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There is a large discrepancy between studies on how the Indian Ocean Dipole (IOD) is defined. Indeed, the definition of historical events varies substantially, resulting in conflicting classifications, hampering hydroclimatic impact assessment of this phenomenon. Therefore, this paper aims to compare the various methods that have previously been adopted to define IOD events and demonstrate an alternative method that specifically captures different "flavours" of IOD. It is shown that the choice of sea surface temperature (SST) data set and climatological baseline has significant impact on the resulting classification of IOD events. Furthermore, not all events display the traditional dipole signature pattern of warming/cooling occurring at opposite poles. Rather, some events could be considered "pseudo dipoles," whereby the SST anomalies in the two poles are not of opposite sign. Importantly, the different types of IOD (i.e., "pseudo and true") are associated with varying impacts on rainfall for surrounding regions. To overcome these issues, this study treats Indian Ocean SST anomalies as a continuum of a finite maximum number of statistically distinguishable patterns using a neural network‐based cluster analysis. Ten individual SST patterns were identified with distinct rainfall teleconnections. In particular, four strong IOD patterns (two positive and two negative) were identified, alongside three pseudo dipoles representing anomalously warm conditions across the entire Indian Ocean Basin, with a SST gradient towards the east (i.e., a pseudo negative dipole) or west (i.e., a pseudo positive dipole). The occurrence of each of the 10 self‐organizing mapping (SOM) patterns is shown to be variable over the past 130 years, with some types more common during certain phases of the El Niño/Southern Oscillation or during the first (or second) half of the 20th century. The implications of these findings are particularly significant for attribution studies, development of proxy climate reconstructions, climate model evaluation and future climate change assessment. The Indian Ocean Dipole (IOD) is known to impact hydroclimate across many Indian Ocean rim counties. However, there is a growing body of research that confirms the IOD is a complex phenomenon with multiple variants, which cannot be captured by a simple index such as the dipole mode index (DMI). This paper uses a novel technique (SOM) to identify 10 individual flavours of the IOD with distinct rainfall teleconnections. Figure shows 10 different Indian Ocean SST "types" derived using SOM. Note that numbers in brackets show the number of occurrences of each type. [ABSTRACT FROM AUTHOR]

In tropical mountains, the way topographical gradients translate in terms of intra-seasonal and interannual climate variability (especially rainfall and evapotranspiration) is relatively unknown, yet it is an important issue for agriculture and food security. The eastern slopes of Mount Kenya, in East Africa, with their wide range of agro-ecological conditions, are appropriate for the study of these aspects. Daily (monthly) rainfall data from 11 (24) stations at different elevations and exposures are collected for the period 1961-2006. For the elevation belts suitable for farming and agro-pastoral activities, mostly below 2000 m, it is found that not all rainfall characteristics co-vary with altitude. Four distinct behaviours are revealed: (1) Parameters which show a relatively regular increase/decrease with elevation. This is the case for the duration of the two rainy seasons found across the region, and the mean onset and cessation dates, (2) Parameters which show a relatively uniform pattern throughout the region, with little differences both horizontally and vertically. This applies to the interannual variability of the October-December seasonal rains, which are quite homogeneously modulated by Indian Ocean sea-surface temperatures (SSTs), (3) Parameters markedly influenced by exposure. This applies to daily rainfall intensities, which peak along the southeast-facing slopes, whatever the altitude. The frequency of rainy days shows both a vertical gradient and some influence of exposure, (4) Parameters which show very weak spatial coherence, or pronounced vertical discontinuities. This is obvious for the cessation date of the March-May rains, whose poorly organized interannual variability contrasts with that of the onset. Variables involved in potential evapotranspiration (ET0) computation display either a strong (maximum temperature) or a weak (global radiation) vertical coherence, reflecting the existence/absence of an elevation control on the mean distribution of these variables. However, the interannual variations of global radiation have the strongest influence on those of ET0. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

The annual SST lag for the world ocean is obtained from a Fourier analysis of the HadISST (1870-2008) and NOAA WOA05 (1900-2005) monthly SST data series. The main properties are a mean of 73 days and a standard deviation of 16 days, with tongues of high SST lag extending poleward and eastward from the equatorial regions where a maximum in lag of about 110 days occurs. The SST lag is the sum of an atmospheric surface heat flux lag and an ocean heat storage lag which is the dominant contributor and provides direct estimates of the damping time for air/sea heat exchange, which are in good agreement with observations in the eastern North Atlantic Ocean, and also predictions of the phase lag of the mid-latitude ocean surface temperature field to El Nino-Southern Oscillation (ENSO) forcing. Hence, SST lag can be used to diagnose global climate variability. Local differences in lag of up to about 15 days occur between the 1991-2005 and 1976-1990 periods, which are accompanied by significant changes in regional climate. The lag time series for grid points in the eastern Indian Ocean, North Pacific Ocean and North Atlantic Ocean show that the variability can be interpreted in terms of bounded random walks of length about 20 years. The CSIRO Mk. 3.5 run for Scenario A2 over the period 1871-2100 has a similar random structure and also shows a secular increase in SST lag in the temperate oceans which is attributable to an expansion of the tropical belt. [ABSTRACT FROM AUTHOR]