ENSO amplitude changes in climate change commitment to atmospheric CO 2 doubling

Transcription

1 GEOPHYSICAL RESEARCH LETTERS, VOL. 33, L13711, doi: /2005gl025653, 2006 ENSO amplitude changes in climate change commitment to atmospheric CO 2 doubling Sang-Wook Yeh, 1 Young-Gyu Park, 1 and Ben P. Kirtman 2,3 Received 9 January 2006; revised 25 May 2006; accepted 5 June 2006; published 15 July [1] Simulations from six climate system models are analyzed to investigate the ENSO amplitude changes in response to a transient increase of the atmospheric CO2. It is found that the ENSO amplitude amplifies as the CO2 concentration increases, then weakens once the concentration is held fixed at 2xCO2 level in the Meteorological Research Institute (MRI) model simulation, but with no significant sensitivity in the other five models. By comparing the MRI and the Geophysical Fluid Dynamical Laboratory (GFDL) models, both of which simulate ENSO reasonably well for the unperturbed climate, we investigate the origin of the sensitivity or lack of sensitivity of the ENSO amplitude to changes in atmospheric CO2 concentration. It is found that in the MRI model the oceanic sensitivity to the equatorial zonal wind stress is significantly decreased in the doubled CO2 period compared to the transient period. Moreover, the vertical stratification at upper levels is weaker in the former period than in the latter period. However, these changes in the GFDL model are negligible. Citation: Yeh, S.-W., Y.-G. Park, and B. P. Kirtman (2006), ENSO amplitude changes in climate change commitment to atmospheric CO 2 doubling, Geophys. Res. Lett., 33, L13711, doi: /2005gl Introduction [2] Whether the El Nino and Southern Oscillation (ENSO) properties would change or not as the atmospheric CO2 concentration becomes higher and subsequently the global climate becomes warmer has been the subject of some debate [Hansen et al., 2000]. As the global temperatures rise, Collins [2000] argued that the amplitude of ENSO would increase, but Knutson et al. [1997] suggested that it would decrease. On the other hand, Meehl et al. [1993] argued that it would not change. The origin of these differences is unknown and remains a difficult problem. Furthermore recent studies have tried to address the question what if atmospheric constituents were suddenly held fixed how much of additional climate changes would be committed [Wigley, 2005]. The intent of this paper is to investigate ENSO variability in transient increase CO2 simulations with six coupled general circulation model (CGCM) experiments. In these experiments, the CO2 concentration increases by 1% per year compound to twice of 1 Korea Ocean Research and Development Institute, Ansan, Korea. 2 Climate Dynamics Program, George Mason University, Fairfax, Virginia, USA. 3 Also at Center for Ocean-Land-Atmosphere Studies, Calverton, Maryland, USA. Copyright 2006 by the American Geophysical Union /06/2005GL the pre-industrial value over a period of 70 years (transient period). Subsequently the concentrations are held fixed at 2xCO2 level for additional 150 years (commitment period). These experiments offer us an opportunity to investigate the sensitivity of ENSO amplitude to transient changes of CO2 as well as to commitment changes at 2xCO2 level. 2. Model [3] We use the selected six CGCM simulations, namely as summarized in Table 1, MRI_CGCM2_3_2a, GFDL_CM2_0, CCCma_CGCM3_1, MPI_ECHAM5, INMCM3_0, NCAR_CCSM3_0, (hereafter, MRI, GFDL, CCCma, MPI, INMCM, and NCAR). The CGCM simulations are made available by the Program for Climate Model Diagnosis and Intercomparison (PCMDI, www-pcmdi.llnl.gov/ipcc/about_ipcc.php). For detail documentation and validation of these models, see the PCMDI web site at 3. Results [4] As shown in Figure 1, the tropical pacific sea surface temperature averaged over 30 N30 S, 120 E270 E increases by about 24 C in response to the CO2 doubling. However, the magnitude is model dependent, indicating uncertainty in climate sensitivity. Tropical Pacific SSTs continue to increase slightly in the commitment period largely due to oceanic thermal inertia in the coupled climate system. In order to analyze the model results, we adopt a concept introduced by Cubasch et al. [2001], transient climate response. It is defined as the difference between a quantity from the transition period, a 20-year average centered at the year of CO2 doubling, and that from the control run. Here, the term control run refers to the entire period simulated by each CGCM with fixed external forcings, such as the atmospheric CO2 is held constant at the pre-industrial level. Another concept used in the analysis is the commitment response, which is defined as the difference between a quantity from the commitment period, which refers to the last 100 years of the integration with a fixed 2xCO2 concentration, and that from the control run. [5] Figures 2a 2f show the transient response of SST anomaly (SSTA) standard deviation, which is equivalent to change in the ENSO amplitude during the transient period relative to that of the control run, for the six different CGCMs. The transient response is markedly different in six CGCM simulations. The standard deviation of the SSTA time series averaged over the NINO3.4 region (5 N 5 S, 170 E 240 E, hereafter, NINO3.4 SST index) which covers the area of the largest SST variability in the MRI model during the transient period is 1.19 C, and that of the control L of5

2 Table 1. CGCM Experiments Used in This Study Model Name (Center a ) Global Oceanic Resolution (Longitude Latitude) Simulation Period Preindustrial Control Experiment 2xCO2 Experiment MRI_CGCM2_3_2a (MRI,Japan) years 220 years GFDL_CM2_0 (NOAA GFDL, USA) years 280 years CCCma_CGCM3_1 (CCCMA, Canadian) years 220 years MPI_ECHAM5 (MPI, Germany) years 220 years INMCM3_0 (INM, Russia) years 220 years NCAR_CCSM3_0 (NCAR, USA) years 220 years a Meteorological Research Institute (MRI), NOAA Geophysical Fluid Dynamics Laboratory (GFDL), Canadian Centre for Climate Modeling and Analysis (CCCMA), Max Planck Institute (MPI), Institute for Numerical Mathematics (INM), National Center for Atmosphere Research (NCAR) run is 0.72 C, showing an increase more than 60% in ENSO amplitude. The ENSO amplitude simulated in the MPI and INMCM model is moderately increased, however, this increase does not pass the chi-square test of 95% significance level. The remaining three models (GFDL, CCCma, NCAR) show negligible change. This result indicates that transient response of ENSO amplitude to increases in greenhouse gases is highly model dependent. [6] We next document how much more ENSO amplitude would be changed during the commitment period. The commitment response of SSTA standard deviation represents change in the ENSO amplitude during the CO2 doubled period relative to that of the control run. In the MRI model, although it is still greater than that of the control run, the commitment response is smaller than that of the transient period as we can see from the comparison of Figures 2a and 3a. In the other five models, there is no additional ENSO amplitude change commitment within the fixed CO2 concentration period. [7] Since it is still under debate on the changes in ENSO amplitude from no anthropogenic forcing to a CO2 increase, we rather focus on ENSO amplitude change from the transient period to the commitment period in the 2xCO2 experiment. For simplicity, we focus on the results from the MRI and GFDL models since these two models produce very different sensitivity of ENSO statistics to a CO2 increase. Note that the ENSO simulated in the control runs of both the MRI and GFDL models was analyzed by Yeh and Kirtman [2006]. They showed that both models capture the observed ENSO variability in terms of the amplitude. [8] We first compute atmospheric and oceanic sensitivities in order to diagnose the strength of the coupled feedbacks during the two periods in each model [Timmermann et al., 1999]. The atmospheric (oceanic) sensitivity is defined as the covariance of the zonal wind stress anomalies averaged over 130 E 210 E, 5 N 5 S and NINO3.4 SST index divided by the variance of the NINO3.4 SST index (the variance of the zonal wind stress anomalies) during the entire analyzed period. Note that the averaged region corresponds to the maximum zonal wind stress variance in the equatorial Pacific in both the MRI and GFDL models. The time evolution of these sensitivity indices in MRI model (Figure 4) show a smaller change in atmospheric sensitivity between the transient period and the commitment period, e.g., the atmospheric sensitivity changes from Pa/ C in the former period to Pa/ C in the latter period. Thus, the atmosphere is likely not to contribute much to the increase in ENSO amplitude. On the other hand, the oceanic sensitivity to changes in the equatorial zonal wind stress show a larger change from 82.4 C/Pa during the transient period to 64.8 C/Pa during the commitment period, indicating a weakening of the sensitivity of NINO3.4 SST variability to changes in the zonal wind stress anomalies from the former to the latter period. An F-test shows that only the changes of the oceanic sensitivity pass the 95% significance level. This significant change in the oceanic sensitivity indicates that oceanic processes must be Figure 1. The time series of tropical Pacific [30 N 30 S, E] mean SST of the control experiment (thin) and the 2CO2 experiment (thick) simulated in the (a) MRI, (b) GFDL, (c) CCCMA, (d) MPI, (e) INMCM and (f) NCAR models for the entire simulation period. Unit is K. 2of5

3 Figure 2. The transient response of SSTA standard deviation simulated in the (a) MRI, (b) GFDL, (c) CCCma, (d) MPI, (e) INMCM and (f) NCAR models. Contour interval is 0.2 C and shading is positive. See the text for the definition of transient response. closely related to the changes in ENSO amplitude between the two periods. However, changes of the both atmospheric and oceanic sensitivities in the GFDL model are very small between these two periods in comparison to those in the MRI model. [9] We first compute atmospheric and oceanic sensitivities in order to diagnose the strength of the coupled feedbacks during the two periods in each model [Timmermann et al., 1999]. The atmospheric sensitivity is defined as the covariance of the zonal wind stress anomalies averaged over 130 E 210 E, 5 N 5 S and NINO3.4 SST index divided Figure 3. (a f) As in Figure 2 except for the commitment response. Contour interval is 0.2 C and shading is positive. Figure 4. The time series of atmospheric (red) and oceanic (black) sensitivity during the transient (thin) and the commitment (thick) period in the MRI model. A 10-year sliding window is used to compute sensitivity. The magnitude of the oceanic sensitivity is indicated on the right of the panel. The straight line indicates ±1.5 standard deviation of the atmospheric (black) and oceanic sensitivity during the commitment period. by the variance of the NINO3.4 SST index during the entire analyzed period. Note that the averaged region corresponds to the maximum zonal wind stress variance in the equatorial Pacific in both the MRI and GFDL models. No significant difference in the atmospheric sensitivity between the transient period ( Pa/ C) and the commitment period ( Pa/ C) is found in the MRI model. Simply put, the atmosphere does not contribute much to the increase in ENSO amplitude. We then computed the oceanic sensitivity to changes in the equatorial zonal wind stress. (The oceanic sensitivity is defined as the covariance of the zonal wind stress anomalies averaged over 130 E 210 E, 5 N 5 S and NINO3.4 SST index divided by the variance of the zonal wind stress anomalies.) In the MRI model we found that the oceanic sensitivity during the transient period (82.4 C/Pa) is larger than that during the commitment period (64.8 C/Pa), indicating that the sensitivity of NINO3.4 SST variability to changes in the zonal wind stress anomalies weakens from the transient period to the commitment period. Note that only the oceanic sensitivity is significantly changed on the F-test of 95% significance level. This significant change in the oceanic sensitivity indicates that oceanic processes must be closely related to the changes in ENSO amplitude between the two periods. We furthermore show the atmospheric and oceanic sensitivity as functions of time using a 10-year sliding window (Figure 4). It is clear that significant changes in the oceanic sensitivity occur between the two periods. On the other hand, the GFDL model does not show significant difference in the sensitivity between the two periods. During the transient period the atmospheric (oceanic) sensitivity is Pa/ C (60.8 C/Pa), and during the commitment period Pa/ C (62.2 C/Pa). [10] Significant changes of the oceanic sensitivity in the MRI model motivate us to investigate changes in the oceanic properties further. It is known that changes in the vertical stratification within the mean thermocline depth are closely related to those in ENSO amplitude (B. Dewitte et al., Rectification of the ENSO variability by interdecadal changes in the equatorial background mean state in a CGCM simulation, submitted to Journal of 3of5

4 Figure 5. Change in the vertical temperature averaged over the equatorial strip (5 N 5 S, 180 E 270 E) during the transient period (TP) and commitment period (CP) from the respective control run in both the MRI and GFDL models. Climate, 2005, hereinafter referred to as Dewitte et al., submitted manuscript, 2005). We therefore compare the changes in the vertical temperature distribution over the equatorial Pacific (average over 5 N 5 S, 180 E 270 E to be more specific) in each period from the control run as shown in Figure 5. Note that the depth of the mean 20 C isotherm, a proxy of the thermocline depth, is around 150m in both the MRI and GFDL models. [11] Both model simulations indicate that the vertical mean ocean temperature in the eastern equatorial Pacific is higher during the transient and commitment periods compared to that in the control run with the greatest warming at surface (Figure 5). In the MRI model, the vertical temperature anomaly profile shows that the warming signal strengthens with depth in the commitment period relative to the transient period, resulting in a weakened vertical stratification for the former period. In the GFDL model, the strength of the warming signal almost does not change with depth, so as to the vertical stratification between these two periods. A result similar to the GFDL model is obtained when the same analysis is applied to CCCma and INMCM models. This suggests that a stronger vertical stratification in the eastern tropical Pacific is associated to higher ENSO amplitude, and vice versa, agreeing with previous studies (e.g., Dewitte et al., submitted manuscript, 2005). 4. Concluding Remarks [12] To understand ENSO variability in climate change commitment to a CO2 increase we compared the 2xCO2 experiments along with their control simulations using preindustrial green house gas concentrations. Two types of results were obtained by six CGCM simulations for future ENSO amplitude changes from gradual increase of CO2 to a fixed CO2 doubled concentration. One is that there would be no significant sensitivity of ENSO amplitude to a CO2 increase. The other is that as the atmospheric CO2 concentration becomes higher or equivalently the global warming progresses the ENSO becomes stronger. Once the CO2 concentration stabilizes at a fixed CO2 doubled period, the amplitude of ENSO, which still is greater than that from the run with the unperturbed CO2 concentration, becomes smaller compared to that from the previous period. The latter scenario has important policy implications in that if we stabilize concentrations of greenhouse cases, strong ENSO events would be less frequent. Moreover, the latter scenario is more seductive than the former because of strong El Niños of 1982/1983 and 1997/1998 during the past few decades in concurrent with the gradual increase of the atmospheric CO2 concentration. In this paper we focused on changes in ENSO amplitude rather than the frequency. As the ENSO amplitude, the power spectra of NINO3 SST indices simulated in the MRI and GFDL models show distinct sensitivity to a CO2 increase. [13] The analysis of atmospheric and oceanic sensitivity indicates that oceanic properties play more important role than the atmosphere in terms of coupled feedback processes in the MRI model. Further analysis indicates that the vertical temperature stratification is weaker during the commitment period than during the transient period as the ENSO amplitude, suggesting close correlation between the two quantities. The time-mean zonal SST gradient or the mean equatorial trade wind has been considered as an important factor controlling the ENSO amplitude. We calculated both quantities during the transient period and during the commitment period in the MRI and GFDL model. It is found that there are no significant differences in the time-mean zonal SST gradient as well as the mean equatorial trade wind between the two periods showing that they are not responsible for the changes in the ENSO amplitude. Investigating the origin of the difference in the vertical thermal structure between the two periods is beyond the scope of this study, but earlier studies suggest that the difference may be associated with subduction processes from the midlatitudes to the tropics as well as the migration of the midlatitude surface water to the tropics [Yang et al., 2005]. Changes in the intensity of Hadley cells or the shallow meridional overturning circulations in the upper Pacific can cause water supply from the midlatitude to the tropics. [14] Acknowledgments. We acknowledge the international modeling groups for providing their data for analysis, the PCMDI for collecting and archiving the model data, the JSC/CLIVAR Working Group on Coupled Modeling (WGCM) and their Coupled Model Intercomparison Project (CMIP) and Climate Simulation Panel for organizing the model data analysis activity, and the IPCC WG1 TSU for technical support. S.-W. Yeh and Y.-G. Park are supported by Korea Ocean Research and Development Institute (PP06401 and PE97005). References Collins, M. (2000), Understanding uncertainties in the response of ENSO to greenhouse warming, Geophys. Res. Lett., 27, Cubasch, U., et al. (2001), Projections of future climate change, in Climate Change 2001: The Scientific Basis, edited by J. T. Houghton et al., pp , Cambridge Univ. Press, New York. Hansen, J., et al. (2000), Climate modeling in the global warming debate, in General Circulation Model Development, edited by D. Randall, pp , Elsevier, New York. 4of5

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