An objective classification of climatic regions in the Pacific and Indian oceans

We have applied a number of objective statistical techniques to define homogeneous climatic regions for the Pacific Ocean, using COADS (Woodruff et al 1987) monthly sea surface temperature (SST) for 1950-1989 as the key variable. The basic data comprised all global 4°x4° latitude/longitude boxes with enough data available to yield reliable long-term means of monthly mean SST. An R-mode principal components analysis of these data, following a technique first used by Stidd (1967), yields information about harmonics of the annual cycles of SST. We used the spatial coefficients (one for each 4-degree box and eigenvector) as input to a K-means cluster analysis to classify the gridbox SST data into 34 global regions, in which 20 comprise the Pacific and Indian oceans. Seasonal time series were then produced for each of these regions. For comparison purposes, the variance spectrum of each regional anomaly time series was calculated. Most of the significant spectral peaks occur near the biennial (2.1-2.2 years) and ENSO (~3-6 years) time scales in the tropical regions. Decadal scale fluctuations are important in the mid-latitude ocean regions

ENSO and precipitation variability over Mexico during the last 90 years

Latin America has been shown to be susceptible to climatic anomalies during El Niño/Southern Oscillation (ENSO) events (eg, Aceituno 1988; Ropelewshi and Halpert 1987; Kiladis and Diaz 1989). While these studies have emphasized ENSO-related rainfall and temperature anomalies over Central and South America, less work has been done on the climatic effects of ENSO over the Mexican region. In this study we are investigating interannual and intraseasonal fluctuation in temperature and precipitation over the southwestern United States and Mexico since the turn of the century. We are particularly interested in the effects of ENSO on the interannual variability over this region. This report focuses on the association between ENSO and interannual variability of precipitation over Mexico

Late winter temperature response to large tropical volcanic eruptions in temperate western North America: Relationship to ENSO phases

February–March temperature reconstructions in western North America from 1500–1980 in the Common Era (CE) are used to evaluate, from a regional perspective, the hypothesis that radiative forcing by large tropical volcanic eruptions induces a tendency in the climate system towards an early post-event El Niño (EN) response followed by a delayed La Niña (LN) response. Post-event spatial composites using superposed epoch analysis (SEA) detect indications for an EN-like pattern in post-event Years 1–2; this result, however, is sensitive to the set of eruptions evaluated. Highly significant LN-like patterns are also observed for two eruptions during Year 1. In contrast, a clear and unique LN-like response is found in both evaluated eruption sets during Years 3–5; Year 3 in particular represents the time of strongest post-event response. No significant EN-like patterns occur during these years. The relative homogeneity of the SEA response for each post-event year is evaluated in terms of the ratio of the amplitude of the SEA composite to its standard deviation across the eruption events. In relation to the same metric determined from random-event-year SEAs, these signal-to-noise ratios are most highly significant in the portions of the domain with the strongest anomalies in Years 1–5, especially Year 3. The signal-to-noise ratios tend towards uniformly low and insignificant values beyond the first half-decade after the eruption, indicating generally reduced coherence across events. In relation to the larger-scale circulation, post-eruption 500mb February–March geopotential height composites from the 20th Century Reanalysis show ENSO-type features that are largely consistent with the SEA results from the primary eruption set during Year 1, but are inconsistent with the EN-like pattern exhibited by the second eruption set during Years 1–2. In Year 3, the pressure composite over North America and the adjacent Pacific and Atlantic is strongly LN-like, consistent with all SEA results; similarly, weakening coherence across events as time progresses beyond Year 3 is also consistent with more variable pressure composites noted after that time. The relatively robust character of the delayed LN-like response is evaluated in terms of the dynamic rebound of the climate system towards its initial energy balance as the radiative impact of immediate post-eruption aerosol cooling dissipates. The LN-like SEA temperature response in Years 3–5 exhibits a slight shift of its southern warm anomaly to the north and west relative to pure composite LN conditions, which is detected as a specifically post-eruption feature in the region

Analysis of spatio-temporal changes in annual and seasonal precipitation variability in South America-Chile and related ocean–atmosphere circulation patterns

Establishing relationships between coupled ocean–atmospheric patterns and precipitation accumulation is important to describe and predict spatio-temporal variability on annual or seasonal scales, and also to evaluate how this variability is influenced by global warming. The objective of this study was to examine the leading modes of interannual and seasonal (summer, autumn, winter, and spring) precipitation variability in South America-Chile, and their significant relationship to seasonally aggregated gridded data and climatic indices. Applying exhaustive data quality control measures to data from 238 rain gauges with different lengths of records between 1893 and 2013, a new data set was created with the objective of obtaining reliable records for further analysis. A comprehensive analysis through empirical orthogonal functions (EOF) allowed for determination of the leading modes of annual and seasonal precipitation and their main spatial patterns for the whole country. The percentage of explained variance in the relationship between seasonally aggregated indices and the leading modes of precipitation confirmed that most of the interannual and winter precipitation variability in Chile is linked to the seasonal aggregation of El Nino Southern Oscillation (ENSO). The leading modes of summer, autumn, and spring precipitation were mostly linked to seasonal aggregations of the Madden and Julian Oscillation (MJO), and the Antarctic Oscillation (AAO)

Recent changes in tropical freezing heights and the role of sea surface temperature

Probabilistic seasonal predictions of rainfall that incorporate proper uncertainties are essential for climate risk management. In this study, three different multi-model ensemble (MME) approaches are used to generate probabilistic seasonal hindcasts of the Indian summer monsoon rainfall based on a set of eight global climate models for the 1982–2009 period. The three MME approaches differ in their calculation of spread of the forecast distribution, treated as a Gaussian, while all three use the simple multi-model subdivision average to define the mean of the forecast distribution. The first two approaches use the within-ensemble spread and error residuals of ensemble mean hindcasts, respectively, to compute the variance of the forecast distribution. The third approach makes use of the correlation between the ensemble mean hindcasts and the observations to define the spread using a signal-to-noise ratio. Hindcasts are verified against high-resolution gridded rainfall data from India Meteorological Department in terms of meteorological subdivision spatial averages. The use of correlation for calculating the spread provides better skill than the other two methods in terms of rank probability skill score. In order to further improve the skill, an additional method has been used to generate multi-model probabilistic predictions based on simple averaging of tercile category probabilities from individual models. It is also noted that when such a method is used, skill of probabilistic forecasts is improved as compared with using the multi-model ensemble mean to define the mean of the forecast distribution and then probabilities are estimated. However, skill of the probabilistic predictions of the Indian monsoon rainfall is too low