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  "source_id": "copernicus-cmip6-monthly",
  "title": "Peru monthly tas projection, SSP5-8.5",
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  "variable": "tas",
  "unit": "degC",
  "measure": "absolute",
  "frequency": "monthly",
  "statistic": "mean",
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    "label": "Peru",
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  "retrieved_at": "2026-09-14T23:30:31Z",
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    "description": "Area-weighted regional monthly means from 26 CMIP6 models under SSP5-8.5. Value is the across-model median; the p10 and p90 fields carry the LOWEST and HIGHEST member values, not interpolated percentiles, because five named models are not a sample a percentile can be read from. Temperature converted from K; precipitation converted from kg m-2 s-1 to mm per month using each model's OWN calendar month length. Native calendars: 360_day: 2 (KACE-1-0-G, UKESM1-0-LL); 365_day: 9 (BCC-CSM2-MR, CAMS-CSM1-0, CESM2, CMCC-CM2-SR5, CanESM5-CanOE, FGOALS-f3-L, FGOALS-g3, INM-CM4-8, INM-CM5-0); gregorian: 6 (CNRM-CM6-1, CNRM-CM6-1-HR, CNRM-ESM2-1, IPSL-CM6A-LR, MIROC-ES2L, MIROC6); julian: 1 (IITM-ESM); noleap: 4 (GFDL-ESM4, MCM-UA-1-0, NorESM2-MM, TaiESM1); proleptic_gregorian: 4 (ACCESS-CM2, AWI-CM-1-1-MR, MPI-ESM1-2-LR, MRI-ESM2-0). Output periods are real calendar months. Each model is weighted on its own grid; the provenance grid figures describe the COARSEST member, which bounds the ensemble, rather than any one model's grid.",
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      "MCM-UA-1-0 (r1i1p1f2)",
      "MIROC6 (r1i1p1f1)",
      "MIROC-ES2L (r1i1p1f2)",
      "MPI-ESM1-2-LR (r1i1p1f1)",
      "MRI-ESM2-0 (r1i1p1f1)",
      "NorESM2-MM (r1i1p1f1)",
      "TaiESM1 (r1i1p1f1)",
      "UKESM1-0-LL (r1i1p1f2)"
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      "from": "2099-12-01"
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    "uncertainty": "26 CMIP6 models, one run each. One run per model is deliberate: a model contributing fifty members would otherwise outvote the rest of the archive. The value is the across-model mean and the p10/p90 fields are the 10th and 90th percentiles of the member distribution, the range the IPCC AR6 Atlas reports for regional projections. Members are weighted by climwip-independence/1: the ClimWIP independence term (Knutti et al. 2017; Brunner et al. 2020), which shares one vote between models whose historical runs resemble each other, with the shape parameter set to this region's median inter-model distance rather than calibrated by a perfect-model test; there is no performance term, so no model is up- or down-weighted for matching observations. The weighting leaves an effective 11.5 of 26 models (Kish), which is the number the spread is actually estimated from. That is under half the roster, so the published spread rests on a small part of the ensemble; inspect before relying on it. This is not a calibrated confidence interval, not a probability distribution over outcomes, and carries no downscaling or bias correction — regional absolute values retain each model's own bias. Ensemble membership is part of the campaign digest, because adding or removing a model changes both the mean and the spread. A month is emitted only where every member supplies it. Realisation note: for CESM2 (r1i1p1f1 historical, r4i1p1f1 SSP5-8.5); MCM-UA-1-0 (r1i1p1f1 historical, r1i1p1f2 SSP5-8.5), the historical run is a different realisation from the projection published here. CDS serves whichever realisation it holds for a named model, and the request carries no variant key. The independence weighting takes its distances from the historical fields, so for these members the run that sets the weight is not the run being weighted. The last 12 month(s) are NOT model output: no member of the ensemble runs past 2099-12-01, so that month's value is repeated to the end of the series, and each such point carries carried_from. A repeated value understates a rising trend, which under a high scenario is steepest at the end, so the closing figure is a floor rather than a projection."
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