climate change projections for the city of cape …cjack/csag_cct report.pdftown region, downscaling...

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


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.














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.



Narrative #3 | Hotter and mixed rainfall change


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.














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.



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.

http://www.wmon.co.za/webclient2/datasets/ff-stations/

http://www.cordex.org/

http://www.fractal.org.za/

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(CHIRPS): Development and Validation.” InAGU Fall Meeting Abstracts, 1, pp. 1417

Sen, P. K. (1968) Estimates of the regression coefficient based on Kendall‟s tau. J. Am.

Statist. Assoc. 63, 1379–1389.

Theil, H. (1950) A rank-invariant method of linear and polynomial regression analysis, I, II,

III, Nederl. Akad. Wetensch. Proc. 53, 386–392; 512–525; 1397–1412.

Tyson, P.D. and Preston-Whyte, R.A., 2000. weather and climate of southern Africa. Oxford

University Press.

Wilks, D.S. Statistical methods in the atmospheric sciences. 3.ed. San Diego: Academic

Press, 2011. 676p.

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


Figure A7: Trends and variability in number of rain days per year in the Cape Town region, based on


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


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


Figure A12: Trends and variability in annual mean of daily minimum temperature in the Cape Town


Figure A13: Trends and variability in number of days with temperature>35° C in the Cape Town


Figure A14: Trends and variability in seasonal mean of daily minimum temperature in the Cape Town


Figure A15: Trends and variability in seasonal mean of daily maximum temperature in the Cape Town


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


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


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


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!!

climate change projections for the city of cape …cjack/csag_cct report.pdftown region, downscaling...

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