Contents Shortcut
Main Menu Shortcut

Climate Information Publications

All research papers and news utilizing APCC's climate information are listed below. If you have published any relevant papers or articles with APCC's climate information and want them to be listed here, please e-mail apcc@apcc21.org.

2020

Dandi, A. R., P. A. Pillai, and J. S. Chowdary, 2020: Inter-annual variability and skill of tropical rainfall and SST in APCC seasonal forecast models. Clim. Dyn., 56, 439-456, https://doi.org/10.1007/s00382-020-05487-w

Jung, E., J.-H. Jeong, S.-H. Woo, B.-M. Kim, J.-H. Yoon, and G.-H. Lim, 2020: Impacts of the Arctic-Midlatitude Teleconnection on Wintertime Seasonal Climate Forecasts. Environ. Res. Lett., 15. 94045, https://doi.org/10.1088/1748-9326/aba3a3

Kim, M., S. T. Kim, and Y. Jeong, 2020: Weather Generator–Based Downscaling of EAWM Strength Prediction to the Climate of a Korean Basin. J. Appl. Meteor. Climatol., 59, 1581–1605, https://doi.org/10.1175/JAMC-D-19-0282.1

Lee, Y.-Y., and J.-H. Oh, 2020: West Pacific teleconnection pattern in dynamical seasonal predictions: how is it connected to the Atlantic atmospheric mean bias?. Clim. Dyn., 54, 3671–3683, https://doi.org/10.1007/s00382-020-05198-2

Myoung, B., J. Rhee, and C. Yoo, 2020: Long-Lead Predictions of Warm Season Droughts in South Korea Using North Atlantic SST. J. Climate, 33, 4659-4677, https://doi.org/10.1175/JCLI-D-19-0082.1

Shin, J. Y., H.-H. Kwon, and J.-H. Lee, 2020: Probabilistic long-term hydrological drought forecast using Bayesian networks and drought propagation. Meteorol. Appl., 27, e1827, https://doi.org/10.1002/met.1827


2019
TOP

Lee, J.-Y., H.-J. Kim, and Y.-R. Jeong, 2019: Influence of Boreal Summer Intraseasonal Oscillation on the 2016 Heat Wave over Korea. Atmos., 29(5), 627–637, https://doi.org/10.14191/ATMOS.2019.29.5.627

Shin, J. Y., H.-H. Kwon, J.-H. Lee, and T.-W. Kim, 2019: Probabilistic long-term hydrological drought forecast using Bayesian networks and drought propagation. Meteorol. Appl., 27, e1827, https://doi.org/10.1002/met.1827

Sohn, S.-J., C.-Y. Tam, and J.-S. Kug, 2019: How does ENSO diversity limit the skill of tropical Pacific precipitation forecasts in dynamical seasonal predictions?. Clim. Dyn., 53, 5815–5831, https://doi.org/10.1007/s00382-019-04901-2


2018
TOP

Alessandri, A., M. D. Felice, F. Catalano, J.-Y. Lee, B. Wang, D. Y. Lee, J.-H. Yoo, and A. Weisheimer, 2018: Grand European and Asian-Pacific multi-model seasonal forecasts: maximization of skill and of potential economical value to end-users. Clim. Dyn., 50, 2719–2738, https://doi.org/10.1007/s00382-017-3766-y

Iizumi, T., Y. Shin, W. Kim, M. Kim, and J. Choi, 2018: Global crop yield forecasting using seasonal climate information from a multi-model ensemble. Clim. Serv., 11, 13-23, https://doi.org/10.1016/j.cliser.2018.06.003

Kim, O.-Y., 2018: Assessment of seasonal prediction of South Pacific Convergence Zone using APCC multi-model ensembles. Clim. Dyn., 50, 3237–3250, https://doi.org/10.1007/s00382-017-3802-y

Kim, O.-Y., J. C. and L. Chan, 2018: Cyclone-track based seasonal prediction for South Pacific tropical cyclone activity using APCC multi-model ensemble prediction. Clim. Dyn., 51, 3209–3229, https://doi.org/10.1007/s00382-018-4075-9

Kim, W., S.-R. Yeo, and Y. Kim, 2018: Development of the Expert Seasonal Prediction System: an Application for the Seasonal Outlook in Korea. Asia-Pacific J Atmos Sci., 54, 563–573, https://doi.org/10.1007/s13143-018-0052-9

Park, H.-J., V. N. Kryjov, and J.-B. Ahn, 2018: One-Month-Lead Predictability of Asian Summer Monsoon Indices Based on the Zonal Winds by the APCC Multimodel Ensemble. J. Climate, 31, 8945–8960, https://doi.org/10.1175/JCLI-D-17-0816.1

Sohn, S.-J., and Coauthors, 2018: The Republic of Korea-Pacific Islands Climate Prediction Services Project. Bull. Amer. Meteor. Soc., 99, 253–257, https://doi.org/10.1175/BAMS-D-17-0075.1

Wu, S., M. Notaro, S. Vavrus, E. Mortensen, R. Montgomery, J. Pieroloa, and P. Block, 2018: Efficacy of tendency and linear inverse models to predict southern Peru's rainy season precipitation. Int. J. Climatol., 38, 2590-2604, https://doi.org/10.1002/joc.5442

Yeo, S.-R., S.-W. Yeh, Y. Kim, and S.-Y. Yim, 2018: Monthly climate variation over Korea in relation to the two types of ENSO evolution. Int. J. Climatol., 38, 811-824, https://doi.org/10.1002/joc.5212

You, Y., and X. Jia, 2018: Interannual Variations and Prediction of Spring Precipitation over China. J. Climate, 31, 655-670, https://doi.org/10.1175/JCLI-D-17-0233.1


2017
TOP

Ham, Y.-G., Y. Chikamoto, J.-S. Kug, M. Kimoto, and T. Mochizuki, 2017: Tropical Atlantic-Korea teleconnection pattern during boreal summer season. Clim. Dyn., 49, 2649–2664, https://doi.org/10.1007/s00382-016-3474-z

Jeong, J.-H., and Coauthors, 2017: The status and prospect of seasonal climate prediction of climate over Korea and East Asia: A review. Asia-Pacific J Atmos Sci., 53, 149–173, https://doi.org/10.1007/s13143-017-0008-5

Kim, O.-Y., H.-M. Kim, M.-I. Lee, and Y.-M. Min, 2017: Dynamical–statistical seasonal prediction for western North Pacific typhoons based on APCC multi-models. Clim. Dyn., 48, 71–88, https://doi.org/10.1007/s00382-016-3063-1

Kim, S. T., S.-J. Sohn, and J.-S. Kug, 2017: Winter temperatures over the Korean Peninsula and East Asia: development of a new index and its application to seasonal forecast. Clim. Dyn., 49, 1567–1581, https://doi.org/10.1007/s00382-016-3402-2

Lee, S.-S., J.-Y. Moon, B. Wang, and H.-J. Kim, 2017: Subseasonal Prediction of Extreme Precipitation over Asia: Boreal Summer Intraseasonal Oscillation Perspective. J. Climate, 30, 2849–2865, https://doi.org/10.1175/JCLI-D-16-0206.1

Min, Y.-M., V. N. Kryjov, S. M. Oh, and H.-J. Lee, 2017: Skill of real-time operational forecasts with the APCC multi-model ensemble prediction system during the period 2008–2015. Clim. Dyn., 49, 4141–4156, https://doi.org/10.1007/s00382-017-3576-2


2016
TOP

Pradhan, P. K., V. Prasanna, D. Y. Lee, and M.-I. Lee, 2016: El Niño and Indian summer monsoon rainfall relationship in retrospective seasonal prediction runs: experiments with coupled global climate models and MMEs. Meteorol. Atmos. Phys., 128, 97-115, https://doi.org/10.1007/s00703-015-0396-y

Shin, J. Y., M. Ajmal, J. Yoo, and T.-W. Kim, 2016: A Bayesian Network-Based Probabilistic Framework for Drought Forecasting and Outlook. Adv. Meteol., 2016, 1-10, https://doi.org/10.1155/2016/9472605

Sohn, S.-J., and C.-Y. Tam, 2016: Long-lead station-scale prediction of hydrological droughts in South Korea based on bivariate pattern-based downscaling. Clim. Dyn., 46, 3305-3321, https://doi.org/10.1007/s00382-015-2770-3


2015
TOP

Jeong, H.-I., J.-B. Ahn, J.-Y. Lee, A. Alessandri, and H. H. Hendon, 2015: Interdecadal change of interannual variability and predictability of two types of ENSO. Clim. Dyn., 44, 1073–1091, https://doi.org/10.1007/s00382-014-2127-3

Lee, D. Y., J.-B. Ahn, and J.-H. Yoo, 2015: Enhancement of seasonal prediction of East Asian summer rainfall related to western tropical Pacific convection. Clim. Dyn., 45, 1025–1042, https://doi.org/10.1007/s00382-014-2343-x

Lee, J.-Y., and K.-J. Ha, 2015: Understanding of Interdecadal Changes in Variability and Predictability of the Northern Hemisphere Summer Tropical-Extratropical Teleconnection. J. Climate, 28, 9634-9647, https://doi.org/10.1175/JCLI-D-15-0154.1

Ye, K.-H., C.-Y. Tam, W. Zhou, and S.-J. Sohn, 2015: Seasonal prediction of June rainfall over South China: Model assessment and statistical downscaling. Adv. Atmos. Sci., 32, 680–689, https://doi.org/10.1007/s00376-014-4047-x

Yim, S.-Y., B. Wang, W. Xing, and M.-M. Lu, 2015: Prediction of Meiyu rainfall in Taiwan by multi-lead physical–empirical models. Clim. Dyn., 44, 3033–3042, https://doi.org/10.1007/s00382-014-2340-0


2014
TOP

Jia, X., H. Lin, and X. Yao, 2014: The Influence of Tropical Pacific SST Anomaly on Surface Air Temperature in China. J. Climate, 27, 1425-1444, https://doi.org/10.1175/JCLI-D-13-00176.1

Jia, X., J.-Y. Lee, H. Lin, A. Alessandri, and K.-J. Ha, 2014: Interdecadal change in the Northern Hemisphere seasonal climate prediction skill: part I. The leading forced mode of atmospheric circulation. Clim. Dyn., 43, 1595–1609, https://doi.org/10.1007/s00382-013-1988-1

Jia, X., J.-Y. Lee, H. Lin, H. Hendon, and K.-J. Ha, 2014: Interdecadal change in the Northern Hemisphere seasonal climate prediction skill: part II. predictability and prediction skill. Clim. Dyn., 43, 1611–1630, https://doi.org/10.1007/s00382-014-2084-x

Kang, S., J. Hur, and J.-B. Ahn, 2014: Statistical downscaling method based on APCC multi-model ensemble for seasonal prediction over South Korea. Int. J. Climatol., 34, 3801-3810, https://doi.org/10.1002/joc.3952

Min, Y.-M., V. N. Kryjov, and S. M. Oh, 2014: Assessment of APCC multimodel ensemble prediction in seasonal climate forecasting: Retrospective (1983–2003) and real-time forecasts (2008–2013). J. Geophys. Res. Atmos., 119, 12 132–12 150, https://doi.org/10.1002/2014JD022230

Yim, S.-Y., B. Wang, and W. Xing, 2014: Prediction of early summer rainfall over South China by a physical-empirical model. Clim Dyn., 43, 1883–1891, https://doi.org/10.1007/s00382-013-2014-3


2013
TOP

Gottschalck, J., P. E. Roundy, C. J. Schreck III, A. Vintzileos, and C. Zhang, 2013: Large-Scale Atmospheric and Oceanic Conditions during the 2011–12 DYNAMO Field Campaign. Mon. Wea. Rev., 141, 4173–4196, https://doi.org/10.1175/MWR-D-13-00022.1

Lee, D. Y., J.-B. Ahn, and K. Ashok, 2013: Improvement of Multimodel Ensemble Seasonal Prediction Skills over East Asian Summer Monsoon Region Using a Climate Filter Concept. J. Appl. Meteorol. Climatol., 52, 1127-1138, https://doi.org/10.1175/JAMC-D-12-0123.1

Lee, D. Y., J.-B. Ahn, K. Ashok, and A. Alessandri, 2013: Improvement of grand multi-model ensemble prediction skills for the coupled models of APCC/ENSMEBLES using a climate filter. Atmos. Sci. Lett., 139-145, https://doi.org/10.1002/asl2.430

Lee, J.-Y., S.-S. Lee, B. Wang, K.-J. Ha, and J.-G. Jhun, 2013: Seasonal prediction and predictability of the Asian winter temperature variability. Clim. Dyn., 41, 573–587, https://doi.org/10.1007/s00382-012-1588-5

Sohn, S.-J., C.-Y. Tam, and J.-B. Ahn, 2013: Development of a multimodel-based seasonal prediction system for extreme droughts and floods : a case study for South Korea. Int. J. Climatol., 33, 793-805, https://doi.org/10.1002/joc.3464

Tang, W., Z.-H. Lin, and L.-F. Luo, 2013: Assessing the Seasonal Predictability of Summer Precipitation over the Huaihe River Basin with Multiple APCC Models, Atmospheric and Oceanic Science Letters, 6:4, 185-190, https://doi.org/10.3878/j.issn.1674-2834.13.0025


2012
TOP

Jeong, H., and Coauthors, 2012: Assessment of the APCC coupled MME suite in predicting the distinctive climate impacts of two flavors of ENSO during boreal winter. Clim. Dyn., 39, 475–493, https://doi.org/10.1007/s00382-012-1359-3

Jia, X., H. Lin, J. Lee, and B. Wang, 2012, Season-Dependent Forecast Skill of the Leading Forced Atmospheric Circulation Pattern over the North Pacific and North American Region. J. Climate, 25, 7248-7265, https://doi.org/10.1175/JCLI-D-11-00522.1

Kosaka, Y., J. S. Chowdary, S. Xie, Y.-M. Min, and J. Lee, 2012: Limitations of Seasonal Predictabiliy for Summer Climate over East Asia and the Northwestern Pacific, J. Climate, 25, 7574-7589, https://doi.org/10.1175/JCLI-D-11-00009.1

Krishnamurti, T. N., and V. Kumar, 2012: Improved Seasonal Precipitation Forecasts for the Asian Monsoon Using 16 Atmosphere-Ocean Coupled Models. Part 2:Anomaly, J. Climate, 25. 65-88, https://doi.org/10.1175/2011JCLI4126.1

Kumar, V., and T. N. Krishnamurti, 2012: Improved Seasonal Precipitation Forecasts for the Asian Monsoon Using 16 Atmosphere-Ocean Coupled Models. Part I:Climatology. J. Climate, 25. 39-64, https://doi.org/10.1175/2011JCLI4125.1

Sohn, S.-J., Y.-M. Min, J.-Y. Lee, C.-Y. Tam, I.-S. Kang, B. Wang, J.-B. Ahn, and T. Yamagata, 2012: Assessment of the longlead probabilistic prediction for the Asian summer monsoon precipitation (1983–2011) based on the APCC multimodel system and a statistical model. J. Geophys. Res., 117, D04102, https://doi.org/10.1029/2011JD016308

Stefanova, L., V. Misra, J. J. O’Brien, E. P. Chassignet, and S. Hameed, 2012: Hindcast skill and predictability for precipitation and two-meter air temperature anomalies in global circulation models over the Southeast United States. Clim. Dyn., 38, 161–173, https://doi.org/10.1007/s00382-010-0988-7


2011
TOP

Min, Y.-M., V. N. Kryjov, and J.-H. Oh, 2011: Probabilistic interpretation of regression-based downscaled seasonal ensemble prediction with the estimation of uncertainty. J. Geophys. Res, 116, D08101, https://doi.org/10.1029/2010JD015284


2010
TOP

Chowdary, J. S., S.-P. Xie, J.-Y. Lee, Y. Kosaka, and B. Wang, 2010: Predictability of summer northwest Pacific climate in 11 coupled model hindcasts: Local and remote forcing. J. Geophys. Res., 115, D22121, https://doi.org/10.1029/2010JD014595

Juneng, L., F. T. Tangang, H. Kang, W. Lee, and Y. K. Seng, 2010: Statistical Downscaling Forecasts for Winter Monsoon Precipitation in Malaysia Using Multimodel Output Variables. J. Climate, 23, 17–27, https://doi.org/10.1175/2009JCLI2873.1

Lee, J., and Coauthors, 2010: How are seasonal prediction skills related to models’ performance on mean state and annual cycle?. Clim. Dyn., 35, 267–283, https://doi.org/10.1007/s00382-010-0857-4


2009
TOP

Kang, H., C.-K. Park, S. N. Hameed, and K. Ashok, 2009: Statistical Downscaling of Precipitation in Korea Using Multimodel Output Variables as Predictors. Mon. Wea. Rev., 137. 1928-1938, https://doi.org/10.1175/2008MWR2706.1

Min, Y.-M., V. N. Kryjov, and C.-K. Park, 2009: A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction. Wea. Forecasting, 24, 812–828, https://doi.org/10.1175/2008WAF2222140.1

Wang, B., and Coauthors, 2009: Advance and prospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction (1980–2004). Clim. Dyn., 33, 93–117, https://doi.org/10.1007/s00382-008-0460-0


2008
TOP

Chu, J.-L., H. Kang, C.-Y. Tam, C.-K. Park, and C.-T. Chen, 2008: Seasonal forecast for local precipitation over northern Taiwan using statistical downscaling, J. Geophys. Res., 113, D12118, https://doi.org/10.1029/2007JD009424

Kug, J.-S., J.-Y. Lee, I.-S. Kang, B. Wang, and C.-K. Park, 2008: Optimal Multi-model Ensemble Method in Seasonal Climate Prediction. Asia-Pacific J Atmos Sci.​, 44. 259-267.

Shin, D. W., S.-D. Kang, S. Cocke, T.-Y. Goo, and H.-D. Kim, 2008: Seasonal probability of precipitation forecasts using a weighted ensemble approach. Int. J. Climatol., 28, 1971-1976, https://doi.org/10.1002/joc.1690

Wang, B., and Coauthors, 2008: How accurately do coupled climate models predict the leading modes of Asian-Australian monsoon interannual variability?. Clim. Dyn., 30, 605–619, https://doi.org/10.1007/s00382-007-0310-5

Zhu, C., C.-K. Park, W.-S. Lee, and W.-T. Yun, 2008: Statistical downscaling for multi-model ensemble prediction of summer monsoon rainfall in the Asia-Pacific region using geopotential height field. Adv. Atmos. Sci., 25, 867–884, https://doi.org/10.1007/s00376-008-0867-x


2007
TOP

Kang, H., and C.-K. Park, 2007: Error analysis of dynamical seasonal predictions of summer precipitation over the East Asian-western Pacific region. Geophys. Res. Lett., 34. L13705, https://doi.org/10.1029/2007GL029392

Kang, H., K.-H. An, C.-K. Park, A. L. S. Solis, and K. Stitthichivapak, 2007: Multimodel output statistical downscaling prediction of precipitation in the Philippines and Thailand. Geophys. Res. Lett., 34, 15, L15710, https://doi.org/10.1029/2007GL030730


2006
TOP

Kar, S. C., A. Hovsepyanm, and C. K. Park, 2006: Economic values of the APCN multi-model ensemble categorical seasonal predictions. Meteorol. Appl., 13. 267-277, https://doi.org/10.1017/S1350482706002271


2005
TOP

Yoo, J. H., and I.-S. Kang, 2005: Theoretical examination of a multi-model composite for seasonal prediction. Geophys. Res. Lett., 32, L18707, https://doi.org/10.1029/2005GL023513

2021

· (2021.04) Brazil will not have La Niña or El Niño in three months

· (2021.01) Rains are expected to be above average in most of Brazil in the first quarter of 2021

· (2021.01) 2021 Summer Forecast: Wetter, Cooler Odisha In May-June

2020

· (2020.10) Update activity "La Niña" strength! Expect not fierce. Bangkok, cold hit the average

· (2020.10) Piauí will have temperatures above historical averages, says Inmet

· (2020.09) Good monsoon tidings may last until year-end

· (2020.07) Scholars warn the phenomenon "La Niña" strikes heavy rain. Especially in Bangkok.

· (2020.07) August Outlook for Australia

· (2020.06) Global models say monsoon yet to reveal its true intent

· (2020.03) Australia Long-Range Outlook

· (2020.03) Early look at India's southwest monsoon

· (2020.03) Korean, Japanese models predict good monsoon

· (2020.02) Monsoon 2020 silver lining: Odisha will see pouring rain in July & August!

· (2020.02) Weather report: Showers may cap day temperatures in East and South this week

· (2020.01) First look: India may have a good monsoon in 2020

2019

· (2019.09) South Asia Climate Forum sees a normal North-East monsoon this year

· (2019.09) Wet spell for South as East braces for welcome rain

· (2019.07) Lows may drive monsoon to peak over Central India

· (2019.07) Monsoon revival to bring more rain to South India

· (2019.03) Global warming hits seaside winter

· (2019.02) More violent weather in the offing for North-West, East India

· (2019.01) Forest fires threaten WV reforested areas – DENR

2018

· (2018.12) DENR to activate emergency response teams for El Niño

· (2018.06) Neutral El Niño/La Niña conditions in June 2018

· (2018.03) El Niño/La Niña today - March 2018

· (2018.03) S-W monsoon: Global models see setback in July, August

· (2018.03) Soaring mercury, looming crisis

· (2018.01) South to get summer showers from February

2015

· (2015.07) BPPT: Sumatra-Java-Bali-Nusa Tenggara Experiencing More Serious Drought

2013

· (2013.04) Skymet forecasts well-distributed, adequate monsoon