Changes in Koppen–Trewartha climate classification over South America from RegCM4 projections

Fernández, Júlio Pablo Reyes; Franchito, Sérgio H.; Rao, V. Brahmananda; Llopart, Marta

doi:10.1002/asl.785

Cited by 28 publications

(15 citation statements)

References 20 publications

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“…Wind energy Fernandez et al 134 50 km 1970-2100 RegCM4 RCPs scenarios Koppen-Trewartha climate classification Lyra et al 59 5 km 1981-2100 Eta RCPs scenarios Only Southeast Brazil Lyra et al 60 20 km 1961-1990 2070-2100 Eta RCPs scenarios Tropical forest Tavares et al 86 5 km 1961-1990 2011-2040 2041-2070 2071-2099 Eta Impacts on coffee in the southeast Brazil 95 30 km 2012 RegCM4 Three seasons with different initial conditions Dutra et al 96 30 km 2013-2014 RegCM4 Forecast for 12 seasons with different initial conditions and physical schemes Reboita et al 17 30 km 2013-2014 RegCM4 Forecast for 12 seasons SAO. Still considering group 2, Reboita et al 26 found in the RegCM4-RCP8.5 ensemble a north-south expansion of the South Atlantic Subtropical Anticyclone (SASA) in the future projections (2070-2098).…”

Section: Km 1979-2098 Regcm4mentioning

confidence: 99%

The state of the art and fundamental aspects of regional climate modeling in South America

Ambrizzi

Reboita

Rocha

et al. 2018

Annals of the New York Academy of Sciences

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Regional climate models have been used since 1989 in order to improve climate simulation in regions where mesoscale forcings modulate the regional climate. These models are driven by time-dependent lateral boundary conditions from global climate models or reanalysis, and this process is called dynamical downscaling. Here, we review the evolution of regional climate modeling, as well as present the studies developed for South America.

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Section: Km 1979-2098 Regcm4mentioning

confidence: 99%

The state of the art and fundamental aspects of regional climate modeling in South America

Ambrizzi

Reboita

Rocha

et al. 2018

Annals of the New York Academy of Sciences

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“…The similarities of the latter to the classical scheme were thoroughly discussed in Belda et al [21]. The K-T climate types have been widely used in previous studies [22][23][24][25] due to its similarities with the native vegetation. In this study, the K-T climate types were used to define the regions of analysis using an updated observational dataset.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of New CORDEX Simulations Using an Updated Köppen–Trewartha Climate Classification

et al. 2019

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A new ensemble of climate and climate change simulations covering all major inhabited regions with a spatial resolution of about 25 km, from the WCRP CORDEX COmmon Regional Experiment (CORE) Framework, has been established in support of the growing demands for climate services. The main objective of this study is to assess the quality of the simulated climate and its fitness for climate change projections by REMO (REMO2015), a regional climate model of Climate Service Center Germany (GERICS) and one of the RCMs used in the CORDEX-CORE Framework. The CORDEX-CORE REMO2015 simulations were driven by the ECMWF ERA-Interim reanalysis and the simulations were evaluated in terms of biases and skill scores over ten CORDEX Domains against the Climatic Research Unit (CRU) TS version 4.02, from 1981 to 2010, according to the regions defined by the Köppen–Trewartha (K–T) Climate Classification types. The REMO simulations have a relatively low mean annual temperature bias (about ± 0.5 K) with low spatial standard deviation (about ± 1.5 K) in the European, African, North and Central American, and Southeast Asian domains. The relative mean annual precipitation biases of REMO are below ± 50 % in most domains; however, spatial standard deviation varies from ± 30 % to ± 200 %. The REMO results simulated most climate types relatively well with lowest biases and highest skill score found in the boreal, temperate, and subtropical regions. In dry and polar regions, the REMO results simulated a relatively high annual biases of precipitation and temperature and low skill. Biases were traced to: missing or misrepresented processes, observational uncertainty, and uncertainties due to input boundary forcing.

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“…Each subregion is categorized as a mainly tropical climate (A), arid climate (B), warm temperate climate (C), snow climate (D), or polar climate (E) according to the climate boundary conditions, which are based on threshold values of monthly temperature and precipitation (e.g., temperatures for climate zones A, C, D, and E; moisture availability is required for plant growth in climate zone B). Due to its simplicity and ecologically meaningful classifications, this method has been widely used in many studies, such as assessments of the impacts of climate change on different climate characteristics ( Lee and Bae, 2015;Fernandez et al, 2017). Table 1 shows a detailed description of the Köppen climate classification.…”

Section: Study Area and Climate Zone Classificationmentioning

confidence: 99%

Intensification characteristics of hydroclimatic extremes in the Asian monsoon region under 1.5 and 2.0 °C of global warming

Kim

Bae

2020

Hydrol. Earth Syst. Sci.

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Abstract. Understanding the influence of global warming on regional hydroclimatic extremes is challenging. To reduce the potential risk of extremes under future climate states, assessing the change in extreme climate events is important, especially in Asia, due to spatial variability of climate and its seasonal variability. Here, the changes in hydroclimatic extremes are assessed over the Asian monsoon region under global mean temperature warming targets of 1.5 and 2.0 ∘C above preindustrial levels based on representative concentration pathways (RCPs) 4.5 and 8.5. Analyses of the subregions classified using regional climate characteristics are performed based on the multimodel ensemble mean (MME) of five bias-corrected global climate models (GCMs). For runoff extremes, the hydrologic responses to 1.5 and 2.0 ∘C global warming targets are simulated based on the variable infiltration capacity (VIC) model. Changes in temperature extremes show increasing warm extremes and decreasing cold extremes in all climate zones with strong robustness under global warming conditions. However, the hottest extreme temperatures occur more frequently in low-latitude regions with tropical climates. Changes in mean annual precipitation and mean annual runoff and low-runoff extremes represent the large spatial variations with weak robustness based on intermodel agreements. Global warming is expected to consistently intensify maximum extreme precipitation events (usually exceeding a 10 % increase in intensity under 2.0 ∘C of warming) in all climate zones. The precipitation change patterns directly contribute to the spatial extent and magnitude of the high-runoff extremes. Regardless of regional climate characteristics and RCPs, this behavior is expected to be enhanced under the 2.0 ∘C (compared with the 1.5 ∘C) warming scenario and increase the likelihood of flood risk (up to 10 %). More importantly, an extra 0.5 ∘C of global warming under two RCPs will amplify the change in hydroclimatic extremes on temperature, precipitation, and runoff with strong robustness, especially in cold (and polar) climate zones. The results of this study clearly show the consistent changes in regional hydroclimatic extremes related to temperature and high precipitation and suggest that hydroclimatic sensitivities can differ based on regional climate characteristics and type of extreme variables under warmer conditions over Asia.

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Changes in Koppen–Trewartha climate classification over South America from RegCM4 projections

Abstract: The Koppen-Trewartha (K-T) classification is used to investigate the biomes change in the future climate over South America (SA).

Cited by 28 publications

References 20 publications

The state of the art and fundamental aspects of regional climate modeling in South America

The state of the art and fundamental aspects of regional climate modeling in South America

Evaluation of New CORDEX Simulations Using an Updated Köppen–Trewartha Climate Classification

Intensification characteristics of hydroclimatic extremes in the Asian monsoon region under 1.5 and 2.0 °C of global warming

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