Nonlinear Indian Ocean Dipole feedbacks...positive/negative IOD events, the response of processes...
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FOR FURTHER INFORMATION Figure 1: Multi-model ensemble mean September-November SST skewness over the tropical Indian Ocean for the (a) historical, and (b) RCP8.5 simulations. The box marks the eastern pole of the Dipole Mode Index (90°E-110°E, 10°S-Eq.) REFERENCES ACKNOWLEDGEMENTS Thanks to the modeling groups for making available their model output. This work is supported by the CSIRO Wealth from Oceans Flagship. Nonlinear Indian Ocean Dipole feedbacks The Indian Ocean Dipole (IOD) is positively skewed with the impacts from its positive phase (pIOD) tending to be stronger [1-5] . These impacts range from floods over east Africa to drought over Australia [1] and the cause of this asymmetry is debated [2-5] . Possible causes of IOD skewness are the atmosphere, through a negative sea surface temperature (SST)-cloud-radiation feedback [3,6,7] , or the ocean, which contributes to IOD skewness via a positive Bjerknes feedback and the thermocline [2,4,5] . Using 19 CMIP5 models, we examine nonlinear feedbacks that are associated with the IOD and their response to climate change. It is found that in the ensemble, the asymmetry of the IOD is primarily driven by the SST response to thermocline depth anomalies. A weakening of this process leads to a reduction in IOD skewness under global warming. CSIRO MARINE AND ATMOSPHERIC RESEARCH Benjamin Ng e [email protected] a Private Mail Box 1, Aspendale, VIC 3195, Australia w www.csiro.au/cmar Conclusions • In the multi-model ensemble, the skewness of the IOD is primarily driven by the thermocline feedback. This feedback weakens under global warming, reducing the positive skewness of the IOD. • Nonlinear dynamic heating also contributes to IOD skewness but its role in reducing the asymmetry under increasing greenhouse gases is comparatively small. • The SCR feedback may weakly contribute to skewness but cannot explain the reduction in RCP8.5. Figure 5: a) September-November multi-model ensemble mean regression coefficients for the three processes associated with the Bjerknes feedback. (b) and (c) IODE SST skewness relationship with SST-ZT asymmetry for the historical and RCP8.5 simulations respectively. Benjamin Ng 1, 2 , Wenju Cai 1 , and Kevin Walsh 2 1 CSIRO Marine and Atmospheric Research, Australia; 2 The University of Melbourne, Australia 1. Cai, W. et al. (2013). Nature Geosci. 6, 999-1007, doi: 10.1038/NGEO2009. 2. Cai, W. & Qiu, Y. (2012). J. Climate 26, 2880-2890, doi:10.1175/JCLI-D-12-00483.1. 3. Hong, C.-C. & Li, T. (2010). Geophys. Res. Lett. 37, L11702, doi:10.1029/2010gl043380. 4. Ogata, T. et al. (2012). J. Climate, 26, 2145-2159, doi:10.1175/jcli-d-11-00615.1. 5. Ng, B. et al. (2014). J. Climate, in press, doi: 10.1175/JCLI-D-13-00527.1. 6. Hong, C.-C. et al. (2008a). J. Climate, 21, 4834-4848, doi:10.1175/2008jcli2222.1. 7. Hong, C.-C. et al. (2008b) J. Climate, 21, 4849-4858, doi:10.1175/2008jcli2223.1. Figure 3: (a) September-November NDH response to IODE SST for ECMWF ORA-S3 reanalysis. (b) and (c) Multi-model ensemble linear regression coefficients for the historical and RCP8.5 runs. (b) shows only positive IODs and (c) only negative IOD values. The values in (b) and (c) are calculated for each model using analysis similar to (a). (b) shows how the NDH reinforcement of positive IODs responds to global warming for each model. (c) is similar to (b) but for negative IODs. Skewness The 19 CMIP5 models were selected for their ability to simulate both positive Dipole Mode Index (DMI) skewness and negative IODE SST skewness in the historical period. Under global warming, the multi-model ensemble mean shows a decrease in IODE SST skewness and DMI skewness (Figs. 1, 2). How will they respond to a warming climate? Figure 2: Change in September-November skewness between the historical and RCP8.5 simulations for (a) eastern pole (IODE) SST and (b) the Dipole Mode Index (DMI). As most models are above and below the dashed diagonal line in (a) and (b) respectively, this shows that the IODE SST skewness and DMI skewness is decreasing under climate change. The circle around the ensemble mean (red diamond) represents the standard deviation of the models for the perpendicular distance from the dashed diagonal line. A larger circle means that there is less agreement amongst the models in the distance from the diagonal line. Figure 4 (right): September-November multi-model ensemble mean reg- ression coefficients for the three processes associated with the SCR feedback. Blue (red) markers represent the historical (RCP8.5) linear regression coefficients. Dashes (crosses) represent the regression coefficient for positive (negative) IODs only. Results Fig. 3a shows the response of NDH to IODE SST anomalies in the ECMWF ORA-S3 reanalysis with pIODs reinforced (blue), whilst negative IODs (nIODs) are weakly damped (red). Most of the models suggest that in RCP8.5, the reinforcement of pIODs due to NDH weakens (Fig. 3b). The damping of nIODs does not appear to change due to increasing greenhouse gases (Fig. 3c). The SCR feedback cannot explain the reduced skewness in RCP8.5 as the asymmetry of the cloud response to SST increases, and the damping of nIODs by incoming SWR also increases under global warming (Fig. 4). The Bjerknes feedback is primarily driven by the thermocline influence on SST (i.e. a thermocline feedback). This shows strong asymmetry in the historical multi-model ensemble mean that decreases in RCP8.5 (Fig. 5a). This can explain the historical IOD skewness and its response to climate change. The asymmetry of the thermocline feedback is correlated with IODE SST skewness (Figs. 5b,c). Data and Methodology Monthly atmosphere and ocean data from 19 CMIP5 models are analysed with a focus on the September-November (SON) season. A 9-year running average is removed to exclude multi-decadal variability. Using linear regression and correlation for only positive/negative IOD events, the response of processes associated with three nonlinear feedbacks are examined for the historical and RCP8.5 simulations. As the IOD is dominated by its eastern pole (90°E-110°E, 10°S-Eq.; IODE), we focus on this region. Feedbacks associated with the IOD The three nonlinear feedbacks focused on in this study are: 1) The anomalous advection of anomalous temperature, which is a nonlinear dynamic heating (NDH) process. There are three terms associated with NDH: Anomalous zonal, meridional, and vertical advection of anomalous temperature but only the total NDH is presented here. 2) A positive Bjerknes feedback which involves the SST response to the thermocline (ZT) anomalies, the zonal wind response to SST, and the ZT response to wind. 3) A negative SST-cloud-radiation (SCR) feedback. This involves the incoming short- wave radiation (SWR) response to SST, the response of cloud cover (PR) to SST, and the SST response to incoming SWR.
Transcript of Nonlinear Indian Ocean Dipole feedbacks...positive/negative IOD events, the response of processes...
FOR FURTHER INFORMATION
Figure 1: Multi-model ensemble mean September-November SST skewness over the tropical Indian Ocean for the (a) historical, and (b) RCP8.5 simulations. The box marks the eastern pole of the Dipole Mode Index (90°E-110°E, 10°S-Eq.)
REFERENCES ACKNOWLEDGEMENTS
Thanks to the modeling groups for making available their model output. This work is supported by the CSIRO Wealth from Oceans Flagship.
Nonlinear Indian Ocean Dipole feedbacks
The Indian Ocean Dipole (IOD) is positively skewed with the impacts from its positive phase (pIOD) tending to be stronger[1-5]. These impacts range from floods over east Africa to drought over Australia[1] and the cause of this asymmetry is debated[2-5]. Possible causes of IOD skewness are the atmosphere, through a negative sea surface temperature (SST)-cloud-radiation feedback[3,6,7], or the ocean, which contributes to IOD skewness via a positive Bjerknes feedback and the thermocline[2,4,5]. Using 19 CMIP5 models, we examine nonlinear feedbacks that are associated with the IOD and their response to climate change. It is found that in the ensemble, the asymmetry of the IOD is primarily driven by the SST response to thermocline depth anomalies. A weakening of this process leads to a reduction in IOD skewness under global warming.
CSIRO MARINE AND ATMOSPHERIC RESEARCH
Benjamin Ng
a Private Mail Box 1, Aspendale, VIC 3195, Australia
w www.csiro.au/cmar
Conclusions • In the multi-model ensemble, the skewness of the IOD is primarily driven by the
thermocline feedback. This feedback weakens under global warming, reducing the positive skewness of the IOD.
• Nonlinear dynamic heating also contributes to IOD skewness but its role in reducing the asymmetry under increasing greenhouse gases is comparatively small.
• The SCR feedback may weakly contribute to skewness but cannot explain the reduction in RCP8.5.
Figure 5: a) September-November multi-model ensemble mean regression coefficients for the three processes associated with the Bjerknes feedback. (b) and (c) IODE SST skewness relationship with SST-ZT asymmetry for the historical and RCP8.5 simulations respectively.
Benjamin Ng1, 2, Wenju Cai1, and Kevin Walsh2
1CSIRO Marine and Atmospheric Research, Australia; 2The University of Melbourne, Australia
1. Cai, W. et al. (2013). Nature Geosci. 6, 999-1007, doi: 10.1038/NGEO2009. 2. Cai, W. & Qiu, Y. (2012). J. Climate 26, 2880-2890, doi:10.1175/JCLI-D-12-00483.1. 3. Hong, C.-C. & Li, T. (2010). Geophys. Res. Lett. 37, L11702, doi:10.1029/2010gl043380. 4. Ogata, T. et al. (2012). J. Climate, 26, 2145-2159, doi:10.1175/jcli-d-11-00615.1. 5. Ng, B. et al. (2014). J. Climate, in press, doi: 10.1175/JCLI-D-13-00527.1. 6. Hong, C.-C. et al. (2008a). J. Climate, 21, 4834-4848, doi:10.1175/2008jcli2222.1. 7. Hong, C.-C. et al. (2008b) J. Climate, 21, 4849-4858, doi:10.1175/2008jcli2223.1.
Figure 3: (a) September-November NDH response to IODE SST for ECMWF ORA-S3 reanalysis. (b) and (c) Multi-model ensemble linear regression coefficients for the historical and RCP8.5 runs. (b) shows only positive IODs and (c) only negative IOD values. The values in (b) and (c) are calculated for each model using analysis similar to (a). (b) shows how the NDH reinforcement of positive IODs responds to global warming for each model. (c) is similar to (b) but for negative IODs.
Skewness The 19 CMIP5 models were selected for their ability to simulate both positive Dipole Mode Index (DMI) skewness and negative IODE SST skewness in the historical period. Under global warming, the multi-model ensemble mean shows a decrease in IODE SST skewness and DMI skewness (Figs. 1, 2).
How will they respond to a warming climate?
Figure 2: Change in September-November skewness between the historical and RCP8.5 simulations for (a) eastern pole (IODE) SST and (b) the Dipole Mode Index (DMI). As most models are above and below the dashed diagonal line in (a) and (b) respectively, this shows that the IODE SST skewness and DMI skewness is decreasing under climate change. The circle around the ensemble mean (red diamond) represents the standard deviation of the models for the perpendicular distance from the dashed diagonal line. A larger circle means that there is less agreement amongst the models in the distance from the diagonal line.
Figure 4 (right): September-November multi-model ensemble mean reg-ression coefficients for the three processes associated with the SCR feedback. Blue (red) markers represent the historical (RCP8.5) linear regression coefficients. Dashes (crosses) represent the regression coefficient for positive (negative) IODs only.
Results Fig. 3a shows the response of NDH to IODE SST anomalies in the ECMWF ORA-S3 reanalysis with pIODs reinforced (blue), whilst negative IODs (nIODs) are weakly damped (red). Most of the models suggest that in RCP8.5, the reinforcement of pIODs due to NDH weakens (Fig. 3b). The damping of nIODs does not appear to change due to increasing greenhouse gases (Fig. 3c).
The SCR feedback cannot explain the reduced skewness in RCP8.5 as the asymmetry of the cloud response to SST increases, and the damping of nIODs by incoming SWR also increases under global warming (Fig. 4).
The Bjerknes feedback is primarily driven by the thermocline influence on SST (i.e. a thermocline feedback). This shows strong asymmetry in the historical multi-model ensemble mean that decreases in RCP8.5 (Fig. 5a). This can explain the historical IOD skewness and its response to climate change. The asymmetry of the thermocline feedback is correlated with IODE SST skewness (Figs. 5b,c).
Data and Methodology Monthly atmosphere and ocean data from 19 CMIP5 models are analysed with a focus on the September-November (SON) season. A 9-year running average is removed to exclude multi-decadal variability. Using linear regression and correlation for only positive/negative IOD events, the response of processes associated with three nonlinear feedbacks are examined for the historical and RCP8.5 simulations. As the IOD is dominated by its eastern pole (90°E-110°E, 10°S-Eq.; IODE), we focus on this region.
Feedbacks associated with the IOD The three nonlinear feedbacks focused on in this study are:
1) The anomalous advection of anomalous temperature, which is a nonlinear dynamic heating (NDH) process. There are three terms associated with NDH: Anomalous zonal, meridional, and vertical advection of anomalous temperature but only the total NDH is presented here.
2) A positive Bjerknes feedback which involves the SST response to the thermocline (ZT) anomalies, the zonal wind response to SST, and the ZT response to wind.
3) A negative SST-cloud-radiation (SCR) feedback. This involves the incoming short-wave radiation (SWR) response to SST, the response of cloud cover (PR) to SST, and the SST response to incoming SWR.
