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Showing posts with the label paleoclimate phanerozoic

CO2 as the Primary Driver of Climate Change During the Phanerozoic

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I saw a s hort presentation  by R.B. Alley a while back that did a marvelous job of explaining why geologists have overwhelmingly concluded that greenhouse gases (and in particular CO2 ) are the primary drivers of climate changes on geologic time scales. This presentation was given prior to the publication of Judd et al 2024 ,[1] so I thought it might be fun to show how his argument would be enhanced even more with the more recent data we now have about Phanerozoic temperature and CO2. But let me set the stage. GMST is set by a balance between incoming absorb solar radiation (ASR) and outgoing longwave radiation (OLR). ASR is affected by changes in how much sunlight reaches the earth ( solar variability ), where and when sunlight reaches the earth ( orbital cycles ) and how much is reflected vs absorbed ( albedo ). Outside influences can also at least theoretically play a role in affecting process on earth ( galactic cosmic rays ) that could change how much incoming solar is a...

The Phanerozoic CO2 and GMST Relationship

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Along with the publication of Emily Judd's reconstruction[1] of global temperatures, she and her team compiled an up-to-date reconstruction of CO2 concentrations from proxy data. I shared in a previous post how this new reconstructions shows a CO2 and GMST as well-correlated on geologic time scales, with GMST increasing by ~8 C for 2xCO2. Her CO2 reconstruction is another step forward for paleoclimate because it makes use of a growing database of proxy evidence, much of which is being compiled at the Paleo CO2 project . For the Paleozoic and Mesozoic, Judd's reconstruction followed Foster et al 2017 [2] pretty closely, especially during the Paleozoic, but she also included additional data from Witkowski et al 2018[4][3]. Differences between Judd's and Foster's reconstruction had to do with different ways of site and time averaging and updates from Witkowski's study. During the Cenozoic, she followed more recent work by Rae et al 2021 [5] which agrees closely with t...

A New Reconstruction of Phanerozoic Temperature and CO2

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In January, I blogged about a lecture I watched from Jessica Tierney, a geologist who has done some fascinating work in paleoclimate, in which she described the research behind a new paper that was at the time still undergoing peer review. You can watch the video and see my previous thoughts about this here . Ever since I watched this lecture, I have been anticipating the publication of the paper and hoping that the text wouldn't be behind a pay wall so I could learn more about what Tierney shared in her lecture. Well, late last week, both of my hopes became a reality. The paper is published and the full version is available. The lead author is Emily Judd[1], and this appears to be a remarkable paper. The tl;dr for this paper is that Emily Judd and her colleagues put together a data analysis (PhanDA) reconstruction of global temperatures and CO2 for the last 485 million years (most of the Phanerozoic), and they found that GMST varied greatly on geologic time scales, ranging from ...

Phanerozoic Climate Reconstructions and Sensitivity

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I watched a lecture recently with Dr. Jessica Tierney, who is a professor of geosciences at the University of Arizona. Her lecture was fascinating to me because she explains how reanalyses (what she calls Data Assimilation or DA) can be used to reconstruct past climates. The lecture also gives us a preview to an upcoming paper with Tierney as a co-author that reconstructs global temperatures over most of the Phanerozoic (almost 500 million years). The method they use is notable to me for it's use in  Osman's recent reconstruction for which Tierney is a co-author. I won't go into a lot of detail here, since she explains it in the above video, but essentially DA starts with  an ensemble of model simulations of past climates as a "prior." The model ensemble is spatially complete, but it isn't real - it's a simulation of what the past may have been like. DA then corrects the model results with proxy data. The proxy data has a huge advantage over models in tha...