So far vegetation have responded positively to increased CO2, a fact that is sometimes called “carbon fertilisation”. The data suggest that terrestrial ecosystems have absorbed more than 90 Gt (Gt = 1 billion metric tons) of additional CO2 since 1959. 90 GtC equivalent is a lot of vegetation. For comparison, all the world’s tropical rainforests = 200GtC equivalent. Even without any greenhouse effect, anthropogenic CO2 emissions have already had a large impact on terrestrial ecosystems.

CO2 record

Atmospheric CO2 has been routinely recorded at Mauna Loa since 1958 and is now recorded at many other locations worldwide. Monthly average data from Mauna Loa are available from the Scripps Institute. The Carbon Dioxide Information and Analysis Center (CDIAC), Oak Ridge National Laboratory, provide historical fossil fuel emissions data, and emissions associated with landuse change. CDIAC also have atmospheric CO2 data obtained from ice core studies, for example from Law Ice Dome in the Antarctic.

The “Global Carbon Budget” from 1959 is summarized by Le Quéré at the University of East Anglia. The fraction of CO2 emissions which remain in the atmosphere (called the Airborne Fraction) is shown in the top chart of the figure below. The mean value of the Airborne Fraction is 43%. The linear fit suggests that a slightly greater proportion of CO2 is remaining in the atmosphere now than in the past.

Le Quéré also gives results of a global ocean calculation of annual CO2 absorbed by the oceans, based on observations.  In this estimate, oceans absorb about 29%  of emissions on average. Whatever is left over (27%) must equal the amount absorbed by terrestrial sinks. In the ocean model, there has been a decrease in the ability of the cold Southern oceans to absorb CO2 (centre chart). This means there is a downward trend in the ocean uptake fraction. This explains the upward trend in the Airborne Fraction. The remaining terrestrial fraction (bottom chart) is highly variable, but has no significant trend.

Two more things are worth noting from the above figure.

(1) the terrestrial uptake is similar in magnitude to the ocean uptake even though only 25% of the surface area of the earth is covered by vegetation.

(2) the terrestrial uptake is much more volatile than the ocean uptake. In some years, vegetation was a source of CO2.

Why is the variability of the terrestrial carbon fluxes so large? The answer is that this reflects vulnerability of vegetation to climatic variability, particularly droughts and fires. The negative effects of the 1987/8 and 1998 El Nino events are obvious on the bottom chart.  The 1991 eruption of Mount Pinatubo apparently lead to increased CO2 uptake.[2]

Accumulation of Carbon by Vegetation

The net cumulative uptake of CO2 by vegetation since 1959 is shown on the left hand plot below. Again this is derived from one specific model of the ocean uptake.

The right-hand plot shows the relation between terrestrial accumulation and cumulative emissions since 1959. This is accurately linear. In some climate models, future global warming causes terrestrial ecosystems to degrade and eventually become net sources of CO2. If this starts to occur, the right hand plot would begin to flatten out.

Where is the extra Vegetation?

For a long time it was believed that the primary terrestrial carbon sink was in growing Northern forests. However more recent work suggests that about 1 Gt of additional CO2 is absorbed per year by mature Tropical Rainforest, as well as 1GtC per year in Northern forests.[3] Of course, deforestation of tropical forests is also the major source of  “land use change” emissions.

It is remarkable that so much uncertainty surrounds such a basic issue. The arrival of new CO2 sensing satellites may improve this situation in the near future.

References

[1]Saturation of the Southern Ocean CO₂ Sink Due to Recent Climate Change, Le Quéré et al http://www.sciencemag.org/cgi/content/abstract/1136188

[2] Anthropogenic and biophysical contributions to increasing atmospheric CO2 growth rate and airborne fraction, M. R. Raupach et al http://www.biogeosciences-discuss.net/5/2867/2008/bgd-5-2867-2008.pdf

[3] Missing carbon mystery: Case solved? Nature Report Climate Change Jane Burgermeister

The U.S. National Climate Data Center (NCDC) maintain a large dataset called the Global Historical Climate Network (ftp site GHCN) – temperature and other data collected at more than 40,000 weather stations. Unfortunately, individual weather stations come and go and there are many gaps in the data. A much smaller database of about 1,000 stations is maintained by the Global Climate Observing System (GCOS) sponsored by World Meteorological Organisation (WMO) and other organisations. We focus on this subset because these stations are up-to-date data and reliable.

The locations of GCOS Global Surface Network (GSN) stations are shown in Google Earth/Maps below.

To see more stations, zoom in to a region of interest and switch to Satellite view. Click on the GCOS logo placemark to see GHCN station ID and other station metadata. If  Google Earth is installed on your system, it may be better to copy the text file gcosgsn, paste into notepad and save as gcosgsn.kml. Clicking on this .kml file opens GE on your system with all GSN stations visible.

gscosgsn was generated using an excel spreadsheet. Improved GSN geo-location data were obtained by cross-referencing the GCOS list with NOAA’s GSOD (0.001^o accuracy).  Other metadata, such as station start year, can easily be added.

How can the climatic variability at a particular GSN station be examined? To do this R will need to be installed on your system. Install it from CRAN if required.

In what follows, a monthly time-series analysis is carried out on a user-selected station. Daily GHCN station data are read into R courtesy of a  data-scraping script from Climate Audit. anything.ts indicates an R time-series object by the way.

1. Start R. You will see the friendly command line prompt in the R console.

>

2. Enter the following command to load required R script.

source("http://joewheatley.net/wp-content/uploads/2009/05/ghcntor.txt")

3. Select a station of interest in GE/GE Plugin. Copy the GHCN station ID from the placemark balloon and enter the following command to load the GHCN data file into R gsn1 <-read.ghcnd("paste_id_here")
4. Plot the mean monthly temperature by copying and paste the following into R
   gsn1.ts <- getTMean.ts(gsn1);
plot(gsn1.ts, main="Mean Monthly Temperature UCCLE Belgium", ylab="o C",xlab="Year",font.axis=2,font.lab=2)
lines(getTrend.ts(gsn1.ts),col=2,lwd=2);
maxTrend <- max(getTrend.ts(gsn1.ts));
maxLine.ts <- ts( rep(maxTrend,length(gsn1.ts)), start= start(gsn1.ts), deltat=1/12)
lines(maxLine.ts, col=2,lty=2);
5. To see the climatic variability
   plot(getResidual.ts(gsn1.ts), main="Monthly Temperature Anomaly UCCLE Belgium", ylab="o C",xlab="Year",font.axis=2,font.lab=2,col=3);

For example, station ID BE000006447 (UCCLE Belgium) generates the plots below.

The periodic black line in the first plot is the monthly average station temperature. Data continuity is from 1831 in this instance. The red line is the long-term trend when seasonal variations and residual random variations are extracted by means of the R stl function. As you can see it indicates that mean temperatures at UCCLE are the highest they have been since 1831.  Compared to the average temperature for 1831-2009, this anomaly is about 1.36^o. A prime candidate for this is global warming, but local factors such as land use change or increased urbanisation also affect individual station data.

The temperature anomaly plot  (green line) shows the residual temperature variations at UCCLE. Of course these have a  larger year-to-year influence than the trend line variations.

If you have followed the above steps you have done something at once both cool and complex. You have combined the most reliable climate data with the best available statistical resource.