Abstract
Abstract. Land use and land cover change (LULCC) impacts local and regional climates through various biogeophysical processes. Accurate representation of land surface parameters in land surface models (LSMs) is essential to accurately predict these LULCC-induced climate signals. In this work, we test the applicability of the default Noah, Noah-MP, and Community Land Model (CLM) LSMs in the Weather Research and Forecasting (WRF) model over Sub-Saharan Africa. We find that the default WRF LSMs do not accurately represent surface albedo, leaf area index, and surface roughness in this region due to various flawed assumptions, including the treatment of the MODIS woody savanna land use and land cover (LULC) category as closed shrubland. Consequently, we developed a WRF CLM version with more accurate African land surface parameters (CLM-AF), designed such that it can be used to evaluate the influence of LULCC. We evaluate meteorological performance for the default LSMs and CLM-AF against observational datasets, gridded products, and satellite estimates. Further, we conduct LULCC experiments with each LSM to determine if differences in land surface parameters impact the LULCC-induced climate responses. Despite clear deficiencies in surface parameters, all LSMs reasonably capture the spatial pattern and magnitude of near-surface temperature and precipitation. However, in the LULCC experiments, inaccuracies in the default LSMs result in illogical localized temperature and precipitation changes. Differences in thermal changes between Noah-MP and CLM-AF indicate that the temperature impacts from LULCC are dependent on the sensitivity of evapotranspiration to LULCC in Sub-Saharan Africa. Errors in land surface parameters indicate that the default WRF LSMs considered are not suitable for LULCC experiments in tropical or Southern Hemisphere regions and that proficient meteorological model performance can mask these issues. We find CLM-AF to be suitable for use in Sub-Saharan Africa LULCC studies, but more work is needed by the WRF community to improve its applicability to other tropical and Southern Hemisphere climates.
Highlights
Land use and land cover change (LULCC) has various biogeophysical impacts on climate by altering land surface albedo, evapotranspiration, and surface roughness that in turn alter atmospheric circulations, energy budgets, and hydrologic cycles (Pielke et al, 2011; Mahmood et al, 2014; Bright, 2015; Smith et al, 2016; Quesada et al, 2017)
We focus on the different ways in which the land surface models (LSMs) prescribe and treat surface parameters such as leaf area index (LAI), albedo, and surface roughness length (RL) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) 21 land category data
The representation of sub-grid land use and land cover (LULC) variability can significantly alter the responses of climate models to LULCC (e.g., Boone et al, 2016), but this functionality is not considered in these experiments since any underlying errors in albedo, LAI, and RL within Noah would be present in both the mosaic tile and dominant LULC configurations
Summary
Land use and land cover change (LULCC) has various biogeophysical impacts on climate by altering land surface albedo, evapotranspiration, and surface roughness that in turn alter atmospheric circulations, energy budgets, and hydrologic cycles (Pielke et al, 2011; Mahmood et al, 2014; Bright, 2015; Smith et al, 2016; Quesada et al, 2017). Glotfelty et al.: Limitations of WRF LSMs for simulating land use and land cover change gions experience warming in response to a reduction in evaporation (e.g., Longobardi et al, 2016; Quesada et al, 2017) This LULCC latitudinal dependence has been shown to occur in observations as well (Zhang et al, 2014). In terms of LULCC applications, Hagos et al (2014) found that WRF model configurations that simulate a climate which is too wet or too dry compared to observations and reanalysis do not produce a strong climate signal from LULCC over Africa This weak signal is a result of the model falling into erroneous moisture- or energy-limited regimes. Understanding the deficiencies in how LSMs represent LULCC is key to accurately representing regional climate signals that impact climate change investigations and coupled natural and human system research regarding human decision-making, air quality, and human–ecosystem health interactions
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