Category Archives: One World

Correlation of earth temperature and global mean CO2

For teaching I need COtime courses. For that purpose we can use the  Hadcrut 4 dataset created earlier Global mean CO2 mix ratios (ppm) can be found at https://data.giss.nasa.gov/modelforce/ghgases/Fig1A.ext.txt.
After unscrambling that file and merging it to Hadcrut4 we can plot it

library(patchwork)
p1 <- ggplot(temp, aes(x=year, y=annual)) + geom_point() + stat_smooth(method="loess", span = .6) +
  scale_y_continuous( name="difference from baseline  [ oC ]", limits=c(-1,1) )

p2 <- ggplot(temp, aes(x=year, y=ppm )) +  geom_point() + stat_smooth(method="loess", span = .6) +
  scale_y_continuous( name=expression('ppm CO'[2]) )
p1+p2

Here are the two time courses

Time course of earth temperature (1850-2018) and CO2 (1850-2011)

while the correlation is higher than I expected

Correlation of earth temperature and CO2

 

References

  • 1850-1957: D.M. Etheridge, L.P. Steele, R.L. Langenfelds, R.J. Francey, J.-M. Barnola and V.I. Morgan, 1996, J. Geophys. Res., 101, 4115-4128,”Natural and anthroupogenic changes in atmospheric CO2 over the last 1000 years from air in Antarctic ice and firn”.
  • 1958-1974: Means of Scripps Institution of Oceanography Continuous Data at Mauna Loa and South Pole provided by KenMaarie (personal communication)
  • 1975-1982: Means of NOAA/CMDL in-situ data at Mauna Loa and South Pole. (P. Tans and K.W. Thoning, ftp://ftp.cmdl.noaa.gov/ccg/co2/in-situ)
  • 1983-2003: Global means constructed using about 70 CMDL CCGG Sampling Network station data. (P.P. Tans and T.J. Conway, ftp://ftp.cmdl.noaa.gov/ccg/co2/flask)
  • 2004-2007: Global mean growth rates. (T. Conway, ftp://ftp.cmdl.noaa.gov/ccg/co2/trends)

Zur Geschichte der Klimaforschung

Es ist nicht einfach, hier einen Überblick zu bekommen.

Jedenfalls sollte man von Qualität der Wettervorhersagen (die für die nächsten 24 Stunden von 75% auf über 90% in den letzten 20 Jahren gestiegen ist), nicht auf die Qualität der Klimavorhersagen schliessen.

Die Geschichte der Klimaforschung kann jedenfalls in drei Beiträgen lückenlos nachgelesen werden:

Johan Rockström talking about tipping points

Last week I had the opportunity to attend a lecture by Johann Rockström explaining his most recent Nature commentary about tipping points “too risky to  bet against”.

The Intergovernmental Panel on Climate Change (IPCC) introduced the idea of tipping points two decades ago. At that time, these ‘large-scale discontinuities’ in the climate system were considered likely only if global warming exceeded 5 °C above pre-industrial levels. Information summarized in the two most recent IPCC Special Reports (published in 2018 and in September this year) suggests that tipping points could be exceeded even between 1 and 2 °C of warming.

Prof. Dr. Johan Rockström Nov 28, 2019 @ 1. Helmholtz Sustainability Summit Max-Dellbrück Zentrum in Berlin explaining his recent Nature paper. The full stream is at https://www.youtube.com/watch?v=Ppj04TGaX-Y&feature=emb_logo

This is a cruel message in particular as probably already one tipping point has been passed @ the Amundsen Sea embayment of West Antarctica. There is a thick ice sheet of about 3 km which forms one of the three major ice-drainage basins of the West Antarctic Ice Sheet. And it is melting rapidly – with the tipping point having been passed in 1996. And the Amazon is burning right now — the world’s largest rainforest. Estimates of the Amazon tipping point ranges between 20% and 40% deforestation.

Panel discussion 1st Helmholtz Sustainability Summit Max-Delbrück Zentrum Berlin. From left to right Prof. Dr. Thomas Hirth, Prof. Dr. Martin Visbeck, Prof. Dr. Otmar Wiestler, Prof. Dr. Heike Graßmann, Prof. Dr. Michael Backes, Heike Leitschuh. More talk than action.

A 498 references paper on climate change and allergy

This is certainly the most comprehensive paper that examines the association of air pollution, climate change and allergen exposure.

Air pollution and climate change are potential drivers for the increasing burden of allergic diseases. The molecular mechanisms by which air pollutants and climate parameters may influence allergic diseases, however, are complex and elusive.

Certainly there is no causal effect of allergens on human allergy, as allergens have been always abundant even without allergy. Nevertheless allergens are drivers aggravating symptoms in allergy-prone patients by basically four factors

  1. Stability effects; influencing the accumulation and degradation of allergenic proteins, the duration of exposure times to cellular receptors, and the process of antigen presentation via major histocompatibility complex (MHC) class II
  2. Epitope effects, i.e., generation of new epitopes or modification of existing epitopes, changing the binding properties of antibodies and receptors, by direct chemical modification or as a result of conformational changes
  3. Adjuvant effects, i.e., generation of new adjuvant functions or modification of existing adjuvant functions such as lipid-binding capacities due to modified ligand binding sites
  4. Agglomeration effects, i.e., multiplication or shielding of epitopes or adjuvant functions by cross-linking (oligomerization) of allergenic proteins, which may enhance the cross-linking

I would add even 5. that the absolute number of pollens increased in some areas as a stress response of dying trees.

Wie mit Wissenschaftsleugnern umgehen?

Hier eine Reihe von Link Tipps (eine Zusammenfassung aus der Scientists for Future Mailing Liste mit eigenen Ergänzungen).

Fakten zählen leider hier wenig, dennoch die Sache ist nicht ganz hoffnungslos wenn man sich die Quellen ansieht, das ganze im Rollenspiel durchgeht, am besten einen Knopf im Ohr hat mit Supportern im Publikum.

Mobilität der Zukunft gemeinsam gestalten

Hier ist mein Mitschrieb der Tagung in Tutzing vom 3. bis 4.11. 2019.

Die Tagung wurde gemeinsam von acatech, der deutschen Akademie der Technikwissenschaften, und TTN Ethik interdisziplinär veranstaltet. In der Begrüßung durch Stephan Schleissing (TTN) und Benjamin Zilker (acatech) wurden generelle Aspekte der Mobilität thematisiert.

Stephan Schleissing, TTN München

Von Rothe (“feste Überzeugung, daß dem Reiche Christi die Er- findung der Dampfwagen und Schienenbahnen eine weit bedeutendere positive Förderung geleistet hat als die Ausklügelung der Dogmen von Nicäa und Chalcedon”) bis zu Schwarke “Transzendenz und Technik“. Oder vom Porschemuseum mit der Himmelsleiter aka Rolltreppe bis zu dem Mooncascade Blog in dem sich zwei älteren Herren über Mobilität unterhalten.

Credit where credit’s true: Hans Rosling

The code for the plot is on Github while I suggest to prettify it using my previous theme.

for(i in 1962:2015){
  p <- ggplot(mydf_filter[mydf_filter$year==i,], aes(fert, life, size = pop, fill=continent)) +
    labs(x="Fertility Rate", y = "Life expectancy at birth (years)", size = "Population (millions)") + 
    xlim(0,10) +
    ylim(30,100) +
    geom_point(alpha=.8,shape = 21 ) +
    scale_color_brewer(type = 'div', palette = 'Spectral') +
    annotate("text", label=i, x=9, y=95, size=8.5) + 
    scale_size(range = c(1,20), name="Population (M)", breaks=c(1,100, 10000))
  fn <- paste("/Users/xxx/Desktop/X/",str_pad(i-1961, 3, pad = "0"),".png",sep="")
  ggsave(p, file=fn, width = 9, height = 6)
}
# ffmpeg -framerate 5 -i /Users/xxx/Desktop/X/%3d.png -r 5 -pix_fmt yuv420p -y /Users/xxx/Desktop/X/Rosling.mp4

 

A new animation of the famous HadCRUT4 climate dataset

Download

Here is the R sample code (PPT aspect ratio is 6:4, Youtube wants 16:9) .

As ggplot2 animation packages have major difficulties to manipulate the single frames, I am combining here raw PNGs using ffmpeg.

# read_cru_hemi() modified from https://mccartneytaylor.com/plotting-climate-change-on-a-spider-graph-using-r

list.of.packages <- c("ggplot2", "reshape", "stringr","RColorBrewer")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
lapply(list.of.packages, require, character.only = TRUE)

read_cru_hemi <- function(filename) {
  tab <- read.table(filename,fill=TRUE)
  nrows <- nrow(tab)
  hemi <- data.frame(
    year=tab[seq(1,nrows,2),1],
    annual=tab[seq(1,nrows,2),14],
    month=array(tab[seq(1,nrows,2),2:13]),
    cover=array(tab[seq(2,nrows,2),2:13])
  )
  hemi[,15:26][ hemi[,15:26]==0 ] <- c(NA)
  return(hemi)
}

url_dat <- "https://crudata.uea.ac.uk/cru/data/temperature/HadCRUT4-gl.dat"
tempdat <- read_cru_hemi(url_dat)
tempmelt <- melt(tempdat[,c(1,3:14)],id="year")

colfunc <- colorRampPalette(c("grey","grey","red"))
FadeToGrey <- colfunc(2019-1850)

new_theme <- theme_classic() + theme(
  text = element_text(size=18, colour="grey"),
  axis.line = element_blank(), 
  axis.text = element_text(colour="grey"),
  axis.ticks = element_line(colour="grey"),
  axis.title.x = element_blank(),
  panel.border = element_blank(),
  panel.grid.major = element_blank(),
  panel.grid.minor = element_blank(),
  panel.background = element_blank(),
  legend.position = "none"
)
theme_set(new_theme)

for(i in 1850:2019){
p <- ggplot(tempmelt[tempmelt$year %in% 1850:i,], aes(x=variable,y=value,color=as.factor(year),group=year)) + 
  geom_line() +
  scale_x_discrete( labels=month.abb) +
  scale_y_continuous( name="difference from baseline  [ oC ]", limits=c(-1,1) ) +
  annotate("text", x=11, y=1, label=i, size=7) +
  scale_color_manual( values=FadeToGrey[ 1:c(i-1849) ]  )
  fn <- paste("/Users/xxx/Desktop/X/",str_pad(i-1849, 3, pad = "0"),".png",sep="")
  ggsave(p, file=fn, width = 16, height = 9)
}

# not run
# ffmpeg -framerate 10 -i /Users/xxx/Desktop/X/%3d.png -r 5 -pix_fmt yuv420p -y /Users/xxx/Desktop/X/out.mp4

 

In comparison here is the original circular plot. Would require blue, green, yellow, red in the Color Ramp Palette…

 

Now it is only a minor step to the warming strips.

colfunc <- brewer.pal(11, "RdBu")
ggplot(tempdat, aes(x = year, y = 1, fill = annual)) +
  geom_tile() +
  scale_y_continuous(expand = c(0, 0)) +
  scale_fill_gradientn(colors = rev(col_strip)) +
  guides(fill = guide_colorbar(barwidth = 1)) +
  theme( axis.ticks= element_blank(),
         axis.text = element_blank(),
         axis.title = element_blank()
  )

Climate crisis and cognitive dissonance

There is an interesting twitter thread by @Psychologists4F about news concerning the climate crisis and how we respond to the cognitive dissonance – the mental discomfort or psychological stress experienced by a person who holds contradictory beliefs or values. There are at least four possibilities how to respond to it

  • Change the behavior (“reduce, refine, replace”)
  • Changing the conflicting situation by just ignoring it
  • Justify own behavior by pseudoexplanations, pointing to others
  • Deny information by devalueing the source

During the discussion the question was asked why the political right wing tends to ignore the dissonance. One commentator points towards a study in Current Biology that may have answer to that. Continue reading Climate crisis and cognitive dissonance

Eine unwissenschaftliche Wissenschaftsgläubigkeit

Sascha Lobo hat ein gutes Beispiel heute gebracht

Eine der häufigsten Formen der Greta-Skepsis aber findet sich bei … Angela Merkel. Sie sprach auf der Uno-Klimakonferenz in New York ein leicht vergiftetes Lob aus, weil in Gretas Rede “aus meiner Sicht nicht ausreichend zum Ausdruck kam, in welcher Weise Technologie, Innovation gerade im Energiebereich, aber auch im Energieeinsparbereich uns Möglichkeiten eröffnet, die Ziele zu erreichen.” … Es ist die Hoffnung, dass eine Technologie der Zukunft die Probleme von heute auf beinahe magisch-mystische Weise lösen werde. Es grenzt an die “Dunkle Technikhörigkeit der Ahnungslosen”, nur dass hier die Akteure sogar Ahnung haben. … Damit steht sie für eine ganze Denkschule.

The biggest problem

The biggest (scientific) problem currently for survival on earth?

The are many more interactive maps at the United Nations website.  And there is a new document out

Ten Key Findings
Citation: United Nations, Department of Economic and Social Affairs, Population Division (2019). World Population Prospects 2019: Ten Key Findings.

Nine countries will make up more than half the projected population growth between now and 2050: The largest increases in population between 2019 and 2050 will take place in: India, Nigeria, Pakistan, Democratic Republic of the Congo, Ethiopia, the United Republic of Tanzania, Indonesia, Egypt and the United States of America (in descending order of the expected increase).