Future-Climate Envirotyping from CMIP6 Downscaled Scenarios
Source:R/env_data_collection_src.R
get_climate_scenario.RdRetrieves downscaled CMIP6 monthly climate (WorldClim 2.1) for a set of
sites and converts it into a DAILY weather series compatible with
processWTH(), env_phenology() and water_balance(), by
applying the monthly change signal to an observed baseline series
(delta-change downscaling).
Usage
get_climate_scenario(
env.id = NULL,
lat = NULL,
lon = NULL,
baseline = NULL,
model = "MPI-ESM1-2-HR",
models = c("IPSL-CM6A-LR", "MPI-ESM1-2-HR", "MRI-ESM2-0", "UKESM1-0-LL"),
scenario = "ssp245",
period = "2041-2060",
resolution = c("2.5m", "5m", "10m", "30s"),
method = c("delta", "raw"),
dir.path = NULL,
baseline.id = "env",
baseline.date = "YYYYMMDD",
precip.scale = c("multiplicative", "additive"),
verbose = TRUE
)Arguments
- env.id
vector (character). Site identifiers.
- lat, lon
vector (numeric). Site coordinates.
- baseline
data.frame. Observed daily weather for the same sites, from
get_weather(). Supplies the day-to-day variability that monthly projections cannot. REQUIRED formethod = "delta".- model
character. CMIP6 GCM name, e.g.
"MPI-ESM1-2-HR". One of.CS_GCMS. Use"ensemble"to average several.- models
vector (character). Used when
model = "ensemble". Default is a four-model spread across climate sensitivities.- scenario
character.
"ssp126","ssp245","ssp370"or"ssp585".- period
character.
"2021-2040","2041-2060","2061-2080"or"2081-2100".- resolution
character. WorldClim tile resolution:
"2.5m"(default),"5m","10m"or"30s". Finer is a much larger download. Resolution does NOT affect which model x scenario combinations exist: archive gaps are per GCM x SSP and are identical at every resolution and period. See.CS_GAPS.- method
character.
"delta"(default) perturbs the baseline;"raw"returns the monthly projected values without downscaling.- dir.path
character. Cache directory for downloaded rasters. Defaults to
tools::R_user_dir("et", "cache").- baseline.id, baseline.date
character. Columns in
baseline.- precip.scale
character.
"multiplicative"(default) or"additive"application of the precipitation delta.- verbose
boolean. Print progress.
Value
For method = "delta", a data.frame in get_weather() layout
(env, YYYYMMDD, daysFromStart, T2M,
T2M_MAX, T2M_MIN, PRECTOT, ...) with the scenario
signal applied, plus scenario, model and period
columns. For method = "raw", a monthly data.frame of projected
tmin, tmax and prec per site.
The attribute "deltas" holds the monthly change factors that were
applied, so the perturbation is auditable.
Details
What delta-change does and does not do. The monthly mean change from the GCM is added to (temperature) or multiplied onto (precipitation) the observed daily series. The result therefore keeps the OBSERVED day-to-day sequence, wet-day frequency and spell structure, and shifts only the monthly means. This is the standard, defensible way to drive a crop model from monthly projections (Hawkins et al. 2013), but note the consequence: changes in variability, in dry-spell length, and in extreme-event frequency are NOT represented. A scenario that in reality brings the same total rain in fewer, heavier events will look identical to one that brings it evenly. Heat-stress day counts under delta-change are driven purely by the mean shift, so they are a lower bound.
If extremes matter to your question, you need daily GCM output and a quantile-mapping bias correction, not this function.
Model choice is not innocuous. Across CMIP6, equilibrium climate
sensitivity ranges roughly 1.8-5.6 K, and several high-sensitivity models
are now regarded as implausibly hot. A single-model result is one draw from
a wide distribution. Prefer model = "ensemble" and report the spread;
a projection whose sign flips across models is not a finding.
Network access. WorldClim is an external host. In restricted
environments the download will fail; pre-populate dir.path with the
GeoTIFFs and the function will use the cache.
References
Fick, S.E. & Hijmans, R.J. (2017) WorldClim 2. Int. J. Climatol. 37(12), 4302-4315.
Hawkins, E. et al. (2013) Calibration and bias correction of climate projections for crop modelling. Agric. For. Meteorol. 170, 19-31.
O'Neill, B.C. et al. (2016) The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461-3482.
Examples
# \donttest{
sites <- data.frame(env = c("PIRA", "SETE"),
lat = c(-22.7, -19.4), lon = c(-47.6, -44.2))
base <- get_weather(env.id = sites$env, lat = sites$lat, lon = sites$lon,
start.day = "2015-10-01", end.day = "2016-03-31")
#> ---------------------------------------------------------------
#> get_weather -- downloads daily weather from NASA POWER
#> Last updated at 09/27/2026
#> ---------------------------------------------------------------
#> ---------------------------------------------------------------
#> get_weather() - daily weather from NASA POWER
#> https://docs.ropensci.org/nasapower
#> ---------------------------------------------------------------
#> Start .............................. Thu Oct 22 18:31:54 2026
#> Environmental units ................ 2
#> Parallel ........................... [ ]
#> [1/2] PIRA downloaded
#> [2/2] SETE downloaded
#> Computing VPD ...................... [ x ]
#> Computing N, RTA ................... [ x ]
#> Computing n ........................ [ x ]
#> Computing TH1 ...................... [ x ]
#> Computing TH2 ...................... [ x ]
#> Computing PAR_TEMP ................. [ x ]
#> Environmental units downloaded ..... 2/2
#> Daily weather features ............. 25
#> Done ............................... Thu Oct 22 18:31:56 2026
#> Total time ......................... 2.3s
## Mid-century, intermediate scenario, 4-model ensemble.
## The default 'models' set covers all four SSPs.
fut <- get_climate_scenario(env.id = sites$env, lat = sites$lat,
lon = sites$lon, baseline = base,
model = "ensemble", scenario = "ssp245",
period = "2041-2060")
#> ---------------------------------------------------------------
#> get_climate_scenario -- CMIP6 downscaled scenario weather
#> Last updated at 09/27/2026
#> ---------------------------------------------------------------
#> ---------------------------------------------------------------
#> CMIP6 downscaled scenario (WorldClim 2.1)
#> Scenario ........... ssp245
#> Period ............. 2041-2060
#> Model(s) ........... IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL
#> Resolution ......... 2.5m
#> Cache .............. /home/runner/.cache/R/et
#> ---------------------------------------------------------------
#> - downloading wc2.1_2.5m_tmin_IPSL-CM6A-LR_ssp245_2041-2060.tif
#> - downloading wc2.1_2.5m_tmax_IPSL-CM6A-LR_ssp245_2041-2060.tif
#> - downloading wc2.1_2.5m_prec_IPSL-CM6A-LR_ssp245_2041-2060.tif
#> - downloading wc2.1_2.5m_tmin_MPI-ESM1-2-HR_ssp245_2041-2060.tif
#> - downloading wc2.1_2.5m_tmax_MPI-ESM1-2-HR_ssp245_2041-2060.tif
#> - downloading wc2.1_2.5m_prec_MPI-ESM1-2-HR_ssp245_2041-2060.tif
#> - downloading wc2.1_2.5m_tmin_MRI-ESM2-0_ssp245_2041-2060.tif
#> - downloading wc2.1_2.5m_tmax_MRI-ESM2-0_ssp245_2041-2060.tif
#> - downloading wc2.1_2.5m_prec_MRI-ESM2-0_ssp245_2041-2060.tif
#> - downloading wc2.1_2.5m_tmin_UKESM1-0-LL_ssp245_2041-2060.tif
#> - downloading wc2.1_2.5m_tmax_UKESM1-0-LL_ssp245_2041-2060.tif
#> - downloading wc2.1_2.5m_prec_UKESM1-0-LL_ssp245_2041-2060.tif
#> - computing monthly baseline climatology
#> - applying monthly delta-change signal
#> Dropping baseline-derived column(s) invalidated by the shift: n, N, RTA, VPD. Re-run processWTH().
#> ---------------------------------------------------------------
#> Mean applied signal across sites and months
#> Tmax +0.80 C
#> Tmin -0.56 C
#> Prec x1.25
#> ---------------------------------------------------------------
## GFDL-ESM4 is fine for ssp126 or ssp370, just not ssp245/ssp585:
fut126 <- get_climate_scenario(env.id = sites$env, lat = sites$lat,
lon = sites$lon, baseline = base,
model = "GFDL-ESM4", scenario = "ssp126",
period = "2041-2060")
#> ---------------------------------------------------------------
#> get_climate_scenario -- CMIP6 downscaled scenario weather
#> Last updated at 09/27/2026
#> ---------------------------------------------------------------
#> ---------------------------------------------------------------
#> CMIP6 downscaled scenario (WorldClim 2.1)
#> Scenario ........... ssp126
#> Period ............. 2041-2060
#> Model(s) ........... GFDL-ESM4
#> Resolution ......... 2.5m
#> Cache .............. /home/runner/.cache/R/et
#> ---------------------------------------------------------------
#> - downloading wc2.1_2.5m_tmin_GFDL-ESM4_ssp126_2041-2060.tif
#> - downloading wc2.1_2.5m_tmax_GFDL-ESM4_ssp126_2041-2060.tif
#> - downloading wc2.1_2.5m_prec_GFDL-ESM4_ssp126_2041-2060.tif
#> - computing monthly baseline climatology
#> - applying monthly delta-change signal
#> Dropping baseline-derived column(s) invalidated by the shift: n, N, RTA, VPD. Re-run processWTH().
#> ---------------------------------------------------------------
#> Mean applied signal across sites and months
#> Tmax +0.57 C
#> Tmin -0.79 C
#> Prec x1.10
#> ---------------------------------------------------------------
## Drop straight into the existing pipeline
fut <- processWTH(fut)
#> ---------------------------------------------------------------
#> processWTH -- derives all agro-meteorological parameters
#> Last updated at 09/27/2026
#> ---------------------------------------------------------------
#> - radiation parameters computed
#> - atmospheric-demand parameters computed
#> - temperature-response parameters computed
ph <- env_phenology(fut, crop = "maize")
#> ---------------------------------------------------------------
#> env_phenology -- maps weather onto thermal-time stages
#> Last updated at 09/27/2026
#> ---------------------------------------------------------------
#> - computing daily thermal time (method = gdd)
#> - assigning developmental stages
#> Warning: 2 environment(s) did not accumulate enough thermal time to reach 'R6': PIRA, SETE. Days beyond the last reached stage are NA. Extend the series or lower the stage thresholds.
#> ---------------------------------------------------------------
#> Thermal-time stages (GDD, Tbase = 10 C, Tupper = 30 C)
#> VE <= 125 C d 18 day(s)
#> V6 <= 475 C d 50 day(s)
#> V12 <= 740 C d 37 day(s)
#> VT <= 1135 C d 55 day(s)
#> R1 <= 1250 C d 16 day(s)
#> R3 <= 1660 C d 56 day(s)
#> R5 <= 2110 C d 63 day(s)
#> R6 <= 2700 C d 71 day(s)
#> ---------------------------------------------------------------
## Compare thermal time now vs mid-century
tapply(ph$dTT, ph$env, sum)
#> PIRA SETE
#> 2611.382 2625.824
# }