Crop models are used for an increasingly broad range of applications, with a commensurate proliferation of methods. Careful framing of research questions and development of targeted and appropriate methods are therefore increasingly important. In conjunction with the other authors in this special issue, we have developed a set of criteria for use of crop models in assessments of impacts, adaptation and risk. Our analysis drew on the other papers in this special issue, and on our experience in the UK Climate Change Risk Assessment 2017 and the MACSUR, AgMIP and ISIMIP projects. The criteria were used to assess how improvements could be made to the framing of climate change risks, and to outline the good practice and new developments that are needed to improve risk assessment. Key areas of good practice include: i. the development, running and documentation of crop models, with attention given to issues of spatial scale and complexity; ii. the methods used to form crop-climate ensembles, which can be based on model skill and/or spread; iii. the methods used to assess adaptation, which need broadening to account for technological development and to reflect the full range options available. The analysis highlights the limitations of focussing only on projections of future impacts and adaptation options using pre-determined time slices. Whilst this long-standing approach may remain an essential component of risk assessments, we identify three further key components: 1. Working with stakeholders to identify the timing of risks. What are the key vulnerabilities of food systems and what does crop-climate modelling tell us about when those systems are at risk?2. Use of multiple methods that critically assess the use of climate model output and avoid any presumption that analyses should begin and end with gridded output.3. Increasing transparency and inter-comparability in risk assessments. Whilst studies frequently produce ranges that quantify uncertainty, the assumptions underlying these ranges are not always clear. We suggest that the contingency of results upon assumptions is made explicit via a common uncertainty reporting format; and/or that studies are assessed against a set of criteria, such as those presented in this paper.

中文翻译：

---

改进作物模型在风险评估和气候变化适应方面的使用


作物模型的应用范围越来越广泛，方法也相应增多。因此，仔细制定研究问题并制定有针对性的适当方法变得越来越重要。我们与本期特刊的其他作者一起制定了一套使用作物模型评估影响、适应和风险的标准。我们的分析借鉴了本期特刊中的其他论文，以及我们在 2017 年英国气候变化风险评估以及 MACSUR、AgMIP 和 ISIMIP 项目中的经验。该标准用于评估如何改进气候变化风险的框架，并概述改进风险评估所需的良好实践和新发展。良好实践的关键领域包括：作物模型的开发、运行和记录，同时关注空间规模和复杂性问题；二.用于形成作物气候整体的方法，可以基于模型技能和/或传播；三.用于评估适应的方法需要扩大，以考虑到技术发展并反映所有可用的选择。该分析强调了仅关注使用预定时间片预测未来影响和适应选项的局限性。虽然这种长期存在的方法可能仍然是风险评估的重要组成部分，但我们确定了另外三个关键组成部分： 1. 与利益相关者合作确定风险发生的时间。粮食系统的主要脆弱性是什么？当这些系统面临风险时，作物气候模型告诉我们什么？2。 使用多种方法严格评估气候模型输出的使用，并避免任何分析应以网格输出开始和结束的假设。3．提高风险评估的透明度和相互可比性。虽然研究经常产生量化不确定性的范围，但这些范围背后的假设并不总是明确的。我们建议通过通用的不确定性报告格式明确假设结果的偶然性；和/或根据一组标准对研究进行评估，例如本文中提出的标准。