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I would like to know your opinion about what are currently the greatest unsolved problems or opportunities for further research in palaeoclimatology.
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How about: humans are the greatest challenge? The idea that a capitalistic economy based on consumerism is the "best"? That we need to have the latest smartphone, buy "fast" fashion, eat meat, follow influencer, follow celebrities...to be humans? Human greed is the greatest challenge to Climate Change. Failure to be capable of being able to cope with less things and enjoy Life for what it is. But we all fear Death, don't we? And we fill this fear with meaningless things. If we were Penguins, we would Live and Die because this is how it is. We would not need more than we need. And we could live in a much better World.
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Are you just going to determine that from facies analysis or are you doing geochemical analysis?
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There are many methods for paleoclimate reconstruction. The choice of methods depends on your goals, objectives and capabilities. In any case, a complex of methods will give a more reliable result.
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Greetings, I was willing to reconstruct paleo-temperature of Last Glacial Maximum (LGM) from the oxygen isotope ( δ18O) or Mg/Ca ratio. Therefore. I obtained data from the Pangaea site and arranged them in 3 columns depth, time (Kyrs BP), parameters. The δ18O data were calculated based on G.ruber and benthic, planktonic foraminifera. While the Mg/Ca ratio was extracted from H. elegans.
Now, I am wondering whether I could use any equation(s) that would take the previously mentioned parameters into variables and reconstruct paleo-temperature. I studied some literature where I found the following equation published by McCrea (1950), which was subsequently revised by Epstein et al. (1953):
T(°C) = 16.5− 4.3(δ18Occ − δ18Osw) + 0.14(δ18Occ−δ18Osw)
where δ18Occ is the measured value in calcium carbonate and δ18Osw is the isotope ratio of the water from which it is precipitated. The slope of this relationship means that a 0.23‰ increase in δ18Occ corresponds to a difference of about 1°C (Paul N. Pearson, 2012). I understand that I would put the Isotope ratio's column in the δ18Occ variable, but what should I do about the δ18Osw variable ? As I am planning to derive the paleo-temperature of the LGM, I was wondering that if there's any standard δ18Osw value of that time or do I need separate dataset for that too?
Regarding the ratio of the water, there was something such as conversion of VSMOW/SMOW to VPDB/PDB scale, I would be grateful if someone clarified these with their expertise. Although I do believe my data is already converted to PDB when I downloaded from Pangaea. It also needs to be mentioned that these dataset are from cores in Bay of Bengal. Finally, I welcome any suggestion, advice or tips to reconstruct temperature from the data I gathered. If my approach is wrong, it would be a great help to me if you could me point me out to the right directions or the right equations. I am attaching some screenshot here as well. Thanks in advance.
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You can find all information needed in this paper, all the main equations used and how to chose.
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Greetings,
I am planning to work with open source paleo-climate data for a thesis, so far the only source for these kind of data i know is : https://www.ncdc.noaa.gov/paleo-search/
Is there any other sources that provides a good amount of paleo-climate data or this the most available source currently ?
With that being said, would any paleo-scientist like to tell me what are some of the special things that you take into consideration while you are dealing with such data, especially because they are from past and mostly climatic reconstruction or proxies ? If you went through that link, you'd see most of them are in (.txt) files, therefore what would be some potential software or programming languages you have used or planning to use, that would be helpful in this regard ? or you'd process it like any usual data (e.g. netCDF are very popular in climatic studies but unfortunately i don't know whether (.nc) files exists for paleo-climate data)?
Any advice or suggestions, in addition to my question would be deeply appreciated.
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I came through to the Canadian climate GCM data download portal (http://www.cccma.ec.gc.ca/data/cgcm4/CanESM2/rcp26/day/atmos/hfls/index.shtml ) in which some ensembles are given. I am not sure which one should be used. I am working on impacts of temperature changes and will be using downscaling techniques.
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This paper on "Representative general circulation models selection and downscaling of climate data" might be helpful:
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Urgent - trying to find a program that will convert a scanned pollen diagram into either graphs, a digital, clearer pollen diagram or data tables.
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You can use the Getdata software, it could precisely get the point (x,y) from the pollen diagram, then use the data to draw another digital diagram.
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Backround research on climate reconstructions around pluvial lakes in the Great Basin, I am finding that a loose correlation between later glacial maxima for alpine glaciers in the southerly portions of western North America, as the continental ice sheets retreat. The alpine glacier retreats then correspond loosly in time to the rise in pluvial lake levels. How does this all relate to the rising sea levels with the retreat of continental ice, especially LIS? Does this increase in sea level in the Pacific have relation to what seems like an increase in moisture seen from the Sierra Nevadas, across the Great Basin, to the southern edge of the Yellowstone ice cap?
Are there any studies that link these? I find it interesting, and relates to my research via lake level to the Chewaucan Basin and the local biome ~15-9 ka.
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Thank you Stuart! I've read Reheis et al. 2014 which is one of the articles that inspired me to see what other resources I've missed on the topic, I'll take a look at the Miller et al 2010 article.
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I would like to see if anyone has studied the publication rate of climate change research in the biological sciences. I have found only one article by Pedersen et al. 2015 that shows the doubling of climate change research in the Nordic Region. I've also checked the IPCC 2014 report and found nothing. Thank you so much for you help.
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Dear Tina,
                You may not be so casual in this global warming effects and aspects. The entire world has awakened  since last 2 decades on the subject matter and you are finding nothing. Your contribution/rating for survival of this planet will be nil.Please be informed on latest developments on the subject matter. Earth is too large as compared to your place of living.
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As I am not familiar with ECMWF data. I have obtained daily Climate Reanalysis data from ECMWF ERA-Interim. I have converted it to the text file and excel file format. The data obtained for various parameters are as follow;
Temperature, Min, Max Temp. Solar radiation, DewPoint Temp, SnowDepth   (Time:12  Step: 03), Precipitation (Time: 00 & 12   Step: 12), Sunshine duration(Time: 00 & 12  Step: 09). 
The required input file format of the model, for example, is as; 
Year--day--Temp--Prec--SRD--Daylength
2011--01---
2011--02---
2011--03---
---
---
2011--365---
2012--1---
2012--2---
---
2012--365--- and so on.....
The data obtained is not on daily format rather is a lot of data on time basis.
Would anybody help how to process this data to get daily data from day 1 to day 365 per year for each parameter?
Your guidance/help in this regard will highly be appreciated.
Thank you and regards,  
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Dear Arshad Ali Shedayi,
I am not able to get your objective properly, but as per my understanding, I can guide you about ECMWF especially ERA-interim datasets. 
First, you have to know about the entities involved in the ECMWF data.
ECMWF data contains both instantaneous as well as accumulated (flux) parameters.  
Now when you use any instantaneous parameters like temperature, dew point temp etc. You will have the value of that parameter at selected time, but in flux parameter, you will have accumulated value from your forecast start. Additionally, for instantaneous parameter, you can start your forecast from four different times (0,6,12,18) and you can select any of the forecast step (0,3,6,9,12), but for  accumulated parameter, you can start your forecast only for two-time (0 and 12) and you will have step options (3,6,9,12) but you can not able to get accumulated parameter on instantaneous time because accumulated means from some time to some time. It means from 0-hour to 3-hour, where instantaneous means at 0-hour or at 3-hour. 
Example.
Temperature:-
Temp is instantaneous value so you will get this at any time
Precipitation:-
Prec. has accumulated means you will get accumulated value between two times.
Now when you select the temperature and start your forecast with 0-hour and 12-hour with forecast step-3  than actually, you are running your model with available measurements at 0-hour and 12-hour and you are forecasting for 3-hours and 15 hours. So you will have temperature value at 3-hour and 15-hour.
Whereas, when you select precipitation for 0-hour and 12-hour with step 3. It means same as above you start your model at 0-hour and 12-hour with available measurements but you are having accumulated values for 0 to 3 hours and 12 to 15 hours. it means you have three three hours accumulated values of precipitation and you can not simply compare this accumulated values with instantaneous values. It is not that simple to work with this.
Hope I am able to make sense about the ECMWF data.
Thank you
With Regards
Piyush Patel
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I am dealing with the various climate proxy data (e.g. geochemistry, pollen, isotopes, etc.) from various locations. I would like to compare statistically (component analysis) all the proxy data, but the problem is missing points.
I am not an expert in the statistical analysis. Is it possible to fill the missing values with the median values and then do a component analysis?
Any help would be greatly appreciated.
Thanks
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as suggested above, impute the means or leave out the records with missing values.
if you use more sophisticated techniques such as rpart, surrogates will be used automatically (to form tree leaves and partitions) for missing values.
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I found data in some papers (mostly concerning marine turtles sexual determination), but I can't find a monitoring of that for a extended period of time (a year, e. g.).
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Good morning Gabriela,
In general the reanalysis data bases, haved data of temperatures of soil is necessary determinate your interest area.
the names of possible data bases for utility are CFS NCEP-NCAR, ERA 40 and ERA Interim
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Generally, it is warm/wet during MCA and cold/dry during LIA in monsoonal Asia. Many proxies show apparent different conditions of these two stages, i.e. lake sediments, speleothem. Questionablly, the proxies mainly relate to precipitaion changes and water vapor sources of a certain area. But the interpretations of speleothem isotopic records are still unclear. Thus we should conceivably consider impacts of water sources on isotope records and rainfall changes.
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And if someone does this, like most climate simulations, is it worth anything?
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Some plant species living in particularly strong environmental stress show different level of 13C absorption as opposed to individuals from the same species living in normal conditions. May this have a reflection also on 14C content even beyond standard laboratory isotopic fractionation correction?
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I am interested in the theories and evidence linking the AMO to Atlantic Meridional Overturning Circulation, solar forcing and possibly other causes in explaining this phenomenon.  Is there literature reviewing these mechanisms? Do you have links to relevant net sources.
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Here's a good link: "Forced and Internal Twentieth-Century SST Trends in the North Atlantic", by Mingfang Ting et al., 2008:  http://journals.ametsoc.org/doi/abs/10.1175/2008JCLI2561.1 .  Their conclusion is that the AMO is an internal mode of variability, not forced by global factors such as anthropogenic warming or solar variability. 
Otherwise, the wikipedia article (https://en.wikipedia.org/wiki/Atlantic_multidecadal_oscillation) is a good place to start and has plenty of further references.
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Some years ago I did a search and noticed that such a software were available only for Mac environment. Now I would like to know about any experiences with free multi-taper technique for PC users, preferably in R but not necessarily. I am working on long proxy climate time series.
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Thank you all for excellent advice and links!
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I'm interested to know more about this field. Can someone help me to know more about paleoclimatology methods to reconstruction of past climate of the Earth?
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You could read one or more text books. "Paleoclimatology" by Raymond S. Bradley is a good one for a general understanding of paleoclimatology. You should also be clear what time-scales and objectives you are going to focus on, and choose to read the specific books or papers in your field.
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I am looking for information on advanced courses in English that would be open for graduate or post-graduate level foreign students. This would be interesting for students specializing on climate reconstruction, past climate dynamics, current issues of climatology, and so on. Is there any web-sites listing such activities? 
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The Urbino summer school in Italy has an extremely good reputation, and all of the details (including course content and logistics) are on their website: http://www.urbinossp.it/
I don't know of others, but I hope that is helpful.
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Proxies generally record frequency-dependent climate signals. I would think that this frequency aspect is perhaps handled by careful selection of proxies or by e.g. choosing proper indexing methods if tree-rings are concerned. Do any of the approaches called climate field reconstruction (CFR methodologies) have clear advantages in this respect? Opinions, views and relevant references welcome!
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Dear Markus,
by frequency-related properties do you mean frequency signals embedded into the reconstruction records you study? 
In that sense, the most critical parameter is time resolution, which forces you to observe a precise frequency range. After the identification of precise signals, each related to a frequency, you can isolate and link them to some property.  May be I'm not catching your question properly, so sorry in advance.
Let me know,
Kind regards,
Gianna
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Transforming proxies into principal components (PCs) and using these PCs in building transfer models is usual in climate reconstruction. A common procedure before PCA is decentering (i.e. subtracting the mean) a group of e.g. tree-ring chronologies before principal components analysis. However, PCA and the resulting PCs seem to be sensitive to decentering. Presuming PCA is based on correlation matrix, what is the rationale behind this? Can anybody elaborate on this or point to a reference?
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It seems to me mean centering is useless procedure before PCA using. Theoretically by itself  in PCA we standartize ((variable-mean)/SD) our variables (e.g. tree-ring chronology) if we are using correlation matrix  or we decenter  (variable-mean) our variables in case of covariance matrix. So it means that we should obtain the same correlation (or covariance) matrices in both cases (with or without  decentering) excluded a sign of PCs.
But Pearson correlation using in most versions of PCA (Statistical packages) is a parametric statistics. And it is sensitive to absolute values of variables involved in analysis. From that point of view it is better to standardize our variables (e.g. tree-ring chronologies). It means to make all averages are equal to 0 and SD - 1 for new variables and then to use PCA.
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who have the skill that using the  carbon isotope composition of loess sediments reconstruct the Precipitation, or who have the article papers that related to that knowledge.
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you might also look at our bibliography (Hatté et al. from 1998 to today) and also at Michael Zech's one. A key point in such a field is to remember to work on typical loess only and not on paleosol (where pedogenesis impacts original vegetation d13C signature), another important point is to adapt chemical treatment to loess organic matter specificities, (aqueous treatment to remove "modern" amino-acid, weak acid concentration to preserve organic matter structure)... for further information contact us!  Christine
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I am seeking an advice on how to read CMORPH precipitation daily data and generate howmoller graphics. The detail specification is a high resolution 8km precipitation data at daily temporal scale. It's possible to use GrAds?
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It seems like there are at least three issues here.
First, there are tons of ways to "read" the CMORPH data. To obtain the data, I assume you have visited http://www.cpc.ncep.noaa.gov/products/janowiak/cmorph_description.html
The files can be downloaded directly from there (and/or via ftp). 
Those files are in binary format, and there is FORTRAN code provided to be able to read the files. (Which you could translate to whatever language you use.)
Second, you specified that you are interested in the 8km data, which has 30-minute temporal frequency. You should think about whether that is the product you really want to use for your purposes. There is also a 0.25-degree 3-hourly product that might be somewhat easier to use. If you decide to use that product, and you know how to use netCDF files, then you can also get the data already converted to netCDF from NCAR: http://rda.ucar.edu/datasets/ds502.1/#!access
Third, you said you want to make Hovmueller plots, and ask if GrAds can be used. Yes, definitely if you know GrAds, then you can use it. Any of the common analysis languages can be used: NCL, Python/Matplotlib/PyNGL, Matlab, IDL, etc. 
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While substage MIS 5e (~120 ka) is considered to have been globally warmer than the second half of the Holocene, and sea-level was several meters above present datum (almost worldwide), a few regions seem to have experienced cooler SST than nowadays. Documenting and understanding this paradox is important for climate modeling efforts in the context of the on-going global warming, especially for these particular regions (eg. S Peru and N Chile).
I am also interested in evidence for Anthropocene cooling trends in coastal regions (like in Central Peru and N Chile).
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Hola Luc, 
When I'm crossing cool with interglacial on my database on coastal sequences, here are the paper related(see attached doc). 
I hope it helps 
Cheers
Kevin 
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I am trying to implement statistical analysis on some data. I have the NCEP2 data set for 4 climate variables and the ERA40 data set for the same variables for a region. Given a grid of 9x10, I have 90 locations and so I have 360 variables for a particular day. I have 12000+ such days. I've implemented PCA on this. What I cannot understand is :
  • How can I get the predicted values of more than one location when I use all variables as the predictors for any location?
  • Am I doing this right or am I missing something?
I don't need the exact implementation. I just want to be clear about how the data flows from the grid format to the output. I'm trying to implement the following papers:
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Dear Gabriel Broadwin,
have you tried to use new methods for downscaling? I worked with artificial neural networks for downscaling (paper attached), for it developed a way to insert data from IPCC AR4-models and observed data (eg rainfall), I am with a Phd student using neural networks to identify uncertainties of the models the AR5, and I can tell you that are encouraging the use of neural networks.
Best, David
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Could some colleagues please suggest a place where I can find high resolution
climate simulations for South America?
I'm looking for something with a resolution of 0.25 x 0.25 or better, covering
southern South America.
I'm collaborating on a project to update the wind hazard map of Argentina using
climate simulations. I'd appreciate any tips regarding high resolution climate simulations to complete the project.
Thanks,
Augusto:
-------------------------
Augusto Sanabria, MSc. Ph.D
Profesor Visitante
Facultad de Ingenieria (http://ing.unne.edu.ar)
Universidad Nacional del Nordeste (UNNE) Resistencia, Argentina
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I recomend to read: Pliscoff, P., Luebert, F., Hilger, H. H., & Guisan, A. (2014). Effects of alternative sets of climatic predictors on species distribution models and associated estimates of extinction risk: A test with plants in an arid environment. Ecological Modelling, 288, 166–177.
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Because Eh influences methane emission...
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Or get a copy of Wetlands by Mitsch and Gosselink. Short answer: you need pretty low Eh to get methane production going, and you will also need enough organics present. What type of soil are you working on?
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Other major oxide values are within limit of PAAS value.
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Higher Fe2O3 is undoubtedly due to oxidizing environment. For CaO, you need to see its source - detrital feldspar or calcite cement. Calcite cement is generally characteristic of warm and arid climate.
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Can anyone help or provide data about the temp rates and climate change in the middle eastern countries, included Iraq, during the period between 1990 to the present?
I am looking for what changes in climate happened there, in particular changes in the temperature and humidity, which are related to drought.
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Dear Mustafa I tried attaching the graph. please see my Dataset: MAPPING HYDROCARBON POLLUTION ON LAND SURFACE USING THERMAL REMOTE SENSING IN SOUTH KUWAIT on RG you will find on page 63 the trend from 1960 - 2009 this is a actual measured data. and on page 64 some landuse changes and ground temperature from satellite datasets (supported by in situ) measurements.
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From my study site I have only 6 years of instrumental records of precipitation and temperature. But I have time series data from 14 nearby climate stations (the farther is 40 Km) and I would like to make one for this region. All climate data are monthly. The time series are from 1963 to 2013. The problem is that not all the series are complete, and the available data in the series are from 10 to 50 years. Some series have only the last, intermediate or the first decades. But the mean monthly values from my study site have a high correlation with these series (r=0.70 to 0.97). I tried the Jones & Hulme 1996 method but it works with a common time period of 20-30 years with continuous values, and with the missing years of my time series I can't apply this method.
Any suggestions are useful.
Thanks
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The R package 'Climatol' is ideal for solving your problem automatically. Temporarily removed from CRAN, you can download it from http:/www.climatol.eu/ where you can find also a manual explaining its use and methodology.
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Significant reorganization of atmospheric circulation, intense uplift of Himalaya-Tibetan Plateau, Significant mammal and floral changes in this time period, How to understand their interaction? I want to know what time witnessed the strongest change, 25 Ma, 23 Ma or 20 Ma? Welcome to discuss.
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25 Ma corresponds to magnetic anomaly 7 in the Indian Ocean, which recorded a global plate reorganisation event, involving the full separation of Arabia from Nubia in the Western Indian Ocean. See Patriat et al., Geology, 2008, for a full discussion. The precise timing of hard collision between Arabia and Eurasia has been recently re-assessed at 27 Ma by kinematic reconstructions (McQuarrie and Van Hinsbergen, Geology, 2013).For the Himalayan uplift or monsoon inception, I think that uncertainties (error bar) in age estimations are too large to propose scenarii of tectonic-climate interactions, based solely on the rough synchronicity of events. Hope this will help you. Regards.
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Most researchers pay attention on temperature and CO2 alternation during life evolution, I mean geological periods, like Paleozoic, Mesozoic and Cenozoic. Is there any authentic data expressing other environmental factors like light, water, and chemical atmosphere component druing the geological periods?
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To some extent, yes, it is preserved in the geologic record -- for instance, we know that hurricanes during the Cretaceous were much stronger than today. The data for this can be found in papers from paleontological journals as well as paleoclimate journals and proceedings. I did find an interesting paper on forests and albedo during the Cretaceous: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1692175/