I have been interested in analysing temperature trends measured by weather stations in the Sahara region. This is for two main reasons. The first is that with so little humidity in the atmosphere any effects caused just by increases in CO2 concentrations should be more evident. Water vapor is the Earth’s dominant greenhouse gas and any changes in humidity either naturally or perhaps linked to climate feedbacks are difficult to untangle from CO2 increases. Consequently arid places with little changes in humidity should help to reduce this uncertainty. The second reason is that the Sahara has not seen such massive human development as seen in other places. I have downloaded the HADCRU station data released in July 2011 from the Met Office available here. I have also been using and modifying the PERL files kindly provided which are used to calculated the gridded data for global temperature “anomalies”. The details of this are taken from the HadCru site below.
“Stations on land are at different elevations, and different countries estimate average monthly temperatures using different methods and formulae. To avoid biases that could result from these problems, monthly average temperatures are reduced to anomalies from the period with best coverage (1961-90). For stations to be used, an estimate of the base period average must be calculated. Because many stations do not have complete records for the 1961-90 period several methods have been developed to estimate 1961-90 averages from neighbouring records or using other sources of data. “
To identify relevant Saharan stations for the study, I selected those stations whose latitude and longitude lie between LAT[15,28] and LON[-12,30]. When doing this I noticed that the files actually use Lon positive heading west which is oposite to normal so I had to correct for this. Each station was also required to have data covering at least the period 1960 – 2000. There were 18 stations which satisfied these criteria as follows:
IN SALAH Algeria TAMANRASSET Algeria BILMA Niger AGADEZ Niger TESSALIT Mali KIDAL Mali TOMBOUCTOU Mali GAO Mali NIORO DU SAHEL Mali NARA Mali HOMBORI Mali MENAKA Mali BIR MOGHREIN Mauritania TIDJIKJA Mauritania SEBHA Libya KUFRA Libya DAKHLA Egypt FAYALARGEAU Chad
For each of these stations the data provide the average monthly temperature over time and the calculated “normals” and standard deviations. Using these temperature values and after subtracting the “normals”, the anomalies for each of the 18 stations are then calculated. An example “snippet” from the station file for FAYALARGEAU, Chad is shown below
Number= 647530 Name= FAYALARGEAU Country= CHAD Lat= 18.0 Long= -19.1 Height= -999 Start year= 1946 End year= 2007 First Good year= 1946 Source ID= 10 Source file= Jones+Anders Jones data to= 1977 Normals source= Data Normals source start year= 1961 Normals source end year= 1977 Normals= 20.2 22.5 26.1 30.6 33.0 34.0 33.1 32.7 32.6 29.4 24.6 21.3 Standard deviations source= Data Standard deviations source start year= 1946 Standard deviations source end year= 1977 Standard deviations= 1.6 1.8 1.6 1.2 1.2 0.9 0.9 1.0 1.2 1.3 1.6 1.6 Obs: 1946 21.9 21.2 26.4 31.8 35.3 35.4 34.3 33.7 34.1 31.2 27.2 22.3 1947 20.9 26.7 26.8 31.2 34.4 36.0 35.0 34.5 34.0 30.9 25.2 23.3 1948 21.4 22.8 23.7 -99.0 34.9 35.4 34.2 33.9 -99.0 28.6 23.7 19.2 1949 20.2 19.7 26.3 28.9 34.0 33.5 32.5 -99.0 31.3 28.8 24.7 19.0 .......
To summarise: The calculation of the grid of global average temperature anomalies used by Hadley-Cru is based on a per station “normal” set of monthly data values and associated standard deviations for 1961-1990. These normal values for each station are the monthly averages for that particular station over the period from 1961 to 1990. For the Saharan study the 18 station anomalies are all plotted together in Figure 1. There appears to be a very slight rise at recent times above the red zero line.
I next take the temperature values for each of the 18 stations and average them all together to get a single time series. This is shown in Figure 2. together with a 50 point rolling averaged smoothing term. There is little evident change in mean temperatures. Figure 3 shows the averaged anomalies for all 18 stations for the full time period, together with a least squares linear fit, and a clear rise in net anomaly now becomes evident. The trends between the averaged temperatures and the averaged anomalies are significantly different. The averaged anomalies as used by the HadCru Gridding algorithm shows an almost linear slow rise of about 1 degree C. in temperature anomaly from 1950 to 2011. However there is no real sign of this at all in the station averaged temperatures over the same time period. Instead it is just the temperature extremes – both positive and negative which seem to increase slightly.
Could there be a systematic effect in the analysis which accentuates any small increase when averaging together all the temperature anomalies ? To investigate this I calculated a new set of anomalies based on all 18 stations. The normals were calculated by averaging temperatures over all 18 stations for each month over the period 1961-1990. The “net” anomalies were then calculated by subtracting these “averaged” normals from the averaged temperatures over the full period. The result is shown in Figure 4.
The anomaly trend is now essentially flat. What can be the difference between using the two different normalisation values ? It seems that averaging the anomalies gives a slightly different result to the anomalies of the averages (figure 5). However the conclusion is crucial as to whether temperatures have risen overall in the Sahara over the last 60 years. In the first case the conclusion is that average temperatures in the Sahara have increased since 1950 by just over 1 degree.C while CO2 concentrations increased by about 80 ppm – in line with IPCC predictions. In the second case the conclusion is that there has been no significant change in temperature at all during the last 60 years. The raw monthly average temperature measurements also support little or zero net change. I have always worried about the use of “anomalies” instead of temperatures because it assumes there is one “normal” period (1961-1990) to which all other temperatures should be referenced. Could this assumption itself introduce a bias ?