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Potential Predictability (PP) of the original (upper), and typical ENSO-related (middle) and -removed (bottom) a SST and b precipitation based on one-month lead monthly mean model simulations. Shading denotes the PP values (see scale at bottom) exceeding the 95% confidence interval based on the Fisher’s F test. The area-averaged PP values for individual models are given at upper right of panels. Contours denote the averaged values (in black lines) of the individual models (in interval of 0.72/0.84/0.65 for the original/typical ENSO-related/-removed SST and 0.31/0.61/0.24 for precipitation, respectively) and the area-averaged correlation values (in blue lines) of persistence (0.77/0.82/0.70 for SST and 0.26/0.52/0.19 for precipitation) for the comparison
Source publication
The prediction skill of tropical Pacific sea surface temperature (SST) and precipitation has been investigated, based on retrospective forecasts from 1983 to 2005 of the APEC Climate Center multimodel ensemble (MME) 6-month climate prediction, with a focus on El Niño-Southern Oscillation (ENSO) and its diversity. It was found that the MME predictio...
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Citations
... The role of sea surface temperatures (SSTs), among other slowly varying lower boundary conditions, in predicting monsoon precipitation variations, was theorized by Charney and Shukla (1981). On seasonal time scales, it was shown that the El Niño Southern Oscillation is key for providing skill at predicting precipitation over the tropics (Shukla and Paolino 1983, Wang et al 2018, Sohn et al 2019, Dunstone et al 2020. On decadal time scales, the North Atlantic and Indian Ocean SSTs also yield a certain amount of predictability for monsoon precipitation , Wang et al 2018, due to the high prediction skill of prediction systems for Atlantic and Indian Ocean SST (Guemas et al 2013, García-Serrano et al 2015 and to the effects of these oceanic basins on monsoon precipitation. ...
Monsoons affect the economy, agriculture, and human health of two thirds of the world’s population. Therefore, predicting variations in monsoon precipitation is societally important. We explore the ability of climate models from the 6th phase of the Climate Model Intercomparison Project (CMIP6) to predict summer monsoon precipitation variability by using hindcasts from the Decadal Climate Prediction Project (Component A). The multi-model ensemble-mean shows significant skill at predicting summer monsoon precipitation from one year to 6-9 years ahead. However, this skill is dependent on the model, monsoon domain, and lead-time. In general, the skill of the multi-model ensemble-mean prediction is low in year 1 but increases for longer-lead times and is largely consistent with externally forced changes. The best captured region is northern Africa for the 2-5- and 6-9-year forecast lead times. In contrast, there is no significant skill using the ensemble-mean over East and South Asia and, furthermore, there is significant spread in skill among models for these domains. By sub-sampling the ensemble we show that the difference in skill between models is tied to the simulation of the externally forced response over East and South Asia, with models with a more skilful forced response capable of better predictions. A further contribution is from skilful prediction of Pacific Ocean temperatures for the South Asian summer monsoon at longer lead-times. Therefore, these results indicate that predictions of the East and South Asian monsoons could be significantly improved.
... The dominance and the persistence of ENSO provides high prediction skills (averaged LEPS (Linear Error in Probability Space) score around 10-20%) over many PICs, particularly those under the direct influence of well-organized canonical ENSO impacts. However, the technical limitations of using only the canonical ENSO index lowers the prediction skills in regions or seasons where the ENSO signal is weak or non-canonical [20][21][22] . Also, any statistical approach is heavily based on the stationarity of the climate state, so it may also suffer under a changing climate when either ENSO or its impact changes significantly 23-25 . ...
Seasonal climate forecasts play a critical role in building a climate-resilient society in the Pacific Island Countries (PICs) that are highly exposed to high-impact climate events. To assist the PICs National Meteorological and Hydrological Services in generating reliable national climate outlooks, we developed a hybrid seasonal prediction system, the Pacific Island Countries Advanced Seasonal Outlook (PICASO), which has the strengths of both statistical and dynamical systems. PICASO is based on the APEC Climate Center Multi-Model Ensemble (APCC-MME), tailored to generate station-level rainfall forecasts for 49 stations in 13 countries by applying predictor optimization and the large-scale relationship-based Bayesian regression approaches. Overall, performance is improved and further stabilized temporally and spatially relative to not only APCC-MME but also other existing operational prediction systems in the Pacific. Gaps and challenges in operationalization of the PICASO system and its incorporation into operational climate services in the PICs are discussed.
... The models were established grid cell by grid cell to consider the diversity of local agronomic management using the representation of actual yield and seasonal climate conditions. The models were designed to use APCC MME monthly average 2-m air temperature and precipitation 6 month forecast data Min et al., 2014;Sohn et al., 2019 , which are issued on the 20th of each month, as inputs to derive yield anomaly forecasts. Once generated, the NARO-APCC Join Crop Forecast report is automatically sent to interested users on the 1st of the following month. ...
An unstable supply of commodity crops and associated increases in food prices are recent and growing concerns due to increasing temperatures, changing precipitation patterns and increasing frequencies of some extreme climate events. Agricultural monitoring and forecasting can support national food agencies, international organizations and commercial entities in better responding to anticipated production shocks induced by seasonal climate extremes. The global seasonal crop forecasting service jointly developed in 2018 by the National Agriculture and Food Research Organization (NARO), Japan and the Asia-Pacific Economic Cooperation Climate Center (APCC), South Korea is an emerging and unique example of agricultural forecasting tailored to major commodity crops (maize, rice, wheat and soybean). The present study evaluates the skills of the NARO-APCC yield forecasts in five countries located in the Southern Hemisphere (the 2019/20 season in Australia and Uruguay and the 2018/19 season in Argentina, Brazil and Paraguay), following the previous assessment for the 2019 season in Northern Hemisphere countries. The results reveal that the NARO-APCC forecasts can capture the major characteristics of reported state yields even six months before harvesting, with variations by crop (the correlation coefficients calculated between the forecasted and reported state yields within a country in a season of interest were frequently over 0.8 for maize, rice and wheat and approximately 0.3 for soybean). In three-fifths of the 122 crop-state combinations assessed here, the NARO-APCC forecasts showed smaller forecast errors than those of the simple forecasts derived solely based on the reported yields. The findings of this study emphasize the novelty of long-range crop forecasting, such as the NARO-APCC forecasts that provide yield forecast information available even just after planting. Together, the NARO-APCC forecasts and existing regional crop forecasts contribute to making objective yield forecast information more seamlessly available throughout the season from planting to harvesting than what is currently available.
... Differences in the longitudinal location and intensity of ENSO events are sensitively associated with different impacts on regional climate throughout the world 10,11 . Such differences in ENSO patterns, referred to as "ENSO diversity" 7 , and their representation in climate models thus strongly influence the skill of impact-prediction systems 12 , and underscore the need for an appropriate characterisation and further mechanistic understanding of ENSO diversity, as well as its projected changes. ...
El Niño-Southern Oscillation (ENSO) shows a large diversity of events that is modulated by climate variability and change. The representation of this diversity in climate models limits our ability to predict their impact on ecosystems and human livelihood. Here, we use multiple observational datasets to provide a probabilistic description of historical variations in event location and intensity, and to benchmark models, before examining future system trajectories. We find robust decadal variations in event intensities and locations in century-long observational datasets, which are associated with perturbations in equatorial wind-stress and thermocline depth, as well as extra-tropical anomalies in the North and South Pacific. Some climate models are capable of simulating such decadal variability in ENSO diversity, and the associated large-scale patterns. Projections of ENSO diversity in future climate change scenarios strongly depend on the magnitude of decadal variations, and the ability of climate models to reproduce them realistically over the 21st century.
... Consequently, significant anomalous rainfall is observed in South China for EP El-Niño but not CP El-Niño (Feng et al. 2011, their Fig. 6). Sohn et al. (2019) investigated the effect of ENSO diversity on tropical rainfall predictions, using seven operational and coupled seasonal forecast models. By separating into EP ENSO related and the residual, Sohn et al. (2019, their Fig. ...
... This study evaluates the seasonal prediction of South China late spring rainfall, in a state-of-the-art operational climate model not included in Lee et al. (2018, their Table 1) nor Sohn et al. (2019, their Table 2). We aim to identify areas for improvement in rainfall prediction, and also to investigate the mechanism of how model mean state bias affects prediction skill of anomalies. ...
Seasonal prediction of South China April to May rainfall is examined based on forecasts by the European Centre for Medium-Range Weather Forecasts (ECMWF) operational model. El-Niño Southern Oscillation (ENSO) is a major source of predictability, conveyed by the lower tropospheric anomalous western Pacific anticyclone and cyclone for El-Niño and La-Niña respectively. By separating ENSO into its diversity of eastern Pacific (EP) and central Pacific (CP) ENSO, different effects on South China rainfall are revealed. From observations, while rainfall is enhanced following EP El-Niño and reduced following EP La-Niña, rainfall remains close to climatology following CP ENSO. However, the seasonal forecast model predicts CP ENSO effect on South China rainfall to be similar to EP ENSO. The model develops excessive westward extension of the eastern Pacific cold tongue within seasonal timescale. While model predicts tropical central Pacific anomalous sea surface temperature (SST) following CP ENSO realistically, the colder mean state is proposed to weaken the anomalous convection, which subsequently induces bias in the western Pacific anomalous rotational flow and hinders South China rainfall prediction. Meanwhile following EP ENSO, the colder mean state is proposed to strengthen the thermocline feedback, inducing stronger tropical eastern Pacific anomalous SST. While bias in the western Pacific anomalous rotational flow is also induced, the bias is far away from South China so rainfall prediction is still realistic. This study highlights the importance of model mean state in the fidelity of model ENSO diversity teleconnections on seasonal timescale.
... Concerns about the predictability of ENSO and the nature of ENSO diversity were expressed in the same WMO 2015 report: "Perplexed by the apparent failures of ENSO forecasts in 2012 and again in 2014, several researchers ask whether changes in ENSO reflect larger shifts or changes in the planet-wide climate system." Some progress in our understanding of ENSO prediction was reported by Sohn et al. (2019), who found that the 6-month prediction skill in APEC Climate Center multi-model ensemble (MME) forecasts depends on both the strength and the flavor of ENSO. Stratifying the sea surface temperature (SST) into that associated with a typical ENSO and its residual, they found that the typical ENSO is the major source of predictability of tropical Pacific SST, while the residual ENSO variability acts to limit tropical rainfall predictability. ...
... Apart from the canonical-or the so-called Eastern-Pacific (EP)-type of El Niño events, Central-Pacific (CP) El Niño, also known as El Niño Modoki which is distinctly different from canonical El Niño Weng et al. 2007;Xu et al. 2020), has also caught the attention of researchers in recent years Kug et al. 2009;Yu and Kim 2010;Ren and Jin 2011;Xu et al. 2012Xu et al. , 2017a. ENSO characteristics thus manifest a robust diversity, with implications on its climatic impacts (Weng et al. 2007(Weng et al. , 2009Xu et al. 2013Xu et al. , 2019Yu and Zou 2013;Yu et al. 2015;Timmermann et al. 2018) and seasonal predictability (Jeong et al. 2012;Sohn et al. 2016Sohn et al. , 2019. The impacts of the two types of El Niño on East Asian (EA) rainfall, throughout the entire ENSO lifecycle, have been studied extensively based on observations and numerical models Nath 2003, 2009;Wu et al. 2003;Kumar et al. 2006;Yuan and Yang 2012;Chen et al. 2013;Lee et al. 2017). ...
In this study, future change of El Niño-related East Asian (EA) rainfall and the diversity of this relationship are investigated on the basis of the historical and representative concentration pathway 8.5 (RCP 8.5) simulations taken from the Coupled Model Intercomparison Project phase 5 (CMIP5). By evaluating the East Asian Summer Monsoon (EASM) climatology and interannual variations in simulations contributing to CMIP5, nine models are verified to be capable of reproducing El Niño diversity and EASM simultaneously. Six of these models are selected for projecting the multi-model ensemble (MME) mean of two types of El Niño-related EA/western North Pacific (WNP) rainfall patterns and low-level atmospheric circulations under global warming, considering the realism in their simulated El Niño and EASM phenomena. It was found that, under a warmer background climate, the general patterns of anomalous circulation and rainfall will persist, but with amplification of the rainfall intensity during mature boreal winter and decaying summer for both Eastern-Pacific (EP) and Central-Pacific (CP) El Niño. Amplification of CP type-related rainfall seems to be stronger than that for EP type El Niño. Further analyses show that a moister atmosphere tends to always strengthen the rainfall variations for both El Niño flavors, regardless of how the El Niño-related circulation amplitude is modulated in various seasons. However, in boreal summer during the El Niño decaying phase, strengthened anomalous circulation also enhances the rainfall variability, with an effect comparable to the background moisture increase. Some of these atmospheric circulation changes might be associated with modified sea surface temperature anomalies (SSTA) of El Niño and its diversity, under global warming. Our results indicate the importance of better preparedness and higher resilience in the EA region to enhanced El Niño-induced hydrological variations under a warming climate.
... The situation reverses during Central Pacific (CP) El Niño events with warm tropical SSTAs centered around the dateline, and these events have occurred frequently in recent years (Ashok et Xu et al., 2012;Yu & Kao, 2007). A CP El Niño could attenuate the WNPSH and suppress rainfall over southern China (e.g., Sohn et al., 2019;Xu et al., 2013;Wang & Wang, 2013;Wang et al., 2019;Zhang et al., 2011Zhang et al., , 2014. In addition, the Indian Ocean Dipole (IOD), which reaches its peak in autumn, is another critical factor affecting East Asian rainfall during and after the wet season (Feng & Chen, 2013;Guan & Yamagata, 2003;Xiao et al., 2015;Zhang et al., 2019). ...
Plain Language Summary
In the post‐monsoon season of 2019, the Mid‐to‐Lower Reaches of the Yangtze River (MLRYR) experienced a record‐breaking drought, which severely disrupted water supplies and affected the planting of crops. At the same time, a super positive Indian Ocean Dipole (pIOD) event occurred, along with a central Pacific (CP) El Niño in the tropical Pacific. The present study indicates that in addition to the CP El Niño, the extremely cold SSTAs in the tropical Southeastern Indian Ocean associated with the super pIOD event was also an important factor in the record‐breaking drought event. This factor first shifted the intertropical convergence zone northward to intensify the post‐monsoon rainfall and its released condensation heating over South Asia. Then, a vertically baroclinic circulation was stimulated to strengthen a descending motion over the MLRYR via an atmospheric teleconnection. On the other hand, the tropical Pacific warm SSTAs related to the strong CP El Niño weakened the western North Pacific anticyclone, which reduced the moisture supply to the MLRYR. In this way, both the pIOD and CP El Niño events jointly resulted in the record‐breaking MLRYR drought in 2019 and explained approximately 40% and 60% of this extreme drought, respectively.
... Besides the difference in the location of warm SSTA, asymmetric amplitudes of EP and CP El Niño also result in different climate impacts (Newman et al. 2011;Yu et al. 2012b;Zhang et al. 2013;Zheng et al. 2016), and also their seasonal predictability (Sohn et al. 2016(Sohn et al. , 2019, over the globe. The variability in ENSO amplitude has been widely studied. ...
... Besides the difference in the location of warm SSTA, asymmetric amplitudes of EP and CP El Niño also result in different climate impacts (Newman et al. 2011;Yu et al. 2012b;Zhang et al. 2013;Zheng et al. 2016), and also their seasonal predictability (Sohn et al. 2016(Sohn et al. , 2019, over the globe. The variability in ENSO amplitude has been widely studied. ...
There exists a pronounced asymmetry between the amplitudes of central Pacific (CP) and eastern Pacific (EP) El Niño sea surface temperature anomalies (SSTA). The present study examines such an asymmetry and its relationship with the North Pacific SSTA. Results indicate that the weaker CP El Niño amplitude can be attributed to the weaker anomalous zonal wind response to the east-west equatorial SSTA gradient during its growing phase compared with EP El Niño. Furthermore, the occurrence of CP El Niño is closely associated with southwesterly surface wind anomalies in the subtropical North Pacific, as well as ocean warming reminiscent of the North Pacific Gyre Oscillation (NPGO) pattern in its vicinity. Both the observations as well as the pacemaker experiments with a coupled global climate model suggest that the anomalous low-level southwesterlies, induced by the North Pacific Oscillation (NPO)-like atmospheric variability, can enhance anomalously positive SST signals and extend them southwestward to the central equatorial Pacific via the wind–evaporation–SST feedback. This will further attenuate the atmospheric response to zonal SSTA gradient, and hence weaken the amplitude of CP El Niño. Therefore, anomalous low-level southwesterlies over the subtropical North Pacific can effectively act as a conduit for tropical–subtropical air–sea interaction in that region, and can play an important role in limiting the intensity of CP El Niño.
We assess skill of the Met Office’s DePreSys3 prediction system at forecasting summer global monsoon precipitation at the seasonal time scale (2–5 month forecast period). DePreSys3 has significant skill at predicting summer monsoon precipitation (r = 0.68), but the skill varies by region and is higher in the northern (r = 0.68) rather than in the southern hemisphere (r = 0.44). To understand the sources of precipitation forecast skill, we decompose the precipitation into several dynamic and thermodynamic components and assess the skill in predicting each. While dynamical changes of the atmospheric circulation primarily contribute to global monsoon variability, skill at predicting shifts in the atmospheric circulation is relatively low. This lower skill partly relates to DePreSys3’s limited ability to accurately simulate changes in atmospheric circulation patterns in response to sea surface temperature forcing. Skill at predicting the thermodynamic component of precipitation is generally higher than for the dynamic component, but thermodynamic anomalies only contribute a small proportion of the total precipitation variability. Finally, we show that the use of a large ensemble improves skill for predicting monsoon precipitation, but skill does not increase beyond 20 members.
