The Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) provides a calibration-based sequential scheme for combining precipitation estimates from multiple satellites, as well as gauge analyses where feasible, at fine scales (0.25° × 0.25° and 3 hourly). TMPA is available both after and in real time, based on calibration by the TRMM Combined Instrument and TRMM Microwave Imager precipitation products, respectively. Only the after-real-time product incorporates gauge data at the present. The dataset covers the latitude band 50°N–S for the period from 1998 to the delayed present. Early validation results are as follows: the TMPA provides reasonable performance at monthly scales, although it is shown to have precipitation rate–dependent low bias due to lack of sensitivity to low precipitation rates over ocean in one of the input products [based on Advanced Microwave Sounding Unit-B (AMSU-B)]. At finer scales the TMPA is successful at approximately reproducing the s...

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The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales

The NCEP Climate Forecast System Reanalysis (CFSR) was completed for the 31-yr period from 1979 to 2009, in January 2010. The CFSR was designed and executed as a global, high-resolution coupled atmosphere–ocean–land surface–sea ice system to provide the best estimate of the state of these coupled domains over this period. The current CFSR will be extended as an operational, real-time product into the future. New features of the CFSR include 1) coupling of the atmosphere and ocean during the generation of the 6-h guess field, 2) an interactive sea ice model, and 3) assimilation of satellite radiances by the Gridpoint Statistical Interpolation (GSI) scheme over the entire period. The CFSR global atmosphere resolution is ~38 km (T382) with 64 levels extending from the surface to 0.26 hPa. The global ocean's latitudinal spacing is 0.25° at the equator, extending to a global 0.5° beyond the tropics, with 40 levels to a depth of 4737 m. The global land surface model has four soil levels and the global sea ice m...

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The NCEP Climate Forecast System Reanalysis

For the tropical Pacific and Atlantic oceans, internal modes of variability that lead to climatic oscillations have been recognized1,2, but in the Indian Ocean region a similar ocean–atmosphere interaction causing interannual climate variability has not yet been found3. Here we report an analysis of observational data over the past 40 years, showing a dipole mode in the Indian Ocean: a pattern of internal variability with anomalously low sea surface temperatures off Sumatra and high sea surface temperatures in the western Indian Ocean, with accompanying wind and precipitation anomalies. The spatio-temporal links between sea surface temperatures and winds reveal a strong coupling through the precipitation field and ocean dynamics. This air–sea interaction process is unique and inherent in the Indian Ocean, and is shown to be independent of the El Nino/Southern Oscillation. The discovery of this dipole mode that accounts for about 12% of the sea surface temperature variability in the Indian Ocean—and, in its active years, also causes severe rainfall in eastern Africa and droughts in Indonesia—brightens the prospects for a long-term forecast of rainfall anomalies in the affected countries.

A Dipole Mode in the Tropical Indian Ocean

A weekly 1° spatial resolution optimum interpolation (OI) sea surface temperature (SST) analysis has been produced at the National Oceanic and Atmospheric Administration (NOAA) using both in situ and satellite data from November 1981 to the present. The weekly product has been available since 1993 and is widely used for weather and climate monitoring and forecasting. Errors in the satellite bias correction and the sea ice to SST conversion algorithm are discussed, and then an improved version of the OI analysis is developed. The changes result in a modest reduction in the satellite bias that leaves small global residual biases of roughly −0.03°C. The major improvement in the analysis occurs at high latitudes due to the new sea ice algorithm where local differences between the old and new analysis can exceed 1°C. Comparisons with other SST products are needed to determine the consistency of the OI. These comparisons show that the differences among products occur on large time- and space scales wit...

An Improved In Situ and Satellite SST Analysis for Climate

Two new high-resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25° and a temporal resolution of 1 day. One product uses the Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data. Because of AMSR’s near-all-weather coverage, there is an increase in OI signal variance when AMSR is added to AVHRR. Thus, two products are needed to avoid an analysis variance jump when AMSR became available in June 2002. For both products, the results show improved spatial and temporal resolution compared to previous weekly 1° OI analyses. The AVHRR-only product uses Pathfinder AVHRR data (currently available from January 1985 to December 2005) and operational AVHRR data for 2006 onward. Pathfinder AVHRR was chosen over operational AVHRR, when available, because Pathfinder agrees better with the in situ data. The AMSR– AVHRR product begins with the start of AMSR data in June 2002. In this product, the primary AVHRR contribution is in regions near land where AMSR is not available. However, in cloud-free regions, use of both infrared and microwave instruments can reduce systematic biases because their error characteristics are independent.

Daily High-Resolution-Blended Analyses for Sea Surface Temperature

A major accomplishment of the recently completed Tropical Ocean-Global Atmosphere (TOGA) Program was the development of an ocean observing system to support seasonal-to-interannual climate studies. This paper reviews the scientific motivations for the development of that observing system, the technological advances that made it possible, and the scientific advances that resulted from the availability of a significantly expanded observational database. A primary phenomenological focus of TOGA was interannual variability of the coupled ocean-atmosphere system associated with El Nino and the Southern Oscillation (ENSO).Prior to the start of TOGA, our understanding of the physical processes responsible for the ENSO cycle was limited, our ability to monitor variability in the tropical oceans was primitive, and the capability to predict ENSO was nonexistent. TOGA therefore initiated and/or supported efforts to provide real-time measurements of the following key oceanographic variables: surface winds, sea surface temperature, subsurface temperature, sea level and ocean velocity. Specific in situ observational programs developed to provide these data sets included the Tropical Atmosphere-Ocean (TAO) array of moored buoys in the Pacific, a surface drifting buoy program, an island and coastal tide gauge network, and a volunteer observing ship network of expendable bathythermograph measurements. Complementing these in situ efforts were satellite missions which provided near-global coverage of surface winds, sea surface temperature, and sea level. These new TOGA data sets led to fundamental progress in our understanding of the physical processes responsible for ENSO and to the development of coupled ocean-atmosphere models for ENSO prediction.

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The Tropical Ocean‐Global Atmosphere observing system: A decade of progress

Since 1995, a large region of Australia has been
gripped by the most severe drought in living memory, the
so-called ‘‘Big Dry’’. The ramifications for affected regions
are dire, with acute water shortages for rural and
metropolitan areas, record agricultural losses, the dryingout
of two of Australia’s major river systems and farreaching
ecosystem damage. Yet the drought’s origins have
remained elusive. For Southeast Australia, we show here
that the ‘‘Big Dry’’ and other iconic 20th Century droughts,
including the Federation Drought (1895–1902) and World
War II drought (1937–1945), are driven by Indian Ocean
variability, not Pacific Ocean conditions as traditionally
assumed. Specifically, a conspicuous absence of Indian
Ocean temperature conditions conducive to enhanced
tropical moisture transport has deprived southeastern
Australia of its normal rainfall quota. In the case of the
‘‘Big Dry’’, its unprecedented intensity is also related to
recent higher temperatures. Citation: Ummenhofer, C. C.,
M. H. England, P. C. McIntosh, G. A. Meyers, M. J. Pook, J. S.
Risbey, A. S. Gupta, and A. S. Taschetto (2009), What causes
southeast Australia’s worst droughts?,

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1029/2008GL036801

What causes southeast Australia's worst droughts?

The Indian Ocean is unique among the three tropical ocean basins in that it is blocked at 25°N by the Asian landmass. Seasonal heating and cooling of the land sets the stage for dramatic monsoon wind reversals, strong ocean–atmosphere interactions, and intense seasonal rains over the Indian subcontinent, Southeast Asia, East Africa, and Australia. Recurrence of these monsoon rains is critical to agricultural production that supports a third of the world's population. The Indian Ocean also remotely influences the evolution of El Nino–Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), North American weather, and hurricane activity. Despite its importance in the regional and global climate system though, the Indian Ocean is the most poorly observed and least well understood of the three tropical oceans.

This article describes the Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction (RAMA), a new observational network designed to address outstanding scientific questions related to Indian Ocean variability and the monsoons. RAMA is a multinationally supported element of the Indian Ocean Observing System (IndOOS), a combination of complementary satellite and in situ measurement platforms for climate research and forecasting. The article discusses the scientific rationale, design criteria, and implementation of the array. Initial RAMA data are presented to illustrate how they contribute to improved documentation and understanding of phenomena in the region. Applications of the data for societal benefit are also described.

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RAMA: The Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction*

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 Nino–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 Nino or La Nina 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 Nino–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 ...

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The Years of El Niño, La Niña, and Interactions with the Tropical Indian Ocean

The expendable bathythermograph (XBT) line Fremantle-Sunda Strait transects the eastern Indian Ocean between northwestern Australia and Java. It was established in 1983 with low-density sampling and upgraded to a frequently repeated line (>18 times per year) in 1987 to monitor currents. The observations during 1983 to 1994 are described and related to the field of wind stress. Variation of thermal structure shows a rich mixture of annual, semiannual, and interannual timescales. Empirical orthogonal function (EOF) analysis of anomalies of sea surface temperature (SST), dynamic height, and depth of the 20°C isotherm D 20 identifies two distinctive signals. The El Nino - Southern Oscillation (ENSO) signal (EOF 1) appears throughout the region and is strongest off the coast of Australia. A modulation of the annual signal (EOF 2) appears off the coast of Java. EOF 2 has a shorter timescale than the ENSO signal, and its temporal coefficients are correlated to zonal winds over the equatorial Indian Ocean. For both EOFs, anomalously low SST and dynamic height occur at the same time as anomalously shallow D 20 and vice versa for opposite anomalies. The XBT data are used with a climatological temperature-salinity relationship to calculate net, relative (0/400 dbar) geostrophic transports T through the section. For long timescales, T is representative of Indonesian throughflow. The variations associated with ENSO show a maximum during the La Nina of 1988-1989 and minima during the El Ninos of 1986-1987 and 1991-1994. The peak-to-trough amplitude of the ENSO signal is 5 Sv. The ENSO signal in throughflow is discussed in terms of earlier studies. For the shorter timescales, T is representative of currents from the Indian Ocean flowing in and out of the region between northwestern Australia and Indonesia, changing the volume of upper layer water stored there. Associated with EOF 2, a sharp peak in westward transport developed during May to October 1994.

Gary Meyers

Papers

The Tropical Ocean‐Global Atmosphere observing system: A decade of progress

What causes southeast Australia's worst droughts?

RAMA: The Research Moored Array for African–Asian–Australian Monsoon Analysis and Prediction*

The Years of El Niño, La Niña, and Interactions with the Tropical Indian Ocean

Variation of Indonesian throughflow and the El Niño‐Southern Oscillation