Asia-Pacific Economic Cooperation Climate Center Research Articles

Predictability of the Wintertime Western Pacific Pattern in the APEC Climate Center Multi-Model Ensemble

The predictability of the wintertime Western Pacific (WP) pattern is evaluated based on seasonal predictions from five models participating in the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model ensemble (MME) for the winters from 1982/1983 to 2021/2022. The temporal correlation coefficient (TCC) between the observed and MME-predicted WP indices was 0.61 (0.37–0.54 for individual models) for the entire series. However, when only three Super El Niño (SEN) years (Niño3.4 ≥ 2.0) out of the 40-year series were excluded, the TCC dropped down to 0.54 (0.27–0.42). During the SEN years, the WP was strongly affected by the SEN-excited anomalies via the PNA. In observations from non-SEN years, the WP pattern was strongly related to the dipole pattern in Northwestern Pacific SST (TCC = 0.8), for the description of which we suggested a Northwestern Pacific (NWP) index, and it was significantly weakly related to the ENSO and IOD, whereas in the model simulations, the main role was played by the ENSO (TCC = 0.6). The NWP index was well predictable in MME (TCC = 0.73) and individual models (0.56–0.71). We showed that the prediction of the WP index polarity is reliable when both predicted WP and NWP anomalies are significant and indicate the same WP sign that has implications for the seasonal forecasting.

Evaluating the 2019 NARO-APCC Joint Crop Forecasting Service Yield Forecasts for Northern Hemisphere Countries

AbstractForecasting global food production is of growing importance in the context of globalizing food supply chains and observed increases in the frequency of climate extremes. The National Agriculture and Food Research Organization–Asia-Pacific Economic Cooperation Climate Center (NARO-APCC) Crop Forecasting Service provides yield forecasts for global cropland on a monthly basis using seasonal temperature and precipitation forecasts as the main inputs, and 1 year of testing the operation of the service was recently completed. Here we evaluate the forecasts for the 2019 yields of major commodity crops by comparing with the reported yields and forecasts from the European Commission’s Joint Research Centre (JRC) and the U.S. Department of Agriculture (USDA). Forecasts for maize, wheat, soybean, and rice were evaluated for 20 countries located in the Northern Hemisphere, including 39 crop-producing states in the United States, for which 2019 reported yields were already publicly available. The NARO-APCC forecasts are available several months earlier than the JRC and USDA forecasts. The skill of the NARO-APCC forecasts was good in absolute terms, but the forecast errors in the NARO-APCC forecasts were almost always larger than those of the JRC and USDA forecasts. The forecast errors in the JRC and USDA forecasts decreased as the harvest approached, whereas those in the NARO-APCC forecasts were rather stable over the season, with some exceptions. Although this feature seems to be a disadvantage, it may turn into an advantage if skillful forecasts are achievable in the earlier stages of a season. We conclude by discussing relative advantages and disadvantages and potential ways to improve global yield forecasting.

Assessment of APCC models fidelity in simulating the Northeast monsoon rainfall variability over Southern Peninsular India

The fidelity of the eight Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) models in representing the inter-annual variability and decadal shift in the northeast monsoon (NEM; October–December) rainfall over Southern Peninsular India (SPI) is evaluated. The hindcast data is used for the period of 28 years from 1983 to 2010 based on September initial conditions. The observations showed a clear inter-annual and inter-decadal variability of NEM rainfall during the study period. The analysis suggests that most of the models exhibited poor skill in representing the inter-annual variability. Only APCC model rainfall is in phase with observed SPI rainfall variations on the inter-annual time scale. It is noticed from the observed NEM rainfall time series that the period 1990–1999 (first decade) displays an above-normal rainfall and the period 2000–2010 (second decade) displays a below normal rainfall over the SPI region. It is also evident from the observations that NEM rainfall for most of the years displayed negative anomalies in the second decade including El Nino and La Nina years, while in the first decade positive anomalies are noted, suggesting the presence of decadal variability in the NEM. Rainfall variations in most of the coupled models are in phase with the observations during the second decade but are out of phase during the first decade. As evidenced from the observations that the intensified deep convection over the Indo-western Pacific region results in too far southward movement of the intertropical convergence zone (ITCZ) during the second decade. The southward shift in the strong upper-level divergence associated with lower-level convergence over the south Indian Ocean caused negative rainfall anomalies over the SPI in this decade. Further, the difference between the second and first decade demonstrates that an anomalous anticyclonic circulation over the Indian subcontinent is accountable for the vigorous dry northerly flow towards the SPI region and the resultant decadal shift in the rainfall pattern. Though the southward shift in the rainfall and large-scale circulation patterns are mildly captured by some models from decade to decade, most of the models completely misrepresented it. This study suggests that the coupled models displayed a very limited skill not only in capturing the inter-annual variability but also in representing the decadal variability of NEM rainfall.

One-Month-Lead Predictability of Asian Summer Monsoon Indices Based on the Zonal Winds by the APCC Multimodel Ensemble

The seasonal predictability of Asian summer monsoon indices characterizing horizontal and vertical zonal wind shear is investigated using 1-month-lead hindcast datasets from seven coupled global circulation models (CGCMs) participating in the operational multimodel ensemble (MME) seasonal prediction system of the Asia–Pacific Economic Cooperation Climate Center (APCC) for the 1983–2010 period. The summer monsoon indices analyzed in this study represent the South Asian summer monsoon (SASM), western North Pacific summer monsoon (WNPSM), and the newly defined northeastern Asian summer monsoon (NEASM). For the WNPSM and NEASM indices, we also analyze the prediction skill of the index components separately. The study demonstrates that the operational APCC MME system reliably predicts most of the summer monsoon indices and their components, with correlation coefficients exceeding the 99% confidence level. Analysis of the ocean sources of the prediction skill of the indices reveals that the strong relationships of most of the monsoon indices and their components with sea surface temperature (SST) are not confined to the equatorial Pacific but rather are dispersed throughout the World Ocean, with the leading role played by the north Indian Ocean SST anomalies. This conclusion is supported by the analysis of correlations between the monsoon indices and the tropical SST indices. The correlations between the SST anomalies and all the summer monsoon indices in the MME predictions are stronger than those in the observations. However, overestimation of the role of the ENSO-related SST anomalies in the seasonal model hindcasts results in some predictability deterioration of the SASM and NEASM indices.

A newly developed APCC SCoPS and its prediction of East Asia seasonal climate variability

The Asia Pacific Economic Cooperation (APEC) Climate Center (APCC) in-house model (Seamless Coupled Prediction System: SCoPS) has been newly developed for operational seasonal forecasting. SCoPS has generated ensemble retrospective forecasts for the period 1982–2013 and real-time forecasts for the period 2014–current. In this study, the seasonal prediction skill of the SCoPS hindcast ensemble was validated compared to those of the previous operation model (APEC Climate Center Community Climate System Model version 3: APCC CCSM3). This study validated the spatial and temporal prediction skills of hindcast climatology, large-scale features, and the seasonal climate variability from both systems. A special focus was the fidelity of the systems to reproduce and forecast phenomena that are closely related to the East Asian monsoon system. Overall, both CCSM3 and SCoPS exhibit realistic representations of the basic climate, although systematic biases are found for surface temperature and precipitation. The averaged temporal anomaly correlation coefficient for sea surface temperature, 2-m temperature, and precipitation from SCoPS is higher than those from CCSM3. Notably, SCoPS well captures the northward migrated rainband related to the East Asian summer monsoon. The SCoPS simulation also shows useful skill in predicting the wintertime Arctic Oscillation. Consequently, SCoPS is more skillful than CCSM3 in predicting seasonal climate variability, including the ENSO and the Arctic Oscillation. Further, it is clear that the seasonal climate forecast with SCoPS will be useful for simulating the East Asian monsoon system.

Seasonal prediction of high‐resolution temperature at 2‐m height over Mongolia during boreal winter using both coupled general circulation model and artificial neural network

The hindcast data of Pusan National University coupled general circulation model (PNU CGCM), a participant model of the Asia‐Pacific Economic Cooperation Climate Center (APCC) Multi‐Model Ensemble Climate Prediction System, and August–October sea‐surface temperature (SST) in the northern Barents–Kara Sea (BKI) and the sea‐ice extent (SIE) in the Chukchi Sea (East Siberian Sea index [ESI]) are used for predicting 20 × 20‐km‐resolution anomalous surface air temperature at 2‐m height (aT2m) over Mongolia for boreal winter. For this purpose, area‐averaged surface air temperature (TI) and sea‐level pressure (SLP) over Mongolia are defined. Then four large‐scale indices, TImdl and SHImdl obtained from PNU CGCM, and TIMLR and SHIMLR obtained from multiple linear regressions on BKI and ESI, are incorporated using the artificial neural network (ANN) method for the prediction and statistical downscaling to obtain the monthly and seasonal 20 × 20‐km‐resolution aT2m over Mongolia in winter. An additional statistical method, which uses BKI and ESI as predictors of TI and SHI together with dynamic prediction by the CGCM, is used because of the relatively low skill of seasonal predictions by most of the state‐of‐the‐art models and the multi‐model ensemble systems over high‐latitude landlocked Eurasian regions such as Mongolia. The results show that the predictabilities of monthly and seasonal 20 × 20‐km‐resolution aT2m over Mongolia in winter are improved by applying ANN to both statistical and dynamical predictions compared to utilizing only dynamic prediction. The predictability gained by the proposed method is also demonstrated by the probabilistic forecast implying that the method forecasts aT2m over Mongolia in winter reasonably well.

Cyclone-track based seasonal prediction for South Pacific tropical cyclone activity using APCC multi-model ensemble prediction

This study aims to predict the seasonal TC track density over the South Pacific by combining the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model ensemble (MME) dynamical prediction system with a statistical model. The hybrid dynamical-statistical model is developed for each of the three clusters that represent major groups of TC best tracks in the South Pacific. The cross validation result from the MME hybrid model demonstrates moderate but statistically significant skills to predict TC numbers across all TC clusters, with correlation coefficients of 0.4 to 0.6 between the hindcasts and observations for 1982/1983 to 2008/2009. The prediction skill in the area east of about 170°E is significantly influenced by strong El Nino, whereas the skill in the southwest Pacific region mainly comes from the linear trend of TC number. The prediction skill of TC track density is particularly high in the region where there is climatological high TC track density around the area 160°E–180° and 20°S. Since this area has a mixed response with respect to ENSO, the prediction skill of TC track density is higher in non-ENSO years compared to that in ENSO years. Even though the cross-validation prediction skill is higher in the area east of about 170°E compared to other areas, this region shows less skill for track density based on the categorical verification due to huge influences by strong El Nino years. While prediction skill of the developed methodology varies across the region, it is important that the model demonstrates skill in the area where TC activity is high. Such a result has an important practical implication—improving the accuracy of seasonal forecast and providing communities at risk with advanced information which could assist with preparedness and disaster risk reduction.

Monthly climate variation over Korea in relation to the two types of ENSO evolution

ABSTRACTThis study documents monthly temperature and precipitation anomalies over Korea during the different phases of El Niño‐Southern Oscillation (ENSO) for the two types of ENSO evolution. The evolution process of ENSO can be classified into two groups based on whether El Niño turns into La Niña in the subsequent year. The first group involves the transition process from El Niño to La Niña, while the second group shows the prolonged El Niño or neutral conditions after the mature phase of El Niño. Because the mid‐latitude atmospheric responses as well as the equatorial heating anomalies for the two groups of ENSO are different each other, the ENSO‐related climate variation over Korea are investigated separately for the two ENSO evolution groups. In particular, this study focuses on the entire monthly evolution of the temperature and precipitation over Korea during the different phases of ENSO. The statistically robust signals can be found in several particular months, which provides statistical basis for predicting monthly climate over Korea. In addition to the observational data analyses, we further investigate the forecast skill of the Asia‐Pacific Economic Cooperation Climate Center (APCC) Multi‐Model Ensemble (MME) in simulating two groups of ENSO evolution and their impacts on Korean climate. The result shows that the MME reasonably predicts the two different evolution of ENSO as in observation but their prediction skills for the ENSO‐related Korean climate are diverse, which largely depends on the phase of ENSO.

Skill of real-time operational forecasts with the APCC multi-model ensemble prediction system during the period 2008–2015

This paper assesses the real-time 1-month lead forecasts of 3-month (seasonal) mean temperature and precipitation on a monthly basis issued by the Asia-Pacific Economic Cooperation Climate Center (APCC) for 2008–2015 (8 years, 96 forecasts). It shows the current level of the APCC operational multi-model prediction system performance. The skill of the APCC forecasts strongly depends on seasons and regions that it is higher for the tropics and boreal winter than for the extratropics and boreal summer due to direct effects and remote teleconnections from boundary forcings. There is a negative relationship between the forecast skill and its interseasonal variability for both variables and the forecast skill for precipitation is more seasonally and regionally dependent than that for temperature. The APCC operational probabilistic forecasts during this period show a cold bias (underforecasting of above-normal temperature and overforecasting of below-normal temperature) underestimating a long-term warming trend. A wet bias is evident for precipitation, particularly in the extratropical regions. The skill of both temperature and precipitation forecasts strongly depends upon the ENSO strength. Particularly, the highest forecast skill noted in 2015/2016 boreal winter is associated with the strong forcing of an extreme El Nino event. Meanwhile, the relatively low skill is associated with the transition and/or continuous ENSO-neutral phases of 2012–2014. As a result the skill of real-time forecast for boreal winter season is higher than that of hindcast. However, on average, the level of forecast skill during the period 2008–2015 is similar to that of hindcast.

Seasonal prediction of June rainfall over South China: Model assessment and statistical downscaling

The performances of various dynamical models from the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) multi-model ensemble (MME) in predicting station-scale rainfall in South China (SC) in June were evaluated. It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain. This could be related to the MME’s ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region. Further assessment of station-scale June rainfall prediction based on direct model output (DMO) over 97 stations in SC revealed that the MME mean outperforms each individual model. However, poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models. In order to improve the performance at those stations with poor DMO prediction skill, a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs. For several models, this scheme can outperform DMO at more than 30 stations, because it can tap into the abilities of the models in capturing the anomalous Indo-Pacific circulation to which SC rainfall is considerably sensitive. Therefore, enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.

Assessment of APCC multimodel ensemble prediction in seasonal climate forecasting: Retrospective (1983–2003) and real‐time forecasts (2008–2013)

AbstractSince 2007, the Asia‐Pacific Economic Cooperation (APEC) Climate Center (APCC) has monthly issued multimodel ensemble (MME) seasonal predictions for 3 months, with 1 month lead time, and disseminated it to APEC member economies. This paper gives a comprehensive documentation of the current status of the APCC operational multimodel performance, with a large set of retrospective (1983–2003) and real‐time (2008–2013) predictions of temperature and precipitation. In order to investigate the enhancement in seasonal predictability that can be achieved by empirically weighted MME (using multiple regression) and calibrated MME (by correcting single‐model prediction using a stepwise pattern projection method) schemes, operationally implemented at the APCC, we compare them with a simple averaged MME (with equal weightings), for predicting seasonal mean temperature and precipitation 1 month ahead. The results indicate that the simple averaged MME consistently outperforms the multiple regression‐based MMEs, when considering all aspects of the predictions from operational prediction systems (i.e., in different variables, regions, and seasons) whereas the calibrated MME shows the capability to reduce errors and improve forecast skills in a large proportion of cases. The possible causes of the failure and success of the different MME methods implemented in the APCC operations are discussed.

Simulation skill of APCC set of global climate models for Asian summer monsoon rainfall variability

The performance of 11 Asia-Pacific Economic Cooperation Climate Center (APCC) global climate models (coupled and uncoupled both) in simulating the seasonal summer (June–August) monsoon rainfall variability over Asia (especially over India and East Asia) has been evaluated in detail using hind-cast data (3 months advance) generated from APCC which provides the regional climate information product services based on multi-model ensemble dynamical seasonal prediction systems. The skill of each global climate model over Asia was tested separately in detail for the period of 21 years (1983–2003), and simulated Asian summer monsoon rainfall (ASMR) has been verified using various statistical measures for Indian and East Asian land masses separately. The analysis found a large variation in spatial ASMR simulated with uncoupled model compared to coupled models (like Predictive Ocean Atmosphere Model for Australia, National Centers for Environmental Prediction and Japan Meteorological Agency). The simulated ASMR in coupled model was closer to Climate Prediction Centre Merged Analysis of Precipitation (CMAP) compared to uncoupled models although the amount of ASMR was underestimated in both models. Analysis also found a high spread in simulated ASMR among the ensemble members (suggesting that the model’s performance is highly dependent on its initial conditions). The correlation analysis between sea surface temperature (SST) and ASMR shows that that the coupled models are strongly associated with ASMR compared to the uncoupled models (suggesting that air-sea interaction is well cared in coupled models). The analysis of rainfall using various statistical measures suggests that the multi-model ensemble (MME) performed better compared to individual model and also separate study indicate that Indian and East Asian land masses are more useful compared to Asia monsoon rainfall as a whole. The results of various statistical measures like skill of multi-model ensemble, large spread among the ensemble members of individual model, strong teleconnection (correlation analysis) with SST, coefficient of variation, inter-annual variability, analysis of Taylor diagram, etc. suggest that there is a need to improve coupled model instead of uncoupled model for the development of a better dynamical seasonal forecast system.

Seasonal Prediction of Distinct Climate Anomalies in Summer 2010 over the Tropical Indian Ocean and South Asia

The characteristics and predictability of climate anomalies over the tropical Indian Ocean (TIO) and South Asian region during the boreal summer (June-July-August) of 2010 are investigated on the basis of atmospheric regional model simulations and five forecasts obtained from Asia-Pacific Economic Cooperation Climate Center coupled models. The robust features of summer 2010 are the basin-wide TIO warming and enhanced (suppressed) rainfall over the north Indian Ocean and maritime continent (head Bay of Bengal and parts of monsoon trough region). Our regional atmospheric model experiments corroborate that rainfall over South Asia was mostly determined by the TIO sea surface temperature (SST) warming during summer 2010. Most of the coupled models and their multi-model ensemble (MME) used in this study successfully predict the robust features over the TIO and/or South Asian region with 01 May 2010 initial condition. The positive rainfall anomalies over the west coast of India, southern Peninsular India, and central Bay of Bengal are qualitatively well predicted by the MME. Suppressed rainfall over the northeast Bay of Bengal associated with the northwestward extension of the northwest Pacific ridge is also reasonably predicted by the MME. On the other hand, the MME has a moderate skill in predicting positive rainfall anomalies over the convective zone of southeast TIO due to weak local SST warming. Further, the coupled models and their MME fail to predict the anomalous positive rainfall in northern Pakistan because of their inability in predicting mid-latitude circulation anomalies. This study reveals that the predictive skill of rainfall and circulation anomalies during summer 2010 over the TIO and South Asia is largely attributable to the Indian Ocean basin-wide warming during the decay phase of El Niño. These results indicate that the accurate simulation of the TIO SST by coupled models is critical in determining the 2010 South Asian summer monsoon rainfall.

Indian summer monsoon rainfall predictability and variability associated with Northwest Pacific circulation in a suit of coupled model hindcasts

The Northwest Pacific (NWP) circulation (subtropical high) is an important component of the East Asian summer monsoon system. During summer (June–August), anomalous lower tropospheric anticyclonic (cyclonic) circulation appears over NWP in some years, which is an indicative of stronger (weaker) than normal subtropical high. The anomalous NWP cyclonic (anticyclonic) circulation years are associated with negative (positive) precipitation anomalies over most of Indian summer monsoon rainfall (ISMR) region. This indicates concurrent relationship between NWP circulation and convection over the ISMR region. Dry wind advection from subtropical land regions and moisture divergence over the southern peninsular India during the NWP cyclonic circulation years are mainly responsible for the negative rainfall anomalies over the ISMR region. In contrast, during anticyclonic years, warm north Indian Ocean and moisture divergence over the head Bay of Bengal-Gangetic Plain region support moisture instability and convergence in the southern flank of ridge region, which favors positive rainfall over most of the ISMR region. The interaction between NWP circulation (anticyclonic or cyclonic) and ISMR and their predictability during these anomalous years are examined in the present study. Seven coupled ocean–atmosphere general circulation models from the Asia-Pacific Economic Cooperation Climate Center and their multimodel ensemble mean skills in predicting the seasonal rainfall and circulation anomalies over the ISMR region and NWP for the period 1982–2004 are assessed. Analysis reveals that three (two) out of seven models are unable to predict negative (positive) precipitation anomalies over the Indian subcontinent during the NWP cyclonic (anticyclonic) circulation years at 1-month lead (model is initialized on 1 May). The limited westward extension of the NWP circulation and misrepresentation of SST anomalies over the north Indian Ocean are found to be the main reasons for the poor skill (of some models) in rainfall prediction over the Indian subcontinent. This study demonstrates the importance of the NWP circulation variability in predicting summer monsoon precipitation over South Asia. Considering the predictability of the NWP circulation, the current study provides an insight into the predictability of ISMR. Long lead prediction of the ISMR associated with anomalous NWP circulation is also discussed.

Development of a multimodel‐based seasonal prediction system for extreme droughts and floods: a case study for South Korea

AbstractAn experimental, district‐level system was developed to forecast droughts and floods over South Korea to properly represent local precipitation extremes. The system is based on the Asia‐Pacific Economic Cooperation (APEC) Climate Center (APCC) multimodel ensemble (MME) seasonal prediction products. Three‐month lead precipitation forecasts for 60 stations in South Korea for the season of March to May are first obtained from the coarse‐scale MME prediction using statistical downscaling. Owing to the relatively small variance of the MME and regression‐based downscaling outputs, the downscaled MME (DMME) products need to be subsequently inflated. The final station‐scale precipitation predictions are then used to produce drought and flood forecasts on the basis of the Standardized Precipitation Index (SPI).The performance of three different inflation schemes was also assessed. Of these three schemes, the method that simply rescales the variance of predicted rainfall to that based on climate records, irrespective of the prediction skill or the DMME variance itself at a particular station, gives the best overall improvement in the SPI predictions. However, systematic biases in the prediction system cannot be removed by variance inflation. This implies that DMME techniques must be further improved to correct the bias in extreme drought/flood predictions. Overall, it is seen that DMME, in conjunction with variance inflation, can predict hydrological extremes with reasonable skill. Our results could inform the development of a reliable early warning system for droughts and floods, which is invaluable to policy makers and stakeholders in agricultural and water management sectors, and so forth and is important for mitigation and adaptation measures. Copyright © 2012 Royal Meteorological Society

Assessment of probability multimodel seasonal forecast based on the APCC model data

The probability multimodel forecast system based on the Asia-Pacific Economic Cooperation Climate Center (APCC) model data is verified. The winter and summer seasonal mean fields T850 and precipitation seasonal totals are estimated. To combine the models into a multimodel ensemble, the probability forecast is calculated for each of single models first, and then these forecasts are combined using the total probability formula. It is shown that the multimodel forecast is considerably more skilful than the single-model forecasts. The forecast quality is higher in the tropics compared to the mid- and high latitudes. The multimodel ensemble temperature forecasts outperform the random and climate forecasts for Northern Eurasia in the above- and below-normal categories. Precipitation forecast is less successful. For winter, the combination of single-model ensembles provides the precipitation forecast skill exceeding that of the random forecast for both Northern Eurasia and European Russia.

Statistical Downscaling Forecasts for Winter Monsoon Precipitation in Malaysia Using Multimodel Output Variables

Abstract This paper compares the skills of four different forecasting approaches in predicting the 1-month lead time of the Malaysian winter season precipitation. Two of the approaches are based on statistical downscaling techniques of multimodel ensembles (MME). The third one is the ensemble of raw GCM forecast without any downscaling, whereas the fourth approach, which provides a baseline comparison, is a purely statistical forecast based solely on the preceding sea surface temperature anomaly. The first multimodel statistical downscaling method was developed by the Asia-Pacific Economic Cooperation (APEC) Climate Center (APCC) team, whereas the second is based on the canonical correlation analysis (CCA) technique using the same predictor variables. For the multimodel downscaling ensemble, eight variables from seven operational GCMs are used as predictors with the hindcast forecast data spanning a period of 21 yr from 1983/84 to 2003/04. The raw GCM forecast ensemble tends to have higher skills than the baseline skills of the purely statistical forecast that relates the dominant modes of observed sea surface temperature variability to precipitation. However, the downscaled MME forecasts have higher skills than the raw GCM products. In particular, the model developed by APCC showed significant improvement over the peninsular Malaysia region. This is attributed to the model’s ability to capture regional and large-scale predictor signatures from which the additional skills originated. Overall, the results showed that the appropriate downscaling technique and ensemble of various GCM forecasts could result in some skill enhancement, particularly over peninsular Malaysia, where other models tend to have lower or no skills.

A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

Abstract A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system that combines a large number of models, with individual model ensembles essentially differing in size and model weights in the forecast and hindcast datasets being inconsistent. Justification for the use of a Gaussian approximation of the precipitation probability distribution function for global forecasts is also provided. PMME retrospective and real-time forecasts are assessed. For above normal and below normal categories, temperature forecasts outperform climatology for a large part of the globe. Precipitation forecasts are definitely more skillful than random guessing for the extratropics and climatological forecasts for the tropics. The skill of real-time forecasts lies within the range of the interannual variability of the historical forecasts.

Statistical downscaling for multi-model ensemble prediction of summer monsoon rainfall in the Asia-Pacific region using geopotential height field

The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific (Asia-Pacific) summer monsoon region (0°–50°N, 100°–150°E) were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center (APCC) multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index (WNPMI) defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height (GPH) can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition (SVD). In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient (ACC) has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble (MME) forecast.

Seasonal probability of precipitation forecasts using a weighted ensemble approach

AbstractA weighted ensemble (WE) method is revisited and employed to issue an improved seasonal probability of precipitation (POP) forecast. Nine boreal summer time seasonal precipitation hindcasts obtained from the APEC (Asia‐Pacific Economic Cooperation) Climate Center (APCC) multi‐model ensemble system are used to assess the suitability of the WE approach for seasonal POP predictions. Owing to its performance‐based selective nature for assigning weights, the WE method produced marginally superior seasonal POP forecasts compared to the conventional approach. Copyright © 2008 Royal Meteorological Society