Whatever happened to the Global Warming Hiatus ?

The last IPCC assessment in 2013 showed a clear pause in global warming lasting 16 years from  1998 to 2012 – the notorious hiatus. As a direct consequence of this  AR5 estimates of climate sensitivity were reduced and CMIP5 models appeared to clearly overestimate trends. Following the first release of HadCRUT4 in 2012  the ‘headline’ that followed was that 2005 and 2010 were now marginally warmer than 1998. This was the first dent in removing the hiatus. Since then each new version of H4 has showed further incremental warming trends, such that by 2019 the hiatus has now completely vanished. Anyone mentioning it today is likely to be ridiculed by the climate science community. So how did this reversal happen within just 7 years? I decided to find out exactly why the post 1998 temperature record changed so dramatically in such a short period of time.

In what follows I always use the same algorithm as CRU for the station data and then blend that with the Hadley SST data. I have checked that I can reproduce exactly the latest HadCRUT4.6 results based on the current 7820 stations from CRU merged with  HadSST3. Back in 2012 I downloaded the original station data from CRU –  CRUTEM3. I have also downloaded the latest CRUTEM4 station data.

Figure 1 compares the latest HadCRUT4.6 results with the last version of HadCRUT3.

Figure 1. Comparison of HadCRUT3 and the latest HadCRUT4.6 Notice how all trends pivot around the 1998 El Nino peak.

I had assumed that the reason for the apparent trend change was because CRUTEM4 had added many new weather stations in the Arctic (removing some in S.America as well), while additionally the SST data had also been updated (HadSST2 moved to HADSST3). However, as I show below, my assumption simply isn’t true.

To investigate I recalculated a ‘modern’ version of HadCRUT3 by using only the original 4100 stations (used by CRUTEM3) from CRUTEM4 station data.  The list of these stations are defined here. I then merged these with  both the older HadSST2 and HADSST3 to derive annual global temperature anomalies. Figure 2 shows the result. I get almost exactly the same values as the full 7820 stations in HadCRUT4. It certainly does not reproduce HadCRUT3 !

Figure 2. The black curve is based on “modern” CRUTEM3 stations combined with HADSST3 and the Yellow  curve is “modern” CRUTEM3 stations with HADSST2

This result provides two conclusions.

  1. Modern CRUTEM3 stations give a different result to the original CRUTEM3 stations.
  2. SST data is not responsible  for the difference between HadCRUT4 and HadCRUT3

To confirm point 1) I used exactly the same code to regenerate HadCRUT3 temperature series using the original CRUTEM3 station data as opposed to the ‘modern’ values based on CRUTEM4.

Figure 3: Comparison of HadCRUT3 with my calculation using the original CRUTEM3 station data.

The original CRUTEM3 station data I had previously downloaded in 2012. These are combined with HADSST2 data. Now we see that  the agreement with the H3 annual temperatures is very good, and indeed reproduces the hiatus.

So the conclusion is very simple. The monthly temperature values in over 4000 CRUTEM3 stations have all been continuously changed, and it is these changes alone that have resulted in transforming the 16 year long hiatus in global warming into a rising temperature trend. Furthermore all these updates have only affected temperatures AFTER 1998! Temperatures before 1998 have hardly changed at all, which is the second requirement needed to eliminate the hiatus.

P.S. I am sure there are excellent arguments as to why pair-wise ‘homogenisation’ is wonderful but why then does it only affect data after 1998 ?

 

Posted in AGW, climate science, IPCC, UK Met Office | 91 Comments

May global temperatures fall 0.16C

The global averaged surface temperature for May 2019 was 0.66C using my spherical triangulation method merging GHCNV3 with HadSST3. This is a significant drop of 0.16C from April 2018. The baseline used is always 1961-1990. I also applied the same calculation to the new V4 data (17400 total stations as compared to 7280 in V3) using both the corrected (V4C) and the uncorrected (V4U) station data.

Global average monthly temperatures. The differences between V4C and V3C appear small, but there is a systematic difference in trend.

Shown below are the annual global average temperatures where the 2019 value is just the average over the first 5 months.

V3 and V4 annual temperatures ( V4 is shown for both the corrected and raw data). Hadcrut3 data shown in green are similar to those used in AR4 showing a hiatus and are based on a 5 degree (lat,lon) weighted average.

I find it  striking how the new V4 data has yet again increased recent warming trends apparently  just by adding more stations. The famous Hiatus which was evident in HadCRUT3 post 1998 has entirely disappeared. You can see this by comparing with the old  2012 HadCRUT3 results as used by AR4. What I find rather strange though is how all the datasets (V3,V4,H3,H4) pivot around exactly the same value for the 1998 El Nino peak. All the datasets, independent of sampling or analysis methods  agree on the same 1998 annual temperature. Why?

One possibility is that adding hundreds of new stations with short time coverage (mostly post 1960) may introduce a post 1990 warming bias simply because they add no new information about pre 1960 temperatures. However, I still can’t understand why 1998 temperatures should remain exactly the same across all datasets, including Cowtan & Way.

1998 was the start of the infamous Hiatus in global warming, so avoiding any increased temperature for 1998 certainly helps in debunking it.

Posted in AGW, Climate Change | Tagged , | 6 Comments

5 Million years of cooling

Why did the earth cool ~6C during the Pleistocene resulting in the current deep ~100 Ky glacial cycles? The most probable cause  is plate tectonics – the opening of the Atlantic and continuing rise of the Himalayas after India collided with Asia. Less well known though is the increasing height of the Andes, Greenland and Western US as shown below. All  this data are from the PaleoDEM project

Comparison of topgraphy 5My ago and Today.

an alternative view of the same data is though contour plots

Topographic contour plot. Coastlines drawn are the current ones

We can quantify the net change in land topography by calculating the surface area of the earth above a certain height. This shows that over the last 5 million years there has been an increase in land surfaces above 3000m altitude by about 5.4 million square km. That figure represents a net global increase of 56% in such high altitude land masses. This land movement is concentrated in the Himalayas, the Western coasts of America and especially Greenland. These last two areas extend into high latitudes where changes in albedo are important factors. So how might all this affect the global climate?

1. High altitudes are colder simply due to the fall in temperature with lapse rate. Above 3000m is something like 20C colder than at sea level.  Moisture falls as snow and glaciers develop.

2. A 50% increase in glaciated areas increases global albedo thereby reducing net incoming solar radiation slightly, which I estimate at about 0.5% or up to 2W/M2.  Perhaps just as important a result is that Milankovitch orbital forcing gets amplified as more land remains permanently glaciated at higher latitudes. This amplification effect is evident in the Ice Volume data.

5 Million year trends in Ice Volume and implied global temperatures

When did Antarctica become permanently ice covered? Prior to 2.5My ago the “West Antarctic Ice Sheet and Antarctic Peninsula Ice Sheets together grew successively larger, with periodic collapses during interglacials. During periods of West Antarctic Ice Sheet absence, the Antarctic Peninsula Ice Sheet remained as a series of island ice caps” (source). This might also explain why initially glacial cycles followed the obliquity cycle since NH insolation and SH insolation are out of phase. Changes in Ice volume partially cancel if Antarctica also contributes to sea levels due to land based melt-back. In this case the MPT (Mid Pleistocene Transition) may represent the end of this cancelation effect  and the start  of NH dominance.

Posted in Ice Ages, Paleoclimatology | Tagged | 4 Comments