دانلود ترجمه مقاله بررسی تاثیر گرم شدن زمین در آینده بر سردی زمستان در استرالیا – نشریه اسپرینگر

فهرست مطالب:

چکیده
مقدمه
روشها
داده های اقلیمی
مدل های سرمادهی
انتخاب مدل گردش اتمسفری-اقیانوسی
درجه حرارت های پیش بینی شده ساعتی
نتایج
بحث
نتیجه گیری

بخشی از ترجمه:

افزایش دما به دلیل متصاعد شدن گازی های گلخانه ای در نتیجه فعالیت های انسانی بر ابعاد کلیدی تولیدات باغی تاثیر می گذارد.تاثیر بالقوه درجه حرارت های بالاتر بر توانایی درختان میوه و خشکبار برای شکستن خواب زمستانه که مستلزم قرار گیری در معرض درجه حرارت های پایین زمستانه است در نظر گرفته شد.سه مدل سرمادهی( ۰-۷، درجه حرارت ۲ سانتی گراد،مدل اصلاح شده یوتاه و دینامیک) برای بررسی تغییرات در انباشت سرما در ۱۳ مکان در استرالیا بر طبق تغییرات موضعی درجه حرارت مربوط به افزایش ۱،۲ و ۳ درجه سانتیگراد در میانگین درجه حرارت جهانی مورد استفاده قرار گرفت. این روش وابسته به برایند های واکنش های متصاعد شدن GHG که تغییر می کنند و یا در معرض تغییر می باشند نمی باشد.تاثیرات منطقه ای و و سرعت کاهش دمای سرد کننده در میان همه مدل های سرمادهی متفاوت می باشد که درآن مدل ۷–.۲۰°C که بیشترین کاهش و مدل دینامیک(پویا) کم ترین سرعت کاهش را نشان دادند. مکان های مرتفع و با عرض جغرافیایی بالا در استرالیا به مقدار کم ترین میزان و در حالی که سه منطقه بحری در غرب استرالیا با ارتفاع کم تر به مقدار بیشترین میزان از گرمایش آینده متحمل شدند.

۱ مقدمه

اقلیم نقشی اساسی در تولید موفق محصولات باغی و خشکبار در مقیاس تجاری ایفا می کند.خواب زمستانه یک بعد مهم و کلیدی چرخه سالانه درختان میوه خزان کننده(برگ ریز) و به تبع آن شکستن حالت خواب محسوب می شود. این وضعیت در دوره زمستانه برای حفاظت درختان در برابر صدمات دمای پایین حفظ می شود(سار ۱۹۸۵). درختان جهت خارج شدن از حالت خواب، بایستی در معرض مقادیر از پیش تعیین شده دماهای سرد در فرایندی موسوم به بهاره سازی(ورنالیزاسیون) قرار بگیرند.سرمادهی ناکافی می تواند موجب رشد نامنظم و جوانه زنی اندک، نمو ضعیف میوه،کوچک ماندن اندازه میوه و زمان های رسیدگی غیر یکنواخت می شود(اکالبی و و همکاران ۲۰۰۳،پتری و لیت ۲۰۰۴، سار ۱۰۸۵،ولر ۱۹۸۶). افزایش درجه حرارت در آینده ناشی از تغییر اقلیم تحت فعالیت های انسانی بر فرایند های سرمادهی یا بهاره سازی تاثیر گذاشته و موجب وارد آمدن اثرات نامطلوب بر تولید می شود.

بخشی از مقاله انگلیسی:

Increases in temperature as a result of anthropogenically generated greenhouse gas (GHG) emissions are likely to impact key aspects of horticultural production. The potential effect of higher temperatures on fruit and nut trees’ ability to break winter dormancy, which requires exposure to winter chilling temperatures, was considered. Three chill models (the 0–۷٫۲°C, Modified Utah, and Dynamic models) were used to investigate changes in chill accumulation at 13 sites across Australia according to localised temperature change related to 1, 2 and 3°C increases in global average temperatures. This methodology avoids reliance on outcomes of future GHG emission pathways, which vary and are likely to change. Regional impacts and rates of decline in chilling differ among the chill models, with the 0–۷٫۲°C model indicating the greatest reduction and the Dynamic model the slowest rate of decline. Elevated and high latitude eastern Australian sites were the least affected while the three more maritime, less elevated Western Australian locations were shown to bear the greatest impact from future warming. Keywords Climate change . Vernalisation . Fruit . Nut Introduction Climate plays a fundamental role in the successful production of commercial scale fruit and nut products. Winter dormancy is one key aspect of the annual cycle of deciduous fruit and nut trees along with the subsequent breaking of the dormant state. This state is maintained through the winter period each year to protect against damaging cold temperatures (Saure 1985). To be released from dormancy trees require exposure to a predetermined quantity of cold temperatures in a process known as winter chilling or vernalisation. Insufficient chilling can lead to sporadic and light bud-break, poor fruit development, small fruit size and uneven ripening times (Oukabli et al. 2003; Petri and Leite 2004; Saure 1985; Voller 1986). Expected future increases to temperature as a result of anthropogenically induced climate change may impact the vernalisation process leading to these adverse effects on production. While the chilling process is not fully understood (Dennis 1994), the physiological response is often estimated by temperature based models (e.g. Cesaraccio et al. 2004; Fishman et al. 1987; Linsley-Noakes et al. 1994; Linvill 1990; Richardson et al. 1974; Shaltout and Unrath 1983; Weinberger 1950). Of the available models, the following are used commonly by researchers and growers; the 0–۷٫۲°C (Bennett 1949; Weinberger 1950), Utah (Richardson et al. 1974) and Modified Utah (Linvill 1990), and the Dynamic (Erez et al. 1990; Fishman et al. 1987) models. All these models, although they contain differing levels of complexity, accumulate chill according to hourly temperature exposure and, once a threshold amount of chill has been amassed, define chilling as satisfied. Different species, and varieties within species, require different amounts of chill to break dormancy. Varietal chill requirements, or thresholds, have been defined according to different chill models resulting in chill thresholds reported in different units (e.g. Table 1). Few studies have quantitatively investigated projected impacts of increased temperatures on chill accumulation, although many discuss potential negative outcomes (Darbyshire et al. 2011; Harrington et al. 2010; Legave et al. 2008; Wand et al. 2008). Hennessy and Clayton-Greene (1995) conducted one of the first investigations into chill accumulation under climate warming conditions. Their study was for Australia and used the Modified Utah model. They implemented two methods to investigate future chill conditions, a sensitivity approach as well as a range of scenarios for the year 2030. The sensitivity study involved adding 1, 2 and 3°C to historical temperature records, meaning a constant temperature increase was applied across all locations. Comparison between sites using this method was not possible, as the rate of warming is likely to differ between regions. Additionally, uniform minimum and maximum temperatures increases were applied, this is also unlikely to eventuate. To allow investigation into site differences they also considered scenarios for 2030 using five climate models and two emission scenarios; however, these projection data was produced in 1992 and are now dated. Recently, Luedeling et al. (2011a) conducted a global analysis of projected changes to chill accumulation according to the Dynamic model only. This model has been shown to equal or out-perform other chill models (Alburquerque et al. 2008; Campoy et al. 2011a; Erez et al. 1990; Luedeling et al. 2009b; Perez et al. 2008; Ruiz et al. 2007; Viti et al. 2010); however, the results may have limited application as few varietal chill thresholds have been measured in chill portions. Luedeling and Brown (2010) compared the output of the 0–۷٫۲°C, Utah and Dynamic models globally and verified that conversion factors between the chill models are regionally dependent and therefore inconsistent. Consequently, thresholds determined for varieties using one chill model cannot be interpreted using output from a different chill model. Some authors have investigated chill concurrently using two or more chill models (Alburquerque et al. 2008; Luedeling et al. 2009b, d, 2011b; Ruiz et al. 2007; Sunley et al. 2006; Viti et al. 2010) but conversion factors between the models were conflicting. Here, three common chill models are assessed for Australian conditions, allowing investigation into chill model sensitivity to warming, consideration of projected chill accumulation measured in different units (e.g. Table 1) and comparison to other studies. Growing support for the Dynamic model and previous research highlighting the higher sensitivities of alternate chill models to warming (Luedeling et al. 2009a) indicate that results from the Dynamic model will be most applicable. Methodology regarding interpretation of climate projection data is a major consideration in this study. Appropriate representation of Atmosphere-Ocean General Circulation Model (AOGCM) uncertainty is important for projection analyses as models can differ greatly in output (Jun et al. 2008; Watterson 2011). Emission scenarios (Nakicenovic and Swart 2000) are intentionally excluded from the analysis. This is because the IPCC’s Special Report on Emission Scenarios (SRES) greenhouse gas (GHG) emission scenario storylines may not eventuate, especially if GHG production continues unabated or if mitigation policies are implemented (e.g. Meinshausen et al. 2009). To improve the applicability of the results, SRES pathways are used for interpretation of results rather than being embedded in the methodology. In this study, chill projections for 13 sites across Australia using three chilling models were calculated. Six AOGCMs were selected, cross-validated against existing model skill assessments (Suppiah et al. 2007; Watterson 2011), to ensure the maximum range of likely outcomes were included in the results. Temperature projections were created using localised monthly minimum and maximum temperature perturbations relating to 1, 2 and 3°C global average temperature increases. Thus, results are comparable across locations and are independent of GHG emission projection uncertainty. Methods Future chill conditions were evaluated at 13 perennial horticultural production locations in Australia (Fig. 1 and Table 2) as used by Darbyshire et al. (2011) for historical chilling analysis. Climate data Historical daily minimum and maximum data from 1911 to 2009 were sourced from 0.05° by 0.05° grids (Jones et al. 2009). This dataset was used by Darbyshire et al. (2011) to investigate historical chilling conditions in Australia as quality historical in situ meteorological data are not available for the major production areas. Climate projection output from 21 AOGCMs were provided by the Queensland Climate Change Centre of Excellence (QCCCE). The projection data were provided as localised monthly minimum and maximum temperature perturbations per 1°C global temperature increase from the 1975–۲۰۰۴ baseline. The pattern scaling methodology used to produce future climates, developed by the Commonwealth Scientific and Industrial Research Organisation (CSIRO), is described in Page and Jones (2001) and Ricketts and Page (2007). Where climate variables for some AOGCMs were not available from CSIRO, they were in-filled by QCCCE using regression methods. Chill models The 0–۷٫۲°C model (Bennett 1949; Weinberger 1950) is a simple step-function which records one chill hour for every hour that temperature is within the 0–۷٫۲°C interval and nil chill hours otherwise. The Modified Utah model (Linvill 1990) builds on the simplicity of the 0–۷٫۲°C model. It incorporates an optimum chilling temperature, which is assigned one chill unit, with temperatures either side of the optimum declining in ability to contribute to the chilling process. This model additionally accounts for the negation effect of high temperatures on chilling, with temperatures over 14°C reversing previously accumulated chill, an aspect lacking in the 0–۷٫۲°C model. These two models are time independent, meaning the effectiveness of chilling temperatures are constant across the chill period. Independence from time using the Modified Utah model means chill accumulated early in winter can be negated by late season warming.