Abstract Winter chill, which temperate trees require in order to overcome dormancy, is expected to decrease substantially in the future in most deciduous fruit tree growing areas. Several mathematical models have been developed in different regions to quantify chill requirements of tree species and cultivars. The Dynamic model has emerged as the most plausible and reliable model, yet all chill models have been found inadequate in at least some growing regions. Accurate models are crucial for the development of quantitatively appropriate climate change adaptation strategies for temperate orchards. To demonstrate the importance of model choice we compared the outputs from 13 agricultural and forest chill models using past and projected future weather data for nine sites in Chile, Tunisia and Germany. To evaluate chill risk, we used a weather generator calibrated with 45 years of temperature data to generate 100 years of synthetic temperature records per scenario for multiple climate scenarios. Chill was computed for 10 past scenarios and projected for 60 future scenarios (for 2050 and 2085 according to greenhouse gas concentration scenarios RCP4.5 and RCP8.5, using projections from 15 climate models). Results show that estimations differ substantially across chill models, even for the same sites and scenarios. The “Chilling Hours” model and the “Chilling Rate” function showed high sensitivity across regions in future scenarios. The “North Carolina”, “Utah”, “Modified Utah” and “Low Chill” models all suggest negative chill levels for past and future scenarios in Tunisia (despite the thriving fruit tree industry there). Only two models projected chill decreases in all sites. In Mediterranean climate areas (central Chile and Tunisia) the “Dynamic” and “Positive Utah” models forecasted similar chill reductions for future scenarios, whereas in temperate locations (Germany) the “Dynamic” model forecasted lower chill increase compared with the “Utah” and “Positive Utah” models. Despite the “Dynamic” and the “Positive Utah” models showing similar performance among climates, the “Dynamic” model appears to be the best current option, due its more physiologically credible structure. However, further research is needed to develop or identify models that are valid across wide climatic gradients. Our results show that a major source of variation and inaccuracy in chilling assessments is the choice of the chill model used to make the assessment.

中文翻译：

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冷却模型选择的重要性——多站点分析

摘要 温带树木克服休眠所需的冬季寒冷预计未来在大多数落叶果树种植区将大幅减少。已经在不同地区开发了几种数学模型来量化树种和栽培品种的寒冷需求。动态模型已成为最合理、最可靠的模型，但至少在某些种植区，所有的寒意模型都被发现不足。准确的模型对于为温带果园制定数量合适的气候变化适应策略至关重要。为了证明模型选择的重要性，我们使用智利、突尼斯和德国 9 个地点的过去和预测的未来天气数据，比较了 13 个农业和森林寒冷模型的输出。为了评估寒冷风险，我们使用了一个用 45 年的温度数据校准的天气发生器，为多个气候场景的每个场景生成了 100 年的合成温度记录。计算了 10 个过去情景的寒冷度，并预测了 60 个未来情景（根据温室气体浓度情景 RCP4.5 和 RCP8.5，使用 15 个气候模型的预测，2050 年和 2085 年）。结果表明，即使对于相同的站点和场景，不同寒冷模型的估计也有很大差异。“Chilling Hours”模型和“Chilling Rate”功能在未来场景中表现出跨区域的高灵敏度。“北卡罗来纳州”、“犹他州”、“修改后的犹他州”和“低寒”模型都表明突尼斯过去和未来情景的寒冷程度为负（尽管那里的果树产业蓬勃发展）。只有两个模型预计所有地点的寒意都会减少。在地中海气候区（智利中部和突尼斯），“动态”和“正犹他州”模型预测未来情景的寒冷减少类似，而在温带地区（德国），“动态”模型预测的寒冷增加低于“犹他州”和“积极的犹他”模式。尽管“Dynamic”和“Positive Utah”模型在不同气候条件下表现出相似的性能，但“Dynamic”模型似乎是目前最好的选择，因为它的结构在生理上更可信。然而，需要进一步的研究来开发或确定在广泛的气候梯度中有效的模型。我们的结果表明，激冷评估中变化和不准确的主要来源是用于进行评估的激冷模型的选择。在地中海气候区（智利中部和突尼斯），“动态”和“正犹他州”模型预测未来情景的寒冷减少类似，而在温带地区（德国），“动态”模型预测的寒冷增加低于“犹他州”和“积极的犹他”模式。尽管“Dynamic”和“Positive Utah”模型在不同气候条件下表现出相似的性能，但“Dynamic”模型似乎是目前最好的选择，因为它的结构在生理上更可信。然而，需要进一步的研究来开发或确定在广泛的气候梯度中有效的模型。我们的结果表明，激冷评估中变化和不准确的主要来源是用于进行评估的激冷模型的选择。在地中海气候区（智利中部和突尼斯），“动态”和“正犹他州”模型预测未来情景的寒冷减少类似，而在温带地区（德国），“动态”模型预测的寒冷增加低于“犹他州”和“积极的犹他”模式。尽管“Dynamic”和“Positive Utah”模型在不同气候条件下表现出相似的性能，但“Dynamic”模型似乎是目前最好的选择，因为它的结构在生理上更可信。然而，需要进一步的研究来开发或确定在广泛的气候梯度中有效的模型。我们的结果表明，激冷评估中变化和不准确的主要来源是用于进行评估的激冷模型的选择。