The Smoking Gun At Darwin Zero

Filed Under: Environmental News on December 9, 2009

Note from Aaron: This article is lengthy and somewhat scientific, but not overly so. Grab a beverage and sit down. I’m only reproducing the first portion of it here. I suggest you follow the link below (author’s name) or at the bottom of this post to the original. It’s well worth reading and will give you some important insight into not only how the data is manipulated (for ill or good), but the kinds of things scientists deal with and why just sticking a thermometer out the window every few hours is not really “getting climate data.”

by Willis Eschenbach

People keep saying “Yes, the Climategate scientists behaved badly. But that doesn’t mean the data is bad. That doesn’t mean the earth is not warming.”

Darwin Airport – by Dominic Perrin via Panoramio

Let me start with the second objection first. The earth has generally been warming since the Little Ice Age, around 1650. There is general agreement that the earth has warmed since then. See e.g. Akasofu . Climategate doesn’t affect that.

The second question, the integrity of the data, is different. People say “Yes, they destroyed emails, and hid from Freedom of information Acts, and messed with proxies, and fought to keep other scientists’ papers out of the journals … but that doesn’t affect the data, the data is still good.” Which sounds reasonable.

There are three main global temperature datasets. One is at the CRU, Climate Research Unit of the University of East Anglia, where we’ve been trying to get access to the raw numbers. One is at NOAA/GHCN, the Global Historical Climate Network. The final one is at NASA/GISS, the Goddard Institute for Space Studies. The three groups take raw data, and they “homogenize” it to remove things like when a station was moved to a warmer location and there’s a 2C jump in the temperature. The three global temperature records are usually called CRU, GISS, and GHCN. Both GISS and CRU, however, get almost all of their raw data from GHCN. All three produce very similar global historical temperature records from the raw data.

So I’m still on my multi-year quest to understand the climate data. You never know where this data chase will lead. This time, it has ended me up in Australia. I got to thinking about Professor Wibjorn Karlen’s statement about Australia that I quoted here:

Another example is Australia. NASA [GHCN] only presents 3 stations covering the period 1897-1992. What kind of data is the IPCC Australia diagram based on?

If any trend it is a slight cooling. However, if a shorter period (1949-2005) is used, the temperature has increased substantially. The Australians have many stations and have published more detailed maps of changes and trends.

The folks at CRU told Wibjorn that he was just plain wrong. Here’s what they said is right, the record that Wibjorn was talking about, Fig. 9.12 in the UN IPCC Fourth Assessment Report, showing Northern Australia:

Figure 1. Temperature trends and model results in Northern Australia. Black line is observations (From Fig. 9.12 from the UN IPCC Fourth Annual Report). Covers the area from 110E to 155E, and from 30S to 11S. Based on the CRU land temperature.) Data from the CRU.

One of the things that was revealed in the released CRU emails is that the CRU basically uses the Global Historical Climate Network (GHCN) dataset for its raw data. So I looked at the GHCN dataset. There, I find three stations in North Australia as Wibjorn had said, and nine stations in all of Australia, that cover the period 1900-2000. Here is the average of the GHCN unadjusted data for those three Northern stations, from AIS:

Figure 2. GHCN Raw Data, All 100-yr stations in IPCC area above.

So once again Wibjorn is correct, this looks nothing like the corresponding IPCC temperature record for Australia. But it’s too soon to tell. Professor Karlen is only showing 3 stations. Three is not a lot of stations, but that’s all of the century-long Australian records we have in the IPCC specified region. OK, we’ve seen the longest stations record, so lets throw more records into the mix. Here’s every station in the UN IPCC specified region which contains temperature records that extend up to the year 2000 no matter when they started, which is 30 stations.

Figure 3. GHCN Raw Data, All stations extending to 2000 in IPCC area above.

Still no similarity with IPCC. So I looked at every station in the area. That’s 222 stations. Here’s that result:

Figure 4. GHCN Raw Data, All stations extending to 2000 in IPCC area above.

So you can see why Wibjorn was concerned. This looks nothing like the UN IPCC data, which came from the CRU, which was based on the GHCN data. Why the difference?

The answer is, these graphs all use the raw GHCN data. But the IPCC uses the “adjusted” data. GHCN adjusts the data to remove what it calls “inhomogeneities”. So on a whim I thought I’d take a look at the first station on the list, Darwin Airport, so I could see what an inhomogeneity might look like when it was at home. And I could find out how large the GHCN adjustment for Darwin inhomogeneities was.

First, what is an “inhomogeneity”? I can do no better than quote from GHCN:

Most long-term climate stations have undergone changes that make a time series of their observations inhomogeneous. There are many causes for the discontinuities, including changes in instruments, shelters, the environment around the shelter, the location of the station, the time of observation, and the method used to calculate mean temperature. Often several of these occur at the same time, as is often the case with the introduction of automatic weather stations that is occurring in many parts of the world. Before one can reliably use such climate data for analysis of longterm climate change, adjustments are needed to compensate for the nonclimatic discontinuities.

That makes sense. The raw data will have jumps from station moves and the like. We don’t want to think it’s warming just because the thermometer was moved to a warmer location. Unpleasant as it may seem, we have to adjust for those as best we can.

I always like to start with the rawest data, so I can understand the adjustments.

Read the rest at this link.