 The Institute of Atmospheric Physics, Chinese Academy of Sciences appreciates the opportunity to be here today. My topic is influence of the Southern Hemisphere Annual Mode on East Asian climate, thanks to my co-authors. The presentation is divided into five parts. Firstly, I'd like to briefly touch on the background. The Southern Hemisphere Annual Mode, also known as the Antarctic Oscillation, is the principal mode of atmospheric circulation in Southern Hemisphere ectropics. The sand activity reflects a seasonal variation between air pressure in middle and high latitude. A positive phase of the sand means the polar law is strengthened, accompanied by a powered shift of the vice-legit. This figure shows the power spectrum density of the sand index. We can see it has several significant peaks, including intracisional inter-annual and decadal timescale. On intracisional timescale, it was found during the phase three of the MGO, the sand index shows significant negative anomalies, describing on average anomalous high pressure over on tactic and low pressure over the surrounding oceans. On decadal and multi-decadal timescale, the sand has co-variability with the underlying Southern Ocean, and a recent study found that the wide-fair activity in Southern South Africa is related to the sand activity on multi-decadal timescale. And on climate change timescale, during the half-past century, the sand index shows a significant positive trend, which is mainly caused by the decreasing of ozone over on tactic and also related to the increasing CO2 and other greenhouse gases. The changes in the Southern annual mode is the driver of Southern Hemisphere climate change. Today, my topic is mainly concentrated on intracisional timescale, and I try to investigate the sand climate influence. Previous studies found that the Boreal Spring Sand could influence East Asian summer monsoon, and the Boreal Autumn Sand could influence East Asian winter monsoon. All the seasons in this presentation indicate Boreal Seasons. This knowledge has been employed in seasonal prediction. In specific about influence of the sand on Boreal summer climate, it was found that a positive phase of Boreal Spring Sand tends to be followed by a weaker East Asian summer monsoon. The mechanism is related to the northward propagation of Indian Ocean sea-subtemperature anomalies caused by the sand. And on the aspect of influence of the sand on Boreal winter climate, the East Asian winter monsoon is also modulated by the sand. The mechanism is related to responses of the Mardinose Collation anomalies to sand-related sea-subtemperature anomalies. And then we will ask whether there are some linked lead correlation between the Boreal Winter Sand and the Spring-Winter Fall, if so, what is the mechanism? And the Seasons winter denotes December to February, and spring means March to May. So let's move on to the next part, linkage between the Boreal Winter Sand and the Spring-Winter Fall. A little bit about the climatology. South China is located in the northern hemisphere subtropics. It is influenced by the descending branch of the Hadelacea and Philaeacea. And the spring precipitation over South China accounts for about 30% of annual precipitation. And South China is a key region for the growth of a variety of crops. Spring-winter anomalies have great impact on social economy. This figure shows the correlation between the Boreal Winter Sand and the Spring-Winter Fall. As we can see, South China is a region with significant negative correlation. That is to say, when a positive winter sand is usually followed by light spring-winter fall, conversely, a negative winter sand is followed by more spring-winter fall. And the partial correlation analysis is carried out to exclude the Ancel signal. The negative correlation in South China is still significant after removal Ancel. To quantify the South China spring-winter fall variability, we define a South China rainfall index, which is a normalized time series of arrowed spring rainfall of 11 stations in the red box. So when do you remove Ancel, do you remove it in the previous winter? Previous winter and spring, both the Ancel signal in winter and spring are removed. And let's have a look at the preceding circulation anomalies associated with South China rainfall index. This figure shows the composite difference in winter signal pressure and 850 million bar horizontal wind between high and low precipitation. As you can see, when there is mild spring rainfall over South China, a negative sand exists in preceding winter. However, when there is light spring rainfall over South China, a positive sand exists in the preceding winter. This result verifies the negative correlation between winter sand and spring rainfall. The question comes to mind is what is the mechanism for this leading correlation? This is an interesting question because the sand signal lead rainfall anomaly one season. Consider the persistence of atmospheric signal is usually not long enough to maintain one season. We pay our attention to the underlying surface, especially the ocean. So I'd like to move to the next part, mechanism for the cross seasonal influence. As mentioned at the beginning, this figure shows the lead like relation between the winter sand and the surface wind at different mouses. As mentioned at the beginning, a positive phase of the sand is filtered by stronger vestilates in high latitudes, but weaker vestilates in middle latitudes. The changes in surface wind would affect sea surface temperature through modulate the ocean head budget. As we can say, in high latitude, sea surface temperature is cooler due to the increased wind speed. In middle latitude, sea surface temperature is warmer due to decreased wind speed. In addition, this sea surface temperature anomalies could persist to the following spring due to large heat capacity of the ocean. And the composite analysis gives similar result. The dipole like sea surface temperature anomalies is significant both in winter and in spring. Where this dipole like sea surface temperature anomalies is called the southern ocean dipole, that is the SOD, to quantify the southern ocean dipole in variability, we define in DAX, which is a normalized difference of zonamine sea surface temperature between the middle and the high latitudes. And the SOD is a medium to stall the winter sand signal and persist to the following spring. As you remember, a positive winter sand corresponds to a positive SOD, and a negative sand corresponds to a negative SOD. So whether the SOD is the bridge linking the winter sand and the spring precipitation, to answer this, we calculate the circulation anomalies associated with the SOD. If you look at the figures, you can say when the SOD is in a positive phase, the subtropical high is weaker and retreat eastward. There is no sea wind anomalies over South China, which lead to less water vapor transport and less precipitation. On the upside, when the SOD is in a negative phase, the circulation anomalies are upside, which lead to more precipitation over South China. We next use the Camps Ray experiment to verify the above result. Two sensitivity experiments correspond to the positive SOD and negative SOD phases. This figure shows the vertical circulation anomalies obtained by experiment A manners the control run. In the northern hemisphere subtropics, there is anomalous descending motion in this region. And meanwhile, in the low troposphere, there is anomalous north-east wind, stronger divergence, and stronger sinking motion. This result are consistent with the statistic analysis from the real analysis data. By now, we generally answered our first question, that is, correlation between boreal winter sand and the spring rainfall over South China. This lead to my next question, because it is well known that the zonal mean precipitation is closely related to the zonal mean meridian circulation. Consider the influence of the SOD on zonal mean meridian circulation. It may also play a role in modulating zonal mean rainfall. So next part, winter sand and spring zonal mean precipitation. This figure shows the partial regression of spring mass refunction on the preceding winter sand after removal and so signal. As we can say, anomalous clockwise and counterclockwise circulations occur alertively from the southern hemisphere, middle latitudes to northern hemisphere subtropics. In specific, a clockwise cell exists in the southern hemisphere, high latitude. And a counterclockwise cell exists in the middle latitudes. And this anomalous circulation also exists in the tropics and northern hemisphere subtropics. Then we will ask whether this regression pattern is just a relax of climate noises, or it is really a significant pattern. To answer this, we need to evaluate the significance of the regression pattern as a whole. A significant test based on 1,000 Monte Carlo simulations is carried out. Details about this method can be found in this paper. Generally, the percentage of area that is significant at 19% confidence level is 22.7%. In the 1,000 Monte Carlo simulations, about 92% members have less significant area than 32.7%. So therefore, the regression pattern as a whole is significant at 92% confidence level. And it is interesting to note that the meridional circulation anomalies related to the SAM is different from those related to the ANSO. And to further show the importance of the SOD in the cross-signal influence of winter SAM on spring circulation, we do partial regression of spring mass refunction on the winter SAM after we move the SOD signal. It is very clear that after we move the SOD signal, no significant relationship exists. This tells us the importance of the SOD in this cross-signal influence. So what is the mechanism for the SOD to influence meridional circulation? We know the SOD activity is accompanied by changes in meridional gradient of sea-sub-temperature. Sea-sub-temperature gradient is enhanced in this region, but reduced south of about 60 degrees. This sea-sub-temperature gradient anomalies have the potential to further modify atmospheric baroclinacy. Baroclinacy increases south of about 50 degrees, but decreases north of 50 degrees. And changes in baroclinicity may trigger real adjustment of framework based on momentum and the head-budget analysis is built to explain the responses of spring meridional circulation to spring SOD. Zonamine momentum equation and zonamine heat equation can help us understand the relationship between southern hemisphere extropical meridional circulation and its relationship with SOD. And to further test the model performance in simulate this relationship, we check semi-file historical simulations in these models. Generally, the multimodal mean give a similar pattern, but the significant in the tropics is a little weak. Although models show difference in simulating this relationship, some models give a very similar result to the observation. And because of responses of spring meridional circulation to the winter SUM, the spring zonamine precipitation is also related to the winter SUM. Less and more precipitation occur alertively from the southern hemisphere middle latitude to the northern hemisphere sub tropics. Next question we are interested is about the linkage between winter SUM and tropical circulation. Here is the result of composite analysis of spring wind at 850 million bar based on the winter SUM. The typical and so years have been excluded before the composite analysis. We can see that when the SUM is in a positive phase, there is anomalous east wind in the central tropical Pacific, means the trade wind is stringent. And the increased trade wind suggests a cooler system of temperature. Indeed, we see the negative correlation between the winter SUM and the system of temperature in central tropical Pacific. To further test the relationship between winter SUM and the spring tropical Pacific system of temperature, we do composite analysis, use ORR data and rainfall data. All this composite analysis, the ENSO signal have been removed. We can see that the result are robust. In the HGCM slab ocean models, in the framework of HGCM slab ocean model, since the temperature variability is determined by some dynamic equation without ocean dynamic process, because the ocean dynamic process is the key mechanism for ENSO variability. So in the framework of HGCM slab ocean model, the ENSO variability is very vague. Therefore, it is a convenient way to remove the climatic influence of ENSO and investigate the role of extra tropical factors in modulating tropical Pacific system of temperature. This table is the list of CMAPS remodels we use. This result are composite analysis from the Arnino case and La Nina case. So I think I missed the slide. Yes, this is the composite analysis using real analysis data, Arnino case and La Nina case. We can see the Arnino-like state of the temperature anomaly corresponding to a positive sum is weaker than that correspond to a negative sum. The differences are significant. Similarly, La Nina-like state of the temperature anomalies in spring correspond to a positive sum is also weaker than that correspond to a negative sum. The difference is also negative. And the framework of CMAPS-3, HGCM slab ocean model, give a similar result. Although the variation of spring tropical Pacific system of temperature, the variability explained by winter sum are smaller than those explained by the winter ENSO. Influence of the sum on spring tropical Pacific system of temperature may overlay on the influence of ENSO. So the slab ocean models, they do have some kind of ENSO type variability still? Yes, it is vague, but I do have some variability. Thank you. And finally, summary and highlight. This study provides new evidence for the influence of tropics on the tropics and beyond. Although the sum itself is mainly confined to the southern hemisphere, its tropics, its influence on zonamine circulation may reach tropics and even the northern hemisphere. This process is an example of ocean-automospheric coupled bridge in which the ocean retains a memory of atmospheric signal and persists to the following season, alert atmospheric circulation and finally influence local and remote climate. And it provides a source of predictability. That's all. Thank you.