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Climate Change Observations - Regional Perspective
- 1. © Yann Arthus-Bertrand / Altitude Climate Change Observations Global and Regional Perspectives Prof. Dr. Edvin Aldrian (LA, Chapter 14) Director of Center for Research and Development BMKG Indonesia BMKG Presented at the IPCC’s Fifth Assessment Report Outreach Event, Bangkok, 18 August 2015
- 2. Outline •Global changes challanges •Human influences on climate •Attribution of climate change •Global Climate parameter changes •Regional changes of Southeast Asia
- 3. 3Rockström, et al. 2009. Planetary boundaries:exploring the safe operating space for humanity. Ecology and Society 14(2): 32
- 4. Population, wealth and emission The future of the climate system (and our survival) depends on our ability to decouple future emissions from the other two factors: population and economic growth Raupach et al. (2007, PNAS)
- 5. Relationship between income and emission 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 Annual income per head (1985 $) Stern Report 2007
- 6. Change in Energy GLOBAL WARMING Humidity • Wind Cloud cover • Rainfall Temperature • Evaporation CLIMATE CHANGE • Forest Fire • Landslide • Drought • Flood • Sea level rise • Cyclone • Tornado Energy resource • Malaria Infrastructure damage • OPT Transportation system • Water resource • Tourism • DBD Mitigation Adaptation : Coping the effect – managing the unavoidable Mitigation : Coping the cause– avoiding the unmanagable Water Cycle Change Adaptation PHYSICAL IMPACT NON PHYSICAL IMPACT GREENHOUSE GASSES RESPONSES
- 7. Reduction of GHG concentration during Nyepi Hindu day Nama Lokasi : Negara Singaraja Karangasem Bedugul Denpasar Koordinat : 8º 20’ 24”S, 114º 36’ 59”E 8º 6’ 57,2”S, 115º 4’ 50,1”E 8º 21’ 53,4”S, 115º 36’ 39,0”E 8º 15’ 1,8”S, 115º 9’ 8,2”E 8º 40’ 44,2”S, 115º 13’ 56,6”E Metode & Frekuensi Data : Indirect Measurement (Sampling); Daily Data (14.00 WITA) Direct Measurement; Continuous Monitoring; (Data tiap 5 menit) Alat : Flask Sampler Flask Sampler Flask Sampler Flask Sampler WolfPack® & IRIS 4600 Data GRK : CO2, CH4 CO2, CH4 CO2, CH4 CO2, CH4 CO2, N2O
- 8. Reduction of GHG concentration during Nyepi Hindu day 360 370 380 390 400 410 420 Negara Karangasem Bedugul Singaraja CO2(ppm) LOKASI Sebelum Saat Nyepi Setelah 375 395 415 435 455 475 495 515 535 0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48 19:12 21:36 CO2(ppm) Jam 8Maret 9Maret 10Maret 11Maret 12Maret 13Maret 14Maret Average reduction 33% 335 336 337 338 339 340 341 342 0:00 2:24 4:48 7:12 9:36 12:00 14:24 16:48 19:12 21:36 N2O(ppb) Jam 8Maret 9Maret 10Maret 11Maret 12Maret 13Maret 14Maret
- 9. Global Warming Major impacts: increase of surface temperature and sea level rise Human vs nature IPCC 2007 IPCC 2001 Natural: annual average of global temperature Anthropogenic: annual average of global temperature (c) Combine: natural and anthropogenic
- 10. Earth temperature with and without human influences IPCC 2001
- 12. Human influence on the climate system is clear (IPCC2013.FigSPM.6)
- 15. Observed changes in Precipitation – AR4 vs. AR5ObservedChanges AR5 AR4
- 16. Projected Changes in Temperature and Precipitation ProjectedChanges
- 18. Changes in Extremes ProjectedChanges Annual Precipitation
- 19. Tropical phenomena: Convergence Zones Rainfall Change (medium confidence) “wet-get-wetter” over CZ regions “warmer-get-wetter” over oceans Figure 14.8: Upper panel: Annual-mean precipitation percentage change (P/P in green/gray shade and white contours at 20% intervals), and relative SST change (colour contours at intervals of 0.2°C; negative shaded) to the tropical (20S–20N) mean warming in RCP8.5 projections, shown as 23 CMIP5 model ensemble mean. Figure 14.9: Seasonal cycle of zonal-mean tropical precipitation change (2081– 2100 in RCP8.5 minus 1986–2005) in CMIP5 multimodel ensemble mean. Eighteen CMIP5 models were used. Stippling indicates that more than 90% models agree on the sign of MME change. The red curve represents the meridional maximum of the climatological rainfall. Adapted from Huang et al. (2013). The seasonal-mean rainfall is projected to increase on the ITCZ equatorward flank More warming and rainfall at north of the equator. Less zonal SST gradient across the equatorial Pacific that contribute to the weakened Walker cells.
- 21. Principles Governing IPCC Work (1998, 2003, 2006) Annex I: Atlas of Global and Regional Climate Projections Maps for 3 time horizons 2016-35, 2046-65, 2081-2100 reference period 1986-2005 Fig.AI.3Fig.AI.4 35 regions 42 global climate models 2 variables Temperature, Precipitation 4 scenarios RCPs 2.6, 4.5, 6.0, 8.5 2 seasons temp: DJF, JJA (for temp) precip: AMJJAS, ONDJFM
- 22. Temperature Change Graph – South Asia (5, 25, 50 75, 95 percentiles)
- 23. Temperature Change Map South Asia – RCP4.5
- 24. Rainfall Change Graph – South Asia (5, 25, 50 75, 95 percentiles)
- 25. Rainfall Change Maps South Asia - RCP4.5 October - March April - September
- 26. © Yann Arthus-Bertrand / Altitude www.climatechange2013.org Further Information BMKG
Hinweis der Redaktion
- Reference only: Figure 12.41: Patterns of temperature (left column) and percent precipitation change (right column) for the CMIP3 models average (first row) and CMIP5 models average (second row), scaled by the corresponding global average temperature changes. The patterns are computed in both cases by taking the difference between the averages over the last twenty years of the 21st century experiments (2080–2099 for CMIP3 and 2081–2100 for CMIP5) and the last twenty years of the historic experiments (1980–1999 for CMIP3, 1986–2005 for CMIP5) and rescaling each difference by the corresponding change in global average temperature. This is done first for each individual model, then the results are averaged across models. For the CMIP5 patterns, the RCP2.6 simulation of the FIO-ESM model was excluded because it did not show any warming by the end of the 21st century, thus not complying with the method requirement that the pattern be estimated at a time when the temperature change signal from CO2 increase has emerged. Stippling indicates a measure of significance of the difference between the two corresponding patterns obtained by a bootstrap exercise. Two subsets of the pooled set of CMIP3 and CMIP5 ensemble members of the same size as the original ensembles, but without distinguishing CMIP3 from CMIP5 members, were randomly sampled 500 times. For each random sample we compute the corresponding patterns and their difference, then the true difference is compared, grid- point by grid-point, to the distribution of the bootstrapped differences, and only grid-points at which the value of the difference falls in the tails of the bootstrapped distribution (less than the 2.5 percentiles or the 97.5 percentiles) are stippled.