DOE BER Climate Modeling PI Meeting, Potomac, Maryland, May 12-14, 2014 Funding for this study was...
Transcript of DOE BER Climate Modeling PI Meeting, Potomac, Maryland, May 12-14, 2014 Funding for this study was...
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 biasesMODELS 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 columnESA (European Space Agency) soil moisture CCI project
RunoffWorld 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 BoutiqueDu, Enhao ([email protected]), Alan Di Vittorio, William D. Collins
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
KolymaRange
Global surface soil moisture cycles are phased out in some years compared to observation
CLM4.0 offline runs
CLM4.0 offline runs
MOAR coupled run meteorology outputs •precipitation•solar radiation•temperature•humidity•wind•surface pressure
MOAR coupled run meteorology outputs •precipitationQian’s 2006 reanalysis•solar radiation•temperature•humidity•wind•surface pressure
MOAR coupled run meteorology outputs•temperature•humidityQian’s 2006 reanalysis•Precipitation•radiation•wind•surface pressure
Qian’s 2006 reanalysis•temperature•humidity•precipitation•radiation•wind•surface pressure
reference runMother 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. humidityRunoff bias
1 1
4 42
3
5
2
3
5
66
Surface soil moisture was dried by humidity/temperature from atmosphere model
driven by atmosphere modeldriven by reanalysis
driven by atm. precipitation driven by atm. humiditySoil 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