Ability to represent interannual variability

Figure 4.10 shows the average of the VI across all GCMs for annual total precipitation,

mean temperature and diurnal temperature range. Excluded from this average is the MMM (multi-model mean), which presented very poor performance. In general, interan- nual variability was poorly captured by the models, although individual models showed strengths in some of the regions. At all grid cells, there was always at least one GCM with VI < 0.5, but in all cases the maximum values were above this threshold. This in-

dicated that errors in models’ representations of interannual variability are generally not geographically consistent.

Interannual variability of mean temperatures in CMIP3 was better represented across WAF and SAF, where values of VI were in the order 0 - 0.5, with only few grid cells (< 15 %) exceeding this range. High skill (VI < 0.5) was observed in 30 - 60 % country-season

combinations. There was also significant spatial variation in the VI, with central EAF

(Kenya and Uganda) and northern SAS (particularly towards Nepal) showing the largest values.

Diurnal temperature range and total precipitation showed very few locations (i.e. less than 10 %) where VI was below 0.5 (Figure 4.10). There was also little geographic consistency

in skill between these two variables. This was evidenced since the areas where VI values

were very high for one variable, were lower for the other and vice versa. High values of VI (VI > 0.5) were found in 50.5 % (MIROC3.2-HIRES) to 100 % (NCAR-PCM1) of the

areas. Such high values (indicating poor skill) for diurnal temperature range were found across SAF and SAS, while the lowest values were found in WAF and EAF (particularly in Ethiopia and Tanzania). For precipitation, VI showed values above 0.5 in at least 72 %

of the areas for all models. The poorest skill was found across the Sahel and in northern SAS, (Figure 4.10).

4.4.4.2 CMIP5 ensemble

CMIP5 interannual variability was also significantly misrepresented, particularly in areas with complex landscape features such as the Himalayas (Figure 4.11). No single climate

Figure 4.10: _{Average CMIP3 climate model skill in reproducing interannual variability} as measured by the variability index (VI, Eq. 3.55) for (a) annual total precipitation (mm/year), (b) annual mean temperature (◦_{C), and (c) diurnal temperature range (}◦_C). Blue areas (where VI < 0.5) indicate high model skill. Values shown are means of all 23

Chapter 4. Climate data assessment 127

Figure 4.11: _{Average CMIP5 climate model skill in reproducing interannual variability} as measured by the variability index (VI, Eq.3.55) for (a) annual total precipitation, (b) number of wet days, (c) annual mean temperature, and (d) diurnal temperature range. Blue areas (where VI < 0.5) indicate high model skill. Values shown are means of all 26

climate model simulations (see Table3.4) per grid cell.

model showed good agreement (VI < 0.5) in more than 30 % of the areas for precipitation

and 15 % for the wet-day frequency. However, the corresponding fractional area is 50 % for temperature range and to 70 % for mean temperatures. Although interannual variability was not adequately captured by most climate models in most areas for precipitation (with most models showing 70 - 80 % areas with low skill, VI > 0.5), areas with high interannual

variability skill (i.e. VI < 0.5) ranged between 27.5 % (MIROC-4h) to 68 % (GFDL-ESM2-

M) for mean temperature, and between 49.8 % (MIROC-4h) to 90.1 % (INM-CM4) for diurnal temperature range.

4.4.5 Comparison between CMIP3 and CMIP5

Here, the actual improvements in climate model skill were assessed using Taylor diagrams (Figure4.12), and the probability density functions of both ensembles (Figure4.13-4.14). Set CMIP3 as a reference, climate models in the CMIP5 ensemble (with no clear difference

Figure 4.12: _{Taylor diagrams for 23 CMIP3 (black lower case letters) and 26 CMIP5 (red} upper case letters). Multi model means are indicated by crossed circles in the respective colour. Only climatological means of simulated annual totals (precipitation and wet-day frequency) and means (mean temperature and diurnal temperature range) for India are shown. In all cases only comparisons with CL-CRU are shown. Due to the overlap between different ensemble members in the CMIP5 ensemble only one ensemble member is shown to improve clarity in the diagram. Note that in the wet day frequency, only

CMIP5 output is shown owing to data availability in the CMIP3 archive.

in skill between Earth System Models ESMs, and Coupled Global Climate Models GCMs) have improved primarily in terms of mean climatology, particularly for mean temperature. The Taylor diagram for annual totals and means over India (Figure4.12) shows less spread in CMIP5 models, and a more accurate representation of standard deviations (i.e. models are closer to the 1:1 arc). This is particularly true for total precipitation and mean tem- perature. Similar trends were observed for all other countries analysed. Despite a general improvement in model skill, however, errors remain large for some variables, particularly for daily temperatures extremes and the wet-day frequency.

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(a) Mean temperature (b) Temperature range

(c) Total precipitation (d) Wet-day frequency

Figure 4.13: _{Probability density functions of the RM SEM for the CMIP3 (blue) and} CMIP5 (red) model ensembles and annual totals or means of precipitation, number of wet days, mean temperature, and diurnal temperature range. Shading indicates ± one standard deviation across the mean PDF (continuous lines). Dashed lines show the multi-

model-mean. Note the differences in the x-axis scale across variables.

In agreement with the above, a more general analysis of the PDFs of the four skill metrics used here (three for mean climates and one for interannual variability) shows a displace- ment of the PDFs of the CMIP5 ensemble (Figure4.13- 4.14). Such displacement is most evident for simulated climatological mean temperatures and total precipitation. Diur- nal temperature range showed no clear trend, and the wet-day frequency could not be analysed. Skill in representing interannual variability showed no improvement at all. The PDFs of skill metrics also indicated that model spread (in the skill metrics) was the largest for diurnal range and wet-day frequency, and the smallest for mean temperatures. Impor- tantly, in all the three variables that could be compared (precipitation, temperature and diurnal temperature range), CMIP5s MMM showed better skill than that of CMIP3.

Figure 4.14: _{Probability density functions of the interannual variability index (VI) for} the CMIP3 (blue) and CMIP5 (red) model ensembles for annual totals or means of (a) precipitation, (b) number of wet days, (c) mean temperature, and (d) diurnal temperature range. Shading indicates one standard deviation around the mean PDF (continuous lines).

Dashed lines show the multi-model-mean.