Evaluating uncertainties in regional climate simulations over South America at the seasonal scale
This work focuses on the evaluation of different sources of uncertainty affecting regional climate simulations over South America at the seasonal scale, using the MM5 model. The simulations cover a 3-month period for the austral spring season. Several four-member ensembles were performed in order to...
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2012
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| Acceso en línea: | https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09307575_v39_n1-2_p59_Solman http://hdl.handle.net/20.500.12110/paper_09307575_v39_n1-2_p59_Solman |
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paper:paper_09307575_v39_n1-2_p59_Solman2023-06-08T15:52:41Z Evaluating uncertainties in regional climate simulations over South America at the seasonal scale Solman, Silvina Alicia Pessacg, Natalia Liz MM5 model Regional climate modeling South America Uncertainty climate modeling ensemble forecasting geographical distribution parameterization precipitation (climatology) regional climate sea level pressure seasonal variation uncertainty analysis South America This work focuses on the evaluation of different sources of uncertainty affecting regional climate simulations over South America at the seasonal scale, using the MM5 model. The simulations cover a 3-month period for the austral spring season. Several four-member ensembles were performed in order to quantify the uncertainty due to: the internal variability; the definition of the regional model domain; the choice of physical parameterizations and the selection of physical parameters within a particular cumulus scheme. The uncertainty was measured by means of the spread among individual members of each ensemble during the integration period. Results show that the internal variability, triggered by differences in the initial conditions, represents the lowest level of uncertainty for every variable analyzed. The geographic distribution of the spread among ensemble members depends on the variable: for precipitation and temperature the largest spread is found over tropical South America while for the mean sea level pressure the largest spread is located over the southeastern Atlantic Ocean, where large synoptic-scale activity occurs. Using nudging techniques to ingest the boundary conditions reduces dramatically the internal variability. The uncertainty due to the domain choice displays a similar spatial pattern compared with the internal variability, except for the mean sea level pressure field, though its magnitude is larger all over the model domain for every variable. The largest spread among ensemble members is found for the ensemble in which different combinations of physical parameterizations are selected. The perturbed physics ensemble produces a level of uncertainty slightly larger than the internal variability. This study suggests that no matter what the source of uncertainty is, the geographical distribution of the spread among members of the ensembles is invariant, particularly for precipitation and temperature. © 2011 Springer-Verlag. Fil:Solman, S.A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Pessacg, N.L. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2012 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09307575_v39_n1-2_p59_Solman http://hdl.handle.net/20.500.12110/paper_09307575_v39_n1-2_p59_Solman |
| institution |
Universidad de Buenos Aires |
| institution_str |
I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
MM5 model Regional climate modeling South America Uncertainty climate modeling ensemble forecasting geographical distribution parameterization precipitation (climatology) regional climate sea level pressure seasonal variation uncertainty analysis South America |
| spellingShingle |
MM5 model Regional climate modeling South America Uncertainty climate modeling ensemble forecasting geographical distribution parameterization precipitation (climatology) regional climate sea level pressure seasonal variation uncertainty analysis South America Solman, Silvina Alicia Pessacg, Natalia Liz Evaluating uncertainties in regional climate simulations over South America at the seasonal scale |
| topic_facet |
MM5 model Regional climate modeling South America Uncertainty climate modeling ensemble forecasting geographical distribution parameterization precipitation (climatology) regional climate sea level pressure seasonal variation uncertainty analysis South America |
| description |
This work focuses on the evaluation of different sources of uncertainty affecting regional climate simulations over South America at the seasonal scale, using the MM5 model. The simulations cover a 3-month period for the austral spring season. Several four-member ensembles were performed in order to quantify the uncertainty due to: the internal variability; the definition of the regional model domain; the choice of physical parameterizations and the selection of physical parameters within a particular cumulus scheme. The uncertainty was measured by means of the spread among individual members of each ensemble during the integration period. Results show that the internal variability, triggered by differences in the initial conditions, represents the lowest level of uncertainty for every variable analyzed. The geographic distribution of the spread among ensemble members depends on the variable: for precipitation and temperature the largest spread is found over tropical South America while for the mean sea level pressure the largest spread is located over the southeastern Atlantic Ocean, where large synoptic-scale activity occurs. Using nudging techniques to ingest the boundary conditions reduces dramatically the internal variability. The uncertainty due to the domain choice displays a similar spatial pattern compared with the internal variability, except for the mean sea level pressure field, though its magnitude is larger all over the model domain for every variable. The largest spread among ensemble members is found for the ensemble in which different combinations of physical parameterizations are selected. The perturbed physics ensemble produces a level of uncertainty slightly larger than the internal variability. This study suggests that no matter what the source of uncertainty is, the geographical distribution of the spread among members of the ensembles is invariant, particularly for precipitation and temperature. © 2011 Springer-Verlag. |
| author |
Solman, Silvina Alicia Pessacg, Natalia Liz |
| author_facet |
Solman, Silvina Alicia Pessacg, Natalia Liz |
| author_sort |
Solman, Silvina Alicia |
| title |
Evaluating uncertainties in regional climate simulations over South America at the seasonal scale |
| title_short |
Evaluating uncertainties in regional climate simulations over South America at the seasonal scale |
| title_full |
Evaluating uncertainties in regional climate simulations over South America at the seasonal scale |
| title_fullStr |
Evaluating uncertainties in regional climate simulations over South America at the seasonal scale |
| title_full_unstemmed |
Evaluating uncertainties in regional climate simulations over South America at the seasonal scale |
| title_sort |
evaluating uncertainties in regional climate simulations over south america at the seasonal scale |
| publishDate |
2012 |
| url |
https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09307575_v39_n1-2_p59_Solman http://hdl.handle.net/20.500.12110/paper_09307575_v39_n1-2_p59_Solman |
| work_keys_str_mv |
AT solmansilvinaalicia evaluatinguncertaintiesinregionalclimatesimulationsoversouthamericaattheseasonalscale AT pessacgnatalializ evaluatinguncertaintiesinregionalclimatesimulationsoversouthamericaattheseasonalscale |
| _version_ |
1768544965410947072 |