Enhanced Rock Weathering (ERW) is a carbon removal solution with the potential to remove gigatons of CO2 per year, but quantifying that removal today requires dense soil sampling and laboratory analysis. We evaluate five weather Foundation Models (FMs) as frozen encoders feeding lightweight decoders, under a leave-one field-out protocol on 5,192 chronosequences from 264 fields across Brazil and the US. To our knowledge, this is the first holistic evaluation of weather FMs for ERW and the largest evaluation on operational ERW data. We find that across weather FMs, representations transfer to ERW: our best model, TerraNova-ERW, built on Aurora 1.5, positively orders measurements within fields it has never seen. Zero-shot prediction of a new field\u2019s mean removal is not yet feasible, and we attribute this to data scale: the same architecture excels on \u223c105 natural weathering sequences, anchoring the scale we expect ERW data must reach. Few shot prediction, blending the model with a few samples per field, is supported today: TerraNova-ERW recovers over 60% of the highest removal fields with two samples per field, roughly halves the samples needed to estimate a field\u2019s mean removal, and can cut sampling on our densest fields by 28\u201398%.<\/p>\n","protected":false},"excerpt":{"rendered":"
Enhanced Rock Weathering (ERW) is a carbon removal solution with the potential to remove gigatons of CO2 per year, but quantifying that removal today requires dense soil sampling and laboratory analysis. We evaluate five weather Foundation Models (FMs) as frozen encoders feeding lightweight decoders, under a leave-one field-out protocol on 5,192 chronosequences from 264 fields […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":[{"type":"text","value":"Sasankh Munukutla","user_id":0},{"type":"user_nicename","value":"Felipe Oviedo","user_id":"39925"},{"type":"text","value":"Danrong Zhang","user_id":0},{"type":"text","value":"Kenji Takeda","user_id":0},{"type":"user_nicename","value":"Juan M. 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