Understanding and Reducing the Uncertainties of Land Surface Energy Flux Partitioning Within CMIP6 Land Models

  • August 21, 2022
  • Blog,Home Page Feature,Science and Technical Highlights
  • Deciduous Forest

    Deciduous forest, one of the different biome types. (Image by Martin Blank | Flickr)


    The Science

    Land surfaces dissipate energy through latent (LE) and sensible (H) heat fluxes that modulate atmospheric temperature and humidity, which in return affect land surface vegetation and soil processes. Within this two-way land-atmosphere coupling, surface energy partitioning (LE versus H) plays a central role in connecting the land and atmospheric states and fluxes. However, considerably large uncertainties still exist in earth system land models, i.e. the phase 6 of the Coupled Model Intercomparison Project (CMIP6). Further, the underlying controls from climate and biological factors on surface energy partitioning over different biome types are not well understood. Figure 1 shows location and biome types of selected sites that were taken into account in this study: Deciduous Broadleaf Forest (DBF), Evergreen Broadleaf Forest (EBF), Evergreen Needleleaf Forest (ENF), Grassland (GRA), and Savanna (SAV).

    Location and biome types

    Figure 1. Location and biome types of selected sites: Deciduous Broadleaf Forest (DBF), Evergreen Broadleaf Forest (EBF), Evergreen Needleleaf Forest (ENF), Grassland (GRA), and Savanna (SAV).

    The Impact

    This study provides a better representation of convection which in turn improves the modeling of precipitation – its global and regional distribution, intensity and frequency.  More accurately simulating precipitation is critical for providing realistic climate change projections for decision making and impacts applications.

    Explained variance of ANN-surrogate

    Figure 2. (a) Explained variance of ANN-surrogate models over five land cover types. (b) Scatterplot of raw CMIP6 Evaporative Fraction (EF) versus observed EF values. (c) Scatterplot of observed EF versus ANN-modelled EF, but with input drivers replaced by FLUXNET and satellite observations. Performance differences before and after driver calibration in term of (d) mean absolute error (MAE) and (e) Pearson correlation coefficient (R).


    Figure 2 shows that CMIP6 models largely overestimated the land surface evaporative fraction. The work also suggests this overestimation has considerable spread at deciduous broadleaf forest (DBF), evergreen needleleaf forest (ENF), and savanna sites (SAV). The research found that accounting for biases in surface forcing variables, the simulated EF in CMIP6 models could be substantially improved (correlation coefficient R between model and observed EF improved from 0.47 to 0.66 averagely across all 5 biomes). Leaf area index, vapor pressure deficit, and precipitation were the most important variables leading to prediction improvement. Furthermore, the ML-based parameterization showed promise to further reduce model biases (R improved from 0.66 to 0.80 on average across all biomes) in spite of the limited improvement at evergreen broadleaf forest sites where model bias may be dominated by structural inaccuracies.

    This study provides an effective framework to understand and reduce model uncertainties in simulating land surface energy flux partitioning and, more importantly, highlights the need of effective model structure improvement for the next generation earth system land model development.


    • Yuan, Kunxiaojia, Qing Zhu, William J. Riley, Fa Li, and Huayi Wu. 2022. “Understanding And Reducing The Uncertainties Of Land Surface Energy Flux Partitioning Within CMIP6 Land Models”. Agricultural And Forest Meteorology 319. Elsevier BV: 108920. doi:10.1016/j.agrformet.2022.108920.


    • This research was supported by the Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation (RUBISCO) Scientific Focus Area and Energy Exascale Earth System Model (E3SM) Project, Office of Biological and Environmental Research of the U.S. Department of Energy Office of Science.


    • Qing Zhu, Lawrence Berkeley National Laboratory
    • William Riley, Lawrence Berkeley National Laboratory
    Send this to a friend