By looking back, scientists see a bright future for climate change?

1. By looking back, scientists see a bright future for climate change? Public release date: 14-Apr-2004 [ Print Article | E-mail Article | Close Window ] Contact: Mary Tobin mtobin@ldeo.columbia.edu 845-365-8607 The Earth Institute at Columbia University By looking back, scientists see a bright future for climate change New climate model predicted every major change For scientists studying climate change, the past is often a key to understanding the future. Dake Chen at Columbia University's Lamont-Doherty Earth Observatory recently used more than a century of climate data to successfully test an improved model of ENSO, the El-Niño/Southern Oscillation that scientists believe is behind climate change in many parts of the world. Chen and his colleagues report in the April 15 issue of the journal Nature that an improved climate model, known as LDEO5, for the first time predicted every major change in the temperature of the tropical Pacific Ocean over the past 150 years with up to two years of advance notice. In addition, their results suggest that ENSO is largely driven by internal relationships between ocean temperature and tropical winds rather than more unpredictable factors such as externally driven wind bursts, making the future of long-term climate prediction much more optimistic.

2. Predictability of El Nino: Recent Developments Francis P. A. Thanks to Prof. B. N. Goswami CENTER FOR ATMOSPHERIC AND OCEANIC SCIENCE Indian Institute of Science Bangalore-12, India

3. Understanding El Nino: Strong winds during La Nina pile up the warm water in the west, causing the thermocline to slope downwards to the west and exposing cold water to the surface in the east. Relaxed winds during El Nino permit the warm water to flow back eastward so that the thermocline becomes more horizontal. The trade wind fluctuations are both the cause and consequence of the sea surface temperature variations. The ocean-atmosphere interactions permit a variety of natural modes of oscillation. The phenomena observed in the tropical Pacific presumably correspond to one of or some combination of those modes.

4. TOGA Array

5. Normalized time series of SST anomaly over Nino 3 area (blue) and Southern Oscillation Index (SOI, red). Nino 3 region : 150W-90W-5S-5N

6. Understanding El Nino: Despite these considerable observational and theoretical advances over the past few decades, many issues are still being debated, and each El Nino still brings surprises. The prolonged persistence of warm conditions in the early 1990s was as unexpected as the exceptional intensity of El Nino in 1982 and again in 1997; nobody knew what to expect of El Nino in 2002. Following are the major issues related to ENSO: Why is the observed ENSO so irregular? How predictable is El Nino? What are the reasons for the decadal modulation of El Nino, the apparent change in its properties that occurred in the 1970s? How will global warming influence El Nino? Various investigators have different views on these issues. In this review, we focus on the predictability of ENSO : Why are different El Nino episodes so different, and so difficult to predict?

7. Understanding El Nino: “The current disagreements concerning El Nino have their origins in the recent history of research on the phenomenon. When the available datasets were scant, during the 1960s and 1970s, we regarded El Nino as a departure from normal conditions, as the response of the coupled ocean-atmosphere to certain triggers. This implied that measurements should be made during the abnormal or anomalous periods when El Nino happens to appear in the Pacific, the way meteorologists make special efforts to observe hurricanes when those cyclones appear. Wyrtki (1975) tentatively identified a sudden collapse of the trade winds as the most important trigger, but intense El Nino of 1982 was not preceded by such a collapse. The availability of more detailed wind datasets in the 1980s led to the identification of sporadic westerly wind bursts that last for a few weeks along the equator in the neighborhood of the dateline as important triggers of El Nino. Such bursts contributed to the development of El Nino in 1997, but similar bursts on other occasions failed to have a similar effect. There must be more to the story than westerly wind bursts. The availability, in the 1980s, of relatively long time-series similar to those in Figure 2 prompted some investigators to question whether each El Nino is an independent, transient phenomenon in response to a trigger, with a definite beginning followed by growth and, finally, decay. Instead, they adopted a radically different perspective and regarded El Nino as part of a continual oscillation without a beginning or end. El Nino therefore has a complement, usually referred to as La Nina. Conditions in the Pacific correspond either to the one or the other; very seldom are conditions normal, as is evident in Figure 2. The main challenge is not to identify triggers but to explain the properties of the oscillation, its period, spatial structure, etc. This new perspective implied a different measurement strategy. Sporadic measurements when the phenomenon happens to appear gave way to arrays of instruments, for example, the one in Figure 1 that monitors the tropical Pacific continuously.” Philander and Fedorov, Is El Nino Sporadic or cyclic? Ann. Rev. Earth Planet. Sci. (2003)

8. Understanding El Nino: It appears that ENSO has a periodicity of 3-7 years Intensity of different events are very different There is longer time scale (of decades or more) variations also

9. Now let us look at the irregularities in ENSO episodes with the help of an idealized coupled ocean-atmosphere model (Neelin 1990) without weather or random atmospheric disturbances-essentially to analyze the effect of ocean atmospheric coupling. a) Weak coupling: ENSO is a damped oscillation. Initial westerly wind burst lasting one month generated ENSO conditions; but decayed fast. b) Moderate coupling: ENSO becomes a self sustaining oscillation. c) Intense coupling : ENSO grows to such a large amplitude that secondary instabilities also appear. Which panel corresponds to reality?

10. Which panel corresponds to reality? Some investigators favor the right-hand panel. In that case, the Southern Oscillation is similar to weather in having as its source of irregularity not externally imposed disturbances, but internal nonlinear processes. However, there is persuasive evidence that random atmospheric disturbances, for example, westerly wind bursts along the equator in the neighborhood of the dateline, influence the development of El Nino on some occasions, as happened in 1997 (McPhaden & Yu 1999). Because of such observations, some scientists believe that the Southern Oscillation is damped and is sustained by noise so that the left-hand panel is the realistic one. If that were the case, then each El Nino would be independent of the next and would depend, for its initiation, on noise. It is then difficult to explain why very similar westerly wind bursts lead to the development of El Nino on some occasions but not others, and why the Southern Oscillation has a distinctive timescale of a few years. A compromise that accommodates the various points of view posits that the Southern Oscillation is weakly damped and is sustained by random disturbances; the panel in Figure that corresponds to reality is then midway between the one on the left and the central one.

11. Predictability of El Nino: The Southern Oscillation involves phenomena with two timescales: an oscillation with a period of several years and rapid developments over a period of weeks or months in response to random disturbances. Hence, it should be possible to anticipate certain aspects of the Southern Oscillation far in advance. For example, since the 1980s, the intensity of El Nino has varied enormously from one event to the next, but the phenomenon has nonetheless appeared with remarkable regularity every five years, in 1982, 1987, 1992, 1997, and 2002. A useful analogy for the Southern Oscillation is a slightly damped, swinging pendulum sustained by modest blows at random times. In the absence of noise, El Nino would be perfectly predictable because the Southern Oscillation would be perfectly periodic while its amplitude slowly attenuates. Noise sustains the oscillation and makes it irregular. The initial conditions do matter because they describe the phase of the Southern Oscillation and strongly influence the impact of random disturbances. For example, a burst of westerly winds when the oscillation enters its El Nino phase is very different from the impact of the same winds when the oscillation enters its La Nina phase. L Fedorov, Harper, Philander, Winter, and Wittenberg, BAMS (2003)

12. Predictability of El Nino: The above discussion thus emphasize the importance of atmospheric noise, particularly the so-called westerly wind bursts in the western equatorial Pacific. In such a scenario, ENSO is a damped oscillation sustained by stochastic forcing, and its predictability is more limited by noise than by initial errors. This implies that most El Nino events are essentially unpredictable at long lead times, because their development is often accompanied by high-frequency forcing. On the other hand, several other studies such as Cane et al (1986), Cane and Zebiak (1988), Barnet (1984), Barnet et al (1988), Graham et al (1987) attempted to model past El Nino events and the results were encouraging that the El Nino events could have been predicted well in advance indicating that the so called 'westerly wind bursts' has only secondary importance.

13. Predictability of El Nino: Inspired from these efforts, Goswami and Shukla (1991) tried to determine the limits on the predictability of the coupled ocean-atmosphere system using the coupled model of Zebiak and Cane (1987) for a period of 1970-1988. In this model the initial conditions for ocean are model simulations forced by observed wind stress. Major results: 1. The predictions starting in the boreal winter have better skill than those starting in spring or summer. 2. Limiting factor of predictability is the growth of small errors in the coupled model which is governed by processes with two different time scales- the faster time scale process has an error doubling time of about 5 months while the slower time scale has a doubling time of about 15 months. 3. It is suggested that the existence of slow growing process gives some hope for predictability and its success will depend on the ability to identify initial conditions that are insensitive to the faster growing process. 4. It is proposed that the fast error growth result from the coupled instability in the ZC model, while the slow error growth is associated with the low frequency mode of the system.

14. Predictability of El Nino: Hence we have two different views about the limiting factors of potential predictability of El Nino: 1) The high frequency westerly wind bursts – Which are highly unpredictable 2) Growth of initial errors – Which can be overcome by identifying initial conditions that are insensitive to faster growing processes In the light of this background knowledge we look in to the 'retrospective model forecasts' by Chen et al., 2004.

15. Retrospective predictions of El Nino and La Nina in the past 148 years (6month lead)- Chen et al 2004. Model Kaplan SST Most of the major El Nino events are predicted Spatial structure of the events are also well predicted.

16. Correlation of predicted anomalies with observed anomalies (as function of lead time) for consecutive 20yr periods 1. Skill is relatively high for the period 1876-1895 and 1976-1995. 2. Skill is relatively poor for the period 1916-1955. 3. The high rms error and low correlations during 1916-1955 is attributed to relatively less number of ENSO events during this period.

17. Long-lead forecasts for six of the largest warm episodes In all cases, the model was able to predict the observed strong El Ninos two years in advance, though some errors exist in the forecasted onset and magnitude of these events. The implication is that the evolution of major ENSO events is largely determined by oceanic initial conditions, and that the effect of subsequent atmospheric noise is generally secondary. It is interesting to note that the model predicts the strong El Nino events in the late nineteenth century, which are notorious for their global impact. These events have been implicated 22 in the deaths of tens of millions of people in India, China, Ethiopia, Northeast Brazil and elsewhere. (The disastrous failure of the Indian monsoon in 1877 prompted the establishment of the Observatory in India, later the venue for the work of Walker that forms the foundation of modern understanding of ENSO.) The predictions shown here are, to our knowledge, the first successful retrospective forecasts of these significant historic events. Observed Nino 3.4 15 Months 21 Months 18 Months 24 Months

18. Summary: 1. It is believed that the limiting factors of potential predictability of El Nino are the high frequency westerly wind bursts – Which are highly unpredictable and growth of initial errors – which can be overcome by identifying initial conditions that are insensitive to faster growing processes. 2. Chen et al show that the predictions depend more on initial conditions that determine the phase of ENSO, than on unpredictable atmospheric noise. 3. Although westerly wind bursts do affect the exact onset time and perhaps the amplitude of El Nino, the gross features of ENSO seem to be coded in the large-scale dynamic state. 4. These results favour the interpretation that the enhanced wind burst activity in the boreal spring preceding large El Nino events is consequence of those ongoing events rather than a cause. 6. A practical consequence of our results is a more optimistic view of the possibility of skillful long-lead forecasts of El Nino.

19. Methods The model used in this study, called LDEO5, is the latest version of an intermediate ocean atmosphere coupled model widely applied to ENSO investigation and prediction. It differs from its predecessor LDEO4 in its improved ability to assimilate SST data, which is crucial here as only reconstructed SST data sets are available for such a long-term experiment. In LDEO5, an assimilated SST field not only directly affects the surface wind field as in LDEO4, but also has a persistent effect on the coupled system. The improvement was achieved by including a bias correction term in the model SST equation that statistically corrects for model deficiencies in parameterizing subsurface temperature and surface heat fluxes. The correction was estimated inversely by fitting model SST tendency to observation using data from 1980-2000, and a regression relating this term to the multivariate model state was obtained in the space of empirical orthogonal functions. Based on this regression, an interactive correction of SST was then implemented in the model. The internal variability of LDEO5 is similar to that of LDEO4; it generates a self-sustaining oscillation with periods of 3-5 yr and amplitudes close to those of observed El Nino's. However, the new version has a higher predictive skill when multiple data sets- sea level, winds, SST are used for initialization, and its skill decreases only slightly when assimilating only SST data. We have to rely on SST data here because tropical Pacific sea level observations are virtually non-existent before 1970, and historic wind information is sparse and poorly calibrated.

20. Note that in the coupled initialization procedure of the LDEO forecast system, assimilated SST data are not simply putting a constraint on the ocean model with SST observations; they translate to surface wind field and subsurface ocean memory. The SST data set used in this study is the reconstructed analysis for the extended period of 1856-2003. Initialized with this monthly analysis, a forecast with lead times up to 24 months was made from each month of the 148-yr period. The same data set was also used to verify the model predictions.