Transcript Diapositiva 1
Regional and subregional climate simulations over Sub-Saharian African regions and the influence on the heat waves hazard
Edoardo Bucchignani, Paola Mercogliano, Myriam Montesarchio, Maria Paola Manzi, Alessandra Zollo
CMCC, Capua
Paolo Capuano, Mariangela Sellerino
AMRA scarl, Napoli Iberohotel Apulia
Marina di Ugento,
12 June 2012
2. Outlook
• • • • • • • Introduction The CLUVA project Regional climate simulations with COSMO-CLM The Lower East domain: validation and climate projections Definition of Heat Waves The Dar Es Salaam case Conclusions
3. CLUVA – Climate change and urban vulnerability in Africa Project Co-ordinator
: AMRA, Center of Competence in the field of Analysis and Monitoring of Environmental Risk, Italy The project objective is to develop methods and knowledge to be applied to African cities, to manage climate risks, to reduce vulnerabilities and to improve their coping capacity and resilience towards climate changes. The project will explore the issues of climate change vulnerability, resilience, risk management and adaptation in selected African cities with local partners.
The aim is to set up methods and work out probabilistic scenarios of climate change affected hazards having a resolution that fits for regional and urban systems (for the 5 selected cities) and related uncertainties. More detailed aims are: • • To produce downscaled regional climate scenarios (IPCC scenarios: RCP4.5 and RCP8.5) for selected African areas surrounding the African cities of interest, at high resolution (about 8 km).
To produce very high resolution projection (about 1-2 using specific and accurate statistical techniques
km
) for the climate of some African cities
4. Areas of interest for CLUVA 1950-2050
Spatial Resolution: 8 km WEST Domain: (18 W -15.17 E; 3.3
– 16.8 N)
465 x 190 grid points
EAST Domains: U (34.4 – 42.9 E; 6.1N – 12.5N)
120 x 90 grid points
L (34.5
– 41.3 E; 11.8S – 2.1S)
95 x 135 grid points
St.Louis (16.5 W, 16.03 N) Ougadougou (1.55 W, 12.37 N) Douala (9.71 E, 4.045 N) Addis Abeba (38.75 E, 9.02 N) Dar es Salaam (39.27 E, 6.82 S)
5. Details of the Numerical simulations
• • • • • • •
8 km resolution Supercomputer used
: Cluster of 30 IBM P575 nodes (32 cores per node)
Driving data
: CMCC-MED 80
km
resolution
Model version
: cosmo_090213_4.8_clm13
Time step
: 40
sec
.
Numerical scheme
: Runge-Kutta 2-time level HE-VI integration
Validation
: CRU data and observed datasets for the 5 cities.
Period considered: 1950-2050 (RCP4.5 and RCP8.5 scenarios)
6. Mean temperature bias with CRU (COSMO-CRU)
1971-2000 In DJF, there is a cold bias between -2 and 3 degrees.
In some parts, up to -5 In JJA, there is a hot bias between 1 and 2 degrees .
DJF JJA
7. Seasonal cycle of temperature (COSMO vs Observations)
Dar es Salaam Max
T
Applied Bias correction (Sperna et. Al 2010):
corr
T
(
T obs
T av
)
T av
:
30-year daily average temperature Mean On the opportunity of using a bias correction technique for impact studies, see e.g.: Wilby et al., EMS, 2000
8. T2m variation: future (2021-2050) vs past (1971-2000)
RCP4.5
In DJF, two different areas are visible, but both characterized by an increase of temperature.
In JJA, a general increase of 1.5
o C is evident. DJF JJA
9. T2m variation: future (2021-2050) vs past (1971-2000)
RCP8.5
With this scenario, the increase of temperature is more uniform and evident with respect to RCP4.5.
DJF JJA
10. Time series of 2-metre temperature
11. What is a heat wave ?
Heat wave generally refers to periods of exceptionally warm temperatures, but there is no universal accepted definition.
The impacts of heat waves on the society are determined also by temporal duration, in addition to their frequency, in fact the capacity of adaptation can be reduced with prolonged exposure to high temperature and humidity.
12. Heat wave definition
In this analysis, Heat Wave is defined as a period in which the
maximum temperatures are over the 90th percentile of the monthly distribution for at least three days
, fixing a minimum threshold for Tmax to exclude months with less hot temperature (winter time), that for Dar Es Salaam was fixed to 32 ° C. The 90th percentile has been evaluated over a climatological base period (1961-1990).
13. PDF of temperature daily values
0.30
0.25
0.20
0.15
0.10
0.05
Obs.
Mod.
DAR ES SALAAM 1961-2010 0.00
24 25 26 27 28 29 30 31 32 Tmax (°C) 33 34 35 36 37 38 39 40
Comparison between observed and bias-corrected modelled daily Tmax.
The differences are partly due to an effect of the incompleteness of observed dataset .
14. PDF change over time (RCP4.5)
Dar Es Salaam_1961-2050 RCP 4.5
0.30
0.25
0.20
61-70 (data coverage 97.4%) 71-80 (data coverage 94.4%) 81-90 (data coverage 93.9%) 91-2000 (data coverage 80.0%) 2001-2010 (data coverage 89.5%) 2011-2020 2021-2030 2031-2040 2041-2050
0.15
0.10
0.05
0.00
Tmax (°C)
Nine periods are considered, according to the legend in the top right-hand corner. Taking into account the data coverage, it is clear that the Tmax peak increases from 30.4
° C (1961-70) to 32.0
° (2041-2050).
All the distribution is moving towards higher temperature in the last decades.
15. PDF change over time (RCP8.5)
Dar Es Salaam_1961-2050 RCP 8.5
0.30
0.25
61-70 (data coverage 97.4%) 71-80 (data coverage 94.4%) 81-90 (data coverage 93.9%) 91-2000 (data coverage 80.0%) 2001-2010 (data coverage 89.5%) 2011-2020 2021-2030 2031-2040 2041-2050
0.20
0.15
0.10
0.05
0.00
Tmax (°C)
Taking into account the data coverage, it is clear that the Tmax peak increases from 30.4
° C (1961-70) to 32.3
° (2041-2050).
All the distribution is moving towards higher temperature in the last decades.
16. Monthly threshold values
THRESHOLD (T °C) - 90°perc.
35 34 33 32 31 30 29 1 2 3 4 5 6 MOD.
OBS.
Comparison between monthly threshold value (evaluated over a climatological base period 1961-1990) for observed and modeled (BIAS-corrected) dataset.
7 8 9 10 11 12 35 34 Monthly threshold value (evaluated over a climatological base period 1961-1990) for dataset BIAS corrected. Fixed minimum threshold for Tmax to exclude months with less hot temperature (winter time) 33 32 31 30 Threshold value_90° Min T 29 1 2 3 4 5 6 7 8 9 10 11 12
17. Rate of hot days per year
Hot days rate (Dar es Salaam) 25 20 15 10 5 0 1971-2000 2021-2050 (RCP4.5) 2021-2050 (RCP8.5)
Low number of heat wave recurrence from 1971 to 2000. The forecasted period 2021 2050 shows a strong increase in the heat waves occurrence, especially in the RCP8.5
scenario.
18. Heat wave duration
18 16 14 12 10 8 6 4 2 0 Average maximum duration of HW (Dar es Salaam) 1971-2000 2021-2050 (RCP4.5) 2021-2050 (RCP8.5)
Key element to evaluate the societal heat wave impacts (reducing the capacity of adaptation). The length (days) of heat wave episodes shows a mean value of about 6 days in 1971-2000.
In the future, a clear increase in the average duration of heat wave is projected.
19. Heat wave duration vs. hot days number
50 45 DAR ES SALAAM - RCP 4.5
40 35 30 25 20 15 10 5 0 0 10 20 30 40 The temperature rise could generate not 50 60 only an increase of heat waves number, but also a longer average duration, that can strongly affect the resilience capacity of the population, particularly the elder people.
y = 5.1Ln(x) - 6.2
R2 = 0.52
70 80 Hot Days 90 100 110 120 130 140 150 40 DAR ES SALAAM - RCP 8.5
35 30 25 20 15 10 5 0 0 10 20 30 40
Heat wave duration and hot days number are strictly correlated
50 60 70 80 Ht Days 90 y = 5.2Ln(x) - 5.3
R2 = 0.58
100 110 120 130 140 150
20. PDF of hot days duration (RCP4.5)
50 48 46 1950-70 1971-90 1991-2010 44 42 40 30 28 26 24 38 36 34 32 12 10 8 6 22 20 18 16 14 4 2 0 2011-2030 2031-2050
DAR ES SALAAM RCP 4.5
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 Length (d) Temporal change of heat wave characteristics. This distribution tends to become longer tailed with time.
21. Focus on the PDF (RCP4.5)
50 48 46 1950-70 1971-90 44 42 40 30 28 26 24 38 36 34 32 12 10 8 6 22 20 18 16 14 4 2 0 3 4 5 6 7 8 9 10 11 12 Length (d) 13 1991-2010 2011-2030 2031-2050
DAR ES SALAAM RCP 4.5
14 15 16 17 18 19 20 The number of events of the maximum length lasting 5 days could increase from 3 to 24 over 100 years (from 1950-70 to 2030-2050).
40 35 30 25 20 15 10 5 0 60 55 50 45 85 80 75 70 65
22. PDF of hot days duration (RCP8.5)
95 1950-69 1970-89 1990-2009 90 2010-29 2030-50 DAR ES SALAAM - RCP 8.5
3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 Length (d) Temporal change of heat wave characteristics. This distribution tends to become longer tailed with time.
40 35 30 25 20 15 10 5 0 60 55 50 45 85 80 75 70 65
23. Focus on PDF (RCP8.5)
95 1950-69 90 3 4 5 6 7 8 9 10 11 12 Length (d) 1970-89 13 14 1990-2009 15 16 2010-29 2030-50 DAR ES SALAAM - RCP 8.5
17 18 19 20 The number of events of the maximum length lasting 5 days could increase from 3 to 33 over 100 years (from 1950-70 to 2030-2050).
24. Conclusions
• Regional Climate Model with a horizontal resolution lesser than 10x10 km can be a useful tool for the description of the climate variability on local scale, suitable for impact studies.
• We have evaluated regional climate projection for some African areas, particularly for Dar Es Salaam. We have used the climate simulations to evaluate the changes in the heat wave occurrence.
• The combined change in the mean and variance of PDF has contributed to an increase in the frequency of hot days and in their duration. Projected future warming in the Dar Es Salaam area shows a further increase in the heat waves parameters.
• The expected persistence of long-lived heat waves lasting approximately 2-3 weeks is clearly longer with respect to the climatological period (1961-1990). In the examined period (100 years), short lived but more intense waves are more than doubled in duration.
• It is evident the needs for the national health services to develop strategies for the mitigation of the heat wave effects, to enhance the resilience of the population, particularly the elder people.
Thanks