1. DIALOGUE ON CLIMATE CHANGE
Climate Change analysis, response and adaptation:
Become aware, informed and prepared
Hannes Rautenbach
South African Weather Service
Rand Water Offices, Glenvista
14 June 2017

2. Generalized Climate Change assumption:
Be AWARE
Generalized Climate Change assumption:
If a “business as usual” Representative Concentration Pathway (RCP) is followed, assuming that 8.5 W.m-2 of heat will be added to the atmosphere by 2100 as a result of increasing CO2 concentrations, the following are projected:
Near-surface temperatures might increase by ±5°C
There is uncertainty about rainfall, although extreme wet and dry events might increase in future
“ADAPT accordingly, with no (or very little) additional reference to weather and climate”

3. GLOBAL CLIMATE

4. SOUTH AFRICAN WEATHER AND CLIMATE
The South African weather and climate is influenced by many factors, and therefore variable in space and time
A positive aspect is that we are coping under these conditions

5. HISTORICAL TEMPERATURE TRENDS - TEMPORAL
The graphs illustrate the change in global surface temperature relative to average temperatures. 
The 10 warmest years in the 136-year record all have occurred since 2000, with the exception of 1998.
The year 2016 ranks as the warmest on record. 
Sources:
TEMPERATURES:
NASA
CO2:
Ed Dlugokencky and Pieter Tans, NOAA/ESRL (
Global average temperature change since °C
Historic and currently observed climate can already provide substantial information about averages, extremes and trends. Note that these might differ from location to location
AR5 historical AR5 projected

6. HISTORICAL TEMPERATURE TRENDS - SPATIAL
South Africa is warming at a slower rate if compared to other continental parts
of the world to 2014 global near-surface temperature trends (°C per decade).
Source: NOAA’s National Climate Data Centre.

7. HISTORICAL TEMPERATURE TRENDS – S. AFRICA
South Africa warms at an average rate of 0.17 °C per decade.
TN: Min. temp., TX: Max. temp., TNN: Annual min of TN, TNX: Annual max of TN, TXN: Annual min of TX and TXX: Annual Max of TX.
Trends in Average Temperature (Tavg) from ~ 1931 to 2015 in °C per decade (filled triangles denote significance of trends at the 5% level).
Temperature changes are happening at a slow rate, allowing for time to “follow” the change….
Kruger, A.C. and Nxumalo, M. (2016) Surface temperature trends from homogenized time series in South Africa: 1931–2015.
Int. J. Climatol., DOI: /joc.4851

8. HISTORICAL RAINFALL TRENDS – S. AFRICA
Trends in total annual rainfall in wet days ~ 1921 to 2015 in mm per decade (filled triangles denote significance of trends at the 5% level).
Rainfall changes are happening at a slow rate, allowing for time to “follow” the change….
Kruger, et al (2017)

9. HISTORICAL RAINFALL TRENDS – S. AFRICA
Trends in the annual mean of daily precipitation intensity ~ 1921 to 2015 in mm per decade (filled triangles denote significance of trends at the 5% level).
Trends in the annual maximum length of dry days ~ 1921 to 2015 in days per decade (filled triangles denote significance of trends at the 5% level).
Rainfall changes are happening at a slow rate, allowing for time to “follow” the change….
Kruger, et al (2017)

10. CLIMATE CHANGE PROJECTION SELECTIONS
With the climate change projection data available at SAWS, a large variety of options are available for producing climate change projections aimed at addressing climate sensitive indicators in the operations and planning.

11. SOUTH AFRICAN PROJECTIONS
Experimental design
COoRdinated Downscaling EXperiment (CORDEX) Grid resolution of 44º x 0.44º (≈45km x 45km).
CGCM name
Country
Resolution
Literature
CanESM2m
Canada
2.8° x 2.8°
Arora et al., (2011)
CNRM-CM5
France
1.4° x 1.4°
Voldoire et al., (2013)
CSIRO-Mk3
Australia
1.9° x 1.9°
Rotstayn et al., (2013)
IPSL-CM5A-MR
1.9° x 3.8°
Hourdin et al., (2013)
MICRO5
Japan
Watanabe et al., (2011)
HadGEM2-ES UK
1.8° x 1.2°
Collins et al., (2011)
MPI-ESM-LR
Germany
Ilyina et al., (2013)
NorESMI-M
Norway
1.9° x 2.5°
Tjiputra et al., (2013)
GFDL-ESM2M
USA
2.0° x 2.5°
Dunne et al., (2012)
CORDEX Africa
Dynamical downscaling: Nine ocean-atmosphere CGCMs provided lateral boundary input to the Rossby Centre Regional Climate Model (RCA4)
Variables:
Temperature
Rainfall
(ensemble means)
30-year periods:
: history
Seasons:
Annual
December-January-February : DJF
March-April-May : MAM June-July-August : JJA
September-October-November : SON
Pathways:
RCP 4.5
RCP 8.5

12. TYPICAL SOUTH AFRICAN PROJECTIONS
RCA4 Regional Climate Model simulations forced by 9 CMIP5 Global Models – 9 member ensemble mean

13. TYPICAL SOUTH AFRICAN PROJECTIONS
RCA4 Regional Climate Model simulations forced by 9 CMIP5 Global Models – 9 member ensemble mean

14. TYPICAL SOUTH AFRICAN PROJECTIONS
RCA4 Regional Climate Model simulations forced by 9 CMIP5 Global Models – 9 member ensemble mean

15. Become INFORMED and PREPARED
But there is more that can be done to become more INFORMED and better PREPARED.
South African’s climate is variable in both space and time, meaning that climate variability and change can differ from location to location.
Generalized Climate Change assumptions do not provide the best basis for adaptation, or the best way to create climate resilience at a known location.
It is important to also become INFORMED about weather and climate variability at the location, especially regarding averages and extremes.
In addition, it is important to become PREPARED by identifying specific climate sensitive sectors / elements at the location and plan, respond or adapt accordingly:
Short-term: Averages, extreme boundaries, frequencies etc.
in historical to currently observed climate.
Long-term: Align trends in historical to currently observed
climate to outlooks and Climate Change projections.

16. Become INFORMED and PREPARED
Analysis based on historical observations
Incorporating seasonal predictions
Incorporating climate change projections
PDF of climate variable
PDF of climate variable
PDF of climate variable
Average
Average
New Average
< Average
> Average
New lower extreme
Lower extreme
Upper extreme
Lower extreme
Upper extreme
New upper extreme
(Number of events or % occurrence)
Frequency
(Number of events or % occurrence)
Frequency
(Number of events or % occurrence)
Frequency
0 - 1
1 - 2
2 - 3
3 - 4
4 - 5
5 - 6
6 - 7
7 - 8
8 - 9
> 9
0 - 1
1 - 2
2 - 3
3 - 4
4 - 5
5 - 6
6 - 7
7 - 8
8 - 9
> 9
0 - 1
1 - 2
2 - 3
3 - 4
4 - 5
5 - 6
6 - 7
7 - 8
8 - 9
> 9
Align maximum production to the maximum frequency of occurrence;
Obtain percentage frequencies of categorical climate events;
Develop categorical risk reduction and production enhancement interventions;
Apply incremental responses as the season progresses;
Follow a long-term investment.
On the long-term, seasonal predictions could add value to existing climate sensitive practices;
Seasonal predictions are given as the % probability of a season to become wetter or drier than the average;
IMPORTANT: always incorporate prediction model skill in decision making.
Give the likelihood of climate
(1) shifting from its average;
(2) shifting across the extreme
boundaries;
(3) shifting according to frequency of occurrence.
Adapt and become climate resilient / contribute to mitigation.

17. Following a ROTATING CLIMATE SENITIVE ROUTE
Climate change adaptation can be achieved by following a sustainable
ROTATING CLIMATE SENSITIVE ROUTE, based on good observations, regular updates and risk assessment and response planning according to observed weather and climate events, while aligning these to weather forecasts, seasonal predictions and climate change projections, as they happen at the location of interest. 2
Updates: Averages,
Extremes, Frequencies
FUTURE
HISTORY
RESPOND, PLAN &
ADAPT
> Climate resilience 1
Observe, Record,
Monitor the environment
Align to Projections,
Predictions,
Forecasts 3
Following a sustainable climate sensitive route 4
Impact
on climate sensitive sectors & elements

18. Thank you