Uncertainty in the terrestrial carbon cycle remains a major constraint in climate projections, partly driven by the uncertainties affecting the land surface representation and variability in Earth system models. To address this limitation, we present a data-driven framework AI4Land, for generating high-resolution historical reconstructions and future projections of key land surface variables. The framework follows a two-phase approach using a U-Net architecture. In the first phase, which is the focus of this work, it reconstructs annual land use and land cover by integrating coarse-resolution scenario data with static geophysical features. In a planned second phase, the resulting high-resolution maps will be used to predict dynamic biophysical variables, particularly leaf area index, at finer temporal scales. Trained on Earth observation data, the models learn to reproduce spatially explicit and physically consistent land surface patterns, extending temporal coverage to periods lacking direct observations. AI4Land was developed and trained on MareNostrum5, demonstrating how GPU-accelerated HPC infrastructure enables global-scale climate AI pipelines. The final product is a suite of open-source emulators designed for real-time coupling with digital twin platforms, such as those developed under the Destination Earth initiative. By delivering realistic and evolving land surface conditions on demand, this work aims to reduce critical uncertainties and improve the predictive power of next-generation climate simulations.

翻译：陆地碳循环的不确定性仍是气候预测中的主要制约因素，其部分原因来自地球系统模型中地表表示及变率的误差。为应对这一局限，我们提出数据驱动框架AI4Land，用于生成关键地表变量的高分辨率历史重建与未来预估。该框架采用基于U-Net架构的两阶段方法：第一阶段（本文核心）通过整合粗分辨率情景数据与静态地球物理特征，重建年度土地利用与土地覆盖；计划中的第二阶段将利用生成的高分辨率地图预测动态生物物理变量（特别是叶面积指数）的精细时间尺度变化。基于地球观测数据训练的模型能够复现空间显式且物理一致的地表模式，并将时间覆盖范围延伸至缺乏直接观测的时期。AI4Land在MareNostrum5超级计算机上开发并训练，证明了GPU加速的高性能计算基础设施如何支撑全球尺度气候人工智能流水线。最终成果是一套开源仿真器套件，专为与数字孪生平台（如"目的地地球"计划开发的平台）实时耦合而设计。通过按需提供动态演变的真实地表条件，本研究旨在减少关键不确定性，提升下一代气候模拟的预测能力。