El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers <s> ENSO Indices for Climate and Health Analyses <s> Abstract El Nino–Southern Oscillation (ENSO) is a natural, coupled atmospheric–oceanic cycle that occurs in the tropical Pacific Ocean on an approximate timescale of 2–7 yr. ENSO events have been shown in previous studies to be related to regional extremes in weather (e.g., hurricane occurrences, frequency and severity of tornadoes, droughts, and floods). The teleconnection of ENSO events to extreme weather events means that the ability to classify an event as El Nino or La Nina is of interest in scientific and other applications. ENSO is most often classified using indices that indicate the warmth and coolness of equatorial tropical Pacific Ocean sea surface temperatures (SSTs). Another commonly used index is based on sea level pressure differences measured across the tropical Pacific Ocean. More recently, other indices have been proposed and have been shown to be effective in describing ENSO events. There is currently no consensus within the scientific community as to which of many indices best captures... <s> BIB001 </s> El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers <s> ENSO Indices for Climate and Health Analyses <s> Data for the 20th century from the International Comprehensive Ocean and Atmosphere Data Set and the Kobe Collection have been used as input data for global objective analyses of sea-surface temperatures (SSTs) and other marine meteorological variables. This study seeks a better understanding of the historical marine meteorological data and an evaluation of the quality of the data in the Kobe Collection. Objective analyses yield gridded data that are less noisy than observed data, which facilitates handling of historical data. The observed data determine the quality of the objective analyses, and quality control specified for historical data is incorporated into the objective analysis to reduce artificial errors. The objective analyses are based on optimum interpolation and reconstruction with empirical orthogonal functions. The final database produced in this study not only contains analysed values, but also analysis errors and data distributions at each time step of the objective analyses. The analysis database contains ample information on historical observations, as well as signals of marine climate variations during the century. Time series of global mean marine temperatures and cloud cover include trends linked to global warming, and local peaks appear commonly in all the time series around the 1940s. Sea-level pressure and sea-surface wind fields show significant linear trends at high latitudes and over the North Pacific Ocean respectively. These trends seem to be artificial. An SST analysis used widely in climatological studies was verified against HadISST from the Hadley Centre and an SST analysis derived from satellite and in situ observations. El Nino and southern oscillation indices for the century are successfully reproduced, even though observations in the tropics are much rarer before 1950 than after 1950. <s> BIB002 </s> El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers <s> ENSO Indices for Climate and Health Analyses <s> El Nino/Southern Oscillation (ENSO) remains the most important coupled ocean–atmosphere phenomenon to cause global climate variability on seasonal to interannual time scales. This paper addresses the need for a reliable ENSO index that allows for the historical definition of ENSO events in the instrumental record back to 1871. The Multivariate ENSO Index (MEI) was originally defined as the first seasonally varying principal component of six atmosphere–ocean (COADS) variable fields in the tropical Pacific basin. It provides for a more complete and flexible description of the ENSO phenomenon than single variable ENSO indices such as the SOI or Nino 3.4 SST. Here we describe our effort to boil the MEI concept down to its most essential components (based on SLP, SST) to enable historical analyses that more than double its period of record to 1871–2005. The new MEI.ext confirms that ENSO activity went through a lull in the early- to mid-20th century, but was just about as prevalent one century ago as in recent decades. We diagnose strong relationships between peak amplitudes of ENSO events and their duration, as well as between their peak amplitudes and their spacing (periodicity). Our effort is designed to help with the assessment of ENSO conditions through as long a record as possible to be able to differentiate between ‘natural’ ENSO behaviour in all its rich facets, and the ‘Brave New World’ of this phenomenon under evolving GHG-related climate conditions. So far, none of the behaviour of recent ENSO events appears unprecedented, including duration, onset timing, and spacing in the last few decades compared to a full century before then. Copyright © 2011 Royal Meteorological Society <s> BIB003 </s> El Niño Southern Oscillation (ENSO) and Health: An Overview for Climate and Health Researchers <s> ENSO Indices for Climate and Health Analyses <s> AbstractThe monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore... <s> BIB004
Faced with a range of ENSO indices, questions that are likely to arise when planning analyses of ENSO health associations are 'which index will be optimal for exploring ENSO related health impacts?' and 'are there different ways of defining ENSO events?'. There is no common consensus in the climate science community as to which ENSO index best describes ENSO phases. This appears to contrast with the climate and health science community in which there appears to be a substantial amount of blind faith applied to the use of ENSO indices for exploring possible ENSO driven health variations at a range of temporal and spatial scales. In many ways the choice of an ENSO index for health analyses may depend on geographical location, its relation to a health-sensitive climate variable or even disease outcome. For example, for regions in close proximity to the Niño oceanic regions, the SST based indices may be appropriate, especially for exploring rainfall, air and sea temperature-related health outcomes. Similarly, the OLR index may be useful for exploring rainfall-related health issues in the central Pacific. ENSO related wind indices, because they represent the surface trade wind strength, are more than likely to be only useful in the trade wind regions of the Pacific Basin. For locations distant from the Pacific Basin, such as southern and eastern Asia, one of the pressure based indices might be more suitable as these represent variations in atmospheric circulation conditions over a larger geographical range. While some researchers might be tempted to use one of the more recently developed blended or multivariate ENSO indices, it is worth bearing in mind that these are complex indices made up of multiple interacting variables. Accordingly, they may not be appropriate if the purpose of an analysis is to uncover a direct link between a specific climate attribute such as temperature and a disease outcome. Further, blended indices have not yet been widely adopted by ENSO forecasting centres. This is most likely because of the prediction error associated with the individual input variables such that the cumulative error for a predicted value of a blended index could be large when compared to a single variable based index. Notwithstanding this, blended indices such that of Wolter and Timlin BIB003 have been applied on a number of occasions in ENSO -health studies (see next section). In selecting ENSO indices, researchers also need to be aware of the producing agency because the way ENSO phases and events are identified can vary between agencies. As noted above, NOAA uses the Niño 3.4 region (5N-5S, 170W-120W) based on ONI and the persistence on SST anomalies in excess of +/-0.5 o C for 5 months for identifying ENSO phases. In contrast, the Japan Meteorological Agency (JMA) uses a slightly different formulation for the calculation of their ENSO index which is a 5-month running mean of spatially averaged SST anomalies over the geographical range of 4°S-4°N, 150°W-90°W; this is essentially a latitude-restricted Niño 3 region. Further, the JMA define an ENSO year as October through to the following September. If the JMA index values exceed +0.5°C or-0.5 o C for 6 consecutive 5-month periods, including October to December, the ENSO year is declared as either El Niño or La Niña. The Australian Bureau of Meteorology (BoM) like NOAA and the JMA use SST anomalies as a basis for ENSO phase definition but set a higher threshold. For an El Niño (La Niña) event to be called, SST anomalies in the Niño 3 and Niño 3.4 regions must exceed +0.8 o C (-0.8 o C). Further to the SST criteria, BoM specifies much weaker (stronger) trade winds over the western or central equatorial Pacific for the previous 3-4 months as well as a SOI value of -7 (+7) to be necessary for an El Niño (La Niña) event to be specified. Note in the case of BoM, the SOI values are quite different from those associated with the NOAA SOI index because the BoM uses the Troup SOI, the standardised anomaly of the mean sea level pressure difference between Tahiti and Darwin. The BoM calculation uses the period 1933 to 1992 as the climatology; this contrasts with the NOAA and JMA. Once the Tromp SOI is calculated, it is multiplied by 10. Using this convention, the BoM SOI takes on values in the range of -35 (strong El Niño) to about +35 (strong La Niña). NOAA, JMA and BoM use SST anomalies in their definitions of the El Niño and La Niña phases of ENSO. In addition to the slight differences in the criteria used for defining events (e.g. SST anomaly, anomaly period and region), a further source of difference between the oceanic indices are the SST data sets employed for constructing the SST anomalies. For example, the JMA uses the Centennial In Situ Observation-Based Estimates (COBE)-SST data set for ENSO monitoring with sliding climatological values based on the most recent 30 year period as described by Ishii et al. BIB002 and JMA  . In contrast, NOAA and BoM use the Extended Reconstructed Sea Surface Temperature, Version 5 (ERSSTv5) data set with anomalies based on centred 30-year periods updated every 5 years as described by Huang et al. BIB004 . Although not described here the United Kingdom's Met Office's Hadley Centre applies yet another SST data set for deriving historical SST based ENSO measures, the HadISST data set  . Important to note in the consideration of possible pre-1950 ENSO health associations is that for this period, because of observational uncertainties, the ERSSTv5 and HadISST from which ENSO indices are derived demonstrate significant differences  . Given this, researchers need to be aware of the differences in SST data sets for deriving ENSO indices in terms of the SST observations drawn upon, statistical and data assimilation methods applied, and spatial resolution of the final SST products  because these data set properties may affect the degree to which meaningful associations between ENSO and health outcomes can be quantified. Hanley et al. BIB001 provided a useful comparison of ENSO indices in terms of their ability to describe ENSO events. They found that El Niño only engenders a weak SST response in the Niño 4 region, whereas La Niña produces quite a strong SST signal in the Niño 1+2 region. They also concluded, based on an analysis of the sensitivity of a range of ENSO indices relative to each other, that the choice of an index for analysing ENSO related risks is somewhat dependent on the ENSO phase. For example, in the case of La Niña events, the JMA ENSO Index was more sensitive than other atmospheric and oceanic indices. In contrast for El Niño events, the SOI, Niño 3.4, and Niño 4 indices are almost equally sensitive but more sensitive than the JMA, Niño 1+2 and Niño 3 indices.