Posts Tagged ‘satellites’

Cowtan and Way global average temperature observations compared to CMIP5 models

November 15, 2013

It is well known that the Arctic is warming up much faster than the rest of the globe. As a consequence, datasets which omit this region (HadCRUT and NOAA) underestimate the global warming trend. A new paper by Cowtan and Way addresses this cool bias by using satellite data to fill in these data gaps. They make a good case that this method also improves upon the NASA GISS dataset, which uses extrapolated data from surface stations to partly fill in the data sparse regions. Combining their new method of infilling with the most up-to-date sea surface temperatures gives a substantially larger trend over the last 15 years than the abovementioned datasets do. The temporary slowdown in global surface warming (also dubbed “the pause”) nearly disappears. As Michael Tobis notes:

This demonstrates is how very un-robust the “slowdown” is.

The corrections don’t amount to a huge change in absolute temperature change, and the new data actually fall inside the uncertainty envelope provided by HadCRUT4. As the paper correctly states:

While short term trends are generally treated with a suitable level of caution by specialists in the field, they feature significantly in the public discourse on climate change.

In the figure below (made by Jos Hagelaars) the global average temperature as calculated by Cowtan and Way (“C&W hybrid”) is compared to both the HadCRUT4 dataset and the CMIP5 multi-model mean as well as its 5% and 95% percentile values (RCP8.5): [Update: The figure below has
been replaced, since the original was found to be in error during discussions on CA). The confidence interval of this corrected graph is substantially narrower than the erroneous original one. Note that the current graph shows the 5 to 95 percentile range of model runs (i.e. the 90% confidence interval), whereas the previous ones showed the 95% confidence interval. At the bottom of the post a similar figure with both confidence intervals as well as the two sigma range is shown.


Also with these data improvements, recent observations are at the low side of the CMIP5 model range. The comparison of observations to models has to be interpreted with caution however. Some people like to jump to preferred conclusions, but it’s good to keep in mind that the expected warming at a specific point in time depends on a combination of factors. Any of these factors -as well as shortcomings in the observational data, such as those discussed by Cowtan and Way- could contribute to a mismatch between observations and models:

– radiative forcing

– equilibrium climate sensitivity

– climate response time

– natural unforced variability

The last factor means that one shouldn’t expect the multi-model mean (in which most variability is cancelled out) to be identical to the observations (which are the result of a particular realisation of natural variability).

Cowtan and Way made a very clear video in which the main results of their paper are explained in just a few minutes. Highly recommended watching:

More commentary on the paper on e.g. RC (Rahmstorf), SkS (Cowtan and Way), Guardian (Nuccitelli), P3 (Tobis), Victor Venema, Neven. See also this very useful background information provided by the authors.

[some typos corrected and clarifications added, 16-11. Erroneous figure replaced 21-11.]

Update: Below a similar figure as above, with different confidence intervals for the model runs shown. 


Update 2 (Feb 2014):

Jos Hagelaars added Cowtan and Way’s data for 2013 to a figure comparing observations to model projections:

Jos Hagelaars - comparison_cmip5_hadcrut4_cowtanway_2013


Response to John Christy’s blog post regarding ‘Klotzbach Revisited’

March 5, 2013

Guest blog by Jos Hagelaars

Dr. John Christy wrote an extensive blog post as a response to my Dutch ‘Klotzbach Revisited’ post (English version here), it is published on “Staat van het Klimaat” and WUWT. I would like to thank Dr. Christy for his interest in my writings.

I have some remarks regarding Dr. Christy’s post, which are addressed in this ‘response-post’ and are built upon some quotes taken from Dr. Christy’s response.
For reference, the original Klotzbach et al 2009 paper (K-2009 in the text) can be found here and the correction paper (K-2010) can be found here.

“Klotzbach et al.’s main point was that a direct comparison of the relationship of the magnitude of surface temperature trends vs. temperature trends of the troposphere revealed an inconsistency with model projections of the same quantities.”

This ‘main point’ is not present at all in the K-2009 paper, the only reference to real data coming from a climate model in the paper is the amplification factor, which was ‘sort of obtained’ by Ross McKitrick from the GISS-ER model. In the abstract a short conclusion is given: “These findings strongly suggest that there remain important inconsistencies between surface and satellite records.”. No word about models.

In my opinion the main point of K-2009 is the suggestion that the surface temperature record is biased. One third of the paper is made up by paragraph 2 with the title: “Recent Evidence of Biases in the Surface Temperature Record”. K-2009 explicitly state:
In our current paper, we consider the possible existence of a warm bias in the surface temperature trend analyses …


Klotzbach Revisited

March 1, 2013

Guest blog by Jos Hagelaars. Dutch version here.

The average surface temperature of the earth, measured by ‘thermometers’, are released by a number of institutes, the most well-known of these datasets are GISTEMP, HadCRUT and NCDC. Since 1979 temperature data for the lower troposphere are released by the University of Alabama in Huntsville (UAH) and Remote Sensing Systems (RSS), which are measured by satellites.
The temperatures of these two methods of measurement show differences, for instance: the NCDC data indicate a trend over land of 0.27 °C/decade for the period 1979 up to and including 2012, while over the same period, the trend based upon the satellite data by UAH over land is significantly lower at 0.18 °C/decade. In contrast, the trends for global temperatures indicate much smaller differences, for NCDC and UAH these are respectively 0.15 °C/decade and 0.14 °C/decade for the same period.

Big deal? Almost everything related to climate is a ‘big deal’, so it is of no surprise that the same applies to these trend differences. In a warming world it is expected that the temperatures of the upper troposphere increase at a higher rate than at the surface, regardless of the cause of the warming. The satellite data (UAH and RSS) do not reflect this. Why is the upper troposphere expected to warm at a higher rate and what is the cause of these trend differences between the surface  and satellite temperatures?

The temperature gradient in the troposphere / the ‘lapse rate’

When you go up in the troposphere it gets colder. This is caused by the fact that rising air will cool down with increasing altitude due to a decrease in pressure with altitude, by means of so-called adiabatic processes. This temperature gradient is called the lapse rate, a concept one will frequently encounter in papers regarding the atmosphere in relation to climate. When the air is dry, this temperature drop is about 10 °C per km. When the air contains water vapor, this vapor will condense to water upon cooling as a result of the rising of the air, which releases heat of condensation. So in this way, heat is transported to higher altitudes and the temperature drop with height will decrease. For air saturated with water vapor, this vertical temperature drop is approximately 6 °C per km.

When the earth gets warmer, air can contain more water vapor. This also has an impact on the lapse rate, since more water vapor means more heat transfer to higher altitudes. This effect on the lapse rate is called the lapse rate feedback. More heat at higher altitudes implies that there will be more emission of infrared light, a negative feedback. This effect is particularly important in the tropics. At higher latitudes, the increase in temperature at the surface is dominant, therefore the change in the lapse rate will turn into a positive feedback. See figure 1 (adapted from the climate dynamics webpage of the University of Leuven).


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