AMV skill, the AMV being a good descriptor of the multi-annual precipitation variability over those regions4. In other areas, like the Asian continent and the Arctic, positive skill coincides with the regions where the relative importance of the linear trend to the interannual variability is the highest (Fig. 3). The positive precipitation skill can be attributed mostly to the specification of the atmospheric concentration variations as the initialization does not substantially improve the skill (Fig. 4, lower panels).


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)

Precipitation forecast quality.(a,b) RMSSS (multiplied by 100) of the ensemble mean of the Init multi-model for predictions averaged over forecast years 2–5 (a) and 6–9 (b). GPCC50 precipitation 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 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. An inference tests at the grid point level was applied to assess if 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, but no point was found significant. Both predictions and the observational reference were smoothed to a 5° grid to reduce the spatial variability of the results.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f4: Precipitation forecast quality.(a,b) RMSSS (multiplied by 100) of the ensemble mean of the Init multi-model for predictions averaged over forecast years 2–5 (a) and 6–9 (b). GPCC50 precipitation 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 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. An inference tests at the grid point level was applied to assess if 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, but no point was found significant. Both predictions and the observational reference were smoothed to a 5° grid to reduce the spatial variability of the results.
Mentions: The skill for land precipitation (Fig. 4) is much lower than the skill for near-surface temperature, with several regions, especially in the Northern Hemisphere, displaying positive values. However, the existence of almost as many areas around the planet with negative as regions with positive skill suggests that near-term precipitation information should be used with great caution. The most that can be done at this early stage is to try to understand the sources of the positive precipitation skill. The skill in areas like Europe and Sahelian Africa might be linked to the positive AMV skill, the AMV being a good descriptor of the multi-annual precipitation variability over those regions4. In other areas, like the Asian continent and the Arctic, positive skill coincides with the regions where the relative importance of the linear trend to the interannual variability is the highest (Fig. 3). The positive precipitation skill can be attributed mostly to the specification of the atmospheric concentration variations as the initialization does not substantially improve the skill (Fig. 4, lower panels).

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.