Climate Change Impact - Part 12 - Kagera Basin (Rwanda, Burundi, Uganda and Tanzania)

Climate Change Impact

Part 12: Example – Kagera Basin (Rwanda, Burundi, Uganda and Tanzania)


The Kagera basin flows into Lake Victoria and as such it forms part of the Nile Basin. An extensive data base of climate and flows was available and was used to calibrate the HYSIM hydrological model to 22 sub-basins. Climate projections show that rainfall is projected to increase but temperature (and hence evapotranspiration) is also expected to increase. Whilst the two changes to some extent balance out, the increase in temperature still has important implications for the future of agriculture.


The Kagera River Basin and its tributaries flow within four countries (Rwanda, Burundi, Uganda and Tanzania). The Kagera River flows into Lake Victoria which in turn forms part of the Nile River Basin. The aim of the study was to assess the water resources potential of the basin and also to estimate the potential effect of climate change.

Figure 1 is a map of the river basin with the sub-basins used for hydrological modelling delineated.

Figure 1 Kagera River basin showing sub-basins for hydrological modelling

Current situation

The project team was provided with a climate and hydrometric data base developed in the Lake Victoria Environmental Management Program, Phase I. This included river flow, precipitation, temperature and other variables such as wind speed, sunshine and relative humidity needed to calculate potential evapotranspiration. Data were available up to the year 2000.

The methodology was based on use of a hydrological model, HYSIM (in its monthly variant). A hydrological model requires continuous input data so gaps in the data had to be infilled by reference to nearby stations with data.

Temporal infilling is standard option from the HYSIM program.  The program does this as follows:

  •         Reads the data into a monthly array of data from all stations.  If there are more than a set number of days with data, then the total for those days is adjusted pro-rata upwards to give the month's total (in the case of precipitation) or monthly mean, in the case of other variables.
  •         Calculates a matrix of totals/means for concurrent periods for all pairs of stations.
  •         Uses the above totals to calculate the ratio of the totals/mean for all pairs of stations.
  •         Uses these ratios to estimate the errors in the relationship between all possible pairs of stations.
  •         Infills the monthly data using whichever station with data gives the lowest error and which has not itself been infilled.

Even when all stations with data had been infilled there were still large parts of the basin without measurements. To complement the observed values, data on a 10’ geographical grid from the Climatic Research Units of the University of East Anglia were also used.

The data were used to calculate potential evapotranspiration and precipitation on a 10’ grid. The following figure shows contours of rainfall minus potential evapotranspiration (PET). As can be seen the western parts of the basin are in surplus but those to the east are in deficit.

Figure 2 Precipitation minus potential evapotranspiration

A similar process was used for other climatic parameters.

The data base of rainfall and precipitation were used with the HYSIM hydrological model to simulate river flows in the basin. Flows were simulated for each of the 22 sub-catchments that are shown on figure 1.

The following figure shows the simulated and observed flows for the Rivubu at Gitega.

Figure 3 Simulated and observed flows - River Rivubu at Gitega

For most of the time the simulation is good however the accuracy drops off markedly after the early 1990s. Whilst the data base is good up to the end of the 1980s the number of climate stations becomes much fewer for the later years and this can be considered to explain the change in accuracy.

Climate change

The A1B scenario has a balanced emphasis on all energy sources. It is generally considered to be the projection of temperature that might occur in the absence of any international agreed and binding protocol to reduce carbon emissions. This scenario was therefore used.

The choice of models was based on those listed on Table 6 of the IPCC “General Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment”, Version 2, June 2007.  Seven models were listed. The projections were based on the average of those models.

The following figure shows the current average annual temperature in the basin and the projected temperature for two different time horizons 2020 to 2049 and 2070 to 2099.

Figure 4 - Observed and projected basin temperature fro two time horizons

For the period 1970 to 1999 the average basin precipitation was 1570 mm/year. For the period 2020 to 2049 it is projected to be 1644 mm/year and the period 2070 to 2099 is projected to be 1740 mm/year.

The overall conclusion was that the increase in precipitation would counter-balance the increased potential evapotranspiration at a basin level but for individual sub-catchments the picture was more complex. Even though the overall water balance might be the same the higher temperature had implication for the types of crops which could be grown and the amount of irrigation required.


Climate Change Impact - Part 10 - Zambia

Climate Change Impact

Part 10: Example –  Zambia


Zambia has a climate typical of Southern Africa with cool dry winters (June to August) with hot wet summers. The potential impact of climate change was studied as part of a project enhancing the country's skills in Integrated Water Resources Management (IWRM). Temperature are expected to rise throughout the year and over the whole country with slightly higher increases in the south-east. The changes in precipitation are less consistent with some months projected to have an increase and others to have a reduction.


The Government of Zambia was fully aware of the principles of Integrated Water Resources Management.  The need for climate change to integrated in water resources planning was recognised in the National Water Plan of 1994. The Water Resources Management Act of 2011 took this a stage further. This act sought to create a National Water Authority. Section 8 of the Act on ‘Functions of the Authority’ said its functions should include:

  •         minimising the effects of climate change
  •         support proactive climate change planning and management
  •         in consultation with the institution responsible for national statistics, establish and maintain an information system, which will be accessible by both gender, in accordance with regulations issued by the Minister providing for the content of the system, which shall include relevant hydrological, hydrogeological, meteorological, climatological, water quality, water storage and supply and use data, and relevant information on potentials for the use of water
  •         publish forecasts, projections and information on water resources

The aim of this project was to assist the Government in the integration of climate change into Integrated Water Resources Management.

The first, and at the time of the project only, National Communication on Climate Change prepared under the auspices of the United Nations Framework Convention on Climate Change was produced by the Ministry of Tourism and Natural Resources (MoTNR) in 2002. The report considered mitigation options. Under the heading ‘Vulnerability and Adaptation Assessment” it was estimated that maize production might fall but sorghum could increase and groundnuts remain steady. There was no clear indication of the effect on livestock. In terms of water resources, it was suggested that southern parts of the country might be particularly vulnerable.

A report on the National Adaptation Programme of Action on Climate Change was produced by the MoTNR. Zambia had experienced a number of climate related hazards over several decades. Using multi-criteria analysis, it had identified most urgent needs to prioritize ten immediate adaptation interventions. Zambia was divided into 3 ecological-climatic regions based on rainfall. The wettest regions were toward the north of the country. According to the report, the projections suggested that the wettest region would have an increase in rainfall but the drier regions would have less rainfall. The driest region is projected to produce less agricultural produce and livestock. Wildlife could be heavily stressed due to reduced rainfall and increased migration. Malaria is likely to increase in areas with increasing rainfall.

Current Climate

The average annual temperatures for four stations are shown on figure 1. All four stations show a similar trend: a maximum around 1930, a general fall until about 1975 and then an increase to a new maximum around 2005. Temperatures during the 5 years 1927 to 1931 were about 0.5 °C higher than temperatures from 2001 to 2004.

Figure 1 Average annual temperature in Zambia - four representative stations

Temperatures are lowest in June and July. In terms of geographical distribution of temperatures, they are highest in the south-east and the north though the variation is not great – most of the is in the range 22°C to 24°C.

Figure 2 shows the seasonal distribution of precipitation. It shows that rainfall is highly seasonal with very little rain in the period June to August.

Figure 2 Average monthly precipitation - three representative stations

There is considerable variation in rainfall from year to year. The wettest station showed a slight increasing trend and the driest station showed a slight decreasing trend. The geographical distribution of rainfall showed it as being higher to the north of the country.

Climate change projections

Climate change projections were based on the A1B scenario. This is considered to be the ‘business-as-usual’ scenario. The projection used was the average of 23 climate models used to inform the IPCC Assessment Report.
Temperatures are expected to increase by from 3.2°C to 3.9°C by the end of the century. Figure 3 shows the geographical distribution of the temperature changes.

Figure 3 Geographical distribution of climate change

In the case of temperature, the increases are fairly uniform throughout the year. In the case of precipitation there is a marked difference in the changes at different times of the year.

Figure 4 Projected change in precipitation

This shows that rainfall will decrease in the currently driest periods of the year and will increase most in the wettest periods.


Temperatures are projected to rise throughout the year and over the whole country with slightly higher increases in the south-east. In the case of precipitation there are seasonal variations in the changes. In January, there are increases in precipitation over the whole country but larger increases in the north. In November, precipitation is expected to fall over the whole country with larger falls in the south.  December could be considered a ‘pivot’ month with increases in the North and reductions in the South.


Climate Change Impact - Part 5 - Great Lakes of Africa (Lakes Victoria,Tanganyika and Malawi)

Climate Change Impact

Part 5: Example – Great Lakes of Africa


The Great Lakes of Africa are an important source of fish. The United Nations Food and Agriculture Organisation (FAO) wished to know to what extent climate change would influence the water temperature in the lakes. A study was carried out which found that lake temperature would increase by around 1°C by the middle of the 21st.century.


FAO initiated an activity to investigate the possible effects and impacts of climate change on fish and fisheries production on the African Great Lakes; Lakes Victoria, Tanganyika and Malawi.
Figure 1Great Lakes of Africa

Figure 1 Great Lakes of Africa

Current climate

The Joint Research Centre (JRC) at Ispra, Italy, maintains databases of unpublished satellite data including water surface temperatures on the African Great Lakes on an 8-day basis. A time series of a number of decades was required to be analysed in order to present the temperature fluctuations on the Great Lakes
The satellite data from which temperatures are estimated are held in TIF (tagged image format) files, one for each year from 1985 to 2008. Each file holds data for 45 passes of the satellite.  For each pass a value is recorded for each cell on a 400 by 250 grid, provided there is no cloud cover and provided it is over water. Each cell is approximately 10 km by 10 km.

One major problem with the data is cloud cover.  For each of the lakes the approximate percentage of time for which temperature can be calculated is:

  • Lake Malawi – 70%
  • Lake Tanganyika – 60%
  • Lake Victoria – 30%
For all lakes, the problem is seasonal and is related to the rainy season.

The method adopted was as follows:
  •          Calculate the average lake temperature for each of the 45 passes and develop a temperature profile.
  •          Assume that although temperature in different parts of the lake would be higher or lower than the average, the distribution throughout the year would be the same.
  •          If data were missing, due to cloud cover, for one of the satellite passes find the value of the previous and of the following passes which had data for that cell.
  •          Base the temperature that satellite pass on the weighted average of the previous and following pass.

This enabled a complete grid of ‘observed’ surface temperature data to be prepared for all three lakes.

The following chart shows an example, for one cell of Lake Malawi.


Figure 2 Example of infilled lake temperature data

Climate change impact

As a first stage in assessing impact, an air temperature record was established for each of the lakes. The data from climate stations was of limited availability; few stations and long gaps in the data. As an alternative, the temperature data based on RSS (Remote Sensing Systems) estimates was used. This is one of two the ‘standard’ temperature records based on (Advanced) Microwave Sounding Unit data (AMSU/MSU). Comparing the limited observed data and the satellite derived data, showed similar trends but less variation. The difference in the temperature variation was due to the fact the satellite data were based on a 2.5 ° grid, not a single point, and therefore represented values over an area. In fact, this data was in that way more suitable than point data.

For each of the lakes a relationship between air temperature and water temperature was developed.

The climate change projections were based on the average of six climate models using the A1B (‘business as usual’) scenario. The models were those used in the IPCC “General Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment”. This gave the projected change in air temperature
The final stage was to use the relationship between lake surface temperature and air temperature to estimate the change in surface temperature of the lakes.

This shows that, for all lakes, the increase in surface water temperature would be around 1°C in the middle of the century.


Climate Change Impact - Part 2 - Southern Bangladesh

Climate Change Impact

Part 2: Example – Southern Bangladesh


This posting is based on a study of the impact of climate change on Southern Bangladesh. It examines current climate related problems which include storm rainfall, drought duration and sea level rise. It concludes that the main changes in climate in the region will be increases in temperature, storm rainfall, sea level and drought severity. Average rainfall will remain similar to the present.


The project area is shown in the following map. It covered 13 Upazilas in southern Bangladesh. The total population of the project areas was 17.6 million. In some Upazalis the population density was more than 1000 people per square kilometre; about the average for Bangladesh.

Figure 1 Map of project area

The project tackled three areas of vulnerability: roads, cyclone shelters and markets. In terms of climate vulnerability, a significant factor in the project area was the fact that much of the it was only a few metres above mean sea level.

Current climate

Climate data were obtained from three main sources shown in the following table.

Table 1Sources of climate data used in report
Data source
National Climatic Data Centre (part of the National Oceanographic and Atmospheric Administration of the USA)
Daily values of rainfall, maximum and minimum temperature.
Climate Explorer, a site run by the Netherland Meteorological Service.
Monthly values of temperature and rainfall.
TuTiempo, a weather site run by a Spanish company.
Daily values of temperature, rainfall, wind speed, atmospheric pressure, relative humidity.

Other data analysed included sea levels (from the Permanent Service for Mean Sea Level (PSMSL)) and Cyclone intensity (from the ‘Joint Typhoon Warning Center’ web site.)

The observed climate data were analysed and some of the conclusions were:
  •         Annual rainfall in the project area is reducing. For the period 1947 to 2015 the annual average is 2072 mm/year and it is falling by 1.3 mm each year.
  •         The monsoon rain, defined as the rainfall in the three wettest consecutive months in a year, is not decreasing.
  •         The monsoon season starts earlier with the wettest month being June in recent years but July in earlier years.
  •         The average temperature in the project area, based on the period 1890 to 2015, is 26.1 °C. It is increasing slowly and over the 125 years rose by 0.35 °C.
  •          Sea level data were obtained for 6 stations in the project area or from nearby sites in the Bay of Bengal. The data were downloaded from Most sites had around 20 years of data but one had 60 years. The conclusion was that sea levels were rising at 4 mm a year but in places the settlement of the sediments in the delta gave an apparent increase of more than 10 mm a year.
  •      Data on cyclones were analysed for the Northern Indian Ocean, an area which encompasses the Bay of Bengal, using the Accumulated Cyclone Index. The data covered the period 1972 to 2014. It showed that cyclone energy was increasing by 1% a year – though there was a lot of inter-annual variability. Additional analysis of this data showed that the increase in energy was mainly related to an increasing number of storms – the energy in individual storms showed little increase. Cyclones are a major problem in the study area and in one incident it is estimated that 300,000 people were killed.

Climate projections

In many countries, you are given a lead as to where to go for climate projections. For example, certain climate models might have been shown to perform well or special models might have been developed. In the case of Southern Bangladesh, I could find no such guidance. I therefore decided to evaluate the accuracy of different climate models. In this case I established a set of criteria related to both temperature and precipitation. For the period of available data, the simulation of the climate models was compared to observed values. The criteria included absolute values (some models are biased having values which are consistently higher or lower than observed) and relative values (based on trend and seasonal variation). There were 8 criteria in all; temperature and precipitation, monthly and annual, bias corrected and uncorrected.

From this analysis, a few points stood out:
  •         Average monthly values were more accurately simulated than annual time series.
  •         Temperature was more accurately simulated than precipitation.
  •         No model scored consistently higher than others on all criteria.
  •         Some models were highly rated for temperature or precipitation but poorly for the other one.

Four models were selected for more detailed analysis. These were:

  •         NOAA GFDL-CM3, had the best average accuracy when all criteria were considered.
  •         MIROC-ESM-CHEM, was ranked 2nd overall.
  •         CESM1-CAM5, was ranked 5th overall. The model ranked 3rd was from the same source as model 1 and it was decided against using two models from the same source.
  •         MPI-ESM-LR, was ranked 6th based on all criteria but it was never lower then 11thon any criteria. This means it was less likely to produce a ‘bad’ projection.

The following chart shows the monthly simulated and observed average temperature. Value for five models are shown – the four retained as the best overall and the model which was best for this criterion.  As can be seen all models performed well on this criterion.

Figure 2 Observed and projected monthly temperature

The next chart shows the simulated and observed annual precipitation for the study area. This supports the statement above the annual values and precipitation are not as accurately simulated as temperature.

Figure 4 Observed and projected annul precipitation

For the projections, the highest value of RCP (RCP 8.5) was used. There were two reasons for this. Firstly, as the value associated with the maximum change in climate, it represents an outer envelope of projections. Secondly, the lower values of RCP assume a more stringent agreement on emissions than the ones achieved so far.

The next chart shows the projected change in average monthly temperature for the period 2040-2060 relative to 1985-2015. Whilst there is a range of projections, about 0.6 °C, the model are generally consistent in projecting a lower temperature increase during the rainy season, May to September, than for the rest of the year.
Figure 4 Projection of monthly temperature in 2050

The chart for annual precipitation shows much less consistency among the models. For each model, there are two traces – a faint one for annual values and a heavy one for the 10-year moving average. The range of value suggest the change could be from minus 150 mm to plus 150 mm. For comparison, the observed annual rainfall is around 2000 mm/year.

Figure 5 Projection of annual rainfall

The projection of other parameters was also considered.

The same models were used for the projection on maximum daily rainfall in each year (RX1 projection from the ETCCDI set).  This showed that intense rainfall would increase. All four models were consistent in projecting this increase. As the precipitation is that for the whole area of the model cell which most closely corresponded the study area, the difference in absolute values is likely to be due to difference in model cell size.
Figure 6 Projection of annual maximum daily rainfall

The study area is a rural area where agriculture is important. To study the impact of climate change on agricultural productivity the projections of projections of variables such as temperature, humidity and others were used to calculate changes in potential evapotranspiration. These values along with precipitation projections were used with a simple irrigation model to show the change in water demand. This shows that annual water demand is likely to increase from 600 mm/year to 800 mm/year for the mid-century
Figure 7 Projection of unsatisfied crop water demand


The main changes in climate in the region will be increases in temperature, storm rainfall, sea level and drought severity. Average rainfall will remain similar to the present.



On 16 May 2012 we posted on temperatures in the Yamal peninsula. This is one of the sites much discussed for its influence on temperature reconstructions. In the earlier post we looked at measured temperature data from two stations. This time we have looked at a group of stations in and around the peninsula as shown in the following map. The data were from the station files used by the CRU for their CRUT3 temperature series.

We used the data from these station to cross-infill all of them. Two of them started before 1820 and eight others started in the 19th century. The method examined each pair of stations separately for each calendar month, calculated the correlation between them, then infilled missing data using whichever station had the best correlation and which had not itself been infilled.
As examples of the infilling we give below sample charts for 5 stations near to the peninsula: Salehard, MYS Kamennyj, Berezovo, Hoseda-Hard and Ostrov-Dikson. The most interesting chart we present is the last which is the estimated temperature for the Yamal peninsula based in the weighted average temperature of 6 stations in or near to the peninsula. This shows that the temperature has risen at about 0.56 °C per century but there is no sign of a sharp, 'hockey-stick' like upturn.



A recurring topic at ClimateAudit has been the use of tree ring data from the Yamal peninsula in Russia. Steve Mcintyre, the author of ClimateAudit, maintains that data from that area have been used selectively by researchers at the CRU to support the idea of a 'hockey stick' . At the RealClimate blog Gavin Schmidt claims that the results by the CRU were obtained after selection of samples following rigorous analysis of the data.

The Yamal peninsula is at around 71N 71 E. Below I plot data from two sites near to Yamal. One, Salehard, is 200 km south of the Peninsula and the other Ostrov Dickson is 200 km to the north east. The data were downloaded from the ClimateExplorer web site. There are few missing months in the data and I replaced them by the average of same calander month for the preceding and following year except for 2011 where I used the average of the temperature for appropriate calendar months.

As can be seen the observed data show no sign of a hockey stick. Both sites show a maximum in the 1940s, a minimum in the 1970s and and an increase since then - similar to the global temperature trend. One site, Ostrov-Dickson, does have a slight upturn this centruy but temperatures are still below those of the 1940s.

This comment was originally posted in the morning of 1 May 2012. It was modified during the course of day with the addition of the data for Ostrov Dickson.


One of the fundamentals of the consensus approach to climate change is that increasing temperature should lead to increasing water vapour and cloudiness. One type of data measured but not readily available is ‘hours of bright sun’. Initially it was measured by the Campbell-Stokes solar recorder, developed in middle of the 19th century. It uses a glass sphere to direct sunlight on to specially prepared card which shows a ‘burn’ mark when the sun is shining. Recently, radiation is measured by more modern methods. The hours of bright sun varies inversely with cloudiness.

Data on hours of bright sun, among other parameters, are posted on the website of Hungarian Met Office for four climate stations for the period 1910 to 2000 ( 

The following chart shows data for 4 sites for the whole of the period when they all have hours of sun data. There appears to be a break in the data measurement method around 1970; before that period there is a greater variability and less consistency between the stations, after that the data seem to be more consistent. (Note that we have presented the full data set even though part of it is not consistent with later data and could legitimately be excluded.) From 1970 to 2000 the data show a rising trend of 0.018 hours per year. This is equivalent to an increase of 0.54 hours over the 30 year period relative to average of 5.3 hours of sun per day.

Another similar data set is available from the Australian Bureau of Meteorology website at Here the data are expressed as daily solar exposure in MJ m-2. In this case they are measuring solar radiation which also has an inverse relationship with cloud cover. We have chosen the 5 stations because they have data and are in different parts of Australia. The trend line shows a steady climb over the 21 years with data again indicating a reduction in cloudiness. (To create the trend line we have replaced the 2005 data which has missing data for all stations by the average of preceding and following year). The trend is equivalent to an increase of 0.45 MJm-2 over the 21 years relative to average of 17.9 MJm-2.

The final data set we consider is from the UK and was downloaded from the Met Office web site at From the stations on the web site we extracted monthly hours of sun data for 10 stations which were more-or-less complete from 1931 to 2011. The next chart shows the data for all 10 stations and the average. There is some sign of trend in the data with a minimum around 1970/80.

As this graph is a bit confusing we have plotted the average of the 10 stations on a separate graph and also a 20-year moving trend line. A moving trend line calculates the slope of the 20-year trend every year from the 20th year with data up to the end of the data set. This shows that in the 20 years up to 1966, the trend was -0.037 hours per year. In the 20 years preceding 2000 the trend was +0.041 hours a year. Given that hours of sun varies inversely with cloudiness, this demonstrates that not only does cloudiness decrease with increasing temperature but increases with decreasing temperature.

These charts using disparate data sets but they all show one thing; temperature and cloudiness are closely related.


We have earlier commented on trends in temperature. (When is a trend not a trend?) Here we look quite simply at the 30-year trend line from three different temperature data sets and a 23-model ensemble hind-cast/projection. The trends were calculated using LINEST function in Excel. The year shown in the chart is the final year of each 30-year regression period. As can be seen the 30-year trend is positive for most of the last hundred or so years. It reached a peak in 2004 or 2005, depending on the data series, and has since declined slightly. Whilst the three data sets, not surprisingly, show very similar trends, the modelled trends are quite different.

Since 1883 there has been a peak or a trough in the trend line roughly every 30 years. Your guess as to what will happen in the future is as good as mine.

On 25 March 2012, the above paragraph was hanged with '30' replacing '25. The following was also added.

In the next graph we look at the 30-ear trend for 7 models. The choice of models was based on those listed on Table 6 of the IPCC “General Guidelines on the Use of Scenario Data for Climate Impact and Adaptation Assessment”, Version 2, June 2007. Where a model has more than one published simulation its results were average before being include in the graph. The siulation results come from the climate explorer site.

Elsewhere we have shown the results of the same seven models. Showing the difference as 30-year trends appears to demonstrate how widely different the simulations of the models were.


In a recent paper Foster and Rahmstorf (F and R, Global temperature evolution 1979–2010) examine the influence of three factors which introduce variability to the temperature record: ElNino/Nina, Volcanoes and Total Solar Irradiance(TSI). They chose to represent the El Nino/Nina effect by the Multivariate El Nino Index (MEI), volcanoes by Aerosol Optical Thickness Data and TSI by sunspot number. They describe their regression as "the multiple regression includes a linear time trend, MEI, AOD, TSI and a second-order Fourier series with period 1 yr." Effectively they assumed the temperature perturbation as the difference from a linear trend plus and allowance for seasonal effects. They chose the period 1979 to 2010 as this included two satellite temperature records in addition to three records based on measurements. They concluded that adjusting the temperature records showed that the underlying temperature trend was upwards for the whole period.

However data for all three variables are available from 1950 and, for some variables, much earlier. We therefore examined the effect of the three factors on temperature from 1950 to 2011.

As we could not assume a linear temperature trend for the whole of the period 1950 to 2011 we calculated the adjustment (or perturbation) as the difference between the three month mean and the 60 month mean. The three month mean represented the short term effect and the 60 month mean the underlying temperature trend. We worked only with HadCRU3V global temperature series. The need to use the 60 month mean means that our series is truncated relative to the F and R series.

The first chart shows the adjustment calculated by this method.

As can be seen the adjustment clearly represents short term effects such as El Nino and volcanoes.

We then regressed the adjustment against the same three variables as F and R. This chart shows a line trend line between the adjustments and the values calculated from the regression equation.The r2 value was 0.3564, a bit lower than the best of the values found by F and R but covering a longer period.

The final plot shows the observed and adjusted temperature series.

For the common period between this plot and that of F and R the results are similar, in particular the effect of 1998 El Nino is reduced but on the other hand the trend is not as uniformly positive as theirs; this is probably due to the use of a different measure of temperature adjustment and the fact that there was no assumption of a linear trend. The plot also shows that in the period up to 1976 the temperature was much flatter than from that year on.

To understand better the differences between F and R and our approach we also tried using the difference from an assumed trend to represent the perturbation. For the period 1976 to 2011 we used a linear temperature increase. For the period 1950 to 1976 we assumed a constant temperature (i.e. completely flat trend).

For the period 1950 to 1976 the two results are virually identical. For the period 1976 to 2011 the results are similar with the use of a linear trend to define the perturbation giving slightly higher values from 2005 onward. The conclusion is that the results show some sensitivity to the method used to calculate the temperature perturbation. It could be argued that a method such as the one we used, which makes no assumptions relative to the underlying trend, has advantages relative to one which does make such an assumption.

(Note: This was first posted on 17 January and revised and extended on 18 January 2012)


On our web site we have a range of climate reconstructions ( One which stands out from the others is the one which was included in the IPCC 1990 Technical Report as Figure 7.1c. This figure is included in Chapter 7.

In the report it is clearly stated that the figures are to be taken as representing global temperature variations. It appears however that the third figure is based on the temperature record for Central England as developed by Lamb (1965). Subsequently a temperature record based on instrumental readings was developed by Gordon Manley (no relation) and others.

The following chart compares that record (downloaded as the file 'cetml1659on.dat') with global and hemisphere temperature estimates produced by the CRU.