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ENSO (Model Inadequacies) -- Summary
Computer model simulations have given rise to three climate-alarmist claims regarding the influence of global warming on El Niņo/Southern Oscillation or ENSO events: (1) global warming will increase the frequency of ENSO events, (2) global warming will increase the intensity of ENSO events, and (3) weather-related disasters will be exacerbated under El Niņo conditions. We have tested the validity of these assertions elsewhere on our website [see ENSO (Relationship to Extreme Weather) and ENSO (Relationship to Global Warming)], and we have demonstrated that these claims are in conflict with the observational record. In this summary, therefore, we highlight findings that suggest that the virtual world of ENSO, as simulated by state-of-the-art climate models, is at variance with reality in still other ways, beginning with several studies that described the status of the problem at the turn of the last century.

In a comparison of 24 coupled ocean-atmosphere climate models, Latif et al. (2001) reported that "almost all models (even those employing flux corrections) still have problems in simulating the SST [sea surface temperature] climatology." They also noted that "only a few of the coupled models simulate the El Niņo/Southern Oscillation in terms of gross equatorial SST anomalies realistically." And they stated that "no model has been found that simulates realistically all aspects of the interannual SST variability." Consequently, because "changes in sea surface temperature are both the cause and consequence of wind fluctuations," according to Fedorov and Philander (2000), and because these phenomena figure prominently in the El Niņo-La Niņa oscillation, it is not surprising that the latter researchers concluded that climate models near the turn of the century did not do a good job of determining the potential effects of global warming on ENSO.

Plain old human ignorance likely also played a role in those models' failure to simulate ENSO. According to Overpeck and Webb (2000), there was evidence that "ENSO may change in ways that we do not yet understand," which "ways" had clearly not yet been modeled. White et al. (2001), for example, found that "global warming and cooling during earth's internal mode of interannual climate variability [the ENSO cycle] arise from fluctuations in the global hydrological balance, not the global radiation balance," and they noted that these fluctuations are the result of no known forcing of either anthropogenic or extraterrestrial origin, although Cerveny and Shaffer (2001) made a case for a lunar forcing of ENSO activity, which also was not included in any climate model of that time.

Another example of the inability of the most sophisticated of late 20th-century climate models to properly describe El Niņo events was provided by Landsea and Knaff (2000), who employed a simple statistical tool to evaluate the skill of twelve state-of-the-art climate models in real-time predictions of the development of the 1997-98 El Niņo. In doing so, they found that the models exhibited essentially no skill in forecasting this very strong event at lead times ranging from 0 to 8 months. They also determined that no models were able to anticipate even one-half of the actual amplitude of the El Niņo's peak at a medium range lead-time of 6 to 11 months. Hence, they stated that "since no models were able to provide useful predictions at the medium and long ranges, there were no models that provided both useful and skillful forecasts for the entirety of the 1997-98 El Niņo."

Given the inadequacies listed above, it is little wonder that several scientists criticized model simulations of ENSO behavior at the turn of the century, including Walsh and Pittock (1998), who said "there is insufficient confidence in the predictions of current models regarding any changes in ENSO," and Fedorov and Philander (2000), who said that "at this time, it is impossible to decide which, if any, are correct."

So what's happened subsequently? Have things improved any?

Huber and Caballero (2003) introduced their contribution to the subject by stating that "studies of future transient global warming with coupled ocean-atmosphere models find a shift to a more El Niņo-like state," although they also correctly reported that the "permanent El Niņo state" -- which has often been hyped by climate alarmists -- "is by no means uniformly predicted by a majority of models." Hence, to help to resolve this battle of the models, they worked with another model, as well as real-world data pertaining to the Eocene, which past geologic epoch -- having been much warmer than the recent past -- provided, in their words, "a particularly exacting test of the robustness of ENSO." More specifically, they used the Community Climate System Model of the National Center for Atmospheric Research, which was said by them to yield "a faithful reproduction of modern-day ENSO variability," to "simulate the Eocene climate and determine whether the model predicts significant ENSO variability." In addition, they compared the model results against middle Eocene lake-sediment records from two different regions: the Lake Gosiute complex in Wyoming and Eckfield Maar in Germany.

In describing their findings, Huber and Caballero reported that the model simulations showed "little change in ... ENSO, in agreement with proxies [italics added]." They also noted that other studies "indicate an ENSO shutdown as recently as ~6000 years ago, a period only slightly warmer than the present." Hence, they concluded that "this result contrasts with theories linking past and future 'hothouse' climates with a shift toward a permanent El Niņo-like state," which conclusion represents a significant setback to climate alarmists who use this unsubstantiated (and now invalidated) theory to induce unwarranted fear of global warming among the general public.

Three years later, Joseph and Nigam (2006) evaluated several climate models "by examining the extent to which they simulated key features of the leading mode of interannual climate variability: El Nino-Southern Oscillation (ENSO)" -- which they described as "a dominant pattern of ocean-atmosphere variability with substantial global climate impact" -- based on "the Intergovernmental Panel on Climate Change's (IPCC) Fourth Assessment Report (AR4) simulations of twentieth-century climate." This evaluation indicated that different models were found to do well in some respects, but not so well in many others. For example, they found that climate models "are still [italics added] unable [italics added] to simulate many [italics added] features of ENSO variability and [italics added] its circulation and [italics added] hydroclimate teleconnections." In fact, they found that the models had only "begun [italics added] to make inroads [italics added] in simulating key [italics added] features of ENSO variability."

Quoting the two scientists who made the evaluations, this study clearly suggests that "climate system models are not quite ready for making projections of regional-to-continental scale hydroclimate variability and change." Indeed, it makes us wonder if they are ready to make any valid projections about anything, seeing they have fared so poorly with respect to simulating the "key [italics added] features of the leading [italics added] mode of interannual climate variability," which is "a dominant [italics added] pattern of ocean-atmosphere variability with substantial [italics added] global [italics added] climate impact."

Yet climate alarmists continue to say -- or actually scream -- The science is settled!!!!! Nothing, however, could be further from the truth, especially since they base their claims on what are still rather crude climate models. Indeed, as Joseph and Nigam concluded, "predicting regional climate variability/change remains an onerous burden [italics added] on models." And that burden has yet to be lifted.

One year later, for example, L'Ecuyer and Stephens (2007) asked themselves how well state-of-the-art climate models reproduced the workings of real-world energy and water cycles, noting that "our ability to model the climate system and its response to natural and anthropogenic forcings requires [italics added] a faithful representation of the complex interactions that exist between radiation, clouds, and precipitation and their influence on the large-scale energy balance and heat transport in the atmosphere," while further stating that "it is also critical to assess [model] response to shorter-term natural variability in environmental forcings using observations."

In the spirit of this logical philosophy, the two researchers decided to use multi-sensor observations of visible, infrared and microwave radiance obtained from the Tropical Rainfall Measuring Mission satellite for the period running from January 1998 through December 1999, in order to evaluate the sensitivity of atmospheric heating (and the factors that modify it) to changes in east-west SST gradients associated with the strong 1998 El Niņo event in the tropical Pacific, as expressed by the simulations of nine general circulation models of the atmosphere that were utilized in the IPCC's most recent Fourth Assessment Report, which protocol, in their words, "provides a natural example of a short-term climate change scenario in which clouds, precipitation, and regional energy budgets in the east and west Pacific are observed to respond to the eastward migration of warm sea surface temperatures."

So what did the two researchers learn from this exercise?

L'Ecuyer and Stephens report that "a majority of the models examined do not reproduce the apparent westward transport of energy in the equatorial Pacific during the 1998 El Niņo event." They also discovered that "the intermodel variability in the responses of precipitation, total heating, and vertical motion [was] often larger than the intrinsic ENSO signal itself, implying an inherent lack of predictive capability in the ensemble with regard to the response of the mean zonal atmospheric circulation in the tropical Pacific to ENSO." In addition, they found that "many models also misrepresent[ed] the radiative impacts of clouds in both regions [the east and west Pacific], implying errors in total cloudiness, cloud thickness, and the relative frequency of occurrence of high and low clouds." And in light of these much-less-than-adequate findings, they concluded that "deficiencies remain in the representation of relationships between radiation, clouds, and precipitation in current climate models," while further stating that these deficiencies "cannot be ignored when interpreting their predictions of future climate [italics added]."

Paeth et al. (2008) compared 79 coupled ocean-atmosphere climate simulations derived from twelve different state-of-the-art climate models forced by six different IPCC emission scenarios with observational data in order to evaluate how well they reproduced the spatio-temporal characteristics of ENSO over the 20th century, after which they compared the various models' 21st-century simulations of ENSO and the Indian and West African monsoons among themselves. With respect to the 20th century, this work revealed that "all considered climate models draw a reasonable picture of the key features of ENSO." With respect to the 21st century, on the other hand, they say that "the differences between the models are stronger than between the emission scenarios," while "the atmospheric component of ENSO and the West African monsoon are barely affected." Their "overall conclusion" was thus that "we still cannot say much about the future behavior of tropical climate." Indeed, they merely considered their study to be "a benchmark for further investigations with more recent models in order to document a gain in knowledge or a stagnation over the past five years." Hence, we must await a similar analysis to be performed with what Paeth et al. call "the meanwhile available Fourth Assessment Report model data base," in order to see if the modelers have learned anything at all over the past five years, as a five-year period of "stagnation" in the gaining of knowledge is implied by them to be a very real possibility.

Jin et al. (2008) investigated the overall skill of ENSO prediction in retrospective forecasts made with ten different state-of-the-art ocean-atmosphere coupled general circulation models with respect to their ability to "hindcast" real-world observations for the 22 years from 1980 to 2001. In doing so, they found that almost all models have problems simulating the mean equatorial SST and its annual cycle. In fact, they say that "none of the models [they] examined attain good performance in simulating the mean annual cycle of SST, even with the advantage of starting from realistic initial conditions," while noting that "with increasing lead time, this discrepancy gets worse," and stating that "the phase and peak amplitude of westward propagation of the annual cycle in the eastern and central equatorial Pacific are different from those of observed." What is more, they found that "ENSO-neutral years are far worse predicted than growing warm and cold events," and they write that "the skill of forecasts that start in February or May drops faster than that of forecasts that start in August or November," which behavior they and others call "the spring predictability barrier," which designation gives an indication of the difficulty of what they were attempting to do. When all was said and done, therefore, Jin et al. concluded that "accurately predicting the strength and timing of ENSO events continues to be a critical challenge for dynamical models of all levels of complexity."

Most recently, McLean et al. (2009) quantified "the effect of possible ENSO forcing on mean global temperature, both short-term and long-term," using Southern Oscillation Index (SOI) data provided by the Australian Government's Bureau of Meteorology, which parameter is defined as "the standardized anomaly of the seasonal mean sea level pressure difference between Tahiti and Darwin, divided by the standard deviation of the difference and multiplied by 10." The temperature data employed in this endeavor were "the University of Alabama in Huntsville lower-tropospheric (LT) temperature data based on measurements from selected view angles of Microwave Sounding Unit (MSU) channel LT 2" for the period December 1979 to June 2008, supplemented by "balloon-based instrumentation (radiosondes)." More specifically, in the case of the latter data going back in time to 1958, they employed the Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC) product (A) of the U.S. National Climatic Data Center, which represents the atmospheric layer between approximately 1500 and 9000 meters altitude.

When their work was completed, McLean et al. found that "change in SOI accounts for 72% of the variance in GTTA [Global Tropospheric Temperature Anomalies] for the 29-year-long MSU record and 68% of the variance in GTTA for the longer 50-year RATPAC record," as well as "81% of the variance in tropospheric temperature anomalies in the tropics," where they say that ENSO "is known to exercise a particularly strong influence." In addition, they determined that "shifts in temperature are consistent with shifts in the SOI that occur about 7 months earlier." Consequently, the three researchers stated, as their final conclusion, that "natural climate forcing associated with ENSO is a major contributor [italics added] to variability and perhaps recent trends in global temperature [italics added], a relationship that is not included [italics added] in current global climate models." We would only add that if this "major contributor" to global tropospheric temperature variability is truly "not included" in current global climate models, one would certainly have to question the validity of the output of those models, which form the sole basis for the fierce climate-alarmist attack on anthropogenic CO2 emissions.

Last of all, and noting that "coral records closely track tropical Indo-Pacific variability on interannual to decadal timescales," Ault et al. (2009) employed 23 coral δ18O records from the Indian and Pacific Oceans to extend the observational record of decadal climate variability back in time to cover the period from AD 1850-1990. In so doing, they identified "a strong decadal component of climate variability" that "closely matches instrumental results from the 20th century." In addition, they report that the decadal variance they uncovered was much greater between 1850 and 1920 than it was between 1920 and 1990. As for what this observation means, the researchers say they "infer that this decadal signal represents a fundamental timescale of ENSO variability," whose enhanced variance in the early half of the record "remains to be explained." We would only add that it also "remains to be explained" why climate models tend to suggest just the opposite of what actually occurs in the real world, i.e., that warming leads to greater climate variability, when real-world data suggest just the reverse.

In conclusion, there remain multiple unknowns with respect to ENSO and long-term climate change; and many of these unknowns raise serious questions about the ability of current climate models to adequately anticipate the multiplicity of climatic effects that the ongoing rise in the air's CO2 content may or may not impose on earth's atmospheric and oceanic environments.

Ault, T.R., Cole, J.E., Evans, M.N., Barnett, H., Abram, N.J., Tudhope, A.W. and Linsley, B.K. 2009. Intensified decadal variability in tropical climate during the late 19th century. Geophysical Research Letters 36: 10.1029/2008GL036924.

Cerveny, R.S. and Shaffer, J.A. 2001. The moon and El Niņo. Geophysical Research Letters 28: 25-28.

Fedorov, A.V. and Philander, S.G. 2000. Is El Niņo changing? Science 288: 1997-2002.

Huber, M. and Caballero, R. 2003. Eocene El Niņo: Evidence for robust tropical dynamics in the "Hothouse." Science 299: 877-881.

Jin, E.K., Kinter III, J.L., Wang, B., Park, C.-K., Kang, I.-S., Kirtman, B.P., Kug, J.-S., Kumar, A., Luo, J.-J., Schemm, J., Shukla, J. and Yamagata, T. 2008. Current status of ENSO prediction skill in coupled ocean-atmosphere models. Climate Dynamics 31: 647-664.

Joseph, R. and Nigam, S. 2006. ENSO evolution and teleconnections in IPCC's twentieth-century climate simulations: Realistic representation? Journal of Climate 19: 4360-4377.

Kerr, R.A. 1998. Models win big in forecasting El Niņo. Science 280: 522-523.

Landsea, C.W. and Knaff, J.A. 2000. How much skill was there in forecasting the very strong 1997-98 El Niņo? Bulletin of the American Meteorological Society 81: 2107-2119.

Latif, M., Sperber, K., Arblaster, J., Braconnot, P., Chen, D., Colman, A., Cubasch, U., Cooper, C., Delecluse, P., DeWitt, D., Fairhead, L., Flato, G., Hogan, T., Ji, M., Kimoto, M., Kitoh, A., Knutson, T., Le Treut, H., Li, T., Manabe, S., Marti, O., Mechoso, C., Meehl, G., Power, S., Roeckner, E., Sirven, J., Terray, L., Vintzileos, A., Voss, R., Wang, B., Washington, W., Yoshikawa, I., Yu, J. and Zebiak, S. 2001. ENSIP: the El Niņo simulation intercomparison project. Climate Dynamics 18: 255-276.

L'Ecuyer, T.S. and Stephens, G.L. 2007. The tropical atmospheric energy budget from the TRMM perspective. Part II: Evaluating GCM representations of the sensitivity of regional energy and water cycles to the 1998-99 ENSO cycle. Journal of Climate 20: 4548-4571.

McLean, J.D., de Freitas, C.R. and Carter, R.M. 2009. Influence of the Southern Oscillation on tropospheric temperature. Journal of Geophysical Research 114: 10.1029/2008JD011637.

Overpeck, J. and Webb, R. 2000. Nonglacial rapid climate events: Past and future. Proceedings of the National Academy of Sciences USA 97: 1335-1338.

Paeth, H., Scholten, A., Friederichs, P. and Hense, A. 2008. Uncertainties in climate change prediction: El Niņo-Southern Oscillation and monsoons. Global and Planetary Change 60: 265-288.

Walsh, K. and Pittock, A.B. 1998. Potential changes in tropical storms, hurricanes, and extreme rainfall events as a result of climate change. Climatic Change 39: 199-213.

White, W.B., Cayan, D.R., Dettinger, M.D. and Auad, G. 2001. Sources of global warming in upper ocean temperature during El Niņo. Journal of Geophysical Research 106: 4349-4367.

Last updated 11 November 2009