A digital twin of Earth is a planet-scale simulation: a continuously updated virtual replica of the whole planet — atmosphere, oceans, land, ice — fed by sensor and satellite data and run on supercomputers to model climate at extreme resolution. The ambition is to forecast weather, climate change, and disaster scenarios decades out, down to the kilometer or finer, so policymakers can test interventions before committing to them in the real world. NVIDIA's Earth-2 effort is the headline example, pitching a digital twin built to simulate and predict climate at scales no current model touches.
My take: the environmental layer is the genuinely useful part, and porting it to an 📝agent-based system to model how climate actually reshapes society is a very viable opportunity. The catch is architectural — agent simulations don't run cleanly on GPUs because of memory threading, so you need GPUs and CPUs mixed to see how climate change moves through human systems, not just the physics. The raw climate fidelity isn't the bottleneck for understanding social change; the agent layer is. And the meter-scale resolution everyone's chasing? For most of what matters, that reads like overkill to me. Build the coupling between environment and agents first — that's where the real signal lives.