Subproject No. 11:
Evaluation of GCM Soil Moisture and Continental Water Budget in AGCM Simulations
Alan Robock1, G. Srinivasan1, Konstantin Y. Vinnikov2
1Department of Environmental Sciences
Rutgers - The State University of New Jersey
14 College Farm Road
New Brunswick, NJ 08901-8551
2Department of Meteorology
University of Maryland
College Park, MD 20742
We are interested in the ability of climate models to simulatethe water
budget of the land surface. The major human impacts of climate change
will be in temperature and hydrology, with predictions of summer drying,
agricultural impacts, and changes in water resources in a greenhouse-warmed
world. It is necessary now to investigate how well models simulate
the land surface in today's climate, to validate the AMIP GCMs for use
in climate change studies.
The design of AMIP allows us to investigate the influence of global SST on the patterns of climate over land. In regions with a strong SST influence, there should be a correspondence between observed and simulated soil moisture, if the models do well at reproducing these relationships. We will investigate the relationship of SST variations to soil moisture variations in both the models and observations in this project.
The connection of snow cover anomalies to Asian monsoon summer rainfall may be through soil moisture anomalies (e.g., Yang and Lau, 1998). In this project, using our extensive Asian soil moisture collection, we will investigate this relationship, by comparing how well the models simulate the observed changes.
The bucket-based and SSiB-based models in AMIP I did not do a good job of simulating the current climate of soil moisture (Robock et al., 1998). The more complex and higher resolution models to be used in AMIP II should do better, but they need to be evaluated. Our extensive new soil moisture data bank (http://climate.envsci.rutgers.edu/~robock) contains observations for Russia, China, Mongolia, India, Illinois, and Iowa, and will allow a more extensive evaluation.
The international GEWEX programs (GCIP, BALTEX, MAGS, ...) attempt to understand the hydrology and water balance of some of the major riversheds of the world, through observational studies and modeling. This project will allow AMIP to be able to make a major contribution to GCIP. The leader is also a participant in PILPS, and perhaps this project should also be linked to PILPS, as is diagnostic subproject 12.
We will study the complete water budget simulated by the models, with particular emphasis on soil moisture variations and their
relationship to the water budget. As such, we will need model output of soil moisture, runoff, evapotranspiration, snow cover and precipitation.
We will compare the simulations
of soil moisture by the AMIP GCMs, both the climatology and interannual
variations, to observations from Russia, China, Mongolia, India, Illinois,
and Iowa in our possession. We will then examine each of the terms
in the land surface water budget, to determine the reasons for the differences
between models and between models and observations. One of the major
terms in the water budget at high latitudes is snow melt, and we will continue
our study of this component (Yang et al., 1997), including the relationship to Indian monsoon rainfall.
We will examine the spatial and temporal scales of soil moisture variability, comparing the scales evident in observations (several hundred km, a few months in the midlatitudes) with those produced by the models. We have found so far (Vinnikov et al., 1996a; Entin et al., 1999b) that the scales of meteorological forcing by precipitation dominate the scales of soil moisture variations, but evaporation is also important. We will evaluate these forcing scales from the models, too.
The design of the AMIP experiment will allow us to examine soil moisture patterns that are related to SST forcing. We will compare patterns of SST variations, such as the SOI index, with patterns of soil moisture variations to extract the component that is forced by SSTs. Unfortunately, there are not long time series of soil moisture observations from the tropics, but we will use the data available, including our new Indian data and shorter time series from the Amazon.
This work will complement our ongoing work in examining the results of the PILPS Phase 2(d) experiment for Valdai, Russia (Schlosser
et al., 1997, 1999), in the Global Soil Wetness Project (GSWP; Entin et al., 1999a), in evaluating land surface schemes with observations
(Robock et al, 1995, 1997), in remote sensing of soil moisture (Vinnikov et al., 1999a), in designing networks for surface observation of soil
moisture (Vinnikov et all, 1999b), evaluation of AMIP precipitation results (Srinivasan et al., 1995), and our new GCIP project evaluating
the LDAS soil moisture simulations for the United States.
Monthly average quantities
will be fine for validation, as the time scale of land surface variations
is on the order of a few months
(Vinnikov et al., 1996a, Entin et al., 1999b). The following quantities will be necessary to calculate the soil water budget:
3. Plant-available soil moisture (liquid + frozen) in the top 0.1 m
4. Plant-available soil moisture (liquid + frozen) in the top 1 m
5. Total (plant-available through the entire model depth) soil moisture
6. Snow melt (water equivalent)
7. Surface runoff
8. Runoff (including drainage) from the top 1 m
9. Total runoff (including drainage)
10. Canopy storage change.
For the snow water budget, the following additional quantities are necessary:
11. Snowfall (water equivalent)
From the documentation in AMIP Newsletter 8, only quantities 1,7, 9, and 11 will be available as part of standard output (Table 2). In addition, it will be possible to approximately calculate 5 from the supplementary output in Table 6. If no changes are made to the requested quantities, then it will not be possible to conduct a complete study of the land surface components of the models as a diagnostic project. In this case, we will study only soil moisture and runoff. However, in order to provide the required quantities, only small modifications would be necessary:
A. Separately save evapotranspiration and sublimation. This will provide quantities 2 and 12, and allow the approximate calculation of 6 as a residual, assuming the snow depth is saved as supplementary output. In order to assure that snow melt (6) is correct, it would be better to also save it as an accumulated value for the month.
B. Save water storage in the vegetation canopy change at the beginning of each month.
C. Save plant-available soil moisture in the top 0.1 m at the beginning of each month.
D. Save plant-available soil moisture in the top 1 m at the beginning of each month.
E. Save monthly-average runoff (including drainage) from the top 1 m.
Entin, Jared, Alan Robock, Konstantin Y. Vinnikov, Shuang Qiu, Vladimir
Zabelin, Suxia Liu, A. Namkhai, and Ts. Adyasuren, 1999a: Evaluation of
Global Soil Wetness Project soil moisture simulations. J. Meteorol. Soc.
Japan, in press. (Invited paper)
Entin, Jared K., Alan Robock, Konstantin Y. Vinnikov, Steven E.
Hollinger, Suxia Liu, and A. Namkhai, 1999b: Temporal and spatial
scales of observed soil moisture variations in the extratropics.
Submitted to J. Geophys. Res.
Robock, Alan, C. Adam Schlosser, Konstantin Ya. Vinnikov, Nina A.
Speranskaya, Jared K. Entin, and Shang Qiu, 1998: Evaluation of AMIP
soil moisture simulations. Global and Planetary Change, 19, 181-208.
Robock, Alan, Konstantin Ya. Vinnikov, C. Adam Schlosser, Nina A.
Speranskaya, and Yongkang Xue, 1995: Use of midlatitude soil moisture
and meteorological observations to validate soil moisture simulations
with biosphere and bucket models. J. Climate, 8, 15-35.
Robock, Alan, Konstantin Ya. Vinnikov, and C. Adam Schlosser, 1997:
Evaluation of land-surface parameterization schemes using observations.
J. Climate, 10, 377-379.
Schlosser, C. Adam, Alan Robock, Konstantin Ya. Vinnikov, Nina A.
Speranskaya, and Yongkang Xue, 1997: 18-Year land-surface hydrology
model simulations for a midlatitude grassland catchment in Valdai,
Russia. Mon. Weather Rev., 125, 3279-3296.
Schlosser, C. A., A. Slater, A. Robock, A. J. Pitman, K. Y. Vinnikov,
Henderson-Sellers, N. A. Speranskaya, K. Mitchell, A. Boone, H. Braden,
P. Cox, P. DeRosney, C. E. Desborough, Y.-J. Dai, Q. Duan, J. Entin, P.
Etchevers, N. Gedney, Y. M. Gusev, F. Habets, J. Kim, E. A. Kowalczyk,
O. Nasonova, J. Noilhan, J. Polcher, A. B. Shmakin, T. Smirnova, D. L.
Verseghy, P. Wetzel, Y. Xue, and Z.-L. Yang, 1999: Simulations of a
boreal grassland hydrology at Valdai, Russia: PILPS phase 2(d).
Submitted to Mon. Weather Rev.
Sellers, P.J., Dickinson, R.E., Randall, D.A., Betts, A.K., Hall, F.G.,
Berry, J.A., Collatz, G.J., Denning, A.S., Mooney, H.A., Nobre, C.A.,
Sato, N., Field, C.B. and Henderson-Sellers, A., 1997: Modeling the
exchange of energy, water, and carbon between continents and the
atmosphere. Science, 275, 502-509.
Srinivasan, G., M. Hulme, and C. Jones, 1995: An evaluation of the
spatial and interannual variability of tropical precipitation as
simulated by GCMs. Geophys. Res. Lett., 22, 1697-1700.
Vinnikov, Konstantin Y., Alan Robock, Nina A. Speranskaya, and C. Adam
Schlosser, 1996a: Scales of temporal and spatial variability of
midlatitude soil moisture. J. Geophys. Res., 101, 7163-7174.
Vinnikov, Konstantin Y., Alan Robock, Shuang Qiu, Jared K. Entin,
Manfred Owe, Bhaskar J. Choudhury, Steven E. Hollinger and Eni G. Njoku,
1999a: Satellite remote sensing of soil moisture in Illinois, USA. J.
Geophys. Res., 104, 4145-4168.
Vinnikov, Konstantin Y., Alan Robock, Shuang Qiu, and Jared K. Entin,
1999b: Optimal design of surface networks for observation of soil
moisture. J. Geophys. Res., in press.
Yang, Zong-Liang, Robert E. Dickinson, Alan Robock and Kostya Ya.
Vinnikov, 1997: On validation of the snow sub-model of the
Biosphere-Atmosphere Transfer Scheme with Russian snow cover and
meteorological observational data. J. Climate, 10, 353-373.
Yang, Song and K.M. Lau, 1998, Influences of sea surface temperature
ground wetness on Asian summer monsoon. J. Climate, 11, 3230-3246.
For further information, contact the AMIP Project Office (email@example.com).
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