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al cyclones comments tue 21 nov 2023 05 53 56 0000 http scienceofdoom com p 11259 what controls the frequency of tropical cyclones here s an interesting review paper from 2021 on one aspect of tropical cyclone research by a cast of luminaries in the field tropical cyclone frequency by adam sobel and co authors plain language summaries are a great idea and this paper has one in this paper the authors review the state of the science regarding what is known about tropical cyclone frequency the state of the science is not great there are around 80 tropical cyclones in a typical year and we do not know why it is this number and not a much larger or smaller one we also do not know much about whether this number should increase or decrease as the planet warms thus far it has not done much of either on the global scale though there are larger changes in some particular regions no existing theory predicts tropical cyclone frequency to see the whole article visit the new science of doom on substack page and please consider suscribing for notifications on new articles https scienceofdoom com 2023 11 21 extreme weather 17 modeling tropical cyclones feed 1 11259 scienceofdoom extreme weather 16 modeling tropical cyclones https scienceofdoom com 2023 10 25 extreme weather 16 modeling tropical cyclones https scienceofdoom com 2023 10 25 extreme weather 16 modeling tropical cyclones respond wed 25 oct 2023 02 42 30 0000 http scienceofdoom com p 11255 one look at the effect of higher resolution models in 15 we looked at one issue in modeling tropical cyclones tcs current climate models have actual biases in their simulation of ocean temperature when we run simulations with and without these errors there are large changes in the total energy of tcs in this article we ll look at another issue model resolution because tcs are fast and small scale climate models at current resolution struggle to model them it s a well known problem in climate modeling and not at all a surprise to anyone who understands the basics of mathematical modeling this is another paper referenced by the 6th assessment report ar6 impact of model resolution on tropical cyclone simulation using the highresmip primavera multimodel ensemble by malcolm roberts and co authors from 2020 the key science questions addressed in this study are the following 1 are there robust impacts of higher resolution on explicit tropical cyclone simulation across the multi model ensemble using different tracking algorithms 2 what are the possible processes responsible for any changes with resolution 3 how many ensemble members are needed to assess the skill in the interannual variability of tropical cyclones in plain english they review the results of a number of climate models each at their standard resolution and then at a higher resolution when they find a difference what is the physics responsible what s missing from the lower resolution model that kicks in with the higher resolution model how many runs of the same model with slightly different initial conditions are needed before we start to see the year to year variability that we see in reality to see the whole article visit the new science of doom on substack page and please consider suscribing for notifications on new articles https scienceofdoom com 2023 10 25 extreme weather 16 modeling tropical cyclones feed 0 11255 scienceofdoom extreme weather 15 modeling tropical cyclones https scienceofdoom com 2023 10 20 extreme weather 15 modeling tropical cyclones https scienceofdoom com 2023 10 20 extreme weather 15 modeling tropical cyclones respond fri 20 oct 2023 23 53 16 0000 http scienceofdoom com p 11253 in 1 6 of the extreme weather series we looked at trends in tropical cyclones tcs from the perspective of chapter 11 of the 6th assessment report of the ipcc ar6 the six parts were summarized here the report breaks up each type of extreme weather reviews recent trends and then covers attribution and future projections both attribution and future projections rely primarily on climate models we looked at some of the ideas of attribution in the natural variability attribution and climate models series ar6 has a section model evaluation on p 1587 before it moves into detection and attribution event attribution how good are models at reproducing tropical cyclones accurate projections of future tc activity have two principal requirements accurate representation of changes in the relevant environmental factors e g sea surface temperatures that can affect tc activity and accurate representation of actual tc activity in given environmental conditions suppose in the future we had a model that was amazing at reproducing tropical cyclones when a variety of climate metrics were accurately reproduced however if the climate model didn t reproduce these metrics reliably we still wouldn t get a reliable answer about future trends in tropical cyclones as a result tropical cyclones are a major modeling challenge to see the whole article visit the new science of doom on substack page and please consider suscribing for notifications on new articles https scienceofdoom com 2023 10 20 extreme weather 15 modeling tropical cyclones feed 0 11253 scienceofdoom natural variability attribution and climate models 12 https scienceofdoom com 2023 10 16 natural variability attribution and climate models 12 https scienceofdoom com 2023 10 16 natural variability attribution and climate models 12 respond mon 16 oct 2023 23 29 45 0000 http scienceofdoom com p 11245 overview of chapter 3 of the ipcc 6th assessment report the periodic ipcc assessment reports are generally good value for covering the state of climate science i m taking about working group 1 the physical science basis which in the case of the 6th assessment report ar6 is 12 chapters they are quite boring compared with news headlines boring and dull is good if you want to find out about real climate if you prefer reading about the end of days then you ll need to stick to press releases here s a quick summary of chapter 3 human influence on the climate system chapter 3 naturally follows on from chapter 2 changing state of the climate system to see the whole article visit the new science of doom on substack page and please consider suscribing for notifications on new articles https scienceofdoom com 2023 10 16 natural variability attribution and climate models 12 feed 0 11245 scienceofdoom natural variability attribution and climate models 11 https scienceofdoom com 2023 10 10 natural variability attribution and climate models 11 https scienceofdoom com 2023 10 10 natural variability attribution and climate models 11 comments tue 10 oct 2023 01 59 02 0000 http scienceofdoom com p 11242 in 9 we looked at an interesting paper van oldenborgh and co authors from 2013 assessing climate models they concluded that climate models were over confident in projecting the future at least from one perspective which wouldn t be obvious to a newcomer to climate their perspective was to assess spatial variability of climate models simulations and compare them to reality if they got the spatial variation reasonably close then maybe we can rely on their assessment of how the climate might change over time why is that one idea behind this thinking is to consider a coin toss if you flip 100 coins at the same time you expect around 50 heads and 50 tails spatial if you flip one coin 100 times you expect 50 heads and 50 tails time there s no strong reason to make this parallel with climate models on spatial and time dimensions but climate is full of challenging problems where we have limited visibility we could give up but we just have the one planet so all ideas are welcome in the paper they touched on ideas that often come up in modeling studies assessing natural variability by doing lots of runs of the same climate model and seeing how they vary comparing the results of different climate models to see the whole article visit the new science of doom on substack page and please consider suscribing for notifications on new articles https scienceofdoom com 2023 10 10 natural variability attribution and climate models 11 feed 19 11242 scienceofdoom natural variability attribution and climate models 10 more on droughts https scienceofdoom com 2023 10 09 natural variability attribution and climate models 10 more on droughts https scienceofdoom com 2023 10 09 natural variability attribution and climate models 10 more on droughts respond mon 09 oct 2023 00 35 31 0000 http scienceofdoom com p 11239 in 1 we saw an example of natural variability in floods in europe over 500 years clearly the large ups and downs prior to the 1900s can t be explained by climate change i e from burning fossil fuels if you learnt about climate change via the media then you ve probably heard very little about natural variability but it s at the top of climate scientists minds when they look at the past even if it doesn t get mentioned much in press releases here s another example this time of droughts in the western usa this is a reconstruction of the pre instrument period to see the whole article visit the new science of doom on substack page and please consider suscribing for notifications on new articles https scienceofdoom com 2023 10 09 natural variability attribution and climate models 10 more on droughts feed 0 11239 scienceofdoom natural variability attribution and climate models 9 https scienceofdoom com 2023 08 13 natural variability attribution and climate models 9 https scienceofdoom com 2023 08 13 natural variability attribution and climate models 9 respond sun 13 aug 2023 06 25 24 0000 http scienceofdoom com p 11235 originally i thought we would have a brief look at the subject of attribution before we went back to the ipcc 6th assessment report ar6 however it s a big subject in 8 and the few articles preceding we saw various attempts to characterize natural variability from the few records we have it s a challenge i recommend reading the conclusion of 8 in this article we ll look at a paper by g j van oldenborgh and colleagues from 2013 they introduce the concept of assessing natural variability using climate models but that s not the principle idea of the paper however it s interesting to see what they say their basic idea we can compare weather models against reality because we make repeated weather forecasts and then can see whether we were overconfident or underconfident for example one time we said there was a 10 chance of a severe storm the storm didn t happen that doesn t mean we were wrong it was a probability but if we have 100 examples of this 10 chance we can see did we get approximately 10 instances of severe storms if we got 0 3 maybe we were wildly overconfident if we got 30 maybe we were very underconfident now we can t compare climate models outputs of the future vs observations because the future hasn t happened yet there s only one planet and climate forecasts are over decades to a century not one week we can however compare the spatial variation of models with reality to see the whole article visit the new science of doom on substack page and please consider suscribing for notifications on new articles https scienceofdoom com 2023 08 13 natural variability attribution and climate models 9 feed 0 11235 scienceofdoom natural variability attribution and climate models 8 https scienceofdoom com 2023 07 31 natural variability attribution and climate models 8 https scienceofdoom com 2023 07 31 natural variability attribution and climate models 8 respond mon 31 jul 2023 06 10 00 0000 http scienceofdoom com p 11226 in 7 we looked at huybers curry 2006 and pelletier 1998 and saw power law relationships when we look at past climate variation over longer timescales pelletier also wrote a very similar paper in 1997 that i went through and in searching for who cited it i came across the structure of climate variability across scales a review paper from christian franzke and co authors from 2020 to summarize many climatological time series exhibit a power law behavior in their amplitudes or their autocorrelations or both this behavior is an imprint of scaling which is a fundamental property of many physical and biological systems and has also been discovered in financial and socioeconomic data as well as in information networks while the power law has no preferred scale the exponential function also ubiquitous in physical and biological systems does have a preferred scale namely the e folding scale that is the amount by which its magnitude has decayed by a factor of e for example the average height of humans is a good predictor for the height of the next person you meet as there are no humans that are 10 times larger or smaller than you however the average wealth of people is not a good predictor for the wealth of the next person you meet as there are people who can be more than a 1 000 times richer or poorer than you are hence the height of people is well described by a gaussian distribution while the wealth of people follows a power law to see the whole article visit the new science of doom on substack page and please consider suscribing for notifications on new articles https scienceofdoom com 2023 07 31 natural variability attribution and climate models 8 feed 0 11226 scienceofdoom natural variability attribution and climate models 7 https scienceofdoom com 2023 07 22 natural variability attribution and climate models 7 https scienceofdoom com 2023 07 22 natural variability attribution and climate models 7 respond sat 22 jul 2023 08 52 37 0000 http scienceofdoom com p 11223 in 6 we looked in a bit more detail at imbers and co authors from 2014 natural variability is a big topic in this article we ll look at papers that try to assess natural variability over long timescales peter huybers william curry from 2006 who also cited an interesting paper from jon pelletier from 1998 here s jon pelletier understanding more about the natural variability of climate is essential for an accurate assessment of the human influence on climate for example an accurate model of natural variability would enable climatologists to make quantitative estimates of the likelihood that the observed warming trend is anthropogenically induced he notes another paper with this comment explained in simpler terms below however their stochastic model for the natural variability of climate was an autoregressive model which had an exponential autocorrelation dependence on time lag we present evidence for a power law autocorrelation function implying larger low frequency fluctuations than those produced by an autoregressive stochastic model this evidence suggests that the statistical likelihood of the observed warming trend being larger than that expected from natural variations of the climate system must be reexamined in plain language the paper he refers to used the simplest model of random noise with persistence the ar 1 model we looked at in the last article he is...