CLIMATE SCENARIOS FOR ALBERTA

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1 CLIMATE SCENARIOS FOR ALBERTA A Report Prepared for the Prairie Adaptation Research Collaborative (PARC) in co-operation with Alberta Environment Elaine Barrow 1 & Ge Yu 2 May Climate Research Services 2229 Princess Street Regina, Saskatchewan S4T 3Z9 2 Prairie Adaptation Research Collaborative Suite 150, 10 Research Drive University of Regina Regina, Saskatchewan S4S 7J7

2 EXECUTIVE SUMMARY The most recent assessment undertaken by the Intergovernmental Panel on Climate Change (IPCC) reached a number of conclusions concerning global climate change, two of which stated that the increasing body of observations gives a collective picture of a warming world and other changes in the climate system and that there is new and stronger evidence that most of the warming observed over the last 50 years is attributable to human activities (IPCC, 2001a). These observed changes in climate are as a result of a global average surface air temperature increase over the 20 th century of about 0.6 C. In contrast to these observed changes, global average surface air temperature is projected to increase between 1.4 C and 5.8 C by 2100, relative to This report explores how these projected global average climate changes may be manifest in Alberta. Following recommendations outlined by the IPCC, scenarios of climate change were constructed using the most recent global climate model (GCM) results available. These GCM experiments used a number of different greenhouse gas emissions scenarios, thus spanning a range of possible socio-economic futures. Although there have been many advances in global climate modelling over the last few years, the output from GCMs is still not sufficiently reliable to be used directly as climate input into impacts studies. Instead, climate change scenarios were constructed by determining the changes in average climate for the 2020s, 2050s and 2080s, relative to the baseline period. There are a large number of GCM experiments available and so a sub-set of climate change scenarios was selected for use based on changes in summer mean temperature and precipitation for the 2050s since this season provided a large range in the scenario results, particularly for precipitation. Five scenarios were selected to represent conditions which were cooler and wetter (NCARPCM A1B), cooler and drier (CGCM2 B2(3)), warmer and wetter (HadCM3 A2(a)) and warmer and drier (CCSRNIES A1FI) than median conditions (HadCM3 B2(b)). Climate change scenarios were constructed for minimum, mean and maximum temperature, precipitation, degree days > 5 C and annual moisture index. For Alberta, changes in annual mean temperature by the 2050s are typically between 3 C and 5 C, although the CCSRNIES A1FI scenario is consistently warmer than the other scenarios by about 2 C. Changes in maximum and minimum temperature are similar to those for mean temperature, although the changes in minimum temperature tend to be slightly greater than those for maximum temperature thus implying a general decrease in the diurnal temperature range. For the 2050s, changes in annual precipitation are generally within the range 10% to +15%, and any decreases in annual precipitation are generally driven by decreases in summer precipitation. By the 2080s, however, all five climate change scenarios indicate increases in annual precipitation of up to 15% in general. Degree days > 5 C and annual moisture index scenarios indicate increases of between 30-50% and 20-30% by the 2050s, respectively. The projected increases in annual moisture index are generally driven by the large increases in degree days above 5 C, rather than by decreases in precipitation. These climate change scenarios were then combined with observed climate information derived from the Alberta Climate Model (Anon., 2004a) to provide actual climate scenarios for the 2020s, 2050s and 2080s. Climate scenario results were described for six sites in Alberta Lethbridge, Medicine Hat, Calgary, Edmonton, Grande Prairie and Fort McMurray. Future minimum, mean and maximum temperatures indicate a similar pattern, with the gradual march north of warmer temperatures. By the 2050s, annual mean temperature at Calgary is projected to be similar to, or warmer than, that currently observed at Lethbridge and Medicine Hat. For this to be the case at Grande Prairie and Fort McMurray we must wait until the 2080s. For precipitation, the scenario results are not as obvious as for temperature and in the earlier time 2

3 periods in particular the changes are generally quite small. There is some overlap with baseline conditions especially in the 2020s and 2050s, with any decreases in annual precipitation generally being driven by decreases in summer precipitation. By the 2080s, precipitation is projected to increase at all sites. The projected increases in degree days > 5 C imply a lengthening of the growing season and/or the availability of more heat units during the growing season. For this variable, Calgary, Edmonton, Grande Prairie and Fort McMurray are projected to approach degree day totals similar to those currently observed at Lethbridge and Medicine Hat by the 2050s. Annual moisture index is projected to increase province-wide, most noticeably in the south around Lethbridge, Medicine Hat and Calgary, with less pronounced increases in central and northern Alberta. These increases are driven by the large increases in degree day totals rather than by large decreases in precipitation. However, the selected scenarios indicate that the largest decreases in precipitation occur during the summer season, which is also the season when most degree-day units are accrued and so moisture stress is likely to occur. For this variable, Calgary, Edmonton, Grande Prairie and Fort McMurray are projected to experience conditions similar to those currently observed at Lethbridge and Medicine Hat by the 2080s. 3

4 TABLE OF CONTENTS EXECUTIVE SUMMARY... 2 LIST OF TABLES AND FIGURES : INTRODUCTION : SCENARIOS : EMISSIONS SCENARIOS : Global Climate Models : CLIMATE CHANGE AND CLIMATE SCENARIOS : Construction of climate change scenarios : Selecting which climate change scenarios to use : Applying climate change scenarios : Climate change scenarios for annual moisture index and degree days above a threshold temperature of 5 C : Climate Change Scenarios for Alberta : Mean temperature changes : Precipitation changes : Maximum temperature changes : Minimum temperature changes : Degree day changes : Annual moisture index changes : Climate Scenarios for Alberta : Mean temperature : Precipitation : Degree days : Annual moisture index : FUTURE RESEARCH DIRECTIONS ACKNOWLEDGEMENTS DETAILS OF DATA AND MAPS ON CD REFERENCES

5 LIST OF TABLES AND FIGURES Figure 1: Projected changes in global-mean temperature ( C) by 2100, in response to a number of different emissions scenarios. 12 Figure 2: Schematic illustration of the SRES scenarios. 14 Figure 3: (a) Analysis of inter-model consistency in regional relative warming (warming relative to each model s global warming). (b) Analysis of inter-model consistency in regional precipitation change. 15 Figure 4: Schematic illustrating the construction of climate change scenarios from GCM output. 19 Figure 5: Scatter plots indicating annual changes in mean temperature ( C) and precipitation (%) for Alberta for the 2020s, 2050s and 2080s. 20 Figure 6: Scatter plots indicating seasonal changes in mean temperature ( C) and precipitation (%) for Alberta for the 2050s. 21 Figure 7: Examples of interpolating coarse-scale climate change scenario information to 0.5 latitude/longitude resolution, using a bilinear 2D interpolation procedure. 23 Figure 8: Examples of the harmonic fit algorithm used to derive daily mean temperature from monthly mean temperature values. 24 Figure 9: Annual mean temperature change ( C) for the 2020s with respect to Figure 10: Annual mean temperature change ( C) for the 2050s with respect to Figure 11: Annual mean temperature change ( C) for the 2080s with respect to Figure 12: Annual precipitation change (%) for the 2020s with respect to Figure 13: Annual precipitation change (%) for the 2050s with respect to Figure 14: Annual precipitation change (%) for the 2080s with respect to Figure 15: Annual maximum temperature change ( C) for the 2020s with respect to Figure 16: Annual maximum temperature change ( C) for the 2050s with respect to Figure 17: Annual maximum temperature change ( C) for the 2080s with respect to Figure 18: Annual minimum temperature change ( C) for the 2020s with respect to Figure 19: Annual minimum temperature change ( C) for the 2050s with respect to Figure 20: Annual minimum temperature change ( C) for the 2080s with respect to Figure 21: Change in degree days > 5 C (%) for the 2020s with respect to Figure 22: Change in degree days > 5 C (%) for the 2050s with respect to Figure 23: Change in degree days > 5 C (%) for the 2080s with respect to Figure 24: Change in annual moisture index (%) for the 2020s with respect to

6 Figure 25: Change in annual moisture index (%) for the 2050s with respect to Figure 26: Change in annual moisture index (%) for the 2080s with respect to Figure 27: Annual mean temperature ( C) for six selected sites in Alberta. 49 Figure 28: Seasonal mean temperature ( C) for six selected sites in Alberta. 50 Figure 29: Annual precipitation total (mm) for six selected sites in Alberta. 51 Figure 30: Seasonal precipitation total (mm) for six selected sites in Alberta. 52 Figure 31: Degree days > 5 C for six selected sites in Alberta. 53 Figure 32: Annual moisture index for six selected sites in Alberta. 54 Figure 33: Annual mean temperature ( C) for the baseline period. 55 Figure 34: Annual mean temperature ( C) for the median scenario (HadCM3 B2(b)) for the 2020s. 56 Figure 35: Annual mean temperature ( C) for the median scenario (HadCM3 B2(b)) for the 2050s. 57 Figure 36: Annual mean temperature ( C) for the median scenario (HadCM3 B2(b)) for the 2080s. 58 Figure 37: Annual precipitation (mm) for the baseline period. 59 Figure 38: Annual precipitation (mm) for the median scenario (HadCM3 B2(b)) for the 2020s. 60 Figure 39: Annual precipitation (mm) for the median scenario (HadCM3 B2(b)) for the 2050s. 61 Figure 40: Annual precipitation (mm) for the median scenario (HadCM3 B2(b)) for the 2080s. 62 Figure 41: Degree days > 5 C for the baseline period. 63 Figure 42: Degree days > 5 C for the median scenario (HadCM3 B2(b)) for the 2020s. 64 Figure 43: Degree days > 5 C for the median scenario (HadCM3 B2(b)) for the 2050s. 65 Figure 44: Degree days > 5 C for the median scenario (HadCM3 B2(b)) for the 2080s. 66 Figure 45: Annual moisture index for the baseline period. 67 Figure 46: Annual moisture index for the median scenario (HadCM3 B2(b)) for the 2020s. 68 Figure 47: Annual moisture index for the median scenario (HadCM3 B2(b)) for the 2050s. 69 Figure 48: Annual moisture index for the median scenario (HadCM3 B2(b)) for the 2080s. 70 Figure 49: A screen capture of a portion of one of the climate scenario data files. 72 Table 1: Summary descriptions of the six illustrative SRES scenarios. 14 Table 2: Details of the SRES simulations currently available on the IPCC DDC (after Parry, 2002). 18 Table 3: Attributes of the five scenarios selected for use in this study. 22 Table 4: Annual mean temperature ( C) at selected sites in Alberta, for (from the Alberta Climate Model) and for the median scenario (HadCM3 B2(b)) for the 2020s, 2050s and 2080s. 49 Table 5: Annual precipitation (mm) at selected sites in Alberta, for (from the Alberta Climate Model) and for the median scenario (HadCM3 B2(b)) for the 2020s, 2050s and 2080s. 51 6

7 Table 6: Degree days > 5 C at selected sites in Alberta, for (from the Alberta Climate Model) and for the median scenario (HadCM3 B2(b)) for the 2020s, 2050s and 2080s. 52 Table 7: Annual moisture index at selected sites in Alberta, for (from the Alberta Climate Model) and for the median scenario (HadCM3 B2(b)) for the 2020s, 2050s and 2080s. 54 Table 8: Information for identification of map files. 72 APPENDIX A Figure A1: Winter (DJF) mean temperature change ( C) for the 2050s with respect to Figure A2: Spring (MAM) mean temperature change ( C) for the 2050s with respect to Figure A3: Summer (JJA) mean temperature change ( C) for the 2050s with respect to Figure A4: Fall (SON) mean temperature change ( C) for the 2050s with respect to Figure A5: Winter (DJF) precipitation change (%) for the 2050s with respect to Figure A6: Spring (MAM) precipitation change (%) for the 2050s with respect to Figure A7: Summer (JJA) precipitation change (%) for the 2050s with respect to Figure A8: Fall (SON) precipitation change (%) for the 2050s with respect to Figure A9: Winter (DJF) maximum temperature change ( C) for the 2050s with respect to Figure A10: Spring (MAM) maximum temperature change ( C) for the 2050s with respect to Figure A11: Summer (JJA) maximum temperature change ( C) for the 2050s with respect to Figure A12: Fall (SON) maximum temperature change ( C) for the 2050s with respect to Figure A13: Winter (DJF) minimum temperature change ( C) for the 2050s with respect to Figure A14: Spring (MAM) minimum temperature change ( C) for the 2050s with respect to Figure A15: Summer (JJA) minimum temperature change ( C) for the 2050s with respect to Figure A16: Fall (SON) minimum temperature change ( C) for the 2050s with respect to APPENDIX B Figure B1: Annual mean temperature ( C) for (from the Alberta Climate Model) and for the cooler, wetter NCARPCM A1B scenario for the 2020s, 2050s and 2080s. 93 Figure B2: Annual mean temperature ( C) for (from the Alberta Climate Model) and for the cooler, drier CGCM2 B2(3) scenario for the 2020s, 2050s and 2080s. 94 7

8 Figure B3: Annual mean temperature ( C) for (from the Alberta Climate Model) and for the warmer, wetter HadCM3 A2(a) scenario for the 2020s, 2050s and 2080s. 95 Figure B4: Annual mean temperature ( C) for (from the Alberta Climate Model) and for the warmer, drier CCSRNIES A1FI scenario for the 2020s, 2050s and 2080s. 96 Figure B5: Mean annual precipitation (mm) for (from the Alberta Climate Model) and for the cooler, wetter NCARPCM A1B scenario for the 2020s, 2050s and 2080s. 98 Figure B6: Mean annual precipitation (mm) for (from the Alberta Climate Model) and for the cooler, drier CGCM2 B2(3) scenario for the 2020s, 2050s and 2080s. 99 Figure B7: Mean annual precipitation (mm) for (from the Alberta Climate Model) and for the warmer, wetter HadCM3 A2(a) scenario for the 2020s, 2050s and 2080s. 100 Figure B8: Mean annual precipitation (mm) for (from the Alberta Climate Model) and for the warmer, drier CCSRNIES A1FI scenario for the 2020s, 2050s and 2080s. 101 Figure B9: Degree days > 5 C for (from the Alberta Climate Model) and for the cooler, wetter NCARPCM A1B scenario for the 2020s, 2050s and 2080s. 103 Figure B10: Degree days > 5 C for (from the Alberta Climate Model) and for the cooler, drier CGCM2 B2(3) scenario for the 2020s, 2050s and 2080s. 104 Figure B11: Degree days > 5 C for (from the Alberta Climate Model) and for the warmer, wetter HadCM3 A2(a) scenario for the 2020s, 2050s and 2080s. 105 Figure B12: Degree days > 5 C for (from the Alberta Climate Model) and for the warmer, drier CCSRNIES A1FI scenario for the 2020s, 2050s and 2080s. 106 Figure B13: Annual moisture index for (from the Alberta Climate Model) and for the cooler, wetter NCARPCM A1B scenario for the 2020s, 2050s and 2080s. 108 Figure B14: Annual moisture index for (from the Alberta Climate Model) and for the cooler, drier CGCM2 B2(3) scenario for the 2020s, 2050s and 2080s. 109 Figure B15: Annual moisture index for (from the Alberta Climate Model) and for the warmer, wetter HadCM3 A2(a) scenario for the 2020s, 2050s and 2080s. 110 Figure B16: Annual moisture index for (from the Alberta Climate Model) and for the warmer, drier CCSRNIES A1FI scenario for the 2020s, 2050s and 2080s. 111 APPENDIX C Figure C1: Winter (DJF) mean temperature ( C) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 122 Figure C2: Spring (MAM) mean temperature ( C) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler 8

9 wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 123 Figure C3: Summer (JJA) mean temperature ( C) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 124 Figure C4: Fall (SON) mean temperature ( C) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 125 Figure C5: Winter (DJF) precipitation (mm) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 127 Figure C6: Spring (MAM) precipitation (mm) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 128 Figure C7: Summer (JJA) precipitation (mm) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 129 Figure C8: Fall (SON) precipitation (mm) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 130 Figure C9: Winter (DJF) maximum temperature ( C) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 132 Figure C10: Spring (MAM) maximum temperature ( C) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 133 Figure C11: Summer (JJA) maximum temperature ( C) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 134 Figure C12: Fall (SON) maximum temperature ( C) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer 9

10 wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 135 Figure C13: Winter (DJF) minimum temperature ( C) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 137 Figure C14: Spring (MAM) minimum temperature ( C) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 138 Figure C15: Summer (JJA) minimum temperature ( C) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 139 Figure C16: Fall (SON) minimum temperature ( C) for (from the Alberta Climate Model) and for the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B), cooler drier (CGCM2 B2(3)), warmer wetter (HadCM3 A2(a)) and warmer drier (CCSRNIES A1FI) scenarios for the 2050s. 140 Table C1: Annual mean temperature ( C) for the baseline and for the 2050s for all five scenarios. 113 Table C2: Winter (DJF) mean temperature ( C) for the baseline and for the 2050s for all five scenarios. 113 Table C3: Spring (MAM) mean temperature ( C) for the baseline and for the 2050s for all five scenarios. 113 Table C4: Summer (JJA) mean temperature ( C) for the baseline and for the 2050s for all five scenarios. 114 Table C5: Fall (SON) mean temperature ( C) for the baseline and for the 2050s for all five scenarios. 114 Table C6: Annual precipitation (mm) for the baseline and for the 2050s for all five scenarios. 114 Table C7: Winter (DJF) precipitation (mm) for the baseline and for the 2050s for all five scenarios. 115 Table C8: Spring (MAM) precipitation (mm) for the baseline and for the 2050s for all five scenarios. 115 Table C9: Summer (JJA) precipitation (mm) for the baseline and for the 2050s for all five scenarios. 115 Table C10: Fall (SON) precipitation (mm) for the baseline and for the 2050s for all five scenarios. 116 Table C11: Annual maximum temperature ( C) for the baseline and for the 2050s for all five scenarios. 116 Table C12: Winter (DJF) maximum temperature ( C) for the baseline and for the 2050s for all five scenarios. 116 Table C13: Spring (MAM) maximum temperature ( C) for the baseline and for the 2050s for all five scenarios. 117 Table C14: Summer (JJA) maximum temperature ( C) for the baseline and for the 2050s for all five scenarios

11 Table C15: Fall (SON) maximum temperature ( C) for the baseline and for the 2050s for all five scenarios. 117 Table C16: Annual minimum temperature ( C) for the baseline and for the 2050s for all five scenarios. 118 Table C17: Winter (DJF) minimum temperature ( C) for the baseline and for the 2050s for all five scenarios. 118 Table C18: Spring (MAM) minimum temperature ( C) for the baseline and for the 2050s for all five scenarios. 118 Table C19: Summer (JJA) minimum temperature ( C) for the baseline and for the 2050s for all five scenarios. 119 Table C20: Fall (SON) minimum temperature ( C) for the baseline and for the 2050s for all five scenarios. 119 Table C21: Degree days > 5 C for the baseline and for the 2050s for all five scenarios. 120 Table C22: Annual moisture index for the baseline and for the 2050s for all five scenarios

12 1.0: INTRODUCTION In 2001, the Intergovernmental Panel on Climate Change (IPCC) released its Third Assessment Report (IPCC, 2001a, b, c), in which the following conclusions were reached: An increasing body of observations gives a collective picture of a warming world and other changes in the climate system. The global average surface air temperature has increased over the 20 th century by about 0.6 C, taking into account urban heat island effects. Although there is a great deal of variability in the instrumental record, most of the warming occurred in the 20 th century, during two periods, 1910 to 1945 and 1976 to The 1990s was the warmest decade globally and 1998 the warmest year in the instrumental record since There is new and stronger evidence that most of the warming observed over the last 50 years is attributable to human activities. Changes in sea level, snow cover, ice extent and precipitation are consistent with a warming climate near the Earth s surface, that is: Global average sea level has risen and ocean heat content has increased; Snow cover and ice extent have decreased; There has been widespread retreat of non-polar glaciers; Heavy precipitation events have increased and shifts in precipitation have occurred. Observed changes in regional climate have affected many physical and biological systems. For example, the duration of ice cover on rivers and lakes has decreased; the growing season has lengthened; plant and animal ranges have shifted poleward and up in elevation; plant flowering, migratory bird arrival and emergence of insects are earlier in the Northern Hemisphere. In contrast to the observed increases in surface air temperature over the last century, global average surface air temperature is projected to warm between 1.4 and 5.8 C by 2100, relative to 1990 (Nakicenovic et al., 2000), i.e., between about 2 and 10 times as fast as the observed 20 th Figure 1: Projected changes in global-mean temperature ( C) by 2100, in response to a number of different emissions scenarios. The IS92a scenario was widely used to represent a business-asusual emissions scenario, prior to the development of the SRES emissions scenarios. The B2 emissions scenario is similar to IS92a, although it has a warmer pathway to a similar increase in global-mean temperature by [Source: IPCC] 12

13 century warming (Figure 1). Canada s location in the high latitudes means that it is likely to experience some of the largest changes in climate, in particular changes in temperature. This report details how mean climate may evolve in Alberta through the use of a number of different climate change scenarios. Some background is provided about the construction and selection of climate change scenarios, before these possible future climates are described, compared and contrasted. 2.0: SCENARIOS The IPCC defines a scenario as a coherent, internally consistent, and plausible description of a possible future state of the world (IPCC, 1994). The large uncertainties associated with the evolution of future conditions, be they population, economic growth, greenhouse gas emissions or climate to name but a few, mean that the level of confidence associated with a particular future is not yet sufficient to permit a scenario to be referred to as a prediction or a forecast. The roles that scenarios may play in the assessment of climate change impacts are described in the IPCC Third Assessment Report (IPCC, 2001b): For the illustration of climate change: for example, by depicting the future climate expected in a given region in terms of the present-day climate currently experienced in a familiar neighbouring region. For communication of the potential consequences of climate change: for example, by specifying a future changed climate to estimate potential future shifts in natural vegetation and identifying species at risk of local extinction. Used in this way, scenarios are awarenessraising devices. For strategic planning: for example, by quantifying possible future sea-level and climate changes to design effective coastal or river flood defences. For guiding emissions control policy: for example, by specifying alternative socio-economic and technological options for achieving some pre-specified atmospheric greenhouse gas concentrations. For methodological purposes: by determining our knowledge (or ignorance) of a system through, for example, the description of altered conditions, the use of a new scenario development technique, or by evaluating the performance of impact models and determining the reasons for any differences in results. To explore the implications of decisions: by examining the impacts resulting from a particular scenario of future climate and the actions taken to ameliorate particular harmful impacts associated with the scenario. For this report, the focus is on the illustration of climate change, and in order to do this we need to consider three types of scenarios emissions scenarios, climate change scenarios and climate scenarios. 2.1: EMISSIONS SCENARIOS Future emissions of greenhouse gases and aerosols into the atmosphere depend very much on factors such as population and economic growth and energy use. For its Third Assessment Report (IPCC, 2001a) the IPCC commissioned a Special Report on Emissions Scenarios (SRES; Nakicenovic et al., 2000), in which about forty different emissions scenarios were developed. These could be classified into four families, depending on whether or not the scenarios had a global or regional development focus or were driven by environmental rather than economic considerations (Figure 2). 13

14 Figure 2: Schematic illustration of the SRES scenarios. The four scenario families are shown, very simplistically, as branches of a twodimensional tree. In reality, the four scenario families share a space of a much higher dimensionality given the numerous assumptions needed to define any given scenario in a particular modelling approach. The schematic diagram illustrates that the scenarios build on the main driving forces of GHG emissions. Each scenario family is based on a common specification of some of the main driving forces. The A1 storyline branches out into four groups of scenarios to illustrate that alternative development paths are possible within one scenario family. [Source: Nakicenovic et al., 2000] Of these forty emissions scenarios, six have been chosen as illustrative, or marker, scenarios: A1FI, A1B, A1T, A2, B1 and B2, and are described briefly in Table 1. The global-mean temperature changes associated with the SRES emissions scenarios are illustrated in Figure 1. The AIFI and B1 emissions scenarios result in the highest and lowest increases in global-mean temperature by 2100, respectively. Emissions of greenhouse gases and aerosols into the atmosphere are the driving force for climate change, since it is the atmospheric concentration of these compounds, produced via both anthropogenic and natural activities, which determines the effect on the energy balance of the Earth-atmosphere-ocean system, and thus on climate. Global climate models (GCMs) are the mechanism through which greenhouse gas and aerosol emissions can be translated into physically-consistent effects on climate. Most global climate modelling centres have carried out experiments using the A2 and B2 emissions scenarios. The results of A2 and B2 climate change simulations carried out with nine GCMs were combined for the IPCC Third Assessment Report (IPCC, 2001a) to identify regions of consistent (and inconsistent) changes in temperature and precipitation (see Figure 3). For the western North America (WNA) region, both winter and summer seasons indicate consistently greater than average warming for the A2 and B2 scenarios. For precipitation, however, both the A2 and B2 Table 1: Summary descriptions of the six illustrative SRES scenarios. Emissions Scenario A1FI A1T A1B A2 Description A future world of very rapid economic growth and intensive use of fossil fuels A future world of very rapid economic growth, and rapid introduction of new and more efficient technology A future world of very rapid economic growth, and a mix of technological developments and fossil fuel use A future world of moderate economic growth, more heterogeneously distributed and with a higher population growth rate than in A1 B1 A convergent world with rapid change in economic structures, dematerialisation, introduction of clean technologies, and the lowest rate of population growth B2 A world in which the emphasis is on local solutions to economic, social and environmental sustainability, intermediate levels of economic development and a lower population growth rate than A2 14

15 Figure 3: (a) Analysis of inter-model consistency in regional relative warming (warming relative to each model s global warming). Regions are classified as showing either agreement on warming in excess of 40% above the global average ( Much greater than average warming ), agreement on warming greater than the global average ( Greater than average warming ), agreement on warming less than the global average ( Less than average warming ), or disagreement amongst models on the magnitude of regional relative warming ( Inconsistent magnitude of warming ). There is also a category for agreement on cooling (which never occurs). A consistent result from at least seven of the nine models is deemed necessary for agreement. The global annual average warming (DJF and JJA combined) of the models used span 1.2 to 4.5 C for A2 and 0.9 to 3.4 C for B2, and therefore a regional 40% amplification represents warming ranges of 1.7 to 6.3 C for A2 and 1.3 to 4.7 C for B2. [Source: IPCC (2001a)] Figure 3: (b) Analysis of inter-model consistency in regional precipitation change. Regions are classified as showing either agreement on increase with an average change of greater than 20% ( Large increase ), agreement on increase with an average change between 5 and 20% ( Small increase ), agreement on a change between -5 and +5% or agreement with an average change between -5 and 5% ( No change ), agreement on decrease with an average change between -5 and -20% ( Small decrease ), agreement on decrease with an average change of less than -20% ( Large decrease ), or disagreement ( Inconsistent sign ). A consistent result from at least seven of the nine models is deemed necessary for agreement. [Source: IPCC (2001a)] experimental results indicate small increases (i.e., between 5 and 20%) during winter, but inconsistent results for the summer season in this region. The inconsistent result during summer is 15

16 not surprising given that convective precipitation has a dominant role in this season. Precipitation in general, and convective precipitation in particular, are difficult to model since these processes occur at scales smaller than a GCM can resolve directly : Global Climate Models Global Climate Models (GCMs) are mathematical models which represent the physical processes of, and the known feedbacks between, the atmosphere, ocean, cryosphere and land surface. They can be used for the simulation of past, present, and future climates and have undergone considerable evolution since their first appearance about forty years ago, in part because of the substantial advances in computing technology during this time. Most GCMs have a horizontal resolution of between 250 and 600 km, with 10 to 20 vertical layers in the atmosphere and as many as 30 layers in the ocean. This resolution is quite coarse, particularly when considered in comparison to the scales at which most impacts studies are conducted, and means that it is impossible to model directly some of the smaller-scale atmospheric and oceanic processes (e.g., precipitation formation). Such processes have to be averaged over larger scales, or parameterised, i.e., related to other variables that are explicitly modelled. Warm-start transient response GCMs are the most advanced models of this type and consist of coupled three-dimensional atmosphere-ocean models. The inclusion of oceanic circulation and transfers of heat and moisture from the ocean surface permit the simulation of the time-dependent response of climate to changes in atmospheric composition, and thus provide useful information about the rate as well as the magnitude of climate change. Warm start GCMs simulate the effects of past changes in radiative forcing, i.e., the effect of historical changes in atmospheric composition (typically from the 18 th or 19 th century) on the radiation balance of the atmosphere. Simulations are then continued into the future using a scenario of future radiative forcing, which is derived from an emissions scenario such as one described in Section 2.1. The output from GCM experiments provides the basis for the main method of climate change scenario construction. 2.2: CLIMATE CHANGE AND CLIMATE SCENARIOS Climate change scenarios can be constructed in a number of different ways, but to ensure that they are of most use for impact researchers and policy makers, the following four criteria were put forward to aid scenario selection (Smith and Hulme, 1998): 1. Consistency at the regional level with global projections: Scenarios should be consistent with a broad range of global warming projections based on increased concentrations of greenhouse gases. This range was given as 1.4 C to 5.8 C by 2100 in the IPCC Third Assessment Report (IPCC, 2001a), relative to 1990 (see Figure 1). Scenario changes in regional climate may lie outside the range of global-mean changes but should be consistent with theory and modelbased results. 2. Physical plausibility: Changes in climate should be physically plausible, such that changes in different climatic variables are mutually consistent and credible, both spatially and temporally. 3. Applicability in impact assessments: Scenarios should describe changes in a sufficient number of climate variables on a spatial and temporal scale that allows for impact assessment. 4. Representativeness: Scenarios should be representative of the potential range of future regional climate change in order for a realistic range of possible impacts to be estimated. Climate change scenarios constructed using global climate model (GCM) output generally conform better with the assumptions listed above than those constructed using other techniques, 16

17 such as synthetic or analogue approaches. For details of full range of climate change scenario construction techniques, see Chapter 13 of the IPCC Third Assessment Report (IPCC, 2001a). Given the number of GCMs currently available and the fact that new experiments are continually being added to the suite available, Smith and Hulme (1998) proposed four criteria for selecting GCM outputs suitable for climate change scenario construction from the large sample of experiments available: 1. Vintage: Recent model simulations are likely (though by no means certain) to be more reliable than those of an earlier vintage since they are based on recent knowledge and incorporate more processes and feedbacks. 2. Resolution: In general, increased spatial resolution of models has led to better representation of climate. 3. Validation: Selection of GCMs that simulate the present-day climate most faithfully is preferred, on the premise that these GCMs are more likely (though not guaranteed) to yield a reliable representation of future climate. 4. Representativeness of results: Alternative GCMs can display large differences in the estimates of regional climate change, especially for variables such as precipitation. One option is to choose models that show a range of changes in a key variable in the study region. More recently, Parry (2002) described the criteria used to determine which GCM experiments are made available through the IPCC Data Distribution Centre (DDC; the web site established by the IPCC Task Group on Data and Scenario Support for Impact and Climate Analysis (TGICA) to facilitate the provision of GCM output and climate change scenarios to the impacts and adaptation research community. The TGICA provides guidelines and recommendations for the construction and use of scenarios in order to encourage consistency in climate change impacts and adaptation research. All GCMs and experiments on the DDC must have met the following criteria: be full three-dimensional coupled ocean-atmosphere GCMs, be documented in the peer-reviewed literature, have performed a multi-century control run 1 (for stability reasons), and have participated in the Second Coupled Model Intercomparison Project (CMIP2; In addition, GCMs which have a resolution of at least 3 3, which have participated in the Atmospheric Model Intercomparison Project (AMIP; and which consider explicit greenhouse gases (e.g., carbon dioxide, methane, nitrous oxide etc.) are preferred. Table 2 indicates the GCMs and associated SRES experiments that are available from the IPCC DDC and which were available for use in this study : Construction of climate change scenarios Although advances in computing technology have enabled large increases in the spatial and temporal resolution of GCMs over the last few years, their model results are still not sufficiently accurate (in terms of absolute values) at regional scales to be used directly in impacts studies (Mearns et al., 1997). Instead, mean differences between the model s representation of current climate (this baseline period is currently ) and some time period in the future are 1 A control run is carried out with all GCMs and is an experiment in which the atmospheric composition is set at or near pre-industrial conditions and there are no changes in forcing for the duration of the run. Output from such a simulation provides valuable information about the stability of the model (e.g., if there are errors in the model formulation, it may drift towards an unrealistic climate over time) and the model s representation of natural climate variability. 17

18 Table 2: Details of the SRES simulations currently available on the IPCC DDC (after Parry, 2002). Climate Modelling Centre Country Model SRES simulations Canadian Centre for Climate Modelling and Canada CGCM2 A2*, B2* Analysis Hadley Centre for Climate Prediction and Research UK HadCM3 A1FI, A2*, B1, B2* Max Planck Institute for Meteorology Germany ECHAM4/ A2, B2 OPYC3 Commonwealth Scientific and Industrial Research Organisation Australia CSIRO-Mk2 A1, A2, B1, B2 Geophysical Fluid Dynamics Laboratory USA GFDL-R30 A2, B2 National Centre for Atmospheric Research USA NCAR-PCM A2, B2, A1B Centre for Climate Research Studies/National Institute for Environmental Studies Japan CCSR/NIES AGCM + CCSR OGCM A1FI, A1T, A1B, A2, B1, B2 *More than one experiment has been carried out for these emissions scenarios. These are known as ensemble experiments and it is common practice for the ensemble mean to be calculated from the individual experiments. This is simply the average of the individual experiments and since the averaging process reduces the noise due to climate variability, the ensemble mean generally represents the climate response ( signal ) to the imposed forcing change. calculated (see Figure 4). Thirty-year periods are used to define the baseline and future time periods since averaging over this length of time gives a better indication of the longer-term trend in climate than does a shorter interval. The changes between the future and baseline periods are calculated on a grid-box by grid-box basis and are referred to as change fields or climate change scenarios. Conventionally, differences (future climate minus baseline climate) are used for temperature variables and ratios (future climate/baseline climate), often expressed in percent terms, are used for other variables such as precipitation and wind speed. The IPCC TGICA currently recommends that three fixed time horizons in the future, the 2020s (i.e., ), the 2050s ( ), and the 2080s ( ), are considered in impacts studies. To obtain a climate scenario, i.e., a representation of the actual future climate rather than simply the change in climate relative to the baseline period, the climate change scenario is combined with some baseline observed climate data set (IPCC, 1994). Most climate change scenarios derived from GCM output are generally at monthly or seasonal time scales, and can be combined with observed baseline climate information from daily to seasonal resolution. Since the climate change scenario is combined with observed climate baseline data, the climate scenario will have the same variability as the observed baseline. This may not necessarily be the case in the future, and methods exist for imposing changes in variability in a scenario (generally at site rather than regional scales) but these are not applied in this study. For this study, the GCMs and associated experiments listed in Table 2 were available for use. However, the ECHAM4 and GFDL-R30 GCMs did not have data available for maximum and minimum temperature and so they were not considered further for climate change scenario construction. Thus, 26 experiments from five GCMs were considered for use in this study. 18

19 2.2.2: Selecting which climate change scenarios to use One of the main problems in climate change scenario work is selecting the number of scenarios to use. The ideal would be to use all available scenarios to build a complete picture of the range of climate (to the best of our current knowledge), and thus impact response to changes in atmospheric composition. Even though some climate change scenarios may appear to be very similar, non-linearities in impact response can lead to quite different results. However, sufficient resources are seldom available for such studies and in any case the amount of data being dealt with soon becomes overwhelming. On the other hand, use of a single climate change scenario is not recommended for anything other than arbitrary exercises exploring the effect of a change in a particular climate variable on impact response. The IPCC TGICA currently recommends that a number of climate change scenarios are used and that these scenarios should attempt to capture the range of possible future climate in a particular region. One way of selecting these scenarios is to examine changes in particular climate variables over the region of interest and then to select scenarios which span the range of changes. If it is known that the particular impact sector under study is most sensitive to changes in particular climate variables in a certain season, then the changes in these variables in that season can be used to define which climate change scenarios are used Mean temperature ( C) Year Figure 4: Schematic illustrating the construction of climate change scenarios from GCM output. The graphic shows the time series of mean surface air temperature for the Canadian land area from the CGCM2 simulation forced by the SRES A2 emissions scenario. The blue line indicates the 30-year mean for the baseline period, whilst the red lines indicate the 30-year mean values for the 2020s ( ), the 2050s ( ) and the 2080s ( ). Scenarios are constructed by calculating the difference, or ratio, between the time means of the future and baseline periods. To create a climate change scenario, this process is carried out for each grid box in the region of interest. 19

20 Annual, 2020s Annual, 2050s Precipitation change (%) Precipitation change (%) Mean temperature change ( C) Mean temperature change ( C) Annual, 2080s Precipitation change (%) Mean temperature change ( C) Figure 5: Scatter plots indicating annual changes in mean temperature ( C) and precipitation (%) for Alberta for the 2020s, 2050s and 2080s. Each symbol and colour represents a different GCM and SRES experiment: A1FI; + A1T; A1; A2; B1; B2; CGCM2 - black; CCSR/NIES - green; CSIROMk2 - pink; ECHAM4 - red; NCAR-PCM - yellow; HadCM3 - blue; GFDL-R30 - cyan. Closed symbols indicate individual experiments, whilst open symbols represent ensemblemeans (i.e., the average of a number of individual experiments using identical SRES forcing). Blue lines indicate the median changes in mean temperature and precipitation and may be used to determine which scenarios are warmer, wetter, cooler or drier than other scenarios in the suite illustrated. The results for the ECHAM4 and GFDL-R30 experiments are shown although they were not considered for selection. The easiest way to make this selection is to use scatter plots of the variables in question. For this study, we have used mean temperature and precipitation change to determine which scenarios to use. Figure 5 shows scatter plots of annual mean temperature and precipitation change for the 2020s, 2050s and 2080s. These changes have been calculated over Alberta as a whole. For the 2020s and 2050s the changes are quite closely clustered together (with the exception of the CCSRNIES GCM experiments in the 2050s) and it is only in the 2080s time period that there is more separation between the individual experimental results. However, by this time there is more uncertainty inherent in the GCM output some of this is due to increased uncertainty associated with the emissions scenarios feeding into the GCMs, and some is due to differences in the GCMs mathematical structure. This means that we have less confidence in the GCMs results for this later time period. Hence, we elected to base our selection on the results for the 2050s, since this time period gives more separation in the experimental results, but at the same time contains less uncertainty than the results for the 2080s. 20

21 Winter, 2050s Summer, 2050s Precipitation change (%) Precipitation change (%) Mean temperature change ( C) Mean temperature change ( C) Spring, 2050s Fall, 2050s Precipitation change (%) Precipitation change (%) Mean temperature change ( C) Mean temperature change ( C) Figure 6: Scatter plots indicating seasonal changes in mean temperature ( C) and precipitation (%) for Alberta for the 2050s. Each symbol and colour represents a different GCM and SRES experiment: A1FI; + A1T; A1; A2; B1; B2; CGCM2 - black; CCSR/NIES - green; CSIROMk2 - pink; ECHAM4 - red; NCAR-PCM - yellow; HadCM3 - blue; GFDL-R30 - cyan. Closed symbols indicate individual experiments, whilst open symbols represent ensemble-means (i.e., the average of a number of individual experiments using identical SRES forcing). Blue lines indicate the median changes in mean temperature and precipitation and may be used to determine which scenarios are warmer, wetter, cooler or drier than other scenarios in the suite illustrated. The results for the ECHAM4 and GFDL-R30 experiments are shown although they were not considered for selection. Further examination of the scatter plots of annual mean temperature and precipitation change for the 2050s indicated that all the climate change scenarios exhibited increases in precipitation. In order to capture the full range of possible future climates (according to the current state of the science) we examined the seasonal scatter plots for this time period (Figure 6). For the summer season scenarios exist which exhibited both increases and decreases in precipitation and so our selection was based on this season. The blue lines in each scatter plot indicate the median changes in mean temperature and precipitation for the suite of scenarios illustrated and can be used to determine which scenarios indicate conditions which are warmer, cooler, drier and wetter than other scenarios in the suite illustrated. Five scenarios were selected, four representing the more extreme changes in mean temperature and precipitation, and one representing median conditions. They were: NCAR-PCM A1B (cooler, wetter), CGCM2 B2(3) (cooler, drier), HadCM3 A2(a) (warmer, wetter), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). The attributes of these scenarios are listed in Table 3 21

22 Table 3: Attributes of the five scenarios selected for use in this study. GCM Acronym Emissions scenario Scenario type Latitude resolution ( ) Longitude resolution ( ) Window limits CGCM2 B2 (3)* Cooler, drier N W CCSRNIES A1FI Warmer, drier N W NCAR-PCM A1B Cooler, wetter N W HadCM3 A2 (a)* Warmer, wetter N W HadCM3 B2 (b)* Median N W *An ensemble of experiments was carried out with this emissions forcing with this GCM. The number or letter is the experiment identifier : Applying climate change scenarios The next step in the climate change scenario process is to calculate the climate scenarios, i.e., the representations of actual climate for the 2020s, 2050s and 2080s. Here, the five climate change scenarios selected for use in Section were combined with the observed baseline climatology constructed using the Alberta Climate Model (ACM) and described in Anon. (2004a, b). This observed baseline climatology is at 1 km resolution ( latitude/longitude) compared with resolutions ranging from 2.5 to for the GCM-derived climate change scenarios. Obviously there is a mismatch of spatial scales and so we interpolated the climate change scenarios to 0.5 latitude/longitude resolution using a bilinear two-dimensional interpolation routine available in the MATLAB software package. This interpolation procedure smoothes the discontinuities between the coarse GCM grid boxes and, given the coarse nature of the climate change scenario data, it was felt that no further value would be added to this data by interpolating to the same resolution as the ACM. Figure 7 illustrates the effect of interpolating coarse-scale data to 0.5 latitude/longitude resolution. The interpolated climate change scenario information was then applied to the observed baseline climatology. Each 0.5 resolution scenario grid box contains 60 grid boxes for the baseline climatology. Climate scenarios were constructed for minimum, maximum and mean temperature and precipitation for the 2020s, 2050s and 2080s by matching the scenario grid boxes with the baseline climatology grid boxes and applying the appropriate scenario changes to the baseline data : Climate change scenarios for annual moisture index and degree days above a threshold temperature of 5 C In addition to the climate scenarios for temperature and precipitation, it was also necessary to construct scenarios for annual moisture index 2 (AMI) and degree days above a threshold temperature of 5 C (DD5), the latter often referred to as growing degree days since it gives a general indication of the warmth of the growing season. Both of these indices are based on daily temperature information. One of the limitations of using GCM information for climate change scenario construction is that one is often constrained by the lack of daily data available from 2 Annual moisture index is degree days >5 C/mean annual precipitation. 22

23 Original GCM resolution Interpolated to 0.5 latitude/longitude Figure 7: Examples of interpolating coarse-scale climate change scenario information to 0.5 latitude/longitude resolution, using a bilinear 2D interpolation procedure. Changes in mean temperature for the 2050s for the CCSRNIES A1FI and NCARPCM A1B scenarios are shown. climate change experiments undertaken with these models. Many climate modelling centres archive only a limited amount of daily data simply because they do not have the resources for storing the huge quantities of data that are quickly generated in climate change experiments. So, although there are some daily data available, they are for a limited number of climate change experiments. In order to construct climate change scenarios for AMI and DD5 using our five selected scenarios, it was therefore necessary to devise a means of determining daily data from the monthly GCM output available. Anon. (2004a) encountered a similar problem when dealing with observed climate data and opted to use a regression approach linking observed monthly average temperature and degree days above a threshold temperature of 5 C. The regression equations derived in that study cannot be assumed to be valid for future climate and that approach would require thorough testing before it could be used in changed climate conditions. Instead, a relatively simple method was adopted for obtaining daily climatological values from monthly means (Epstein, 1991). This method is based on a harmonic fit and improves upon a simple linear interpolation between monthly means since it allows the daily values to exceed the maximum monthly value and to be less than the minimum monthly value whilst linearly interpolated daily values will always lie within the range of the monthly means. In this method (Epstein, 1991), the unknown daily climatology can be represented by the sum of harmonic components: 23

24 Mean temperature ( C) Julian day Mean temperature ( C) Julian day Series1 Series2 Series1 Series2 Mean temperature ( C) Julian day Series1 Series2 Figure 8: Examples of the harmonic fit algorithm used to derive daily mean temperature from monthly mean temperature values. GCM monthly mean temperatures are indicated by the pink squares (series 2), whilst the daily mean temperature values are shown by the black line (series 1). Each graphic illustrates output from a different grid box for a single year. 2πjt 2πjt yt () = a + a cos + j b sin 0 j, j=1,6 (1) j where t is time (months), and Y T a 0 = 12 a b j a j T j j jt = π π 2π / sin Y cos / 6 T T 12 j=1,5 j j jt = π π 2π / sin Y sin / 6 T T 12 j=1,5 [ ( πt) 12] = 6 π 2 π 2 /sin Y T cos / T b =

25 The value of t in Equation (1), for the mth day of month number T is given by t=(t-0.5)+(m-0.5)/d, where D is the number of days in month T. Y T are the climatological monthly mean values. This approach was tested using monthly mean temperature for a number of individual grid boxes to ensure that this algorithm performed well (Figure 8). This algorithm was applied for each year of the four time periods ( , , and ) and for each grid box in the region of interest. Degree days above a threshold of 5 C were then calculated by summing the difference between the daily mean temperature and the threshold temperature on days when the mean temperature exceeded the threshold temperature. Again, this was carried out for each year of the four time periods. Once the DD5 had been calculated, it was possible to calculate the AMI for each year by dividing the degree day totals by the annual mean precipitation. In order to construct climate change scenarios for these two variables, the 30-year mean values were calculated for each time period and the changes between the baseline and future time periods expressed in percentage terms. The climate change scenarios were then interpolated to 0.5 latitude/longitude resolution and applied to the observed baseline climatology for these two variables to obtain climate scenarios for the 2020s, 2050s and 2080s : Climate Change Scenarios for Alberta This section describes climate change scenarios for Alberta constructed according to the methodology described above. It must be remembered that the five scenarios were selected on the basis of mean temperature and precipitation changes averaged over the whole of Alberta during the summer season. This means that the annual changes described herein will not necessarily reflect the scenario type, e.g., the warmer wetter scenario changes may not be the warmest and wettest of the five scenarios at the annual time scale. Maps for annual changes only are contained in the main text, whilst maps for seasonal changes for the 2050s are illustrated in Appendix A : Mean temperature changes Figures 9 to 11 illustrate the changes in annual mean temperature for the 2020s, 2050s and 2080s, respectively. For the 2020s, there is little difference among the scenarios, with the change in annual mean temperature generally lying within the range 0-2 C (Figure 9). By the 2050s, the increase in annual mean temperature is generally between 0 C and 3 C, the exception being the warmer and drier scenario (CCSRNIES A1FI) which exhibits warming of between 3 C and 5 C. The median scenario (HadCM3 B2(b)) indicates an increase in annual mean temperature of between 2 C and 3 C, with the greater warming being in the south (Figure 10). The median scenario (HadCM3 B2(b)) indicates an increase in annual mean temperature of between 2 C and 4 C by the 2080s. For this time period, the four extreme scenarios show a general increase of between 2 C and 5 C, the exception being the warmer and drier scenario (CCSRNIES A1FI) which indicates an increase of at least 7 C to in excess of 9 C in northern Alberta (Figure 11). Seasonal changes in mean temperature for the 2050s are contained in Appendix A (Figures A1 to A4). In Winter, the largest increases in mean temperature of between 5 C and 7 C are exhibited by the warmer drier scenario (CCSRNIES A1FI). In contrast to this, the warmer and wetter scenario (HadCM3 A2(a)) indicates the smallest increases of between 0 C and 2 C. The three other scenarios exhibit increases in mean temperature of between 1 C and 4 C (Figure A1). In Spring, the warmer drier scenario (CCSRNIES A1FI) again indicates the largest increases in mean temperature of between 4 C and 7 C compared to increases between 1 C and 3 C in the other scenarios (Figure A2). The cooler wetter (NCARPCM A1B) and cooler drier (CGCM2 B2(3)) scenarios indicate the smallest increases in mean temperature in Summer, of between 1 C and 3 C, compared to between 2 C and 4 C in the other scenarios (Figure A3). In the Fall, the 25

26 median (HadCM3 B2(b)) scenario exhibits increases in mean temperature of between 2 C and 3 C, compared to between 1 C and 2 C for the cooler drier (CGCM2 B2(3)) scenario and between 3 C and 5 C for the warmer drier (CCSRNIES A1FI) scenario (Figure A4) : Precipitation changes Changes in annual mean precipitation are illustrated in Figures 12 to 14 for the 2020s, 2050s and 2080s, respectively. For the 2020s, four out of the five scenarios indicate changes in precipitation in the range 5% to +10%, although the warmer, drier scenario (CCSRNIES A1FI) exhibits a general decrease in precipitation over the whole province of up to 10%, with the largest decreases in precipitation occurring in the far south. By the 2050s, the median scenario (HadCM3 B2(b)) indicates a decrease in annual mean precipitation of about 5% in the south, but increases of between 5% and 10% in the north-west of the province. The two wetter scenarios (NCARPCM A1B and HadCM3 A2(a)) indicate increases in annual mean precipitation of between 0% and 15%. For the two drier scenarios (CGCM2 B2(3) and CCSRNIES A1FI) the range of annual mean precipitation change is between 10% and +15% during this time period. For the 2080s, all scenarios show increases in annual mean precipitation of between 0% and 15%, the exception being the warmer, drier scenario (CCSRNIES A1FI) which indicates a small area of precipitation decrease in the far south of the province, but increases of at least 15% to in excess of 25% in other areas of the province. Figures A5-A8 in Appendix A illustrate the seasonal changes in precipitation for the 2050s. In Winter, all scenarios except that of CGCM2 B2(3) (cooler drier) indicate increases in precipitation. For this cooler drier scenario there are bands of precipitation decrease in the north (5% to 15%) and south (0% to 5%) of the province separated by an area of precipitation increase of between 5% and 10% in central Alberta. The remaining four scenarios indicate precipitation increases of at least 10%, with the median (HadCM3 B2(b)) and warmer wetter (HadCM3 A2(a)) scenarios exhibiting areas with increases in excess of 25% (Figure A5). In Spring, a similar picture exists with all scenarios except that of CGCM2 B2(3) (cooler drier) indicating precipitation increase. In general, increases in precipitation are between 5% and 15%, although the median (HadCM3 B2(b)) scenario indicates increases of between 15% and 20% in the south and between 5% and 10% in the north. The warmer drier scenario (CCSRNIES A1FI) exhibits precipitation increases in excess of 25% in the northern half of the province (Figure A6). In contrast in Summer all scenarios show some areas of precipitation decrease. The median (HadCM3 B2(b)) scenario indicates a gradient of precipitation decrease by as much as 15% in the south-west through to an increase of between 5% and 15% in the north-east of the province. The cooler drier (CGCM2 B2(3)) scenario continues to exhibit a pattern of precipitation increase in central Alberta (of about 5%) bounded by areas of precipitation decrease in the north (10%) and south (10% to 15%) of the province. The patterns of change for the remaining three scenarios are different: the warmer drier (CCSRNIES A1FI) scenario indicates decreases in precipitation by as much as 20% in the north and west, with increases of between 10% and 15% in the south-east; the cooler wetter (NCARPCM A1B) scenario shows an opposite pattern to that of the cooler drier scenario with increases of between 5% and 20% in the north and south and an area of precipitation decrease in central Alberta of about 5%, whilst the warmer wetter scenario (HadCM3 A2(a)) indicates precipitation increases of between 5% and 20% in the north and decreases of up to 10% in the south and west (Figure A7). In Fall, all scenarios indicate increases in precipitation, although the warmer wetter (HadCM3 A2(a)) scenario also has some areas of slight precipitation decrease (5%). The cooler wetter scenario (NCARPCM A1B) indicates increases of between 5% and 20%, the cooler drier (CGCM2 B2(3)) scenario of between 0% and 10%, and the warmer drier (CCSRNIES A1FI) scenario indicates larger increases in the north of the province (10%-20%) compared to 0% to 5% in the south. For the median scenario (HadCM3 26

27 B2(b)) increases are between 0% and 20%, with the largest increases occurring in the northern half of the province (Figure A8) : Maximum temperature changes Changes in annual maximum temperature are very similar to those for annual mean temperature. For the 2020s, all scenarios exhibit increases in annual maximum temperature of between 0 C and 2 C (Figure 15). By the 2050s, the general increase in annual maximum temperature is between 1 C and 3 C, with the exception being the warmer and drier scenario (CCSRNIES A1FI) where increases are projected to be between 3 C and 5 C (Figure 16). For the 2080s, the cooler wetter scenario (NCARPCM A1B) exhibits the smallest increases in annual maximum temperature of between 2 C and 3 C, whilst the warmer drier scenario (CCSRNIES A1FI) indicates the largest increases of at least 7 C, but to in excess of 9 C. In general, increases in annual maximum temperature are between 2 C and 4 C for this time period (Figure 17). Seasonal changes in maximum temperature for the 2050s are illustrated in Appendix A (Figures A9-A12). In Winter, the largest increases (4 C to 7 C) are exhibited by the warmer drier scenario (CCSRNIES A1FI), with the largest of these increases occurring in the north-east of the province. In contrast the warmer wetter scenario (HadCM3 A2(a)) indicates increases in maximum temperature of, at most, 2 C. The remaining three scenarios indicate similar magnitudes of change, with increases of between about 1 C and 4 C, although the cooler wetter scenario (NCARPCM A1B) exhibits increases of up to 5 C in the north of the province (Figure A9). In Spring, maximum temperature increases are generally between 1 C and 2 C, although the cooler drier scenario (CGCM2 B2(3)) and the warmer drier scenario (CCSRNIES A1FI) indicate larger increases in maximum temperature of between 1 C and 4 C and between 4 C and 7 C, respectively (Figure A10). In Summer, the two scenarios designated as cooler than the median (NCARPCM A1B and CGCM2 B2(3)) exhibit the smallest increases in maximum temperature of between 1 C and 2 C, whilst the two warmer scenarios (HadCM3 A2(a) and CCSRNIES A1FI) exhibit a similar pattern of increase with the largest warming occurring in the south (3 C to 4 C) and a smaller increase of between 2 C and 3 C occurring in the north. The median scenario (HadCM3 B2(b)) shows a similar pattern of maximum temperature increase (Figure A11). In Fall, the warmer drier scenario (CCSRNIES A1FI) indicates the largest increases in maximum temperature of between 4 C and 5 C in the northern half of the province and of between 3 C and 4 C in the south. The cooler drier scenario (CGCM2 B2(3)) indicates the smallest increases in maximum temperature, of between 1 C and 2 C, whilst the remaining three scenarios (HadCM3 B2(b), NCARPCM A1B and HadCM3 A2(a)) all exhibit increases of between 2 C and 3 C (Figure A12) : Minimum temperature changes On the whole, the projected increases in annual minimum temperature for all five scenarios are slightly greater than those for annual maximum temperature. This is in keeping with the faster rise observed in minimum temperatures compared to maximum temperatures in continental areas (Easterling et al., 1997). For the 2020s, increases in annual minimum temperature are generally between 0 C and 2 C (Figure 18). By the 2050s, the cooler wetter scenario (NCARPCM A1B) indicates the smallest increase in annual minimum temperature (0 C to 2 C), whilst the warmer drier scenario (CCSRNIES A1FI) exhibits the largest increases of between 3 C and 5 C. The median (HadCM3 B2(b)) increase in annual minimum temperature is between 2 C and 4 C (Figure 19). For the 2080s, the smallest increases in annual minimum temperature are between 2 C and 4 C (NCARPCM A1B), whilst the largest increases exhibited are of at least 8 C to in excess of 9 C (CCSRNIES A1FI). The median scenario (HadCM3 B2(b)) increases in annual minimum temperature are between 2 C and 5 C for this time period (Figure 20). 27

28 Seasonal changes in minimum temperature for the 2050s are similar to those of maximum temperature (see Appendix A, Figures A13-A16). In Winter, the warmer drier scenario (CCSRNIES A1FI) exhibits the largest increases in minimum temperature, with the largest warming (6 C to 7 C) occurring in the north-east of the province. In contrast, the median scenario (HadCM3 B2(b)) exhibits the largest warming (4 C to 5 C) in the far south of the province, and increases of between 2 C and 4 C elsewhere. The warmer wetter scenario (HadCM3 A2(a)) exhibits the smallest increase in minimum temperature, of between 0 C and 2 C, whilst the cooler wetter (NCARPCM A1B) and cooler drier (CGCM2 B2(3)) scenarios both indicate minimum temperature increases of between 4 C and 6 C, although the NCARPCM A1B scenario indicates slightly reduced warming (3 C to 4 C) in the south of the province (Figure A13). In Spring, the minimum temperature increases are generally less than those occurring in winter both the median (HadCM3 B2(b)) and warmer wetter (HadCM3 A2(a)) scenarios indicate increases of between 1 C and 2 C, whilst the warmer drier (CCSRNIES A1FI) and cooler drier (CGCM2 B2(3)) scenarios indicate increases of between 4 C and 6 C in general. The cooler wetter (NCARPCM A1B) scenario exhibits minimum temperature increases of between 1 C and 3 C, with the smallest changes occurring in the south-east and central Alberta (Figure A14). In Summer, the median (HadCM3 B2(b)) and warmer wetter (HadCM3 A2(a)) scenarios exhibit similar patterns of minimum temperature increase, of between 2 C and 3 C in general, but as much as 3 C to 4 C in the far south-east corner. The cooler wetter scenario (NCARPCM A1B) exhibits the smallest increases in summer minimum temperature, of between 1 C and 2 C over the whole province. The cooler drier scenario (CGCM2 B2(3)) indicates a similar magnitude of warming, although minimum temperature increases are slightly larger in the north (2 C to 3 C). The warmer drier scenario (CCSRNIES A1FI) shows increases in minimum temperature of a similar magnitude as the median scenario (Figure A15). In Fall, the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B) and warmer wetter (HadCM3 A2(a)) scenarios indicate a similar magnitude of minimum temperature increase, of between 2 C and 3 C. The cooler drier scenario (CGCM2 B2(3)) shows increases which are about half of this, whilst the warmer drier scenario (CCSRNIES A1FI) exhibits the largest increases in minimum temperature, of between 3 C and 4 C in the southern half of the province and between 4 C and 5 C in the northern half of the province (Figure A16) : Degree day changes For all time periods there is a general increase in the number of degree days above a threshold temperature of 5 C (Figures 21-23). For the 2020s, this increase is generally between 0% and 20% (Figure 21). For the 2050s and 2080s, a similar pattern of change is exhibited in the median (HadCM3 B2(b)), cooler wetter (NCARPCM A1B) and warmer wetter (HadCM3 A2(a)) scenarios, with the largest degree-day increases in south-west Alberta. The NCARPCM and HadCM3 GCMs have a higher resolution than the CGCM2 and CCSRNIES GCMs and this pattern of increase is likely due to their better representation of the Rocky Mountains. For the 2050s, these three scenarios exhibit a general increase of 30-50%, with larger increases in the south-west, whilst the remaining two scenarios indicate a general increase in the number of degree days of between 20% and 50% (Figure 22). For the 2080s, the largest degree-day increases are exhibited by the warmer drier scenario (CCSRNIES A1FI) and warmer wetter (HadCM3 A2(a)) scenarios, with increases in excess of 90%, (i.e., probably an approximate doubling in the number of degree days) and between 70% and 80%, respectively. In general, however, increases of between 40% and 50% are indicated (Figure 23). 28

29 : Annual moisture index changes Changes in the annual moisture index (i.e., degree days >5 C/mean annual precipitation) exhibit similar patterns to those of degree-day changes (Figures 24-26). For the 2020s, increases in annual moisture index are between 0% and 20% (Figure 24). By the 2050s, increases are generally between 20% and 30%, with larger increases in the south-west in the median scenario (HadCM3 B2(b)). For the 2080s, the largest increases in annual moisture index are exhibited by the warmer drier (CCSRNIES A1FI) and warmer wetter (HadCM3 A2(a)) scenarios, with increases between 70% and 90% and between 50% and 70%, respectively, again with largest increases in the south-west. There is a general increase in the annual moisture index of between 20% and 30% in this time period (Figure 26). 29

30 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 9: Annual mean temperature change ( C) for the 2020s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 30

31 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 10: Annual mean temperature change ( C) for the 2050s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 31

32 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 11: Annual mean temperature change ( C) for the 2080s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 32

33 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 12: Annual precipitation change (%) for the 2020s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 33

34 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 13: Annual precipitation change (%) for the 2050s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 34

35 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 14: Annual precipitation change (%) for the 2080s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 35

36 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 15: Annual maximum temperature change ( C) for the 2020s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 36

37 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 16: Annual maximum temperature change ( C) for the 2050s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 37

38 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 17: Annual maximum temperature change ( C) for the 2080s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 38

39 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 18: Annual minimum temperature change ( C) for the 2020s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 39

40 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 19: Annual minimum temperature change ( C) for the 2050s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 40

41 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 20: Annual minimum temperature change ( C) for the 2080s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 41

42 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 21: Change in degree days > 5 C (%) for the 2020s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 42

43 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 22: Change in degree days > 5 C (%) for the 2050s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 43

44 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 23: Change in degree days > 5 C (%) for the 2080s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 44

45 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 24: Change in annual moisture index (%) for the 2020s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 45

46 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 25: Change in annual moisture index (%) for the 2050s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 46

47 Cooler, wetter Warmer, wetter Cooler, drier Warmer, drier Median Figure 26: Change in annual moisture index (%) for the 2080s with respect to Climate change scenarios are shown for NCARPCM A1B (cooler, wetter), HadCM3 A2(a) (warmer, wetter), CGCM2 B2(3) (cooler, drier), CCSRNIES A1FI (warmer, drier) and HadCM3 B2(b) (median). Each scenario is shown at the original GCM resolution and the window depicted is the area used for interpolation to 0.5 latitude/longitude resolution. 47

48 2.2.5: Climate Scenarios for Alberta The climate change scenarios described in Section were applied to the observed baseline climate constructed using the Alberta Climate Model (Anon., 2004a) to produce climate scenarios for Alberta. The method used to construct the observed baseline climate includes the effects of elevation (Anon., 2004a) and thus the underlying topography of Alberta dominates the climate maps. The effects of elevation may be seen on temperature (colder higher elevations and warmer valleys) and on precipitation (generally higher precipitation in higher elevation areas and some rain shadow effects to the east of the Rocky Mountains). This section describes the mean annual future climate for the 2020s, 2050s and 2080s resulting from the application of the five selected scenarios. This discussion is tailored to the results at six sites in Alberta Lethbridge, Medicine Hat, Calgary, Edmonton, Grande Prairie and Fort McMurray and focuses on mean temperature, precipitation, degree days > 5 C and annual moisture index. The results for maximum and minimum temperature are very similar to those of mean temperature and so are not discussed further. Maps for annual mean climate for the median climate scenario (HadCM3 B2(b)) for the three future time periods are included in the main text, whilst maps for the annual mean future climates corresponding to the four extreme scenarios are illustrated in Appendix B. Seasonal climates for the 2050s corresponding to all five of the selected climate change scenarios are illustrated, where applicable, in Appendix C. Tables containing the scenario results for the six sites for the 2050s are also provided in Appendix C (Tables C1-C22) : Mean temperature Figure 33 indicates annual mean temperature for Alberta and the topographic effects described above are easily apparent, with the general picture indicating warmer conditions in the south-east of the province and cooler conditions in the north. Since this baseline climate forms the foundation for climate scenario construction, similar patterns are apparent in the maps for the 2020s, 2050s and 2080s (Figures and Figures B1-B4 in Appendix B), although future conditions are obviously warmer. Table 4 summarises the annual mean temperature for the six selected sites for the median scenario (HadCM3 B2(b)). By the 2050s, annual mean temperature at Grande Prairie and Fort McMurray, the two northern-most sites, is projected to be similar to that currently observed at Calgary and Edmonton, respectively, and this is reflected in Figures 34-36, which show the gradual march north of warmer annual mean temperatures. Figure 27 illustrates the range of scenario results for annual mean temperature for the six sites for the baseline period and the 2020s, 2050s and 2080s. This range has been derived from the five selected scenarios and indicates simply the minimum and maximum values of this group of five scenarios. It is apparent from this figure that the scenario range increases over time. Some of this increase will be due to climate change and some is due to increasing uncertainty in factors such as the emissions scenarios and the climate models themselves. This type of plot also allows for easy comparison across the sites and shows, for example, that by the 2050s the annual mean temperature range at Calgary is warmer than current conditions at Lethbridge and Medicine Hat, and that by the 2080s, future conditions at Grande Prairie and Fort McMurray may also be warmer than current conditions at these two sites. It also indicates that the annual mean temperature range for the 2050s exceeds that of the 2020s, but that there is some overlap between the ranges in the 2050s and 2080s. Without exception, the largest increases in mean temperature are derived at all sites from the CCSRNIES A1FI scenario which consistently gives values which are approximately 1 C to 2 C warmer than the other scenarios. 48

49 Table 4: Annual mean temperature ( C) at selected sites in Alberta, for (from the Alberta Climate Model) and for the median scenario (HadCM3 B2(b)) for the 2020s, 2050s and 2080s s 2050s 2080s Lethbridge Medicine Hat Calgary Edmonton Grande Prairie Fort McMurray Figure 28 illustrates the range of future seasonal mean temperature for the six sites for the 2020s, 2050s and 2080s. On this figure, four blocks of data are given for each site and, from left to right these represent Winter (DJF), Spring (MAM), Summer (JJA) and Fall (SON) conditions, respectively. Spring and Fall mean temperatures are very similar both for the baseline and future scenarios, whilst the range in scenario results tends to be larger for the Winter season compared to the Summer season. Winter mean temperature at the three northern-most sites may approach that currently observed at Lethbridge, Medicine Hat and Calgary by the 2050s for Edmonton and by the 2080s for Grande Prairie and Fort McMurray. It is generally the 2080s before Summer mean temperature at Calgary, Edmonton, Grande Prairie and Fort McMurray approaches that currently observed at Lethbridge and Medicine Hat. 16 Annual 14 Mean temperature ( C) Lethbridge Medicine Hat Calgary Edmonton Grande Prairie Fort McMurray Figure 27: Annual mean temperature ( C) for six selected sites in Alberta. At each site baseline ( ) conditions (black square), and the scenario ranges for the 2020s (blue high-low lines), the 2050s (black high-low lines) and the 2080s (red high-low lines) are illustrated. The scenario range has been calculated from the results for the five selected scenarios. 49

50 30 25 JJA 20 Mean temperature ( C) MAM SON -10 DJF Lethbridge Medicine Hat Calgary Edmonton Grande Prairie Fort McMurray Figure 28: Seasonal mean temperature ( C) for six selected sites in Alberta. At each site there are four blocks of data [baseline ( ) conditions (black square), and the scenario ranges for the 2020s (blue high-low lines), the 2050s (black high-low lines) and the 2080s (red high-low lines]. Each block of data represents a single season: from left to right Winter (DJF), Spring (MAM), Summer (JJA) and Fall (SON). The scenario range has been calculated from the results for the five selected scenarios : Precipitation Maps of annual total precipitation averaged over and for the 2020s, 2050s and 2080s for the median (HadCM3 B2(b)) scenario are illustrated in Figures The scenario results are not as easily apparent as those for temperature. This is confirmed by viewing Table 5 and Figure 29 which indicate that, for the earlier time periods in particular, the changes in precipitation are quite small. For Lethbridge and Medicine Hat, in particular, the scenario range overlaps with the baseline annual precipitation total in both the 2020s and 2050s, and it is only by the 2080s that the scenario range exceeds the baseline value. For Edmonton, Grande Prairie and Fort McMurray, the annual precipitation total exceeds the average by the 2050s. The increase in uncertainty over time is also not so apparent as with the results for annual mean temperature, and it is really only at the three northern-most sites that there is a larger range in precipitation values in the 2080s. All scenarios indicate, however, that annual precipitation totals are projected to increase after the 2050s. Where annual precipitation totals are sometimes less than the baseline value, this decrease is generally driven by decreases in summer precipitation (see Figure 30). 50

51 Table 5: Annual precipitation (mm) at selected sites in Alberta, for (from the Alberta Climate Model) and for the median scenario (HadCM3 B2(b)) for the 2020s, 2050s and 2080s s 2050s 2080s Lethbridge Medicine Hat Calgary Edmonton Grande Prairie Fort McMurray Annual Precipitation total (mm) Lethbridge Medicine Hat Calgary Edmonton Grande Prairie Fort McMurray Figure 29: Annual precipitation total (mm) for six selected sites in Alberta. At each site baseline ( ) conditions (black square), and the scenario ranges for the 2020s (blue high-low lines), the 2050s (black high-low lines) and the 2080s (red high-low lines) are illustrated. The scenario range has been calculated from the results for the five selected scenarios : Degree days Degree days are a temperature-based index and can be used with any relevant threshold temperature. In this case, 5 C has been used as the threshold temperature since this value is widely recognised as indicative of general plant growth. Since this index is temperature based, similar patterns of increase are seen in this index as are apparent in the maps for mean temperature increase (see Figures and Figures 33-36, respectively). The gradual increase in this index over time implies a lengthening of the growing season and/or the availability of more heat units for plant growth during the growing season. Table 6 indicates the magnitude of this index for the baseline climate and for the median (HadCM3 B2(b)) scenario for the 2020s, 2050s and 2080s at the six Alberta sites. For this scenario, increases are generally between 900 and 1200 degree-day units, although this number is reduced at Fort McMurray where the increase is approximately 600 units, by the 2080s. Figure 31 summarises the results of all scenarios for the 2020s, 2050s and 2080s for the six Alberta sites. As has been the case for all climate variables, the largest range in scenario results occurs for the 2080s. By the 2050s, Calgary, Edmonton, Grande Prairie and Fort McMurray may approach degree-day conditions similar to those currently observed at Lethbridge and Medicine Hat. 51

52 Precipitation total (mm) JJA MAM SON 50 DJF 0 Lethbridge Medicine Hat Calgary Edmonton Grande Prairie Fort McMurray Figure 30: Seasonal precipitation total (mm) for six selected sites in Alberta. At each site there are four blocks of data [baseline ( ) conditions (black square), and the scenario ranges for the 2020s (blue high-low lines), the 2050s (black high-low lines) and the 2080s (red high-low lines]. Each block of data represents a single season: from left to right Winter (DJF), Spring (MAM), Summer (JJA) and Fall (SON). The scenario range has been calculated from the results for the five selected scenarios. Table 6: Degree days > 5 C at selected sites in Alberta, for (from the Alberta Climate Model) and for the median scenario (HadCM3 B2(b)) for the 2020s, 2050s and 2080s s 2050s 2080s Lethbridge Medicine Hat Calgary Edmonton Grande Prairie Fort McMurray

53 Degree days > 5 C Lethbridge Medicine Hat Annual Calgary Edmonton Grande Prairie Fort McMurray Figure 31: Degree days > 5 C for six selected sites in Alberta. At each site baseline ( ) conditions (black square), and the scenario ranges for the 2020s (blue high-low lines), the 2050s (black high-low lines) and the 2080s (red high-low lines) are illustrated. The scenario range has been calculated from the results for the five selected scenarios : Annual moisture index Annual moisture index, defined as the ratio of the annual degree day total (using a threshold temperature of 5 C) to annual total precipitation, combines temperature and precipitation information into an index which can be used to give an indication of moisture availability for plant growth. Increases (decreases) in this index indicate either increases (decreases) in the degree day total, or decreases (increases) in annual precipitation. Figures illustrate baseline ( ) and median scenario (HadCM3 B2(b)) conditions for the 2020s, 2050s and 2080s. Increases in this index are apparent province-wide, with the most noticeable increases being apparent in the south-east of the province around Lethbridge, Medicine Hat and Calgary. This increase is driven by the large increases in degree days at these sites, rather than by a large decrease in precipitation. In fact, annual precipitation increases at all these sites by the 2080s but not by an amount sufficient to offset the degree day increase. This implies that although conditions are warmer and therefore favourable for enhanced plant growth, the precipitation increase is insufficient to support this and moisture stress is likely to occur. Table 7 lists the results for the six Alberta sites for baseline and median scenario conditions and Figure 32 summarises the range of results for the 2020s, 2050s and 2080s for all five scenarios. It is apparent from this figure that the largest increases occur at Lethbridge and Medicine Hat, the two southern-most sites, and that increases are less pronounced at the more central and northern sites. For Grande Prairie and Fort McMurray, in particular, there is little difference between the scenarios until the 2080s when the scenario range increases. It is also true to say that the largest range of scenario results occurs for the 2080s for all sites, although for the more southern sites the range for the 2050s is also pronounced. For Calgary, Edmonton, Grande Prairie and Fort McMurray, annual moisture index values generally only approach those currently observed at Lethbridge and Medicine Hat by the 2080s. 53

54 Table 7: Annual moisture index at selected sites in Alberta, for (from the Alberta Climate Model) and for the median scenario (HadCM3 B2(b)) for the 2020s, 2050s and 2080s s 2050s 2080s Lethbridge Medicine Hat Calgary Edmonton Grande Prairie Fort McMurray Annual 10 Annual Moisture Index Lethbridge Medicine Hat Calgary Edmonton Grande Prairie Fort McMurray Figure 32: Annual moisture index for six selected sites in Alberta. At each site baseline ( ) conditions (black square), and the scenario ranges for the 2020s (blue high-low lines), the 2050s (black high-low lines) and the 2080s (red high-low lines) are illustrated. The scenario range has been calculated from the results for the five selected scenarios. 54

55 N W E S Fort McMurray Grande Prairie Edmonton Mean annual temperature (C) < > Kilometers Calgary Lethbridge Medicine Hat Figure 33: Annual mean temperature ( C) for the baseline period. Results are from the Alberta Climate Model, as described in Anon. (2004a). 55

56 N W E S Fort McMurray Grande Prairie Edmonton 2020s HADb mean annual temperature (C) < > Kilometers Calgary Lethbridge Medicine Hat Figure 34: Annual mean temperature ( C) for the median scenario (HadCM3 B2(b)) for the 2020s. 56

57 N W E S Fort McMurray Grande Prairie Edmonton Mean annual temperature (C) < > Kilometers Calgary Lethbridge Medicine Hat Figure 35: Annual mean temperature ( C) for the median scenario (HadCM3 B2(b)) for the 2050s. 57

58 N W E S Fort McMurray Grande Prairie Edmonton 2080s HADb mean annual temperature (C) < > Kilometers Calgary Lethbridge Medicine Hat Figure 36: Annual mean temperature ( C) for the median scenario (HadCM3 B2(b)) for the 2080s. 58

59 N W E S Fort McMurray Grande Prairie Edmonton Mean annual precipitation (mm) > 2200 No Data Kilometers Calgary Lethbridge Medicine Hat Figure 37: Annual precipitation (mm) for the baseline period. Results are from the Alberta Climate Model, as described in Anon. (2004a). 59

60 N W E S Fort McMurray Grande Prairie Edmonton 2020s HADb annual precipitation (mm) > 2200 No Data Kilometers Calgary Lethbridge Medicine Hat Figure 38: Annual precipitation (mm) for the median scenario (HadCM3 B2(b)) for the 2020s. 60

61 N W E S Fort McMurray Grande Prairie Edmonton Mean annual precipitation (mm) > 2200 No Data Kilometers Calgary Lethbridge Medicine Hat Figure 39: Annual precipitation (mm) for the median scenario (HadCM3 B2(b)) for the 2050s. 61

62 N W E S Fort McMurray Grande Prairie Edmonton 2080s HADb annual precipitation (mm) > 2200 No Data Kilometers Calgary Lethbridge Medicine Hat Figure 40: Annual precipitation (mm) for the median scenario (HadCM3 B2(b)) for the 2080s. 62

63 N W E S Fort McMurray Grande Prairie Edmonton Growing degree-days > 5 C > Kilometers Calgary Lethbridge Medicine Hat Figure 41: Degree days > 5 C for the baseline period (output from the Alberta Climate Model). 63

64 N W E S Fort McMurray Grande Prairie Edmonton 2020s HADb growing degree days < 5C > Kilometers Calgary Lethbridge Medicine Hat Figure 42: Degree days > 5 C for the median scenario (HadCM3 B2(b)) for the 2020s. 64

65 N W E S Fort McMurray Grande Prairie Edmonton Growing degree-days > 5 C > Kilometers Calgary Lethbridge Medicine Hat Figure 43: Degree days > 5 C for the median scenario (HadCM3 B2(b)) for the 2050s. 65

66 N W E S Fort McMurray Grande Prairie Edmonton 2080s HADb growing degree days < 5C > Kilometers Calgary Lethbridge Medicine Hat Figure 44: Degree days > 5 C for the median scenario (HadCM3 B2(b)) for the 2080s. 66

67 N W E S Fort McMurray Grande Prairie Edmonton Annual moisture index > Kilometers Calgary Lethbridge Medicine Hat Figure 45: Annual moisture index for the baseline period (output from the Alberta Climate Model). 67

68 N W E S Fort McMurray Grande Prairie Edmonton 2020s HADb annual moisture index > Kilometers Calgary Lethbridge Medicine Hat Figure 46: Annual moisture index for the median scenario (HadCM3 B2(b)) for the 2020s. 68

69 N W E S Fort McMurray Grande Prairie Edmonton Annual moisture index > Kilometers Calgary Lethbridge Medicine Hat Figure 47: Annual moisture index for the median scenario (HadCM3 B2(b)) for the 2050s. 69

70 N W E S Fort McMurray Grande Prairie Edmonton 2080s HADb annual moisture index > Kilometers Calgary Lethbridge Medicine Hat Figure 48: Annual moisture index for the median scenario (HadCM3 B2(b)) for the 2080s. 70

71 3.0: FUTURE RESEARCH DIRECTIONS This report has described the construction of climate change scenarios for Alberta using global climate model output in accordance with criteria recommended by the IPCC. It is important to conform with these criteria to ensure consistency amongst climate change impacts assessments, thus permitting conclusions to be drawn concerning the likely impacts of future climate change. For the construction of physically-plausible and internally-consistent climate change scenarios for a large region like Alberta, the techniques used within this report really represent the only currently available means of developing regional-scale scenarios. To advance this work, there are a few of options: 1. To expand the number of scenarios used, so that the scenario ranges, such as those illustrated in Figure 32, can be expressed in terms of box-and-whisker plots. In this type of statistical plot, the box represents 50% of the scenario results and the whiskers the extreme scenarios. A box-and-whisker plot would give a much better idea of the spread of the scenario results and prevents the range of results being dominated by any one scenario (as is the case for the temperature scenarios described herein). Use of only five scenarios means that there is insufficient information to construct such a plot with any statistical robustness. 2. To link the scenarios with information about GCM-simulated natural climate variability and to express the projected scenario changes in terms of their significance, i.e., whether or not the projected changes are within the range of model-simulated natural climate variability. 3. To update the scenarios when the next set of global climate model results are released for use in the IPCC s Fourth Assessment Report (due out in 2007). These results are not currently available. The climate change scenarios described within this report represent plausible changes in future average climate. Thus, changes in climate variability are not included and it is these changes in variability which are likely to have the largest effect on the frequency and magnitude of extreme climate events which, in turn, tend to have the largest impact on our environment. Working at the regional scale means that the inclusion of changes in climate variability as well as changes in mean climate is not a trivial task. Techniques (such as stochastic weather generators) exist which allow the perturbation of observed time series by both changes in means and variability, but these are generally best applied at the site scale, so one option would be to focus on specific locations in Alberta, such as the six sites used in this report. If a location-specific approach is taken, then statistical downscaling of the climate change scenarios is another possible avenue for research. These techniques, in which statistical relationships are developed (where possible) between the coarse-resolution GCM output and individual site information, may add value to the scenarios by identifying the coarse-resolution drivers of the local climate conditions. Typical drivers are mean sea level pressure, relative or specific humidity and air flow, and more confidence can be placed in these GCM-derived climate variables than in variables such as precipitation. However, there are a number of disadvantages associated with the statistical downscaling approach to scenario construction, and if one of the main objectives of any future scenarios research is to ensure that the range of plausible future climates is considered, then statistical downscaling is not recommended. Another option for consideration is the use of past climate information, i.e., from prior to the beginning of the instrumental record. Where palaeo-climate information exists, this may be used to contextualise GCM-derived climate change scenarios and also to provide valuable information about environmental responses to particular climate conditions or events. 71

72 ACKNOWLEDGEMENTS This work was funded by the Prairie Adaptation Research Collaborative in co-operation with Alberta Environment. The authors thank Alberta Sustainable Resource Development for supplying the baseline data from the Alberta Climate Model which was used in the construction of the climate scenarios described in this report. The authors also thank Tammy Kobliuk of Alberta Sustainable Resource Development for technical assistance in obtaining the data. DETAILS OF DATA AND MAPS ON CD A number of CDs accompany this report. Three CDs contain the data for the climate scenarios described in Section and six CDs contain copies of the maps featured in this report. The data files are in ASCII format with 6 header lines followed by the data values (see Figure 49). Each block of data consists of 1680 columns and 1800 rows, with the data starting in the top left-hand corner of the Alberta window (i.e., the north-west) and working east (columns) and south (rows). The lower left corner of this window corresponds to 47 N, 122 W, and the resolution is latitude/longitude. A file exists for each month (jan-dec), season (djf, mam, jja, son) and annual (ann) data field, for each scenario, for each time period. Figure 49: A screen capture of a portion of one of the climate scenario data files. The maps illustrated in this report are reproduced on CD. Table 8 provides information to aid in the identification of the map files. Table 8: Information for identification of map files. Scenario Description GCM Name Short Form Median HadCM3 B2(b) hadb Cooler, wetter NCARPCM A1B ncar Cooler, drier CGCM2 B2(3) cgcm Warmer, wetter HadCM3 A2(a) hada Warmer, drier CCSRNIES A1FI csrn 72

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