Agro‐climate changes over Northeast Asia in <scp>RCP</scp> scenarios simulated by <scp>WRF</scp>

Ahn, Joong‐Bae; Hong, J. K.; Shim, Kang-Bo

doi:10.1002/joc.4423

Cited by 8 publications

(15 citation statements)

References 40 publications

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“…However, the difference between HadGEM2‐AO and ENS seems to be marginal compared to the increases in temperature and heat indices in response to enhanced GHGs concentrations. In line with many other studies (e.g., Suh et al, ; Ahn, Hong, et al, ; Ahn, Jo, et al, ), temperature increases are very clear and unequivocal across the entire target region, and these changes all satisfy the statistical significance at the 95% confidence interval based on the two‐tailed t test. The common feature appearing in all T as , T sw , and T ap data is an increasing rate that is roughly proportional to the GHG concentrations.…”

Section: Resultssupporting

confidence: 89%

Worsening of Heat Stress Due To Global Warming in South Korea Based on Multi‐RCM Ensemble Projections

Choi

Ahn

2017

JGR Atmospheres

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This study assesses the future changes in summer (June–July–August; JJA) heat stress over South Korea under global warming. To better resolve the region‐specific changes in terms of geographical patterns and severity of heat stress in the Korean peninsula, four regional climate models (RCMs) are used for dynamical downscaling of Hadley Centre Global Environmental Model version 2—Atmosphere and Ocean global projections forced by two Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios. Dynamically downscaled simulations (horizontal resolution of 12.5 km and output interval of 3 h) facilitate in‐depth analysis of diurnal variation and extremes over South Korea, as well as focusing on the particular location, Daegu, that is characterized by high vulnerability to rising temperature. Both maximum temperature and heat stress indices such as wet bulb globe temperature and apparent temperature, which include the effect of humidity, are examined in order to comprehensively interpret the behaviors of heat stress in response to anthropogenic climate change. Ensemble projections reveal robust patterns of temperature and resultant humidity increases that are roughly constrained by the approximate 7%/K increase in the moisture holding capacity. The changes in temperature and humidity are directly transmitted to the heat stress indices, showing a significant increase. The heat stress is exacerbated in a differentiated way, with more intensification in diurnal variation at nighttime and in regional variation at low‐elevation basins. Both RCP4.5 and RCP8.5 scenarios project the statistical likelihood of a notable increase of extreme heat stress indices, much stronger and more extended heat waves, and the emergence of a long period of consecutive tropical nights.

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Section: Resultssupporting

confidence: 89%

Worsening of Heat Stress Due To Global Warming in South Korea Based on Multi‐RCM Ensemble Projections

Choi

Ahn

2017

JGR Atmospheres

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“…Frost occurs when the surface minimum temperature drops below 0°C due to radiation cooling or cold air advection (Meehl et al, ), and frost arriving during dormancy and after blooming of plants will adversely affect crops by freezing cellular tissue and damaging foliage (Ahn et al, ). It can have significant impacts on the emergence or maturation of plants especially if they occur early or late during transition seasons (Alexander & Tebaldi, ).…”

Section: Results and Analysismentioning

confidence: 99%

Seasonal and Regional Patterns of Future Temperature Extremes: High‐Resolution Dynamic Downscaling Over a Complex Terrain

et al. 2018

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The combined impacts of projected/simulated climate change and variability on temperature extremes over a complex topographical terrain along the eastern Mediterranean are assessed in this study. High-resolution dynamical temperature downscaling is conducted for the past (2008) and the near future (until 2050) during eight extreme hot and dry years under two Representative Concentration Pathways (RCP 4.5 and RCP 8.5). The Weather Research and Forecasting (WRF) model is used for the downscaling process with two nested resolutions of 9 and 3 km, forced by the global High-Resolution Atmospheric Model (HiRAM) at a resolution of 25 km. Climate indices resultant from daily simulated temperature illustrate considerable changes in daily maximum summer and minimum winter temperature extremes during the future simulated years in comparison with the historic 2008 selected as reference and control period. While average yearly temperatures increase in both scenarios and most regions as expected, the seasonal variability is forecasted to intensify even more significantly resulting in colder winter and warmer summer conditions. Moreover, the interseasonal variability was most pronounced in the years with the highest averaged temperatures. A notable increase in the annual incidence of hot nights and heat wave events relative to 2008 conditions is also expected. The orographic complexity resulted in significant regional differences, with the most affected regions experiencing more than a doubling of extreme indices during the hottest and driest years of near-future decades. This strong spatial variability highlights the need for high-resolution downscaling.Intergovernmental Panel on Climate Change (IPCC) (2013) defines an extreme climatic event as one that is rare (i.e., falling on the extremes of a probability density function) at a particular place and time of year. In Key Points:• A downscaled GCM predicts exacerbated seasonal swings in Lebanon, including decreasing winter and spring temperatures and increasing temperature in summer and fall • Projections of increases in maximum summer temperatures and extreme heat indices are severe over the study area • Regional differences in impact over the focus area's complex terrain are very significant, require high-resolution downscaling El-Fadel, M. (2018). Seasonal and regional patterns of future temperature extremes: High-resolution dynamic downscaling over a complex terrain.

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“…Accordingly, the earliest heading date is July 1, being 71 days after the earliest optimal seeding date (April 21), and the latest heading date is September 18, being 110 days after the latest optimal seeding date (May 31). Considering these and following Ahn et al (2016), 1 July to 18 September is selected as the heading date period of the rice in this study. In order to verify that the CYP defined in this study (Hanyu et al, 1966) can be applied to Korea in the present climate , the actual rice production in South Korea (RP_OBS) provided by the Korean Statistical Information Service (KOSIS) was compared with rice production calculated using the CYP index (RP_CYP) during the 30-year period of 1981-2010.…”

Section: Model and Datamentioning

confidence: 99%

“…The analysis variables are daily mean temperature (°C) and daily sunshine duration (hours). The daily sunshine duration for each grid was estimated by accumulating the duration when the hourly incident shortwave radiation (SR) is greater than zero and the hourly precipitation is zero at each grid (Ahn et al ., 2016).…”

Section: Model and Datamentioning

confidence: 99%

“…CYP is defined as follows.

CYP ((), kg / 10^{3} m^{2}) = italicDS ((), α - β {(T_{normala} - T_{1})}^{2})

where DS is the daily accumulated sunshine duration and T a the average surface air temperature during the 40 days after the heading date. T 1 (21.4°C) is the most proper mean temperature for rice during the grain‐filling period and α (4.14) and β (0.13) are regression coefficients, which can be changed depending on the variety of rice (Hanyu et al ., 1966; Ahn et al ., 2016). According to the Korea Rural Development Administration (RDA) (2004, 2019) and Han (2016), the optimum temperature range is 20–22°C during the grain‐filling period for high quality rice production in South Korea.…”

Section: Model and Datamentioning

confidence: 99%

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Climatic yield potential of Japonica‐type rice in the Korean Peninsula under RCP scenarios using the ensemble of multi‐GCM and multi‐RCM chains

Ahn

Kim

Shim

et al. 2020

Intl Journal of Climatology

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Rice production in the Korean Peninsula (KP) in the near future (2021-2050) is analysed in terms of the climatic yield potential (CYP) index for Japonicatype rice. Data obtained from the dynamically downscaled daily temperature and sunshine duration for the Historical period (1981-2010) and near future under two Representative Concentration Pathway (RCP4.5 and RCP8.5) scenarios are utilized. To reduce uncertainties that might be induced by using a Coupled General Circulation Model (CGCM)-a Regional Climate Model (RCM) chain in dynamical downscaling, two CGCM-three RCM chains are used to estimate the CYP index. The results show that the mean rice production decreases, mainly due to the increase of the temperature during the grainfilling period (40 days after the heading date). According to multi model ensemble, the optimum heading date in the near future will be approximately 12 days later and the maximum CYP will be even higher than in the Historical. This implies that the rice production is projected to decrease if the heading date is selected based on the optimum heading date of Historical, but to increase if based on that of near future. The mean rice production during the period of ripening is projected to decrease (to about 95% (RCP4.5) and 93% (RCP8.5) of the Historical) in the western and southern regions of the KP, but to increase (to about 104% (RCP4.5) and 106% (RCP8.5) of the Historical) in

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Agro‐climate changes over Northeast Asia in RCP scenarios simulated by WRF

Cited by 8 publications

References 40 publications

Worsening of Heat Stress Due To Global Warming in South Korea Based on Multi‐RCM Ensemble Projections

Worsening of Heat Stress Due To Global Warming in South Korea Based on Multi‐RCM Ensemble Projections

Seasonal and Regional Patterns of Future Temperature Extremes: High‐Resolution Dynamic Downscaling Over a Complex Terrain

Climatic yield potential of Japonica‐type rice in the Korean Peninsula under RCP scenarios using the ensemble of multi‐GCM and multi‐RCM chains

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