Projected extreme climate indices in the java island using cmip5 models (original) (raw)
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Kurniadi et al., 2022
The ability of 42 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), consisting of 20 low resolution (LR) and 22 medium resolution (MR), are evaluated for their performance in simulating mean and extreme precipitation over Indonesia. Compared to Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), the model climatologies and interannual variability are investigated individually and as multimodel ensemble means (MME-mean) at monthly and seasonal time scales for the historical simulation over the period 1988-2014. Overall, results show that both LR and MR CMIP6 model skills in simulating mean and extreme precipitation indices vary across specific Indonesian regions and seasons. The individual and MME-mean tend to overestimate the observed climatology, being largest over drier regions, yet MR models perform better compared to the LR regarding the mean bias presumably due to increased resolution. CMIP6 models tend to simulate extreme precipitation better in the dry seasons compared to the wet season. The MME-means of the LR and MR groups mostly outperform the individual models of each group in simulating wet extremes (R95p and Rx5d) but not for the dry extremes (CDD). Among the 42 CMIP6 models, three models consistently perform poorly in simulating Rx5d and R95p, namely FGOALS-g3, IPSL-CM6A-LR, and IPSL-CM6A-LR-INCA, and one model in consecutive dry day (CDD) simulation, MPI-ESM-1-2-HAM, and caution is warranted. Given the knowledge of such biases, the LR and MR CMIP6 climate models can be suitably applied to assist policy makers in their decision on climate change adaptation and mitigation action.
Atmosphere
Extreme climate change events are major causes of devastating impacts on socioeconomic well-being and ecosystem damage. Therefore, understanding the performance of appropriate climate models representing local climate characteristics is critical for future projections. Thus, this study analyses the performance of 24 GCMs from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and 6) and their multi-model ensembles in simulating climate variables including average rainfall, maximum (Tmax), and minimum (Tmin) temperatures at annual and seasonal scales over the Chungcheong region of South Korea from 1975 to 2015. A trend analysis was conducted to estimate the future trends in climate variables in the 2060s (2021–2060) and 2080s (2061–2100). Inverse distance weighting and quantile delta mapping were applied to bias-correct the GCM data. Further, six major evaluating indices comprising temporal and spatial performance assessments were used, after which a comprehensive GCM ra...
Scientific reports, 2024
Selecting the best region-specific climate models is a precursor information for quantifying the climate change impact studies on hydraulic/hydrological projects and extreme heat events. A crucial step in lowering GCMs simulation-related uncertainty is identifying skilled GCMs based on their ranking. This research performed a critical assessment of 30 general circulation models (GCMs) from CMIP6 (IPCC's sixth assessment report) for maximum and minimum temperature over Indian subcontinent. The daily temperature data from 1965 to 2014 were considered to quantify maximum and minimum temperatures using a gridded spatial resolution of 1°. The Nash-Sutcliffe efficiency (NSE), correlation coefficient (CC), Perkins skill score (PSS), normalized root mean square error (NRMSE), and absolute normalized mean bias error (ANMBE) were employed as performance indicators for two different scenarios, S1 and S2. The entropy approach was used to allocate weights to each performance indicator for relative ranking. Individual ranking at each grid was achieved using a multicriteria decision-making technique, VIKOR. The combined ranking was accomplished by integrating group decision-making, average ranking perspective, and cumulative percentage coverage of India. The outcome reveals that for S1 and S2, NRMSE and NSE are the most significant indicators, respectively whereas CC is the least significant indicator in both cases. This study identifies ensemble of KIOST-ESM, MRI-ESM2-0, MIROC6, NESM3, and CanESM5 for maximum temperature and E3SM-1-0, NESM3, CanESM5, GFDL-CM4, INM-CM5-0, and CMCC-ESM2 for minimum temperature. Temperature and precipitation are the most widely used climatic parameter that unveils the impact of climate change over a region. Alteration in local water availability for irrigation purposes, occurrences of extreme events like droughts and floods, change in temperature patterns and severe heat wave occurrences are some of the common climate change impacts on society 1 . To tackle the above problems and have better infrastructure planning for the future, it is important to predict the impacts of climate change in terms of temperature and/ or precipitation. Global Climate Models (GCMs) are used for projecting future climatic data that can be used for hydro-climatological studies. Several studies worldwide consider climatic variables like maximum and minimum temperature, precipitation, surface mean temperature, and sea surface temperature for simulating GCMs in combination with the observed data 2-11 . The factors like complex topography of a region, monsoon dynamics with its onset, strength, and duration are influenced by the atmosphere, land, and ocean's complex interaction . The natural climate variabilities like El Nino-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) often biases GCMs' maximum and minimum temperatures in different seasons . The correlation between surface temperature and precipitation involving 17 CMIP5 GCMs, observed that models performed better in the cold season than in the warm season, and better over the land than over the oceans of the Indian subcontinent 16 . Low-frequency air eddies may alter global and regional climate over decadal periods . There are various other uncertainties associated with the GCM simulations such as inappropriate parameterization of aerosols, initial and boundary conditions, greenhouse gas emission, systematic model errors, and socio-economic factors making it challenging to use at local and regional scales. The additional uncertainties like
Determining best East Java monthly rainfall projection using spatial-based validation
IOP Conference Series: Earth and Environmental Science, 2019
In production of climate change information, determining the best projection is a very important step. Missed projection can lead to loss of public trust in climate change. In some previous studies, most analyses were conducted on point approach due to the lack of observational data covering large spatial areas. In this study, rainfall data from 197 observation points are used to get correction coefficients by comparing it to monthly rainfall historical model from 1972-2005. Area of study is 110.89°E–116.27° E and 8.78°S–5.04°S. Two RCP scenarios (4.5 and 8.5) under CSIROMK3.6 RegCM4 BMKG SEA-Cordex are compared to get its ensemble weighting factor. Validation parameters used are mean absolute error and quartiles of error. Shifting correction is introduced in this study with some evidence based on visual analysis and high correction coefficients from the unshifted one. It is also shown that bilinear resampling gives better result than nearest neighbour. Ensemble weighting factors ar...
Climate predictions are very important for the people in various development sectors, such as agriculture, forestry, fisheries, and industry. Java Island has the biggest population in Indonesia, causing almost all sectors of development centered on the island. The impact of extreme climate like dry season and rainy season that is longer than normal conditions, due to the phenomenon of ENSO and IOD greatly affect various sectors of life in Java Island. Therefore, climate predictions in Java Island is very interesting to study, consider this will be very useful for the community. This study was conducted in 1995-2014 observations using software Climate Predictability Tool (CPT) with Canonical Correlation Analysis (CCA) methods. Predictors used was the monthly data index of Niño3.4 + Dipole Mode (DMI), one month prior to the observation season (November for rainy season Desember-January-February (DJF) and May for dry season June-July-August (JJA)) while predictant was the rainfall data Climate Hazards InfraRed Precipitation Group with Station (CHIRPS) region of the Java island. The results of rainfall prediction CPT years 2013-2014 were compared with the results of spatial analysis using GrADS, which both have the same spatial distribution of the rainfall values average of 250-550 mm/month (DJF) and average of 0-300 mm/month (JJA). The accuracy of the model CPT was also indicated by the Relative Operating Characteristic (ROC) curve of the results of Pearson correlation of 5 representative points of observation (Jakarta, Bandung, Semarang, Yogyakarta and Surabaya), which were mostly located in the top line of non-skill, so that a reliable model of the CPT to use. Rainfall prediction of Java Island on rainy season DJF 2014/2015 shows rainfall value ranges from 200-600 mm/month with the highest peak rainfall in February while forecast on dry season JJA 2015 was in the range of 0-250 mm/month with the lowest rainfall peaks in August. Keywords: CCA; CHIRPS; CPT; rainfall; ROC curve
A Projection on Climate Change Impact towards Meteorological Droughts over Java Island, Indonesia
2016
Java Island, as one of the main Indonesian islands with the largest population and rice production, is considered highly vulnerable towards climate change impacts. Identification of the increasing risk due to climate change enhanced meteorological disasters is very important to support local community resilience.This study aims to analyze the meteorological drought potential in Java Island, as projected by IPCC climate change scenarios. Monthly time series of rainfall data from 1985-2004 are used to determine the current drought potential. Standardized Precipitation Index (SPI) is applied to the observed (1985-2004) and projected (2010-2030) drought index. Geographic Information system (GIS) analysis is utilized to depict the spatial distribution of drought events. The result reveals that although there is a different trend between the western and the eastern part of the island, the drought frequency in general is increasing. The result also indicates that the daily rainfall tendenc...
Atmospheric Research, 2021
The objective of this research was to assess the difference in historical simulations and future projections of rainfall and temperature of CMIP5 (RCP4.5 and 8.5) and CMIP6 (SSP2-4.5 and 5-8.5) models over Southeast Asia (SEA). Monthly historical rainfall and temperature estimations of 13 global climate models common to both CMIPs were evaluated to assess their capability to reproduce the spatial distribution and seasonality of European Reanalysis (ERA) rainfall and temperature. Models were used to determine uncertainty with spatiotemporal variability of rainfall and temperature projections. The CMIP6 GCMs did not appear to perform better than the older CMIP5 in SEA unlike other parts of the globe, except for rainfall. The CMIP6 models showed Kling-Gupta Efficiency (KGE) values in the range of-0.48-0.6, 0.21-0.85 and 0.66-0.91 in simulating historical rainfall, maximum temperature and minimum temperature compared to 0.13-0.46, 0.3-0.86 and 0.42-0.92 for CMIP5. The improvement in CMIP6 models in SEA was in the low uncertainty in ensemble simulation. The projections of CMIP5 and CMIP6 showed a relatively smaller increase in temperature with the CMIP6 ensemble when compared to CMIP5 models, while rainfall appeared to decrease. The geographical distribution of the changes indicated a greater increase in temperature in the cooler region than in the warmer region. In contrast, there was increase in rainfall in the wetter region and a smaller improvement in the drier region. This indicates increased homogeneity in temperature spatial variability, but more heterogeneity in rainfall, in the SEA region under climate warming scenarios.
2020
Climate change is likely to pose enormous challenges for agriculture, water resources, infrastructure, and livelihood of millions of people living in South Asia. Here, we develop daily bias-corrected data of precipitation, maximum and minimum temperatures at 0.25° spatial resolution for South Asia (India, Pakistan, Bangladesh, Nepal, Bhutan, and Sri Lanka) and 18 river basins located in the Indian sub-continent. The bias-corrected dataset is developed using Empirical Quantile Mapping (EQM) for the historic (1951-2014) and projected (2015-2100) climate for the four scenarios (SSP126, SSP245, SSP370, SSP585) using output from 13 CMIP6-GCMs. The bias-corrected dataset was evaluated against the observations for both mean and extremes of precipitation, maximum and minimum temperatures. Bias corrected projections from 13 CMIP6-GCMs project a warmer (3-5°C) and wetter (13-30%) climate in South Asia in the 21st century. The bias-corrected projections from CMIP6-GCMs can be used for climate ...
Stochastic Environmental Research and Risk Assessment, 2016
Projections of changes in climate are important in assessing the potential impacts of climate change on natural and social systems. However, current knowledge on assembling different GCMs to estimate future climate change over the Pear River basin is still limited so far. This study examined the capability of BMA and arithmetic mean (AM) method in assembling precipitation and temperature from CMIP5 under RCP2.6, RCP4.5 and RCP8.5 scenarios over the Pearl River basin. Results show that the BMA outperforms the traditional AM method. Precipitation tends to increase over the basin under RCP2.6 and RCP4.5 scenarios, whereas decrease under RCP8.5. The most remarkable increase of precipitation is found in the northern region under RCP2.6 scenario. The linear trend of the monthly mean near-surface air temperature increases with the growing CO 2 concentration. The warming trends in four seasons are distinct. The warming rate is prominent in summer and spring than that in other season, meanwhile it is larger in western region than in other parts of the basin. The findings can provide beneficial reference to water resources and agriculture management strategies, as well as the adaptation and mitigation strategies for floods and droughts under the context of global climate change. Keywords Climate change Á Multi-model ensemble projections Á Bayesian model averaging Á The Pearl River basin
Theoretical and Applied Climatology, 2017
We present the climate change impact on the annual and seasonal precipitation over Rajang River Basin (RRB) in Sarawak by employing a set of models from Coupled Model Intercomparison Project Phase 5 (CMIP5). Based on the capability to simulate the historical precipitation, we selected the three most suitable GCMs (i.e. ACCESS1.0, ACCESS1.3, and GFDL-ESM2M) and their mean ensemble (B3MMM) was used to project the future precipitation over the RRB. Historical (1976-2005) and future (2011-2100) precipitation ensembles of B3MMM were used to perturb the stochastically generated future precipitation over 25 rainfall stations in the river basin. The B3MMM exhibited a significant increase in precipitation during 2080s, up to 12 and 8% increase in annual precipitation over upper and lower RRB, respectively, under RCP8.5, and up to 7% increase in annual precipitation under RCP4.5. On the seasonal scale, Mann-Kendal trend test estimated statistically significant positive trend in the future precipitation during all seasons; except September to November when we only noted significant positive trend for the lower RRB under RCP4.5. Overall, at the end of the twenty-first century, an increase in annual precipitation is noteworthy in the whole RRB, with 7 and 10% increase in annual precipitation under the RCP4.5 and the RCP8.5, respectively.