用于开展系外行星模式相互比较项目的 CUISINES 框架，1.0 版
The CUISINES Framework for Conducting Exoplanet Model Intercomparison Projects, Version 1.0
AI总结：
本文提出 CUISINES 1.0 框架，以五项原则规范社区驱动的系外行星模式相互比较项目，支撑模式验证、观测对比与开放协作。
摘要：
随着 JWST 开始传回观测结果，系外行星气候模式能否一致且正确地预测系外行星的可观测性、反演其数据并根据这些数据解释行星环境，变得比以往任何时候都更加重要。在这一背景下，尤其是在当前可用于验证模式预测的数据很少的时候，模式相互比较发挥着关键作用。NASA 系外行星系统科学协作组织（NExSS）的 CUISINES 工作组支持采用系统化方法评估系外行星模式的性能，并在此提供一个用于开展社区组织的系外行星模式相互比较项目（exoMIP）的框架。CUISINES 框架借鉴并调整了地球气候界的实践，以专门满足系外行星研究人员的需求，涵盖多种模式类型、行星目标和参数空间研究。它旨在帮助研究人员朝着共同目标进行集体、公平和开放的协作。CUISINES 框架建立在五项原则之上：1）预先界定 exoMIP 旨在解决的研究问题。2）创建可最大限度提高社区参与度的实验设计，并广泛宣传。3）规划项目时间表，使所有 exoMIP 成员都能充分参与。4）根据模式输出生成数据产品，以便与观测结果直接比较。5）制定当前可行且未来可扩展的数据管理计划。在成立后的最初几年内，CUISINES 已经为 10 个 exoMIP 提供后勤支持，并将继续举办年度研讨会，以获取进一步的社区反馈并展示新的 exoMIP 想法。
英文摘要：
As JWST begins to return observations, it is more important than ever that exoplanet climate models can consistently and correctly predict the observability of exoplanets, retrieval of their data, and interpretation of planetary environments from that data. Model intercomparisons play a crucial role in this context, especially now when few data are available to validate model predictions. The CUISINES Working Group of NASA's Nexus for Exoplanet System Science (NExSS) supports a systematic approach to evaluating the performance of exoplanet models, and provides here a framework for conducting community-organized exoplanet Model Intercomparison Projects (exoMIPs). The CUISINES framework adapts Earth climate community practices specifically for the needs of exoplanet researchers, encompassing a range of model types, planetary targets, and parameter space studies. It is intended to help researchers to work collectively, equitably, and openly toward common goals. The CUISINES framework rests on five principles: 1) Define in advance what research question(s) the exoMIP is intended to address. 2) Create an experimental design that maximizes community participation, and advertise it widely. 3) Plan a project timeline that allows all exoMIP members to participate fully. 4) Generate data products from model output for direct comparison to observations. 5) Create a data management plan that is workable in the present and scalable for the future. Within the first years of its existence, CUISINES is already providing logistical support to 10 exoMIPs, and will continue to host annual workshops for further community feedback and presentation of new exoMIP ideas.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
