DOE BER Climate Modeling PI Meeting, Potomac, Maryland, May 12-14, 2014 Funding for this study was provided by the US Department of Energy, BER Program. Contract # DE-AC02-05CH11231 SCIENCE DRIVER Development of Land surface model depends on the rigorous calibration and validation against observations. Hydrologic components of Community Earth System Model (CESM) and other CMIP5 climate models have not been fully assessed at pixel scale. Surface soil moisture controls partitioning of sensible and latent heat, and surface runoff and infiltration. Feedback between soil moisture and precipitation may affect atmosphere circulation in large scale. Future trend of runoff, hence water supply, as a result of increasing concentrations of greenhouse gas remains debated. The objectives of this study are to evaluate fidelity of the hydrology components in climate models against observation identify sources of uncertainty and factors that are responsible for the biases MODELS AND DATA (SOIL MOISTURE AND RUNOFF) SCIENCE IMPACT CMIP5 models (historical runs, ensemble: r1i1p1) CCSM4 (NCAR), HadCM3 (Hadley Centre), MIROC5 (AORI ), GFDL-CM3 (NOAA/GFDL), CSIRO-Mk3 (CSIRO), BCC-csm1 (BCC), MRI-ESM1 (MRI), FGOALS-g2 (IAP), GISS-E2-R (NASA/GISS) Moisture content in upper portion of soil column ESA (European Space Agency) soil moisture CCI project Runoff World Meteorological Organization GRDC (Global Runoff Data Center) Meteorologic forcing crosscheck experiment design Conclusions: 1.Areas where hydrologic variable biases prone to occur include high latitude (permafrost), mountains, and densely-vegetated tropical zones 2.Precipitation from climate model has led to overestimation of runoff in mountain ranges and tropical zones. Temperature and humidity offset the precipitation effects in the land surface modeling, but caused drier surface in high latitudes 3.CLM algorithms need to be improved in Amazon and permafrost on evapotranspiration (tropical forest) and freeze-thaw (permafrost) mechanisms RESULTS AND DISSCUSIONS Historical Evaluation of Hydrologic Components of CESM and CMIP5 Models Integrated Assessment Boutique Du, Enhao Alan Di Vittorio, William D. Global water balance P=Q+E+ ΔS runoff storage change CLM4 runoff CLM4 10cm soil moisture vs. vs. GRDC data ESA satellite data Positive biases over world’s major mountain ranges and central Africa. Negative bias in Amazon runoff bias normalized by precipitation Rocky Mountain Andes Kjolen Mts. Brazilian Highland Kolyma Range Global surface soil moisture cycles are phased out in some years compared to observation CLM4.0 offline runs MOAR coupled run meteorology outputs precipitation solar radiation temperature humidity wind surface pressure MOAR coupled run meteorology outputs precipitation Qian’s 2006 reanalysis solar radiation temperature humidity wind surface pressure MOAR coupled run meteorology outputs temperature humidity Qian’s 2006 reanalysis Precipitation radiation wind surface pressure Qian’s 2006 reanalysis temperature humidity precipitation radiation wind surface pressure reference run Mother Of All Runs Precipitation from atmosphere model changed the bias sign from negative to positive on runoff simulation driven by reanalysis driven by atmosphere model driven by atm. precipitation driven by atm. humidity Runoff bias Surface soil moisture was dried by humidity/temperature from atmosphere model driven by atmosphere modeldriven by reanalysis driven by atm. precipitationdriven by atm. humidity Soil moisture bias Surface soil moisture is better correlated in areas where hydrologic cycles are intensive 10-cm soil moisture correlation Himalaya Precipitation by climate model affect runoff mainly in spring when northern hemisphere snowmelt occurs Monthly runoff Monthly surface soil moisture