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

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 - 2100. 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”

GLOBAL CLIMATE

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

HISTORICAL TEMPERATURE TRENDS - TEMPORAL The graphs illustrate the change in global surface temperature relative to 1951-1980 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 https://climate.nasa.gov/vital-signs/global-temperature/ CO2: Ed Dlugokencky and Pieter Tans, NOAA/ESRL (www.esrl.noaa.gov/gmd/ccgg/trends/) Global average temperature change since 1880 + 0.99 °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

HISTORICAL TEMPERATURE TRENDS - SPATIAL South Africa is warming at a slower rate if compared to other continental parts of the world - 1985 to 2014 global near-surface temperature trends (°C per decade). Source: NOAA’s National Climate Data Centre.

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: 10.1002/joc.4851

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)

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)

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.

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: 1976-2005: history 2036-2065 2066-2095 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

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

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

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

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.

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.

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

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