Performance of CMIP5 global climate models for climate simulation in Southeast Asia

Kamworapan, Suchada; Surussavadee, Chinnawat

doi:10.1109/tencon.2017.8227954

Cited by 4 publications

(7 citation statements)

References 10 publications

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“…Previous studies have evaluated the performances of these GCMs to be used for specific regions, e.g., eastern Tibetan Plateau [10], Australia [11], US Pacific Northwest [12], northeastern Argentina [13], northern Eurasia [14], US continental areas [15], and Southeast Asia [16,17]. Since climate is different for different regions and GCMs also perform differently for different regions, results for different regions cannot be directly compared.…”

Section: Introductionmentioning

confidence: 99%

“…Since climate is different for different regions and GCMs also perform differently for different regions, results for different regions cannot be directly compared. Despite the importance of climate change studies for Southeast Asia as it is one of the most vulnerable regions to climate change, there are only few previous studies [16,17] that evaluate the performances of CMIP5 GCMs in the region. Raghavan et al [16] have evaluated the performance of CMIP5 GCMs for Southeast Asia with the focus on only historical precipitation simulations for 20 years of 1986-2005 without considering temperature simulations.…”

Section: Introductionmentioning

confidence: 99%

“…Even though our preliminarily study [17] has evaluated all 40 CMIP5 GCMs for climate simulations for Southeast Asia, it does not address detailed results for each performance metric and does not consider the performances of ensemble averages of different GCMs. e performances of all 40 CMIP5 GCMs for simulating climatological temperature and precipitation in the twentieth century are evaluated in further details in this study using observation and reanalysis datasets, where 19 performance metrics are employed for evaluation.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of CMIP5 Global Climate Models for Simulating Climatological Temperature and Precipitation for Southeast Asia

Kamworapan

Surussavadee

2019

Advances in Meteorology

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This study evaluates the performances of all forty different global climate models (GCMs) that participate in the Coupled Model Intercomparison Project Phase 5 (CMIP5) for simulating climatological temperature and precipitation for Southeast Asia. Historical simulations of climatological temperature and precipitation of the 40 GCMs for the 40-year period of 1960–1999 for both land and sea and those for the century of 1901–1999 for land are evaluated using observation and reanalysis datasets. Nineteen different performance metrics are employed. The results show that the performances of different GCMs vary greatly. CNRM-CM5-2 performs best among the 40 GCMs, where its total error is 3.25 times less than that of GCM performing worst. The performance of CNRM-CM5-2 is compared with those of the ensemble average of all 40 GCMs (40-GCM-Ensemble) and the ensemble average of the 6 best GCMs (6-GCM-Ensemble) for four categories, i.e., temperature only, precipitation only, land only, and sea only. While 40-GCM-Ensemble performs best for temperature, 6-GCM-Ensemble performs best for precipitation. 6-GCM-Ensemble performs best for temperature and precipitation simulations over sea, whereas CNRM-CM5-2 performs best over land. Overall results show that 6-GCM-Ensemble performs best and is followed by CNRM-CM5-2 and 40-GCM-Ensemble, respectively. The total errors of 6-GCM-Ensemble, CNRM-CM5-2, and 40-GCM-Ensemble are 11.84, 13.69, and 14.09, respectively. 6-GCM-Ensemble and CNRM-CM5-2 agree well with observations and can provide useful climate simulations for Southeast Asia. This suggests the use of 6-GCM-Ensemble and CNRM-CM5-2 for climate studies and projections for Southeast Asia.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Evaluation of CMIP5 Global Climate Models for Simulating Climatological Temperature and Precipitation for Southeast Asia

Kamworapan

Surussavadee

2019

Advances in Meteorology

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“…This architecture is described in Figure 3 Model optimisation was carried out with some minor improvements in MSE, but were not significant for the aims of the study and complicate dataset comparisons unnecessarily. Within the CMIP6 and CMIP5 deck of experiments, there are GCMs which are able to capture the seasonal and climatic patterns present in SEA better than others (Raghavan et al, 2018;Kamworapan and Surussavadee, 2017). Typically, performance is evaluated via the models' ability to capture historical trends, and large seasonal processes such as the monsoon (McSweeney et al, 2015).…”

Section: Stacked (St)mentioning

confidence: 99%

Downscaling using Deep Convolutional Autoencoders, a case study for South East Asia

Levers

Herremans

Dipankar³

et al. 2022

Preprint

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Abstract. Inspired by recent advancements in the field of computer vision, specifically models for generating higher-resolution images from low-resolution images, we investigate the utility of a deep convolutional autoencoder for downscaling and bias correcting climate projections for South East Asia (SEA). Downscaled projections of 2 m surface temperature are generated, using autoencoders trained with data from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and data from the fifth generation ECMWF atmospheric reanalysis (ERA5) project. Using CMIP5 projections as an input, three sets of downscaled data are generated using three methods of autoencoder training, which allow us to determine how autoencoder downscaling and bias correction modify temperature values. Where possible, the downscaled outputs are compared against the Southeast Asia Regional Climate Downscaling/Coordinated Regional Climate Downscaling Experiment–Southeast Asia (SEACLID/CORDEX–SEA) project and outputs from available CMIP6 experiments, to evaluate performance. The autoencoders are found to excel at the rapid generation of highly spatially-resolved climate projections for surface temperature. Realistic spatial features due to coastal and topographic variation are generated by the autoencoder, which are not present in the CMIP5 projections. Additionally, the autoencoders are capable of generating forecast data with regional temperature profiles exceeding that of those appearing in the training set (out-of-sample extrapolation). Seasonal temperature cycles are retained after downscaling throughout the region, despite the absence of temporal information provided to the model. However, autoencoders trained to carry out bias correction display a tendency to smooth daily average temperatures and reduce daily highs and lows beyond that which can be expected to be realistic. Without bias correction, downscaled outputs have a reduced improvement in spatial resolution but the daily temperature profiles of the CMIP5 input forecasts are maintained. Autoencoders rely on the presence of structural features in the datasets to carry out downscaling, and so performance over the oceans is reduced as strong temperature gradients are absent. For this reason, ocean warming is not well represented, an artefact which is not immediately clear in the downscaled outputs. This study demonstrates the importance of rigorous analysis of 'black-box' methods, which can generate non-obvious artefacts that could potentially create misleading results. Despite these limitations, Autoencoders are clearly capable of generating much needed high-resolution climate projections, and strategies to improve upon shortcomings are numerous and well established.

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“…WorldClim 2.0 provides climate data for the period 2021-2040 but at coarser resolution, and a stronger impact of climate change on the area for expansion and carbon sequestration potential from agroforestry can be expected using the 2041-2060 data. CNRM-CM5 is one of the best climate models for South East Asia [24]. Because we only considered the main perennial crop component of each agroforestry system for expansion in the land suitability analysis, the impact of future climate was also assessed for the main crop component only to represent the impact on each agroforestry system, neglecting the potential of agroforestry to modify micro-climates through an integration of multiple plant components that can reduce the intensity of climate change impact.…”

Section: Step 4: Select Agroforestry Systems For Expansion and Land Suitability Analysismentioning

confidence: 99%

Enhancing Vietnam’s Nationally Determined Contribution with Mitigation Targets for Agroforestry: A Technical and Economic Estimate

Mulia¹,

Nguyen

Nguyen³

et al. 2020

Land

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The Nationally Determined Contributions (NDCs) of several non-Annex I countries mention agroforestry but mostly without associated mitigation target. The absence of reliable data, including on existing agroforestry practices and their carbon storage, partially constrains the target setting. In this paper, we estimate the mitigation potential of agroforestry carbon sequestration in Vietnam using a nationwide agroforestry database and carbon data from the literature. Sequestered carbon was estimated for existing agroforestry systems and for areas into which these systems can be expanded. Existing agroforestry systems in Vietnam cover over 0.83 million hectares storing a 1346 ± 92 million ton CO2 equivalent including above-, belowground, and soil carbon. These systems could be expanded to an area of 0.93–2.4 million hectares. Of this expansion area, about 10% is considered highly suitable for production, with a carbon sequestration potential of 2.3–44 million ton CO2 equivalent over the period 2021–2030. If neglecting agroforestry’s potential for modifying micro-climates, climate change can reduce the highly suitable area of agroforestry and associated carbon by 34–48% in 2050. Agroforestry can greatly contribute to Vietnam’s 2021–2030 NDC, for example, to offset the greenhouse gas emissions of the agriculture sector.

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Performance of CMIP5 global climate models for climate simulation in Southeast Asia

Cited by 4 publications

References 10 publications

Evaluation of CMIP5 Global Climate Models for Simulating Climatological Temperature and Precipitation for Southeast Asia

Evaluation of CMIP5 Global Climate Models for Simulating Climatological Temperature and Precipitation for Southeast Asia

Downscaling using Deep Convolutional Autoencoders, a case study for South East Asia

Enhancing Vietnam’s Nationally Determined Contribution with Mitigation Targets for Agroforestry: A Technical and Economic Estimate

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