increase time step to 900s. (#1756)

Author: kmdeck

NEWS.md

@@ -2,6 +2,7 @@ ClimaLand.jl Release Notes
 ========================
 main
 ----
+- Change timestep to 900s from 450s in calibration, longruns and benchmarks; reduce the number of short/default diagnostics PR[#1756](https://github.com/CliMA/ClimaLand.jl/pull/1756)
 
 v1.8.3
 -----

experiments/benchmarks/bucket.jl

@@ -28,7 +28,7 @@ const FT = Float64;
 ## This result is from a benchmark run on an A100 on the clima cluster
 const PREVIOUS_GPU_TIME_S = 0.13
 ## This result is from a benchmark run with a single process on the clima cluster
-const PREVIOUS_CPU_TIME_S = 2.0
+const PREVIOUS_CPU_TIME_S = 2.15
 ######################################################################
 include("benchmark_utils.jl")
 

experiments/benchmarks/richards.jl

@@ -47,7 +47,7 @@ const FT = Float64;
 ## This result is from a benchmark run on an A100 on the clima cluster
 const PREVIOUS_GPU_TIME_S = 0.40
 ## This result is from a benchmark run with a single process on the clima cluster
-const PREVIOUS_CPU_TIME_S = 1.18
+const PREVIOUS_CPU_TIME_S = 1.23
 ######################################################################
 
 include("benchmark_utils.jl")

experiments/benchmarks/snowy_land.jl

@@ -7,7 +7,7 @@
 # Number of spatial elements: 180, 360, 15
 # Soil depth: 50 m
 # Simulation duration: 6 hours
-# Timestep: 450 s
+# Timestep: 900 s
 # Timestepper: ARS111
 # Fixed number of iterations: 3
 # Jacobian update: every Newton iteration
@@ -36,9 +36,9 @@ const FT = Float64
 
 ######################################################################
 ## This result is from a benchmark run on an A100 on the clima cluster
-const PREVIOUS_GPU_TIME_S = 0.515
+const PREVIOUS_GPU_TIME_S = 0.279
 ## This result is from a benchmark run with a single process on the clima cluster
-const PREVIOUS_CPU_TIME_S = 15.8
+const PREVIOUS_CPU_TIME_S = 19.0
 ######################################################################
 
 include("benchmark_utils.jl")
@@ -55,7 +55,7 @@ function setup_snowyland()
     start_date = DateTime(2008)
     duration = Dates.Hour(6)
     stop_date = start_date + duration
-    Δt = 450.0
+    Δt = 900.0
     time_interpolation_method = LinearInterpolation(PeriodicCalendar())
     nelements = (180, 360, 15)
     earth_param_set = LP.LandParameters(toml_dict)

experiments/benchmarks/soil.jl

@@ -2,7 +2,7 @@
 # Number of spatial elements: 101 in horizontal, 15 in vertical
 # Soil depth: 50 m
 # Simulation duration: 6 hours
-# Timestep: 450 s
+# Timestep: 900 s
 # Timestepper: ARS111
 # Fixed number of iterations: 3
 # Jacobian update: every new Newton iteration
@@ -31,7 +31,7 @@ const FT = Float64;
 
 ######################################################################
 # This result is from a benchmark run on an A100 on the clima cluster
-const PREVIOUS_GPU_TIME_S = 0.13
+const PREVIOUS_GPU_TIME_S = 0.0794
 ## This result is from a benchmark run with a single process on the clima cluster
 const PREVIOUS_CPU_TIME_S = 1.12
 ######################################################################
@@ -49,7 +49,7 @@ function setup_soil()
     start_date = DateTime(2008)
     duration = Hour(6)
     stop_date = start_date + duration
-    Δt = 450.0
+    Δt = 900.0
     nelements = (101, 101, 15)
     domain = ClimaLand.Domains.global_box_domain(FT; context, nelements)
     forcing = ClimaLand.prescribed_forcing_era5(

experiments/calibration/models/snowy_land.jl

@@ -10,7 +10,7 @@
 # Number of spatial elements: 101 in horizontal, 15 in vertical
 # Soil depth: 50 m
 # Simulation duration: 730 d
-# Timestep: 450 s
+# Timestep: 900 s
 # Timestepper: ARS111
 # Fixed number of iterations: 3
 # Jacobian update: every new Newton iteration
@@ -74,7 +74,7 @@ function ClimaCalibrate.forward_model(
     # Determine start date and end date from the sample date ranges
     start_date = first(sample_date_ranges[minimum(minibatch)]) - spinup
     stop_date = last(sample_date_ranges[maximum(minibatch)]) + extend
-    Δt = 450.0
+    Δt = 900.0
 
     # Convert to ITimes
     t0 = ITime(0, Dates.Second(1), start_date)

experiments/long_runs/snowy_land_pmodel.jl

@@ -10,7 +10,7 @@
 # Number of spatial elements: 101 in horizontal, 15 in vertical
 # Soil depth: 50 m
 # Simulation duration: 730 d
-# Timestep: 450 s
+# Timestep: 900 s
 # Timestepper: ARS111
 # Fixed number of iterations: 3
 # Jacobian update: every new Newton iteration
@@ -111,7 +111,7 @@ end
 # March so that a full season is included in seasonal metrics.
 start_date = LONGER_RUN ? DateTime("2000-03-01") : DateTime("2008-03-01")
 stop_date = LONGER_RUN ? DateTime("2019-03-01") : DateTime("2010-03-01")
-Δt = 450.0
+Δt = 900.0
 domain =
     ClimaLand.Domains.global_box_domain(FT; context, mask_threshold = FT(0.99))
 

experiments/long_runs/soil.jl

@@ -9,7 +9,7 @@
 # Number of spatial elements: 101 in horizontal, 15 in vertical
 # Soil depth: 50 m
 # Simulation duration: 2-20 years, based on LONGER_RUN setting
-# Timestep: 450 s
+# Timestep: 900 s
 # Timestepper: ARS111
 # Fixed number of iterations: 3
 # Jacobian update: every new Newton iteration
@@ -57,7 +57,7 @@ outdir =
 # If LONGER run, run for 20 years, with the correct forcing each year.
 start_date = LONGER_RUN ? DateTime(2000) : DateTime(2008)
 stop_date = LONGER_RUN ? DateTime(2020) : DateTime(2010)
-Δt = 450.0
+Δt = 900.0
 domain = ClimaLand.Domains.global_box_domain(FT; context)
 toml_dict = LP.create_toml_dict(FT)
 
@@ -90,7 +90,7 @@ simulation =
 CP.log_parameter_information(toml_dict, joinpath(root_path, "parameters.toml"))
 ClimaLand.Simulations.solve!(simulation)
 
-short_names = ["swc", "sie", "si", "et", "wvpa"]
+short_names = ["swc", "sie", "si"]
 LandSimVis.make_annual_timeseries(simulation; savedir = root_path, short_names)
 LandSimVis.make_heatmaps(
     simulation;

src/diagnostics/default_diagnostics.jl

@@ -405,23 +405,23 @@ function add_diagnostics!(
     model::EnergyHydrology,
     subcomponent::ClimaLand.AtmosDrivenFluxBC,
 )
-    append!(diagnostics, ["soilrn", "soillhf", "soilshf", "salb"])
+    append!(diagnostics, ["salb"])
     return nothing
 end
 function add_diagnostics!(
     diagnostics,
     model::EnergyHydrology,
     subcomponent::ClimaLand.Soil.Runoff.SurfaceRunoff,
 )
-    append!(diagnostics, ["sr", "tr"])
+    append!(diagnostics, ["sr"])
     return nothing
 end
 function add_diagnostics!(
     diagnostics,
     model::EnergyHydrology,
     subcomponent::ClimaLand.Soil.Runoff.TOPMODELRunoff,
 )
-    append!(diagnostics, ["sr", "ssr", "tr", "sfsat", "sath", "infc"])
+    append!(diagnostics, ["sr", "ssr"])
     return nothing
 end
 function add_diagnostics!(
@@ -615,13 +615,13 @@ end
 Return a shortened list of highlighted diagnostics for the given model.
 """
 function get_short_diagnostics(model::EnergyHydrology)
-    return ["swc", "si", "sie", "tsoil", "et"]
+    return ["swc", "si", "sie", "tsoil"]
 end
 function get_short_diagnostics(model::SoilCO2Model)
-    return ["sco2", "hr", "soc", "soc_int", "sco2_ppm"]
+    return ["sco2", "hr", "so2"]
 end
 function get_short_diagnostics(model::CanopyModel)
-    diagnostics = ["gpp", "ct", "lai", "trans", "er", "sif"]
+    diagnostics = ["gpp", "lai", "trans", "er"]
     add_diagnostics!(diagnostics, model, model.biomass)
     return diagnostics
 end
@@ -632,7 +632,7 @@ function get_possible_diagnostics(model::ClimaLand.InlandWater.SlabLakeModel)
     return ["lkie", "tlake", "qlake", "alake", "shflake", "lhflake", "rnlake"]
 end
 function get_short_diagnostics(model::ClimaLand.InlandWater.SlabLakeModel)
-    return ["tlake", "alake"]
+    return ["tlake"]
 end
 function get_short_diagnostics(model::SoilCanopyModel)
     component_diagnostics =
@@ -656,18 +656,13 @@ function get_short_diagnostics(model::LandModel)
         "lwu",
         "swu",
         "shf",
-        "swd",
-        "lwd",
         "tair",
         "precip",
         "lhf",
         "msf",
-        "lwp",
         "iwc",
-        "sdr",
         "nee",
         "ra",
-        "cveg",
     ]
 
     # Add conditional diagnostics based on soil runoff type, since this