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Documentation Index

Fetch the complete documentation index at: https://docs.climatifai.com/llms.txt

Use this file to discover all available pages before exploring further.

The /agri/climate endpoint returns two complementary datasets for a given coordinate: a historical monthly series sourced from ERA5 reanalysis data, and a CMIP6 future projection for the scenario you choose. Both datasets share the same structure — monthly rows with temperature, precipitation, and soil moisture — making it straightforward to compare past conditions against projected future ones.
1

Make the request

Send a GET request to /agri/climate. The from and to parameters define the historical period; scenario sets the CMIP6 emissions pathway for the projection.
curl "https://api.climatifai.com/agri/climate?lat=-34.6&lon=-58.4&from=1991&to=2020&scenario=SSP3-7.0"
ParameterTypeDefaultDescription
latfloatrequiredLatitude (WGS 84, decimal degrees)
lonfloatrequiredLongitude (WGS 84, decimal degrees)
frominteger1991First year of the historical series
tointeger2020Last year of the historical series
scenariostringSSP3-7.0CMIP6 scenario for the projection
2

Read historical data

The historical array contains one object per month for the requested year range. Each object covers a single calendar month across the full period — months are averaged over the years you specified (e.g., 1991–2020 gives you the 30-year climatological mean for each month).
"historical": [
  { "year": 1991, "month": 1, "temp_c": 24.1, "precip_mm": 112.3, "soil_moisture": 0.228 },
  { "year": 1991, "month": 2, "temp_c": 23.7, "precip_mm": 98.6,  "soil_moisture": 0.221 },
  { "year": 1991, "month": 3, "temp_c": 21.4, "precip_mm": 87.2,  "soil_moisture": 0.215 },
  "..."
]
Fields per row:
FieldUnitDescription
year—Calendar year
month—Calendar month (1 = January)
temp_c°CMean 2-metre air temperature
precip_mmmmTotal precipitation
soil_moisturem³/m³Volumetric soil water content (ERA5 layer 1)
Any field may be null if the upstream data source did not return a value for that period.
3

Read projected data

The projected array has the same structure as historical but contains CMIP6 ensemble output for the future period associated with the chosen scenario. Rows represent monthly means in the mid- to late-21st century horizon provided by Open-Meteo’s climate projection service.
"projected": [
  { "year": 2071, "month": 1, "temp_c": 27.3, "precip_mm": 95.1,  "soil_moisture": 0.198 },
  { "year": 2071, "month": 2, "temp_c": 26.8, "precip_mm": 88.4,  "soil_moisture": 0.191 },
  { "year": 2071, "month": 3, "temp_c": 24.9, "precip_mm": 76.0,  "soil_moisture": 0.185 },
  "..."
]
Compare projected[n].temp_c against historical[n].temp_c for the same month to estimate the projected temperature anomaly at your location.
4

Choose a different scenario

Change the scenario query parameter to switch between CMIP6 Shared Socioeconomic Pathway (SSP) scenarios:
ValuePathwayDescription
SSP1-2.6SustainabilityStrong mitigation; warming likely limited to ~1.5–2 °C by 2100
SSP2-4.5Middle of the roadModerate mitigation; ~2–3 °C warming
SSP3-7.0Regional rivalryHigh emissions; ~3–4 °C warming (default)
SSP5-8.5Fossil-fuelled developmentHighest emissions; ~4–5 °C warming
curl "https://api.climatifai.com/agri/climate?lat=-34.6&lon=-58.4&from=1991&to=2020&scenario=SSP1-2.6"
For a risk-based analysis, you might run all four scenarios and compare the projected precipitation totals to understand the range of possible outcomes for your crop.
5

Use climate data with the crop advisor

The /agri/advisor endpoint uses the same historical climate data internally. Calling /agri/climate first lets you inspect the raw inputs — annual precipitation, mean temperature, and soil moisture — before the advisor converts them into weighted factor scores.For example, if the historical series shows average annual precipitation of 450 mm for a location where you plan to grow coffee (ideal: 1 200–2 200 mm), you can anticipate a low rainfall adequacy score before calling the advisor. This lets you validate that the climate data looks reasonable for your region before building an irrigation or crop-selection decision on top of it.The projected series also reveals whether conditions are expected to improve or deteriorate under a given scenario, giving you a longer-term view for capital investment decisions.
The response includes a _upstream_hints object with historical and projected keys. If the Open-Meteo service returns an error for either dataset — for example, a temporary rate-limit or an unsupported coordinate — the corresponding hint field will contain the error message from upstream instead of being null. When you see a non-null hint, the returned data may be stale (served from cache) or partially missing.
"_upstream_hints": {
  "historical": null,
  "projected": "Open-Meteo climate API: model data not available for this coordinate"
}