One could contemplate synthesizing all measurements of rain into a global ENSO precipitation index, which would be calculated by adding the rainfall anomalies in all areas which experience excess rain during warm ENSO phases and subtracting the anomaly in those areas which experience a deficit. The problem with such a proposition is that regions which experience excess rain during warm phases do not always experience rain deficits during cold phases and vice versa. In other words, the maps of the rainfall anomalies during warm and cold ENSO phases do not appear to be mirror images of one another.

Indices are used in many walks of life to try to describe complex fluctuations and distill them into a single number which can be tracked or compared for different occurences of the same phenomenon. Examples include the stock market indices, the earthquake magnitude indices, human burn severity indices, and the Gross Domestic Product. Such indices are typically obtained by adding up several positive quantities, to end up with a strictly positive number. In the context of climate change, to quantify a departure from normal conditions, one must first subtract from each quantity its long-term average, to obtain the "anomaly". Furthermore, some of the "weights" used in the index may well be negative, to reflect the fact that under certain circumstances some quantities may tend to decrease systematically whenever others increase.

A more serious problem with the proposition of subtracting deficit areas from excess areas is that, by subjectively selecting only those areas which have a consistently sustained correlation with ENSO, one would be ignoring those regions which are less significantly affected by the phenomenon, and which could be responsible for a large proportion of the global rainfall variability. The accumulated evidence still begs the question: how can one objectively quantify the importance of ENSO in the global (land and ocean) variability of surface rainfall? Indeed, without any a priori awareness of ENSO, is it possible to examine the rainfall remote sensing record and condense its overall variability into a simple metric, then look for those physical processes which best correlated with this metric?

Until quite recently this question had remained unaddressed largely because the systems required to monitor precipitation over the oceans simply did not exist. This dire situation changed dramatically in the 1980's with the availability of data from low-earth-orbiting multiple-frequency microwave radiometers such as the Special Sensor Microwave Imagers (SSMI), and from geostationary visible/infrared (Vis/IR) imagers. The latter are useful in the sense that they can gauge the height of the cloud tops (and hence, at least in convective systems, the depth of the clouds, and hence, by a further stretch of the imagination, the amount of rain which these clouds are producing), with frequent updates. With less frequent updates, the low-earth-orbit microwave radiometers provide a handful of radiances in which the surface emissivity effects and the competition between the absorption/emission and the scattering from rain and ice can be approximately sorted out to produce an estimate of the rainfall amount at rather poor resolution. An "ENSO precipitation index" (ESPI) is currently calculated from the merged Vis/IR and SSMI measurements, essentially by subtracting the precpitation anomaly over the region around the Maritime Continent (10^oS to 10^oN by 90^oE to 150^oW) from that over the eastern Pacific (10^oS to 10^oN by 160^oE to 100^oW) -- the exact boundaries of the boxes are "dynamically" calculated in real-time to maximize the contrast. By design, ESPI correlates very well with the "El Niño" and "Southern Oscillation" indices. But it leaves unanswered the question of characterizing the global variability of rain objectively, let alone identifying its physical sources.

Further progress was limited by the shortcomings of the sources of the data, namely the IR and SSMI estimates. The former relies on the very tenuous correlation between cloud top heights and surface rain. While SSMI is more directly sensitive to the rain itself, the poor resolution of the instrument forces one to make homogeneity assumptions about the precipitation which are likely to introduce large biases in the estimates (because the average rain quantities one would like to estimate are related in a very non-linear way to the average radiances one measures). Most important, over land, the relation between either the IR or the microwave radiances and the surface precipitation is very weak. It is precisely to remedy the shortcomings of these systems that the Tropical Rainfall Measuring Mission was conceived. In addition to having a very low resolution-enhancing orbit (originally 350 km), TRMM's advantage is that it carries the first spaceborne precipitation-profiling radar in addition to a nine-channel microwave radiometer and a visible/infrared imager. Although the clutter from the overwhelming surface echo severely limits the swath of the PR and, therefore, limits its ability to sample the precipitation as frequently as a radiometer, the vertical detail with which it can probe the atmosphere, its insensitivity to the characteristics of the surface, and its high horizontal resolution make it an ideal instrument with which to "calibrate" the rain retrievals of the radiometer within the narrow common swath of the radar, and subsequently carry this calibration over to the TMI-only retrievals over the wide swath of the radiometer.

Of particular interest are the surface rainfall estimates produced by the "TRMM-combined" radar/radiometer algorithm from December 1997 until February 2003. These estimates are available in the form of monthly rain maps over the region between 40^oS and 40^oN at a resolution of 5^o by 5^o. The following image synthesizes the information in these maps objectively:

The map shows the "first principal component" of the surface rain, essentially an index of the month-to-month change in rainfall. Areas in red experience an increase whenever areas in blue experience a decrease in rainfall, and vice versa. Areas with the weakest changes are in green. Not surprisingly, this intra-annual rainfall change index is evidently obtained essentially by subtracting the pixels with a November-to-April rain peak from the ones with a May-to-October rain peak, reflecting the simple fact that the seasons are indeed the major driver of the change in rainfall patterns from month to month.

Much more interesting is the characterization of the variation of the monthly rain anomaly. Using the 60 months' worth of TRMM-combined data from January 1998 to December 2002 as the baseline to establish the monthly mean for each pixel, we performed a principal component analysis on the monthly TRMM-combined rain anomaly. The coefficients of the first principal component, the "anomaly index", are shown in the following figure:

(Clik on the image for a movie of the anomaly from December 97 to February 03)

Areas in blue receive more rain whenever areas in red experience a shortfall, and vice versa. The year-on-year change is smallest in the green areas. One readily notes that the variability of the rainfall anomaly is strongly sensitive to the precipitation over the oceans, in rather sharp contrast with the variability of the rainfall itself which is more sensitive to continental rain. This is due to the more rapid and pronounced response of the continents to summer heating (resp. winter cooling), which enhances (resp. inhibits) the rain-producing convection. In contrast, the tropical Western Pacific and the equatorial Eastern Pacific have large coefficients in the first principal component of the rain anomaly. This is undoubtedly due in no small part to the fact that the TRMM record starts in the middle of one of the strongest ENSO warm phases of the twentieth century. However, the coefficients over the various pixels within the Pacific are not entirely consistent with the ENSO pattern. For example, the coefficients over Micronesia/western-Pacific region are similar to those over Indonesia/New-Guinea, yet Ropelewski and Halpert have shown that while the rain anomalies during low-index ENSO phases are similar in both regions, they differ during high index phases. More striking, the coefficients over the Eastern and Western Indian Ocean appear to highlight the recently identified Indian Ocean Dipole oscillation, a phenomenon that has not been directly tied to ENSO.

In order to understand better how the rain anomaly correlates with the underlying physical mechanisms, one must look for ways to extend the TRMM record in general, and the anomaly index in particular, back beyond the five years' worth of TRMM data. This was achieved by making use of a global dataset of monthly surface rain accumulations from over 20,000 surface stations. We started by distinguishing those TRMM pixels whose coefficients in the first three anomaly principal components are large in absolute value, and for which there exists at least one surface station in the surface station database with a reasonably complete observational record reaching back at least to the 1950's or earlier. The fact that the surface station accumulations are not perfectly representative of the amounts TRMM would have estimated over the corresponding pixel was accounted for by applying a local seasonal adjustment to the surface-station measurements, and a "proxy" for the rain anomaly index was obtained by using the adjusted surface-station data instead of the TRMM measurements before 1998. The results are summarized in the following figure:

The "Nino-3" index is shown in black, the SOI index in green, and our rain anomaly index in blue.

The results appeared in the September 2004 issue of the Journal of Geophysics Research.
A copy of the article can be downloaded here.

---

Here is a link to the relevant NASA press release.
Click here for links to various TRMM-related web sites.

---