The status and prospect of seasonal climate prediction of climate over Korea and East Asia: A review

Jeong, Jee‐Hoon; Lee, Hyunsoo; Yoo, Jin; Kwon, Min Serk; Yeh, Sang‐Wook; Kug, Jong‐Seong; Lee, Jun-Yi; Kim, Baek-Min; Son, Seok‐Woo; Min, Seung‐Ki; Lee, Hansu; Lee, Woo-Seop; Yoon, Jin‐Ho; Kim, Hyun-Kyung

doi:10.1007/s13143-017-0008-5

Cited by 18 publications

(20 citation statements)

References 168 publications

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“…In a practical hydrologic forecasting system of Korea, The KMA provides climate forecast as a form of simple probability mass function, which refers to the application of cutting edge technologies to predict the atmosphere state on a given location for the near future. Since May 2014, KMA has been operationally running climate prediction system named GloSea5 (Global Seasonal Forecasting System version 5) which is the joint seasonal forecasting system with the UK Met Office [40]. The atmospheric initial conditions come from KMA's 4D-VAR system.…”

Section: Probabilistic Climate Forecastmentioning

confidence: 99%

Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction

2020

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In order to enhance the streamflow forecast skill, seasonal/sub-seasonal streamflow forecasts can be post-processed by incorporating new information, such as climate signals. This study proposed a simple yet efficient approach, the “Bivar_update” model that utilizes bivariate climate forecast to update individual probabilities of the ensemble streamflow prediction. The Bayesian updating scheme is used to update the joint probability mass function derived from historic precipitation and temperature data sets. Thirty-five dam basins were used for the case study, and the modified Tank model was embedded into the ensemble streamflow prediction framework. The performance of the proposed approach was evaluated through a comparison with a reference streamflow forecast model, the “Univar_update” model, that reflects only precipitation forecast, in terms of deterministic and categorical streamflow forecast accuracy. For this purpose, multiple cases of probabilistic precipitation and temperature forecasts were synthetically generated. As a result, the Bivar_update model was able to decrease the errors in forecast under below-normal conditions. The improvements in forecasting skills were found for both measures; deterministic and categorical streamflow forecasts. Since the proposed Bivar_update model reflects both precipitation and temperature information, it can compensate low predictability especially under dry conditions in which the streamflow’s dependency on temperature increases.

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Section: Probabilistic Climate Forecastmentioning

confidence: 99%

Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction

2020

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“…Under the background of climate change, the current skill of seasonal and regional climate predictions is not enough yet to meet various needs from industrial and public sectors Seo et al 2014;Jeong et al 2017), partly due to deficiencies of climate models. The correlation between the observed and simulated China SAT for 1880-1999 is still lower than that for either the global or Northern Hemispheric average, and thus the mechanisms leading to this warming remain an open question (Zhou and Yu 2006).…”

Section: Introductionmentioning

confidence: 99%

“…In addition, seasonal climate variability in East Asia is strongly influenced by many internal and external factors including the East Asian monsoon, tropical ocean variability, and other atmospheric low-frequency modes. Therefore, a comprehensive understanding of these factors is essential for better seasonal climate predictions, as well as future climate projections over East Asia (Ding et al 2014;Jeong et al 2017;Luo and Wang 2017).…”

Section: Introductionmentioning

confidence: 99%

Decomposing East-Asian winter temperature and monsoonal circulation changes using timeslice experiments

Ren

Chadwick

et al. 2020

Clim Dyn

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Based on a set of pilot atmosphere-only experiments from the Cloud Feedback Model Intercomparison Project Phase 3 (CFMIP-3), in this study, the winter surface air temperature (SAT) and monsoonal circulation changes in East Asia as a response to the 4 × CO 2 forcing in coupled model are decomposed into the four parts in terms of the responses to the uniform SST warming, 4 × CO 2 radiative effect, SST pattern changes, and plant physiological effect. The uniform SST warming presents the most significant influence on the increase of SAT change, which strengthens the East Asian winter monsoon (EAWM) circulation. The CO 2 radiative effect can also induce the SAT increase over East Asia but with a magnitude smaller compared to the uniform SST warming, in which more warming is in land than ocean and the EAWM circulation could be weakened consequently due to the decreased land-sea thermal contrast in response to the CO 2 radiative effect. The SAT changes in response to the SST pattern change show inconsistencies over the eastern and southern parts of East Asia between the two models, associated with the large difference for EAWM circulation changes, indicating that the SST pattern change could be the primary source of inter-model uncertainties in the East-Asian SAT change. As for the influence of plant physiological effect, it could generate a SAT rise in many highly vegetated regions. Further analyses for different areas show that both the uniform SST warming and CO 2 radiative effects could induce more intense SAT increase in northern East Asia, while the plant physiological effect has a more significant influence on that in southern/eastern part of East Asia.

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“…An empirical statistical model showed skill (anomaly correlation coefficient: r~0.5) for Korean winter temperature over 1954-2003, using a number of lagged predictors (Kim et al, 2007). Kim et al (2017) showed weak positive skill for Korean winter temperature of r = 0.35 over 1983-2006 by defining a new sea level pressure (SLP)-based East Asia winter monsoon (EAWM) index and r = 0.19 based on multi-model ensemble (MME) from APEC Climate Center (see also Jeong et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Skilful seasonal prediction of Korean winter temperature

Lim

Dunstone

Scaife

et al. 2019

Atmospheric Science Letters

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Korean winter temperature exhibits significant year-to-year variability, an extreme example of which was the severe cold winter of 2012/2013. Such events can have significant societal and financial impacts. We investigate the seasonal forecast skill for Korean winter temperature using Met Office initialised climate prediction systems. We find significant skill using two independent hindcasts covering the last decades. Observed Korean winter temperature variability appears to be driven by large-scale dynamical circulation anomalies with centers over the northwest Pacific High (PH) and the Siberian High (SH). While the model has a good simulation of these observed teleconnections, we find that skilfully predicted PH variability dominates the model skill. This is due to the relatively strong PH teleconnection to highly predictable tropical Pacific and Indian Ocean variability. Whereas, observed SH variability is currently much less predictable in seasonal forecast systems. Furthermore, we find that the modelled SH exhibits lower predictability than the observed SH, suggesting a model "signal-to-noise" problem over extratropical East Asia which is similar to that reported over northern Europe. We explore one possible pathway, the representation of stratosphere-troposphere coupling, by examining the model simulation of stratospheric sudden warming (SSWs) events. While the SSW frequency and the spatial pattern of surface circulation response appears well simulated, the amplitude of the model response over SH appears anomalously weak. Nevertheless, we conclude that current seasonal forecasts of Korean winter temperature are skilful and have the potential to provide useful climate services.

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The status and prospect of seasonal climate prediction of climate over Korea and East Asia: A review

Cited by 18 publications

References 168 publications

Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction

Utilizing Bivariate Climate Forecasts to Update the Probabilities of Ensemble Streamflow Prediction

Decomposing East-Asian winter temperature and monsoonal circulation changes using timeslice experiments

Skilful seasonal prediction of Korean winter temperature

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