Impact model: GOTM

The General Ocean Turbulence Model (GOTM) was used for simulating the thermal dynamics of 41449 lakes (pixels). More details about the selected lakes for simulations can be found here: https://github.com/icra/ISIMIP_Lake_Sector. More information about basic validation of simulation here: https://github.com/icra/GOTM_ISIMIP3_validation. GOTM is an open-source ocean model adapted to lakes, which assumes a one-dimensional water column model for studying hydrodynamic and biogeochemical processes in marine and limnic waters. It models the state-of-the-art of main physical processes in lakes: vertical turbulent fluxes of momentum, heat, and dissolved and particulate matter.

ISIMIP3b
ISIMIP3a
ISIMIP2b

Person responsible for model simulations in this simulation round

Daniel Mercado: damebet@gmail.com, 0000-0003-4572-3029, Blanes Centre for Advanced Studies (Spain)

Output Data

Experiments: ssp585_2015soc-from-histsoc_default, picontrol_2015soc-from-histsoc_default, historical_histsoc_default, picontrol_histsoc_default, ssp370_2015soc-from-histsoc_default, ssp126_2015soc-from-histsoc_default
Climate Drivers: GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL
Date: 2024-01-05

Basic information

Model Version: 5.3

Model Output License: CC0

Model Homepage: https://gotm.net/

Resolution

Spatial aggregation: regular grid

Horizontal resolution: 0.5°x0.5°

Vertically resolved: Yes

Number of vertical layers: For lake depth <1m, 4 levels were simulated (e.g, every 0.2m for a 0.8m lake); For 1m <= lake depth < 20m, the level simulated double the depth (e.g, for a 15m lake, 30 levels were simulated); For lake depth >= 20, the lake was simulated every meter (e.g., 100 levels for a 100m lake)

Temporal resolution of input data: climate variables: daily

Input data

Simulated atmospheric climate data sets used: MRI-ESM2-0, IPSL-CM6A-LR, MPI-ESM1-2-HR, UKESM1-0-LL, GFDL-ESM4

Other human influences data sets used: Lakes area fraction (ISIMIP3b)

Other data sets used: Lakes static information

Climate variables: hurs, sfcWind, tas, rlds, rsds, ps, pr

Spin-up

Was a spin-up performed?: Yes

Spin-up design: 20 years spin-up. A long-term spin-up was used to avoid any error in the initial condition.

Calibration

Was the model calibrated?: Yes

Which dataset was used for calibration?: Mean values of the parameter values resulting from ISIMIP2a local lakes calibration were used.

Vegetation

Is co2 fertilisation accounted for?: Yes

Person responsible for model simulations in this simulation round

Daniel Mercado: damebet@gmail.com, 0000-0003-4572-3029, Blanes Centre for Advanced Studies (Spain)

Output Data

Experiments: counterclim_histsoc_default, obsclim_histsoc_default
Climate Drivers: 20CRV3, 20CRV3-ERA5, 20CRV3-W5E5, GSWP3-W5E5
Date: 2023-03-23

Basic information

Model Version: 5.3

Model Output License: CC0

Model Homepage: https://gotm.net/

Simulation Round Specific Description: Graphical validation of the simulation can be found here: https://github.com/icra/GOTM_ISIMIP3_validation

Resolution

Spatial aggregation: regular grid

Horizontal resolution: 0.5’ x 0.5’

Temporal resolution of input data: climate variables: daily

Input data

Observed atmospheric climate data sets used: GSWP3-W5E5 (ISIMIP3a)

Climate variables: hurs, sfcWind, tas, rlds, rsds, ps, pr

Spin-up

Was a spin-up performed?: Yes

Spin-up design: 20 year spin-off

Dams & Reservoirs

Dam and reservoir implementation: They are part of the selected lakes for simulation, more details here: https://github.com/icra/ISIMIP_Lake_Sector

Calibration

Was the model calibrated?: No

Which years were used for calibration?: 1979-2016

Which dataset was used for calibration?: Average parameters to run local lakes in ISIMIP2a EWEMBI simulations

Person responsible for model simulations in this simulation round

Malgorzata Golub: gosia.golub@ebc.uu.se, Uppsala University (Sweden)

Daniel Mercado: damebet@gmail.com, 0000-0003-4572-3029, Blanes Centre for Advanced Studies (Spain)

Output Data

Experiments: II, III, VIII (only historical and future periods, partial data)
Climate Drivers: None
Date: 2020-06-22

Basic information

Model Version: GOTM v5.3

Model Output License: CC BY 4.0

Model Homepage: https://gotm.net

Model License: https://gotm.net

Simulation Round Specific Description: Atmospheric modeling forcing: IPSL-CM5A-LR Scenarios: historical, piControl, rcp26, rcp60, rcp85

Resolution

Spatial aggregation: regular grid

Horizontal resolution: 0.5°x0.5°

Temporal resolution of input data: climate variables: daily

Input data

Simulated atmospheric climate data sets used: IPSL-CM5A-LR, HadGEM2-ES, GFDL-ESM2M, MIROC5

Observed atmospheric climate data sets used: EWEMBI

Climate variables: hurs, sfcWind, tasmax, tas, tasmin, uas, rlds, vas, rsds, ps, pr

Spin-up

Was a spin-up performed?: Yes

Spin-up design: The first year of the simulation outputs should be taken as spin-up period.

Additional questions 1

How did you initialise you lake temperature profile?: The initial temperature conditions were configured as 8 and 6ºC at 1m and 50m depths, except for the tropical pixels (-30 to 30º latitude) with more than 10m depth, due to the unreal low temperatures presented in these particular lakes (thermal inertia issue, even after the one-year spin-up), then a re-run was implemented using temperatures initial values of 25 and 23ºC at 1m and 50m depths.

How did you set lake depth?: The model was run with a fixed water level at 0m and the number of layers been simulated are equal to the total depth of the lake (pixel). Although, there are different lake depths only 10 layers were saved in the NetCDF outputs, according to this exponential funcions: Lake depth -> layers saved depth <=10m -> 1-10 meters 10 1-9, 15 meters 15 1-5, 7, 9, 12, 15, 20 meters 20 1-5, 7, 9, 12, 20, 30 meters 30 1-5, 7, 9, 12, 30, 50 meters 50 1-3, 5, 7, 9, 15, 27, 50 , 100 meters depth > 100 -> 1-3, 5, 7, 9, 15, 27, 100 , 1000 meters

How did you set water transparency?: Light extinction methods based on non-visible fraction of shortwave radiation and e-folding depth of non-visible shortwave radiation