 Yeah, we can start. So Fred, maybe can I suggest that. Yes, should we start? Yes, please. You want to give a citation? Yeah, okay. Yeah, maybe I can. Let's start. So good afternoon, everybody. It's my pleasure to be to welcome and congratulate the 2021 ICTP prize winners who are present there at ICTP today. Rondruth Tiana, Barry Malala and Narendra Oja. I wish I also could be present, but I have to join by Zoom. I would like to just say a few words about the ICTP prize, which was created in 1982. It recognizes young scientists from developing countries who work and live in those countries and who have made outstanding and original contributions to physics. Each year, the ICTP prize is given in the honor of a scientist who has made outstanding contributions to the field in which the prize is given. The 2021 ICTP prize is dedicated to the memory of Jacob Bueblis, who discovered the connection between sea surface temperature and easterly winds anomalies in the equatorial Pacific. He helped towards an understanding of the El Nino Southern Oscillation by suggesting that an anomalously warm spot in the eastern Pacific can weaken the east-west temperature difference, disrupting trade winds and which push warm water to the west. So these results have been important in the climate science and the winners of this year's ICTP prize are their own field of scientific research is closely connected. I would like to now first read the citations for Rondruth Yama Bari Malala and then the citation for Narendra Orja. And perhaps then I will hand it over to Fred to give a more detailed scientific introduction to the two winners this year. So the 2021 ICTP prize is awarded to Dr. Rondruth Yama Bari Malala for her outstanding and pioneering contributions that have advanced our understanding of the role of the Indian Ocean on the climate of Madagascar and South Africa and its response to climate change through the application of modeling tools and careful data analysis. The citation for Dr. Narendra Orja is as follows. The 2021 ICTP prize is awarded to Dr. Narendra Orja for his outstanding contributions to the field of atmospheric chemistry and physics by performing in situ measurements satellite data analysis and chemistry climate model is over South Asia. Dr. Orja's studies have had an enormous impact on our understanding of the influence of anthropogenic and biomass burning emissions on the Southeast Asia air quality. So I will now give the floor back to Fred. So we will proceed with the with the handing of the document. Okay, first. Okay, okay. Congratulations. Congratulations. Okay, congratulations to both of you. So before we start the scientific presentations, I will just very briefly not talk very much very unforn, you know, in form we'll talk about a little bit of the history of the candidates. So Rondro Barimala started her career in climate science, at least when he came when she came to join our diploma program at ICTP. And if I remember correctly, you came actually from a solid earth background but then you decided to do what kind of ocean atmosphere interactions and. And so Rondro was so keen and so I was also her diploma thesis tutor together with Annalisa Braco. She was so keen to study further that she also joined the our common ICTP, the University of Trieste and OGS PhD program. And she conducted her PhD studies also here under the supervision of the also Annalisa Braco and myself I was a tutor. And so she wrote her PhD thesis already on variability of the Indian Ocean from into annual to decade of time scales. And after this important step she moved on actually her first step but then some shorter periods I think at Georgia Tech. You moved on to the George Mason University and Professor Shukla and David Strauss I hear from that university and had your first research experience actually as a postdoc there. And then you moved on to Singapore and continued to research there, then Cape Town and now you're also affiliated with the, you know, with the Bjergne Center in Bergen right. And so, while at the very beginning, the focus was more on, on just variability of the Indian Ocean, then in later years, Rondo focus the research more on the impacts of Indian Ocean variability, and what's going on in the Indian Ocean in on the climate of Madagascar and, and also specifically on South Africa. In a particular we should also mention that Rondo has made important contribution to the latest IPCC report she was part of working group one assessing the climate and in that region, which is extremely vulnerable to climate change and has therefore made an important contribution to this report to. So, thanks for giving us a presentation then, and look here we can have it. Then we have, of course, Narendra Arya, who actually you finished your PhD in India right, and I understood correctly if your first experience was more on experimental analyzing experimental data but always focused on air pollution and then composition of air and how that may impact the health of humans. But then, in your post a period, you went to the Max Planck Institute in mind side where you probably you, you met Andrea Potts, I guess there, and who was a former staff member of ICTP. So, and I was so that this it was then when you came on to the combining modeling with observed data and you combine these two methods to to gain even more insight into this phenomenon and in fact today we had your presentation at the conference at our conference on end so in monsoon dynamics. It was very interesting for us to learn how, actually, we are used to, to think of how aerosols, for example, can influence the monsoon. That's what I've learned in the past. That's the first time I've heard it presented a detailed study on how actually the monsoon dynamics can influence the composition of the atmosphere and so there's an interaction between these two phenomenon which is extremely interesting and now you're back to India. You are doing extremely well and performing high performance computing modeling and combining this still with the observational data and experimental data. Thank you very much to be here and to get your presentation. Hi everyone and thanks very much Fred for the very nice introduction. It's always nice to be back here at ICTP and as Fred said, I started my career in climate science here at ICTP so this is like my academic home. So for the past five years, my work has been focusing on sovereign African climate and extending a bit to the eastern African climate. So in sovereign Africa, it's home to 180 million people. And the majority of the population actually heavily rely on rain-fed agriculture so it's really important for us to understand the mechanisms that drive the climate over the area. But also on the other side, it's important for us to understand both processes so that we can do some process-based analysis of the state-of-the-art models that are used for future projection and they are heavily used over the continent, over the subcontinent for future planning. So the work I'm going to present here was done under the umbrella of future climate for Africa. And this is really a teamwork so I want to acknowledge my collaborators, Ross Blemie, Fabian Tezbiol, and Chris Reason. What we see here, it's a simple sketch of the drivers of the sovereign African climate. So the colors are the topography. As you can see that the sovereign African area, it's characterized by a very complex topography. But then there are also different large-scale signals that force the climate of the subcontinent, for instance, and so. What we're having here is the sub-tropical high, the masculine high and the sub-Atlantic high pressure. We have the impacts of the warm Indian Ocean and the cold Bangalab welding system from the Atlantic. And then we have moisture transports from the Indian Ocean itself from the tropical Pacific and from the Congo basin. And the climate of the area, it's driven by a very complex mix of these processes. Now, if we look at what the state of the art models are doing over the area, so this figure shows the precipitation bias in the ensemble mean of the semi-plugged models. So we have this large excess in rainfall over mainland Africa and then deficit in rainfall over Madagascar. And this precipitation bias, it's not only in the semi-plugged five, but it's been persisting from semi-plugged three, semi-plugged five, and it's still there in semi-plugged six. So the question is why, what we do not understand by having such a large bias. And if I am to draw a box here over mainland Africa and another box over Madagascar and look at this precipitation, this is what we have in the semi-plugged models. So again, positive biases over southern Africa and negative over Madagascar and it's pretty consistent in all the, in most of the couple models. And some of the models actually have more than 300% rainfall compared to, compared to the observation. So that motivates us in trying to understand what actually drives precipitation over over this area. This figure it's been shown in the activity on NSOL teleconnection this week. So this is the global rainfall during January, February, March from observation. I think we use CMAP here. And one of the particularities of the sovereign hemisphere, it's the presence of these sovereign convergence on. So we have the sovereign Indian Ocean Convergence on over the Indian Ocean, the ACC or sovereign Atlantic Convergence on over the Atlantic and this as PCC over the Pacific. So it's very particular with Indian Ocean that as VSO CZ, there is actually Madagascar on the way. So it's really in the middle of the sovereign Indian Ocean Convergence on that we have Madagascar. So if the moisture flux or the circulation in the southwestern Indian Ocean, then we clearly see this Easterlies from the Indian Ocean, somehow blocked by Madagascar before the moisture is going inside mainland Africa. So that's it's because of this Mozambique channel drop a trap that forms between Madagascar and mainland Africa. So the main focus of this talk is really to understand what's the role of this Mozambique channel trap for the climate in the for the rainfall in the region. Just to explain a bit for Mozambique channel trust so it's like the pressure low pressure area in the Mozambique channel. And as I said, it's on the path of the soft Indian Ocean Convergence on. So it could impact the southern African rainfall. Two possibilities that can trigger the formation of the NCT for Mozambique channel trap. It's the dynamical adjustment of Easterlies flowing through Madagascar. So we saw these Easterlies then blocked by a topography, and then various with slow pressure on the side of the mountain. So that's one case but the other case also that it's thermally forced so forced by the formal forcing over over Madagascar. Now what we did was, we quickly looked at this parameters net by recent at all, which compares the impact of the topography by dynamically adjusting the flow or by thermally forcing the flow. Significantly less than one means that the effect of the topography is dominant compared to the effect of the thermal forcing and inferior analysis we saw that it's 0.07 so it's, we were quite confident that the formation of the MCT is dominated by the topography of Madagascar. So what we did is that we did some sensitivity tests with regional climate models. And we gradually reduced photography of Madagascar in this test, and then completely removed Madagascar from the map. So what I'm showing here is the control run where we have a full topography and the flat where we flattened the topography to 300 meter in between these we did some experiments by gradually reducing the topography by 75% 50% and then the flatten and then completely removed and I will go back to this no Madagascar experiment later on, because what we did in this one is that we replaced Madagascar with an interpolated SST from the surrounding grid points so that kind of creates like an artificial warm anomaly in that area. So the model we used it's worth run at 10 kilometer horizontal resolution and we run it for 17 summers. So as I said the physical parametric session in all the experiments, they're all the same. But we just changed the topography. So the plot in the bottom here, it gives the latitude and our profile of the topography of Madagascar in was experiment so the dashed lines are the maximum topography and the bold lines the mint topography. The first thing we got from that experiment. When we plot the 850 topascal wind and geopotential height. The first one the first figure here from CFSR, which is used as our boundary condition in the experiment. So we have this beautiful MCT over here. And then in the control, we get the we still get the MCT, but then in flat and see we do have this anomalous Easterlies going straight from the Indian Ocean to Southern Southern Africa, and that's expected because we removed whatever blocks, the moisture. So the mechanism for that is mainly by having this flow, going through a mountain, it conserves its authenticity and then on the east side of the mountain. We have this stretching because of the conservation of the forticity and form formation of the cyclonic circulation on the east side of the mountain. So just to, to look at how locked and unblocked the flow is so we use the food number. It's a common number that's used to characterize blocked flow and the food number close to zero it's like the flow is completely blocked. And then when it goes higher at the blocking is is becoming weak. So in the control run we have 0.15. If we remove, if we keep 75% of the topography of Madagascar, then the front number is 0.43. And if we flat Madagascar, then it's 0.40. The number shows that there is actually a non-linearity in the impact of this blocking on the flow because 75% of the topography it's still pretty much the same. It's almost the same as the control run, but if we look at the front number, it's almost the same as the flattened topography here. So that's something that we need to understand is fairly threshold in the blocking that that gets this flow going straight to the mainland Africa. So if we take the MCT index as the area of range of relative vorticity over the Mozambique channel and plot it in the experiment. So what we see here on the left side, it's the annual cycle of the MCT. One, it's the CFSR, which is used as a boundary condition, the magenta of the control and the green and blue are the experiments. And as you can see here that from December to April, April was the end of our run. We have this clear negative relative vorticity in the Mozambique channel, whereas when we remove the topography and again here by keeping 75% of the topography, it's almost the same as flattening or completely removing Madagascar from the map. So if we do the mean of the January, February, March, which is below the highest relative vorticity is a negative value with respect to the mean topography. That's what we get in the on this figure on the right here. So the red and magenta are respectively the CFSR and control, and then we get this large jump in relative vorticity by changing slightly the topography. So what we did is we looked at the difference in mean between flat and control and C and control, and that's because we want to know what's the effect of flat. So the arrows here are the moisture flats, anomalies, and the colors are the divergence. So it's pretty clear here that the various this large easterly anomalies going from around Madagascar in the Mozambique channel, going into mainland Africa, and also this increase in convergence over mainland Africa here. Now, if, if, sorry, if I drew a line here between Madagascar and Mozambique and calculate like how much moisture is actually going into mainland Africa in that. So in the control, it's 49 in the flat and C 79 and 89. And again, that shows that by changing the topography, then we almost double the amount of moisture that's transported from the eastern Indian ocean to to mainland Africa. So what's the impact of that on the precipitation? This is a difference between flat and control in the precipitation, large rainfall over mainland, and deficit in rainfall over Madagascar. The harsh areas are where the signals are significant. And same for the sea experiment, it just that the amplitude it's intensified because that's quite an extreme case. So we have this type of like precipitation anomaly with significant increase over mainland and decrease over Madagascar. So back to the interpolated as I said at the very beginning that the way we did the sea experiment is to interpolate the ocean grid points in the surrounding area to where Madagascar is. So that's good creates very artificial signal in our, in our results. So what we did is we performed another experiment hit. It's exactly the same as see so like no Madagascar, but we double the amount of heat we put where Madagascar is. So by assuming that there is a linear response, of course, there was some non linearities but we assume that there is a linear response. And that's what we get by having warm SST over Madagascar, we have this strong cyclonic circulation here, and then less moisture and less rainfall over mainland Africa in more rainfall over over Madagascar. And that's to say that the, the, the signal we have here. It's actually, it's not, of course, there is an impact of the heating over Madagascar, but it's not really by increasing the rainfall over mainland Africa. So, one of the questions that I got when I proposed this study was, but no one is going to remove Madagascar from the map. So why would you do that that's very theoretical, and I completely agree with that. It's very theoretical but at least we have an understanding of why the topography is actually important representation of the topography is important. And so, Callum Maday produced his figure. He looked at the topography profiles over Madagascar in the CME5 models. So this 12 degree south, it's in the northern northern tip of Madagascar, and going south to 2020 software at the end of Madagascar. The different lines are the different topography in the CME5 models. And as you can see, as we go southern, there is this large discrepancy in the topography in the models. And here, for instance, it's not even removing 75% of the observed topography. It's more than that. It's more like flattening completely flat in Madagascar. And if we look at the difference between the low topography models and high topography models, we look at the difference in precipitation. This is what we get. Precipitation and low level wind at HST at Topascal, very strong easterly anomalies from the Indian Ocean, and this deficit in rainfall over Madagascar and if I bring us back to this one, the bias in the CME5 models, we have this typo-like signal. And of course, I'm not claiming here that all the biases in the CME5 models are from the topography of Madagascar or the most ambiguous channel trough, but it kind of gives us a hint that varies contribution of the topography of Madagascar and the most ambiguous channel trough in these large biases in the model. So far, I hope I have convinced you that the topography of Madagascar actually modulates the southern African rainfall through the most ambiguous channel trough. And the strength of the most ambiguous channel trough is actually modulates the amount of moisture that's transported from the Indian Ocean going into mainland Africa. And lastly, the difficulties that climate models have in representing the mean rainfall in the area could partly due to a misrepresentation of the topography of Madagascar and or the MCT. So that's the mean rainfall for January, February, March. The question is, does the MCT have any variability? Does it vary year to year? Does it have any inter-annual variability? What we did is we looked at the MCT indices. Here we tried different indices. The red one, it's the one that we used in the model by just taking the relative vorticity of the most ambiguous channel area. The blue one is by taking the geopotential height and the green one is by looking by taking the first component, first UF component of a relative vorticity. And of course all of them show like inter-annual variability. However, this discrepancy is here. We don't fully quite understand why it is in the PC one but then in the height. It's because the height is kind of sensitive to large scale signals. So if you have changed in the large scale, like the masculine height for instance, then you have this peak here. So we're more confident to use the original MCT index for relative vorticity over the area. So what we did is we classified these years as weak and strong MCTs and then look at the vorticity budget in the weak MCT years and strong MCT years. So the figure we have here on the left, it's the vorticity budget of a strong MCT year. And as we can see that the tilton is dominant and that's very obvious in the strong years and also the vertical advection is dominant in the strong MCT. If we take all the weak MCT years and of course we still have a tilton because that's from the dynamical adjustment of the flow, but the vertical advection completely disappeared. So if we do the same analysis for the specific humidity by doing a composite of strong MCT years and weak MCT years, then we have this positive anomaly over from Mozambique channel extending to the surface, surface of the Indian Ocean, negative anomaly over mainland Africa and the opposite for the weak MCT years. So these kind of suggest that the MCT variability can be associated with most convection over the Mozambique channel. And what's the impact of that on the rainfall variability? So again doing the composite analysis using the CHIPS rainfall data and SIMUP data and these signals are pretty consistent that we have this positive anomaly, excessive rainfall over Madagascar and negative anomaly over mainland Africa by having a very strong MCT and vice versa for weak MCT. And then again looking at the anomaly cloud cover, these are very consistent with our precipitation results. If we look at moisture transport and divergence in the analysis by the strong and weak MCTs, then for the strong MCTs we have this anomalous westerlies going into the Indian Ocean. So it's like we have less moisture transported into mainland Africa whereas we are opposite this in the weak MCT. So to summarize, we know that the MCT has an internal variability and it's associated to the most convection within the Mozambique channel and strong MCT years tend to have excessive rainfall over mainland and southern Africa and every city of Madagascar. One thing with this Mozambique channel trap, the MCT index is that now it's been used as one of the automatized evaluation metrics for southern African rainfall in the SIMUP models. So hopefully this will appear soon in the ESM cartoons. And thanks very much. Any questions, right? You do have questions. Hi, thanks for the wonderful talk. I had a question about the representation of the Mozambique trough in SIMUP five models. And I was wondering if they're able to represent the variability correctly or it's just a general bias that's in the SIMUP five models. So we haven't really checked the variability in the SIMUP five, but we've looked at the mean MCT. And yes, it's there. So when you have weak MCT you tend to have this large rainfall over mainland Africa in the SIMUP five models. Because I've also been looking at some similar things. And from what I've seen it seems like in the SIMUP five models that the variability is completely overwhelmed by the Mozambique trough. And maybe that's something we can talk about afterwards. Andrew, I was wondering that giving this very important role of the topography that you have found, have you had the chance to look at the ensemble of the Cortex Africa to see if you get any improvement? No, the reason why we didn't really look at that, it's that the boundary of the Cortex Africa, it's very close to Madagascar. And I wouldn't really make sense to, yeah, we could look at that, but I think it would be affected by that boundary really very close to Madagascar. I would be curious when you show this high mountain minus low topography case. Is this one to one with high resolution with this low resolution or do some models have a fix to pose some you call it envelope or graphy or I don't know, is it a one to one? Yeah, so that's by doing a mean of models that have topography higher than the observation and then mean of the models having topography lower than the what's in the era or CFSR. So it's not a comparison like one by one, one to one model, but it's like a mean of the models classified by the topography. So it would be interesting to look to compare with the high resolution version of one model, for instance. Okay, if there are no more questions we can thank again Rondra. It was exciting. So get very good afternoon to all of you. And thanks very much for coming to this talk. So, now I will take you to another part of developing word in it is Indian subcontinent or what we call as South Asia. We will focus mainly on atmospheric trace gases and aerosols so Earth's atmosphere mainly is composed by nitrogen, oxygen and organ about 99.9% of atmosphere, but then this remaining 0.1% is really really important and plays major role in the air pollution and climate change. So that is how we develop a lot of interest into it, but somehow a series of complex processes related to physical process, atmospheric dynamics and then a lot of chemical reactions, which happen in presence of sunlight that we call as photochemistry. And then whatever pollution is at one place it keeps on getting transported through horizontal winds are through convection and other vertical moments. So it keeps on basically all different type of process they come together and then the net result is what we see the distribution of stress gases and aerosols in the atmosphere. So as you can see the major sources are there are really diverse sources like even natural trees forest they also emit different type of organic compounds, agriculture, you know a lot of ammonia and other gases are emitted from them. They can also convert into particles like aerosols. Then biomass burning is also of different types, for example, they can be in the natural wildfires in the forests. And we expect that in the climate warming they can increase more, but there are of other types also like crop residue burning done by farmers to clear their fields after they basically get their crops. So these type of all diverse emissions then they undergo a lot of processes chemistry and transport, and then what they can affect these gases and aerosols they can affect human health, especially the particles which are smaller in size they can really penetrate through the lungs. And they can create haze, and they also affect vegetation and crop productivity. So with this, they are really important and we really need to know their amount. And then we need to pinpoint their sources, not just at global or regional skills but to the local skills to actually inform policies like how to improve the air quality and how to mitigate their impact on the climate. So in this direction, space-based observations did provide some good clues. This is satellite data of nitrogen oxide. You can see some large enhancement in the, this region is called the Indo-Gangetic Plain in North India. And this is Eastern Asia. And if you can see this Indo-Gangetic Plain, so this is the most densely populated region in the world. So you can see the population distribution here. So while more population, they can have more emissions but at the same time, a larger population is at a greater health risk in these regions. And some global model studies, so basically these models take the information on emissions from different sources, and they use our knowledge of physics and chemical reactions to calculate the concentration of aerosols and trace gases. And then they use metrics to calculate what could be the impact on human health. They find that especially human health can be more in the East and South Asian regions. These numbers are basically telling in one degree by one degree or 100 kilometer by 100 kilometer, how many excess premature probabilities can be caused by aerosols and greenhouse and trace gases like ozone. And in another study, they use another model and they compute the ozone distribution over India. And they estimate that the effect of ozone pollution on agriculture is that loss is sufficient to feed about 94 million people who live below the poverty line. The impact is not only on health but is also on agricultural productivity and many of the, many of the sustainable goals of the United Nations, they cannot be achieved until we achieve kind of clean air and reduce the levels. And another process that is becoming more important is climate warming. So in Europe, they found that with increasing the temperature, there can be more ozone in the future. One because the chemistry can be more intense with higher temperatures and another because with climate warming the forests and trees they can emit more biogenic compounds. But in the studies in Indian subcontinent they are still lacking. So we need to because Indian conditions in both the chemical environment wise and also climatic condition wise they are very different. And these studies not only are important for the atmospheric composition or South Asia, but through monsoon the air can get uplifted vertically and then the air masses are trapped into what we call as monsoon anticyclone. And then the atmospheric composition, as far as the Mediterranean, all are impacted. So Lelywild et al wrote that monsoon can has two phases like a generous head. So it can, it can inject the pollution from lower troposphere to higher altitudes, but at the same time it also is a remarkably strong cleansing mechanism. So it removes a lot of pollution and it creates a lot of hydroxyl radicals which help cleaning the atmosphere. So with this background, basically the studies and especially studies combining experimental data with models, they are really important in South Asian region. We use models and we especially try to use regional scale models. So the idea is that they take the transport from outside regions from global model datasets. And then from within that small region, we do the high resolution simulation. And then for we divide the region in small, small boxes, and we calculate the all the production loss and influx from outside and what goes outside from that box. And this is done for each small, small box within that region. And we use a high performance computing facility at my Institute in Ahmedabad. And the model that I'm using is called weather research and forecasting. It's a very popular model. It is used very widely to give you weather forecasts in US, Europe and in India also. But the major thing is that we are coupling it with detailed chemistry of the atmosphere. And then we are running it a high resolution. Generally, global models, their resolution is about 100 kilometer by 100 kilometer. But to go really up to the local skills, we are doing simulations at 12 kilometer by 12 kilometer. So and then we have to adopt some simplifications of the chemistry. And then we have, because we have to India is really large region so we have to cover entire India so to save the computations we have to use simple chemistry. And I will show what those simplifications how they affect the model performance. Currently, since these models were developed for different parts of the world and when we are applying to developing Indian regions, then we have to do rigorous evaluation of the model before we conclude much on health impacts or policy formulation. We need to have a rigorous evaluation, which I think was the biggest challenge because data sets are very scarce in developing Indian regions. So I will show a lot of our efforts that we have made in this direction. And besides our own modeling we also use reanalysis data sets like Rundhra was also showing some data sets. So similar to that, here we are using some chemical reanalysis. So, like if we have satellite data set that can be assimilated with model. So these type of data we can also use for India. So this is our domain of the model this is covering almost entire India some extra simulation we are doing to cover even bigger regions. And what information goes into the model is basically the emissions of all different gases and aerosols from different sources, most importantly anthropogenic sources like fossil fuel burning, then biomass burning is coming. I think emissions are calculated online because as I said earlier that emissions from trees vegetation that depend upon the temperature so they are those are calculated using the weather conditions. And then we compare we got several stations although they are not kind of covering a lot at high resolution in India, but we can make some comparison with the observations. There's a lot of uncertainty in the emission data itself. So we use three different emission inventories for India, and we found that output can really change if we use one emission inventory as compared to other. So this is an important challenge for developing regions that emission input they are not very fixed they are not very accurate. There is substantial uncertainty into our conclusions can pop up from that and not just the emission inventories. It is also important how much simplification is done into writing the chemical mechanism of the model. So if we use one chemistry mechanism if we focus just on this blue line and this pink line. So when we basically compare, we keep everything same just we keep more details of the chemistry. The inputs are changed a lot. So by comparing a lot of data sets, we came up with the configuration with which we can really reproduce in a better way the observations that we had in India. So for example here I am comparing our simulation with a ship cruise based experiment in the Bay of Bengal. So you can see that these black dots are basically the observations collected by during the ship campaign and this blue line continuous line is the our simulation. So there was some difference in the levels, but if you see the variability so model is capturing that the same way we are comparing ozone here. We did additional simulation in which we turned off all the emissions happening in India, and then this line become this dotted almost flat type of line. Notice that the variations that ship based experiment list are seeing in the Bay of Bengal to great extent they were controlled by the outflow from the Indian processes like so this is we are putting into a simple simpler form here that air is entering from this Arabian sea and while leaving India towards reaching the ship based instrument, it is the photo chemistry is happening exactly in this part of the region. So a lot of information we can get from the when we combine the observations and model. So this was ship data now let's see how the data of model compares with the aircraft based experiment. So, aircraft based data was collected you from by putting instruments into Lufthansa airline, which used to fly between Frankfurt and Chennai. After takes off from Chennai they get kind of one profile up to about 200 hectopascal that is the cruising altitude of long, long flights, and then when it comes back to Chennai and lands then they get another profile. So taking this type of experimental data, we compare with our model, and this is basically distribution of water vapor. The model captured this type of vertical distribution of water vapor, we get confidence that they are getting the monsoonal convection right. So we go again to the satellite based observations, they can also help filling the gap of experimental data. And what interesting we find is the left this panel is basically our simulation, and these are data from three different OMEs, NASA's OZN monitoring instrument, GOM2 and CMHER from European Space Agency. So here we are comparing one particular species called formaldehyde. The significant scientific significance is that most of the hydrocarbons when they undergo different process in the atmosphere they are converted into formaldehyde. So if formaldehyde is high, that means that that particular region is experiencing intense photochemistry. And the technical importance of this species that it can be, it has some spectral signatures, so it can be measured from satellites. Other species are also important but they cannot be measured through satellites. So with this, we compared our model output with the different satellites. So what we noticed is that our model predicts other than the eastern Indo-Gangetic plane, besides that it is also showing a lot of hot spots in the regions which are not that much dominated by anthropogenic activities. And these regions are basically dominated more by forest area and natural emissions. So that was one important finding that because in the future when climate warming will happen, probably this type of process will become more important, the natural biogenic emissions, they can be more enhanced. The other important point that we conclude by comparing these satellites is this, that there is some differences between the model and satellite. But there are also substantial differences if you compare one satellite with another satellite. So this means that we need to establish experimental network of stations within India so that we can validate both satellites and then we can pinpoint the uncertainties in the model. So we applied this model to a particular problem which was of interest for wider public interest also and for scientific interest also. That in India, every towards winter, we see a lot of hedge covering entire Indo-Gangetic plane. This is really widespread. So these are numbers are called fine particulate matter. There, as I was saying that small particles they affect health more strongly. So this is the distribution of those. And to make sense of these numbers, generally the level should be less than 25. Generally the levels should be less than 25 to do not affect our health too much. But you can see that most of the regions it is really widespread and levels are exceeding by order of magnitude. So the impact will be really remarkable on health. And it happens typically towards every post monsoon and winter that most of them are this Indo-Gangetic plane that experiences really, really high levels of the particulate matter. So we did the simulations. These are our simulation plots and we try to compare it with some of the stations located in the Gangetic plane. And although model does not perform perfectly, but there were some agreements between the in the time variations that we saw in the observations when we compared with the model output. So what we did, we removed the biomass burning in the model and calculated what is the difference that is coming because of the biomass burning emissions. So we find that in the Punjab-Haryana regions where most of the crop residue burning happens, so their strong effects were there from the fires. And they were also persisting and spreading around during post monsoon like November. But when we have the maximum pollution loading that is in winters, then the or Gangetic plane, the effect of biomass burning was not there. So it clearly tells that biomass burning effects, they are only important in the post monsoon. And when the PM 2.5 was the highest, then fire effects were really not important, at least for the Gangetic plane. Some biomass burning is still visible or the Himalaya, but this is related to the forest fires. And these emissions, they do not sink down and they are more confined within the Himalaya. So with this, we try to kind of communicate what are the important sources and what are not and why this type of enhancement are suddenly appearing. You can see that towards winter, temperature is really, really low and boundary layer depth that tells us how vertically the dilution of pollution can happen. That is very, very shallow. So all the aerosols, they are confined within a very small volume near the surface. So at that time, if sources like other anthropogenic sources, they are high, so the pollution problem is really, really very intense. Then in the previous talk also there was a lot of highlight about representing the topography of complex regions. In India also we have a really highly complex terrain that is Himalaya and it is, I was mentioning, I was mentioning that it was just above this Indo-Gangetic plane and kind of which is also affected by a lot of biomass burning fires. This region is highly complex in terms of topography. So when I look at this region, you see a lot of houses. This is basically what you call Indo-Gangetic plane. And for this I was mentioning that this is most densely populated part in the world. But if you see that very closely, these mountains are located. So within this region, if you want to do modeling, so they often models do not resolve a highly, basically plain regions placed very close to highly complex terrain of the Himalaya. So we go to very high resolutions. So this star is basically this location. This is in the central Himalaya and this is basically an astronomical observatory. I was basically doing my PhD from this institution. It is called Aries. So I basically downscaled the model to really, really high resolutions at one kilometer by one kilometer. And we tried to see if the models are becoming any better. So this is common problem of the global climate models that in mountains, they kind of overestimate temperature because they do not represent the mountain tops very well. So when we go to from course resolution to high resolutions, we really improve the predictions of temperature for the central Himalaya. So that was kind of bit bit promising, but there was some discrepancies that in the local scale atmospheric dynamics model is not getting the winds direction properly. So this is the observation observation says that most of the winds are coming from east, but at course resolution this component was missing at high resolution. It appeared, but it also appeared with some discrepancies, right. So the result is that we could better reproduce the temperature variations in Himalaya, but discrepancies in winds are still substantial. And there are local scale processes. So probably it will still be challenged to even even at such high resolution, it will be a challenge to reproduce this type of environments. So when the human intelligence was kind of giving limited performance we tried artificial intelligence also for Himalayan environments. So this is one exercise that we have recently initiated, we use machine learning algorithms. And the first we try to train the machine learning algorithm algorithm with two, three years of observations, and then we try to test whether it can predict the remaining observation independent data sets. But to really exploit or really test the ability of machine learning, we took a long term data from the chemical reanalysis model data, like temperature humidity boundary layer height winds and some precursors of ozone like carbon monoxide nitrogen oxide etc. And when we put all those areas into the machine learning algorithm, then model performance was really good. So we so we can say that machine learning based models, they can really perform well and they can help in the prediction of ozone and probably this type of exercise we can extend for aerosols and we can try to explore more potential of machine learning to complement the conventional regional modeling that we are doing. And we also analyze this type of long term data and find one important interesting result that while sulfur dioxide that is a tracer of emissions for from for energy generation like power plants etc. Cold burning etc. So, earlier the trends were very fast or India, but in recently they are kind of slightly select they are showing some signatures of stabilization and trends are becoming smaller now. So, this type of information can also be generated when we combine the satellite observations with models. So to summarize what all I discussed is that several important features that we saw in ground based ship based aircraft and satellite data sets for Indian subcontinent. We could reproduce many of them with good ability. Because there are some more biases and we need to work them out. And we use the model to discuss an important problem of widespread winter time pollution across Indo-Gangetic plan. And we found that biomass burning does not have the strong role at least in the winter time. It's role was important but only up to the post monsoon. So, first simulations we could also predict that some natural sources are also important and their role is can be enhanced in the case of climate warming. And it can be more important in the future as we see a slow down in anthropogenic tracers like sulfur dioxide. And we could resolve the Himalayan topography better as compared to previous studies at high resolutions. And we could improve the temperature variations, but still there are substantial discrepancies in the simulating the local scale dynamics of the Himalaya. So it will take more efforts to really study the transport of pollution from Gangetic plan to the Himalaya and see what could be its impact on climate. And we did some initial exercises using machine learning methods. And we find that they can help especially in the regions where conventional models are not performing very well. And especially if we can have some long term experimental data sets to train the machine learning algorithms. So I also like to thank many experimentalist friends and collaborators who have been helping throughout the work. And thank you for your attention. Thanks for the questions. Very nice lecture. Dr. The role of biomass burning. We really hear different claims about how bad it is and how important it is. Obviously, newspaper reports are filled with during the winter season. The whole not the whole Indo-Gangetic plane gets kind of quite affected. So what is the evidence based on which you're saying that it is not a serious problem during the during the winter, because the impacts are there. So what is the main reason for that winter problem. So, as you rightly pointed out that it was an impression that biomass burning especially for field clearing that is kind of responsible for this widespread haze. But these type of fires they can be detected from satellites. So the emissions from fire that we included in the model, they are based on satellite based observations of fires. So once we included in the model, then our model was reproducing the day to day changes in the aerosols over this region. And then when we remove that fire emissions from the model, then we see that there was there was not much effect on the Gangetic planes air quality. So fires role was important and it is really, really important at least during post monsoon when the crop residue burning is happening. But after some time these type of aerosols their lifetime is two weeks or so. So after that their effect is gone. So then the major thing is happening that it is too cold conditions across the Gangetic plane and whatever the remaining anthropogenic aerosols are there from non fire sources. They are trapped into very shallow stagnant atmospheric conditions. So to counter this type of effect produced by sharp change in mutuality, we have to rigorously bring down the other sources also just expand on the non fire related anthropogenic sources. What are they just can you mention a few of them. So towards winter some traditional, the other sources like fossil fuel burning industrial emission they are all time there, but towards winter the additional other sources come for heating purposes, heating purposes and residential purposes. So they are also getting enhanced. I would be interested if they are in this in the footheads in this pollution area. If they if you know if they are strong dynamically induced into annual variations of this pollution or if it's essentially every year almost the same. So every year. This is a typical cycle that towards post monsoon and especially winters. The haze is the most most pronounced, and it really affects a lot of flights and they have to get cancelled due to poor visibility. So this is a typical phenomena, and a lot of research is happening on this. And, but within the one season to other season, they lot lot of change in the air quality. And one year to the other, just expect the same kind of, yes, more or less the same kind of suggest that important seasonal changes, they have some hard control on it. And some some new studies are coming up and they are telling that there are some chemical environments which allow more condensation and aerosol growth in this environment. So those angles are also being investigated right now. So there are some puzzles is still in this program. Your variability of circulation from year to year can be a source because the inversion layer once it has become strong. So that has nothing to do that has to do with the sort of climate variables. It can it is playing a role. Is that correct. There is a significant some contribution also just the weather and climate state itself. Right, so in the years which are kind of colder than other years, the effect can be more. There are no further questions or comments. What is the next item on the refreshments on the terrace is there. There's no other photo right the photos we have done with the photos. Okay, okay, okay, okay. Let's thank again then both. And we have a refreshments on the terrace.