Regional climate simulations over South America: sensitivity to model physics and to the treatment of lateral boundary conditions using the MM5 model

Solman, Silvina Alicia; Pessacg, Natalia Liz

doi:10.1007/s00382-011-1049-6

Cited by 23 publications

(27 citation statements)

References 55 publications

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“…However, it is important to note that the maximum precipitation associated with the SACZ is underestimated by around 3 mm/day. The systematic underestimation of precipitation in tropical areas with the Grell scheme has also been documented by Solman and Pessacg (2012a) with the MM5 regional model, by Fernán-dez et al (2006) and da Rocha et al (2009) with the RegCM3 regional model, and by Chou et al (2012) with the Eta regional model. While the MM5 model captures the observed maximum of precipitation in areas of high topography, its magnitude is overestimated.…”

Section: Validation Of Surface Variables For Presentsupporting

confidence: 55%

“…Moreover, it has been shown that IV is sensitive to the simulated period, the domain size and geographic location. This has motivated several studies focused on evaluating IV, such as those that aim for assess the magnitude of IV in RCMs and characterize both spatial and temporal distribution, as well as its dependence on the number of ensemble members and the domain size (Giorgi and Bi, 2000;Christensen et al, 2001 over the Mediterranean region, southern Europe and northern Africa; Caya and Biner, 2004;Alexandru et al, 2007;Lucas-Picher et al, 2008 in North America; Vanvyve et al, 2008 for western Africa;and Solman and Pessacg, 2012a for South America). Moreover, the IV represents the lowest level of uncertainty that cannot be reduced, but should be characterized in order to assess the extent to which the response of the regional climate represents a significant signal or is immersed in the inherent uncertainty of the RCM.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Climate downscaling over southern South America for present-day climate (1970-1989) using the MM5 model. Mean, interannual variability and internal variability

Cabré¹,

Solman²,

Núñez³

2014

Atmósfera

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Este trabajo evalúa la capacidad del modelo regional MM5 para representar las principales características del clima actual de Sudamérica. Se evalúa la distribución espacial de los valores medios estacionales, la variabilidad interanual y el ciclo anual de la precipitación y la temperatura, así como la variabilidad interna. El análisis tuvo dos objetivos: cuantificar la capacidad de la regionalización (downscaling) dinámica para representar el clima actual e identificar los aspectos críticos de la simulación climática regional en Sudamérica para interpretar las proyecciones hacia fines de siglo XXI en el escenario de emisión SRES A2 con cierto grado de confiabilidad. En general, el modelo MM5 representa de forma adecuada las características regionales, el ciclo estacional y la variabilidad interanual de las variables de superficie en el sur de Sudamérica. La distribución espacial de la temperatura está bien representada; sin embargo, se encuentran algunos errores sistemáticos, como una sobreestimación en el centro y norte de Argentina y una subestimación en las regiones montañosas a lo largo del año. La distribución espacial de la precipitación también está bien representada por el modelo regional, sin embargo se encuentra una sobreestimación de la precipitación en la región andina (especialmente en el centro y sur de Chile) en todas las épocas del año y una subestimación de la precipitación en latitudes tropicales. El ciclo anual de la precipitación está representado de manera adecuada en todas las regiones analizadas, sin embargo su representación es mejor en la cuenca del Plata (LPB, por sus siglas en inglés), Cuyo (CU) y sur de Buenos Aires (región denominada sureste Pampas [SEP]). El ciclo anual de la temperatura media también está bien representado. En líneas generales, el modelo sobreestima la variabilidad interanual de la temperatura y subestima la variabilidad interanual de la precipitación. De la evaluación de la variabilidad interanual, la variabilidad interna y los sesgos, puede concluirse que independientemente de la época del año, la precipitación simulada por el modelo regional MM5 es confiable en latitudes subtropicales, Uruguay, el sur de Brasil y el centro-este de Argentina, pero es poco confiable en áreas montañosas. La temperatura es confiable en latitudes subtropicales, Uruguay y el sur de Brasil solamente durante el invierno, pero es poco confiable o se encuentra en el límite de confiabilidad en el centro y sur de Chile a lo largo del año. De esta manera, puede concluirse que el modelo MM5 es una herramienta de mucha utilidad para la generación de escenarios regionales de cambio climático de alta resolución en el sur de Sudamérica y constituye un interesante punto de partida para evaluar los escenarios regionales de cambio climático en dicha región. ABSTRACTThis work focuses on evaluating the ability of the MM5 regional model to represent the basic features of present climate over South America. The spatial distribution of seasonal means and the interannual variability, as well as annual cy...

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Section: Validation Of Surface Variables For Presentsupporting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Climate downscaling over southern South America for present-day climate (1970-1989) using the MM5 model. Mean, interannual variability and internal variability

Cabré¹,

Solman²,

Núñez³

2014

Atmósfera

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“…The WRF3.1.1 used the KF cumulus scheme and the YSU PBL scheme, which have been tested over the region with good results by Tadross et al (2005) using MM5 andCr etat et al (2012) using WRF3.1.1. Solman and Pessacg (2012) show the KF scheme to be most suitable in subtropical regions, so it is unclear to the authors why WRF3.1.1 should introduce such a wet bias to the region. With the exception of WRF3.1.1, the models produce a dry bias over the northeast of the domain more specifically over Mozambique and the south part of Malawi (Nikulin et al 2012;Kim et al 2013), which is more likely related to circulation anomalies that shift the south Indian convergence zone west (Cook 2000) than directly to the cumulus parameterization.…”

Section: B Model Biasesmentioning

confidence: 99%

“…Tchotchou and Kamga (2010) caution in their study of the West African monsoon using RegCM3 that unrealistic soil moisture resulting from the way albedo has been parameterized was a leading cause of an excessive northward penetration of monsoon flow. Solman and Pessacg (2012) attributed the underestimation of precipitation over the La Plata Basin by the MM5 to be largely due to a misrepresentation of the moisture flux convergence. New et al (2003), Steiner et al (2009), Wu et al (2009, Alo and Wang (2010), Xue et al (2010), and Ruti et al (2011) draw attention to the importance of the land surface scheme in simulating the West African monsoon.…”

Section: B Model Biasesmentioning

confidence: 99%

A Diagnostic Evaluation of Precipitation in CORDEX Models over Southern Africa

et al. 2013

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The authors evaluate the ability of 10 regional climate models (RCMs) to simulate precipitation over Southern Africa within the Coordinated Regional Climate Downscaling Experiment (CORDEX) framework. An ensemble of 10 regional climate simulations and the ensemble average is analyzed to evaluate the models' ability to reproduce seasonal and interannual regional climatic features over regions of the subcontinent. All the RCMs use a similar domain, have a spatial resolution of ;50 km, and are driven by the Interim ECMWF Re-Analysis (ERA-Interim;-2008. Results are compared against a number of observational datasets.In general, the spatial and temporal nature of rainfall over the region is captured by all RCMs, although individual models exhibit wet or dry biases over particular regions of the domain. Models generally produce lower seasonal variability of precipitation compared to observations and the magnitude of the variability varies in space and time. Model biases are related to model setup, simulated circulation anomalies, and moisture transport. The multimodel ensemble mean generally outperforms individual models, with bias magnitudes similar to differences across the observational datasets. In the northern parts of the domain, some of the RCMs and the ensemble average improve the precipitation climate compared to that of ERA-Interim. The models are generally able to capture the dry (wet) precipitation anomaly associated with El Niño (La Niña) events across the region. Based on this analysis, the authors suggest that the present set of RCMs can be used to provide useful information on climate projections of rainfall over Southern Africa.

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“…(Grell et al 1994). Solman and Pessacg (2011) summarizes the overall model performance over SA. All the simulations in this study were performed using the explicit moisture scheme by Hsie et al (1984).…”

Section: Itczmentioning

confidence: 99%

Evaluating uncertainties in regional climate simulations over South America at the seasonal scale

Solman

Pessacg

2011

Clim Dyn

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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. Keywords Regional climate modeling Á South America Á Uncertainty Á MM5 model Abberivations MM5 Fifth-generation Pennsylvania-State University-NCAR non-hydrostatic mesoscale model ERA40 European reanalyses SA South America RCM Regional climate model GCM General circulation model IV Internal variability KF Kain-Fritsch cumulus scheme GR Grell cumulus scheme BM Betts-Miller cumulus scheme KF2 Updated version of Kain-Fritsch cumulus scheme PBL Planetary boundary layer MRF Medium range forecast model ETA ETA planetary boundary layers scheme RMSD Root mean square difference slp Sea level pressure SST Sea surface temperature SACZ South Atlantic convergence zone

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Regional climate simulations over South America: sensitivity to model physics and to the treatment of lateral boundary conditions using the MM5 model

Cited by 23 publications

References 55 publications

Climate downscaling over southern South America for present-day climate (1970-1989) using the MM5 model. Mean, interannual variability and internal variability

Climate downscaling over southern South America for present-day climate (1970-1989) using the MM5 model. Mean, interannual variability and internal variability

A Diagnostic Evaluation of Precipitation in CORDEX Models over Southern Africa

Evaluating uncertainties in regional climate simulations over South America at the seasonal scale

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