Projected impacts from future warming on grapevine phenology have been modelled for two important varieties across six representative wine-growing regions in Australia. Various regional warming projections are based on a range of future greenhouse gas emission scenarios and patterns of climate change from a suite of climate models. Results are compared and contrasted regionally and the sensitivity of grapevine phenology to different climate futures is assessed. Impacts on budburst vary from region to region. Cabernet Sauvignon budburst in Coonawarra is projected to occur earlier by four to eight days in the year 2030, and by six to 11 days in 2050. Season duration (from budburst to harvest) is compressed in all regions studied and harvest is earlier in most cases. Given the highest warming scenario, harvest could be 45 days earlier in Coonawarra by 2050. Some regions may be adversely affected by the chilling requirement not being met in future warmer climates. For example, in the Margaret River region budburst is projected to be later. An important finding of this analysis is that harvest is projected to occur both earlier in the year and in a warmer climate, i.e. a dual warming impact. Harvesting in warmer temperatures can negatively impact grape quality. AbbreviationsGHG greenhouse gas; GCM global climate model

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We examined the effects of past and future climate change on natural snow cover in southeastern mainland Australia and assessed the role of snowmaking in adapting to projected changes in snow conditions. Snow-depth data from 4 alpine sites from 1957 to 2002 indicated a weak decline in maximum snow depths at 3 sites and a moderate decline in mid-to late-season snow depths (August to September). Low-impact and high-impact climate change scenarios were prepared for 2020 and 2050 and used as input for a climate-driven snow model. The total area with an average of at least 1 d of snow cover per year was projected to decrease by 10 to 39% by 2020, and by 22 to 85% by 2050. By 2020, the length of the ski season was projected to have decreased by 10 to 60%, while by 2050 the decrease was 15 to 99%. Based on target snow-depth profiles from May to September nominated by snowmaking managers at various ski resorts, the snow model simulated the amount of snow that is needed to be made each day, taking into account natural snowfall, snow-melt and the pre-existing natural snow depth. By the year 2020, an increase of 11 to 27% in the number of snow guns would be required for the low impact scenario, and 71 to 200% for the high impact scenario. This corresponds to changes in total snow volume of 5 to 17% for the low impact scenario to 23 to 62% for the high impact scenario. Therefore, with sufficient investment in snow guns, the Australian ski industry may be able to manage the effect of projected climate change on snow cover until at least 2020.KEY WORDS: Snow · Depth · Area · Duration · Australia · Climate · Change · Snowmaking Resale or republication not permitted without written consent of the publisherClim Res 35: [255][256][257][258][259][260][261][262][263][264][265][266][267][268][269][270] 2008 annual snow-cover extent since 1966, largely due to decreases in spring and summer snow cover since the mid-1980s over both the Eurasian and American continents (Robinson & Frei 2000). Surface observations for the northern hemisphere from show no significant change in winter snow extent, but a decrease in spring (Brown 2000). At most locations below 1800 m in northwestern USA, large decreases in waterequivalent snow depth from 1950-2000 coincide with significant increases in temperature, despite increases in precipitation (Groisman et al. 2004, Mote et al. 2005. Since the late 1940s, there has been a shift toward earlier snow-melt runoff in many rivers of northwestern America (Stewart et al. 2005).In the Australian region, there has been a warming of 0.9°C since 1900, most of which has occurred since 1950 (Nicholls & Collins 2006). Australian rainfall exhibits large annual and regional variability, including a decline in annual rainfall in the east since 1950 (Nicholls & Collins, 2006). Climate trends are likely to have had an effect on the Australian snowfields, but the large annual variability in snow season characteristics in the mainland Australian alpine region makes it difficult to detect trends. Fig. 1 shows ...

[1] It is well understood that Australian climate is affected by natural climate variability such as El Niño-Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Southern Annular Mode (SAM), depending on seasons and regions. However, studies on Australian climate extremes associated with natural climate variability remain limited. This study examines the observed impact of natural climate variability on inter-annual changes in seasonal extremes of rainfall and temperature over Australia during 1957-2010. We use non-stationary Generalized Extreme Value (GEV) analysis, where GEV parameters are specified as a linear function of modes of climate variability, and compare results with the case when climate variability is not considered. Results from two station-based observational data sets consistently suggest that extreme responses overall resemble mean responses to climate variability. Anomalously drier and hotter conditions occur over northeastern Australia and the southern coast during El Niño and a positive phase of the IOD in the cold seasons, while wetter and cooler conditions are observed during La Niña and a negative phase of IOD. A positive (negative) phase of the SAM brings wetter and cooler (drier and warmer) conditions over much of the eastern continent in summer. Covariation and relative importance of ENSO and the IOD as well as an inverse relationship between rainfall and daily maximum temperature are also found to hold for extremes. This suggests that teleconnection mechanisms responsible for seasonal mean variations may be at work for inter-annual changes in extremes, providing important implications for climate model evaluations and regional climate change projections.

An extensive assessment of historical trends in winegrape maturity dates from vineyards located in geographically diverse winegrape growing regions in Australia has been undertaken. Records from 44 vineyard blocks, representing a range of varieties of Vitis vinifera L., were accessed. These comprise 33 short-term datasets (average 17 years in length) and 11 long-term datasets, ranging from 25 to 115 years in length (average 50 years). Time series of the day of the year grapes attain maturity were assessed. A trend to earlier maturity of winegrapes was observed in 43 of the 44 vineyard blocks. This trend was significant for six out of the 11 long-term blocks for the complete time period for which records were available. For the period 1993-2009, 35 of the 44 vineyard blocks assessed displayed a statistically significant trend to earlier maturity. The average advance in the phenology was dependent on the time period of observation, with a more rapid advance over more recent decades. Over the more recent 1993-2009 period, the average advance was 1.7 days year, whereas for the period 1985-2009 the rate of advance was 0.8 days yr À1 on average in the 10 long-term vineyard blocks assessed for cross-regional comparison. The trend to earlier maturity was associated with warming temperature trends for all of the blocks assessed in the study.

This study uses a model of snow-cover duration, an observed climate data set for the Australian alpine area, and a set of regional climate-change scenarios to assess quantitatively how changes in climate may affect snow cover in the Australian Alps. To begin, a regional interannual climate data set of high spatial resolution is prepared for input to the snow model and the resulting simulated interannual and spatial variations in snow-cover duration are assessed and compared with observations. The model provides a reasonable simulation of the sensitivities of snow-cover duration to changes in temperature and precipitation in the Australian Alps, although its performance is poorer at sites highly marginal for snow cover. (In a separate comparison, the model also performs well for sites in the European Alps.) The input climate data are then modified in line with scenarios of regional climate change based on the results of five global climate models run in enhanced greenhouse experiments. The scenarios are for the years 2030 and 2070 and allow for uncertainty associated with projecting future emissions of greenhouse gases and with estimating the sensitivity of the global climate system to enhanced greenhouse forcing. Attention focuses on the climate changes most favourable ('best-case scenario') and least favourable ('worst-case scenario') for snow cover amongst the range of climate changes in the scenarios. Under the best case scenario for 2030, simulated average snow-cover duration and the frequency of years of more than 60 days cover decline at all sites considered. However, at the higher sites (e.g., more than 1700 m) the effect is not very marked. For the worst case scenario, a much more dramatic decline in snow conditions is simulated. At higher sites, simulated average snow cover duration roughly halves by 2030 and approaches zero by 2070. At lower sites (around 1400 m), near zero average values are simulated by 2030 (compared to durations of around 60 days for current climate).These simulated changes, ranging between the best and worst case, are likely to be indicative of how climate change will affect natural snow-cover duration in the Australian Alps. However, note that the model does not allow directly for changes in the frequency and intensity of snow-bearing circulation systems, nor do the climate-change scenarios allow possible changes in interannual variability (particularly that due to the E1Nifio-Southem Oscillation) and local topographical effects not resolved by global climate models. The simulated changes in snow cover are worthy of further consideration in terms of their implications for the ski industry and tourism, water resources and hydroelectric power, and land-use management and planning.

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Various agricultural sectors are likely to be sensitive to projected climate change. Winegrapes are particularly sensitive to climate change because of the intrinsic link between the climate and the characteristic and often unique quality of the resulting wine. Here we present results from a study exploring the impact of projected climate change on the Australian wine industry. In the present study, impact models based upon existing viticultural and winegrape market data are used to estimate how projected regional temperature increases might affect the winegrape and wine industry throughout Australia by 2030 and 2050. The effect on winegrape quality is determined for different premium winegrape varieties separately. Differential impacts were determined across a range of base-climates, climate change regimes and varietal crush profiles. This represents the first national study of the impact of climate change on winegrape quality that is regionally specific, and that integrates varietal differences in temperature sensitivity. The impact of warming was found to be negative overall, assuming no adaptation is implemented, for all Australian winegrowing regions. It is found that the reduction to winegrape quality varied regionally, with greater quality reductions calculated for the inland regions. Without adaptation, winegrape quality may be reduced at a national scale in Australia from 7% with lower warming to 39% with higher future warming by the year 2030, and from 9% with lower warming to 76% with higher warming by the year 2050 (all uncertainties considered). KEY WORDS: Climate change · Winegrape quality · Winegrape varieties Resale or republication not permitted without written consent of the publisherClim Res 36: [99][100][101][102][103][104][105][106][107][108][109][110][111] 2008 (Bindi et al. 1996), or shifting regional suitability for viticulture (Kenny & Harrison 1992, Hayhoe et al. 2004, none have directly and quantitatively assessed the impact on winegrape quality, nor attempted to determine the impact at a national industry scale.Earlier studies have discussed the impact of climate change on the wine industry without any detailed spatial modelling of projected impacts, or projection uncertainties (Dry 1988, Smart 1989, Schultz 2000, Pincus 2003. Both global and regional climate change projection uncertainties are examined in this assessment. A range of greenhouse gas (GHG) emission scenarios and also a selection of global climate models (GCMs) is utilised to incorporate uncertainties in climate projections. We have undertaken a spatial assessment and included 9 future climate possibilities and 2 outlook periods. Because projected climate change is not anticipated to be uniform across Australia, with more warming inland than near the coast (Whetton & Hennessy 2001), we applied spatially varying climate projections to a fine-resolution grid of baseline temperature. This allows unique regional impacts to be calculated. The method also enables an analysis of the sensitivity of the industry to ...