Impacts of a New Solar Radiation Parameterization on the CPTEC AGCM Climatological Features

Barbosa, H. M.; Тарасова, Т. А.; Cavalcanti, Iracema F. A.

doi:10.1175/2007jamc1760.1

Cited by 8 publications

(10 citation statements)

References 56 publications

(54 reference statements)

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“…Here the values of the bias derived from 48 CMIP5 models are similar, at 8.6 and 11.9 W m −2 compared with GEBA without and with observations over China, respectively. This may be attributed to an underestimation of clouds in the climate simulations in CMIP5 over land [ Barbosa et al ., ; Bodas‐Salcedo et al ., , ; Klein et al ., ; Lauer and Hamilton , ; Pueschel et al ., ; Wild and Roeckner , ]. In addition, it has been shown that the clear‐sky fluxes over land in global climate models are too high (Figure ), a known long standing problem in climate modeling [ Wild et al ., , , ].…”

Section: Resultsmentioning

confidence: 99%

Impact of geolocations of validation data on the evaluation of surface incident shortwave radiation from Earth System Models

Wang

Wild

2015

JGR Atmospheres

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Ground-based observations of surface incident solar radiation (R s ) have been used to evaluate simulations of global climate models. Existing studies have shown that biases in simulated clouds have a significant spatial pattern, which may be transferred to the simulated R s . Therefore, the evaluation results of R s simulations may depend on the locations of the ground-based observations. In this study, R s simulations of 48 models participating in the fifth phase of the Coupled Model Intercomparison Project (CMIP5) were first evaluated with ground-based observations from different networks (446 stations in total) from 2000 to 2005. The global mean biases of the CMIP5 R s simulations were found to vary from 4.8 to 11.9 W m À2 when R s observations from different networks were used as reference data. To reduce the location impact on the evaluation results, CMIP5 simulated R s was then evaluated with the latest satellite R s retrieval at 1°× 1°spatial resolution by the Clouds and the Earth's Radiant Energy System, Energy Balanced and Filled (CERES EBAF). It was found that the CMIP5 simulated multimodel mean R s has a small bias of 2.

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Section: Resultsmentioning

confidence: 99%

Impact of geolocations of validation data on the evaluation of surface incident shortwave radiation from Earth System Models

Wang

Wild

2015

JGR Atmospheres

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“…The simplified physical parameterization schemes used in BAM‐v0 in comparison to the first operational version (BAM‐v1) are: shortwave radiation CLIRAD scheme (Chou and Suarez, ) modified by Tarasova and Fomin () and validated by Barbosa et al . (), longwave radiation (Harshvardhan et al ., ), cloud microphysics (Ferrier et al . ), deep convection (Grell and Dévényi, ), shallow convection (Tiedtke, ), total cloud fraction (Slingo, ).…”

Section: Methodsmentioning

confidence: 99%

“…The model overestimated the net short wave radiation related to the underestimation of the clouds fraction, subject discussed in Tarasova and Cavalcanti (2002). The implementation of CLIRAD scheme for solar radiation reduced the bias in several variables, as shown in Barbosa et al (2008). The evolution of this model led to the AGCM3, which was used in studies of vegetation changes in Amazonia (Sampaio et al, 2007), experiments of sea ice anomalies in Antarctica (Cunningham and Bonatti, 2011), seasonal predictions (Coelho et al, 2012) and probabilistic global operational NWP (Cunningham et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Climate variability over South America‐regional and large scale features simulated by the Brazilian Atmospheric Model (BAM‐v0)

Cavalcanti

Silveira

Figueroa

et al. 2019

Intl Journal of Climatology

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The reliability of climate prediction by a global model is directly related to the ability to simulate the observed climate variability and the main teleconnection patterns. Precipitation anomalies in certain regions are strongly affected by these features, and it is important to know if models are able to reproduce such patterns and influences. The main objective of this article is to analyse some global features of the Brazilian Atmospheric Model with simplified physics (BAM‐v0), and to discuss several aspects of climate variability over South America. Especially, the ability of the model in simulating the main teleconnection patterns that affect South America and the precipitation variability in several regions of Brazil associated with the Pacific and Atlantic Sea Surface Temperature. The model is the atmospheric component of the Brazilian Earth System Model‐Ocean–Atmosphere (BESM), which can be used to long integrations due to the simplified physics, considering computer limitations. Climate variability is investigated through analyses of variance and correlations, and teleconnections such as Southern Annular Mode (SAM) and Pacific South American (PSA) are obtained from EOF analyses. El Niño Southern Oscillation (ENSO) features are analysed through the Southern Oscillation Index and precipitation anomalies. BAM‐v0, even at coarse resolution, represents many climate variability features. It captures the influences of tropical Pacific and Atlantic Oceans on Northeast Brazil precipitation and reproduces the influences of ENSO over South America. SAM and PSA teleconnections are well simulated. Observed features of the South America Monsoon System are captured by the model, although the intensities of precipitation variability need to be improved. There are some deficiencies related to global budget, precipitation variance in some regions of the globe and precipitation anomalies in certain regions of South America. Identification of model deficiencies and variability analyses are important to model development and contribute to climate prediction improvements.

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“…This model uses a deep cloud convection parameterization scheme developed by Kuo () and the short wave radiation scheme as described in Barbosa et al . (). Ensemble hindcasts for the period 1979–2008 were produced using a lagged initialization approach based on 10 atmospheric initial conditions from NCEP/NCAR reanalysis (Kalnay et al ., ; Kanamitsu et al ., ).…”

Section: Verification Strategymentioning

confidence: 99%

Comparative skill assessment of consensus and physically based tercile probability seasonal precipitation forecasts for Brazil

Coelho

2013

Meteorological Applications

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This study aimed to perform a comparative skill assessment of consensus and physically based tercile probability seasonal precipitation forecasts for Brazil produced during the last decade. Two fundamental forecast attributes have been examined: discrimination and reliability. The discrimination assessment revealed that forecast quality is seasonally dependent and that consensus and physically based forecasts are complementary. During spring and summer consensus forecasts were generally found to have better discrimination ability than physically based forecasts. During autumn and winter physically based forecasts were found to have better discrimination ability than consensus forecasts. However, discrimination is a necessary but not sufficient forecast skill attribute, and therefore only provides indication of potential forecast quality provided forecasts are reliable (i.e. well calibrated). The analysis of tendency diagrams has revealed that both consensus and physically based forecasts suffer from systematic errors (biases) for the three forecast categories. Both forecasts under-forecasted the below-normal category and over-forecasted the above normal category. This over-forecasting feature was stronger for physically based forecasts when compared to consensus forecasts. The normal category was more severely over-forecast for consensus forecasts when compared to physically based forecasts. The assessment through the computation of the reliability component of the Brier Score has revealed that consensus forecasts are better calibrated than CPTEC/AGCM physically based forecasts.

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Impacts of a New Solar Radiation Parameterization on the CPTEC AGCM Climatological Features

Cited by 8 publications

References 56 publications

Impact of geolocations of validation data on the evaluation of surface incident shortwave radiation from Earth System Models

Impact of geolocations of validation data on the evaluation of surface incident shortwave radiation from Earth System Models

Climate variability over South America‐regional and large scale features simulated by the Brazilian Atmospheric Model (BAM‐v0)

Comparative skill assessment of consensus and physically based tercile probability seasonal precipitation forecasts for Brazil

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