Thank you, chair. Good morning, ladies and gentlemen. I'm Tianjin Zhou from IAP, Chinese Academy of Sciences. First, I should give my thanks to the organizing committee for the invitation. Today, I will talk about the decade of variability of the Asian summer monsoon. Here is the outline for my talk. First, I will show some observational evidence of the monsoon changes over East Asia and South Asia. After that, I will show some evidence regarding the connections among different monsoon domains. Finally, I will give a discussion on the potential mechanism behind all these phenomena, and finally followed by an overview of the monsoon map for the coming CMAP 6. The first question, how about the changes of the East Asian summer monsoon during the 20th century? Here, please note, because the meridional coverage of East Asian monsoon is larger than other monsoon systems. So, a stronger monsoon means we have more rainfall in the North China, but the less rainfall is central and the South China. And here is the monsoon circulation index derived from the analysis. There are many analysis, and the results are nearly the similar. You can find in the second half of the 20th century, the East Asian summer monsoon circulation has been weakening. This kind of trend is significant. And following this kind of weakened monsoon circulation, we have rainfall anomalies at this. You can find in the summer time, while there is a drawing trend in the northern part of China, there is more rainfall in the southern part, especially along the Yangtze River valley. If we check the monsoon rainfall anomalies at different decades, you can find this kind of decadal variability more clearly. You can find starting from the end of the 1970s, the monsoon rainbed has moving southward, and thus we have dropped in the northern part of China. This kind of inter-decadal variability has a large social impact. One example here is the water vapor short-range. Here, for example, to solve the problem of water resources short-range in North China, the central government has carried out the so-called South to North water diversion project. That means we transport water from here, from the Yangtze River to the North China by channels. It costs billions of dollars. And starting from last year, about one-third of the water used in Beijing actually come from here, Yangtze River. This is the decadal variability in the past 15 years occurred over the East Asian summer monsoon domain. How about the changes of the South Asian summer monsoon? Here, I show two figures. The first is the monsoon area, monsoon rainfall intensity, and the total monsoon rainfall averaged over the land area of Indian monsoon. You can find in the past, in the second half of the 20th century, there is also a decline trend, especially starting from the end of the 1970s. If we focus on this core area, or the northern part of the Indian monsoon, you can find, as shown by the black line here, based on the CRIO data set, the weakening trend is even more evident. So, the South Asian monsoon also shows a weakening trend in the second half of the 20th century. We further extend the story of the South Asian monsoon to the past 100 years. And here is the Indian monsoon rainfall derived from different data sets. One key point of this figure is that the weakening trend of South Asian monsoon in the recent decades is not a phenomenon that covered the whole 100 years. It's only inter-decadal variabilities. So, based on all this evidence, we can see both the East Asian monsoon and the South Asian summer monsoon, or Indian summer monsoon, have shown inter-decadal variabilities. Next point I want to note is that, actually, there are connections among regional monsoon changes. Here I show an example beginning from the connection between South Asian and East Asian monsoon. And here is the trend of summer rainfall during the post-1950 period. You can find the drought in the northern part of India actually is consistent with that with the north China. In the meantime, we have more rainfall in the southern part of China. Please note here, this kind of phenomenon is the inter-decadal trend of rainfall associated with summer monsoon circulation over the East Asian domain. Based on this figure, we can find there are connections between East and the South Asian monsoon. Before the extent of our figure to the African monsoon system, here is a figure from Mardin-Hurley. You can hear it rainfall over the northern part of the North African monsoon area, and here is the time series over the southern part. You can find in the second half of the 20th century the North African monsoon has also shown a weakening trend. Here, again, I show the East Asian summer monsoon circulation index here and the rainfall anomalies here. If we compare the East Asian summer monsoon index with that of the African monsoon rainfall, we can find a nearly similar weakening trend. This kind of comparison also indicates there are connections, maybe there are connections between the African monsoon and the East Asian monsoon. Before the extent of our analysis to the global monsoon domains, what I show here, this color is the monsoon area, here is the monsoon rainfall intensity, and here is the monsoon rainfall amount. It's a trend for this period. Here is the trend of these three index average over the northern hemisphere monsoon area. You can find all the index shows a weakening trend. Before the compare, this kind of index over the North Africa, South Africa, and the individual regional monsoon domains, you can find the weakening trend of the northern hemisphere monsoon system actually is dominated by that from North Africa, South Africa, and part of the North American monsoon system. This evidence further suggests there are connections among different monsoon system. So we extend our eye into the global monsoon system. There are two ways of definition. The first definition is based on the precipitation defined by being one, and here is the definition done by Jianping Li based on the surface bend. You can find two kinds of definitions actually games have given us nearly the similar story. The coverage of the global monsoon includes the Asian-Australian monsoon system, the American monsoon system, and the African monsoon system. So based on this domain, we can define a global monsoon precipitation index. That means if we add the rainfall that fall in the land monsoon domain, we can get an accumulated global monsoon precipitation index. The result is given here as shown by the bar. You can find for the second half of the 20th century, the global monsoon has been weakening. Here I also show the East Asian summer monsoon index. You can find the evolution of the East Asian summer monsoon is almost consistent with that of the global monsoon system. So for the East Asian people, we should say the story occurred over the East Asian domain has a much bigger picture. It's not a regional phenomenon. All this evidence suggests from East Asian, South Asian, and African monsoon system. In the second half of the 20th century, they have shown interdicated variabilities. So next question you might ask is how about the mechanism behind all these kinds of phenomena? Here I first show the result for the East Asian summer monsoon. What I show here, first is the July East Asian summer monsoon index is given as a bar. And also I show the ideal index in the same time. You can find in the second half of the 20th century, the change of the monsoon circulation index is just out of face with the PDO index if we focus on decadal time scale. I further show the summer precipitation average over the northern part of China and the bar also show the PDO index here. You can find in the precipitation is also negatively correlated with the PDO index. These are based on the time series in the second half of the 20th century. If we extend the time period to the past about 100 years, we have result at this. Here is the PDSI index average over northern China and here again is the PDO index. You can find even in the past 100 years, we still can find this kind of out of face relationship between room for the northern part of China and the PDO index. So all this evidence suggests the long-term or decadal changes of the East Asian monsoon should be modulated or partly dominated by the face changes of PDO. And here is a recent study given by Professor Bhima from University of Hawaii. Here he has defined a wind shear index for the northern hemisphere summer monsoon. The index is given by the dashed line. You can find, he also showed the index of the natural IPO AMO. You can find in the second half of the 20th century and the beginning of this century, the change of the northern hemisphere wind shear monsoon index is significantly correlated with the minus IPO index and also the AMO index. Please note in his paper, he defined this kind of tropical zonal gradient or sea surface temperature at the mega n-zone, but I believe actually is still a manner of IPO in the tropical low. So based on this relationship, Bhima suggests if we can predict PDO and AMO at decadal timescale, then we can make a successful prediction of the global or northern hemisphere monsoon system. This idea actually can be examined by new market models. Here I show one example is the in-map time simulation. Here is the list of the models we used. We have done two sets of simulation. The first is the GAGA. That means we use global SST to drive our AGCM. The second is GAGA. That means we only use the observed tropical SST to drive the AGCM and then check the response. How about the result? If we focus on the global monsoon, here we show the global land monsoon precipitation index. The green is a simulation. You can find the weakening trend in the observation is partly reproduced in the model world. Because in this kind of simulation to the monsoon system, the only external forcing is sea surface temperature anomalies. So next question we should answer is which part of SST anomalies can cause this kind of weakening trend? Here I show the sea surface temperature anomalies congruent with the weakening trend of the global land monsoon precipitation. You can find in both the observation and the model world the tropical lobe of the positive phase or IPO stand out. So it's due to the positive phase or IPO that the global land monsoon precipitation has been weakening in the second half of the 20th century. However, I should note that the scale of the model actually is very low over the Asian monsoon area. And here is a trend of summer precipitation in the observation and in the simulation. You can find why the scales of the model are reasonable over the northern hemisphere and the other north Africa and other monsoon domains. The scale over the Asian monsoon area is very low. So for the east Asian summer monsoon, we should focus on the circulation change rather than precipitation. How about the circulation change? Here I show the east Asian summer monsoon index derived from the analysis and the two set of simulations. First, you can find again in the observation the monsoon circulation has been weakening. Second, this kind of weakening trend can be well reproduced in the model world. And third, if we compare the result of global SST driven or Gauguin red with that of the tropical SST driven or Tauguin red, you can find the response are nearly the same. This kind of similarity suggest is due to the tropical SST forcing that the east Asian summer monsoon circulation has been weakening. All this evidence suggests the observed weakening change of Asian summer monsoon or global monsoon are actually dominated by the face change or PDO. PDO is regarded as an internal variability of the coupled ocean atmosphere system. So when Mary said this kind of change is an internal model, in the meantime, we should note external forcing may also have contributions. And here are two figures. For example, in 2002, my known published paper in science, Shane highlighted that the emission of the black carbon may, okay thank you, induce a drought in the south Asian and east Asian domain. In a recent paper, GFDR scientists have done numerical simulations by using GFDR model. And this suggests if we specify aerosols to a coupled model, the observed drought in the northern part of India can be reproduced in the model world. How about the result of the east Asia domain? Here I show the result based on the multi-model ensemble CMAT5. Here is the trend of surface wind derived from the analysis. You can find a southward wind anomaly. Please note for the mean state, it goes from south to the north. So this suggests a weakening of the summer monsoon circulation. And here is the result of the model world. You can find the weakening trend can be well reproduced except with the weaker magnitude. We further checked the response of the monsoon system to a different kind of fossions. You can find the response in the off fossing actually is dominated by the aerosol fossing. For the greenhouse gases fossing, we can find a yin host rather than weakened summer monsoon. For the contribution of natural fossing, that means volcano fossing in the CMAT5 simulation, the response is not significant. In a recent study, here is again B1's paper. He suggests the recent cooling of the eastern Pacific has favored a yin host global monsoon. Similar phenomena can be found in other data set. And here is a global monsoon index derived from different data set. You can find the monsoon has been recovering in this period in recent decades. And we further examined the trend of this monsoon index over different domains. North African, Asian monsoon, North American, South African, Australia and the South American monsoon. You can find positive trend nearly in all monsoon domains except for the North African monsoon domain. The trend is weak and also the analysis has weakness in this area. The last point I want to note that the recovering trend of the East Asian summer monsoon index actually lacks that of the northern hemisphere summer monsoons in about 11 years. You can find for the recovery of East Asian summer monsoon index, it mainly occurred since the early 1990s. All this evidence suggests both internal variability such as PDO or IPO and external fossing can contribute to the changes of Asian monsoon. But one question we cannot answer is how about the relative contributions? To answer this question we have proposed a global monsoon map, a global monsoon modeling intercomparison project named GM map. It's one of the 17 maps endorsed by WGCM for CMAP6. One key objective of this map is try to answer the question what are the relative contributions of internal processes such as IPO and AMO and the external fossing that have driven the 20th century historical evolution of global monsoons. We have suggested three tiered experiment. The tier one is an extended AMAP simulation but covers more than 100 years. The tier two is a pacemaker experiment. That means we use fully coupled model to do historical simulation but we not observed SST either at the IPO or the AMO domain. These are the major experiments. The tier two experiments need coordination with DCCP C component. Chris is over there. We can have discussion for the coordination of the design for this. To make a summary, the vacancy of the South and East Asian summer monsoon during the second half of the 20th century are local manifestations of global monsoon changes. Both the global monsoon changes and Asian monsoon changes are dominated by the interdicative phase change or PDO or IPO. CMAP5 fossing suggests the areas of fossing with driven awakened summer monsoon while the amnesia of greenhouse gases favors a stronger monsoon circulation over East Asia. Finally, for the monsoon for CMAP6 we hope to by this kind of international cooperation to understand the internal processes and external fossing in driving the past evolution of global monsoon system. Thank you.


Let's go hi, so we're going hi, and we're gonna talk about a bit Sustainability right and so the thing we all do together and and how it Is this working yeah, I think okay, and okay, yeah, and how it impacts our planet and what we can do about it And how we think about it so first Mm-hmm Okay, yeah, oh no this is not oh Yay, so this is me. Hi. So my name is Ori Peckleman. I'm the chief strategy officer at platform of the sage and and and we're kind of going to do the contrast here, so I like slow things slow cooking slow reading and and I Used to be like the chief product officer at platform to the safety there'll be a thing about that and and An important thing usually I would not say something like this in a talk But I have kids and it's going to be somewhat of Important in this talk and and I'll switch over immediately to Oh, I'll try to okay. Okay. Let's be on that side of the Hello everyone Welcome to this session. My name is Fabien Potentier. I'm the CPU the new CPU of platform SH and I'm the opposite of already in the sense that I like things that go that go fast It's it's true when it comes to websites of course, I'm trying to run fast as well I'm not sure that that works really well. So it's me during the Paris marathon And to be very honest, I was quite slow Anyway, so I'm also the creator of symphony which is used in Drupal as well And I really like when I'm able to actually optimize the performance of you know, any website and and of course whenever we are able to optimize Drupal or Symphony or PHP or Linux or whatever you're using That's great for many reasons and and we are going to talk about these reasons today Um Okay, I also have kids That's the thing that we have in common Anyway, so we are how many of you are actually developers Okay, almost everyone So we are Developing things all day long and maybe at some point You you don't even remember that a software is actually running on hardware, right? Even if you are using a serverless architecture, right? You are all aware of that right at some point. We have some hardware That's very important and the digital World represents something like 4% of carbon emissions in the world. So that's huge It's I think it's even bigger than Plains and yeah, it's basically the same size air travel. Yeah, so it's really huge So we should never forget that Whenever we write a piece of code it runs somewhere can be on a mobile phone a Server somewhere in a data center, whatever. So it has some consequences in terms of Carbon emissions Okay So how does that work? So we have you know, you develop a small website with Drupal you asked it somewhere It works. It's nice and then you have some more Traffic, so you are adding more servers because that's the thing that we are doing, right? Oh, by the way, do you know what is the game here on the screen? pitonk That's French That's a French game That's the kind of things that you are doing when you are a slow man This guy, right When you're not walking you play the pitonk And you have a glass of pastis in the other hand, which is like a and now and For equilibrium something really really slow. It's really nice Okay So that's just because When something is getting slower you are throwing metal at it, right? That's just the analogy here get it Yeah, okay Okay, you're sleeping Thank you Yeah, so and and you can do that, you know Many times and at the end of the day you have many servers running your crappy software or I mean You might have also a lot of traffic And do you need to care? Maybe not right and for many years nobody cared because It is the cheap way of actually scaling, right? During the last 20 years Everybody is going to say tell you that Adding more servers is much cheaper than hiring more software developers Right. So the right thing to do is to actually higher Scaling by adding more servers and if you don't care about the environment That's something that you can do if you have us if you are a startup You have a lot of money so you don't care Optimizing the code is hard adding more servers is just you know writing a bigger check. That's all no consequences and of course What we are trying to what we are going to try today is to demonstrate that This is the wrong thing to do and how we can actually Do something better So I was talking about the serverless architecture and nowadays most software developers They are not even you know aware of where The code is actually running 20 years ago and and that's a true story 20 years ago. I add a lot of different websites running on Servers that I own so I add racks in a data center In Paris downtown That's it sounds crazy, but it was it was actually the case so I had some racks with servers real ones and you know whenever something went wrong like a memory Problem or CPU issue or whatever I add to take my bike and get there So I add this relationship with the hardware. It was you know real machines So adding more machines meant that I needed to buy the machine to rack the machine Install Linux and you know everything and taking care of that Nowadays not so much. How many people are actually still having some servers in their own data centers or racks really your Yeah, of course, so you need to migrate And there is a cool company nowadays named platform is H and they can help you if you want Anyway, and and the thing is you know On top of hardware we have so many layers now, so you're not You're not contracting with a data center directly. We have yes. We have Platinum as a service we have long does and things like that So you're not even thinking about the machines anymore, but they are there, of course and the same goes for Code as well. How many of you are still writing SQL statements directly? Yeah, more or less sometimes, right, but most of the time you are using an abstraction layer and or M or whatever And so it means that you are not even thinking about The performance of the code that you are writing because you are not even aware of the SQL statement that are actually executed on the machines and most of the time That's really bad and plus one issues for instance, you are all aware of that So the code can get slower really fast Okay, so anyway, I Think that you know And it's not just about Software development as in websites. It's also about Games for instance I'm not into games, but I know that you know Whenever you start to work on a new game Because or thanks to the Molo, you know that the machines that the people are going to get in three years from now are going to be much faster So there is no need to optimize your code because you are going to run on really fast machine. Is it good? I'm not sure And again, we are going to talk about that so the conclusion for that part is that we need to change our mindset from Optimizing the coast to Taking care of the planet and the impact on the environment whenever we are doing things and of Course you can tell me that Yeah, Fabien you're right, but again optimization and cut optimizations are really hard so adding machines It's kind of green nowadays, right? If you are having a look at Google Azure Amazon they're all saying that your machines are actually Green in the sense that they are using green energy. So should be okay, right? Do you agree with that? Not really Yeah, that's not really the case, right? So there and we can recognize that they are Trying really hard To lower the impact that they have on the planet They have a lot of initiatives and and and some of them so it's about a lot of different acronyms I've listed some of them here They're not really important, but the point is so so maybe I can talk about one One or two maybe so the first one is Power Usage effectiveness so when you are running some out where it consumes some energy and ideally There is no overhead So that that's 100% 20 years ago in my data center It was more like 1.6 so 60% overhead on top of what you can get Ideally nowadays we are at 1.2 Maybe 1.1 so that's great, but we are at the limit We will never get to one anyway, right? There's always some kind of hover it so it's done we can't do better than that so they are doing a lot of things and They're also Purchasing You know power agreement so they are Buying green energy that is injected directly into the grid But at the end of the day, we are still consuming some resources also because it's not just about the power It's also about the machines themselves the digital Industry is growing 10% per year which means that we are adding more and more machines So maybe not 10% more per year, but at some point we are still building machines Which means and everything around the machines so it means concrete buildings rare metals and things like that They are trying to actually Recycle some of the old machines But we are not really good at that either So at the end of the day, we are still adding more machines. It's still bad for the planet So if we can do something better if we can optimize the things that we are doing that's Even even better for the planet Yeah, but I don't like the gloom and doom kind of Environmental talks so let's remember some other things that are kind of important right a lot of what we're doing is actually Creating avoidance and you know whenever you're going so we're we are starting on our journey of Actually understanding the math and the accounting the carbon counting around this and and potentially the most complicated Subject are those of avoidance. It's not like has nothing to do with what you do It's with what doesn't need to be done. So like If we do a computer system where suddenly you don't need to go to the DMV Drive there In order to get something done all of these carbon emissions don't happen, right? And and like when we collaborate on these beautiful digital systems. We have created Probably there's less paper happening and paper bad so And in a way, there's something really interesting about the past of our industry Actually over the last 20 years there was an explosion of usage and the carbon footprint of our domain Hasn't actually gone up It's basically stable for for mostly 20 years because of this PUE going from 1.8 to you know And now Google says 1.1 and so we've been able up until now to Compensate and probably we have potentially been contributing to some serious levels of avoidance But maybe that's over now, right again on the efficiency level Maybe we're not going to gain much and maybe a lot of the terms like the digital transformation and the avoidance that can be created through that probably already done and it's gonna continue on Growing in in like next 10 years, which is kind of the good moment for us to stop throwing metal at stuff because you know Again, I have to be on this side of the thing for for the clicker to work because you know like Again, and I've been reading through so much of their marketing and you know, actually interesting papers You know Ernst and young these days would give you a carbon audit and it will be Ernst and young style with lots of tables and and a lot of acronyms and all of the CFP's and PUE's and all of that and The kind of amusing amazing thing that always happens. I don't know how it ends up with a zero and And the thing is with heroes is that it's really really hard to do math with heroes Like you add zeros and you have zero and you subtract yours and you have zero and you multiply Zeroes and you have zeros you divide by zero. You're in trouble And and no and it's kind of a thing. So you can't really use it. Yeah, right? So We're gonna assume That you know, even if it's like let's assume. It's not zero, right? Let's use an assumption that says it's ain't actually zero and we Whatever it is, whatever the number is We're gonna try to make it lower and and one of the interesting things is that when you look at these zeros These are your scope one and scope two friends. These are the things that take into account the carbon emissions of running as ever while most And I don't know what number is right But most models say something like 50% of the carbon emissions of a workload of a computer Have been created at the moment in which it was put into the rack, right? It's the shipping it and building it and and everything else around it. So Zero if you can actually not be zero and and replacing those even if you recycle them a recycling extremely energy Non-efficient waste recycling stuff. You can usually you need a lot of heat to recycle. So I put here a Good friend. I was looking like for quotes for because it's nice to have quotes for a talk and like So one of the interesting things how many of you are aware of this is a damning Him he's aware of everything like come here do the talk and so he's like the father of statistical process management and You may have heard about things like, you know Kanban and Kaizen Kanban Kaizen Yes, like you like the Japanese being really really good and we learning from the Japanese about how to do actual process management The interesting thing here is that it was it actually an American that after Second World War went to Japan and they Like and he inspired all of that. So it has Japanese names, but it's basically an American so and and and and You know and I think you know this one everybody knows like in God we trust all others have to bring data Now the really interesting thing about this and because we are going to talk about math and really really bad math is that he one of the better known things of damning is the seven deadly sins of management and one of them is You know the KPI story is looking at numbers and Looking only the numbers you can have because more often than not Actually making a process better is going to depend on a lot of stuff that you don't know how to measure yet and Because you know when you have numbers people love KPIs they're gonna look at what they can actually count so We'll try to go and do some counting. Oh Yeah so Luckily enough Even those of you who are not like math majors are like this function It's an easy one, right so You know we have to start thinking about it. So how do we think about it? Kind of easy. We take the resource usage We The resources we multiply them by the usage. We take our PUE 1.1 1.2 and some of those are published yay We look at the carbon carbon intensity of the grid of the actual Production of energy that is used there we multiply all of that yay we get a number that is That is going to sell tell us how many grams kilos tons of CO2 Our usage has created Evidently the problem here is that you can't actually get all of this this data No, like nobody's gonna give you this like even the nice ones that are going to give you Their carbon emissions. They're not going to give you the kilowatt hours So you kind of have to to use their modeling which again tends to end up with a zero Some numbers we can trust actually and and if you haven't looked at it electricity map org I Enjoying you to do so. It's a Zen moment because you can actually see like the live Variations through the day of the carbon mix and that's part of the complexity This actually moves around within the day within the day. It might be all solar All hydro all fun and another hour. It's all like carbon and fumes going out and so again and when you're going to this like beautiful map here one of the interesting things is that like we live on the Internet's right and Even if you know like hey My servers there if all of your clients are here It's going to be potentially quite complicated to actually understand. What's the global? Carbon impact you're going to have and And this is by the way where all of the experts are going to be yelling at each other will go to some yelling afterwards Generally what we want to try to achieve, you know, we Grab some level of numbers That at least are going to be Relative so if we can't get to absolute numbers if I can't get to actually being able to tell you this Website over three months generated this amount of carbon emissions If we believe that it's non-zero What would be interesting is just being able to go and figure out a delta Making whatever that number is as a bit smaller So in a way our question would be What are the things we can actually have control of over as developers not as the ones running data centers? Because none of you have that answer except him. He has but like all of the others So the question is basically now what can we measure and what can we measure that we have levers on? And that actually has an impact Fabien, what can we measure? Okay, so We need to measure something we need to measure the performance of your applications because that's what you can optimize, right? As a developer I can optimize the software that I actually write So I want it to be very concrete I wanted a good example a Drupal one, so I found one And there are many many other ones out there. So this one is based on Drupal Performance optimization that was done for Drupal 8.8 I think Yeah, that's it and it's very interesting because It was about an optimization on the JSON API endpoint Making it 30 percent faster than it was. So that's huge. It's not a few percent. That's 30 percent So how was it possible? It's possible because we have tools nowadays To do that because yeah, so as human beings we are really really bad at understanding how a computer actually works behind the scenes right Whenever I've tried to optimize some piece of code because I thought that you know I had a good understanding on how the code actually worked I was wrong and the only way to actually check if you're right or wrong is using a tool That tells you that your code is actually faster or slower So one such tool in the PHP world is black fire Remember, I like you know things to go fast That's why about ten years ago I Decided to create my own profiler for the PHP world. So that that's black fire You can get there and and and use it to optimize your not to optimize the code but actually to understand how your code is actually behaving on Production servers or your development machines as well so So do you want a demo? Yeah, you can say no, you will have a demo anyway, right? It's not a live demo. Oh Too bad Anyway, so the way it works is that black fire actually instruments your code And it gets a lot of data about what's going on when your code is actually running Basically, all the function calls Right, so whenever there is a function that is called in your code We record that number of time and the CPU usage the IO Memory as well Network activities things like that and then we have several representations This one is what we call the call graph. So basically it is the function calls and The the hierarchy between the function calls So when function a call function B how many of times and things like that and if you have a look at the left side of the screen you can see that some Functions are actually called a lot of times many many many times so the basics of Optimizing something is trying to find Such problems a method of function that is called many times and see why and how you can optimize that or by the way almost 100% of the time the problem comes from SQL you have too many SQL statements for a request 99% of the case that's you don't even need a profiler You need a profiler to actually Understand where the SQL statement is actually called That's that's the goal here. So it's nothing fancy really so here the fix was actually not to optimize the number of calls or Optimize the way the function actually works and trying to optimize the things it's doing it was Just about adding a layer of cash It's kind of cheating I would say but that's good enough So you had a layer of cash and you're done right No, you need to check that you have actually made the code faster because most of the time Cash is makes code slower Because the cash is actually slower than the code that you are replacing. I can tell you that I've seen that many many many times and that's counter-intuitive you are adding some cash so Intuitively it's going to be faster, but that's not the case because you know if your cash is on redis For instance or my SQL whatever you have some latency and it can be huge Okay, so here we are profiting again the code after adding the cash You can see the core graph is very different and now all the method calls to the Symphony normalizer or Serializer component they are gone replaced by some redis calls if you have a look at The numbers at the top of the screen you can see that the code is actually faster So we are using less CPU time less SQL statement as well and less memory There's a secret feature on blackfire and that's how you can compare two different profiles So you don't even need to do that yourself. What you can do is asking blackfire Can you compare profile a and profile B and Here everything in blue means that it's faster Or the same Everything in red means that it's getting slower. So here we can see that the code is actually faster 30% That's what I told you and basically we have more calls to Redis which makes sense because that's the cash layer. So that's efficient So it was committed in the code and part of triple 8.8 That's the solution that's one solution. It's not, you know, it's not about solving all the problems out there But and that's the important point here for you developers as developers We have a responsibility If you're making Drupal faster, it's not just about your websites. It's about all the websites out there using Drupal That's huge, right? And the same goes if you are optimizing some libraries used by Drupal like Symphony It's also the case if you can optimize PHP itself. It's even, you know, the Impact is going to be even bigger than that. And if you optimize Linux as well So as a developer, even if you think that, you know, it's a small optimization It adds up really quickly just because, you know, PHP, Symphony, Drupal They are really popular out there. A lot of different people are using it and Okay, so before talking about Numbers here Or we are some some some math for you. Yeah. Oh, yeah. Yeah, we were starting with We're just starting with a really bad math. It's gonna work at, you know, downhill really fast. So so and again and We kind of have to talk with a bunch of humility because we're, you know We're taking this incredibly seriously and taking this incredibly seriously is first and foremost knowing that we don't actually You know are the experts of that? So we're trying to, you know, learn and Google stuff like this very beautiful article from February That has, you know, this very strong correlation between global temperatures and UFO sightings So we're gonna give you bad math, not that bad And again and about who's doing the bad math and we said a bit earlier that Network traffic is, you know, there are controversies here So this is like the shift project some of the most serious people around Modeling carbon emissions and the IEA and they kind of disagrees by a factor Ethics, botanics, so someone did a bit to bite Our own auditors when we did our carbon audit He found that so I was like what this this looks like So much and it was like a 10x problem again like 8x problem someone, you know didn't divide and and so In a way you can you know, you know when you go on in this journey early on you can kind of throw your hands in the air and and the thing is that You should be okay with doing the bad math Because at any rate if you're making it better, you're not making it worse right if what we are absolutely sure is that Performance density all of these things are perfect process boxes for carbon emissions. We know that so we might not know precisely the Values of the factors, but if we're relatively Gaining 30% there. We know it 30% of avoided emissions. So some really bad math now So as I said the digital world is Consuming 4% of the carbon emissions are actually emitting 4% Out of the 4% 2% are for terminals or your computers mobile phones and things like that and The other 2% is about network and the machines and the machines are about 1% Okay We know that PHP is actually powering almost 75% of all the websites out there and That's the number of websites. It's not really about the traffic, right? But anyway, let's say that it's a huge number, right? Then the Drupal JSON API has been made Really fast so it's 30% faster than before. So that's again This one is not an assumption actually. It's it's it's it's for sure We know that Drupal is almost 2% of the websites out there We're not so sure and again, that's the number of websites. It's not the traffic It's not we are not even sure that they are using JSON API actually, but you know, we are throwing numbers here We are Guest teammate that 1% of the traffic goes to the JSON API Endpoint So it's all about hypothesis here, right? They are all wrong for sure And that's okay. It's not a problem. But if we are doing the math we are at 14 Megaton or kiloton a year Maybe it's twice more than that Maybe it's half of that, but it's non-zero, right? It is significant in any case So we can say for sure that this optimization has a huge impact on the planet It's not zero. It's not insignificant And by the way, it took maybe two hours for someone somewhere to figure that out You know, it's not like it took month to actually optimize that that's not the case But then if you can do that with Drupal You can do that with something else You don't need to optimize Drupal actually you can get more performance for free By doing what? You know that That's hard running better code, you know writing better code is hard. That's possible. I want something simple HTTP cache Sure, it's too hard something else upgrading PHP PHP is getting faster not not for every new release, but almost Any new release is faster than a previous one. It's easy, right? Just upgrade to the latest PHP version and again we can do some math here and again the server represents You know 1% PHP powers 75% of the websites the market for PHP 8.1 is actually 24% and PHP 8.1 is 30% faster than the previous version or 20% or 10% we don't care We know that it is faster than the previous version so if Everyone in the room and everyone in the world Actually upgraded to PHP 8.1 from day one That represents a large number 81 megaton of CO2, so that's huge again and again it's wrong It's not very serious, but we know that's a huge number. It's non-zero How many of you are not using PHP 8.1? What a shame and So maybe Maybe your hosting company is not supporting 8.1 yet You need to switch to a better one I mean I can't talk about the product so I'm not going to say the the product name anyway And and maybe you cannot upgrade because you are still on Drupal 7. Are you? Yeah So you're stuck, right? So and the good news is The bad news is that it's not just about Drupal 7, right? It's about WordPress, Magento some version of Magento and things like that So a very small thing because I see Nicola. Nicola is looking at me saying, oh, that's not the case with Symphony, right? And yeah, and that's something that we've been doing for many years now Making sure that even the oldest version of Symphony the ones that are still supported We make sure that they do support the latest version of PHP because we know that it can have an impact so even if you cannot migrate from Symphony 4 4 to Symphony 5 4 at least you can upgrade PHP And that's useful But there are more problems about running Drupal 7. It's not just about a carbon emission It's not just about the planet. It's about a lot of different things first It is slower because you know Drupal 7 is slower than Drupal 8 and PHP 7.4 or 7 something is slower than PHP 8 Which means that from an ACO perspective you're missing out It also means that your revenue if you have an e-commerce website It's lower and the revenue is lower than it should be because we know that is if Website is faster than the revenue goes up. There are many many many articles about that From Amazon and all the big players out there e-commerce big players. So that's an issue. So you should really See if you can upgrade to a new version of PHP and Drupal just because it's better for the planet, but it's also better for your revenue and Engagement as well. So it's not just about e-commerce website, of course And then you you you might also have security issues and Last but not least as a developer I mean, I'm not a Drupal developers developer, but if I were a Drupal developer, I would not want to use Drupal 7 anymore I would want to use the little version right as developers We love to work with the latest and the greatest, right? So that's a lot of different things and they are all going in the same direction, right? It's better for the planet is better for Recruitment is better for the revenue. It's better for everything. So Why are you waiting? You need to upgrade, right? We need to work on that and again, it has some big impact It's not insignificant. It's really huge Okay, so it's all about, you know Getting better Having faster code and upgrading. That's the easiest thing that you can do I'll try to go fast now because we're kind of late now. Yeah, so Again as we've repeated all along Current accounting is not yet a science most of the data you're going to Get is is is a flow quality and If you can't really actually figure out where to start Start with making apps faster And again and not all of the Data is going to be that bad electricity map really good So actually running the same application in Portland or in Paris It's precisely five times like it Paris is actually five times better So one of the things you can, you know, actually do is Try to have the influence you can on well What clouds are going to be running because that's like five times he got 30% and It's yeah, and the PHP 8.1 30% this is like a lot of percent. This is five times I don't know how to translate times to two percent because I'm really good at math Okay, and and you know and when we we look at it. It's actually, you know, it's kind of easy to go And and try to figure out the bank for the buck, right? What's under my responsibility? Where do I have levers and you know, and it's kind of a pyramid and you know And you know, okay, maybe I can get up to 20 times better By writing better code and maybe I can get 10 times better by choosing the workload location if I have customers that are mostly in Europe and I can decide between Ireland and Sweden Don't use Ireland We like the Irish, but it's very carbon-intensive and and again and the Between Sweden and Germany. This is huge Evidently when you do that you actually have to think about the The other effects because speed is important if your customers are in Australia Maybe don't put it in Sweden because again, we don't forcefully understand very well the network model and potentially you're just going to create so much more carbon emissions over the network and And when you're people like us running actual workloads on servers density There are so many levers we can get by simply saying, you know Let's get more workloads on a single server. It's possible. We have the science for that. That's actually stuff We know how to do We've started with a carbon audit. So what one of the like on this journey before, you know You know, we have all of these questions of Relative change and positive change, but the first thing is to establish a baseline even if this baseline is wrong And I do believe it is It's important because that's the only way you have to measure something you can optimize right something that needs to get better and And and you know and we have this like number of 94 tons a year for the company And which is the marketing number, which is a damn lie because we're a distributed company So evidently a no commute and no commute not a lot of carbon emissions already But the number that we need to look at is the actual number that is generated by the workloads of our customers And this is what we know, I don't want to change You know, like what computer can people buy not it's not the first thing we should do, right? buying maybe one that has a high repair index better, but that's like this is 3700 tons we need to look at and figure out Every one of the levers we can have and we have you know, like we're moody cloud We you can run on hundreds of data centers. That's a lot of optimization points we can use So if it's not really important if it's in Norway or Sweden That just between Norway and Sweden you have a 2x thing. No, no Norway is just like wow and and Again, and it's about going and measuring and trying to figure out all of the small places that make big impact and and as you've seen it's kind of Quickly enough you get into the kilotons and and this is you know, like you yourself You can be responsible in your company us as for our customers for actually You know significant change and So part of what you know, as we said, we're not climb climate experts, but There are actually climate experts around so I'm all the time repeating. It's not a science But there are actual scientists working on this we've hired one and and like She was at the IPCC before like she wouldn't work with us if you know It's well, yeah, let's let's continue on like And I think you know like at the end of the day, you know, I we started with a thing that we have kids So this is a beautiful kind of representation of projected carbon emissions and global temperature rise and like and you can personalize it you can Google it it's by Sophie Lewis and and like these are the actual numbers of And you know and this is like the this is the horrible business as usual Scenario like if we don't actually now actually now start in our Daily lives in our work environment start this only happens if like if you take us seriously If you don't take this really really seriously, this is the scenario that happens and this is You know now and it's not about, you know, even carbon and you know Paris Accords in 2030 All of that is actually too late. Look at the graph. It's too late. So Yeah you know, but My kid 11 years old told me he doesn't want to have kids because at school they told him about you know Climate change and he was like, okay humans they pollute. I'm not going to create new humans And that was anyway, and I'm not okay if he doesn't want to have kids It's great, but not for that reason. That sounds like a horribly depressing reason and so yeah, we can Get better at being good Any questions? I'm so once upon a time. I almost quit Doing Drupal commerce work to do utility analytics. So helping power companies optimize The production of energy Because even if we reduce like the the consumption of energy by any one application That doesn't mean that that power company now gets to turn on one fewer generators right like because like The energy exists. It's been produced at what point, you know, how do you how do you measure the impact then of optimization? Because they may still have the same number on It's not so if you look, you know, like if you look at And again, Google are the bell. Sorry Google are the best at least at marketing on this And you will see that they're complaining about all the others that are doing market-based. So basically they're buying a Green electricity somewhere else than where it's actually being used Okay, but and what they're saying and which is true that the others don't do hourly counting So if you look at the electricity map, you will see that the carbon mix actually changes by hour and therefore Absolutely, if you you know if there's a lot of consumption on the grid, it will go towards the least green Sources because you know, they're not always there.  Yeah, possibly And like because this is how electric grid actually work, but Yeah, so so it actually has an impact if you're generating less Consumption they actually don't have to turn on the carbon emitting energy sources anything else Questions So I was just kind of wondering so what do you think about at this time like we're kind of going through the phase of People are starting to now get into like web 3 and as you said like playing newer faster games and like Metaverse augmented rally everything that takes so much more data all the extra data that's being used and energy that's being created Do you think? like as you're saying Do you think there's a way that we can become more efficient as you're saying like, you know would take if all of us kind of, you know Slow down one thing or optimize our code and optimize everything a little bit better Do you think there's there will be a time where that will actually be able to get close to Being able to like look at our own code or look at sites or whatever we're doing to say like hey We're getting closer to net zero by doing, you know, x y and z or something. Is there a possibility? Again net zero can't possibly exist. It's and it's you know, and it's really Bying into the lie and we cannot we must not what we can do is actually ask ourselves for the service rendered Right because what we are doing is useful, right? If what we're doing is useless then anyway, we shouldn't be doing it and what we're doing is potentially useful for avoidance and great so the question is can we actually make systems that are extremely useful and less carbon-intensive and Because we don't want to you know turn off all of the things We still want the the phones and the games and but but but currently we are Incredibly inefficient and if you know and when we're introducing new stuff like web 3 I'm not allowed to draw but When something is by definition and by construction energy inefficient like Proof of stake doesn't work and proof of work is basically proving that you're burning the planet So I would say you know from my standpoint. I will not participate in NFT web 3 stuff Personally, I don't foresee I won't forcefully yell at people that do I will probably yell at people that do Just shut down the machines. Yeah doing web 3 That's that's a nightmare So and I kind of I actually tested Deploying like a gas so a netherium thing and doing a solidity workflow on platform to see and it's actually really cool because Like developing in solidity is a is just a shicho. It's not not good And it was like fun and then and then I was like, yeah, but I'm not telling anyone about it So we'll cut this part of the video and don't run web 3 things on platform, please. We won't We won't like you So the thing is to So basically being mindful really rather than actual he trying to solve Every problem really because the way technology is I'm scaling with Bitcoin and all of these things as long as you have a lot of people Using the internet has just going to be A lot More of carbon Energy so is It's just better to be mindful really rather than trying to solve You see I like if you look at the graphs of growth of carbon emissions related to cloud usage and If people actually took too hard they thought one PHP thing we would have been flat That's enough to keep the planet like on our domain Probably flat. So no, I don't believe that You know that we there's nothing we can do and it's like okay usage is going to grow and therefore carbon emissions The the truth of the matter is that when I I think that Fabien also, but I think many of it when I see softer I'm horrified. I mean we're 2022 and this is horribly written and this is horribly run and this is incredibly inefficient and We can actually do better. I mean we look, you know in the in the stuff that I don't want to again publicize my my company of this but You can earn factors in Indensity like this is so incredibly inefficient people are running these VMs and and and you know Even you look at a Kubernetes cluster and how it's built and the level of duplication of work that happens in these things How incredibly inefficient they are there are factors to be gained? So if we're growing by 10% a year, but every year we got gained a factor I mean if we actually work in this we know we we can actually have sustainable Growth of the digital domain with zero growth at least of the carbon emissions. It is feasible I mean and I mean you've seen the numbers, right? Bad math, but that's actually saying I mean this bad math actually says that if we work on this and this is top You know top one priority. We actually can be Participants to the growth of the economy the growth of human consciousness like the the Twitter guide said and we can allow consciousness And not burn the planet or not be the ones responsible for it And maybe the conclusion here is that there is hope Right. There are so many things that we can optimize. It's not like it's super optimized right now So there are so many things that we can we can do all together to be sure that we can optimize more So that you know, it's not growing as fast as we can anticipate Okay, thank you very much


Now, we always talk about necessary proportional, we talk about surveillance, and we're living in a world where a lot more things than necessary and proportionality is different. So you can easily see things being snuck in that's not related because it's an exceptional time and I do worry about that.


Hello and welcome to another edition of Whiteboard Friday. My name is Elisa Sharf. I'm the director of SEO here at Sierra Interactive and today I'm gonna be your host. So my talk today is about harnessing generative AI for content and SEO success. So AI is here. How do we feel about it? We're talking to a lot of people at Sierra and people are feeling different ways about it. Some people are feeling really skeptical about it and they're really worried that the outputs from generative AI aren't going to comply with EEAT principles. That's experience, expertise, authority and trust. So how can a machine capture all of those principles? We're also talking to some people who are kind of annoyed or dismissive about the tool and they say it's a plagiarism engine. It has no role in good quality content production. But at Sierra we're feeling the love, we're really excited about it and what we're gonna talk about today is a framework that offers a way to leverage generative AI in a way that's safe but also effective. So let's just plan ourselves in the task at hand. Say we want to create a content brief and for the sake of the example today we're gonna be talking about GPT-4 and so there's two different ways that we can do this. One way is to say look, GPT-4 is really, really smart. It can pass the bar, it can earn an MBA. So surely it's smart enough to do some SEO and content research. So you can just go to GPT-4 and say here's my keywords, write me a content outline, copy and paste the output, give it to somebody to review, deliver, publish to your website. So you can certainly do that but if you're going to rely totally on the vast knowledge of GPT-4 and say, hey, build me a content brief, there's gonna be some issues that you might find with that process. So one of them is gonna be that you might have low confidence in the quality of your recommendations relative to the SERPs that you're targeting. So how exactly does GPT-4 know what's present and what elements Google is rewarding on those SERPs? So that's gonna be an issue. You're also potentially gonna have low confidence in the uniqueness of those recommendations. So if you can follow this very simple process, what's stopping your competitors from doing the exact same thing and coming up with a very similar output? And lastly, we get back to the EEAT principles. So what exactly about this process is going to demonstrate experience and expertise? It's going to be a very fast process but is it going to be good? Hard to say. So what we're gonna talk about today is the process and the framework that we've been using at SERP and that framework is called PARS. It stands for Populate, Activate, Retrieve and Scrutinize. So I'm gonna take you step by step through each level of this framework. One of the things we really like about this framework is that it's going to blend your data, your experience, your insights with the vast knowledge of all of those large language models. It's going to allow you to combine the power of those models with the existing and unique knowledge and their ability to ingest and contextualize new information. And lastly, it's going to come with a very robust review period that should make your compliance team very happy. So let's get into it step by step. So the first step is Populate. And let's take a step back. Imagine you were starting at SERP as a new hire. I wouldn't say go to the library and learn everything you can about everything. That person would come back a very intelligent person but they still wouldn't know our process and our approach and what our clients are expecting. So instead, what I would do is we would put them through our training program, share with them our training material. We would share with them our templates, our best in class examples so they can contextualize themselves with what it is we deliver to clients. So let's start by doing that with GPT. We can import all of that information directly to GPT-4 so it really understands the task at hand. Next, we wanna make sure that the output we're building is as high quality as it can be. And so we're not just going to give it keywords. We're going to designate, here's the primary keywords, here's the secondary keywords, here's data we've pulled from the SERPs to say here's the features that are present so we wanna make sure we're optimizing for them. Here's people also ask questions so we understand what the audience is really looking for with this content and here's related keywords. And we're not going to stop there either. We also wanna make sure that we're integrating the subject matter experts into the content process. So how would we do that without GPT? I would have my team talk to people like sales representatives, customer service representatives, or directly with our clients' audiences to better understand their pain points, their motivations and their triggers. And so all of the outputs of those conversations can also be ingested directly into GPT. Lastly, we wanna make sure that the content we're creating aligns with the brand guidelines, the brand vision and the brand tone. That's really important. That's the first step. If you were to just stop there, you're gonna have a pretty good output, but there's three more steps. The second is activate. And this is basically what people are referring to when they talk about prompt engineering. So there's a ton of information out there about prompt engineering. Mike King has a great resource that he's built for the community that you should definitely check out when it comes to prompt engineering. So I'm just going to focus on the high level steps that we're taking here at Sierra. The first thing is you wanna make sure that you are diagnosing the problem that GPT needs to solve for you. The second step is to break down that problem into functional elements. The third step is to reframe that problem. So say it a different way to make sure that GPT understands the context. Lastly, you're going to outline the constraints. So what are the must haves and what are the nice to haves? So the third step is retrieve. If you've done step one and two very well, retrieve is gonna be very easy. What you're going to do is just copy and paste the output. You can either copy and paste it directly from chat GPT or you can export it to a file type like a PDF, a Word doc, or an Excel, or you could be working directly within GPT for sheets for an easier output. The last step is scrutinize. So this is the review period. What's really exciting is there is a very near future where GPT is gonna be able to do this step for us. For now, what we're gonna focus on is the who, the what, and the how. And so first of all, who should review it? When you're initially building out this framework, you should be having anybody review it who has a stake in the final output. And so that could be SEOs, that could be subject matter experts, product managers, anybody who wants the best possible output for this work product. The next step is gonna be the what. So what exactly are you reviewing for? You wanna review for everything. You wanna review for grammar, make sure the information is accurate, make sure the brand guidelines are followed, make sure we've got the subject matter expertise incorporated, and most importantly, make sure that the output is unique. You can be doing this with a variety of tools and people. Lastly, how do you do this? Ideally, you just latch this on to your existing acceptance criteria or SOPs in order to create a checklist to run through. The last tip here is any feedback that you identify as part of that review period, you can feedback to step one or step two and make sure that you're applying that feedback to the framework to make it better for next time. And so that is the process. That's pars, populate, activate, retrieve, and scrutinize. And this is a process, again, that you can use to leverage generative AI for SEO and content in a way that is both safe but also effective. So thank you for spending your time here with me today. For more ideas like this, please follow us on the SEAR newsletter. Thanks.