We examine the dynamics and thermodynamics of four El Niño and five La Niño events in March 2000 to June 2011 with the following satellite datasets: ASCAT-on-MetOp-A and SeaWinds-on-QuikSCAT 10-m height wind vector; MISR-on-Terra wind vector at many heights throughout the troposphere; and TMI-on-TRMM sea surface temperature and rainfall. The geographical coordinates of the global equatorial region were 1°S-1°N and 2°-longitudinal boxes. Findings included: (1) westward wind speed at 10 m in the west Pacific (150°E-150°W) was weaker in El Niño than in La Niña; (2) westward wind speed at 10 m in the east Pacific (150°W-90°W) was stronger in El Niño than in La Nina, which was contrary to conventional wisdom and did not match in-situ measurements and model-produced thermocline depths; (3) westward wind speeds at 700-m height confirmed result observed at 10 m; (4) westward wind speed at 10 m in Atlantic and Indian provided no evidence of of El Niño and La Niña events; (5) eastward wind speed at 14 km in west Pacific was weaker in El Niño than in La Niña; (6) eastward wind speed at 14 km in Atlantic stronger in El Niño than in La Niña; (7) eastward wind speed at 14 km in Indian showed no evidence of El Niño and La Niña; and (8) rainfall was considerably larger in west Pacific in El Niño compared to La Niña. Clouds will also be evaluated. The western component of the Walker Cell over the Pacific shifted eastward in El Niño and westward in La Niña. The 10- to 14,000-m shear of the zonal wind component in the Atlantic was larger in El Niño and, consequently, reduced the tendency for hurricane development in the Atlantic. The response of the Indian monsoon to El Niño/La Niña conditions will be described.

The analysis of Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) satellite observations and European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis data identify an asymmetric rainfall pattern in the southeast Indian Ocean (SEIO) between the positive and negative Indian Ocean Dipoles (IODs). During positive (negative) IODs, the precipitation anomaly is more confined to equatorial (off-equatorial) regions, even though their associated sea surface temperature anomalies almost reside in the same region. It is argued that the rainfall pattern asymmetry is attributed to the effect of the rainfall mean state. While the negative rainfall anomalies associated with positive IODs are confined in the region of mean precipitation, the positive rainfall anomalies associated with negative IODs do not have such a constraint. The so-induced asymmetric rainfall pattern exerts a great impact on the upper ocean salinity and bushfires over the western maritime continent.

The composite analysis of the eastern equatorial Pacific SST anomaly reveals an asymmetric evolution characteristic between El Niño and La Niña. While the composite El Niño is characterized by a rapid decay after its peak and a phase transition from a positive to a negative SST anomaly (SSTA), the composite La Niña is characterized by a weaker decay after its peak and a re-intensification of the cold SSTA in the later of the second year.

The physical mechanisms responsible for the distinctive asymmetric evolutions are investigated through a mixed layer heat budget analysis. The result shows that the faster decay of El Niño is attributed to stronger dynamic and thermodynamic damping. The former is attributed to an asymmetric low-level wind response in the western Pacific, whereas the latter is due to asymmetric cloud and latent heat flux responses. A positive SSTA in the eastern equatorial Pacific during the mature phase of El Niño induces, through local air-sea interaction processes, an anomalous anticyclone in the WNP. Easterly anomalies south of the anticyclone trigger upwelling Kelvin waves, promoting a fast transition from a warm to a cold SSTA by summer of the El Niño decaying year. Strengthened air-sea coupled instability in northern fall further amplified the cooling. In contrast, La Niña induces an anomalous cyclone west of Philippines. Because of this asymmetric wind response, the equatorial thermocline anomaly is much weaker during the La Niña decaying phase. This weak dynamic forcing effect, along with a weaker negative cloud-SST and evaporation-SST feedback, leads to a slow damping of La Niña. The negative SSTA re-develops in northern fall of the second year and leads to re-occurrence of La Niña episode.

Predictive skill for ENSO in the early 21^st century declined sharply relative to the last two decades of the 20^th century despite ongoing improvements of seasonal forecast systems. This decline coincides with a shift in Pacific climate after 1998 to increased trade winds and stronger Walker circulation together with warmer/colder temperatures in the western/eastern Pacific, which has previously been associated with the recent pause in global-mean surface warming. Here we show, using seasonal forecast sensitivity experiments, that this shift in background climate has also acted to reduce ENSO predictability because the atmosphere-ocean coupling that drives ENSO is weakened. These findings help explain the fickle development and disparate predictions of El Niño in 2014, which temporarily decayed during boreal summer after developing strongly in spring.

This study investigated the meteorological and climatic conditions during the episode of extreme precipitation event from 21 – 24 December 2014 that led to the worst ever recorded flood in the northeast region of Peninsular Malaysia. The analysis showed that the wind circulations during the episode were dominated by anomalously stronger easterlies from the Pacific Ocean. These strong easterly winds were the tropical Rossby-Kelvin wave response to the active MJO phase when the center of active convection was located over the eastern Indian Ocean during the episode. Due to these easterlies, low-level moistures were transported from Pacific Ocean to northeast region of Peninsular Malaysia to fuel intense convection over the region. The presence of an anomalously strong high-pressure system over the central-north Pacific Ocean strengthened the equatorial easterlies. Conspicuously, the strong cold surges were absent during this period and this may explain why the location of maximum rainfall and hence the flood was in the northeast region of the Peninsular rather than in the southern region during the December 2006 / January 2007 flood. The inhibition of strong cold surges was due to MJO influence and weaker Siberian-high pressure system during the period.

Keywords: Extreme precipitation, Flood, East Coast Peninsular Malaysia, Madden-Julian Oscillation, Cold Surges

This study examined the predictive skills of Thailand seasonal rainfall forecast from statistical model and statistical downscaling of GCM output. Canonical Correlation Analysis (CCA) was used to construct forecast models based on empirical relationship between sea surface temperature (SST) in the tropical Indo-Pacific sector and observed rainfall for the period of 1951-2014. The model exhibited high forecast skills during pre-monsoon period (from February to May) with lead time up to six months. However during monsoon period (from June to September) and other seasons, the forecast skills were generally weak. The source of predictive skill in pre-monsoon season was associated with ENSO whereby positive pre-monsoon rainfall was associated with La Nina condition. In contrast, Indian Ocean SST showed no skill in predicting Thailand rainfall. Additionally, CCA was used to downscale seasonal rainfall forecast from CFSv2. In general, the predictive skills of CFSv2 downscaling were comparable to statistical model based on SST.

The springtime snowpack over the Himalayan-Tibetan Plateau (HTP) region and Eurasia has long been suggested to be an influential factor on the onset of Indian summer monsoon.

Here, we examine a suite of coupled ocean-atmosphere forecasts using the Seasonal Forecast System of the European Centre for Medium-Range Weather Forecasts (ECMWF), to assess the impact of realistic initialization of springtime snow over HTP on the onset of the Indian summer monsoon.  Twin sets of 4-month ensemble simulations were initialized on 1 April every year for the period 1981-2010.  The “realistic” set comprises of the System 4 seasonal hindcasts routinely performed at ECMWF, where the snow is initialized with ERA-Interim/Land Re-analyses. The “unrealistic” set is identical in all aspects except that that initial conditions for snow-related land surface variables over the HTP region are scrambled.

We show that high snow depth over HTP influences the meridional tropospheric temperature gradient reversal that marks the monsoon onset. Composites difference based on a normalized HTP snow index reveal that, in high snow years, the onset is delayed by about 7 days with clear signatures on surface temperature, precipitation and moisture fluxes. We further demonstrate that high April snow depths over HTP does not uniquely relate to any climate patterns prevailing over the previous winter, such as ENSO, the Indian Ocean dipole or the North Atlantic Oscillation. Accurate initialization of spring snow depth over the HTP region could hence have strong implication for dynamical prediction of the Indian summer monsoon onset.

We analyzed long-term change of extreme significant wave heights (SWH) in the western North Pacific (WNP) derived from WAVEWATCH-III forcing by 3-hourly surface wind fields of the global model GME for 1980-2009 years. We also demonstrated significant interaction between SWH in the WNP and SST conditions in the equatorial Pacific (EP) by using both the EOF/PC analysis and linear regression analysis. It was identified that the changes of SWH was associated with activity of tropical cyclones (TCs) in the WNP which links with SST anomalies in the Nino-3.4 region over the EP. These are impacts of the ENSO on the inter-annual variability of SWHs in the WNP with the TCs activity.

As results, the dominant patterns of SWH in the WNP were investigated by applying an EOF and PC methods to SWH in the summer. In the leading EOF mode, we were able to see the changes of a clear amount that was formed around the edge of the WNP basin and in the south of Japan. In addition, leading PCs were employed to identify the relationship with SST anomalies. The linear regression coefficients between leading PC of SWH and SST anomalies showed a positive relationship in the EP indicating that inter-annual variability of SWH was associated with the ENSO in the summer. To investigate the relationship more clearly, we had set up an experiment investigating four extreme cases. Then, we investigated the changes in both the variability of SWH and TCs activity in WNP. Therefore, we could suppose the variability of SWH through TCs activity owing to changes of SST conditions in the EP in the future.

This Research has been performed as a collaborative research project of Strengthening the National Supercomputing Service Infrastructure by the KOREA INSTITUTE of SCIENCE and TECHNOLOGY INFORMATION (KISTI).

Bridging the gap between weather and climate prediction, intraseasonal prediction has received wide scientific and societal attention during recent decade since it is a great challenge at a relatively early stage of development. Achieving a better understanding of intraseasonal variability, predictability, and its associated weather and climate impacts are of importance for extending forecast beyond its current limit and developing seamless suite of weather and climate prediction. Physical basis for the intraseasonal prediction lies in both atmospheric initial conditions and external boundary forcings in the state of the ocean, land, and sea ice. For the first time, multi-national and multi-institutional efforts have been made on assessment of the current state of the intraseasonal prediction in the state-of-the-art dynamical models under the coordinated Intraseasonal Variability Hindcast Experiment (ISVHE) since 2009 supported by several international programs. The multi-model ensemble prediction for the last 20 years of 1989-2008 using the ISVHE data has useful skill up to 15 to 25 days for the tropical Intraseasonal variability depending on region and up to 30 days for the Madden-Julian Oscillation, the most predictable component on intraseasonal time scale, during boreal winter, demonstrating significant improvement in intraseasonal prediction within recent decade. It is further noted that the MME skill for the MJO depends on initial MJO phase and El Nino and Southern Oscillation.

The hindcast results of SINTEX-F2 CGCM with one month lead are downscaled over the Australia region using the Weather Research and Forecasting (WRF) regional model. The downscaling was for the austral summer (winter) from Dec to Feb (June to Sep). During austral winter, the southwest of West Australia receives seasonal rainfall and is important for the agriculture of the region. The northern and the eastern parts of Australia receive rainfall during the austral summer. Generating high resolution forecasts during the winter and summer season is essential for the economics of the regions.

Various metrics such as anomaly correlation, relative operating characteristic (ROC) and relative operating level (ROL) are evaluated for the WRF generated hindcasts. The metrics showed that the WRF generated high resolution hindcasts do not add value to the SINTEX-F2 hindcasts due to the mean biases in the SINTEX-F2 model. Various experiments such as coupling with ocean models were carried out to improve the WRF downscaled hindcasts. Of all the experiments it was found that the mean bias correction in the SINTEX-F2 hindcasts improves the anomaly correlation and ROC/ROL scores of the downscaled hindcasts and adds value to the original SINTEX-F2 generated hindcasts. The improvements are found to be due to a better representation of the SST and atmospheric high pressure systems in the bias corrected SINTEX-F hindcasts.

Austral summer (December-January-February) season climate over the period 1991/1992 to 2010/2011 was dynamically downscaled by the weather research and forecasting (WRF) model at 9 km resolution for South Africa. Lateral boundary conditions for WRF were provided from the European Centre for medium-range weather (ECMWF) reanalysis (ERA) interim data. The model's sensitivity of the simulated precipitation to the cumulus parameterization schemes was analysed by employing three different convective parameterization schemes, namely the (1) Kain–Fritsch (KF), (2) Betts–Miller–Janjic (BMJ) and (3) Grell–Devenyi ensemble (GDE) schemes. The model biases in the rainfall were evaluated over the South Africa as a whole and its nine provinces separately by comparing with the observed rainfall. The results show that the three cumulus schemes simulate positive rainfall biases over South Africa, with the KF scheme simulating large biases and mean absolute errors. It is found that the BMJ scheme could reproduce the observed intensity of rainfall anomalies, and exhibits a high correlation with observed interannual summer rainfall variability. Analysis shows that the model run with the KF scheme simulates a transport of significantly high amount of moisture from the tropics into South Africa   leading to the large positive biases in precipitation over the South Africa landmass. The model run with the GDE scheme simulated a negative bias in moisture, along with a stable atmosphere and negative biases of vertical velocity resulting in negative rainfall biases over South Africa, especially over the Limpopo Province. The variability of the precipitation over South Africa due to the various climate modes such as El Niño/Southern Oscillation (ENSO) and subtropical dipole modes shows that the performance of the WRF model with BMJ cumulus scheme is superior to the model runs with either KF or GDE schemes. The model could reproduce the observed ENSO-South Africa rainfall relationship and could successfully simulate three wet (dry) years that are associated with La Niña (El Niño). The results obtained in this study are essential for the seasonal forecasting experiments which will be carried out in the future.

The impacts of the Madden-Julian Oscillation (MJO) on rainfall diurnal cycle over the Peninsular Malaysia at early phases (1 and 2) of MJO were examined based on the Real-time Multivariate MJO (RMM) index. Multiple datasets with sub-daily and daily resolutions were employed where anomalies were composited into 8 active and one “weak” MJO phases. The composite of daily rainfall during boreal summer (June-July-August (JJA)) indicated positive (negative) anomaly during the early phases of MJO over west (east) coast of Peninsular Malaysia. In these phases, over west coast of Peninsular Malaysia, the maximum precipitation tends to occur during 1400 to 2000 Malaysian Standard Time. This is due to convection induced by anomalous low-level moisture which is modulated by a stronger sea breeze and hence causes stronger moisture convection. During normal period, afternoon rainfall is uncommon during JJA but prevalent in March-April-May (MAM). Hence we hypothesized that the presence of active MJO convection center over western and central Indian Ocean weakens the southwesterly monsoonal wind over the Peninsular Malaysia, thus creating favourable conditions for local convection to occur in the afternoon to late afternoon periods. We also proposed that reduced cloudiness during phase 1 and 2 over Peninsular Malaysia enhances sea breeze (superimposed with monsoonal flow) over west coast resulting in anomalous moisture transport to fuel the local convection. In the east coast, the impact is reduced rainfall due to the opposing direction of sea breeze to monsoonal wind flow resulting in less moisture transport and hence, suppressing convection over the region.

The global warming and the Interdecadal Pacific Oscillation (IPO) started influencing the coastal ocean off Western Australia, leading to a dramatic change in the regional climate predictability. The warmer ocean started driving rainfall variability regionally there after the late 1990s. Because of this, rainfall predictability near the coastal region of Western Australia on a seasonal time scale was drastically enhanced in the late 1990s; it is significantly predictable 5 months ahead after the late 1990s. The high prediction skill of the rainfall in recent decades is very encouraging and would help to develop an early warning system of Ningaloo Niño/Niña events to mitigate possible societal as well as agricultural impacts in the granary of Western Australia.

Infectious diseases are one of the leading causes of deaths in today's changing world. With global economy growth, increasing human interactions, altering biodiversity and ongoing changes in climate, disease outbreaks can occur suddenly or gradually, becoming more intensified and widely spread. This intensification of infectious diseases has a great impact on developing and newly industrialized countries. In the sub-Saharan African region, malaria is the most deadly vector-borne disease, in which 90% of all malaria deaths occur in these countries, with most cases in young children under the age of five. 

Malaria is usually transmitted from human to human through bites of Anopheles mosquitoes which previously fed on an infected human. The infective mosquito carries a parasite (Plasmodium genus) responsible for transmitting malaria and continuously feeds on other humans thus spreading the disease. The disease is transmitted as long as the mosquito lives and feeds on other humans. In this way, the life cycle of the mosquito is an important aspect in the spread of malaria. All mosquito species exhibit a life cycle that is highly influenced by climate, such as temperature and rainfall. When climatic factors fall within an optimal range, mosquito survival and reproduction rates increase, therefore resulting in a high abundance of active mosquitoes, which increases the chances of malaria transmission. 

This study aims to identify spatiotemporal patterns in malaria occurrence in the Limpopo province in South Africa with the use of machine learning methods and investigate the relationship of malaria incidence with environmental and climatic factors. Climate predictions from the SINTEX-F global climate model will assist in providing an outlook on how the distribution of malaria might change in the future and possibly spread to other provinces in South Africa.