Box 24 SRESbased projections of climate variability and extremes

Possible changes in variability and the frequency/severity of extreme events are critical to undertaking realistic CCIAV assessments. Past trends in extreme weather and climate events, their attribution to human influence, and projected (SRES-forced) changes have been summarised globally by WG I (IPCC, 2007) and are reproduced in Table 2.2.

Table 2.2. Recent trends, assessment of human influence on the trend, and projections for extreme weather events for which there is an observed late 20th century trend. Source: IPCC, 2007, Table SPM-2.

Phenomenon and direction of trend

Likelihood9 that trend occurred in late 20th century (typically post-1960)

Likelihooda of a human contribution to observed trend

Likelihood3 of future trends based on projections for 21st century using SRES scenarios

Warmer and fewer cold days and nights over most land areas

Very likelyb

Likely0

Virtually certain°

Warmer and more frequent hot days and nights over most land areas

Very likelyd

Likely (nights)°

Virtually certain°

Warm spells/heatwaves. Frequency increases over most land areas

Likely

More likely than note

Very likely

Heavy precipitation events. Frequency (or proportion of total rainfall from heavy falls) increases over most areas

Likely

More likely than note

Very likely

Area affected by droughts increases

Likely in many regions since 1970s

More likely than not

Likely

Intense tropical cyclone activity increases

Likely in some regions since 1970

More likely than note

Likely

Increased incidence of extreme high sea level (excludes tsunamis)*

Likely

More likely than note g

a The assessed likelihood, using expert judgement, of an outcome or a result: Virtually certain >99% probability of occurrence, Extremely likely >95%, Very likely >90%, Likely >66%, More likely than not >50%. b Decreased frequency of cold days and nights (coldest 10%). c Warming of the most extreme days and nights each year. d Increased frequency of hot days and nights (hottest 10%).

e Magnitude of anthropogenic contributions not assessed. Attribution for these phenomena based on expert judgement rather than formal attribution studies.

f Extreme high sea level depends on average sea level and on regional weather systems. It is defined here as the highest 1% of hourly values of observed sea level at a station for a given reference period. g Changes in observed extreme high sea level closely follow the changes in average sea level. It is very likely that anthropogenic activity contributed to a rise in average sea level. h In all scenarios, the projected global average sea level at 2100 is higher than in the reference period. The effect of changes in regional weather systems on sea-level extremes has not been assessed.

Notes:

a The assessed likelihood, using expert judgement, of an outcome or a result: Virtually certain >99% probability of occurrence, Extremely likely >95%, Very likely >90%, Likely >66%, More likely than not >50%. b Decreased frequency of cold days and nights (coldest 10%). c Warming of the most extreme days and nights each year. d Increased frequency of hot days and nights (hottest 10%).

e Magnitude of anthropogenic contributions not assessed. Attribution for these phenomena based on expert judgement rather than formal attribution studies.

f Extreme high sea level depends on average sea level and on regional weather systems. It is defined here as the highest 1% of hourly values of observed sea level at a station for a given reference period. g Changes in observed extreme high sea level closely follow the changes in average sea level. It is very likely that anthropogenic activity contributed to a rise in average sea level. h In all scenarios, the projected global average sea level at 2100 is higher than in the reference period. The effect of changes in regional weather systems on sea-level extremes has not been assessed.

2005 was about 379 ppm (Forster et al., 2007) and was projected in the TAR using the Bern-CC model to rise by 2100 to reference, low, and high estimates for the SRES marker scenarios of B1: 540 [486 to 681], A1T: 575 [506 to 735], B2: 611[544 to 769], A1B: 703 [617 to 918], A2: 836 [735 to 1080], and A1FI: 958 [824 to 1248] ppm (Appendix II in IPCC, 2001a). Values similar to these reference levels are commonly adopted in SRES-based impact studies; for example, Arnell et al. (2004) employed levels assumed in HadCM3 AOGCM climate simulations, and Schröter et al. (2005b) used levels generated by the IMAGE-2 integrated assessment model. However, recent simulations with coupled carbon cycle models indicate an enhanced rise in [CO2] for a given emissions scenario, due to feedbacks from changing climate on the carbon cycle, suggesting that the TAR reference estimates are conservative (Meehl et al., 2007).

Elevated levels of ground-level ozone (O3) are toxic to many plants (see Chapter 5, Box 5.2) and are strongly implicated in a range of respiratory diseases (Chapter 8, Section 8.2.6). Increased atmospheric concentrations of sulphur dioxide are detrimental to plants, and wet and dry deposition of atmospheric sulphur and nitrogen can lead to soil and surface water acidification, while nitrogen deposition can also serve as a plant fertiliser (Carter et al., 2001; see also Chapter 4, Section 4.4.1; Chapter 5, Section 5.4.3.1). Projections with global atmospheric chemistry models for the high-emissions SRES A2 scenario indicate that global mean tropospheric O3 concentrations could increase by 20 to 25% between 2015 and 2050, and by 40 to 60% by 2100, primarily as a result of emissions of NOx, CH4, CO2, and compounds from fossil fuel combustion (Meehl et al., 2007). Stricter air pollution standards, already being implemented in many regions, would reduce, and could even reverse, this projected increase (Meehl et al., 2007). Similarly, the range of recent scenarios of global sulphur and NOx emissions that account for new abatement policies has shifted downwards compared with the SRES emissions scenarios (Smith et al., 2005; Nakicenovic et al., 2007).

For the purposes of CCIAV assessment, global projections of pollution are only indicative of local conditions. Levels are highly variable in space and time, with the highest values typically occurring over industrial regions and large cities. Although projections are produced routinely for some regions in order to support air pollution policy using high-resolution atmospheric transport models (e.g., Syri et al., 2004), few models have been run assuming an altered climate, and simulations commonly assume emissions scenarios developed for air pollution policy rather than climate policy (see Alcamo et al., 2002; Nakicenovic et al., 2007). Exceptions include regionally explicit global scenarios of nitrogen deposition on a 0.5° latitude x 0.5° longitude grid for studying biodiversity loss in the Millennium Ecosystem Assessment (Alcamo et al., 2005) and simulations based on SRES emissions for sulphur and nitrogen over Europe (Mayerhofer et al., 2002) and Finland (Syri et al., 2004), and for surface ozone in Finland (Laurila et al., 2004).

2.4.6.3 Sea-level scenarios

A principal impact projected under global warming is sea-level rise. Some basic techniques for developing sea-level scenarios were described in the TAR (Carter et al., 2001). Since the TAR, methodological refinements now account more effectively for regional and local factors affecting sea level and, in so doing, produce scenarios that are more relevant for planning purposes. Two main types of scenario are distinguished here: regional sea level and storm surges. A third type, characterising abrupt sea-level rise, is described in Section 2.4.7. Analogue approaches have also been reported (e.g., Arenstam Gibbons and Nicholls, 2006). More details on sea level and sea-level scenarios can be found in Bindoff et al. (2007), Meehl et

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