Earth Sciences Division (ESD) Department of Energy (DOE) Lawrence Berkeley National Laboratory (LBNL)

Climate Sciences Core Capability: Modeling biogeochemical processes and ecosystem-climate interaction

The LBNL-Earth Sciences Division’s Climate Science Department uses several land surface models and coupled land surface-climate models.  Our models include:

  • CLM
  • WRF
  • MM5
  • coupled CLM-WRF

We are developing a fully coupled integrated assessment and Earth system model, or iESM (integrated Earth System Model).  We apply these capabilities in a number of projects.

Model platforms being used at LBNL, showing intra- and inter-SFA linkages.  Task abbreviations used for this SFA are Atm C (Atmospheric Carbon), Terr C (Terrestrial Carbon) and Clouds (Cloud Forcing). IA (Integrated Assessment) and Prediction are in the Climate Change Modeling SFA.

Model Name

Model Reference


Tasks Used


Dai et al. (2003)

Land-surface C and energy modeling

Atm. C, IA, Terr C


To be developed

Land-surface modeling; 13C, 18O, and 14C modeling

Atm. C, IA, Terr. C


Riley et al. (2002)

Land-surface and isotope C and energy fluxes

Atm. C, IA, Terr. C


Skamarock et al. (2008)

Mesoscale climate

Atm. C, Clouds, IA


Subin et al. (2009)

Land-surface atmosphere interactions

Atm. C, IA


Lin et al. (2003), Kort et al. (2008)

Inversions and footprint analysis

Atm. C


Gurney et al. (submitted)

Fossil CO2 attribution

Atm. C

14C box model

Trumbore & Torn (2005)

Estimatte turnover time from 14C data

Terr. C


Riley et al. (2009)

Simulates fine root turnover and C fluxes to SOM

Terr. C


Riley and Matson (2000)

Coupled soil N and C cycles

Terr. C


Maggi et al., (2008), Gu et al., (2008)

Coupled soil N and C cycles

Terr. C


Collins et al. (2006a, b)

Climate simulations

IA, Prediction

Radiation model

Clough et al. (2005)

GHG and radiation interaction

IA, Prediction

Our land-surface model (ISOLSM) for the SGP dominant vegetation types uses a model parameter fitting analysis and measurements from our portable and Central Facility (CF) eddy covariance systems. These tools allow us to demonstrate that characterizing land-surface cover and its status (e.g., leaf area index) at the spatial scale of variability are critical to accurately estimating LH, SH, and CO2 fluxes (Riley et al., 2002, 2003; Cooley et al., 2005, Riley et al. 2009). In fact, it is necessary to deal with land-cover heterogeneity at a fine scale even for estimates of fluxes at the regional scale (Riley et al. 2009).

To develop confidence in our regional estimates, we have tested our CO2 and latent and sensible heat exchange predictions (Aranibar et al., 2006, Riley et al., 2002; 2003; Cooley et al., 2005, Riley et al., 2009) against multiyear site-level eddy covariance measurements in the dominant land-cover types. We have also integrated stable isotopic tracers (18O, 13C) into our modeling framework for testing and diagnosis (Still et al., 2009; Mcdowell et al., 2008; Lai et al., 2006; Riley, 2005).