Data from: "Reduced evapotranspiration decreases land precipitation despite enhanced moisture convergence: Insights from perturbed parameter ensembles of earth system models"
Data files
Jun 17, 2026 version files 2.26 GB
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et_precip_ppe.zip
2.26 GB
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README.md
7.75 KB
Abstract
Both canonical theory and climate model simulations predict an increase in globally averaged precipitation with warming from radiative forcing. However, terrestrial processes control surface water and energy fluxes, and therefore can impact surface temperatures independently of radiative forcing. Here, we investigate the sensitivity of the global hydrological cycle to changes in land surface conditions using perturbed parameter ensembles from two climate models, which sample a broad range of evapotranspiration (ET) states through modifications to land model parameters. The parameters are perturbed within the range of observationally-constrained uncertainty and alter ET through different land surface pathways. We find that reduced ET drives warming and decreases land-mean precipitation, which is strongly linked to large decreases in near-surface relative humidity. The decline in relative humidity from warming overcomes increased specific humidity and moisture convergence over land driven by enhanced moisture following Clausius-Clapeyron scaling and shifts in large-scale circulation. We explain the tropical precipitation response to ET forcing with a simple scaling that relates changes in the land-ocean contrast in relative humidity to land-mean precipitation change. Further, the suppressed ET induces a shortwave cloud response that amplifies warming. Our results highlight that changes to land surface processes within parametric uncertainty influence warming and precipitation, which is not directly captured by scalings of the global hydrological cycle with radiative forcing, quantifying the potential significance of terrestrial ecosystem functioning and response to climate change.
This repository contains output from two perturbed parameter ensembles: one from the Community Earth System Model Version 2 (CESM2; Danabasoglu et al. 2020) and one from the Hadley Centre Coupled Model Version 3 (HadCM3; Booth et al 2012).
File structure
The dataset is organized into two directories within et_precip_ppe.zip
cesm2- CESM2 PI PPEhadcm3- HadCM3C Historical and A1B PPE
The directory structure is as follows:
et_precip_ppe
├── cesm2
│ ├── climatology
│ │ ├── COUP_PPE.cam.h0.climatology.LHFLX.nc
│ │ ├── COUP_REF.cam.h0.climatology.LHFLX.nc
│ │ ...
│ │
│ ├── time_average
│ │ ├── COUP_PPE.cam.h0.time_mean.LHFLX.nc
│ │ ├── COUP_REF.cam.h0.time_mean.LHFLX.nc
│ │ ...
│ │
│ ├── paramfiles
│ │ ├── OAAT0000.nc
│ │ ...
│ │
│ ├── namelist_mods
│ │ ├── OAAT0000.txt
│ │ ...
│ │
│ ├── cesm2_atmgrid_weights.nc
│ └── CESM2-LE-1850-1880-F_u-F_v.nc
│
├── hadcm3
│ ├── a1b
│ │ └── time_average
│ │ ├── HadCM3_PPE.a1b.time_mean.surface_upward_water_flux.nc
│ │ ├── HadCM3_STD.a1b.time_mean.surface_upward_water_flux.nc
│ │ ...
│ │
│ ├── control
│ │ └── time_average
│ │ ├── HadCM3_PPE.control.time_mean.air_temperature.nc
│ │ ├── HadCM3_STD.control.time_mean.air_temperature.nc
│ │ ...
│ │
│ └── hadcm3_grid_weights.nc
│
└── README.md
Variables
The native variable naming conventions for each model are retained in this dataset. The full CESM variable names can be found in the CESM output variable look-up table and the mapping used in the manuscript can be found on Zenodo in bbuchovecky/et_precip_ppe-v1.0.0. The following variables are included for each PPE:
CESM2 PI PPE
LHFLX: surface latent heat flux (W m$^{-2}$)calculated_PRECT: total precipitation flux computed as the sum of the large-scale preciptation (PRECL) and convective precipitation (PRECC) (mm day$^{-1}$)TREFHT: reference height air temperature (K)calculated_RHREFHT: reference height relative humidity computed from specific humidity, air pressure, and air temperature using the Goff-Gratch method for saturation vapor pressure (unitless)SWCF: shortwave cloud radiative effect (W m$^{-2}$)Q: specific humidityU: zonal wind (m s$^{-1}$)V: meridional wind (m s$^{-1}$)PS: surface pressure (Pa)FSNS: net shortwave radiative flux at the surface (W m$^{-2}$)FSDS: downwelling shortwave radiative flux at the surface (W m$^{-2}$)
HadCM3 PPE
air_temperature: near-surface air temperature (K)precipitation_flux: total precipitation (mm day$^{-1}$)relative_humidity: near-surface relative humidity (%)surface_downwelling_shortwave_flux_in_air: downwelling shortwave radiative flux at the surface (W m$^{-2}$)surface_net_downward_shortwave_flux: net shortwave radiative flux at the surface (W m$^{-2}$, positive down)surface_upward_water_flux: evapotranspiration flux (mm day$^{-1}$)toa_outgoing_shortwave_flux_assuming_clear_sky: upwelling shortwave radiative flux assuming clear sky at the top of atmosphere (W m$^{-2}$)toa_outgoing_shortwave_flux: upwelling shortwave radiative flux at the top of atmosphere (W m$^{-2}$)
Postprocessing
Files with the PPE prefix contain all perturbed parameter simulations along a member dimension. Files with the REF or STD prefix contain a single default parameter simulation.
All files are the time average of monthly output variables. We average the last 100 years of the CESM2 PI PPE and the last 50 years of the HadCM3 PPEs. The time_average directories contain a single time average over specified time period while the climatology directory contains the climatological monthly values over the same time period.
The cesm2_atmgrid_weights.nc and hadcm3_grid_weights.nc files contain the grid cell area weights for the CESM2 PI PPE and the HadCM3 PPE. The CESM2-LE-1850-1880-F_u-F_v.nc file contains the zonal and meridional components of the vertically-integrated horizontal moisture flux (F_u and F_v, respectively) computed using 6-hourly specific humidity and wind output from the first ensemble member of the CESM2 Large Ensemble (Rodgers et al. 2021) over the period 1850-1880.
Parameter perturbations
The parameter peturbations for the CESM2 PI PPE are provided in the cesm2/paramfiles and cesm2/namelist_mods directories. The cesm2/CLM5PPE_coupledPPE_crosswalk.csv file maps the parameter perturbation (name and minimum or maximum) to the corresponding parameter file ID (e.g., OAAT0013) and location (either in the NetCDF parameter file or namelist modification). The "pft specific" colunm indicates if each plant functional type (PFT) has its own parameter value.
References
Bogenschutz, P. A., A. Gettelman, C. Hannay, V. E. Larson, R. B. Neale, C. Craig, and C.-C. Chen, 2018: The path to CAM6: coupled simulations with CAM5.4 and CAM5.5. Geoscientific Model Development, 11, 235–255, https://doi.org/10.5194/gmd-11-235-2018.
Cox, P. M., 2001: Description of the ‘TRIFFID’ Dynamic Global Vegetation Model. Met Office.
Cox, P. M., R. A. Betts, C. D. Jones, S. A. Spall, and I. J. Totterdell, 2000: Acceleration of global warming due to carbon-cycle feedbacks in a coupled climate model. Nature, 408, 184–187, https://doi.org/10.1038/35041539.
Danabasoglu, G., and P. R. Gent, 2009: Equilibrium Climate Sensitivity: Is It Accurate to Use a Slab Ocean Model? Journal of Climate, 22, 2494–2499, https://doi.org/10.1175/2008JCLI2596.1.
Danabasoglu, G., and Coauthors, 2020: The Community Earth System Model Version 2 (CESM2). Journal of Advances in Modeling Earth Systems, 12, e2019MS001916, https://doi.org/10.1029/2019MS001916.
Gordon, C., C. Cooper, C. A. Senior, H. Banks, J. M. Gregory, T. C. Johns, J. F. B. Mitchell, and R. A. Wood, 2000: The simulation of SST, sea ice extents and ocean heat transports in a version of the Hadley Centre coupled model without flux adjustments. Climate Dynamics, 16, 147–168, https://doi.org/10.1007/s003820050010
Lawrence, D. M., and Coauthors, 2019: The Community Land Model Version 5: Description of New Features, Benchmarking, and Impact of Forcing Uncertainty. Journal of Advances in Modeling Earth Systems, 11, 4245–4287, https://doi.org/10.1029/2018ms001583.
Nakicenovic, N., R. Swart, J. Alcamo, G. Davis, B. de Vries, and J. Fenhann, 2000: IPCC Special Report on Emissions Scenarios. Cambridge University Press.
Rodgers, K. B., and Coauthors, 2021: Ubiquity of human-induced changes in climate variability. Earth System Dynamics, 12, 1393–1411, https://doi.org/10.5194/esd-12-1393-2021.
Zarakas, C. M., and Coauthors, 2024: Land Processes Can Substantially Impact the Mean Climate State. Geophysical Research Letters, 51, e2024GL108372, https://doi.org/10.1029/2024GL108372.
The CESM2 PI PPE, described further in Zarakas et al. (2024), was run at 1.9° by 2.5° resolution in a coupled atmosphere-land-slab-ocean configuration under preindustrial boundary conditions using the Community Land Model Version 5 (CLM5; Lawrence et al. 2019), the Community Atmosphere Model Version 6 (CAM6; Bogenshutz et al. 2018), and a slab ocean model (SOM; Danabasoglu and Gent 2009). The perturbed parameter files are included in addition to the output for the CESM2 PI PPE. The HadCM3 PPE contains two scenarios: one forced with historical greenhouse gas emissions and aerosol concentrations from 1850-1990 and the other forced with projected atmospheric CO2 emissions from the 1990-2100 A1B SRES scenario (Nakicenovic et al. 2000). HadCM3 was run in a configuration using a flux-adjusted version of HadCM3 (Gordon et al. 2000) with fully dynamic atmosphere and ocean components coupled to a land surface model with dynamic vegetatino (TRIFFID; Cox 2001, Cox et al. 2000).