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Abstract
The Indian Ocean zonal dipole is a mode of variability in sea surface temperature that seriously affects the climate of many nations around the Indian Ocean rim, as well as the global climate system. It has been the subject of increasing research, and sometimes of scientific debate concerning its existence/nonexistence and dependence/independence on/from the El Niño–Southern Oscillation, since it was first clearly identified in Nature in 1999. Much of the debate occurred because people did not agree on what years are the El Niño or La Niña years, not to mention the newly defined years of the positive or negative dipole. A method that identifies when the positive or negative extrema of the El Niño–Southern Oscillation and Indian Ocean dipole occur is proposed, and this method is used to classify each year from 1876 to 1999. The method is statistical in nature, but has a strong basis on the oceanic physical mechanisms that control the variability of the near-equatorial Indo-Pacific basin. Early in the study it was found that some years could not be clearly classified due to strong decadal variation; these years also must be recognized, along with the reason for their ambiguity. The sensitivity of the classification of years is tested by calculating composite maps of the Indo-Pacific sea surface temperature anomaly and the probability of below median Australian rainfall for different categories of the El Niño–Indian Ocean relationship.
Abstract
The Indian Ocean zonal dipole is a mode of variability in sea surface temperature that seriously affects the climate of many nations around the Indian Ocean rim, as well as the global climate system. It has been the subject of increasing research, and sometimes of scientific debate concerning its existence/nonexistence and dependence/independence on/from the El Niño–Southern Oscillation, since it was first clearly identified in Nature in 1999. Much of the debate occurred because people did not agree on what years are the El Niño or La Niña years, not to mention the newly defined years of the positive or negative dipole. A method that identifies when the positive or negative extrema of the El Niño–Southern Oscillation and Indian Ocean dipole occur is proposed, and this method is used to classify each year from 1876 to 1999. The method is statistical in nature, but has a strong basis on the oceanic physical mechanisms that control the variability of the near-equatorial Indo-Pacific basin. Early in the study it was found that some years could not be clearly classified due to strong decadal variation; these years also must be recognized, along with the reason for their ambiguity. The sensitivity of the classification of years is tested by calculating composite maps of the Indo-Pacific sea surface temperature anomaly and the probability of below median Australian rainfall for different categories of the El Niño–Indian Ocean relationship.
An account is given of the Antarctic First Regional Observing Study of the Troposphere (FROST) project, which has been organized by the Physics and Chemistry of the Atmosphere Group of the Scientific Committee on Antarctic Research. The goals of FROST are to study the meteorology of the Antarctic, to determine the strengths and weaknesses of operational analyses and forecasts over the continent and in the surrounding ocean areas, and to assess the value of new forms of satellite data that are becoming available. FROST is based around three one-month Special Observing Periods (SOPs)—July 1994, 16 October–15 November 1994, and January 1995 for which comprehensive datasets have been established of model fields and in situ and satellite observations. High quality manual surface and upper-air analyses are being prepared for these periods to determine the extent to which non–Global Telecommunications System data can improve the interpretation of the synoptic situation. Over the ocean areas during SOP-1, incorporation of the late data resulted only in a limited improvement in the analyses, indicating that the models are correctly analyzing most of the major weather systems. Over the continent, the production of 500-hPa heights from the automatic weather station data greatly helped in the analysis process. The lack of data around west Antarctica was a major handicap in the analysis process. The rms errors in the forecasts of 500-hPa height for the Antarctic were about 20% greater than those for midlatitude areas. The forecasts from the European Centre for Medium-Range Weather Forecasts were the most accurate of those received.
An account is given of the Antarctic First Regional Observing Study of the Troposphere (FROST) project, which has been organized by the Physics and Chemistry of the Atmosphere Group of the Scientific Committee on Antarctic Research. The goals of FROST are to study the meteorology of the Antarctic, to determine the strengths and weaknesses of operational analyses and forecasts over the continent and in the surrounding ocean areas, and to assess the value of new forms of satellite data that are becoming available. FROST is based around three one-month Special Observing Periods (SOPs)—July 1994, 16 October–15 November 1994, and January 1995 for which comprehensive datasets have been established of model fields and in situ and satellite observations. High quality manual surface and upper-air analyses are being prepared for these periods to determine the extent to which non–Global Telecommunications System data can improve the interpretation of the synoptic situation. Over the ocean areas during SOP-1, incorporation of the late data resulted only in a limited improvement in the analyses, indicating that the models are correctly analyzing most of the major weather systems. Over the continent, the production of 500-hPa heights from the automatic weather station data greatly helped in the analysis process. The lack of data around west Antarctica was a major handicap in the analysis process. The rms errors in the forecasts of 500-hPa height for the Antarctic were about 20% greater than those for midlatitude areas. The forecasts from the European Centre for Medium-Range Weather Forecasts were the most accurate of those received.
