Evaluation of hydrologic components of community land model 4 and bias identification

Journal Article · Wed Apr 01 00:00:00 EDT 2015 · International Journal of Applied Earth Observation and Geoinformation

DOI:https://doi.org/10.1016/j.jag.2015.03.013· OSTI ID:1421795

Du, Enhao ^[1]; Vittorio, Alan Di ^[1]; Collins, William D. ^[2]

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1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Climate Science Dept.
2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Climate Science Dept.; Univ. of California, Berkeley, CA (United States). Dept. of Earth and Planetary Science

Runoff and soil moisture are two key components of the global hydrologic cycle that should be validated at local to global scales in Earth System Models (ESMs) used for climate projection. Here, we have evaluated the runoff and surface soil moisture output by the Community Climate System Model (CCSM) along with 8 other models from the Coupled Model Intercomparison Project (CMIP5) repository using satellite soil moisture observations and stream gauge corrected runoff products. A series of Community Land Model (CLM) runs forced by reanalysis and coupled model outputs was also performed to identify atmospheric drivers of biases and uncertainties in the CCSM. Results indicate that surface soil moisture simulations tend to be positively biased in high latitude areas by most selected CMIP5 models except CCSM, FGOALS, and BCC, which share similar land surface model code. With the exception of GISS, runoff simulations by all selected CMIP5 models were overestimated in mountain ranges and in most of the Arctic region. In general, positive biases in CCSM soil moisture and runoff due to precipitation input error were offset by negative biases induced by temperature input error. Excluding the impact from atmosphere modeling, the global mean of seasonal surface moisture oscillation was out of phase compared to observations in many years during 1985–2004. The CLM also underestimated runoff in the Amazon, central Africa, and south Asia, where soils all have high clay content. We hypothesize that lack of a macropore flow mechanism is partially responsible for this underestimation. However, runoff was overestimated in the areas covered by volcanic ash soils (i.e., Andisols), which might be associated with poor soil porosity representation in CLM. Finally, our results indicate that CCSM predictability of hydrology could be improved by addressing the compensating errors associated with precipitation and temperature and updating the CLM soil representation.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE; USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
• Grant/Contract Number: AC02-05CH11231
• OSTI ID: 1421795
• Alternate ID(s): OSTI ID: 1254663
• Journal Information: International Journal of Applied Earth Observation and Geoinformation, Journal Name: International Journal of Applied Earth Observation and Geoinformation Journal Issue: C Vol. 48; ISSN 0303-2434
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

54 ENVIRONMENTAL SCIENCES
Bias test
CLM4
Historical evaluation
Runoff
Surface soil moisture