THE CLIMATE SYSTEM ANALYSIS GROUP,
UNIVERSITY OF CAPE TOWN
CLIMATE CHANGE PROJECTIONS FOR THE CITY OF CAPE TOWN
An update based on the most recent science
Contributors to this report:
Chris Jack
Piotr Wolski
Anna Steynor
Chris Lennard
June 2016
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Contents
Executive Summary ................................................................................................................... 5
1. Introduction ........................................................................................................................ 7
2. The City in context............................................................................................................. 8
Intra-city ................................................................................................................................. 8
Regional ................................................................................................................................. 8
Global ..................................................................................................................................... 9
3. City Regional Climate...................................................................................................... 11
Regional Climate processes ................................................................................................. 11
4. Historical analysis data and methods ............................................................................... 13
Datasets ................................................................................................................................ 13
Spatial Domain..................................................................................................................... 14
Methods................................................................................................................................ 14
Climate regions ................................................................................................................ 14
Long-term trends in observed climate indices ................................................................. 14
Decadal time scale variability in climate indices ............................................................. 16
Rainfall zones in the Cape Town region .......................................................................... 16
5. Trends and Variability in the historical climate of Cape Town ........................................ 18
Summary points ................................................................................................................... 18
Rainfall ................................................................................................................................. 18
Trends and variability in annual rainfall indices .............................................................. 18
Trends and variability in seasonal rainfall indices ........................................................... 21
Air temperature .................................................................................................................... 23
Trends and variability in annual temperature indices ...................................................... 23
Trends and variability in seasonal temperature indices ................................................... 23
6. Climate change projections: methodology and limitations .............................................. 25
7. Climate change projections for Cape Town: .................................................................... 27
Summary points ............................................................................................................... 27
Global Climate Model Projections....................................................................................... 27
Seasonal changes in rainfall and temperatures ................................................................ 29
Downscaled Projections ....................................................................................................... 29
Seasonal changes in rainfall and temperatures ................................................................ 31
8. Climate change projections technical interpretation and discussion ............................... 32
Summary points: .............................................................................................................. 32
Self-organising maps ....................................................................................................... 32
Category trajectory analysis ............................................................................................. 33
Performance analysis ....................................................................................................... 34
Downscaling contradictions ............................................................................................. 35
9. Narratives of the future climate ....................................................................................... 36
Narratives ............................................................................................................................. 38
Narrative #1 | Hotter and drier ......................................................................................... 38
Narrative #2 | Warmer and no rainfall change ................................................................. 39
Narrative #3 | Hotter and mixed rainfall change .............................................................. 40
10. Reflections on the participatory workshop ................................................................... 42
Feedback on the narratives: ................................................................................................. 42
11. Conclusions and recommendations............................................................................... 44
References ................................................................................................................................ 45
Appendices ............................................................................................................................... 46
Appendix 1: .......................................................................................................................... 46
Appendix 2: .......................................................................................................................... 70
Executive Summary This report represents the first phase in a series of activities focussed on developing
effective climate adaptation strategies across the City of Cape Town. During this phase, the
most recent science is interrogated in order to provide an updated understanding of climate
science for the City of Cape Town. The report captures the current general understanding of
the regional climate historical trends and future projections, highlighting progress that has
been made as well as ongoing and newly emerging sources of uncertainty or lack of
knowledge. The report then goes further to explore the use of narratives / storylines rather
than traditional science communication modes and describes both the process of developing
climate change narratives or “stories of the future” as well as engagement with a range of
city practitioners on the utility of these narratives.
In order to draw the bounds of the research, it is important to first put the city in context. The
City of Cape Town does not exist in isolation from the surrounding region, but rather is
dynamically related to the surrounding region with respect to water supply, food supply,
tourism, and other economic activities. Therefore, the climate data analyses are carried out
for a spatial domain covering a loosely-defined Cape Town “region”, which broadly covers
the City of Cape Town and relevant hydrological catchments on which Cape Town draws its
water resources as well as relevant agricultural areas.
Variability and trends in rainfall have been assessed over the long-term (1901 – current) and
for a mid-term period (1979-present). As station based observations of climate variables
covering the recent period were not available for this project, analyses of the historical
climate are based on surrogate datasets (satellite-derived rainfall estimates) and global data
compilations. For this reason, there is some discrepancy in the signals across the different
datasets which are unpacked in detail in the relevant section of this report. However, in
general, the long-term trends in rainfall seem to indicate an overall increase in rainfall in the
north of the Cape Town region (West Coast) and a decline in the southern part (other
regions). The trends are significant at some locations, although not significant in the region-
averages. The overall mid-term (1979-2013) trends in annual rainfall are predominantly
negative, although not significant. However it is clear that the mid-term period is
characterized by above average rains in its first part (1980s), followed by below average
rains in the mid- to late 1990s, with some recovery towards wetter conditions in the late
2000s. Mean daily rainfall shows a negative trend while the number of days with rain shows
a positive trend. Temperature shows a clear trend, both in the long-term and mid-term, with
all indices of temperature showing a positive trend.
On a seasonal basis, there is generally a weakly negative trend in the summer and autumn
total seasonal rainfall and weakly positive trend for winter and spring total seasonal rainfall.
Daily maximum and minimum air temperatures show a positive trend, particularly in the West
coast and Swartberg regions, with the strongest trend in spring and summer.
When looking to the projections, both Global Climate Model (GCM) projections and
statistically downscaled projections are presented. The GCMs all show a continuation of
natural variability into the future up until around 2030-2040 after which almost all models
show a significant shift towards a drier future. There is a projected reduction in rainfall in all
seasons, although the strongest reduction is projected for autumn and winter. All the GCMs
show temperatures continuing to rise into the future.
Due to the complex topography and land-ocean boundaries present in the City of Cape
Town region, downscaling has been used to explore finer scale responses to large scale
circulation shifts simulated by the GCMs. Downscaled projected temperature changes do not
differ significantly from the GCM projections and show temperature continuing to rise into the
future. However, projected rainfall time series are significantly different for the downscaled
data. In particular, downscaled projections of rainfall change into the future for all regions
show an almost equal split between a wetter future and a drier future. On a seasonal basis,
the summer season shows a non-statistically significant drying.
This contradiction in messaging between the GCMs and the downscaling required further
analysis to determine the sources contributing to the contradictions. The analysis shows
that none of the GCMs performs markedly worse than the others so none of the GCMs can
be ruled out of consideration. However initial analysis does indicate that the downscaling
approach used is potentially failing to capture climate variability signals that drive changes in
rainfall. This means that the downscaling is possibly unable to represent the true sources of
rainfall variability. These results are not conclusive and require significantly more analysis
before firm conclusions can be made.
In order to contextualise the technical detail provided in the report, the methodology of
“narratives” or “stories have change” has been included. Narratives represent a new method
that is being tested to try and aid the communication of uncertain climate projections. Each
of the three narratives (seen as equally likely) represents a possible future, within the range
of uncertainty of future projections. The narratives address the need for greater clarity of
what a future climate may look like and provide the opportunity to develop viable futures
representing the inter-linkages between the various climate variables. These narratives were
tested for their effectiveness and utility during a participatory workshop held with the City of
Cape Town officials as part of this project. In general, the narratives were received positively
and the feedback gathered from the workshop will enable future enhancements in the
narrative methodology.
While there are recommendations around investing in observational networks and research
into unpacking contradictions between GCMs and downscaling, what the report, the
narratives, and the engagement workshop have all shown is that the City of Cape Town
needs to prepare in earnest for a drier warmer future over the next decades. While there
remains uncertainty in the climate science, the evidence for drying and warming is strong
and planning that ignores this evidence is at significant risk of vulnerability to a changing
climate. There is now sufficient science evidence to motivate for serious consideration of
climate adaptation planning and implementation in the city.
1. Introduction The City of Cape Town is a rapidly growing coastal urban area functioning under a highly
variable and complex regional climate. With increasing demands on water and constrained
water supply alternatives, wide ranging climate related risks including annual extensive
flooding, coastal erosion and infrastructure damage, it is critical that the City of Cape Town
considers climate risk in its planning and development and implementation actions.
Climate information plays a potentially important role in the city‟s planning and
implementation actions by guiding selection of options, resource allocations, and strategic
timing of interventions. However, the nature of the information required across the various
functions of the city is largely unclear. Likewise, there is lack of clarity and certainty in
climate science and scientists‟ understanding of the drivers of the regional climate. It is
therefore essential that innovative approaches to co-produce relevant and actionable climate
information are developed and adopted.
This report captures the current general understanding of the regional climate historical
trends and future projections, highlighting progress that has been made as well as ongoing
and newly emerging sources of uncertainty or lack of knowledge. The report then goes
further to explore the use of narratives rather than traditional science communication modes
and describes both the process of developing climate change narratives or “stories of the
future” as well as engagement with a range of city practitioners on the utility of these
narratives.
The report concludes with some suggestions on ways forward, both within climate science,
but also with respect to integrating climate science into decision making. This report
represents the first phase in a series of activities focussed on developing effective climate
adaptation strategies across the City of Cape Town
2. The City in context Cities generally do not exist in isolation. They exist within a broader region, within a country,
and indeed, within a global context. They are dependent on and contributors to the broader
region through flows of resources, finances, energy, and people. They are subject to law
and policy from outside as well as being increasingly significant influences of national and
global law and policy. As a result, a city's exposure to climate risk needs to be considered
across multiple spatial scales.
Intra-city Within a city itself, climate and weather can have direct impacts and contribute to various
types of risk. There are obvious first order risks such as flooding, infrastructure damage, or
economic losses through extreme events where the climate is the direct cause. There are
second order risks such as health where weather extremes or specific conditions can be
contributors or aggravators of existing drivers to increased health problems. There are also
third order risks where climate causes behaviour changes that increase risk, such as
increased use of air conditioning which places strain on electricity supply.
The City of Cape Town experiences all of the above climate impacts and climate-related
risks. Flooding in low lying areas is an annual event, particularly in informal settlements with
limited drainage infrastructure and challenges with solid waste. However, even formalised
areas with good infrastructure regularly experience flooding and disruptions to traffic and
other activities are significant.
Regional The most common regional climate risk is water supply. It is very uncommon that a city‟s
water supply comes entirely from within the spatial bounds of the city. In most cases water
is transported from dams, rivers, or other sources in the surrounding region. In some cases
sources can be hundreds of kilometers away. It is therefore important to consider potential
shifts in climate in water catchments areas distant from the city. While groundwater
extraction is becoming increasingly common in many developing nation cities and this can
take place within the city bounds, challenges with recharge through surface hardening
(paved areas) as well as contamination from industrial pollution are significant.
The City of Cape Town currently sources most of its water from catchments and dams in the
surrounding mountains of the Hottentots Holland range. These include Theewaterskloof,
Berg River, Brandvlei, Steenbras, Wemmershoek and others. The map in Figure 1 below
details the location of the major dams and other significant water bodies in the City region
and highlights the proximity of most dams to the regional mountain ranges. This highlights
the importance of understanding and considering regional mountain climate in the context of
city climate risk.
Figure 1: The location of the major dams and other significant water bodies in the City region
It is common across many large urban areas that significant food supplies for the city are
sourced from very large distances away. In many cases food sources are even
international. Globalised food trade means that exposure to climate risk becomes extended
to a global scale. A drought in India impacts global rice prices which can impact national
and local food prices. However, this also potentially increases options and resilience. For
example, local droughts can impact on local food production, but international trade can
allow for food imports to compensate, though often with higher costs.
Cape Town has both within city agriculture, mostly focused in the Philippi Horticultural Area
(PHA), and extensive regional agriculture including wheat, table and wine grapes, various
fruits, and barley. However, Cape Town, like many metropolitan areas, also source
significant amounts of food from more distant areas and is therefore vulnerable to climate
impacts on yields and prices in those areas.
Global Globally, cities are increasingly prominent players due to their increasing economic leverage
as well as increasing role in carbon emissions and resource consumption. As the world
moves towards agreements, such as the Paris Agreement, that aim to reduce global
emissions, cities need to be able to respond by reducing or limiting emissions while, at the
same time, planning for adaptation to changing climates. However, global drivers are not
limited to international policy. As climate change influences various nations differently,
diverse responses, either reactive or anticipatory, have repercussions across the globe.
For the City of Cape Town it is unclear how shifts in global climate might impact. One
possibility is a shift in global shipping routes as sea-ice retreat around the Arctic allows for
re-routing of shipping and potentially reduces shipping traffic through or past Cape Town.
3. City Regional Climate
Regional Climate processes
Cape Town experiences a mediterranean climate with distinctive seasonal weather
processes. Firstly, winter rainfall results from mid-latitude cyclones (cold fronts) that
propagate from west to east over the southern parts of the country (Figure 2). If particularly
deep, these frontal systems are associated with extreme rainfall and gale force winds.
Passing fronts may spawn cut-off lows that can be associated with extreme rainfall.
Secondly, a semi-permanent high-pressure band is located in an east-west orientation
stretching over the interior of the country between the oceans that border it (Figure 2). This
system is associated with descending air (subsidence) and clear, dry conditions. In Cape
Town the south Atlantic high pressure component of this high pressure band results in the
south easterly winds and associated weather to dominate characteristic of the Cape Town
summer. In winter the high pressure band moves toward the equator and generally does not
affect the cities‟ weather.
Figure 2: Idealised figure of typically winter synoptic patterns. The red area labelled “A”
represents the semi-permanent band of high pressure stretching over the country that moves
equatorward in winter and poleward in summer. Rain-bearing frontal systems (labelled “B”)
move equatorward in winter and poleward in summer. Based on Tyson and Preston-Whyte,
(2000).
Lastly, south of the country, transient high pressure systems move from the south Atlantic to
the south Indian Oceans (ridging high pressure system). During summer (Figure 3) these
ridging highs bring dry, windy weather to the region and during winter may follow in the wake
of cold fronts and bring very cold polar air into the region.
Figure 3: Idealised figure of typically summer synoptic patterns over South Africa. The red
area labelled “A” represents the semi-permanent band of high pressure, which in summer has
moved poleward and displaced cold fronts (labelled “B”) to the south of the country. The
Atlantic high pressure results in the characteristic summer south easter and may also form a
ridging high that moves from the Atlantic to the Indian ocean as indicated by the arrows
labelled “C”. Based on Tyson and Preston-Whyte, (2000).
4. Historical analysis data and methods It is useful to examine historical climate change and variability, both to explore if the city-
region has already experienced changes, and secondly, from a climate science perspective,
to understand what drives variations in our regional climate. Understanding what changes
have already occurred can help us explain or unpack perceptions of change by
society. Have Cape Town winters become drier? Has Cape Town become
warmer? Understanding what drives variations in climate is critical to understanding what
climate model projections of the future might mean for the city-region and to what extent we
might trust or rely on model projections.
Datasets Station-based observations of climate variables covering the recent period were not
available for this project, and thus the analyses are based on surrogate datasets (satellite-
derived rainfall estimates) and global data compilations (Table 1). These datasets have
relatively coarse spatial resolutions ranging from ~25km to ~50km, and cover different
periods. They were selected to provide an overview of trends and variability of various
attributes of rainfall over a range of timescales - from a century to the recent decade.
Table1: Analysed rainfall (P) and air temperature (T) datasets
Dataset Time
period
Data Temporal
resolution
Resolution
(degrees)
Code
CHIRPS v2.0 1981- to
date
P Daily 0.25 CHIRPS2.0
CRU v.3.23 1901-2012 P Monthly 0.5 CRU3.23
Technical box: CHIRPS, CRU and WATCH datasets
CHIRPS: Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS, Peterson
et.al, 2013). The CHIRPS data comprises daily rainfall data only. It is a combination of
satellite and weather station rainfall data, and is available for the period 1981-2014, gridded
to 0.25 x 0.25 degree spatial resolution
CRU: Climate Research Unit (CRU TS 3.21, Harris et al., 2014). The CRU TS data is made
up of monthly time series of various climate variables, which include maximum and minimum
temperature and rainfall. The data is based on over 4000 global weather stations, is
available for the period 1901 – 2012, and is gridded to 0.5 x 0.5 degree spatial resolution.
WATCH WFDEI: WATCH-Forcing-Data-ERA-Interim (WFDEI) was produced using Watch
Forcing Data methodology applied to ERA-Interim data. It is a meteorological forcing dataset
extending into early 21st C (1979 – 2014). Eight meteorological variables are available at 3-
hourly time steps, and as daily averages.
WATCH WFDEI
(gpcc)
1979-2009 P,T Daily 0.5 WFDEI
Spatial Domain
As described above, the City of Cape Town does not exist in isolation from the surrounding
region, but rather is dynamically related to the surrounding region with respect to water
supply, food supply, tourism, and other economic activities. Therefore, the climate data
analyses are carried out for a spatial domain covering a loosely-defined Cape Town “region”,
which broadly covers the City of Cape Town and relevant hydrological catchments on which
Cape Town draws its water resources as well as relevant agricultural areas. The spatial
domain is illustrated in Figure 4.
Methods
Climate regions
As the City of Cape Town and surrounding region is composed of widely varying topography
and hence annual rainfall patterns, it was decided to divide the city-region into four different
rainfall sub-regions. This was done by hierarchical clustering of mean monthly rainfall at grid
level of each of the gridded rainfall datasets, with Ward‟s method of cluster aggregation. A
range of cluster numbers were tried and it was considered that four clusters are enough to
capture the main features of rainfall heterogeneity in the region. These can be see in Figure
4.
Long-term trends in observed climate indices
Quantifying long-term trends of climate variables is challenging. This is because climate
variables, particularly rainfall, typically exhibit large variations from year to year and even
from decade to decade (see for example Figure 5). These variations are typically referred to
as variability or, where it is assumed that climate change is not involved, natural
variability. Where a time series of historical observations exhibits high variability, a
calculated trend can easily be spurious and dependent on the particular historical time
period selected. The longer the time period the more reliable the calculated trend is
assumed to be. There are also a variety of methods for detecting the presence of a long
term trend as well as determining the magnitude of a trend. These methods have been
developed to avoid problems with large outliers near the start or end of a time series as well
as dealing with other assumptions about the time series.
In this analysis, temporal trends in climate statistics were assessed using the Mann-Kendall
𝛕 (tau) statistic (Mann, 1945; Helsel & Hirsch, 1992). This method is commonly used to
detect the presence of a trend in a time series. However, the magnitude of the trend, i.e.
trend‟s slope expressed in variable units/unit time was calculated as Theil-Sen slope (Theil,
1950; Sen, 1968). Theil-Sen slope is a linear (uniform) slope that is compatible with the
assumptions of Mann-Kendall test and is also typically less sensitive to outliers in the time
series than the more standard linear regression-based trend.
Technical Box: Temporal trends
Climate trend analysis aims at detecting systematic change of a climate variable with time,
i.e. finding whether there is tendency in a climate variable, such as rainfall or air
temperature, to increase (or decrease) in time. There are a number of ways of capturing and
expressing a trend, and which one is used depends on the character of the trend, data,
purpose of the analysis and sometimes personal preference of the analyst. Linear
regression is one of the most common ways of trend detection, but it has a number of
disadvantages. Firstly, it is sensitive to outliers (it means that a single outlier value in data
can strongly affect the value of the trend slope), and secondly, it considers that a trend is
linear in nature (i.e. that the trend does not speed up or slow down), which may lead to
underestimation of significance of non-linear trends. As a result, the presence and strength
of trend may be falsely estimated. The method adopted here, based on Mann-Kendall 𝛕 and
Theil-Sen slope, is more robust, i.e. it is less likely for the trend slope to be affected by
outliers, and the method is more likely to detect significant trends with changing rate that the
linear trend. The Theil-Sen slope is an expression of the average trend rate only, and thus,
unfortunately, the method does not provide information on whether trend is speeding up or
slowing down.
Technical Box: Significance testing
A key concept in time series analysis such as trend analysis, is statistical significance
testing. Significance testing is a class of statistical methods used to determine if a particular
statistic, such as the magnitude of an historical trend, is likely to have been a result of
random chance versus being the result of some underlying process or cause. Trends can
occur by chance because of noise or variability in a time series. This noise means that
using a different time period or sampling a different subset of values results in a different
statistic. However, if a result passes a significance test then we have more confidence that,
regardless of what time period or subset of samples we analyse, we will get similar
results. It is then considered likely that there is some common underlying cause for the
observed statistic such as global climate change influencing temperatures in Cape Town.
The analysis presented here uses an approach called “bootstrapping” to determine statistical
significance. Bootstrapping produces multiple re-samplings of a time series. Calculating the
same statistic, such as a trend on multiple re-samplings allows for a test of the consistency
of the statistic and hence a measure of statistical significance. In order to account for the
influence of autocorrelation (eg. If it rained yesterday it is more likely to rain tomorrow than if
it was dry yesterday) in data on the significance of calculated trends, the procedure of
stationary bootstrapping (block bootstrapping with randomized block length) (Effron and
Tibshirani, 1993; Wilks, 2011) was used to calculate significance levels of trends. Trend
significance was calculated as a percentile of distribution of 𝛕 obtained from a 1000 block-
bootstrap re-samplings of data. The significance of trend was expressed as a two-tailed p-
value. While interpreting the results, we consider p-value of 0.05 as a threshold of
significance.
Decadal time scale variability in climate indices
The Cape Town region, in fact the entire southern African region, is characterized by strong
climate variability occurring at multiannual to multi-decadal time scales. While methods exist
that allow for rigorous statistical analysis of such behaviour, considering the relatively small
scale of the domain and data uncertainties in this project a qualitative visualisation approach
was adopted instead, which is based on LOWESS scatterplot smoothing approach
(Cleveland and Devlin, 1988) which allows for the generation of time smoothed time series
that reflect well the underlying longer-term variability.
Rainfall zones in the Cape Town region
The climate of the Cape Town region (as described above), is determined by the interaction
of atmospheric circulation features such as cold fronts, with the relatively complex
topography of the Cape. General knowledge and prior analyses of observational data
suggests that several zones or sub-regions can be distinguished in the Cape Town region,
that differ in the amount of rainfall they receive, while maintaining a very similar seasonality
(i.e. domination of winter rainfall). The highest rainfall (>800 mm/year and reaching 1500
mm/year) falls over the Table Mountain and the southern section of the Cape Fold
mountains - Hottentots-Holland range extending towards Jonkershoek and Kogelberg in the
south, and Hawequas range towards Cederberg in the North. The plains of the Overberg,
the West Coast as well as the Cape Town metropolitan area are characterized by moderate
rainfall (~400mm/year), while the areas to the north (north of Saldanha-Piketberg line) and to
the east (east of the Cape Fold range) are characterized by low rainfall (~200mm/year).
Technical Box: Long-term variability and temporal smoothing
Climatic variables show considerable variability (differences) at a number of time scales -
from daily to decadal and longer. Seasonal variability is usually the most obvious and
strongest, but in the context of anthropogenic climate change, we are usually concerned with
time scales longer than a year. At these time scales, the variability manifests by multi-year
periods of e.g. above average conditions. Importantly, these periods are followed by reversal
towards near-average, or below average conditions. Such periods can extend to 20-30 years
and may be mistaken for trends. Importantly, they have different causes than a systematic
trends, i.e. are caused by internal processes within a climate system rather than by
anthropogenic forcing. It is therefore important to distinguish between the two. In this work,
we have adopted a smoothing approach to visualize the longer-term variability. The longer-
term variability is usually weaker than year-to-year variability, and thus is not clearly
identifiable by visual inspection of a time series plot. However, it can be brought up by
smoothing of the time series. Smoothing reduces year-to-year variability, and exposes
underlying longer-term behaviour. The smoothing is usually done with so called moving
average, where each point (date) in the time series is given the value of the mean of the
averaging period (for example 20 years) centered on that date. Here we have used a more
complex, but more robust method of smoothing - lowess smooth. The smooth line values are
derived from locally-weighted regression calculated within the moving window.
This relatively intricate pattern of rainfall differences as determined by the topography
(mountain ranges mostly) cannot be precisely replicated in the coarse datasets that are the
basis for the analyses, but the WATCH WFDEI-based zoning was considered adequate and
was used as a basis for further analyses (Figure 4).
Figure 4: Broad rainfall zones in the Cape Town region. Colours in the climatology and time
series plot correspond to the colours in the map. The Same colours are used to show zonal
trends in subsequent figures.
The four sub-regions in Figure 4 are named (from north to south): West Coast (in dark grey),
Swartland (in yellow-ish), Cape Town (in blue) and Overberg (in light grey), and these
names are used in the remainder of this report.
5. Trends and Variability in the historical climate of
Cape Town
Summary points
Annual trends
Variability and trends in rainfall have been assessed over the long-term (1901 –
current) and for a mid-term period (1979-present)
The long-term trends in rainfall seem to indicate an overall increase in rainfall in the
north of the Cape Town region (West Coast) and a decline in the southern part (other
regions). The trends are significant at some locations, although not significant in the
region-averages.
The overall mid-term (1979-2013) trends in annual rainfall are predominantly
negative, although not significant. However it is clear that the mid-term period is
characterized by above average rains in its first part (1980s), followed by below
average rains in the mid- to late 1990s, with some recovery towards wetter conditions
in the late 2000s. Mean daily rainfall shows a negative trend while the number of
days with rain shows a positive trend
Temperature shows a clear trend, both in the long-term and mid-term, with all indices
of temperature showing a positive trend.
Seasonal trends
Generally, there is a weakly negative trend in the summer and autumn total seasonal
rainfall and weakly positive trend for winter and spring total seasonal rainfall
The positive trends in daily maximum and minimum air temperatures are strongest in
spring and summer with the West coast and Swartberg regions indicating the
strongest trend
Rainfall
The variability and trends in rainfall have been assessed at two time scales:
Long-term period, or centennial time-scale (1901-present)
Mid-term period, or multidecadal time scale (1979-present)
Trends and variability in annual rainfall indices
Long term (1901-present):
Analyses of time series of annual rainfall indicate that there are weak long-term trends and
a decadal-scale variability in rainfall in the Cape Town city-region (Figures 5 - 7). Although
the decadal scale anomalies are mostly consistent across the various datasets, there are
some discrepancies between them in the sign of anomalies in the recent (post 2000) period.
In the 20th century; 1900s, 1950s and 1980/1990s were generally above average, while
1930s, 1970s and 2000s were below average (Figure 5). The long-term trends seem to
indicate an overall increase in rainfall in the north of the CT sub-region (West Coast), with
trend in the order of 1.5 mm/decade, and a decline in the southern part (other sub-regions),
with trends in the order of 2-5 mm/decade. The trends are significant at some locations,
although not significant in the sub-region averages.
Mid-term (1979-2013):
Variability within the mid-term:
The variability within the mid-term (1979-2013) appears to be generally consistent across the
CRU and WFDEI datasets (Figures 5 and 6). Both indicate increase in rainfall in the 1980s,
however, the former indicates reduction of rainfall in the post 1990s period, while the latter
indicates an upward trend in the post-2003 period. The pattern of variability in the CHIRPS
dataset is different - it indicates a relatively consistent decline in rainfall since the early
1980s across the entire city-region (Figure 7). The analyses of the limited observed rainfall
data indicates that, in the recent years, after 2003, there is an increase in annual rainfall.
The pattern present in the WFDEI dataset reflects that finding the best (see Technical Box
below). Further analyses will be conducted using the CRU dataset for the century-scale
trends, and WFDEI dataset for the recent decadal-scale trends, skipping the interpretation of
CHIRPS dataset.
Overall mid-term trend:
The overall mid-term (1979-2013) trends in annual rainfall as presented in the WATCH
WFDEI dataset are predominantly negative reaching 13 mm/decade (Figure 6). However,
from Figure 6 it is clear that the mid-term period is characterized by above average rains in
its first part (1980s), followed by below average rains in the mid- to late 1990s, with some
recovery towards wetter conditions in the late 2000s. The trend may thus be an artefact
resulting from that sequencing. This is confirmed by the fact that the detected trends are, in
general, not statistically significant.
The above describes trends and variability in total annual rainfall. In the context of changing
climate it is also important to determine properties of other rainfall indices, particularly those
describing rainfall intensity and frequency of rainfall events. Both these indices show decadal
scale variability of similar pattern to that of the total annual rainfall, i.e. high in late 1980s, low
in late 1990s and early 2000s, and recovery towards 2013 (Figure A6 and Figure A7 in the
Appendix). Unlike for the total annual rainfall, this decadal scale variability is weaker and
superimposed on overall trends that are different for each of the indices.
For mid-term mean daily rainfall, there is an overall negative trend in all four rainfall sub-
regions, the strongest (~-0.4 mm/decade), and statistically significant in the Cape Town sub-
region (Figure A6 in the Appendix). For the number of rain days, there is a positive trend
(Figure A7 in in the Appendix), in the order of 1-2 days/decade, and it is statistically
significant at individual locations, although not significant in sub-region averages. Since the
total annual rainfall is a combination of rainfall intensity and frequency of rainfall events, it
seems that in the mid-term period, the opposite overall trends in the latter has resulted in the
weak overall trend in the former.
Figure 5: Long-term (1901-2014) trends and variability in CRU 3.23 dataset
Technical box: Differences between rainfall datasets
The three datasets used here have different origin, and this may cause discrepancies
between them. CRU is based on interpolation of station data, WFDEI dataset uses station
data to bias-correct results of climate model simulations, while CHIRPS integrates satellite-
derived product with observations. In case of CRU, there is a very clear decline in the
number of stations used to derive rainfall values for grid cells in the Cape Town city-region
(Figure A3 in the Appendix), that might explain the recent trend. The WATCH WFDEI
dataset is primarily model driven, that allows for maintaining variables‟ consistency across
time (and thus likelihood that trends and variability are captured adequately). It is not clear
what factors could underlie the discrepancy present in the CHIRPS dataset, as it is one of
the most recently-developed and most technologically advanced satellite rainfall products
and it takes observations into consideration. The investigation of the nature of the
discrepancies are beyond the scope of this project.
Figure 6: Trends and variability in total annual rainfall in the Cape Town city-region, based on WFDEI
1979-2013 dataset
Figure 7: Trends and variability in total annual rainfall in the Cape Town city-region, based on
CHIRPS 1981-2015 dataset
Trends and variability in seasonal rainfall indices
When disaggregated into seasonal components, mid-term rainfall trends are different for
different seasons (Figure 8). Generally, there is a weakly negative trend in December,
January, February (DJF) and March, April, May (MAM) total seasonal rainfall (0.5 to 4
mm/decade), and weakly positive trend for June, July, August (JJA) and September,
October, November (SON) (1 to 4 mm/decade). Only the DJF trends in Swartberg sub-
region and MAM trend in the grid cell representing Cape Town reach levels of statistical
significance. The strong decadal-scale variability is present in each of the seasons, although
the pattern of that variability in DJF is different from that observed in the annual data and in
other seasons. That DJF pattern corresponds to the decadal scale variability observed in the
summer rainfall sub-regions of southern Africa (e.g. Jury, 2013).
Figure 8: Trends and variability in total seasonal rainfall in the Cape Town city-region, based on
WFDEI 1979-2013 dataset
The mid-term trends in mean daily rainfall are predominantly negative in each of the seasons
and each of the rainfall sub-regions and are in the range of -0.05 to -0.5 mm/decade,
although there are individual locations where trends are weakly positive (Figure A9 in
Appendix). These trends reach statistical significance only in the Cape Town/Swartberg
rainfall sub-regions in DJF season.
The mid-term trends in number of rain days are weak and mixed in DJF and MAM, but
consistently positive in JJA and mostly positive in SON (Figure A10 in Appendix). The JJA
trends reach 1.75 days/decade. These trends are, however, almost exclusively not
significant.
In terms of differences between the rainfall sub-regions, some generalizations can be made:
the trends are generally weaker in sub-regions with lower rainfall, i.e. West Coast and
Overberg, and stronger in high rainfall sub-regions - i.e. Cape Town and Swartland. As the
above analysis indicates, the trends may also differ in sign between these sub-regions.
Air temperature
Trends and variability in annual temperature indices
Mid-term trends in air temperature (illustrated in the Figure 9 below through the annual mean
of maximum daily temperature) are different in nature to trends in rainfall. They are almost
exclusively positive and statistically significant. The decadal scale variability is present, but
plays lesser role in determining prevalent temperatures.
Trends in the annual mean of maximum daily temperature reach 0.29°C/decade (Figure 9),
with the strongest trend in Overberg and Swartland, and lower in Cape Town and West
Coast sub-regions.
Trends in annual mean of minimum daily temperature are stronger, reaching 0.34°C/decade
(Figure A12 in Appendix), with the strongest trend in the Cape Town sub-region.
The trend in number of days with maximum air temperature exceeding 35 °C is strongly
positive in the north and east (inland) of the Cape Town sub-region, reaching 2 days per
decade (Figure A13 in Appendix).
Figure 9: Trends and variability in annual mean of daily maximum temperature in the Cape Town city-
region, based on WFDEI 1979-2014 dataset
Trends and variability in seasonal temperature indices
The disaggregation of air temperature trends into seasons reveals that trends in daily
maximum air temperatures are strongest in DJF and SON, but weak to the level where they
are not significant in JJA and MAM (Figure A14 in Appendix). The DJF trends fall within
0.42-0.52°C/decade, while SON are weaker, in the order of 0.25-0.35°C/decade.
Trends in daily minimum air temperature have similar pattern, with strongest in DJF
(reaching 0.6°C/decade) and SON (0.35°C/decade), weaker in MAM (up to 0.28°°C/decade)
and the weakest and not statistically significant in JJA, where the trend does not exceed
0.2°C/decade (Figure A15 in Appendix).
The general spatial pattern in the air temperature trends is that trends are stronger in West
Coast and Swartberg sub-regions, and weaker in the Overberg and Cape Town, with the
exception of daily minimum air temperature in DJF, where the strongest trend is in the Cape
Town sub-region.
6. Climate change projections: methodology and
limitations The primary source of information on large scale changes to the global climate is Global
Climate Models (GCMs) and more specifically, coupled Atmosphere-Ocean Global Climate
Models (AOGCMs). AOGCMs simulate responses to changing atmospheric concentrations
of greenhouse gases (GHG) - mostly carbon dioxide and methane, and other emissions
including sulphates and black carbon. AOGCMs simulate both ocean dynamics and
atmospheric dynamics and many modern models also simulate vegetation and other land
surface processes.
Global Climate Model Projections AOGCMs involved in the CMIP5 experiment operate at relatively low spatial precision. This
means that they simulate average conditions over fairly large spatial areas. For example, a
typical AOGCM member of CMIP5 would simulate an area of 200km x 200km as a
homogenous area with no variations in rainfall, temperature, or wind across that
area. Clearly, such low resolution is unable to accurately represent the complexity of an
area like the City of Cape Town region. It is unlikely that such a model could capture any
Technical Box: CMIP
The Coupled Model Inter-comparison Project (CMIP) has been instrumental in increasing
the utility of AOGCMs by coordinating experiments across multiple modelling centres and
involving multiple AOGCMs. Coordinated experiments allow for far more complex analyses
of sources of uncertainty because the experiments control various boundary conditions while
allowing different AOGCMs to represent different responses. A number of CMIP
experiments have now been run with the most recent being CMIP5.
Technical Box: RCPs
The primary coordinated parameter in the CMIP experiments has been emissions
scenarios. This has allowed all models to simulate the future climate under a controlled set
of concentrations of greenhouse gases (GHG) and other key atmospheric gases. In the
CMIP5 experiment Representative Concentration Pathways (RCPs) were used to capture
different future emissions scenarios. Whereas CMIP5 used socio-economic scenario
derived emissions (SRES), RCPs avoid the complexity of defining socio-economic scenarios
and instead focus directly on equivalent radiative forcing. So, for example, RCP 8.5
represents a GHG concentration pathway that results in an equivalent increase in solar
radiation of 8.5 W/m2 (Watts per square meter at the top of the atmosphere) by
2100. Likewise RCP 4.5 represents a GHG concentration pathway that results in the
equivalent of 4.5 W/m2 by 2100. In this analysis RCP8.5 and RCP4.5 are considered as
these are the most likely upper and lower bounds of global emissions given current trends
and international agreements.
local mountain rainfall effects or the rapid temperature transitions from ocean to inland areas
experienced in the Cape Town city-region. However, AOGCMs are able to capture large
scale shifts in circulation patterns and processes such as the high pressure systems, the
continental low pressures, and mid-latitude jet dynamics of relevance to Cape Town and
described above. It is for this reason that we still use AOGCMs to explore the future climate
of the city.
7. Climate change projections for Cape Town:
Summary points The GCMs all show a continuation of natural variability into the future up until around
2030-2040 after which almost all models show a significant shift towards a drier
future. There is a projected reduction in rainfall in all seasons, although the strongest
reduction is projected for autumn and winter
Significant shifts in temperature are projected to have already occurred (ie. 2011-
2015) with the GCMs showing temperature continuing to rise into the future.
Because of the complex topography and land-ocean boundaries present in the City
of Cape Town region, downscaling has been used to explore finer scale responses to
large scale circulation shifts simulated by the GCMs.
Projected rainfall time series are significantly different for the downscaled data. In
particular, downscaled projections of rainfall change into the future (for all regions)
show an almost equal split between a wetter future and a drier future (the possible
reasons for this are unpacked in the discussion section). The summer season shows
a non-statistically significant drying.
Downscaled projected temperature changes do not differ significantly from the GCM
projections and show temperature continuing to rise into the future
Global Climate Model Projections In the plots below (and in the more comprehensive set of plots in the Appendix - Figure
A16Figure A26), timeseries of projected rainfall and temperature are presented. These time
series are produced by running a 20 year moving average over annual or seasonal rainfall
and temperature values produced by a suite of 16 CMIP5 AOGCMs area averaged over
each of the rainfall sub regions of the city-region. Estimates of uncertainty resulting from
natural variability are represented by shaded areas surrounding the projected values and
significance of the projected changes (i.e. when the changes exceed the bounds of what we
have experienced in the past) are highlighted by a change in colour from blue to
orange. This allows for some estimation of when in the future we are likely to be operating
under a climate that is distinctly different from the climate we currently experience.
We can see in Figure 10 below that the CMIP5 GCMs all show a continuation of natural
variability into the future up until around 2030-2040 after which almost all models show a
significant shift towards a drier future ranging from small reductions through to as much as a
50% reduction in rainfall.
Figure 10: Simulations and projections of total annual rainfall for the Cape Town city-region based on
GCM CMIP5 MME
Figure 11: Simulations and projections of maximum air temperature for the Cape Town city-region
based on GCM CMIP5 MME.
Projected changes in temperature derived from the CMIP5 models operating under the
RCP8.5 pathway, and visualised in Figure 11 above, show that significant shifts in
temperature are projected to have already occurred (i.e. 2011-2015) with temperature
continuing to rise into the future. By mid-century most sub-regions show increases of
between 1°C and 2°C.
Seasonal changes in rainfall and temperatures
Seasonal disaggregations indicate that the reduction in rainfall is projected in all seasons,
although the strongest reduction is projected for JJA and MAM, while DJF has weakest
change (Figure A33 and Figure A34). There are no noticeable differences between the
rainfall sub-regions in terms of seasonal signal.
There seems not to be any noticeable between-season differences in changes in air
temperature (both minimum and maximum). The GCM ensemble projects more or less
uniform increases in air temperatures in each of the seasons (Figure A35Figure A37).
Downscaled Projections Due to the complex topography and land-ocean boundaries present in the City of Cape
Town region, downscaling has been used to explore finer scale responses to large scale
circulation shifts simulated by GCMs. In this analysis, statistical downscaling has been
used. Statistical downscaling uses historical records of rainfall and temperature as well as
histories of circulation patterns to calibrate a statistical model relating circulation patterns to
local rainfall responses. A unique calibration is performed for each fine resolution grid cell in
the observed rainfall and temperature datasets described above, which means that the
unique local relationship can be captured rather than a broad regional relationship. In
theory, this should mean that the effect of local topography or land-ocean boundary can be
captured in the statistical model and future projected changes in rainfall and temperature can
be developed for these fine scale grid boxes across the region.
Similarly to the GCM projections, plume plots have been developed using the downscaled
rainfall and temperature derived from each GCM. These are presented below in Figures 12
and 13 (and more comprehensively in the Appendix in Figure A27- Figure A37) While
downscaled projected temperature changes do not differ significantly from the GCM
projections (because regional temperature shifts over time are not particularly sensitive to
local topography), projected rainfall time series are significantly different for the downscaled
data. In particular, downscaled projections of rainfall change into the future, for all regions,
show an almost equal split between a wetter future and a drier future. The range of
projected changes by the 2050 period extends from -20% to +20% with the largest absolute
and relative changes projected for the Cape Town region.
Some possible explanations for these strong differences and suggestions for further
avenues of research are described in the follow section.
Figure 12: Simulations and projections of total annual rainfall for the Cape Town city-region based on
downscaling (SOMD) of CMIP5 MME.
Figure 13: Simulations and projections of maximum daily air temperatures for the Cape Town city-
region based on downscaling (SOMD) of CMIP5 MME.
Seasonal changes in rainfall and temperatures
Seasonal disaggregations of projected rainfall indicate generally similar pattern to that
observed in the annual downscaled projections, i.e. the individual ensemble members time
series fluctuating mostly around current rainfall levels. Only in DJF there seems to be a
relatively consistent decline in rainfall, although mostly not reaching statistical significance.
Similarly to the GCM projections, there seems not to be any noticeable between-season
differences in changes of air temperature (both minimum and maximum). The SOMD
ensemble projects more or less uniform increases in air temperatures in each of the
seasons.
8. Climate change projections technical interpretation
and discussion
Summary points: The analysis shows that none of the GCMs performs markedly worse than the
others.
However initial analysis does indicate that the downscaling approach used is
potentially failing to capture climate variability signals that drive changes in
rainfall. This means that the downscaling is possibly unable to represent the true
sources of rainfall variability.
These results are not conclusive and require significantly more analysis before firm
conclusions can be made.
As a consequence it is currently felt that the GCM messages of reduced rainfall in the
future do need to be considered as fairly strong messages while acknowledging that
the statistical downscaling continues to suggest more mixed messages.
The projections presented above tell a confident story of increasing temperatures into the
future. However they also tell a story involving contradictions and disagreement regarding
changes in rainfall for the city region. While the GCM projections show consistent messages
of drying, the projected magnitude of rainfall reductions show a wide spread from very small
to quite significant (up to 50%). This level of disagreement is common and has led climate
scientists to explore the possibility that some GCMs are “better” than others with the view to
focussing on the projections produced by relatively good models.
Such an analysis was done for the city region projections. While the technical details of the
analysis are extensive, the principle is as follows: Identify the key climate processes driving
rainfall for the city region, then determine how well each GCM captures the evolution of
those processes through the seasons of the year. So for the City of Cape Town we would
expect that a GCM should be able to simulate the annual cycle of increasing and decreasing
high pressure systems, the north-south variations in the mid-latitudes, and variations in the
continental heat low.
Self-organising maps Rather than use subjective approaches to identify these key climate features or processes
we use an objective data analysis method call Self Organising Maps (SOM). SOMs are
really just a form of clustering that divide all the monthly or daily climate conditions
(circulation features) into different categories where days or months within a particular
category are more similar than days or months in different categories. The one key strength
of SOMs is that they represent a continuum of categories or clusters of states in a grid
pattern. This means that days that fall within one category are very similar. Days that fall
within an adjacent category in the two dimensional grid or SOM “map” of categories are
more different but less different than a category on the opposite side of the SOM map. The
actual circulation categories determined using this method can be seen in Figure 14 below.
In this case monthly mean circulation states analysed.
Category trajectory analysis The next step in this analysis involves exploring how each model represents the seasonal
evolution of climate processes or patterns. This seasonal evolution can be visualised as a
pathway or trajectory through the SOM category grid (SOM map). Models that capture
realistic seasonal evolutions of climate processes or patterns should produce pathways
through the SOM map that are similar to observed pathways. The results of this analysis
can be seen in Figure 15 below where multiple GCM pathways have been plotted and
compared to pathways determined from observed data.
Figure 14: Archetype maps for 700mb geopotential height in a 7x9 SOM trained on monthly 1979-
2014 ERA-Int fields (q, t, u, v, all at 700mb)
Figure 15: “Trajectories” through SOM-space in ERA-Int reanalyses, and in GCM simulations. WCP
domain, SOM trained on monthly data in 1979-2014 period.
Performance analysis The final step in the analysis is to quantify the difference between each GCM pathway and
the observed pathways. A number of approaches to this have been explored. The
approach used here involves calculating the average distance between the GCM pathway
and the observed pathway through the seasons. This is the most intuitive approach though
it may miss other nuances that more sophisticated methods capture. However, at this point
the simple distance measure is the most understandable. The results of this distance
measure are presented in Figure 16 below. It can be seen that while there is some
difference in performance (using this analysis approach) between the different GCMs, none
of the GCMs performs markedly worse than the others. While it may be defensible to ignore
the CanESM2 model and possibly the MPI-ESM-LR model, the difference are not marked
enough to really justify such a removal.
Figure 16: Index of similarity between SOM-space trajectories of individual GCMs and ERA-Int. SOM
trained on monthly data for 1979-2014 period.
Downscaling contradictions What emerges therefore is that the most significant contradictions in the rainfall projections
arise from the introduction of the downscaled projections which significantly alter the
dominant message of drying towards a message of split wetting or drying in the future. It is
clear that this is quite a strong contradiction and one that needs far more exploration.
Some initial analysis (not presented here) does indicate that the downscaling approach used
is potentially failing to capture climate variability signals that drive changes in rainfall. The
downscaling approach used also uses SOM maps, just like the prior analysis has
demonstrated. This allows us to unpack the downscaling results using a similar
approach. The results indicate that rainfall variability does not map well to changing
frequency of different climate process states as represented by the SOM and that rainfall in
the city region varies strongly under identical climate process states. This means that the
downscaling is possibly unable to represent the true sources of rainfall variability.
However these results are far from conclusive but require significantly more analysis before
we come to firm conclusions. However, as a consequence, it is currently felt that the GCM
messages of reduced rainfall in the future do need to be considered as fairly strong
messages while acknowledging that the statistical downscaling continues to suggest more
mixed messages.
9. Narratives of the future climate Narratives, or stories of change, are a new method that is being tested to try and aid the
communication of uncertain climate projections. The narratives offered here were presented
at a City workshop on 21 June in order to test their effectiveness and receive feedback on
their potential utility for decision-making at a city scale (see the next section for reflections on
that workshop). The feedback from the workshop is very useful in terms of nuancing these
types of narratives for future use. Unfortunately it has not yet been possible, within the time
frames and scope of this project, to refine these narratives based on feedback from the
workshop.
In using these narratives there are important points to consider:
Each of these narratives represents a possible future, within the range of uncertainty,
but should not be seen as a definitive projection or representing a certainty about a
particular future. They represent speculative futures based on the current evidence
and scientific judgement
Each of these narratives should be seen as equally likely. There is no probability or
likelihood associated with each. Therefore, in considering decision-making using
these narratives, all the narratives should be taken into account.
These narratives should be used in conjunction with the underlying evidence
available in the detailed projections analysis (available through the initial sections of
this report). They should not be used as stand-alone evidence as they do not
represent the entire range of possible futures.
Technical box: Considerations in the construction of the narratives
It is not enough to give a statement of future climate, as in receiving any narrative message
of projected climate change it is imperative to understand the rationale of how the narrative
was constructed. This is predicated on a consideration of the driving processes, the
information limits and implications of the tools and techniques, and cognizance of the
assumptions involved. Absent of these considerations any scenario is at danger of being
over-interpreted. Thus, the following initial statements are crucially important:
a) The experienced local climate is a product of multiple atmospheric processes on
different spatial and temporal scales: from local processes such as cloud formation
through to the hemispheric scales of, for example, the westerlies and El Nino. These
work in combination to drive the nature of the local weather which collectively,
defines the climate. However, because they change at different rates, the combined
effect of their change can vary into the future.
b) These processes intersect with other factors such as the location, altitude, and
aspect of the local topography. Thus the historical rainfall at the Cape Town Airport
and in the mountains behind Gordon‟s Bay can differ by a factor of three, while
similar mountains further inland have a very different interaction. Likewise the
temperatures in the Worcester valley can be dramatically different to those on the
coastal plain. These complex interactions with topography can play a critical role in
how climate change will be manifest.
c) A homogeneous climate region does not mean that the climate change will be
equally manifest across this region. For example, in the Western Cape one might
consider the south coast, or the west coast, or the winelands as each being relatively
homogeneous climates, yet the climate change may be manifest differently within as
well as between these regions.
d) Any type of averaging will obscure some characteristics of change. For example,
averaging in time (seasonal, monthly or annual averages) will obscure the
information of how events occur (such as cold front duration or intensity, dry spell
length, etc). Likewise, spatial averages, whether because the model has a resolution
limit, or because the data has been spatially averaged, will mask local climate
change signals that may not be spatially homogeneous within the averaged areas.
Thus, considering (c) above, any messages about especially precipitation that arise
from spatial averaging across areas of complex topography, or where topography
with flat areas have been mixed together, should be treated with extreme caution – if
they are considered at all.
e) Climate models on which projections are based inherently include some measure of
spatial averaging, and depending on the data source being used may include some
temporal averaging. Hence these tools include significant limitations on what
legitimate details may be interpreted.
f) Climate change occurs on top of natural variability, hence there will still be years that
are wetter/drier, warmer/cooler, more intense/mild, relative to the prevailing average
(which will itself be slowly changing).
g) Historical trends are identifiable, but may or may not yet all be statistically significant
as a change in climate due to the complications of separating the change signal from
natural variability. Nonetheless the spatial consistency of both statistically significant
and statistically non-significant change coveys a clear message of how the climate
has been evolving, and may (but not necessarily) evolve in the future.
h) On the basis of the above, narratives of projected change must necessarily assess
primarily the drivers of change, consider how these individually respond to climate
change, and then how they collectively combine and then interact with other factors
to give rise to the experienced local change in climate.
Narratives
Narrative #1 | Hotter and drier
The Cape Town region continues to experience cycles of wet and dry seasons and wet and
dry decades due to cycles of natural variability in the climate system. However, failure of the
Paris agreement results in global mean temperatures reaching 2C above pre-industrial by
the early 2040s. This means that Cape Town mean temperatures are 2.5C above pre-
industrial and 1.5C above the 1986-2005 baseline period. The result is far more frequent
extremely hot days, as well higher extreme temperatures. The number of days per month
exceeding 36C in inland locations is double that of the baseline period. The average
summer is now hotter than the hottest summer during the 1986-2005 period. 40C days in
the inner city are now fairly common in mid-summer.
Higher summer temperatures has increased demand for power for cooling in the city as well
as resulting in an increase in heat related health problems, particularly in low income areas
where housing is less well adapted to higher temperatures.
Higher temperatures have reduced runoff into dams from light rainfall events due to
increased evaporation. High wind speeds combined with higher temperatures and low
relative humidity results in higher evaporative losses from dams. Water quality in rivers is
now an increasing concern as high water temperatures allow for algae growth decreasing
oxygen levels. Rain fed agriculture is impacted by higher evaporation and resultant drier
soils. Irrigated crops demand more irrigation. Some export targetted winter fruit crops and
wine grapes are no longer viable due to insufficient cool periods required for these varieties.
Stronger sub-tropical high pressure systems combined with a more intense continental heat
low has set up a stronger land-ocean pressure gradient resulting in strong summer south
easterly winds. Stronger winds and shifting wind directions, combined with a warmer ocean,
has influence the fishing industry negatively. Stronger winds is also resulting in more
frequent closure of the container port. Global changes in container ship traffic due to
reduced sea-ice opening up arctic shipping routes has also impacted port activity negatively.
Higher temperatures, decrease relative humidity, and drier vegetation, combined with
stronger winds means that wildfire is more common and more intense, impacting natural
ecosystems and tourism, as well as human settlements.
While cycles of wet and dry seasons do continue, long term mean rainfall has decreased
due to the southerly shift of the mid-latitude jet stream and other changes in system
dynamics. This means that while seasons considered normal during the 1986-2005 baseline
period do still occur, they occur less frequently, and seasons such as the 2015 winter season
are now more common and multi-year droughts also occur more frequently, placing severe
stress on the cities water supply which has already been impacted both on the supply and
demand side by higher temperatures and evaporation.
However, higher moisture content (not the same as humidity), and changes in the mid-
latitude jet dynamics, means that periodic winter storms have become less frequent but
more intense causing frequent widespread flooding in low lying areas as well as damage to
infrastructure. Coastal storm damage caused by a combination of more intense wind events
combined with sea-level frequently impact coastal developments and infrastructure.
Note: For the purposes of these narratives, the City of Cape Town includes the actual
municipal area as well as surrounding water catchment and satellite locations.
Narrative #2 | Warmer and no rainfall change
The Cape Town region continues to experience cycles of wet and dry seasons and wet and
dry decades due to cycles of natural variability in the climate system. However, the Paris
agreement is successful in reducing global emissions and as a result global mean
temperatures are kept below 1.5C above pre-industrial by the early 2040s. This means that
Cape Town mean temperatures are 2C above pre-industrial and 1C above the 1986-2005
baseline period. The result is more frequent extremely hot days, as well higher extreme
temperatures. The number of days per month exceeding 36C in inland locations is 1.5 times
that of the baseline period. The average summer is now hotter than most of the hottest
summers during the 1986-2005 period. 39C days in the inner city are now fairly common in
mid-summer.
Higher summer temperatures has increased demand for power for cooling in the city as well
as resulting in an increase in heat related health problems, particularly in low income areas
where housing is less well adapted to higher temperatures.
Higher temperatures have reduced runoff into dams from light rainfall events due to
increased evaporation. High wind speeds combined with higher temperatures and low
relative humidity results in higher evaporative losses from dams. Water quality in rivers is
now an increasing concern as high water temperatures allow for algae growth decreasing
oxygen levels. Rain fed agriculture is impacted by higher evaporation and resultant drier
soils. Irrigated crops demand more irrigation. Some export targetted winter fruit crops and
wine grapes are no longer viable due to insufficient cool periods required for these varieties.
Stronger sub-tropical high pressure systems combined with a more intense continental heat
low has set up a stronger land-ocean pressure gradient resulting in strong summer south
easterly winds. Stronger winds and shifting wind directions, combined with a warmer ocean,
has influence the fishing industry negatively.
While cycles of wet and dry seasons do continue, long term mean rainfall has not changed
significantly. Even though high pressure systems are more dominant, counteracting shifts in
the mid-latitudes and interactions with a strong continental heat low result in fairly normal
long term mean rainfall. However, higher moisture content (not the same as humidity), and
changes in the mid-latitude jet dynamics, means that periodic winter storms have become
less frequent but more intense causing frequent widespread flooding in low lying areas as
well as damage to infrastructure. Coastal storm damage caused by a combination of more
intense wind events combined with sea-level frequently impact coastal developments and
infrastructure.
Note: For the purposes of these narratives, the City of Cape Town includes the actual
municipal area as well as surrounding water catchment and satellite locations.
Narrative #3 | Hotter and mixed rainfall change
The Cape Town region continues to experience cycles of wet and dry seasons and wet and
dry decades due to cycles of natural variability in the climate system. However, failure of the
Paris agreement results in global mean temperatures reaching 2C above pre-industrial by
the early 2040s. This means that Cape Town mean temperatures are 2.5C above pre-
industrial and 1.5C above the 1986-2005 baseline period. The result is far more frequent
extremely hot days, as well higher extreme temperatures. The number of days per month
exceeding 36C in inland locations is double that of the baseline period. The average
summer is now hotter than the hottest summer during the 1986-2005 period. 40C days in
the inner city are now fairly common in mid-summer.
Higher summer temperatures has increased demand for power for cooling in the city as well
as resulting in an increase in heat related health problems, particularly in low income areas
where housing is less well adapted to higher temperatures.
Higher temperatures have reduced runoff into dams from light rainfall events due to
increased evaporation. High wind speeds combined with higher temperatures and low
relative humidity results in higher evaporative losses from dams. Water quality in rivers is
now an increasing concern as high water temperatures allow for algae growth decreasing
oxygen levels. Rain fed agriculture is impacted by higher evaporation and resultant drier
soils. Irrigated crops demand more irrigation. Some export targetted winter fruit crops and
wine grapes are no longer viable due to insufficient cool periods required for these varieties.
Stronger sub-tropical high pressure systems combined with a more intense continental heat
low has set up a stronger land-ocean pressure gradient resulting in strong summer south
easterly winds. Stronger winds and shifting wind directions, combined with a warmer ocean,
has influence the fishing industry negatively. Stronger winds is also resulting in more
frequent closure of the container port. Global changes in container ship traffic due to
reduced sea-ice opening up arctic shipping routes has also impacted port activity negatively.
Higher temperatures, decrease relative humidity, and drier vegetation, combined with
stronger winds means that wildfire is more common and more intense, impacting natural
ecosystems and tourism, as well as human settlements.
While cycles of wet and dry seasons do continue, long term mean rainfall in low altitude
areas has decreased due to the southerly shift of the mid-latitude jet stream and other
changes in system dynamics. However, this is partly counteracted by increased rainfall in
the mountains caused by high moisture content. The higher moisture content is also
enhanced by higher ocean temperatures to the south as a result of a strong Algulhas
current. The result is a shift in rainfall spatially with more rain falling in the mountains and
less in low lying areas. Increased temperatures and evaporation do however partly offset
the increase mountain rainfall with the result that water supply systems are still frequently
under stress.
The higher moisture content (not the same as humidity), and changes in the mid-latitude jet
dynamics, means that periodic winter storms have become less frequent but more intense
causing frequent widespread flooding in low lying areas as well as damage to infrastructure.
Coastal storm damage caused by a combination of more intense wind events combined with
sea-level frequently impact coastal developments and infrastructure.
Note: For the purposes of these narratives, the City of Cape Town includes the actual
municipal area as well as surrounding water catchment and satellite locations.
10. Reflections on the participatory workshop
On the 21st June 2016, CSAG facilitated a City-hosted workshop to disseminate the results
of this report to City staff. Approximately 90 staff were invited to the workshop and 28
participants attended, representing a spectrum of departments across the City.
The workshop began with an introduction to the findings of the report and an opportunity to
ask questions. This was followed by an introduction to the process of development of the
narratives which led into a breakout session during which groups were given the opportunity
to work with the narratives and provide feedback on their utility.
The narratives developed for this report are the result of an experimental design aimed at
enhancing the manner in which climate information is communicated for use. This process
is as a direct result of feedback and learning received during several years of interactions
with users of climate information. The uncertainty in the climate change message is often
expressed as a major hurdle to the use of climate information. Although it is recognised that
there will always be uncertainty in climate science, the narratives were developed in order to
try and address the need for greater clarity of what a future climate may look like.
Additionally, the narratives provide the opportunity to develop viable futures representing the
inter-linkages between the various climate variables.
The workshop provided an opportunity for feedback on the utility of the narratives for
decision-making. This feedback has been documented and is being used to revisit the
narratives and revise them, where possible, in light of the feedback. It has also provided a
first opportunity for CSAG to test whether the notion of narratives should be pursued as a
mechanism of communication.
Feedback on the narratives: Overall the narratives were received as a viable means of communicating climate science
and are seen as useful for advocating action, but there were also many recommendations
made in order to refine the narratives further. There was some confusion around the
purpose of the narratives, and their potential use and interpretation. In particular there were
concerns that they could be used to discredit the motivation for mitigation.
There was a mixed message communicated back from the groups about whether the
narratives were too complex or too simple. This was dependent on the target audience.
Impacts modellers require more specific data, even if this is ranges, in order to make
decisions, whereas others require simpler messages and uncomplicated science. In
essence, the recommendation was that the narratives are always provided in combination
with the underlying data to a) provide the context and b) provide the more complex
information for those that need it. With this combined approach, the narratives could be
technically simpler and shorter to cater for both audiences. At the moment, the information
in the narratives is thought to be too detailed and not structured enough for easy use.
A strong message that was communicated back from all the groups was the need for a
greater emphasis on impacts in the narratives. The impacts provide the impetus to act. It is
recognised that CSAG do not possess the expertise to make strong inferences to impacts
and that further interaction with the city would be required in order to include detailed
impacts in the narratives. There was a suggestion that these impacts, once co-developed,
could be grouped into sectoral themes so as to provide easy access to departmental
managers. However, on the other hand, there is a concern that this will stand to reinforce
the siloed nature of decision making in the City, which was noted as an impediment to
climate change planning.
The participants at the workshop expressed a strong desire for a sense of scientific
judgement as to the probabilities / likelihoods associated with each of the narratives. One
suggestion was to identify common aspects across the narratives which could be construed
as fairly certain eg temperatures increasing. One participant went further to note that, given
multiple narratives, identifying commonalities across the narratives would be his first task in
trying to use them if this were not done for him. Translating these likelihoods into risk
(likelihood of change x magnitude of impact) would provide additional utility as it would
highlight areas that need particular attention. In theory, this requirement sounds sensible,
however, the implications for communication of robust science and whether it is even
feasible needs to be further investigated. This has been noted for further investigation.
The last major group of recommendations was around the desire for “scenario flags” to
indicate when the trajectory of change may be migrating towards or away from particular
narratives. Given multiple narratives, it would be particularly useful if points could be
identified (in the future) when the narratives should be reassessed for their viability and
respective likelihoods. This may help to decide on when and how to make a decision as it
will provide a measure to assess against. How these flags would be established is yet to be
ascertained but this is useful feedback for re-evaluating the narratives.
Minor points raised included the need to incorporate sea level rise as a variable in the
narratives. As Cape Town is particularly vulnerable to this potential, it should be included as
a variable. Also, the narratives seem overly pessimistic and if there are positive impacts to
include these should be investigated.
A brief write up of the workshop breakout group sessions is provided in the appendix.
11. Conclusions and recommendations
It is clear that the climate of the City of Cape Town region is a complex phenomenon and our
ability to explain long term variations in climate remains limited. To some extent this relates
to our lack of observations. The report describes the challenges with observations and the
disagreements between different observational products. This is a severe constraint and
therefore drives a key motivation: Investment in observational networks. This is best done
through investment in existing networks and in partnership with organisations such as CSAG
who run the GEF funded Fynbos Fire weather station network
(http://www.wmon.co.za/webclient2/datasets/ff-stations/) and with the South African
Environmental Observation Network (SAEON) who also maintain and install various weather
and river monitoring networks.
The report has revealed strong agreement on a drier future in the global model based
projections. While the magnitude of drying is still quite broad, it seems clear that a drier City
of Cape Town must certainly be a scenario that is very seriously considered. However, it is
also clear that much work needs to be done to understand the contradictions between the
global model based projected changes in rainfall and the statistically downscaled projections
which have the potential to capture some of the more complex regional climate differences.
CSAG is actively involved in the Coordinated Regional Downscaling Project (CORDEX:
http://www.cordex.org) with membership on the Scientific Advisory Team as well as regional
focus group membership. There is currently a strong focus within CORDEX on unpacking
contradictions between GCM and downscaled projections and this needs to be pursued
actively in the context of the City of Cape Town to resolve the uncertainty around the rainfall
projections.
The use of a narrative format to communicate future climate projections was found to be a
useful exercise and feedback from the engagement workshop has been seriously engaged
with and the approach will continue to be developed in the context of other activities such as
FRACTAL (http://www.fractal.org.za).
Finally, what the report, the narratives, and the engagement workshop have all shown is that
the City of Cape Town needs to prepare in earnest for a drier warmer future over the next
decades. While there remains uncertainty in the climate science, the evidence for drying
and warming is strong and planning that ignores this evidence is at significant risk of
vulnerability to a changing climate. There is now sufficient science evidence to motivate for
serious consideration of climate adaptation planning and implementation in the city.
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Appendices
Appendix 1:
Trends and variability in past and projected climate
Figure A1: Long-term (1901-2014) trends and variability in CRU 3.23 dataset
Figure A2: Mid-term (1981-2014) trends and variability in the total annual rainfall in CRU 3.23 dataset
Figure A3: Number of stations used to derive grid cell values for Cape Town region in CRU 3.23
dataset.
Figure A4: Mid-term (1981-2014) trends and variability in the total annual rainfall in CHIRPS dataset
Figure A5: Trends and variability in total annual rainfall in the Cape Town region, based on WFDEI
1979-2013 dataset
Figure A6: Trends and variability in mean daily rainfall per year in the Cape Town region, based on
WFDEI 1979-2013 dataset
Figure A7: Trends and variability in number of rain days per year in the Cape Town region, based on
WFDEI 1979-2013 dataset
Figure A8: Trends and variability in total seasonal rainfall in the Cape Town region, based on WFDEI
1979-2013 dataset.
Figure A9: Seasonal trends and variability in mean daily rainfall in the Cape Town region, based on
WFDEI 1979-2013 dataset
Figure A10: Trends and variability in number of rain days per season in the Cape Town region, based
on WFDEI 1979-2013 dataset
Figure A11: Trends and variability in annual mean of daily maximum temperature in the Cape Town
region, based on WFDEI 1979-2013 dataset
Figure A12: Trends and variability in annual mean of daily minimum temperature in the Cape Town
region, based on WFDEI 1979-2013 dataset
Figure A13: Trends and variability in number of days with temperature>35° C in the Cape Town
region, based on WFDEI 1979-2013 dataset
Figure A14: Trends and variability in seasonal mean of daily minimum temperature in the Cape Town
region, based on WFDEI 1979-2013 dataset
Figure A15: Trends and variability in seasonal mean of daily maximum temperature in the Cape Town
region, based on WFDEI 1979-2013 dataset
Figure A16: Simulations and projections of total annual rainfall for the Cape Town region based on
GCM CMIP5 MME.
Figure A17: Simulations and projections of mean daily rainfall for the Cape Town region based on
GCM CMIP5 MME.
Figure A18: Simulations and projections of high intensity rainfall events for the Cape Town region
based on GCM CMIP5 MME.
Figure A19: Simulations and projections of minimum air temperature for the Cape Town region based
on GCM CMIP5 MME.
Figure A20: Simulations and projections of maximum air temperature for the Cape Town region
based on GCM CMIP5 MME.
Figure A21: Simulations and projections of hot days (days with max air temp>35° C) for the Cape
Town city-region based on GCM CMIP5 MME.
Figure A22: Simulations and projections of total seasonal rainfall for the Cape Town city-region based
on GCM CMIP5 MME.
Figure A23: Simulations and projections of mean daily rainfall per season for the Cape Town region
based on GCM CMIP5 MME.
Figure A24: Simulations and projections of minimum air temperature per season for the Cape Town
city-region based on GCM CMIP5 MME.
Figure A25: Simulations and projections of maximum air temperature per season for the Cape Town
city-region based on GCM CMIP5 MME.
Figure A26: Simulations and projections of hot days per season (days with max air temp>35° C) for
the Cape Town region based on GCM CMIP5 MME.
Figure A27: Simulations and projections of total annual rainfall for the Cape Town city-region based
on downscaling (SOMD) of CMIP5 MME.
Figure A28: Simulations and projections of mean daily rainfall for the Cape Town region based on
downscaling (SOMD) of CMIP5 MME.
Figure A29: Simulations and projections of high intensity rainfall events for the Cape Town region
based on downscaling (SOMD) of CMIP5 MME
Downscaled projections of air temperature
Figure A30: Simulations and projections of minimum daily air temperatures for the Cape Town region
based on downscaling (SOMD) of CMIP5 MME.
Figure A31: Simulations and projections of maximum daily air temperatures for the Cape Town region
based on downscaling (SOMD) of CMIP5 MME.
Figure A32: Simulations and projections of hot days (days with max air temp>35° C) for the Cape
Town region based on downscaling (SOMD) of CMIP5 MME.
Figure A33: Simulations and projections of total seasonal rainfall for the Cape Town city-region based
on downscaling (SOMD) of CMIP5 MME.
Figure A34: Simulations and projections of mean daily rainfall per season for the Cape Town city-
region based on downscaling (SOMD) of CMIP5 MME.
Figure A35: Simulations and projections of minimum daily air temperatures per season for the Cape
Town region based on downscaling (SOMD) of CMIP5 MME.
Figure A36: Simulations and projections of maximum daily air temperatures per season for the Cape
Town city-region based on downscaling (SOMD) of CMIP5 MME.
Figure A37: Simulations and projections of of hot days per season (days with max air temp>35° C)
for the Cape Town city-region based on downscaling (SOMD) of CMIP5 MME.
Figure A 38: Archetype maps for 700mb geopotential height in a 9x7 SOM trained on daily 1979-
2014 ERA-Int fields (q, t, u, v, all at 700mb)
Figure A39: JJA rainfall associated with each of the SOM nodes, for each of the regions (color coded
as in Figure 1).
Figure A40: Mean daily rainfall (JJA) associated with each of the SOM nodes, for each of the
regions (colour coded as in Figure 1).
Figure A41: Temporal evolution of SOM node frequency in ERA-Int data (lowess smooth of JJA
counts). Numbers denote statistically significant Pearson‟s correlation coefficient between JJA counts
and total JJA region‟s rainfall, colour-coded for each of the sub-regions.
Figure A42: Lowess smooth of SOM node frequency (JJA season counts) in each member of the
GCM ensemble
Appendix 2: City of Cape Town Workshop: Notes from the discussions
22 June 2016
_________________________________________________________________________
__
Question 1: Where/how can this climate information feed into your
Work?
Question 2: What more information would you like to see in/alongside these narratives in
order to be able to apply them effectively?
Questions 3: Are the impacts described in the narrative on your table feasible? What other
impacts do you think there would be?
Group 1
Q1
● Transport has environmental benefits but no core business. Rather focus on efficient
movement of people and goods, plan 20-30 years ahead.
● We should build in an environmental argument/justification but it shouldn't be the
main focus, just a short narrative.
● Optimized future land use scenario - quantifying environmental benefits of realizing
this scenario
● ERMD biodiversity - focus on multiple benefits of conservation through an open
space system - interested in linkage between mountains and lowlands.
● Water and Sanitation - WDM
○ Climate change is not core but there are implemented strategies for water
sustainability in response to drought eg. water pressure reduction,
○ Climate Change info used to strengthen argument for sustainability.
○ Climate change recognized as one of the multiple pressures
○ But the response is quite reactive
● ERMD: CAPA looking at projects for implementing adaptation based on broad climate
change narratives
● SPUD: (unsuccessfully) try to guide spatial development of city, especially reducing
risk along coast and now to fire working with TCT and TOD on integrating land use
modelling
● Plan generally for resilience & increased efficient use of resources
● The answer is not in planning (approved plans don't stick) but in DM (political
finance) need for integrated urban mitigation
● Build CC into SDBIPs
● Need for cross disciplinary/transversal engagement around budget allocations
● Departments if left alone, fiddle with the existing system, not make big changes/new
investments.
Q2.
● When read problems immediately try to link up solutions
● Immediately focus on impact/problems
● Scientists must get clearer because the message now looks like “nothing is going to
change”
● Scientists sound ambivalent and vague - you must tell us “the bottom line is „this‟
might happen”
● We need simple messages, not complex science
● “Science isn't useful for us!”
● Suggestion: You could group impacts into sectoral theme
● We have to halt species loss
● We have to cut emissions
● Narratives should include the bigger global picture → i.e sea level rise is important
for Cape Town
● Certainties - water is always going to be a problem so we should focus on that.
● “BE CLEAR DON'T BE SCIENTISTS!”
● “Show us one graph → We are going to hell in a handbasket
● Can you express information in probabilities? = H,M,L risk on X scenarios
● It is better to have narrative
● Need to fine tune effort
● Currently we just know that climate change is bad but not how bad.
● The local government is crude - they don't need details, they need big messages.
● Scientists need to convey more confidence - what do we need to be especially
precautionary about.
● Suggestion :List things from certainty to uncertainty………. And a menu of impacts.
Q3
● Busses are starting to overheat
● Water shortages
● Water - increased in demand for :
○ Water table won't recover because more boreholes are being drilled
○ Wasted water children in informal settlements will play with the taps - leave
them on.
○ Increase in power useage for aircon
○ Increased water usage for treatment plant cooling.
○ Increase in fires requires more water
● Shift from surface water supply to ground water supply.
Group 2
● Air Quality?
○ Inversions and persistence
○ Other factors
● When will we know our trajectories?
● Narratives are all negative - should include positive impacts
● Narratives are good in combination with data
● When do we act?? Reactive vs Preemptive
● Need for evidence to promote action
● More focussed
● User friendly
Group 3
Q1
● Infrastructure investment
● Use as a tool /evidence base toward mitigation adaptation
● Resource planning
● How we plan around our open land areas
● “This is where we don't want to be”
Q2
● More specific data required for decisions
● Room for more narrow series of narratives around specifics eg. sea level rise
● Shorter more specific one lines
● Scenario Flags - Flags that let us know we are headed down a specific scenario
route.
● More specific evidence - shorter time horizons
● A range of probable outcomes
○ This helps determine thresholds, goal posts.
● Advantage - city won't be caught off guard
Q3
● If climate is not suitable for grapes then what is it suitable for?
● Impacts on wind speed and wind power
● Impacts elaborated beyond environmental to more human based impacts
● Economic impacts - more relatable
● Food security
● Growth patterns
● Tourism impacts
● Job creation
● Winder economic zone - beyond the city
● Demand management
● Multisectoral impacts
● Extrapolating story lines for specific groups
● Pick up the top sectors
General feedback discussion
Group 1
● Need for more integrated decisions.
● Planning is not the biggest impact in decision making –
● Narratives that motivate for particular directions of action. Climate used as an
additional benefit for using something.
● Scenarios = be as clear and specific and confident. Don‟t sound ambivalent. Give us
the bottom line.
● So that when we spend a million rand we feel more confident….
● Say it in terms of: what is certain, what isn‟t certain, what is unlikely.
● Don‟t give us too much science – tell us what the problems are don‟t give us
“vauguries”
● Some contradictions - we want specific message. While others said give us broad
clear statements.
Group 2
● When will we know which way we are headed. If we continue monitoring continual
monitoring.
● Impacts are negative – can we tie in positive impacts too?
● Need data – numbers.
● CJ posed a question : are any decisions actually constrained by lack of science or
lack of information?– response : is not do we make a decision but rather when do we
make the decision???when are we reactive and preemptive? Maybe narratives could
unpack that more??
● Narratives as evidence to promote/advocate for action.
● They are a focussed way of presenting user information and quite friendly.
Group 3
Q1
● Information is useful but we need numbers “ In order to stress test infrastructure
models,
● To determine whether our infrastructures can accommodate the events stressed in
our narrative (hotter mixed rainfall changer).
● When doing infrastructure planning we need to motivate for buying expensive
infrastructure with costs up to millions - having the numbers helps with this
● Environment research management - We need climate data for river systems and
open spaces and understanding to what extent these systems need to be managed
in order to accommodate for impacts.
Q2
● Narrative are a powerful device for synthesizing information , good for internalising
information
● There is a concern that they cannot be applied in terms of getting resources from
politicians - politicians need scary numbers.
● Useful for certain ends but in addition we would need to really see some flags –
events or triggers that would flag that we are moving into a certain scenarios
Q3
● Incorrect use of the word feasible - meaningful/correct would be better
● We need interaction between sectors. Useful to understand human impacts,
economic impacts, job creation., bring this common narrative - something that is
tangible to stakeholders. The impacts need to be taken further.
Final question posed to Workshop : What will you do when given all three?
● We would choose one
● We would have to make a range of solutions that can account for all possibilities - but
this would make it meaningless.
● Scenarios as flags. – we need to know where we are. We are going to have to
choose one we think is more likely… but this is around scenarios….
● “If I got three I would work backwards and look at common problems, and which ones
are specific to one narrative.” What we need – reducing everything to numbers – only
way we can bring everything together.
● We need ranges - We understand it‟s not a matter of probabilities - so we must have
ranges
● We need to test our thresholds, we can then do risk analysis , a cost-benefit
assessment. We can use ranges very effectively. Narratives can't stand in isolation.
● Group impacts around themes – eg. Water. Themed narratives.
● Range. Worst case and best case scenario – this is when range is helpful.
● CJ –. We don‟t know how to get to those thresholds or those flags. So we need to
work through that together in these discussions. Are we going to have 3 years of
winter rainfall – is that a flag?
● Resilience officer.
● “Using this message to mitigate!!