Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates

Semenov, Mikhail A.; Brooks, Roger J.; Barrow, Elaine; Richardson, C. W.

doi:10.3354/cr010095

Cited by 530 publications

(392 citation statements)

References 11 publications

(18 reference statements)

Supporting

Mentioning

345

Contrasting

Unclassified

Order By: Relevance

“…LARS-WG (Semenov et al, 1998) is a stochastic model widely used for downscaling of future climate large scale projections, in scenario studies related to climate change impact on agroecosystems Richter & Semenov, 2005;Streck & Alberto, 2006a, 2006b). This stochastic model generates data series using semi-empirical probability distribution functions that keep the statistics of the original data series (Semenov et al, 1998).…”

Section: Methodsmentioning

confidence: 99%

“…Climate change scenarios were created with the LARS-WG Weather Generator (Semenov et al, 1998), using as database the meteorological data records (daily minimum and maximum air temperature) from 1969 to 2003, collected at a standard Meteorological Station of Instituto Nacional de Meteorologia (8 o Distrito de Meteorologia). LARS-WG (Semenov et al, 1998) is a stochastic model widely used for downscaling of future climate large scale projections, in scenario studies related to climate change impact on agroecosystems Richter & Semenov, 2005;Streck & Alberto, 2006a, 2006b). This stochastic model generates data series using semi-empirical probability distribution functions that keep the statistics of the original data series (Semenov et al, 1998).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Impact of increasing mean air temperature on the development of rice and red rice

Lago

Streck

Alberto

et al. 2008

Pesq. agropec. bras.

View full text Add to dashboard Cite

-The objective of this study was to assess the development response of cultivated rice and red rice to different increases in minimum and maximum daily air temperatures, in Santa Maria, Rio Grande do Sul State, Brazil. One hundred years climate scenarios of temperatures 0, +1, +2, +3, +4, and +5 o C, with symmetric and asymmetric increases in minimum and maximum daily air temperatures were created, using the LARS-WG Weather Generator, and a 1969-2003 database. Nine cultivated rice genotypes (IRGA 421, IRGA 416, IRGA 417, IRGA 420, BRS 7 TAIM, BR-IRGA 409, EPAGRI 109, EEA 406 and a hybrid), and two red rice biotypes (awned black hull-ABHRR, and awned yellow hull-AYHRR) were used. The dates of panicle differentiation (R1), anthesis (R4), and all grains with brown hulls (R9) were estimated with a nonlinear simulation model. Overall, the duration of the emergence-R1 phase decreased, whereas the duration of the R1-R4 and R4-R9 phases most often increased, as temperature increased in the climate change scenarios. The simulated rice development response to elevated temperature was not the same, when the increase in minimum and maximum temperature was symmetric or asymmetric.Index terms: Oryza sativa, global warming, phenology, simulation, weeds. Impacto do aumento na temperatura média do ar sobre o desenvolvimento do arroz e do arroz-vermelhoResumo -O objetivo deste trabalho foi avaliar a resposta do desenvolvimento do arroz cultivado e do arroz vermelho a diferentes aumentos nas temperaturas mínima e máxima diárias do ar, em Santa Maria, RS, Brasil. Foram criados cenários climáticos de cem anos de 0, +1, +2, +3, +4 e +5 o C, com aumentos simétricos e assimétricos nas temperaturas mínima e máxima diária do ar, usando o Weather Generator LARS-WG e uma base de dados de 1969-2003. Foram usados nove genótipos de arroz cultivado (IRGA 421, IRGA 416, IRGA 417, IRGA 420, BRS 7 TAIM, BR-IRGA 409, EPAGRI 109, um híbrido e EEA 406) e dois biótipos de arroz-vermelho (casca preta aristado e casca amarela aristado). A data da diferenciação da panícula (R1), a antese (R4) e todos os grãos com casca marrom (R9) foram estimados com um modelo não linear. Em geral, a duração da fase emergência-R1 diminuiu, enquanto a duração das fases R1-R4 e R4-R9 aumentaram, com o aumento da temperatura nos cenários de mudança climática. A resposta simulada do desenvolvimento do arroz à elevação da temperatura não foi a mesma, quando o aumento na temperatura mínima e máxima foi simétrico ou assimétrico.Termos para indexação: Oryza sativa, aquecimento global, fenologia, simulação, planta daninha.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Impact of increasing mean air temperature on the development of rice and red rice

Lago

Streck

Alberto

et al. 2008

Pesq. agropec. bras.

View full text Add to dashboard Cite

show abstract

“…Esses cenários foram criados com base em Siqueira et al (2001). O cenário sem mudança climática consistiu de um conjunto de dados sintéticos de 100 anos, criados com o "weather generator" LARS-WG (Semenov et al, 1998), a partir dos dados observados no período de 1969 a 2003, cuja série (35 anos) é suficiente, pois é maior que o mínimo de 20 anos preconizado para usar o modelo (Semenov et al, 1998;Weiss et al, 2003).…”

Section: Introductionunclassified

“…Esse modelo estocástico pode gerar séries meteorológicas (que quando têm duração maior que 30 anos podem ser considerados séries climáticas), através de distribuições de probabilidade e correlações entre elementos meteorológicos que mantêm as estatís-ticas da série original, cujas estatísticas das séries climáticas geradas devem ser comparadas com as estatísticas da série de dados meteorológicos observados (Semenov et al, 1998;Weiss et al, 2003).…”

Section: Introductionunclassified

Estudo numérico do impacto da mudança climática sobre o rendimento de trigo, soja e milho

Streck

Alberto

2006

Pesq. agropec. bras.

View full text Add to dashboard Cite

Resumo -O objetivo deste trabalho foi avaliar, por meio de estudo numérico, a existência de impacto da possível mudança climática sobre o rendimento das culturas do trigo, soja e milho, em Santa Maria, RS. Foram criados cenários de mudança climática, dobrando-se a quantidade de CO 2 , com diferentes aumentos de temperatura do ar, com aumento e sem aumento de precipitação pluvial. O rendimento das três culturas foi simulado com modelos matemáticos disponíveis na literatura. Concluiu-se que a mudança climática, projetada pela simulação, influenciará o rendimento de grãos de trigo, soja e milho, em Santa Maria, RS. O aumento de 2, 3 e 6 o C na temperatura do ar pode anular os efeitos benéficos do aumento de CO 2 no rendimento de trigo, soja e milho, respectivamente.Termos para indexação: Triticum aestivum, Glycine max, Zea mays, aquecimento global, suprimento de alimentos, agricultura. Numerical study of the impact of climate change on the yield of wheat, soybean and maizeAbstract -The objective of this work was to evaluate through a numerical study the impact of a possible climate change on the yield of wheat, soybean and maize, in Santa Maria, RS. Climate change scenarios were created by doubling CO 2 , with different increases in air temperature, and with increases and without increases in rainfall.Yield of the three crops was simulated with models available in the literature. It was concluded that projected climate change will affect wheat, soybean and maize yield in Santa Maria, RS, Brazil. The increase of 2, 3 and 6 o C may cancel the benefits of increasing CO 2 on yield of wheat, soybean and maize, respectively.

show abstract

“…Among many applications, they can serve as a computationally effective tool to produce site-specific data sets at the required time resolution (Semenov et al 1998).…”

Section: Weather Generatorsmentioning

confidence: 99%

Projected Change—Models and Methodology

Wibig

Maraun

Benestad

et al. 2015

Regional Climate Studies

View full text Add to dashboard Cite

General (global) circulation models (GCMs) are a useful tool for studying how climate may change in the future. Although GCMs have high temporal resolution, their spatial resolution is low. To simulate the future climate of the Baltic Sea region, it is necessary to downscale GCM data. This chapter describes the two conceptually different ways of downscaling: regional climate models (RCMs) nested in GCMs and using empirical and/or statistical relations between large-scale variables from GCMs and small-scale variables. There are many uncertainties in climate models, including uncertainty related to future land use and atmospheric greenhouse gas concentrations, limits on the amount of input data and their accuracy, and the chaotic nature of weather. The skill of methods for describing regional climate futures is also limited by natural climate variability. For the Baltic Sea area, the lack of an oceanic component in RCMs and poor representation of forcing by aerosols and changes in land use are major limitations. IntroductionThe development of general circulation models (GCMs) has created a useful tool for projecting how climate may change in the future. Such models describe the climate at a set of grid points, regularly distributed in space and time and with the same density over land and ocean. Their temporal resolution is relatively high, but their spatial resolution is limited by computing power. Many important processes, such as cloud formation, convection, and precipitation, occur at spatial scales much smaller than the distance between grid points. This means that these so-called sub-grid processes are not explicitly simulated by the models, but must be approximated with simplifying algorithms referred to as parameterisations. The low spatial resolution also means that the topography, coastline, and processes at the land-air, ocean-air, and land-ocean boundaries are coarsely represented in GCMs.

show abstract

Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climates

Cited by 530 publications

References 11 publications

Impact of increasing mean air temperature on the development of rice and red rice

Impact of increasing mean air temperature on the development of rice and red rice

Estudo numérico do impacto da mudança climática sobre o rendimento de trigo, soja e milho

Projected Change—Models and Methodology

Contact Info

Product

Resources

About