Climate Change Impact - Part 10 - Zambia

Climate Change Impact


Part 10: Example –  Zambia


Summary

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.

Introduction

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.

Conclusions

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.





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Climate Change Impact - Part 2 - Southern Bangladesh




Climate Change Impact

Part 2: Example – Southern Bangladesh


Summary

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.

Introduction

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
Data
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 www.psmsl.org. 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

Conclusions

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.



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PRECIPITATION PROJECTIONS

This is the time of year when climate data sets are updated to include annual totals for the preceding year (in this case 2011). Most sites concentrate on temperature - though sometimes include not just observed atmospheric temperature but also variables such as modelled projections and temperature in the oceans. One variable which is often forgotten is precipitation. After all, the positive feedback from water vapour assumes that it remains in the atmosphere rather than becoming precipitation.

On the chart below we use two data sets. The first is the NCDC 5° gridded precipitation anomaly at http://www1.ncdc.noaa.gov/pub/data/ghcn/v2/grid/grid_prcp_1900-current.dat.gz. To get a monthly global figure we averaged the data, cosine weighted on latitude to compensate for reducing grid sizes. The values are in millimetres. The second data set was of precipitation hind-cast/projected downloaded from the Climate Explorer web site at http://climexp.knmi.nl. The data set used was described as “all models, 20c3m/sresa1b” and included 23 models. These data were in mm/day so to convert then to equivalent units they were multiplied by the number of days in the month. They were adjusted to give values relative to the period 1980 to 2010. As trends were masked by month-to-month variations the five year centred moving averages are also plotted.




This shows, as stated in the IPCC TAR4 report, that the variance of the simulated precipitations is less than that of the observed values (TAR4 section 9.5.4.2.1). The difference, based on the 5-year moving average, is as high at 2 mm/month which is equivalent to 1.88 W/m2. (1 mm evaporation over 1 m2 weighs 1 kg. The latent heat of evaporation of 1 kg of water is 2.45 Mj. 1 kWh is 3.6 Mj.)
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PRECIPITATION AND EL NINO

Recently there has been a number of blog postings linking temperature and El Nino. Tamino (http://tamino.wordpress.com/2011/12/06/the-real-global-warming-signal/) based on a paper by Foster and Rahmstorf used the link to show that global warming had not levelled off. Meanwhile Frank Lansner at WUWT did a similar analysis to show that there had been a levelling off.

One thing which always surprises me is the importance given to temperature by comparison with that given to precipitation. This is very much the case with the previous IPCC report and I am posting it here in the hope that this will change. Within certain wide limits what matters to people is not temperature, and by inference evaporation, but the balance between evaporation and temperature.

The following graph shows a regression plot of precipitation and an El Nino index. The precipitation is based on the NCDC global precipitation anomaly with a 5-month centred moving average. The El Nino index is the NOAA's Oceanic El Nino Index (ONI).



This shows that global precipitation and El Nino are correlated.

The next plots shows the variation in precipiation and the ONI. The precipitation is as above and the ONI is inverted (i.e. the original value multipied by -1).



As can be the two are closely related in both magnitude and timing. The fact that the two are synchronous suggests that neither is forcing the other but that both are a result of a common factor.

In TAR4 very little information was given on the simulation of precipitation by climate models. One of the reasons might have been the fact that the models did not represent the sort of variations shown above.

This should be addressed in TAR5.
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