Assessment of the long‐lead probabilistic prediction for the Asian summer monsoon precipitation (1983–2011) based on the APCC multimodel system and a statistical model

Abstract

[1] The performance of the probabilistic multimodel prediction (PMMP) system of the APEC Climate Center (APCC) in predicting the Asian summer monsoon (ASM) precipitation at a four-month lead (with February initial condition) was compared with that of a statistical model using hindcast data for 1983-2005 and real-time forecasts for 2006-2011. Particular attention was paid to probabilistic precipitation forecasts for the boreal summer after the mature phase of El Niño and Southern Oscillation (ENSO). Taking into account the fact that coupled models' skill for boreal spring and summer precipitation mainly comes from their ability to capture ENSO teleconnection, we developed the statistical model using linear regression with the preceding winter ENSO condition as the predictor. Our results reveal several advantages and disadvantages in both forecast systems. First, the PMMP appears to have higher skills for both above-and below-normal categories in the six-year real-time forecast period, whereas the cross-validated statistical model has higher skills during the 23-year hindcast period. This implies that the cross-validated statistical skill may be overestimated. Second, the PMMP is the better tool for capturing atypical ENSO (or non-canonical ENSO related) teleconnection, which has affected the ASM precipitation during the early 1990s and in the recent decade. Third, the statistical model is more sensitive to the ENSO phase and has an advantage in predicting the ASM precipitation after the mature phase of La Niña.

Six month–lead downscaling prediction of winter to spring drought in South Korea based on a multimodel ensemble

“…For model data, historical retrospective forecasts from five different coupled models participating in the APEC Climate Center (APCC) one-tier MME 6 month prediction [Sohn et al, 2012a] were considered. The APCC one-tier MME comprises the APCC seasonal prediction system based on the Community Climate System Model [Jeong et al, 2008], Predictive Ocean Atmosphere Model for Australia from the Bureau of Meteorology Research Center [Wang et al, 2008c], the National Centers for Environmental Prediction Coupled Forecast System [Saha et al, 2006], and coupled general circulation models from Seoul National University [Ham and Kang, 2010] and Pusan National University [Sun and Ahn, 2011].…”

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

“…The detailed information of individual models and their initial conditions are available in Sohn et al (2012). For each model, ensemble mean anomalies are calculated on the basis of monthly ensemble-mean climatology for each lead time.…”

“…Monthly hindcast data are derived from five CGCMs (see Table 1) that are participating in the semi-operational 6-month lead one-tier multi-model ensemble (MME) prediction at Asia-Pacific Economic Cooperation Climate Center (APCC; Jeong et al 2012;Sohn et al 2012) in cooperation with the Climate Prediction and its Association to Society project (Wang et al 2009;Lee et al 2010). We use individual models integrations initialized on 01 May 2010 (one-month lead) and their MME.…”

“…The southwest monsoon rainfall intensifies over the Arabian Sea and weakens over the northwest Pacific during summer following the El Niño years. Numerous studies have found that precipitation anomalies are more predictable during summer (June, July, August; JJA (1) 1 ) following the mature phase of El Niño over the Indo-western Pacific region (Wang et al 2009;Liang et al 2009;Chowdary et al 2011;Lee et al 2011a, b;Sohn et al 2012). Figure 1a shows composites of JJA(1) SST and 850 hPa circulation anomalies over the Indo-western Pacific region.…”

Long-lead station-scale prediction of hydrological droughts in South Korea based on bivariate pattern-based downscaling

“…Despite the advances in prediction skills brought about by the MME method, there are still deficiencies and limitations in long-lead predictions of climate variability and extreme phenomena on the local scale. This is especially the case over the extratropics, due to inherently low climate predictability in this region (Lee et al 2011(Lee et al , 2013aSohn et al 2011Sohn et al , 2012. Other factors such as the relatively coarse resolution of global models, uncertainties in initial conditions, forecast lead-time and error growth also limit the skill of long-lead regional climate predictions.…”