Download Between-dataset difference in climate variables Between

Exploring impact of forcing data on hydrologic simulations and climate sensitivity
Naoki
1
Mizukami ,
Martyn
1
Clark ,
and Pablo
1,2
Mendoza
1. National Center for Atmospheric Research
2. University of Colorado, Boulder
Introduction
Results
When assessing climate impact on hydrologic processes,
we face a number of different modeling approaches,
including forcing dataset, downscaling of atmospheric
model outputs, spatial scales, hydrologic model. It’s been
shown that different modeling approaches result in
different hydrologic simulations (e.g. Vano et al. 2012).
The study objective is to examine how different forcing
datasets affect hydrologic simulations. Ultimately we will
evaluate between-dataset difference in sensitivity to
climate variability
Between-dataset difference in climate variables
Figure 3 shows overall comparison of two datasets.
• The most pronounced differences are seen in the highest
elevation band (e.g., Precipitation, humidity, SW radiation)
• The variable that exhibit the most difference is SW (i.e., SW in
NLDA is much lower than VIC SW radiation).
Summary
Between-dataset difference in CLIM simulations
It appear that SW radiation difference is translated into
difference in CLM simulations of SWE during winter time and
also evapotranspiration during summer. As a results, VIC
produces less runoff. This radiation effect is more significant in
higher elevation.
Variables
NLDAS
Figure3. Comparison of monthly values for 23 water years for 3 elevation bands (left panel) and spatial
pattern of annual means (right panel) between NLDAS and VIC.
Figure 4 shows difference in seasonality of SW radiation and
relative humidity. Difference in SW is greater during summer.
NLDAS exhibit higher RH during winter.
SW radiation
Relative humidity
Figure 6. Comparison of climatological annual cycles of elevation-band average SWE, ET, and Runoff
between NLDAS and VIC.
VIC
Precipitation
CPC gage + elev adjust
Co-op Gage
Temperature
NARR + elev adjust
Co-op Gage
Specific humidity
NARR + elev adjust
MT-CLIM algorithm1
SW radiation
NARR + GOES adjust
MT-CLIM algorithm1
• We illustrated how differences in climate forcing were
propagated into hydrologic simulation using CLM
simulation with NLDAS and VIC climate forcing.
Ultimate objective of this study is to examine how
hydrologic sensitivity to climate variability differ between
the two data sets.
• In a comparison between two forcing datasets and CLM
simulations, difference in SW radiation affects hydrologic
simulations among the forcing variables. NLDAS has
less precipitation and higher temperature and higher
humidity and higher wind speed than VIC data, but more
snow accumulation and less ET, as a result more runoff.
Therefore SW radiation and humidity impact on model
simulation difference. In VIC forcing dataset, this SW
radiation difference appears to be caused by difference
in diurnal temperature range between two dataset.
Diurnal temperature range is used to estimate SW
radiation and humidity in VIC forcing data.
Approaches
Community Land Model (CLM; Olsen et al. 2010; Figure
1) was run over the upper Colorado River Basin (Figure 2)
at 1/8 degree from 10/1/1980 to 9/30/2008. CLM was
forced by two historical climate datasets 1) North
American Land Data Assimilation System (NLDAS; Xia et
al. 2012) and University of Washington dataset (VIC;
Maurer et al. 2003) to examine the impact of different
forcing on hydrologic simulation and sensitivity to climate
variability. Major difference in data source are summarized
in Table below.
AGU 2012 Fall Meeting
H41-A1158
Between-dataset difference in model sensitivity to climate
variables
Figure 7 shows how hydrologic states are related to climate
variables at an annual scale. A slope for each group (i.e. elev.
band and product) indicates sensitivity of a state variable to a
climate variable.
• An analysis of sensitivity difference to climate variables
is still working in progress. Based on a preliminary
analysis, both datasets appear to exhibit similar
hydrologic sensitivity to temperature and precipitation,
though hydrologic simulations with two datasets
produced different magnitudes of the hydrologic states.
Different elevation bands exhibit different impacts of
climate variables on the processes. Sensitivity of snow
accumulation to temperature/precipitation still needs to
be examined.
Figure4. Comparison of annual cycle of SW radiation and RH between NLDAS and VIC.
LW radiation
NARR+ elev adjust
TVA Algorithm (1972)
Pressure
NARR+ elev adjust
Lapse rate from Sea
level pressure
Wind Speed
NARR
NCEP-NCAR
1Hungerford
et al. 1989; Kimball et al. 1997; Thornton and Running 1999
Ele. band 1 (< 2 km)
Since VIC uses a set of empirical algorithm with daily Tmax,
Tmin, daily precipitation to estimate radiation and humidity,
annual cycle of Tmax, Tmin, DTR, and wet-day are further
examined (Figure 5).
• Both have similar climatological wet day
• Both have similar Tmax, but VIC has lower Tmin making greater
DTR than NLDAS.
• The large DTR in VIC may cause larger VIC SW radiation.
Tmin
Ele. band 2 (2km - 3 km)
Ele. band 3 (> 3 km)
NLDAS
VIC
Hungerford, R.D., Nemani, R.R., Running, S.W., Coughlan, J.C., 1989. MT-CLIM: a mountain
microclimate simulation model. US Forest Service Research Paper, INT-414.
Kimball, J. S., S. W. Running, and R. Nemani, 1997: An improved method for estimating surface
humidity from daily minimum temperature. Agricultural and Forest Meteorology, 85, 87-98.
Maurer, E.P., Wood, A.W., Adam, J.C., Lettenmaier, D.P., Nijssen, B., 2002. A Long-Term
Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United
States*. Journal of Climate, 15(22): 3237-3251.
Tmax
Figure 7. Scatter plots of simulated hydrologic states vs. climate variables (T & P) for 3 elevation-bands.
Figure 8 shows composite daily time series based on annual
precipitation and temperature over the highest elevation band.
Figure 1. Schematics of land surface processes simulated in Community Land Model
Mean over top 5 warmest yrs
Mean over top 5 coldest yrs
Diurnal Temperature Range (DTR)
Reference
Wet days
Oleson, K. W., D. M. Lawrence, G. B. Bonan, M. G. Flanner, E. Kluzek, P. J. Lawrence, S. Levis,
S.C. Swenson, P.E. Thornton, A. Dai, M. Decker, R. Dickinson, J. Feddema, C.L. Heald, F.
Hoffman, J.-F. Lamarque, N. Mahowald, G.-Y. Niu, T. Qian, J. Randerson, S. Running, K.
Sakaguchi, A. Slater, R. Stockli, A. Wang, Z.-L. Yang, Xi. Zeng, and X. Zeng, 2010: Technical
Description of version 4.0 of the Community Land Model (CLM), 257 pp.
Thornton, P. E., and S. W. Running, 1999: An improved algorithm for estimating incident daily
solar radiation from measurements of temperature, humidity, and precipitation. Agricultural and
Forest Meteorology, 93, 211-228.
Vano, J. A., T. Das, and D. P. Lettenmaier, 2012: Hydrologic sensitivities of Colorado River runoff
to changes in precipitation and temperature. Journal of Hydrometeorology.
Xia, Y. et al., 2012. Continental-scale water and energy flux analysis and validation for the North
American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and
application of model products. J. Geophys. Res., 117(D3): D03109.
Acknowledgement
This was funded by Bureau of Reclamation and U.S. Army Corps of Engineers. Authors
acknowledge many NCAR scientists as well as Levi Brekke (BoR) and Jeff Arnold
(USACE) for scientific discussions and technical supports.
Figure 2. Upper Colorado River Basin. Three elevation bands of the UCRB.
Figure 5. Comparison annual cycle of Tmax, Tmin, and DTR between NLDAS and VIC.
Figure 8. Daily time series averaged over the top wettest and driest years (left two columns) and top 5
warmest and coldest years (right two columns)
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