The CMIP5 projection of extreme climate indices in Indonesia using simple quantile mapping method

Putra, I Dewa Gede Arya; Rosid, Mohammad Syamsu; Sopaheluwakan, Ardhasena; Sianturi, Yesi

doi:10.1063/5.0000849

Cited by 10 publications

(7 citation statements)

References 8 publications

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“…To bias-correct the RCMs, the quantile mapping approach, also called the quantilequantile method or distribution mapping was applied for precipitation and temperature datasets. The quantile mapping was used considering its good results in different climatic zones all over the world [12,[40][41][42][43][44][45][46], and in other West African catchments similar to the Mono River Basin [8,[47][48][49]. Furthermore, previous studies in the Mono River Basin have reported good performances with the quantile method [17,18,20].…”

Section: Bias Correctionmentioning

confidence: 99%

A Multi Criteria Decision Analysis Approach for Regional Climate Model Selection and Future Climate Assessment in the Mono River Basin, Benin and Togo

2022

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Regional climate models (RCMs) are key in the current context of global warming, and they are increasingly used to support decision-making and to identify adaptation measures in response to climate change. However, considering the wide range of available RCMs, it is important to identify the most suitable ones prior to climate impact studies, especially at small scales like catchments. In this study, a multicriteria decision analysis approach, namely the technique for order preferences by similarity to an ideal solution (TOPSIS) was applied to select the best performing RCMs in the Mono River Basin of Benin and Togo (West Africa). The TOPSIS method was used to systematically rank 15 RCMs accessed from the coordinated regional downscaling experiment (CORDEX) database. Six RCMs were finally selected and averaged into an ensemble to assess the future climate in the Mono River Basin until 2070 compared to the period 1966–2015. Two climate change scenarios, RCP 4.5 and RCP 8.5, were considered. The results show that under both climate change scenarios, the annual temperature has an increasing trend during the period 1966–2070, whereas annual rainfall for the next 50 years presents high variability and no statistically significant trend. Furthermore, seasonal cycles of rainfall are expected to change in the different parts of the catchment with delayed onset of rainfall, longer dry seasons, and rainfall intensification. In response to the projected changes, impact studies and risk assessments need to be carried out to evaluate potential implications for human security in the Mono River Basin and to provide adequate adaptation measures.

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Section: Bias Correctionmentioning

confidence: 99%

A Multi Criteria Decision Analysis Approach for Regional Climate Model Selection and Future Climate Assessment in the Mono River Basin, Benin and Togo

2022

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“…The ability of the CORDEX model to simulate the historical climate condition in different regions of Tanzania was evaluated by Luhunga et al (2016) and found reasonable model skills, suggesting their potential use in representing the climate condition in different regions of Tanzania. It is important to understand that although the outputs from climate models are used in this study or were used in previous studies (Tölle et al 2018;Luhunga & Songoro 2020;Putra et al 2020), their results should be interpreted to account for the inability of the models to simulate the small-scale processes including convection processes inside the model grids that play a role in modulating the intensity and magnitude of extreme climatic events. extreme climatic events in different regions of Tanzania 3.1.1.…”

Section: Resultsmentioning

confidence: 99%

Projection of extreme climatic events related to frequency over different regions of Tanzania

Luhunga

2022

Journal of Water and Climate Change

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In recent years, extreme climatic events such as heavy rainfall and droughts are common and have contributed to the loss of lives, damage of properties, destruction of the environment and socio-economic livelihood of people predominantly in many developing countries. Characterizing these events to understand their temporal and spatial evolution is of great considerable benefit to different sectors, for instance, energy, agriculture, health and water resource sectors. In this study, we use the outputs of regional climate models to characterize the temporal and spatial evolution of extreme climatic events over Tanzania. Results reveal that all regions across Tanzania are projected to experience a statistically significant increased frequency of extreme climatic events related to temperatures. However, the frequency of extreme climatic events related to rainfall is projected to increase at a non-significant level across most regions. The presented increase in extreme climatic events is likely to pose significant damage to the agriculture sector, water sector and other socio-economic livelihoods of people over many regions in Tanzania. It is therefore recommended that appropriate policies should be put in place to help different sectors and communities at large to adapt to the projected increase in extreme climatic events, especially on the projected warming of near-surface temperatures.

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“…We additionally analyzed extreme events by computing the ETCCDI indices-introduced by the Expert Team on Climate Change Detection and Indices (Peterson et al, 1998;Karl et al, 1999)-which describe moderate to extreme aspects in daily temperature and precipitation statistics. These indices have been extensively used to study changes of extreme weather in observational data (Frich et al, 2002;Kiktev et al, 2003;Alexander et al, 2006;Min et al, 2011;Morak et al, 2011), model simulations of historical climate (Sillmann et al, 2013a) and for future climate projections as well (Sillmann and Roeckner, 2007;Tebaldi et al, 2007;Sillmann et al, 2013b;Rajczak and Schär, 2017;Putra et al, 2020;Wei et al, 2022).…”

Section: Climate Extreme Indicesmentioning

confidence: 99%

Impact of bias correction on climate change signals over central Europe and the Iberian Peninsula

Ugolotti¹,

Anders²,

Lanssens³

et al. 2023

Front. Environ. Sci.

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Vegetation models for climate adaptation and mitigation strategies require spatially high-resolution climate input data in which the error with respect to observations has been previously corrected. To quantify the impact of bias correction, we examine the effects of quantile-mapping bias correction on the climate change signal (CCS) of climate, extremes, and biological variables from the convective regional climate model COSMO-CLM and two dynamic vegetation models (LPJ-GUESS and CARAIB). COSMO-CLM was driven and analyzed at 3 km horizontal resolution over Central Europe (CE) and the Iberian Peninsula (IP) for the transient period 1980–2070 under the RCP8.5 scenario. Bias-corrected and uncorrected climate simulations served as input to run the dynamic vegetation models over Wallonia. Main result of the impact of bias correction on the climate is a reduction of seasonal absolute precipitation by up to −55% with respect to uncorrected simulations. Yet, seasonal climate changes of precipitation and also temperature are marginally affected by bias correction. Main result of the impact of bias correction on changes in extremes is a robust spatial mean CCS of climate extreme indices over both domains. Yet, local biases can both over- and underestimate changes in these indices and be as large as the raw CCS. Changes in extremely wet days are locally enhanced by bias correction by more than 100%. Droughts in southern IP are exacerbated by bias correction, which increases changes in consecutive dry days by up to 14 days/year. Changes in growing season length in CE are affected by quantile mapping due to local biases ranging from 24 days/year in western CE to −24 days/year in eastern CE. The increase of tropical nights and summer days in both domains is largely affected by bias correction at the grid scale because of local biases ranging within ±14 days/year. Bias correction of this study strongly reduces the precipitation amount which has a strong impact on the results of the vegetation models with a reduced vegetation biomass and increases in net primary productivity. Nevertheless, there are large differences in the results of the two applied vegetation models.

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The CMIP5 projection of extreme climate indices in Indonesia using simple quantile mapping method

Cited by 10 publications

References 8 publications

A Multi Criteria Decision Analysis Approach for Regional Climate Model Selection and Future Climate Assessment in the Mono River Basin, Benin and Togo

A Multi Criteria Decision Analysis Approach for Regional Climate Model Selection and Future Climate Assessment in the Mono River Basin, Benin and Togo

Projection of extreme climatic events related to frequency over different regions of Tanzania

Impact of bias correction on climate change signals over central Europe and the Iberian Peninsula

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