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Seasonal rainfall forecasts are an important tool for risk management across many sectors. However, significant challenges arise in the development of skilful and practically useful seasonal forecasts for regions where the temporal and spatial variability of rainfall is large and/or knowledge about what causes this variability is in its infancy. This is evident in the state of South Australia (SA), where seasonal rainfall currently has low predictive skill. The key climate processes have yet to be fully identified in SA and therefore may not be adequately represented in forecast models. The aim of this paper is to identify and quantify relationships between large-scale ocean-atmospheric processes and seasonal rainfall variability across SA. We identify two distinct climate zones: (1) the arid northern region, where rainfall is mostly influenced by climate processes stemming from the tropical Indian and/or Pacific Oceans and (2) southern SA, which is dominated by Southern Ocean processes. The average percent of variability of SA rainfall accounted for by any single large-scale climate process (i.e. linear regression using a single predictor) is 8% in summer, 19% in autumn, 33% in winter and 24% in spring. However, when two or more processes are considered in combination (through multiple linear regression), this rises to 13, 26, 46, and 33%, respectively, highlighting the importance of capturing the interaction among multiple climate processes. Importantly, the findings from this study provide a set of metrics against which existing statistical and dynamical forecasting schemes can be tested and highlight processes that should be focused on in order to improve (or develop new) forecasting schemes. The study also recommends the need for further investigations into non-linear relationships between rainfall and large-scale ocean-atmospheric processes and the development of more objective methods for determining which climate process, or combination of processes, are most important for a certain season or location. [ABSTRACT FROM AUTHOR]

ABSTRACT Seasonal rainfall forecasts are an important tool for risk management across many sectors. However, significant challenges arise in the development of skilful and practically useful seasonal forecasts for regions where the temporal and spatial variability of rainfall is large and/or knowledge about what causes this variability is in its infancy. This is evident in the state of South Australia ( SA), where seasonal rainfall currently has low predictive skill. The key climate processes have yet to be fully identified in SA and therefore may not be adequately represented in forecast models. The aim of this paper is to identify and quantify relationships between large-scale ocean-atmospheric processes and seasonal rainfall variability across SA. We identify two distinct climate zones: (1) the arid northern region, where rainfall is mostly influenced by climate processes stemming from the tropical Indian and/or Pacific Oceans and (2) southern SA, which is dominated by Southern Ocean processes. The average percent of variability of SA rainfall accounted for by any single large-scale climate process (i.e. linear regression using a single predictor) is 8% in summer, 19% in autumn, 33% in winter and 24% in spring. However, when two or more processes are considered in combination (through multiple linear regression), this rises to 13, 26, 46, and 33%, respectively, highlighting the importance of capturing the interaction among multiple climate processes. Importantly, the findings from this study provide a set of metrics against which existing statistical and dynamical forecasting schemes can be tested and highlight processes that should be focused on in order to improve (or develop new) forecasting schemes. The study also recommends the need for further investigations into non-linear relationships between rainfall and large-scale ocean-atmospheric processes and the development of more objective methods for determining which climate process, or combination of processes, are most important for a certain season or location. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper analyses the influence of different types of El Niño-Southern Oscillation ( ENSO) events on South American precipitation under a climate change scenario. We use two sub-selections of Coupled Model Intercomparison Project Phase 5 (CMIP5) models that have a good representation of teleconnections to the region, albeit with reduced magnitude anomalies. Comparing historical simulations and the RCP 8.5 scenario experiments, after removing the trend in the mean due climate change, the sub-ensemble means show similar global sea surface temperature anomalies and South American precipitation anomaly patterns in the future. No robust changes in the pattern or magnitude of precipitation anomalies are found, despite significant ENSO-related shifts in the Pacific basin, as highlighted in other studies. However, if the models are analysed individually, some show relatively strong changes in ENSO-related precipitation anomalies for both canonical and Modoki events. We show that, in individual models, tropical teleconnection changes associated with ENSO are primarily driven by changes in the magnitude of ENSO-related circulation but with no agreement on the sign. Increases in background moisture, which have been hypothesized to drive an intensification of ENSO-related precipitation anomalies, are of secondary importance. [ABSTRACT FROM AUTHOR]

Precipitation is a significant parameter in many aspects such as agriculture, water management and climate variability. To characterize rainy season variations is important to understand precipitation variability under the effect of climate change. In this study, rainy season features (i.e., onset, retreat and rainy‐season precipitation) over the Huaihe River basin (HRB) and the response to different types of El Niño–Southern Oscillation (ENSO), that is, central Pacific warming (CPW), eastern Pacific warming (EPW), eastern Pacific cooling (EPC), conventional ENSO and ENSO Modoki in the developing and decaying phases are evaluated. The multi‐scale moving t test was used to capture onset and retreat of rainy season. The possible dynamic causes of ENSO‐induced precipitation over the HRB, such as monsoon and atmospheric circulation, were also explored. Results show that (a) onset (retreat) of rainy season progressed northwards (westwards), with rainy‐season precipitation increasing from north to south; (b) rainy‐season precipitation showed a strong correlation to Sea Surface Temperature (SST) in Niño regions. Dry and wet signals were identified in different regions of the HRB in the developing and decaying phases of CPW, EPC and EPW. Special attention should be paid on decaying EPW, where totally dry signals were found, which can reach down to 25% below average precipitation; (c) developing El Niño Modoki and decaying El Niño showed totally dry signals, with decaying El Niño Modoki and conventional La Niña demonstrating overall increasing precipitation; (d) different performances of ENSO‐induced precipitation during rainy season over the HRB are attributable to the combined influences of the monsoon from the India Ocean and the anticyclonic flow in the western North Pacific (WNP). Stronger anticyclone and monsoon are generally associated with increasing rainy‐season precipitation. These results can improve predictability of rainy season features and ENSO‐induced precipitation over the Huaihe River basin. The graphical abstract map for this research. [ABSTRACT FROM AUTHOR]

China has experienced alarming drought events during recent decades. In this study, we investigated monthly drought severity related to the eastern Pacific cooling (EPC), eastern Pacific warming (EPW), and central Pacific warming (CPW), and analysed the differential influence of canonical ENSO (c‐ENSO) and El Niño Modoki (ENSO_M) events on monthly droughts severity based on the SPEI at multiple timescales in China. The results show that (a) EPC episodes negatively affected 1‐ and 3‐month drought severity in southeast and northeast China with negative anomalies lower than −0.4 (<−0.4) during November–February the next year. EPW and CPW episodes significantly negatively affect drought severity in central north China (<−0.8) and the coastal areas of southeast China (<−0.6) during the most months, respectively. (b) La Niña negatively affected drought severity in the most parts of northeast China (<−0.2) and southeast China (<−0.4) during the most months, and La Niña Modoki caused dry conditions in the most parts of China (<−0.2) during a few months. Significant dry conditions were observed in central north China (<−0.8) in many months during El Niño, but dry conditions covered south China (<−0.4) in the most months during El Niño Modoki. (c) SST anomalies in Niño3 and ENSO_M regimes and wind anomalies might explain the spatial distribution of dry conditions across China during c‐ENSO and ENSO_M phases. The results could provide valuable information for predicting droughts associated with the c‐ENSO and ENSO Mokodi. Spatial distribution of meteorological stations in China. [ABSTRACT FROM AUTHOR]

Previous study detected an intensified impact of the El Niño‐Southern Oscillation (ENSO) Modoki sea surface temperature (SST) anomalies on the tropical cyclone (TC) activity over the western North Pacific (WNP) after the early 1990s and attributed it to an expansion in areal coverage of the equatorial central Pacific (ECP) SST anomalies. This study identifies the contribution of SST anomalies in several other regions to this inter‐decadal change in the relationship between ENSO Modoki SST and the WNP TC genesis. Before the early 1990s, the positive ECP SST anomalies induce an anomalous lower‐level cyclone and consequently an increase in the TC genesis frequency over the southern part of the WNP, and the positive ECP and tropical Indian Ocean SST anomalies together induce an anomalous lower‐level anticyclone and accordingly a decrease in the TC genesis frequency over the northern part of the WNP. As such, the relationship between the ECP SST and the WNP TC genesis frequency is weak. After the early 1990s, the positive ECP SST anomalies with a large areal coverage induce a large anomalous lower‐level cyclone covering most of the WNP and thus an increase in the TC genesis frequency over the WNP. Meantime, the tropical northern Atlantic and western South Pacific SST anomalies during spring enhance the succeeding summer–autumn WNP atmospheric circulation response to the ECP SST anomalies through an Atlantic–Pacific teleconnection and a wind‐evaporation positive feedback, respectively. This strengthens the relationship between the ECP SST and the WNP TC genesis frequency. [ABSTRACT FROM AUTHOR]

This study evaluates the forecast skill of the fourth version of the Canadian coupled ocean-atmosphere general circulation model (CanCM4) and its model output statistics (MOS) to forecast the seasonal rainfall in Malaysia, particularly during early (October-November-December) and late (January-February-March) winter monsoon periods. CanCM4 is the latest component of the Canadian Seasonal to Inter-annual Prediction System (CanSIPS), which is a multi-seasonal climate prediction system developed particularly for Canada but applicable globally. Generally, CanCM4's skill in reproducing the climatology during winter is not as good as in other seasons because of the model's inability to simulate the regional synoptic circulations over the western Maritime Continent. In particular, the model fails to forecast the cold surges and Borneo vortex circulations that play critical roles in moisture horizontal advection. Moreover, its forecast skill during the early winter monsoon period is poorer than during the late period. Interestingly, forecast skill is enhanced when MOS models are applied as the MOS utilizes the predictive signals in the quasi-global predictors from the CanCM4 forecast system. The predictability can be traced to the conventional El Niño--Southern Oscillation (ENSO) and ENSO Modoki signals that are present in the CanCM4 forecast MOS predictor fields. The quasi-global sea-surface temperature and quasi-global sea-level pressure fields are found to be the most useful predictors. Interestingly, CanCM4 forecast signals associated with the Indian Ocean Dipole also contribute to the skill. Skill enhancement is particularly significant for northern Borneo during early monsoon periods in medium- and long-lead forecasts when the CanCM4 has minimal direct skill in the region. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) anomalies in the Pacific Ocean during El Niño-Southern Oscillation (ENSO) episodes exhibit different spatial patterns from year to year. In most ENSO studies, SST anomalies have been analysed in the 3.4 or 3.0 El Niño regions. Recent analyses have considered different SST anomalies areas in the Pacific, such as ENSO Modoki, which takes into account anomalies in the central Pacific which are bounded by opposite anomalies in the eastern and western Pacific. In order to analyse the influence of different Pacific spatial patterns on South American precipitation and on the Southern Hemisphere atmospheric circulation, composites obtained from Canonical ENSO were compared with those from ENSO Modoki in cases when strong anomalies were present in the Central Pacific. During the Canonical El Niño (La Niña), there tends to be a precipitation increase (decrease) in the La Plata Basin (LPB, 45°W-65°W, 15°S-35°S) and a decrease (increase) over northern South America during all seasons. In ENSO Modoki exhibiting strong anomalies in the Central Pacific, these typical patterns are not observed, and in some regions the anomalies even show opposite signs. Precipitation anomaly differences or similarities over South America between the two cases occur in different areas and different seasons. In both cases, differences in tropical South American precipitation during both ENSO types are related to differences in the Walker circulation. In extra-tropical South America, the precipitation differences are due to differences in the Pacific wavetrains and differences in moisture flux intensity over the continent. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]