Periodic Trends Guided Note Sheet Answers: Fill & Download for Free

Before Using a graph in UPSC Mains Examination, One should ask Why?Why Should one use graphs in Mains Exam Answers?> It helps examiner grasp visually the answer that has written.> It takes examiner less time to understand your answer.> It gives examiner a better understanding of your though process behind writing the answer.> It takes less time to explain great deal of details in very limited time.> It helps display a lot of information in an easy way.> Line graph Helps showing a trend over a period.> Bar graphs show comparison stats in a simple way.> pie charts display group breakdowns neatly as well.> It limits the data to show which helps your to put a limit and only required effort.> Even if you are not sure about the actual data/fact, you can show a relative presentation solve the purpose up to large extent.> With the help of a graph, you can easily restrict the content of your answer as per requirement.> A graph in the answer always reduce your effort of writing a lengthy boring paragraphs. Max word limit is mentioned in the question paper, not the min limit. So It wont matter how short your answer paragraph is. All that matter is, your answer should be informative and complete, which can easily be achieve with the help of graphs.Note: You should be cautious using graphs in terms of relevancy and only when required. Too many graphs in answer sheet is not encouraged.Does this impact the time limit?This actually helps saving the time in terms of reducing the effort of writing a lengthy paragraphs which takes relatively longer time than drawing a graph.But too many graphs would lead to imbalance of time limit. The graphs should be used only when you are sure about the information you want to convey. A meaningless/vague would certainly be exposing your weakness directly.How does this affect the word limit?>> This has been answered above.Basic of Graphs:Before learning the graphs, there are few concepts to understand:X-axis -- > The line horizontal to the planY-axis -- > The line vertical to the planX-axis and Y-axis meets only once on a plain and they are perpendicular to each other.Dimensions: A dimension object is the object being tracked; in other words, it can be considered the focus of the analysis.Measure: A measure object which can be derived by aggregate functions like sum, count, avg etc. In simple words, these are numerical values which can be manipulated based on any related dimension.Any graph has at least one dimension and one measure. There could be more than one in one graph. For better understanding, please go through examples.Example of Measure:No. of Student,No. of States,Count of UN CountriesTotal No of Biodiversity Hot spot in IndiaExample of Dimensions:CountryMahaJanpadaStateCitizenshipComponents of a graph:This diagram would help you understand the components of a graph. Red color marked are something that is required in answers and rest are optional.What are the most common graphs that can be used in Main Exam Answers?The below diagram would answer this question- Pie ChartPie charts are a visual way of displaying how the total data are distributed between different categories. Pie Chart can be used to show the percentage of the total votes received by each party in an election. Pie charts should only be used for displaying nominal data (i.e. data that are classed into different categories). They are generally best for showing information grouped into a small number of categories and are a graphical way of displaying data that might otherwise be presented as a simple table. The study guide Pie Charts gives more details about designing pie charts and using them to compare data. Below are few relevant examples:- Bar GraphBar charts are one of the most commonly used types of graph and are used to display and compare the number, frequency or other measure (e.g. mean) for different discrete categories or groups. The graph is constructed such that the heights or lengths of the different bars are proportional to the size of the category they represent. Since the x-axis (the horizontal axis) represents the different categories it has no scale. The y-axis (the vertical axis) does have a scale and this indicates the units of measurement. The bars can be drawn either vertically or horizontally depending upon the number of categories and length or complexity of the category labels. There are various ways in which bar charts can be constructed and this makes them a very flexible chart type. For example, if there is more than one set of values for each category then grouped or component bar charts can be used to display the data. Further details about each of these different types of bar chart can be found in the associated study guide Bar Charts.This is again am easy graph to draw on paper without worrying about accuracy. This gives a very clear understanding of your answer. Examiner love to have such kind of representations since it helps the to evaluate your answer and the thought behind the answer.Check Examples for better understanding:- Line GraphThis is one of the most popular types of graph because this is very easy to create and easy to understand.This represent data in clear manner and show relationship between the data. This show the change in direction of an output.Line graphs are usually used to show time series data - that is how one or more variables vary over a continuous period of time. Typical examples of the types of data that can be presented using line graphs are monthly rainfall and annual unemployment rates. Line graphs are particularly useful for identifying patterns and trends in the data such as seasonal effects, large changes and turning points. As well as time series data, line graphs can also be appropriate for displaying data that are measured over other continuous variables such as distance. For example, a line graph could be used to show how pollution levels vary with increasing distance from a source, or how the level of a chemical varies with depth of soil. However, it is important to consider whether the data have been collected at sufficiently regular intervals so that estimates made for a point lying half-way along the line between two successive measurements would be reasonable. In a line graph the x-axis represents the continuous variable (for example year or distance from the initial measurement) whilst the y-axis has a scale and indicates the measurement. Several data series can be plotted on the same line chart and this is particularly useful for analyzing and comparing the trends in different data sets.During UPSC Mains Exam, you would come across many questions where you can show a trend using this graph.To draw this graph, you don’t need to worry about the accuracy because it gives an easy to draw flexibility. And In this exam, the purpose of a hand draw graph won't be expected to accurate. It is just for the representation purpose. You can take any dimension and plot the line graph for any related measure.Below are few examples of Line Diagrams- Time lineA timeline is a way of displaying a list of events in chronological order, sometimes described as a project artifact. It is typically a graphic design showing a long bar labelled with dates alongside itself and usually events labelled on points where they would have happened.This helps in questions where a historical evolution has been asked such as show the evolution of Mauryan Empire. Here we can highlight major events with the help of a timeline.This is very easy to draw. you can simply draw a line, mark pointer and show events. Check below Examples for better understanding:- Organizational ChartAn organization chart (sometimes called an organizational chart, an org chart, or an organogram) is a diagram that illustrates the structure of an organization, the relationships and relative ranks of its business units/divisions, and the positions or roles assigned to each unit/division. This can be used in answers of such questions where it has been asked to describe the structure as an organisation/administration/Govt. body/Institutions etc.This can be also be used in questions where the flow of a process of cascading impact has been asked for. Check out Examples- Venn Diagrams:Venn diagrams enable students to organize information visually so they are able to see the relationships between two or three sets of items. They can then identify similarities and differences. A Venn diagram consists of overlapping circles. Each circle contains all the elements of a setTypes of Venn diagram that could be included in Mains Answer writing- Overlapping: This is a diagram that shows all possible logical relations between a finite collection of different subjects/topics. This helps examiner to know how well you understand the subject matterand well you can build relations with others. It requires a deep understanding on the topic. Below are few relevant examples which would help understand better.- Nested: In this type of Venn Diagram, one is super set of another. For Example an India State - Maharastra and India would be in a nested Venn.- Mixed: This is mix of both Overlapping and Nested. Look out below examples:One more form of diagrams, a favorite of all of us, which could be more extensively used in answer writing is Free Form.Free Form: Here we group and draw as per our need. No specific rule is to followed in this case. ans example is given below:Advantages of Venn Diagrams in mains Answers:Good to use when concepts overlapGood for visual representation of your answerUsually requires higher-order thinking - Helps examiner about your though process on subjectQuick way to illustrate a complicated issueAppeals to examiner with easy and smooth understanding of your answerShows the depth your understanding on concept and subject matterDirectly Impress the examiner.Gives your answer a cut above the average marking.Check out my other posts for answer writing examples with the use of various graphs from previous year question papers.

Climate models don't "predict." They project what will happen given a set of initial conditions and may include several scenarios. If a certain scenario didnt happen, it does not mean the prediction was wrong. It means the conditions for that particular scenario didnt happen. The scenarios are often grouped as most likely and worst case etc.All studies shows models have underestimated the realities.Part of the problem here stems from people either misunderstanding or deliberately misrepresenting how predictive models work. Many people have the unrealistic expectation that the observed data need to be a near perfect match for the prediction line, but that’s not actually how things work.Some are still "under the mistaken impression that concern about global warming is based on climate models, which in reality play little role in our understanding -- our understanding is based mainly on how the Earth responded to changes of boundary conditions in the past and on how it is responding to on-going changes."- Dr. James HansenSome people argue that climate models are unreliable if they don't make perfect short-term predictions. However, a number of unpredictable influences such as ocean and solar cycles have short-term influences on climate. Over the long term, these effects average out, which is why climate models do so well at long-term predictions.It’s basic physics which follows the same principles as other laws of nature.CLIMATE PROJECTIONS HAVE BEEN VERY ACCURATE:We’ll check the models, but first:Latest 2020:Climate models published between 1970 and 2007 provided accurate forecasts of subsequently observed global surface warming. This finding shows the value of using global observations to vet climate models as the planet warms.The first climate models to numerically describe an evolving and interacting atmosphere, ocean and land surface on a grid covering the entire Earth date back to the 1970s (for example, refs 1–3). Since then, the planet’s surface has warmed, in large part because of increased emissions of greenhouse gases. Writing in Geophysical Research Letters, Hausfather et al.4 retrospectively assessed the forecasting skill of climate models published between 1970 and 2007. Their results show that the physics in these early models was accurate in predicting subsequently observed global surface warming.Early climate models successfully predicted global warmingThe First Climate Model Turns 50, And Predicted Global Warming Almost PerfectlyEven 50-year-old climate models correctly predicted global warmingThe Worst-Case Scenario for Global Warming Tracks Closely With Actual EmissionsStudy Confirms Climate Models are Getting Future Warming Projections RightStudy Confirms Climate Models are Getting Future Warming Projections Right – Climate Change: Vital Signs of the PlanetModels that were used in the IPCC 4th Assessment Report can be evaluated by comparing their approximately 20-year predictions with what actually happened. In this figure, the multi-model ensemble and the average of all the models are plotted alongside the NASA Goddard Institute for Space Studies (GISS) Surface Temperature Index (GISTEMP). Climate drivers were known for the ‘hindcast’ period (before 2000) and forecast for the period beyond. The temperatures are plotted with respect to a 1980-1999 baseline. Credit: Gavin SchmidtUpdate day 2020!Nov 2019:"According to the research published today, almost every peer-reviewed climate model of human-caused global temperature rise dating back to 1970 lines up with the warming we see today.“Taken together,” he added, “these climate models have always been quantitatively accurate.”How good have climate models been at truly predicting the future?Hausfather et al 2019Evaluating the performance of past climate model projectionsCONTENTCLIMATE MODELS HAVE BEEN VERY ACCURATEIPCC MODELSTHE HISTORY OF CLIMATE MODELSBONUSES - INCLUDING DEBUNKING OF MODEL MYTHSThe reason why long term climate predictions ARE possible, and 2–3 days weather predictions are NOT possible, is very logical:A number of unpredictable influences such as ocean and solar cycles have short-term influences on climate. Over the long term, these effects average out, which is why climate models do so well at long-term predictions.It’s basic physics which follows the same principles as other laws of nature.“The physics we use to understand the earth’s climate system is the same physics that explains how stoves, fridges, airplanes and more work. And most people don’t really have a problem with the physics of non-linear fluid dynamics and radiative transfer that have been well understood for decades, even centuries.” (Climate scientist Katharine Hayhoe.)Just check out world known and verified Quora account climate scientist Katharine Hayhoe:Katharine Hayhoe's answer to What climate models do you believe to be the most accurate? In other words, what scenario do you think will most likely play out in the near future and far future? How do you communicate this to the public?Now, let’s check the models:" the close match between projected and observed warming since 1970 suggests that estimates of future warming may prove similarly accurate."Here are some actual predictions from Global Climate Models all of which have proven correct. Im even linking to the paper directly so you can go and read for yourself.- That the Earth would warm, and about how fast, and about how much(Arrhenius 1896, Callendar 1938, Plass 1956, Sawyer 1972,Broecker 1975; validated by Crowley 2000, Philipona et al 2004,Evans and Puckrin 2006, Lean and Rind 2008, Mann et al. 2008, etc)- That nighttime temperatures would increase more than daytime temperatures(Arrhenius 1896; validated by Dai et al. 1999, Sherwood et al. 2005, etc)- That winter temperatures would increase more than summer temperatures(Arrhenius 1896, Manabe and Stouffer 1980, Rind et al 1989; validated by Balling et al 1999, Volodin and Galin 1999, Crozier2003, etc)- Polar amplification (that temperatures increase more as you move toward the poles)(Arrhenius 1896, Manabe and Stouffer 1980; validated by Polyakov et al 2001, Holland and Bitz 2003, etc)- That the Arctic would warm faster than the Antarctic(Arrhenius 1896, Manabe and Stouffer 1980; validated by Doran et al 2002, Comiso 2003, Turner et al 2007, etc)- That the Earth’s troposphere would warm and the stratosphere would cool(Manabe and Wetherald 1967, Manabe and Stouffer 1980; validated by Ramaswamy et al. 1996, 2006, De F. Forster et al 1999, Langematz et al 2003, Vinnikov and Grody 2003, Fu et al 2004, Thompson and Solomon 2005, etc)- The near constancy of relative humidity on global average(Manabe and Wetherall 1967; validated by Minschwaner and Dessler 2004, Soden et al 2005, Gettelman and Fu 2008, etc)- Scientists made a retrodiction (a model prediction based on established physics) for Last Glacial Maximum sea surface temperatures which was inconsistent with the paleo evidence for those times; better paleo evidence showed the models were right(Rind and Peteet 1985; validated by Farreral et al 1999, Melanda et al 2005, etc)- The clear sky super greenhouse effect from increased water vapor in the tropics(Vonder Haar 1986; validated by Lubin 1994, etc)- That coastal upwelling of ocean water would increase(Bakun 1990; validated by Goes et al 2005, McGregor et al 2007, etc)- The magnitude (0.3 C) and duration (two years) of the cooling from the Mt. Pinatubo eruption(Hansen et al 1992; validated by Hansen et al 1996, Soden et al 2002, etc)- The amount of water vapor feedback due to ENSO(Lau et al 1996; validated by Soden 2000, Dessler and Wong 2009, etc)- The rising of the tropopause and the effective radiating altitude(Thuburn and Craig 1997, Kushner et al 2001; validated by Santer et al 2003, Seidel and Randel 2006, etc)- The response of southern ocean winds to the ozone hole(Fyfe et al 1999, Kushner et al 2001, Sexton 2001; validated by Thompson and Solomon 2002, etc)- The expansion of the Hadley cells(Quan et al 2002; validated by Fu et al 2006, Hu and Fu 2007, etc)- They predicted a trend significantly different in amount and different in nature from UAH satellite temperatures, and then a bug was found in the satellite data which showed that surface temperatures were more accurate and reliable than UAH temperature data.(Christy et al 2003; validated by Santer et al 2003, Mears and Wentz 2005, Santer et al 2005, Sherwood et al 2005, etc)- The poleward movement of storm tracks(Trenberth and Stepaniak 2003; validated by Yin 2005, etc)NASA scientist Gavin Schmidt:“In this new study, NASA scientists analyzed the GISTEMP data to see if past predictions of rising temperatures were accurate. They needed to know that any uncertainty within their data was correctly accounted for. The goal was to make sure that the models they use are robust enough to rely on in the future. The answer: Yes they are. Within 1/20th a degree Celsius. Kudos.”Here's How Scarily Accurate NASA's Long-Term Climate Predictions Have Been So FarReality check december 2018:"Twenty years ago in Nature we concluded that recent warming was unprecedented in at least six centuries"https://www.nature.com/articles/33859This year in Nature, scientists concluded it's unprecedented in at least eleven millenniahttps://www.nature.com/articles/nature25464SUMMARY:Global climate models aren’t given nearly enough credit for their accurate global temperature change projections. As the 2014 IPCC report showed, observed global surface temperature changes have been within the range of climate model simulations.Now a new study shows that the models were even more accurate than previously thought. In previous evaluations like the one done by the IPCC, climate model simulations of global surface air temperature were compared to global surface temperature observational records like HadCRUT4. However, over the oceans, HadCRUT4 uses sea surface temperatures rather than air temperatures.Climate models are even more accurate than you thought | Dana NuccitelliWere the predictions we made about climate change 20 years ago accurate? Here's a lookLiterature that suggests the models are on track after all. And while that may be worrisome for the planet, it’s good news for the scientists working to understand its future. Climate models are even more accurate than you thought The difference between modeled and observed global surface temperature changes is 38% smaller than previously thought. Global climate models aren’t given nearly enough credit for their accurate global temperature change projections.As the 2014 IPCC report showed, observed global surface temperature changes have been within the range of climate model simulations.Worrisome first quarter of 2017 climate trends » Yale Climate ConnectionsClimate Models - OSS FoundationSlow climate mode reconciles historical and model-based estimates of climate sensitivityRobust comparison of climate models with observations using blended land air and ocean sea surface temperatureshttps://www.researchgate.net/pub...Factcheck: Climate models have not 'exaggerated' global warmingBonus:CMIP6: the next generation of climate models explained | Carbon Brief2. IPCC PREDICTIONS:The IPCC predictions have been accurate, but conservative. They have in fact underestimated the warming.“Comparisons to the most recent data consistently finds that climate change is occurring more rapidly and intensely than indicated by IPCCpredictions.”“Brysse et al. (2012) suggests that the IPCC and climate scientists in general tend to be too conservative in their predictions because they are "erring on the side of least drama" (ESLD). However, they point out that an underprediction is just as wrong as an overprediction. Climate scientists may be introducing bias into their predictions for fear of being called "alarmist," but this conservative bias may leave us unprepared for the magnitude of future climate change.”Science is conservative by nature and climate science even more.“one thing is certain: Several fundamental projections found in U.N. Intergovernmental Panel on Climate Change reports have consistently underestimated real-world observations, potentially leaving world governments in doubt as to how to guide climate policy.”The IPCC, for instance, failed to anticipate China's economic growth, or resistance by the United States and other nations to curbing greenhouse gases.“We really haven't explored a world in which the emissions growth rate is as rapid as we have actually seen happen,” Fields said.IPCC Predictions: Then Versus NowClimate Science Predictions Prove Too ConservativeClimate change prediction: Erring on the side of least drama?https://skepticalscience.com/ipc...Let’s look at the realities:IPCC models fits well when put on end of other predictions. But ,as mentioned, a bit conservative:Assessment of the first consensus prediction on climate changeComparing CMIP5 & observationsRobust comparison of climate models with observations using blended land air and ocean sea surface temperaturesA global perspective on CMIP5 climate model biasesAssessment of the first consensus prediction on climate changehttps://skepticalscience.com/ipc...IPCC PREDICTIONS HAVE DONE WELL AND MUCH BETTER THAN CONTRARIANS PREDICTIONS:BONUS: CONFUSING SHORT TERM MODELS WITH LONG TERM CMIP MODELS.Some people argue that climate models are unreliable if they don't make perfect short-term predictions. However, a number of unpredictable influences such as ocean and solar cycles have short-term influences on climate. Over the long term, these effects average out, which is why climate models do so well at long-term predictions.IPCC explains this difference here:IPCC confirms that short term internal climate variability, in any given 15-year period is hard to predict."For the period from 1998 to 2012, 111 of the 114 available climate-model simulations show a surface warming trend larger than the observations"Then they confirms their CMIP5 models are accurate and explains recent 15-year period short term predictions, showing a surface warming trend larger than the observations, was because of El Ninò:"There is hence very high confidence that the CMIP5 models show long-term GMST trends consistent with observations, despite the disagreement over the most recent 15-year period. Due to internal climate variability, in any given 15-year period the observed GMST trend sometimes lies near one end of a model ensemble, an effect that is pronounced in Box TS.3, Figure 1a, b as GMST was influenced by a very strong El Niño event in 1998."https://www.ipcc.ch/pdf/assessme...Risbey et al (2014) found that climate models actually generate good estimates of recent and past trends provided they also took into account natural variability, particularly the key El Nino-La Nina phases in the Pacific.“You’re always going to get periods when the warming slows down or speeds up relative to the mean rate because we have these strong natural cycles,” Dr Risbey said.In roughly 30-year cycles, the Pacific alternates between periods of more frequent El Ninos - when the ocean gives back heat to the atmosphere - to La Ninas, when it acts as a massive heat sink, setting in train relatively cool periods for surface temperatures.By selecting climate models in phase with natural variability, the research found that climate models have provided good estimates of 15-year trends, including for recent periods and for Pacific spatial trend patterns.By selecting climate models in phase with natural variability, the research found that climate models have provided good estimates of 15-year trends, including for recent periods and for Pacific spatial trend patterns.Well-estimated global surface warming in climate projections selected for ENSO phase3. LETS TAKE A LOOK AT THE HISTORY OF CLIMATE MODELS:“The researchers compared annual average surface temperatures across the globe to the surface temperatures predicted in 17 forecasts. Those predictions were drawn from 14 separate computer models released between 1970 and 2001. In some cases, the studies and their computer codes were so old that the team had to extract data published in papers, using special software to gauge the exact numbers represented by points on a printed graph.Most of the models accurately predicted recent global surface temperatures, which have risen approximately 0.9°C since 1970. For 10 forecasts, there was no statistically significant difference between their output and historic observations, the team reports today in Geophysical Research Letters”Even 50-year-old climate models correctly predicted global warmingThe First Climate Model Turns 50, And Predicted Global Warming Almost PerfectlyModeling the Earth's climate is one of the most daunting, complicated tasks out there. If only we were more like the Moon, things would be easy. The Moon has no atmosphere, no oceans, no icecaps, no seasons, and no complicated flora and fauna to get in the way of simple radiative physics. No wonder it's so challenging to model! In fact, if you google "climate models wrong", eight of the first ten results showcase failure. But headlines are never as reliable as going to the scientific source itself, and the ultimate source, in this case, is the first accurate climate model ever: by Syukuro Manabe and Richard T. Wetherald. 50 years after their groundbreaking 1967 paper, the science can be robustly evaluated, and they got almost everything exactly right.The effects and dangers of human made climate change was well known and understood by the US military and the president already in the 1960s.Fifty four years ago: The White House knew all about climate changeOn November 5, 1965, President Lyndon B. Johnson’s White House released “Restoring the Quality of our Environment”, a report that described the impacts of climate change, and foretold dramatic Antarctic ice sheet loss, sea level rise, and ocean acidification.That 1965 White House report stated:“Carbon dioxide is being added to the earth’s atmosphere by the burning of coal, oil, and natural gas at the rate of 6 billion tons a year. By the year 2000 there will be about 25 percent more CO2 in our atmosphere than present.”[…] “This will modify the heat balance of the atmosphere to such an extent that marked changes in climate, not controllable through local or even national efforts, could occur.On the 50th anniversary of the White House report, CO2 concentrations in the atmosphere are indeed at 399 ppm: 25 percent over 1965 levels, exactly as predicted 50 years ago.http://ourchildrenstrust.org/sit...Scientists warned the President about global warming 50 years ago today | Dana NuccitelliEven oil company Exxon knew everything in the 70’s:Exxon Knew about Climate Change Almost 40 Years AgoExxon predicted in 1982 exactly how high global carbon emissions would be todayAccording to a graph displaying the “growth of atmospheric CO2 and average global temperature increase” over time, the company expected that, by 2020, carbon dioxide in the atmosphere would reach roughly 400 to 420 ppm. This month’s measurement of 415 ppm is right within the expected curve Exxon projected under its “21st Century Study-High Growth scenario.”IN 1982, EXXON SCIENTIST PROJECTED CARBON DIOXIDE CONCENTRATIONS IN THE ATMOSPHERE. (CREDIT: EXXON VIA INSIDE CLIMATE NEWS)Not only did Exxon predict the rise in emissions, it also understood how severe the consequences would be.“At the high end, some scientists suggest there could be considerable adverse impact including the flooding of some coastal land masses as a result of a rise in sea level due to melting of the Antarctic ice sheet,” it continued, stating this would only take place centuries after temperatures warmed by 3 degrees Celsius.”Watch to see Exxon scientist show how accurate their prediction was:https://insideclimatenews.org/sites/default/files/documents/1982%20Exxon%20Primer%20on%20CO2%20Greenhouse%20Effect.pdfComparing what climate scientists said in the early 80s with today:Comparing what climate scientists said in the early 80s with today:Listening to James Hansen on Climate Change, Thirty Years Ago and Now30 years ago global warming became front-page news – and both Republicans and Democrats took it seriously40 years ago, scientists predicted climate change. And hey, they were rightThe 1984 Climate Change DocumentaryThe 1984 Climate Change DocumentaryClimate models have been correct for literally 40 yearsEven 50-year-old climate models correctly predicted global warmingBONUS 1:Latest:Humans Have Caused the Most Dramatic Climate Change in 3 Million Years1. By including dropping CO2 levels and the removal of the sediment overburden atop the bedrock underneath the ice sheets and glaciers (the ice scoured the sediments down & into the bedrock itself), scientists were able to successfully reproduce the observed climate as found in the various proxy records over that same time interval2. The mid-Pleistocene transition from 40,000 year glacial phases to 100,000 year phases was successfully replicated using the new forcing values3. The Earth's climate system has a strong response to even small variations in CO2 (3 C climate sensitivity in the model)4. CO2 levels during that interval have never been as high as they are today (~410 ppm)5. A continuance of Business As Usual Emissions Pathways, as at present, would push our climate beyond the bounds of climate experienced over the Quaternary Period (the period covered by this study).First successful model simulation of the past 3 million years of climate changeHumans Have Caused the Most Dramatic Climate Change in 3 Million YearsMid-Pleistocene transition in glacial cycles explained by declining CO2 and regolith removalBONUS 2:“The physics we use to understand the earth’s climate system is the same physics that explains how stoves, fridges, airplanes and more work. And most people don’t really have a problem with the physics of non-linear fluid dynamics and radiative transfer that have been well understood for decades, even centuries.” (Climate scientist Katharine Hayhoe.)Q&A: How do climate models work?https://www.carbonbrief.org/qa-h...There is an excellent description of climate models evaluation in the following IPCC report:http://www.ipcc.ch/pdf/assessmen...If anything else, I suggest you read page 600-601 that address how reliable current models are.The last paragraph states:"In summary, confidence in models comes from their physical basis, and their skill in representing observed climate and past climate changes. Models have proven to be extremely important tools for simulating and understanding climate, and there is considerable confidence that they are able to provide credible quantitative estimates of future climate change, particularly at larger scales. Models continue to have significant limitations, such as in their representation of clouds, which lead to uncer- tainties in the magnitude and timing, as well as regional details, of predicted climate change. Nevertheless, over several decades of model development, they have consistently provided a robust and unambiguous picture of significant climate warming in re-sponse to increasing greenhouse gases."BONUS 3:DENIERS ATTACK ON CLIMATE MODELSROY SPENCERS BIG LIE AND CHEAT WAS BASED ON FALSELY CALIBRATED DATA“Climate scientists have been terrible at predicting temperatures.”This is a claim that's often made by climate skeptics: that predictions about the near-term future have been bad, so we shouldn't trust predictions about the longer-term future. But is that true? Have predictions historically been bad?Have predictions historically been bad?The evidence they cite is from Dr. Roy Spencer, who showed in 2013 that 95% of climate models over predict the temperature rises due to greenhouse gases. The chart showing that is below. Unfortunately, that chart itself is based on falsely calibrated data.Unfortunately, that chart itself is based on falsely calibrated data.“In 2014, the truth came out: Spencer’s UAH team had made a huge mistake in the calibration of their data. Instead of negligible upper-atmosphere warming, they found that the upper atmosphere had been warming at +0.14 degrees per decade, double the 1880-2014 rate of 0.07 degrees per decade. The other major satellite data set, RSS, also found a calibration error, meaning the Earth warmed 140% faster since 1998 than previous conclusions indicated. At the same time, the ground-based data from NOAA, NASA, the Hadley center and BEST all displayed agreement with one another. Once the 2014, 2015 and 2016 data are also included, the graph shows the scientific truth: the models are very much in line with what we observe.”The correctly adjusted chart:JOHN CHRISTYS LYING non-peer reviewed GRAPH:DEBUNK:In addition to various statistical sizes, Christy uses different physical measures in comparison when comparing temperatures at the surface with the temperature of 15 km of the atmosphere."Christy showed a graph of only mid-troposphere temperatures. The mid-troposphere is the atmospheric layer from about 25,000–50,000 feet, or about 8–15km in altitude. One might reasonably ask why Christy only showed data for such high altitudes. For perspective, the highest point on the Earth’s surface is on Mount Everest at 29,000 feet (8.8km), and the highest elevation city in the world is La Rinconada, Peru at 16,700 feet (5.1km). Humans live in the lower troposphere, not the mid-troposphere.However, climate models have done a good job matching the observed temperature change at the surface and in the lower troposphere, where humans live. We understand the workings of the Earth’s climate much better than Christy suggests, especially where it matters most to humans."Christy compared the average of 102 climate model simulations with temperature from satellite measurements (average of three different analyses) and weather balloons (average of two analyses). This is a flawed comparison because it compares a statistical parameter with a variable.Temperature from satellites are also model resultsIt is fair to compare the satellite record with model results to explore uncertainties, but the satellite data is not the ground truth and cannot be used to invalidate the models. The microwave sounding unit (MSU), the instrument used to measure the temperature, measures light in certain wavelength bands emitted by oxygen molecules. Satellite data are 5 timers less accurate than ground data.Different types of numbersThe upper left panel in Fig. 1 shows that Christy compared the average of 102 climate model simulations with temperature from satellite measurements (average of three different analyses) and weather balloons (average of two analyses). This is a flawed comparison because it compares a statistical parameter with a variable.A parameter, such as the mean (also referred to as the ‘average’) and the standard deviation, describe the statistical distribution of a given variable. However, such parameters are not equivalent to the variable they describe.The comparison between the average of model runs and observations is surprising, because it is clearly incorrect from elementary statistics (This is similar statistics-confusion as the flaw found in the Douglass et al. (2007)).http://www.realclimate.org/index...http://www.realclimate.org/index...Climate scientists, using current science, are successful in predicting temperatures.https://skepticalscience.com/graphics/ChristyChart500.gifhttps://skepticalscience.com/gra...https://skepticalscience.com/republicans-favorite-climate-chart-has-serious-problems.htmlCongress manufactures doubt and denial in climate change hearing | Dana NuccitelliDEBUNK#2Graph shows a comparison between the average of an ensemble of 102 model calculations and observations (average of 3 satellite measurements and 4 balloon measurements). They have clearly not understood that they compare different sizes, so that they can not dismiss model calculations based on such.The model calculations shown by Christy are derived from the Dutch data portal ClimateExplorer. However, model calculations of temperatures at different heights above the ground in this web portal can not be found, only the temperature near the ground. The satellite measurements and balloons, on the other hand, represent the average temperature in a volume that stretches from the ground to a height of about 15 km.In addition to various statistical sizes, Christy uses different physical measures in comparison when comparing temperatures at the surface with the temperature of 15 km of the atmosphere. Increased greenhouse effect causes the lower part of the atmosphere (troposphere, which goes up to about 10km) to get warmer while the above layers of the stratosphere become colder. Does anyone see the problem with this comparison?Not only that. The satellite measurements are also model calculations,. In fact, they base on similar models that show that CO2 provides global warming. Ironically, neither Spencer nor Christy have realized this fact. In addition, the satellite curve is sewn together by different satellites with short lifespan, and the measurements from the different satellites are scattered. It is not so easy to put them together to a reliable temperature curve. They have been corrected several times.In other words, the figure of Christy and Spencer reveals basic deficiencies in understanding both statistics and physics.https://www.theguardian.com/envi...BONUS 4:With the increased knowledge on climate sensitivity added , there is every reason to believe future climate models will be even better:A new study by Kate Marvel, Gavin Schmidt, Ron Miller, and Larissa Nazarenko at the NASA Goddard Institute for Space Studies appears to have found the answer. They drew upon previous research by Drew Shindell and Kummer & Dessler, who identified a flaw in studies taking the energy budget approach. Those studies had assumed that the Earth’s climate is equally sensitive to all forcings.In reality, as world-renowned climate scientist James Hansen noted in a 1997 paper, some forcings are more efficient at causing the Earth’s surface temperature to change than others. Those in which the effects are focused in the northern hemisphere tend to be more efficient, for example. [...]The NASA study shows that the previous estimates were indeed biased low, and correcting for that bias brings them into agreement with estimates using other approaches. A number of independent studies using near-global satellite data find positive feedback and high climate sensitivity. [...] Climate sensitivity is a measure of how much our climate responds to an energy imbalance. The most common definition is the change in global temperature if the amount of atmospheric CO2 was doubled. If there were no feedbacks, climate sensitivity would be around 1°C. But we know there are a number of feedbacks, both positive and negative. So how do we determine the net feedback? An empirical solution is to observe how our climate responds to temperature change. We have satellite measurements of the radiation budget and surface measurements of temperature. Putting the two together should give us an indication of net feedback.Less than 2 °C warming by 2100 unlikely The likely range of global temperature increase is 2.0–4.9 °C, with median 3.2 °C and a 5% (1%) chance that it will be less than 2 °C (1.5 °C). Population growth is not a major contributing factor. Our model is not a ‘business as usual’ scenario, but rather is based on data which already show the effect of emission mitigation policies. Net positive feedback is confirmed by many different lines of evidence.Spread in model climate sensitivity traced to atmospheric convective mixingA Less Cloudy Future: The Role of Subtropical Subsidence in Climate SensitivityThe Impact of Global Warming on Marine Boundary Layer Clouds over the Eastern Pacific—A Regional Model StudyGreater future global warming inferred from Earth’s recent energy budget.Tan I, Storelvmo T, and Zelinka MD: Observational constraints on mixed-phase clouds imply higher climate sensitivity. Science 352(6282):224–27, 2016.Shaffer G, Huber M, Rondanelli R, and Pepke Pedersen JO: Deep-time evidence for climate sensitivity increase with warming. Geophysical Research Letters 43(12):6538–45, 2016.Armour KC: Energy budget constraints on climate sensitivity in light of inconstant climate feedbacks. Nature Climate Change 7(5):331–35, 2017The most likely value of ECS constrained by different lines of evidence is 3 °C, not lower than that.Knutti R, Rugenstein MA, and Hegerl GC: Beyond equilibrium climate sensitivity. Nature Geoscience 10:727–736, 2017.Greater future global warming inferred from Earth’s recent energy budgethttps://www.nature.com/articles/...All the models and evidence confirm a minimum warming close to 2°C for a doubling of atmospheric CO2 with a most likely value of 3°C and the potential to warm 4.5°C or even more. Even such a small rise would signal many damaging and highly disruptive changes to the environment. In this light, the arguments against reducing greenhouse gas emissions because of climate sensitivity are a form of gambling.Papers on climate sensitivity estimateshttps://agwobserver.wordpress.co...

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