Download Validation of Downscaled Climate Change Scenarios of

Validation of Downscaled Climate Change Scenarios of Precipitation and Temperature for Kitui
Mwingi and Mutomo Districts, Kenya
*W N Githungoa, A Oyoob , A K Njogua, A M Kibuea, P K Njugunaa, K Kwenac,, and Henry F.
Mahood
*Corresponding Author email [email protected], and [email protected]
a
Kenya Meteorological Department P. O Box 30259 00100, Nairobi
b
International Crop Research for Semi Arid Tropics - ICRISAT
c
Kenya Agricultural Research Institute
d
Sokoine University of Agriculture. P.O. Box 3003, Morogoro, Tanzania
ABSTRACT
Over the coming decades, global climate change will have an impact on food and water availability in
significant and uncertain ways. There are strong indications that developing countries will bear the
brunt of the adverse consequences, particularly from climate change. The project “Managing Risk,
Reducing Vulnerability and Enhancing Agricultural Productivity in a Changing Climate” sponsored
by Climate Change Adaptation in Africa (CCAA) seeks as one of its outputs to evaluate the impacts
of climate change on vulnerability of agricultural production systems and focuses on climate change
impacts and options for adaptation in the Greater Horn of Africa (GHA). To achieve this the project
seeks to understand how the local climate will change, how it can exacerbate/ ameliorate conditions in
relation to agricultural production and what actions can/can not be taken at the local scale to mitigate
impacts. Validation of climate models is an important task before outputs of Global Circulation
Models (GCM) and/or climate change scenarios are used in climate change impact studies. Statistical
downscaling methods of GCM climate change scenarios were therefore tested in terms of their ability
to construct indices of extremes of daily precipitation and temperatures from large-scale atmospheric
variables with the aim of developing a tool for the construction of future scenarios of the extremes at
local levels. In this study, statistically downscaled GCM data for daily mean temperature and
precipitation for Kitui in Kenya are validated. The validation characteristics included temporal
autocorrelations and higher-order statistical moments for daily temperature and precipitation. Reanalysis data from six climate models, namely: CCCMA_CGCM3-1, CNRM_CM3, GFDL_CM2-0,
CSIRO-MK3-0, CSIRO-MK3-5, MPI-ECHAM-5 and MRI-CGCM2-3-2a were checked for quality
and used in validation by comparing NCEP (National Centre for Environmental Prediction) reanalysis
climate for the year 1979 – 2004 at reference grid stations.
Results indicated that the models performed fairly well in reproducing indices of precipitation in the
rainy seasons but performance for dry seasons was compounded by greater variability of of simulation
of mean rainfall. Performance of temperature indices was better than the corresponding indices of
precipitation. Differences in seasonal variation were less prominent. These validation results have
helped to choose the following models Precipitation: CCCMA_CGCM2-1, CNRM_CM2 and
MRI_CGCM2-3-2, Tmax:
MPI-ECHAM5 and MRI –CGCM2-3-2. Tmin: CCMA-CGCM2-1,
CNRM-CM2, CSIRO-MK3-0, CSIRO-MK3-5, MPI-ECHAM5 and MRI-CGCM2-3-2] which would
be applied to construct scenarios of the extremes for the end of the 21st century using predictor sets
simulated by the GCM.
Keywords: Adaptation, Climate, Downscaling, Scenario, Validation, Kenya
1. INTRODUCTION
Validation of climate models is an important task before the outputs of Global Circulation Models
(GCM) and climate change scenarios are used in climate change impact studies in agriculture. In this
study, the statistically downscaled GCM data for daily mean precipitation, maximum and minimum
temperature for Kitui, Mwingi and Mutomo districts in Kenya was validated. Nowadays the global
climate models developed and exploited by the world climate centres are rather reliable for providing
realistic projections for the synoptic scale patterns and evolution of the climate. However, they are
insufficient for detailed local scale estimations. There are several methods to interpret the results of
these global coupled atmospheric-ocean general circulation models for regional and local scales. Most
recently the dynamical downscaling of the global results with the use of regional (limited area)
climate models is the most widely-applied procedure. Knowledge of the observed distribution of
climate, and its changes on both short and long term timescales, is fundamental to understanding
current and future trends and variability in climate change as well as in evaluating climate models
(Osborn and Hulme, 1998; Dai and Trenberth, 2004; Dai et al,
2006).
The validation characteristics included temporal autocorrelations and higher-order statistical moments
for daily temperature and precipitation. Re-analysis (control) data from eight
climate models,
Canadian Center for Climate Modeling and Analysis (CCCMA_CGCM3-1), Centre National de
Recherches Météorologiques (CNRM_CM3), Geophysical Fluid Dynamics Laboratory (GFDL_CM20), Commonwealth Scientific and Industrial Research Organization (CSIRO-MK3-0), and (CSIROMK3-5), Max-Planck-Institute for Meteorology in Hamburg Fifth generation of the atmospheric
GCM (MPI-ECHAM-5) and Meteorological Research Institute, Japan (MRI-CGCM2-3-2a) were
checked for quality and used in validation by comparing with the National Centre for Environmental
Prediction NCEP reanalysis climate for the year 1979 – 2004 at reference grid stations. The
evaluation of the climate models was aimed at obtaining an appropriate model for downscaling
climate to evaluate the impacts of climate change on vulnerability of agricultural systems in arid and
semi arid parts of the south eastern parts of eastern province of Kenya. The paper presents briefly part
of the validation analysis of the models for the Kitui, Mwingi and Mutomo stations.
The main objective of this study was to compare downscaled mean daily precipitation as well as mean
daily maximum and minimum temperatures and their variances and higher moments in arid and
semiarid areas of Kitui, Mwingi and Mutomo districts of Kenya. The comparison was performed by
plotting the derived indices of the downscaled daily data as well as by performing statistical inference
for the indices of observed data and the different models. Hewitson (2006) indicated that in some
regions, climate change may be manifested as changes in the histogram of daily synoptic-scale events,
with or without a change in the mean itself. The daily, decadal, monthly, seasonal and annual
averages, variations and normalities of precipitation, Tmax and Tmin separately for each datasets
were computed and compared. The plots and statistical inference proved that the downscaled mean
daily precipitation as well as daily maximum and minimum temperatures agreed generally well.
However, the observed but varied significantly with the simulated results of extreme values.
2. METHODOLOGY
2.1 STUDY AREA AND DATA
The study area covered Kitui, Mwingi and Mutomo districts in one of the the semi-arid to arid zones
of Kenya. The area is located at latitude 00° 30S and 2° 00 south, and longitudes 37° 45 and 38° 30
East. The central part of Kitui and Mwingi Districts consists of an undulating plateau with an altitude
of about 1100m above sea level. The ridges and hills rise to about 1700m above sea level. Downhill
towards the Lower Midlands including Mutomo district, the climate is rather dry. The area
experiences two rainy seasons. The long rainy season covers the months of March April and May and
the short rainy season is from October, November to December. These two seasons are the growing
periods for crops in the area with average rainfall amounts of 250 – 390 mm and 280 – 490 mm in the
two seasons respectively. In Mutomo District, rainfall varies between 185mm to 350 mm in the
growing periods. The growing seasons are separated by two distinct dry seasons. The total annual
average rainfall is between 750 and 1 150 mm. Temperature in the region range between 10°C to
32°C.
2.2 DATA
The validation of the downscaled climate change scenario data presented here utilises data from four
primary sources of observational data and model output.
2.2.1 Historical Observed Climate data
Observation data for all the stations was acquired from the Kenya Meteorological Department data
archives. This included continuous time series of daily observations of the surface climate field of
interest (precipitation Tmax and Tmin) for Kitui Mwingi and Mutomo. Katumani research station data
was used as an alternative temperature station due to the poor status of the temperature data at Kitui.
The station data utilised is listed below:

Kitui Water office Station: precipitation data 1961 – 2004, Temperature data 1972 –
1995

Ithokwe Agricultural Research Centre: precipitation data 1961 – 2004, Temperature
data 1995 – 2006

Mitinyani Rainfall Station: precipitation data 1961 – 2004

Mwingi Agriculture office station: precipitation data 1961 – 2004

Mutomo Agriculture office station:

Katumani Agriculture research station: Temperature data 1991 – 2004
precipitation data 1961 – 2004
2.2.2 Quality Control and Homogeneity of Historical Data
A thorough investigation of the data revealed that the station precipitation data had few missing
records. For example, in the case of precipitation, the missing data for each station is shown in
brackets: Kitui Water office Station (5%), Ithokwe Agricultural Research Centre (2%), Mitinyani
Rainfall Station (4%), , Mwingi Agriculture office station (8%), , Mutomo Agriculture office station
(9%), , and Katumani Agriculture research station Temperature 3%. Estimation of missing data for
precipitation was done by use of the inverse distance method utilising neighbouring station records
while that of temperature data utilised the arithmetic mean method. In the case of temperature,
missing data for Ithokwe Agricultural Research Centre Temperature was 6%, while that of Katumani
Agriculture research station was 3%.
2.3 NCEP Re-analysis Data
NCEP/NCAR daily reanalyses fields for the years 1979-2004, are provided at 2.5°X2.5° spatial
resolution. The area of study is located within 36°E and 39°E Longititude and 0° and 2° South. Given
this scenario, the station locations were well extracted at 4 grid points (matching the stations located
at Kitui, Mwingi, Mutomo and Katumani) given a mesh of 2.5°X2.5°. NCEP re-analysis output fields
for precipitation, Tmax and Tmin at relevant station grid points representing the four locations,
covering the historical period (1979-2004) the GCM simulations were extracted.
2.4 Downscaled GCM Data
Downscaled GCM data fields of precipitation and Tmax and Tmin, covering the period 1961-2000 for
the control climate (IPCC, 2007) was used. Downscaled GCM models re-analysis output fields for
precipitation, maximum and minimum temperature at relevant station grid points representing the four
locations Kitui, Mwingi, Mutomo and Katumani were as follows; CCMA_CGCM, CNRM_cgcm2,
GFDL_CM2, CSIRO-MK3, CSIRO-MK3-5, MPI-ECHAM-5, MRI-CGCM2, control data
2.5 Consistency, Homogeneity and Quality Control of Model Data
The focus on homogeneity and quality control is to identify significant problems in the data. When
the homogeneity testing software identifies a likely problem, the station dataset is consulted and
scrutinised closely to understand why and where possible to pick the anomalous data. Non-climatic
jumps in the time series have resulted in some stations not being used in the analyses or used only for
the period after the discontinuity. Once quality control and homogeneity testing have been
accomplished, the calculation of the analysis commences. A description of models and in particular
their comparisons relative to improvements of various versions and with respect to response to
increasing greenhouse-gas forcing can be found in the relevant respective model literature (IPCC,
2001, Christensen, J.H 2007)
2.6 Validation Approach
Three different comparisons were done to evaluate the capability of the GCM models to reproduce the
observed climate and its changes as given by Lu, X. (2006), Clare et al (2003).
Step 1: the NCEP reanalysis was validated for its ability to reproduce the observed rainfall over Kitui,
Mwingi and Mutomo and Tmax and Tmin at Katumani. The comparison was performed by plotting
the means and variances of the observed NCEP reanalysis and downscaled GCM datasets as well as
by performing statistical significance tests for the differences of means and equality of variances of
datasets populations. Upon perfect validation of observed Vs NCEP, the NCEP reanalysis was
considered the control climate.
Step 2: The downscaled GCM were validated for their ability to reproduce the NCEP reanalysis
climate. Summary statistics for decadal, monthly and annual values of precipitation, Tmax and Tmin
were compared with NCEP values. Comparison was also done as above.
Step 3: models found to agree with step 2 above were validated with station observed datasets with
similar analysis to Steps 1 and 2 above.
2.7 Statistical Approaches to Analysis
For purposes of visualization, graphical data analysis technique utilised in the comparison of data sets,
included autocorrelation function (ACF) plots, frequency histograms and density functions, boxplots,
line plots and double mass curves. Graphical representation gives visual indications of similarities
and/or differences in the derived statistics. Comparison of the datasets was based on the frequency
and mean patterns, differences and the ratio of standard deviation and coefficient of variation of the
datasets. The ratio of the standard deviation was used to check for the magnitude of differences in the
standard deviation of the monthly and dekadal precipitation, and mean diurnal Tmax and Tmin
(Sinedovich, 1995). This was done both for the observed against NCEP and for NCEP against
observed datasets. The significance of the differences in the mean values was examined with aid of
student-Fisher t-test at the 0.05 level of significance. The variations were also tested for equality using
the F-test. Finally the relationship between downscaled and observed, precipitation, Tmax and Tmin
was evaluated using linear correlation analysis. Statistical tests were performed using WINKS SDA
Software (Texasoft, Cedar Hill, TX.) Statistical decisions were made at p=0.05.
3. RESULTS AND DISCUSSION
Autocorrelation plots of precipitation, Tmax and Tmin of observed, NCEP and downscaled GCM
show autocorrelation values between -0.5 – 0.5 for both observed, NCEP reanalysis and downscaled
GCM datasets. This is an indication that the data is randomly distributed. Figure 1 shows an
autocorrelation plot of the rainfall at Kitui water Office station.
(Insert Figure 1)
3.1 COMPARISON OF NCEP WITH STATION OBSERVED DATASETS
3.1.1 Precipitation
Summary statistics of station observed and NCEP data for precipitation were prepared. Box plots
were used to compare the mean decadal, monthly, MAM and OND seasonal rainfall and annual
precipitation for Kitui, Mwingi & Mutomo each with corresponding NCEP statistics. Figure 2 shows
a box plot of annual precipitation of observed rainfall plotted against NCEP reanalysis for three
stations Kitui, Mwingi and Mutomo plotted against respective NCEP reanalysis.
(Insert Figure 2)
The box plot results indicate that the annual precipitation for Kitui, Mwingi and Mutomo are in
agreement with the NCEP while Mwingi and Mutomo indicate slight differences in the median.
Differences are visible on the box plots of the total season rainfall both for the long rains season
March April and May (MAM) and the short rainy season October, November and December (OND)
(Figures 3 and 4 respectively).
(Insert Figure 3 and Figure 4 )
The difference is mainly in the level of rainfall amounts, being large for the observed datasets, while
NCEP seasonal rainfall exhibits small values of total rainfall. Mutomo particularly observed peculiar
seasonal rainfall with amounts ranging between 250 mm and 800 mm compared to NCEP simulation
amounts of about 250 mm to 400mm. In general NCEP reanalysis rainfall agrees quite well in the
seasonal patterns of rainfall. A separate analysis of variability of extreme was also done. The double
mass curve (Figure 5). indicates perfect agreement of NCEP with observed rainfall at Kitui, Mwingi
and Mutomo
(Insert Figure 5)
The time series plots of mean decadal, monthly and seasonal and annual rainfall totals, show
agreement in the seasonal trends of rainfall both for observed and for NCEP (Figures 6 and 7).
(Insert Figure 6 and 7)
While the NCEP reanalysis data agrees quite well with the observed mean values of rainfall, it
however does not capture the extreme values of high precipitation whenever they occurred.
3.1.2 Temperature
Comparison of observed temperature with NCEP was done by plotting mean values of Tmax and
Tmin. The double mass curve of mean Tmax and Tmin indicated perfect agreement of NCEP
reanalysis with observed temperature at Katumani and Kitui. Time series plots of Katumani observed
Tmax and Tmin along with NCEP reanalysis shows perfect agreement of the two. The double mass
curve confirms the same .
3.2 COMPARISON OF DOWNSCALED GCM WITH NCEP
3.2.1 Precipitation
The histogram and density plots of daily rainfall (Figures 8 & 9) of downscaled GCM revealed a
distinctly skewed distribution towards the left (that is, toward smaller values) during the dry periods
of January, February and June July and August.
(Insert Figure 8 and 9)
Similarly the histograms and density plots of the wet season was skewed towards the right (that is,
toward higher values). This was observed to agree for both NCEP and all the downscaled GCM
models. Visual inspection of the box plot of annual rainfall total for downscaled GCM and NCEP
indicated only two GCM agree with NCEP (MPI-ECHAM-5 and CNRM_CM2) in the distribution of
total annual rainfall amounts. However the mean and median positions of all the models were
observed to lie far from the position exhibited by NCEP. The decadal, monthly and seasonal box plots
of precipitation exhibited similar characteristics.
Line plots of mean diurnal precipitation of NCEP plotted against downscaled GCM models revealed
that the CCCMA_CGCM2-1, CNRM_CM2, MRI_CGCM2-3-2 models agreed with the annual
seasonal patterns of rainfall simulated by NCEP. Time series plots of mean decadal, monthly,
seasonal and annual total precipitation of NCEP plotted against the downscaled GCM also revealed
good agreement with the models CCCMA_CGCM2-1, CNRM_CM2 AND MRI_CGCM2-3-2.
Figure 13 shows a plot of mean diurnal precipitation of downscaled GCM plotted against NCEP.
3.2.2 Temperature
Histograms and density plots of mean diurnal Tmin and Tmax respectively indicated skewness of the
datasets with respect to seasons. The dry seasons of Jan, Feb and March showed the values to be
skewed to the right while the cool/cold season of June, July and August showed values to be skewed
to the left. This was observed for both NCEP and the downscaled GCM datasets (Figure 10 and
Figure 11).
(Insert Figure 10 and 11)
Tmin: Seasonal curves of mean daily Tmin indicated perfect agreement for the models, CCMACGCM2-1, CNRM-CM2, CSIRO-MK3-0, CSIRO-MK3-5, MPI-ECHAM5 AND MRI-CGCM2-3-2.
Figure 13 shows a plot of mean diurnal Tmin of the downscaled GCM and NCEP.
(Insert Figure 13)
Tmax: From the seasonal curves of Tmax, only two (2) models were in perfect agreement with
NCEP, MPI-ECHAM5 and MRI –CGCM2-3-2. On the other hand, CSIRO-MK3-0 AND CSIROMK3-5 agreed with the seasonal trends of NCEP Tmax but differed slightly on the peak values of
Tmax during the dry season of February and March.
3.2.3 Comparison of Variations
Differences in variability of the mean conditions were compared by use of the standard deviation and
coefficient of variation for precipitation and the standard deviation, the mean diurnal range and mean
intra annual range for temperature.
Rainfall
The ratio of the standard deviation was used to check for the magnitude of differences in the standard
deviation of the monthly and dekadal precipitation. Except for the low rainfall season of June, July
and August, the ratio of standard deviation of NCEP and the downscaled GCM models lies between 0
and 2 for most of the models. Only MPI-ECHAM5 model rainfall which differed slightly. This is an
indication that the variability of precipitation simulated by the downscaled GCM is not much different
from NCEP. Downscaled GCM exhibited coefficient of variation not very much different from
NCEP.
Temperature
The ratio of the standard deviation was used to check for the magnitude of differences in the standard
deviation of the mean diurnal Tmax and Tmin. The ratio of standard deviation of NCEP and the
downscaled GCM models lies between 0 and 2 for all of the downscaled GCM. This is an indication
that the variability of Tmax and Tmin simulated by the downscaled GCM is not much different from
NCEP. The ratio of standard deviation, the mean diurnal temperature range, the mean intra annual
range, and the mean intra annual variance were used to measure the variability in the temperature
variables. The mean diurnal temperature range of the downscaled GCM was plotted against the NCEP
reanalysis. Comparisons do not indicate any peculiar differences of the mean diurnal temperature
range.
Statistical Tests
Test for equality of Variance.
Box plots and line plots provided some preliminary visual information about equality of variances of
the population samples. However, that equality cannot be confirmed unless a relevant statistical test is
performed at relevant significance level. As such, the equality of variances has been tested for station
observed datasets with NCEP reanalysis and also for NCEP reanalysis with each of the downscaled
GCM at 95% significance using F-test and decision was made based on the p-value. If the p-value is
greater than 0.05, then the groups have equal variance. Tables 1 -5 show the results of the F-test. The
test hypothesis for each independent group of F-test was as follows:
Ho: There is no difference between variances.
H1: The variances are different
Observed variables and NCEP datasets: Results of the F-test for the annual totals of rainfall show
equal variance for all observed station datasets and NCEP. Un- equal variances were tested for the
decadal, monthly and seasonal totals.
F-test for station observed Tmax and Tmin indicated unequal variances when tested with NCEP.
The F-test performed on the downscaled GCM of the annual rainfall showed equal variances with
NCEP. The F-test results were however exceptional for CSIRO-MK3-O model which indicated unequal variance with NCEP
The F-test performed on the downscaled GCM Tmin indicated equal variance for two downscaled
GCM i.e. MRI-CGCM2-3-2A and MPI-ECHAM5. Other GCM indicated unequal variance with
NCEP.
The F-test performed on the downscaled GCM Tmax indicated unequal variance for most of the
GCM. Only three models showed equal variance CSIRO-MK3-0, CSIRO-MK3-5 and GFDL.
Test for Differences of Means.
Mean values of monthly and decadal precipitation computed were used to test the difference between
the means of observed downscaled GCM and NCEP reanalysis data respectively. Similarly the mean
diurnal values of Tmax and Tmin were used to test for differences of means. A preliminary test for
equality of variance for each pair of groups was used to determine the type of t-test to perform. Where
the test for equality of variance indicated significant difference between the variances of the group,
then a two-sample t-test was performed that did not assume equal variances; otherwise the t-test was
performed assuming equal variances. The test hypothesis for each independent group t-test was as
follows:
H0: There is no difference between means.
H1: The means are different.
Statistical tests using the student t-test on the mean of the datasets is indicated in Tables 6-8.
The t-test performed for observed annual rainfall and NCEP show no differences of means for
Ithokwe and Mutomo station at 95% confidence level.
The t-test performed for observed Tmin and Tmax also indicated no difference in means for Katumani
observed Tmin and Tmax with NCEP reanalysis respectively.
The t-test performed on the downscaled GCM with NCEP for the annual rainfall indicates no
difference in the means for models CCMA-CGCM, CNRM, MPI-ECHAM5 and MRI-CGCM2. The
downscaled GCM CSIRO-MK3-0, CSIRO-MK3-5 AND GFDL indicate difference of means with
NCEP. The t-test performed on the downscaled GCM with NCEP for Tmax and Tmin however
indicate no differences in the means.
3.3 CORRELATION OF STATION OBSERVED WITH DOWNSCALED GCM MEANS
Table 21 indicates the linear Pearson correlation coefficient computed between observed datasets and
the downscaled GCM datasets. High correlation coefficient was found for annual rainfall total, mean
decadal rainfall, mean monthly rainfall, mean diurnal Tmax and mean diurnal Tmin for the relevant
downscaled GCM datasets. The seasonal rainfall for MAM and OND were found to have no
relationship for most of the downscaled GCM except for CSIRO=MK3-0 and MRI-CGCM2-3-2A
which indicated high correlation for MAM and OND respectively.
CONCLUSIONS and RECOMMENDATIONS
CONCLUSIONS
The following conclusions can be drawn from this study:

Observed Tmax and Tmin agreed perfectly well with NCEP re analysis both in annual trends
and in the means. Likewise the seasonal trends and means of observed precipitation agreed
quite well with NCEP reanalysis. However, NCEP was not able to simulate accurately the
extreme conditions of observed rainfall. Thus NCEP reanalysis may be applied to estimating
likely distributions of daily rainfall, Tmax and Tmin.

Graphical comparisons and statistical hypothesis tests proved that certain downscaled models
agreed well with NCEP while others indicated significant differences in the estimates of
means and variances of downscaled daily GCM.

The following downscaling models have been identified as suitable for use in agriculture
impacts studies for Kitui, Mwingi and Mutomo.
 Precipitation: CCCMA_CGCM2-1, CNRM_CM2 and MRI_CGCM2-3-2
 Tmax: MPI-ECHAM5 and MRI –CGCM2-3-2.
 Tmin: CCMA-CGCM2-1, CNRM-CM2, CSIRO-MK3-0, CSIRO-MK3-5, MPIECHAM5 and MRI-CGCM2-3-2
RECOMMENDATIONS
Separate analysis to determine the extreme value analysis needs to be undertaken.
ACKNOWLEDGEMENT:
The project “Managing Risk, Reducing Vulnerability and Enhancing Agricultural Productivity in a
Changing Climate’ is supported by the Climate Change Adaptation in Africa (CCAA) program, a
joint initiative of Canada’s International Development Research Centre (IDRC) and the United
Kingdom’s Department for International Development (DFID).
The views expressed are those of the authors and do not necessarily represent those of DFID or IDRC.
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APPENDIX 1 Tables & Figures
Table 1 F-TEST for annual rainfall total, of station Observed & NCEP
F
DF
P-value
Decision
Mutomo
1.68
21
0.134
Equal variance
Mwingi
1.6
21
0.143
Equal variance
Ithokwe
1.75
21
0.102
Equal variance
Table 2 F-TEST for NCEP & GCM Derived Mean Dekadal Rainfal
F
DF
P-value
Decision
1.12
35
0.366
Equal variance
Cnrm-cm2
1.07
35
0.424
Equal variance
Csiro-mk3-0
1.29
35
0.226
Equal variance
Csiro-mk3-5
1.64
35
0.075
Equal variance
gfdl
2.29
35
0.008
unequal
Cccmacgcm2-3-1
variance
Mpi-echam5
1.03
35
0.461
Equal variance
Mri-cgcm2
1.93
35
0.028
unequal
variance
Table 3 F-TEST for NCEP & GCM Derived Annual Rainfal
F
DF
P-value
Decision
1.24
21
0.312
Equal variance
Cnrm-cm2
1.02
21
0.482
Equal variance
Csiro-mk3-0
1.91
21
0.073
unequal
Cccmacgcm2-3-1
variance
Csiro-mk3-5
2.24
21
0.036
Equal variance
gfdl
1.7
21
0.116
Equal variance
Mpi-echam5
1.04
21
0.466
Equal variance
Mri-cgcm2
1.85
21
0.083
Equal variance
Table 4 F-TEST for NCEP & GCM Derived Mean Diurnal Tmin
Cccma-
F
DF
P-value
Decision
3.08
21
0.006
Unequal
cgcm2-3-1
Cnrm-cm2
Variance
3.36
21
0.004
Unequal
Variance
Csiro-mk3-0
8.43
21
0.001
Unequal
Variance
Csiro-mk3-5
1.49
21
0.183
equal
Variance
gfdl
4.94
21
0.001
Unequal
Variance
Mpi-echam5
1.44
21
0.207
equal
Variance
Mri-cgcm2
1.88
21
0.079
equal
Variance
Table 5 F-TEST for NCEP & GCM Derived Mean Diurnal Tmax
Cccma-
F
DF
P-value
Decision
1.53
21
0.168
Unequal
cgcm2-3-1
Cnrm-cm2
Variance
1.54
21
0.167
Unequal
Variance
Csiro-mk3-0
9.04
21
0.001
Equal variance
Csiro-mk3-5
7.72
21
0.001
Equal variance
gfdl
7.11
21
0.001
Equal variance
Mpi-echam5
1.88
21
0.079
Unequal
Variance
Mri-cgcm2
1.05
21
0.457
Unequal
Variance
Table 6 t-TEST for annual rainfall total, of station Observed & NCEP
t-statistic
DF
P-value
SE
95%
Decision
Confidence
Mutomo
Mwingi
Ithokwe
-1.23
-3.96
-1.2
42
43
42
0.236
0.001
0.236
91.634
89.1433
91.634
-295.615 –
Means are
174.77
not different
-583.38 –
Means are
174.77
different
-295.615 –
Means are
174.77
not different
Table 7 t-TEST for Mean Diurnal Tmin, of station Observed & NCEP
t-statistic
DF
P-value
SE
95%
Decision
Confidence
Katumani
0.18
10.4
0.86
0.39208
-0.747-0.888
Means are
not
different
Table 8 t-TEST for Mean Diurnal Tmax of station Observed & NCEP
t-statistic
DF
P-value
SE
95%
Decision
Confidence
Katumani
0.18
20
0.858
0.39208
-0.747-0.888
Means are
not
different
Table 9 t-TEST for NCEP & GCM Derived Annual Rainfal
Cccma-
t-statistic
DF
P-value
SE
Decision
0.86
42
0.386
75.278
Means are
cgcm2-3-1
Cnrm-cm2
not different
-0.57
42
0.574
79.645
Means are
not different
Csiro-mk30
4.32
36.6
0.001
69.139
Means are
different
Csiro-mk3-
1.96
42
0.057
67.395
5
Means are
not different
gfdl
4.43
42
0.001
70.59
Means are
different
Mpi-
-0.43
42
0.672
80.00674
echam5
Means are
not different
Mri-cgcm2
-1.25
42
0.219
94.60202
Means are
not different
Table 10 t-TEST for NCEP & GCM Derived Mean Diurnal Tmax
t-statistic
DF
P-value
SE
95%
Decision
Confiden
ce
Cccma-cgcm2-3-
1.4
42
0.169
0.7758
0.7758
1
Cnrm-cm2
Means are
not different
1.34
42
0.188
3.3776
3.3776
Means are
not different
Csiro-mk3-0
1.02
42
0.316
Means are
not different
Csiro-mk3-5
1.12
42
0.273
0.06413
0.06413
Means are
not different
gfdl
0.97
42
0.343
0.06444
0.06444
Means are
not different
Mpi-echam5
1.53
42
0.134
0.07471
0.07471
Means are
not different
Mri-cgcm2
1.71
42
0.094
0.08432
0.08432
Means are
not different
Table 11 Person Correlation Coefficient of Station Observed against Downscaled GCM
Derived Parameters
cccma-
cnrm-
csiro-
csiro-
gfdl
mpi-
mri-
echam
cgcm
Annual Total precipitation
cgcm2-3-1
cm2
mk3-0
mk3-5
0.997
0.999
0.997
0.997
0.155
0.003
0.534
0.076
5
2
0.997
0.994
0.994
0.106
-0.340
-
(Ithokwe)
MAM season Total
precipitation (Ithokwe)
OND season Total
0.212
-0.264
-0.137
0.219
-0.166
-0.085
-0.226
0.5
0.923
0.810
0.827
0.511
0.519
0.648
0.821
0.918
0.936
0.845
0.460
0.512
0.686
0.821
Mean Diurnal Tmax
0.920
0.798
0.620
0.549
-
0.863
0.798
Mean Diurnal Tmin
0.879
0.861
-
0.655
0.857
0.861
precipitation (Ithokwe)
Mean dekad precipitation
(Ithokwe)
Mean month precipitation
(Ithokwe)
Figure 1 Autocorrelation of Observed Rainfall - Kitui water office
Station
1.0
Correlation
0.5
0.0
-0.5
-1.0
0
5
10
15
Lag
20
25
Figure 2
Figure 5
Figure 6
Figure 3
Figure 7
Figure 4
Figure 8 Frequency Histograms April
Rainfall Total
Frequency Histograms Daily Maximum
Temperature – January
Figure 10
Frequency Histograms February Rainfall
Total
Figure 7
Frequency Histograms Month Mean
Minimum Temperature – February
Figure 8
Fig 12: Downscaled GCM mean Daily Tmin mpi-echam5, mri-cgcm2-3-2a, NCEP