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http://dx.doi.org/10.5532/KJAFM.2022.24.4.201

Improvement in Regional-Scale Seasonal Prediction of Agro-Climatic Indices Based on Surface Air Temperature over the United States Using Empirical Quantile Mapping  

Chan-Yeong, Song (Department of Atmospheric Sciences, BK21 School of Earth and Environmental Systems, Pusan National University)
Joong-Bae, Ahn (Department of Atmospheric Sciences, Pusan National University)
Kyung-Do, Lee (Climate Change Assessment Division, National Institute of Agricultural Sciences)
Publication Information
Korean Journal of Agricultural and Forest Meteorology / v.24, no.4, 2022 , pp. 201-217 More about this Journal
Abstract
The United States is one of the largest producers of major crops such as wheat, maize, and soybeans, and is a major exporter of these crops. Therefore, it is important to estimate the crop production of the country in advance based on reliable long- term weather forecast information for stable crops supply and demand in Korea. The purpose of this study is to improve the seasonal predictability of the agro-climatic indices over the United States by using regional-scale daily temperature. For long-term numerical weather prediction, a dynamical downscaling is performed using Weather Research and Forecasting (WRF) model, a regional climate model. As the initial and lateral boundary conditions of WRF, the global hourly prediction data obtained from the Pusan National University Coupled General Circulation Model (PNU CGCM) are used. The integration of WRF is performed for 22 years (2000-2021) for period from June to December of each year. The empirical quantile mapping, one of the bias correction methods, is applied to the timeseries of downscaled daily mean, minimum, and maximum temperature to correct the model biases. The uncorrected and corrected datasets are referred WRF_UC and WRF_C, respectively in this study. The daily minimum (maximum) temperature obtained from WRF_UC presents warm (cold) biases over most of the United States, which can be attributed to the underestimated the low (high) temperature range. The results show that WRF_C simulates closer to the observed temperature than WRF_UC, which lead to improve the long- term predictability of the temperature- based agro-climatic indices.
Keywords
United States; Regional climate model; Empirical quantile mapping; Temperature; Agro-climatic indices;
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Times Cited By KSCI : 8  (Citation Analysis)
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1 Lu, Y., and L. Kueppers, 2015: Increased heat waves with loss of irrigation in the United States. Environmental Research Letters 10(6), 064010. https://doi.org/10.1088/1748-9326/10/6/064010   DOI
2 MacLachlan, C., A. Arribas, K. A. Peterson, A. Maidens, D. Fereday, A. A. Scaife, M. Gordon, M. Vellinga, A. Williams, R. E. Comer, J. Camp, P. Xavier, and G. Madec, 2015: Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. Quarterly Journal of the Royal Meteorological Society 141(689), 1072-1084. https://doi.org/10.1002/qj.2396   DOI
3 McMaster, G. S., and W. W. Wilhelm, 1997: Growing degree-days: one equation, two interpretations. Agricultural and Forest Meteorology 87(4), 291-300. https://doi.org/https://doi.org/10.1016/S0168-1923(97)00027-0   DOI
4 Michaelsen, J., 1987: Cross-Validation in Statistical Climate Forecast Models. Journal of Applied Meteorology and Climatology 26(11), 1589-1600. https://doi.org/10.1175/1520-0450(1987)026<1589:Cviscf>2.0.Co;2   DOI
5 Mishra, V., and K. A. Cherkauer, 2010: Retrospective droughts in the crop growing season: Implications to corn and soybean yield in the Midwestern United States. Agricultural and Forest Meteorology 150(7), 1030-1045. https://doi.org/10.1016/j.agrformet.2010.04.002   DOI
6 Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. Journal of Geophysical Research: Atmospheres 102(D14), 16663-16682. https://doi.org/10.1029/97JD00237   DOI
7 Molteni, F., T. Stockdale, M. Balmaseda, G. Balsamo, R. Buizza, L. Ferranti, L. Magnusson, K. Mogensen, T. Palmer, and F. Vitart, 2011: The new ECMWF seasonal forecast system (System 4). ECMWF Tech. Memo. No. 656, European Centre for Medium Range Weather Forecasts, 49pp. [Available online at https://www.ecmwf.int/sites/default/files/elibrary/2011/11209-new-ecmwf-seasonal-forecast-system-system-4.pdf]
8 Moonen, A. C., L. Ercoli, M. Mariotti, and A. Masoni, 2002: Climate change in Italy indicated by agrometeorological indices over 122 years. Agricultural and Forest Meteorology 111(1), 13-27. https://doi.org/10.1016/S0168-1923(02)00012-6   DOI
9 Mueller, B., M. Hauser, C. Iles, R. H. Rimi, F. W. Zwiers, and H. Wan, 2015: Lengthening of the growing season in wheat and maize producing regions. Weather and Climate Extremes 9, 47-56. https://doi.org/https://doi.org/10.1016/j.wace.2015.04.001   DOI
10 Pacanowski, R. C., and S. M. Griffies, 2000: MOM 3.0 Manual. NOAA/GFDL, 682pp. [Available online at https://www.gfdl.noaa.gov/wp-content/uploads/files/model_development/ocean/mom3_manual.pdf]
11 Paulson, C. A., 1970: The Mathematical Representation of Wind Speed and Temperature Profiles in the Unstable Atmospheric Surface Layer. Journal of Applied Meteorology and Climatology 9(6), 857-861. https://doi.org/10.1175/1520-0450(1970)009<0857:Tmrows>2.0.Co;2   DOI
12 Piani, C., J. O. Haerter, and E. Coppola, 2010: Statistical bias correction for daily precipitation in regional climate models over Europe. Theoretical and Applied Climatology 99, 187-192. https://doi.org/10.1007/s00704-009-0134-9   DOI
13 RDA, 2014: The Construction of Agrometeorological Information and Climate Modeling in Major Crop Production Area. Rural Development Administration, 138pp. [Available online at https://scienceon.kisti.re.kr/srch/selectPORSrchReport.do?cn=TRKO201400011432]
14 RDA, 2018: Production of Dissemination of Agroclimate Information in Major Crop Production Areas. Rural Development Administration, 86pp. [Available online at https://scienceon.kisti.re.kr/srch/selectPORSrchReport.do?cn=TRKO201800043070&dbt=TRKO]
15 RDA, 2020: Development of Environment Information and Monitoring Service System for grain yield in foreign countries. Rural Development Administration, 232pp. [Available online at https://scienceon.kisti.re.kr/srch/selectPORSrchReport.do?cn=TRKO202000030378]
16 Song, C.-Y., S.-H. Kim, and J.-B. Ahn, 2021: Improvement in Seasonal Prediction of Precipitation and Drought over the United States Based on Regional Climate Model Using Empirical Quantile Mapping. Atmosphere 31(5), 637-656. https://doi.org/10.14191/Atmos.2021.31.5.637 (in Korean with English abstract)   DOI
17 Monier, E., L. Xu, and R. Snyder, 2016: Uncertainty in future agro-climate projections in the United States and benefits of greenhouse gas mitigation. Environmental Research Letters 11(5), 055001. https://doi.org/10.1088/1748-9326/11/5/055001   DOI
18 Schlenker, W., and M. J. Roberts, 2009: Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proceedings of the National Academy of Sciences, USA, 15594-15598. https://doi.org/10.1073/pnas.0906865106   DOI
19 Shim, K.-M., G.-Y. Kim, K.-A. Roh, H.-C. Jeong, and D.-B. Lee, 2008: Evaluation of Agro-Climatic Indices under Climate Change. Korean Journal of Agricultural and Forest Meteorology 10(4), 113-120. (in Korean with English abstract)   DOI
20 Skamarock, W. C., J. B. Klemp, J. Dudhia, D. O. Gill, D. M. Barker, W. Wang, and J. G. Powers, 2008: A description of the Advanced Research WRF version 3. Tech Rep. No. NCAR/TN-468+STR, NCAR/TN-468+STR, National Center for Atmospheric Research, 88pp. [Available online at https://opensky.ucar.edu/islandora/object/technotes:500]
21 Teutschbein, C., and J. Seibert, 2012: Bias correction of regional climate model simulations for hydrological climate-change impact studies: Review and evaluation of different methods. Journal of Hydrology 456-457, 12-29. https://doi.org/10.1016/j.jhydrol.2012.05.052   DOI
22 Themessl, M. J., A. Gobiet, and A. Leuprecht, 2011: Empirical-statistical downscaling and error correction of daily precipitation from regional climate models. International Journal of Climatology 31(10), 1530-1544. https://doi.org/10.1002/joc.2168   DOI
23 Themessl, M. J., A. Gobiet, and G. Heinrich, 2012: Empirical-statistical downscaling and error correction of regional climate models and its impact on the climate change signal. Climatic Change 112(2), 449-468. https://doi.org/10.1007/s10584-011-0224-4    DOI
24 Choi, S.-C., J.-K. Kim, and J. Yang, 2022: The Current Status of Korean Agriculture in the World. Korea Rural Economic Institute, 194pp.
25 Ahn, J.-B., J.-Y. Hong, and K.-M. Shim, 2010: Agro-Climatic Indices Changes over the Korean Peninsula in CO2 Doubled Climate Induced by Atmosphere-Ocean-Land-Ice Coupled General Circulation Model. Korean Journal of Agricultural and Forest Meteorology 12(1), 11-22. (in Korean with English abstract)   DOI
26 Ahn, J.-B., K.-M. Shim, M.-P. Jung, H.-G. Jeong, Y.-H. Kim, and E.-S. Kim, 2018: Predictability of temperature over South Korea in PNU CGCM and WRF hindcast. Atmosphere 28(4), 479-490. https://doi.org/10.14191/Atmos.2018.28.4.479 (in Korean with English abstract)   DOI
27 Chen, F., and J. Dudhia, 2001: Coupling an Advanced Land Surface-Hydrology Model with the Penn State-NCAR MM5 Modeling System. Part I: Model Implementation and Sensitivity. Monthly Weather Review 129(4), 569-585. https://doi.org/10.1175/1520-0493(2001)129<0569:Caalsh>2.0.Co;2   DOI
28 Chung, U., S. Gbegbelegbe, B. Shiferaw, R. Robertson, J. I. Yun, K. Tesfaye, G. Hoogenboom, and K. Sonder, 2014: Modeling the effect of a heat wave on maize production in the USA and its implications on food security in the developing world. Weather and Climate Extremes 5-6, 67-77. https://doi.org/https://doi.org/10.1016/j.wace.2014.07.002   DOI
29 DelSole, T., and J. Shukla, 2006: Specification of Wintertime North American Surface Temperature. Journal of Climate 19(12), 2691-2716. https://doi.org/10.1175/jcli3704.1   DOI
30 Dudhia, J., 1989: Numerical Study of Convection Observed during the Winter Monsoon Experiment Using a Mesoscale Two-Dimensional Model. Journal of the Atmospheric Sciences 46(20), 3077-3107. https://doi.org/10.1175/1520-0469(1989)046<3077:Nsocod>2.0.Co;2   DOI
31 Hersbach, H., B. Bell, P. Berrisford, S. Hirahara, A. Horanyi, J. Munoz-Sabater, J. Nicolas, C. Peubey, R. Radu, D. Schepers, A. Simmons, C. Soci, S. Abdalla, X. Abellan, G. Balsamo, P. Bechtold, G. Biavati, J. Bidlot, M. Bonavita, G. De Chiara, P. Dahlgren, D. Dee, M. Diamantakis, R. Dragani, J. Flemming, R. Forbes, M. Fuentes, A. Geer, L. Haimberger, S. Healy, R. J. Hogan, E. Holm, M. Janiskova, S. Keeley, P. Laloyaux, P. Lopez, C. Lupu, G. Radnoti, P. de Rosnay, I. Rozum, F. Vamborg, S. Villaume, and J.-N. Thepaut, 2020: The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146(730), 1999-2049. https://doi.org/10.1002/qj.3803   DOI
32 Donat, M. G., L. V. Alexander, H. Yang, I. Durre, R. Vose, R. J. H. Dunn, K. M. Willett, E. Aguilar, M. Brunet, J. Caesar, B. Hewitson, C. Jack, A. M. G. Klein Tank, A. C. Kruger, J. Marengo, T. C. Peterson, M. Renom, C. Oria Rojas, M. Rusticucci, J. Salinger, A. S. Elrayah, S. S. Sekele, A. K. Srivastava, B. Trewin, C. Villarroel, L. A. Vincent, P. Zhai, X. Zhang, and S. Kitching, 2013: Updated analyses of temperature and precipitation extreme indices since the beginning of the twentieth century: The HadEX2 dataset. Journal of Geophysical Research: Atmospheres 118(5), 2098-2118. https://doi.org/10.1002/jgrd.50150   DOI
33 Feng, S., and Q. Hu, 2004: Changes in agrometeorological indicators in the contiguous United States: 1951-2000. Theoretical and Applied Climatology 78(4), 247-264. https://doi.org/10.1007/s00704-004-0061-8   DOI
34 Gudmundsson, L., J. B. Bremnes, J. E. Haugen, and T. Engen-Skaugen, 2012: Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations-a comparison of methods. Hydrology and Earth System Sciences 16(9), 3383-3390. https://doi.org/10.5194/hess-16-3383-2012 HESS   DOI
35 Hong, S.-Y., and J.-O. J. Lim, 2006: The WRF single-moment 6-class microphysics scheme (WSM6). Asia-Pacific Journal of Atmospheric Sciences 42(2), 129-151.
36 Hur, J., Y.-S. Kim, S. Jo, K.-M. Shim, J.-B. Ahn, M.-J. Choi, Y.-H. Kim, M. Kang, and W.-J. Choi, 2021: Estimation of Waxy Corn Harvest Date over South Korea Using PNU CGCM-WRF Chain. Korean Journal of Agricultural and Forest Meteorology 23(4), 405-414. https://doi.org/10.5532/KJAFM.2021.23.4.405 (in Korean with English abstract)   DOI
37 Hong, S.-Y., Y. Noh, and J. Dudhia, 2006: A New Vertical Diffusion Package with an Explicit Treatment of Entrainment Processes. Monthly Weather Review 134(9), 2318-2341. https://doi.org/10.1175/mwr3199.1   DOI
38 Hunke, E. C., and J. K. Dukowicz, 1997: An Elastic-Viscous-Plastic Model for Sea Ice Dynamics. Journal of Physical Oceanography 27(9), 1849-1867. https://doi.org/10.1175/1520-0485(1997)027<1849:Aevpmf>2.0.Co;2   DOI
39 Hur, J., and J.-B. Ahn, 2015: The change of first-flowering date over South Korea projected from downscaled IPCC AR5 simulation: peach and pear. International Journal of Climatology 35(8), 1926-1937. https://doi.org/10.1002/joc.4098   DOI
40 Im, E.-S., S. Ha, L. Qiu, J. Hur, S. Jo, and K.-M. Shim, 2021: An Evaluation of Temperature-Based Agricultural Indices Over Korea From the High-Resolution WRF Simulation. Frontiers in Earth Science 9(357). https://doi.org/10.3389/feart.2021.656787   DOI
41 Jo, S., K.-M. Shim, J. Hur, Y.-S. Kim, and J.-B. Ahn, 2020: Future Changes of Agro-Climate and Heat Extremes over S. Korea at 2 and 3 ℃ Global Warming Levels with CORDEX-EA Phase 2 Projection. Atmosphere 11(12), 1336. https://doi.org/10.3390/atmos11121336   DOI
42 Kain, J. S., 2004: The Kain-Fritsch Convective Parameterization: An Update. Journal of Applied Meteorology 43(1), 170-181. https://doi.org/10.1175/1520-0450(2004)043<0170:Tkcpau>2.0.Co;2   DOI
43 Kim, Y.-H., M.-J. Choi, K.-M. Shim, J. Hur, S. Jo, and J. B. Ahn, 2021: A Study on the Predictability of the Number of Days of Heat and Cold Damages by Growth Stages of Rice Using PNU CGCM-WRF Chain in South Korea. Atmosphere 31(5), 577-592. https://doi.org/10.14191/Atmos.2021.31.5.577 (in Korean with English abstract)   DOI
44 Kiehl, J. T., J. J. Hack, G. B. Bonan, B. A. Boville, B. P. Briegleb, D. L. Williamson, and P. J. Rasch, 1996: Description of the NCAR community climate model (CCM3). Tech Rep. No. NCAR/TN-420+STR, National Center for Atmospheric Research, 152pp. [Available online at https://opensky.ucar.edu/islandora/object/technotes:187]
45 Kim, H.-J., and J.-B. Ahn, 2015: Improvement in Prediction of the Arctic Oscillation with a Realistic Ocean Initial Condition in a CGCM. Journal of Climate 28(22), 8951-8967. https://doi.org/10.1175/jcli-d-14-00457.1   DOI
46 Kim, Y.-H., E.-S. Kim, M.-J. Choi, K.-M. Shim, and J.-B. Ahn, 2019: Evaluation of Long-Term Seasonal Predictability of Heatwave over South Korea Using PNU CGCM-WRF Chain. Atmosphere 29(5), 671-687. https://doi.org/10.14191/Atmos.2019. 29.5.671 (in Korean with English abstract)   DOI
47 Kukal, M. S., and S. Irmak, 2018: U.S. Agro-Climate in 20th Century: Growing Degree Days, First and Last Frost, Growing Season Length, and Impacts on Crop Yields. Scientific Reports 8(1), 6977. https://doi.org/10.1038/s41598-018-25212-2   DOI
48 Leng, G., 2021: Maize yield loss risk under droughts in observations and crop models in the United States. Environmental Research Letters 16, 024016. https://doi.org/10.1088/1748-9326/abd500   DOI
49 Lim, E.-P., H. H. Hendon, S. Langford, and O. Alves, 2012: Improvements in POAMA2 for the prediction of major climate drivers and south eastern Australian rainfall. CAWCR Tech. Rep. No. 051, Centre for Australian Weather and Climate Research, 23pp. [Available online at https://www.cawcr.gov.au/technical-reports/CTR_051.pdf]