Seasonal Prediction of Surface Air Temperature across Vietnam Using the Regional Climate Model Version 4.2 (RegCM4.2)

Van, Tan Phan; Nguyen, Hiep Van; Tuan, Long Trinh; Quang, Trung Nguyen; Ngo‐Duc, Thanh; Laux, Patrick; Nguyen-Xuan, Thanh

doi:10.1155/2014/245104

Cited by 12 publications

(7 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since this study is the first attempt to perform dynamical downscale of CFSv2 focusing on the Korean peninsula, it is not allowed to benchmark our results against other literatures. However, we found a study that demonstrated the performance of dynamical downscaling of CFSv2 in Vietnam using the RegCM4 (Phan- Van et al, 2014). Their downscaling results show the severe cold temperature exceeding −5 • C at a month lead time.…”

Section: Summary and Discussionmentioning

confidence: 94%

“…The first focus of the analysis is to quantify the added value of the dynamically downscaled simulations over the driving global model prediction (i.e., CFSv2). Although CFSv2 is widely used for future predictions at various timescales worldwide (e.g., Phan- Van et al, 2014;Sangelantoni et al, 2019), its downscaled results as well as its performance itself over Korea have not yet been intensively evaluated. The regional details and temporal variations of daily mean temperature (Tmean) and daily maximum temperature (Tmax) simulated from WRF nested domain and CFSv2 are compared against 90 in-situ observations.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Evaluation of Temperature-Based Agricultural Indices Over Korea From the High-Resolution WRF Simulation

Qiu

et al. 2021

Front. Earth Sci.

View full text Add to dashboard Cite

This study evaluates the performance of dynamical downscaling of global prediction generated from the NOAA Climate Forecast System (CFSv2) at subseasonal time-scale against dense in-situ observational data in Korea. The Weather Research and Forecasting (WRF) double-nested modeling system customized over Korea is adopted to produce very high resolution simulation that presumably better resolves geographically diverse climate features. Two ensemble members of CFSv2 starting with different initial conditions are downscaled for the summer season (June-July-August) during past 10-year (2011–2020). The comparison of simulations from the nested domain (5 km resolution) of WRF and driving CFSv2 (0.5°) clearly demonstrates the manner in which dynamical downscaling can drastically improve daily mean temperature (Tmean) and daily maximum temperature (Tmax) in both quantitative and qualitative aspects. The downscaled temperature not only better resolves the regional variability strongly tied with topographical elevation, but also substantially lowers the systematic cold bias seen in CFSv2. The added value from the nested domain over CFSv2 is far more evident in Tmax than in Tmean, which indicates a skillful performance in capturing the extreme events. Accordingly, downscaled results show a reasonable performance in simulating the plant heat stress index that counts the number of days with Tmax above 30°C and extreme degree days that accumulate temperature exceeding 30°C using hourly temperature. The WRF simulations also show the potential to capture the variation of Tmean-based index that represents the accumulation of heat stress in reproductive growth for the mid-late maturing rice cultivars in Korea. As the likelihood of extreme hot temperatures is projected to increase in Korea, the modeling skill to predict the ago-meteorological indices measuring the effect of extreme heat on crop could have significant implications for agriculture management practice.

show abstract

Section: Summary and Discussionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

An Evaluation of Temperature-Based Agricultural Indices Over Korea From the High-Resolution WRF Simulation

Qiu

et al. 2021

Front. Earth Sci.

View full text Add to dashboard Cite

show abstract

“…The four climate scenarios projected by MoNRE [20] consist of: low GHG concentration—RCP2.6; average GHG concentration—RCP4.5; relatively high GHG concentration—RCP6.0; and high GHG concentration—RCP8.5. These scenarios were developed based on adjustments to five regional climate change models, including AGCM/MRI [34], Providing Regional Climates for Impacts Studies (PRECIS) [35], Conformal Cubic Atmospheric Model (CCAM) [36], Regional Climate Model (RegCM) [37], and Weather Research and Forecast (clWRF) [38], and based on the global climate predictions described in the IPCC’s Fifth Assessment Report [39]. Accordingly, the regional climate change models were calibrated with temperature and rainfall data and extreme climate events (typhoon, hot, and cold spells) observed at 150 hydro-meteorological stations in Vietnam.…”

Section: Methodsmentioning

confidence: 99%

Modeling ENSO impact on rice production in the Mekong River Delta

Yen¹,

Quyen

Duong³

et al. 2019

PLoS ONE

View full text Add to dashboard Cite

The Mekong River Delta is the rice production hub in South-east Asia and has a key role in determining rice prices in the world market. The increasing variability in the local climate due to global climate changes and the increasing severity of the ENSO phenomenon threatens rice production in the region, which has consequences for local and global food security. Though existing mapping efforts delineate the consequences of saline water intrusion during El Niño and flooding events during La Niña in the basin, research to predict future impacts in rice production is rather limited. The current work uses ORYZA, an ecophysiological model, combined with historical climate data, climate change scenarios RCP4.5 and 8.5 and climate-related risk maps to project the aggregate productivity and rice production impacts by the year 2050. Results show that in years of average salinity intrusion and flooding, the winter-spring rice crop in the MRD would experience an average annual decrease of 720,450 tons for 2020–2050 under the RCP4.5 scenario compared to the baseline of 2005–2016 average and another 1.17 million tons under the RCP8.5 scenario. The autumn-winter crop would decrease by 331,480 tons under RCP4.5 and 462,720 tons under RCP8.5. In years of severe salinity intrusion and flooding, the winter-spring rice crop would decrease by 2.13 million tons (10.29% lower than the projection for an average year) under RCP4.5 and 2.5 million tons (13.62%) under RCP8.5. Under severe conditions, the autumn-winter crop would have an average decrease of 1.3 million tons (7.36%) under RCP4.5 and 1.4 million tons (10.88%) for the RCP8.5 scenario. Given that most of the rice produced in this area is exported, a decline in rice supply at this scale would likely have implications on the global market price of rice affecting global food security. Such decline will also have implications for the rural economy and food security of Vietnam. Suggestions for corrective measures to reduce the impacts are briefly discussed.

show abstract

“…Two experiments were designed to further examine the skill of the RegCM model in the tercile seasonal forecasting of the TC frequency with respect to the observed climatology (EXP_1) and model climatology (EXP_2). For these experiments, the observed TC and model TC climatology are obtained from the number of TCs ob served during the 1981−2010 period and from the model simulations (RegCM_ CFSR2.5) during the 1995− 2010 period, based on values for the 33rd (ob served: q33o; model: q33m) and 66th (q66o; q66m) percentiles (see Phan et al 2014). For specific evaluation of the tercile forecasts of the TC activity, the number of TCs obtained from RegCM_CFS1.0 (NTCs) during the 2012−2013 seasons is compared against the observed climatology (i.e.…”

Section: Real-time Tc Frequency For Wpacmentioning

confidence: 99%

Seasonal forecasting of tropical cyclone activity in the coastal region of Vietnam using RegCM4.2

Phan-Van

Trinh-Tuan²,

Bui³

et al. 2015

Clim. Res.

View full text Add to dashboard Cite

This study presents an experimental seasonal forecast of tropical cyclone (TC) activity for Vietnam's coastal region during the 2012−2013 typhoon seasons, using the Regional Climate Model (RegCM, version 4.2) to downscale the global Climate Forecasting System (CFS) forecast. Using an improved vortex tracking algorithm that detects vortex centers efficiently, RegCM reasonably forecasts the general distribution of TC counts in time as well as the TC track pattern during the entire experimental period from February to July 2012 and 2013, despite significant underestimation of the TC counts in the global CFS forecasts that are used as initial and lateral boundary conditions for the RegCM model. Further examination of the storm activity in the Vietnam East Sea that directly influences Vietnam's coastal region shows, however, that RegCM tends to overestimate the TC frequency in this sub-region compared to observation. This suggests that direct applications of the RegCM model for seasonal forecasts of TC activity in Vietnam's coastal region has a significant bias that will need to be corrected before the model can provide useful information.

show abstract

Seasonal Prediction of Surface Air Temperature across Vietnam Using the Regional Climate Model Version 4.2 (RegCM4.2)

Cited by 12 publications

References 27 publications

An Evaluation of Temperature-Based Agricultural Indices Over Korea From the High-Resolution WRF Simulation

An Evaluation of Temperature-Based Agricultural Indices Over Korea From the High-Resolution WRF Simulation

Modeling ENSO impact on rice production in the Mekong River Delta

Seasonal forecasting of tropical cyclone activity in the coastal region of Vietnam using RegCM4.2

Contact Info

Product

Resources

About