climate change on UAE - Stockholm Environment Institute-US Center

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system biases between observed local <strong>climate</strong> and <strong>climate</strong> generated by GCMs. Downscaling does not necessarily provide more reliable information nor increase our confidence in a particular GCM scenario for <strong>climate</strong> <strong>change</strong>. Downscaling techniques generally fall into several classes, including simulated (dynamical), statistical and bias-correction/disaggregation, and sensitivity methods. Dynamical downscaling involves the use of regional <strong>climate</strong> models run at a relatively high resolution over a limited area with boundary conditions (and sometimes interior domain information as well) prescribed from the lower resolution GCM. Statistical downscaling methods involve deriving statistical relationships between observed small-scale (often station level) variables and larger (GCM) scale variables, using analogue methods (circulation typing), regression analysis, or neural network methods (Mearns, 1999; Yates et al., 2003, Clark and Hay, 2004). Our <strong>climate</strong> Scenarios are based on repeating the observed <strong>climate</strong> from 1994 through 2008, but then adding an absolute, random <strong>change</strong> in temperature (T’ = dt + T) and a percent <strong>change</strong> in precipitation P’ = dp*P to the historical record. This results in new time series of temperature and precipitation for the period 2008 through 2050, with <strong>change</strong>s in temperature and precipitation bound by the magnitude of the <strong>change</strong>s suggested by GCMs. These Scenarios are akin to a sensitivity analysis of <strong>climate</strong> <strong>change</strong>, as we are assuming that the <strong>climate</strong> of the past repeats itself into the future, with a predefined, albeit random, <strong>change</strong> in temperature and precipitation dictated by GCM results. For the worst-case or pessimistic scenario, the <strong>climate</strong> models suggest a 2.7ºC annual average Table ‎4‐7. Summary of <strong>climate</strong> <strong>change</strong>s included in WEAP modeled scenarios. Climate Outcome Climate Changes over 1961-90 baseline Temperature Precipitation Change <strong>change</strong> 1) Optimistic + 1.7 o C +10% 2) Pessimistic + 2.7 o C -20% 3) Middle of the Road MOR) + 2.2 o C +5% warming and a 20% decrease in total annual precipitation, with the best-case or optimistic scenario includes a more modest 1.0ºC warming and 10% increase in precipitation over the next 50 years. Note that because of the extreme aridity in the UAE, the optimistic scenario of a 10% increase in precipitation is practically insignificant, while a 20% decline in precipitation suggests that the ADE would continue to need to manage its water resources from the perspective of even greater aridity. No scenario suggests that the future <strong>climate</strong> <strong>change</strong> will mean increased renewable water supplies for the ADE. Climate <strong>change</strong> will likely have its most important impacts if there were substantial warming in the winter months, as the summers are already extremely warm and dry. Climate <strong>change</strong> could mean more winter season evaporative demands and thus more water demands for the domestic, amenity, and agricultural sectors. This sensitivity analysis was used to understand the response of the hydrologic system to a warmer <strong>climate</strong>, with particular emphasis on water demand. In an effort to make the scenario analysis efficient and informative given the broad range of GCM results and the uncertainty in future socio-economic conditions, we developed three categorical Scenarios which attempt to bracket the range of possible future <strong>climate</strong> and socioeconomic outcomes referred to as: Optimistic, Pessimistic, and Middle-of-the-Road. The Scenarios are explained in more detail in the following section but are summarized here by Table 4-7, and Figures 4-7 and 4-8. In Figure 4-7, we graph the average monthly temperature for the Middle-of-the-Road (MOR) scenario for both the coastal (light blue) and interior (dark blue) areas. A 5-year moving average is included in the plot to demonstrate the embedded warming trend of about 2ºC for this scenario. Figure 4-8 graphs precipitation trends for all Scenarios, including the “Optimistic”, “Pessimistic” and “Middle-ofthe-Road”. 4.8. Summary of Modeled Scenarios Using Optimistic, Pessimistic, and Middle-ofthe-Road assumptions about future <strong>climate</strong> as 112 Climate Change Impacts, Vulnerability & Adaptation
Avg. Monthly Air Temperature - MOR Scenario Impacts, Vulnerability & Adaptation for Water Resources in Abu Dhabi Figure 4-7. The average monthly temperature for the Middle-of-the-Road (MOR) Scenario. Annual Precipitation Scenarios Figure ‎4‐8. Total annual precipitation for the three <strong>climate</strong> <strong>change</strong> Scenarios. 113
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Acknowledgements The authors are de
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Foreword There are many levels at w
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Table of Contents (Part 2) Water Re
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9.2. Types of ecosystem models ....
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APF AML CARA CO 2 CReSIS CSI DEM EG
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Table ‎1‐1. Climate Change and
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2. Sea-level Rise Impacts on Coasta
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differences in observed sea levels
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Sea Level Change (mm) 20 15 10 5 0
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Impacts, Vulnerability & Adaptation
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Gulf is that the shallower depth al
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ago when sea level was a few meters
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Scientists have every reason to bel
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amongst others. Sea level rise may
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Figure ‎4‐2. Data displaying ar
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processed SRTM topographic dataset
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water height of each tidal day obse
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Table ‎4‐3. UAE Coastal Cities.
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Figure ‎4-10: 1 meters above mean
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Figure ‎4-12: 3 meters above mean
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as well as parts of the Industrial
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up to help to assess technology nee
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framework that has relevance to the
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to be estimated reliably and hence
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6. Conclusions and recommendations
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7. List of References ADEA (2006)
Page 61 and 62: Assessment Report of the Intergover
Page 63 and 64: 8. Glossary Adaptation: Adjustment
Page 65 and 66: To calculate areas inundated by lan
Page 67 and 68: Annex 1: Elevation Data Sensitivity
Page 69 and 70: Annex 2: Inundation maps 3. Urban V
Page 71 and 72: Figure A2-3 3 meters sea level rise
Page 73: Figure A2-6 1,3,9 meters sea level
Page 77 and 78: Page Figure 4‐6. WEAP estimates o
Page 79 and 80: in storage occurs in the Liwa lens
Page 81 and 82: Figure‎ 2‐1. Percentage of tota
Page 83 and 84: Total installed capacity equals 648
Page 85 and 86: Without demand management strategie
Page 87 and 88: Impacts, Vulnerability & Adaptation
Page 89 and 90: Figure 3-1 Global average temperatu
Page 91 and 92: The CO 2 storylines include both
Page 93 and 94: of topography on the region’s pre
Page 97 and 98: Temperature Change (degrees C) Temp
Page 99 and 100: neglected infrastructure, regional
Page 101 and 102: and magnitude of the ENSO (Haines e
Page 103 and 104: Table ‎4‐1. Irrigated Hectares
Page 105 and 106: including estimates of population a
Page 107 and 108: 4.4. Representing Irrigation Demand
Page 109 and 110: Table 4-5. Comparison of Historical
Page 111: 4.6. Scenarios and Key Assumptions
Page 115 and 116: Table ‎4‐8. Summary of scenario
Page 117 and 118: 5. Results and Discussion The <stro
Page 119 and 120: total water demand estimate at abou
Page 121 and 122: for the Optimistic and Pessimistic
Page 123 and 124: eport of the IPCC holds out hope th
Page 125 and 126: Table 6-1. Adaptation options for w
Page 127 and 128: CIA, (1990), Middle East Area Oil a
Page 129 and 130: Figure A1‐2. Map of Abu Dhabi key
Page 131 and 132: Data Sources And Assumptions We rel
Page 133 and 134: Table A1-3. continued Liwa- Hammim
Page 135 and 136: Table A2-1. Summary of 36 emission
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Page 143 and 144: List of Tables Page Table 3-1: Majo
Page 145 and 146: 2. Potential risks to dryland ecosy
Page 147 and 148: 4. Major terrestrial ecosystems in
Page 149 and 150: Peninsula in the north to close to
Page 151 and 152: 5. Important flora in Abu Dhabi Abu
Page 153 and 154: over a one third of the known speci
Page 155 and 156: 6. Fauna of the terrestrial ecosyst
Page 157 and 158: include the urban development and i
Page 159 and 160: (Arnell, 1999; Milly et al., 2005).
Page 161 and 162: camels and other large herbivores i
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8. Biodiversity, ecosystem threshol
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Alternatively, established shrublan
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e functionally deficient. We projec
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Adaptation can be planned or it can
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is based on a theoretical understan
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species to adapt to a changing <str
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that remediation steps begin soon t
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10 References Adler, PB, DA Raff, W
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Nordad, (2007), Climate Change Fact
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Annex 2: Expected effects of global
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Annex 4: Main drivers of ecosystem
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Sn Species Scientific name Family S
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Sn Species Scientific name Family S
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Name of species/sub-species Habitat
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Annex 8: Native species list of ter
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The cover picture represents a Brid
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