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

Abstract

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.

A Case Study of Bias Correction and the Dynamical Downscaling of CFSv2 S2S Forecasts Using a WRF Model: Heatwave in 2018 over South Korea

“…In Figure 12, the averages of the accumulated heat stress index for 94 stations derived from observations and NOBC and BC simulations are shown in the bar graph. Although the analysis period for calculating the index is different, as in a previous study [4], the index calculated by the WRF's simulation was found to be less than half of the actual level in extreme heatwave cases, such as the case in 2018. Similar to the underestimation of the average 2 m temperature in Figures 5 and 6, the heat stress index showed a lower value than the actual one.…”

“…Because of on the purpose of this study to provide S2S forecasts for agriculture, the agro-meteorological indexes produced related to the mean and maximum temperature were evaluated. We calculated the accumulated heat stress index as the threshold 24 • C of the mean temperature [32] according to the equation introduced in Im et al (2021) [4].…”

“…Over the last decades, extreme weather events such as heatwaves and droughts that impact the growth of crops have increased, and are expected to continue to increase [1][2][3]. In this respect, weather and climate forecast information is important for the agricultural sector to inform agricultural practices, planning, and projects, ensure reliable crop yields, and aid in managing the stability of the food crop market [3][4][5]. A sub-seasonal to seasonal (S2S) forecast range (more than 2 weeks and less than a season) is particularly useful for end users in the agricultural application sector, and is useful for managing water resources, energy, unexpected disasters, etc., in order to respond quickly to weather-related hazards; thus, the demand for seamless forecasts is continually growing [6,7].…”

“…Despite development and efforts to improve S2S predictions, there is a limitation of GCMs when applying these models' results to agriculture. Global circulation models (GCMs) have spatial resolutions that are too coarse; for example, CFSv2 has grid resolutions of nearly 100 km for driving dynamical crop model simulations at local scales, where agriculture practices are mostly managed [4,15,16]. Dynamic downscaling with regional climate models (RCMs), which are forced by initial and lateral boundary conditions from GCMs, can transfer global predictions from the GCMs to regional scales [17,18].…”

“…The regional climate simulations showed the occurrence of about 80% of the total heatwaves in the 1970-2010 period for Europe; however, the results were relatively poor over complex topographical areas. Im et al (2021) [4] demonstrated that the dynamic downscaling of CFSv2 operational forecasts using the WRF model leads to significant improvements in extreme heatwave years. They evaluated the performance of CFSv2 and WRF-nested simulations for monthly temperatures during a 10-year summer season compared with in situ observational data, and concluded that dynamic downscaling with RCM resolved the regional variability with topography better and reduced cold biases over the Korean Peninsula, which is a characteristic of CFSv2.…”