Rainfall anomaly prediction using statistical downscaling in a multimodel superensemble over tropical South America

Johnson, Bradford; Kumar, Vinay; Krishnamurti, T. N.

doi:10.1007/s00382-013-2001-8

Cited by 7 publications

(12 citation statements)

References 53 publications

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“…As it was mentioned in Johnson et al (2014) there are few studies that utilize the statistical downscaling over South America (i.e., Valverde Ramírez et al, 2006;Solman and Nuñez, 1999;Mendes and Marengo, 2010) and are more focused on the climate of some regions of Brazil or in the southern part of South America. The work of Johnson et al (2014) employs a statistical downscaling methodology for an ensemble of CMIP5 models, but it is applied to the analysis of present climate. There are some more papers related to dynamical downscaling, but also devoted to present climate such as Carril et al (2012) and Chou et al (2012).…”

Section: Conclusion and Discussionmentioning

confidence: 97%

“…Some of these are focused on areas located in the east and south of the continent, below the Equator, over areas of Brazil, Uruguay, Paraguay, and northern Argentina and Chile, and most of them use dynamical downscaling (Carril et al, 2012;Exbrayat et al, 2014;Giorgi et al, 2014;Llopart et al, 2014). However, little research has focused on using statistical downscaling over South America (Valverde Ramírez et al, 2006;Mendes and Marengo, 2010;Johnson et al, 2014). Ramirez-Villegas et al (2012) compare linear and nonlinear downscaling for rainfall forecasting over southeastern Brazil.…”

Section: Introductionmentioning

confidence: 98%

“…Mendes and Marengo (2010) used a downscaling method based on temporal neural networks to reproduce the monthly mean precipitation values, their trends and their possible long-term implications over the Amazon Basin. In a subsequent study, Johnson et al (2014) developed a synthetic super-ensemble model (SEE) to study the predictability of rainfall variations over South America, particularly in the Amazon Basin. They found that the SEE is a tool capable of reducing SD model bias, providing climate information at the smaller scales needed for many climate impact studies.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Summer precipitation projections over northwestern South America from CMIP5 models

Palomino-Lemus¹,

Córdoba-Machado²,

Gámiz‐Fortis³

et al. 2015

Global and Planetary Change

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Section: Conclusion and Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Summer precipitation projections over northwestern South America from CMIP5 models

Palomino-Lemus¹,

Córdoba-Machado²,

Gámiz‐Fortis³

et al. 2015

Global and Planetary Change

View full text Add to dashboard Cite

“…So, actually, comparing our results with those of other authors is problematic and limited because of the differences between the regions studied, the periods selected, the seasons considered, and the GCMs and scenarios analyzed. Additionally, the methodologies used can differ, with most studies employing dynamical downscaling and more focused on the climate of some specific regions as Brazil, Ecuadorian Andes or southern South America than on tropical America and many of them for present climate (Buytaert et al 2010, Johnson et al 2014, Valverde Ramírez et al 2006, Solman and Nuñez 1999, Mendes and Marengo 2010, Sánchez et al 2015. Therefore, the present study offers the novelty of being one of the few works dedicated to high-resolution change projections of boreal summer precipitation for tropical America using CMIP5 models.…”

Section: Discussionmentioning

confidence: 99%

Climate change projections of boreal summer precipitation over tropical America by using statistical downscaling from CMIP5 models

Palomino-Lemus

Córdoba-Machado

Gámiz‐Fortis

et al. 2017

Environ. Res. Lett.

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Climate change projections for the last 30 years of the 21st century, for boreal summer precipitation in tropical America, have been made by developing a statistical downscaling (SD) model applied to the SLP outputs of 20 GCMs of CMIP5, for present climate , and for future (2071-2100) under the RCP2.6, RCP4.5 and RCP8.5 scenarios. For present climate, many SD GCMs faithfully reproduce the precipitation field in many regions of the study area. For future climate, as the radiative forcing increases, the projected changes intensify and the regions affected expand, with higher coherence between models. The zone between central and southeastern Brazil registered the most pronounced precipitation changes by a large number of SD models, even for the RCP2.6. Except for this region in Brazil, in general, the changes in rainfall range from moderate (± 25%) to intense (from ±70% to ±100%) as the radiative forcing increases from the RCP2.6−RCP8.5. For this latter scenario, all SD models present significant precipitation changes for more than 50% of the area, in some cases reaching 75% of area with significant changes. For the ensemble mean, the results show three extensive regions with significant changes under the three scenarios, the most highlighted changes being for the RCP8.5: a northwest-southeast band that extends from northern Mexico to eastern Brazil, crossing through northern Colombia, along with the regions in the south of the study area, with generally moderate precipitation increases; and a band that extends from eastern Ecuador to southeastern Brazil, with major decreasing changes. This pattern of change could be related with a possible strengthening in frequency in terms of La Niña events for the end of the century.

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“…Statistical downscaling procedures have been used to improve the horizontal resolution of the member models [4][5][6][7][8][9]. By doing so, the regional details of the dry and wet patches of rainfall bulge out.…”

Section: Introductionmentioning

confidence: 99%

Performance of Multimodel Schemes for Seasonal Precipitation over Indian Region

Kumar

Ghosh

2018

Advances in Meteorology

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This study uses downscaled rainfall datasets from 16 coupled climate models at high resolution of 25 km from 1987 to 2001. The multimodel superensemble scheme is widely tested for rainfall forecast over mid-latitude, subtropical, and, especially, various regions of the monsoonal belt. A well-known statistical estimation theoretic approach, namely, Best Linear Unbiased Estimator (BLUE), is examined on 16 member models. The results are compared with superensemble methodology based on various skill scores. Results show that BLUE is providing promising forecasts. As far as comparative studies are concerned BLUE and superensemble schemes compete and show their importance from normal years to extreme rainfall years. BLUE methodology is capable of predicting draughts very well compared with other multimodel schemes. One basic advantage of BLUE is computationally less expensive than superensemble scheme. These statistical schemes like downscaling, BLUE, and superensemble can improve rainfall forecasts further, if a dense rain gauge data is provided.

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Rainfall anomaly prediction using statistical downscaling in a multimodel superensemble over tropical South America

Cited by 7 publications

References 53 publications

Summer precipitation projections over northwestern South America from CMIP5 models

Summer precipitation projections over northwestern South America from CMIP5 models

Climate change projections of boreal summer precipitation over tropical America by using statistical downscaling from CMIP5 models

Performance of Multimodel Schemes for Seasonal Precipitation over Indian Region

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