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C. Adam Schlosser
,
Alan Robock
,
Konstantin Ya Vinnikov
,
Nina A. Speranskaya
, and
Yongkang Xue

Abstract

Off-line simulations of improved bucket hydrology and Simplified Simple Biosphere (SSiB) models are performed for a grassland vegetation catchment region, located at the Valdai water-balance research station in Russia, forced by observed meteorological and simulated actinometric data for 1966–83. Evaluation of the model simulations is performed using observations of total soil moisture in the top 1 m, runoff, evaporation, snow depth, and water-table depth made within the catchment. The Valdai study demonstrates that using only routine meteorological measurements, long-term simulations of land-surface schemes suitable for model evaluation can be made. The Valdai dataset is available for use in the evaluation of other land-surface schemes.

Both the SSiB and the bucket models reproduce the observed hydrology averaged over the simulation period (1967–83) and its interannual variability reasonably well. However, the models’ soil moisture interannual variability is too low during the fall and winter when compared to observations. In addition, some discrepancies in the models’ seasonal behavior with respect to observations are seen. The models are able to reproduce extreme hydrological events to some degree, but some inconsistencies in the model mechanisms are seen. The bucket model’s soil-moisture variability is limited by its inability to rise above its prescribed field capacity for the case where the observed water table rises into the top 1-m layer of soil, which can lead to erroneous simulations of evaporation and runoff. SSiB’s snow depth simulations are generally too low due to high evaporation from the snow surface. SSiB typically produces drainage out of its bottom layer during the summer, which appears inconsistent to the runoff observations of the catchment.

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Alan Robock
,
Konstantin Ya Vinnikov
,
C. Adam Schlosser
,
Nina A. Speranskaya
, and
Yongkang Xue

Abstract

Soil moisture observations in sites with natural vegetation were made for several decades in the former Soviet Union at hundreds of stations. In this paper, the authors use data from six of these stations from different climatic regimes, along with ancillary meteorological and actinometric data, to demonstrate a method to validate soil moisture simulations with biosphere and bucket models. Some early and current general circulation models (GCMS) use bucket models for soil hydrology calculations. More recently, the Simple Biosphere Model (SiB) was developed to incorporate the effects of vegetation on fluxes of moisture, momentum, and energy at the earth's surface into soil hydrology models. Until now, the bucket and SiB have been verified by comparison with actual soil moisture data only on a limited basis. In this study, a Simplified SiB (SSIB) soil hydrology model and a 15-cm bucket model are forced by observed meteorological and actinometric data every 3 h for 6-yr simulations at the six stations. The model calculations of soil moisture are compared to observations of soil moisture, literally “ground truth,” snow cover, surface albedo, and net radiation” and with each other.

For three of the stations, the SSIB and 15-cm bucket models produce good simulations of seasonal cycles and interannual variations of soil moisture. For the other three stations, there are large errors in the simulations by both models. Inconsistencies in specification of field capacity may be partly responsible. There is no evidence that the SSiB simulations are superior in simulating soil moisture variations. In fact, the models are quite similar since SSiB implicitly has a bucket embedded in it. One of the main differences between the models is in the treatment of runoff due to melting snow in the spring-SSiB incorrectly puts all the snowmelt into runoff. While producing similar soil moisture simulations, the models produce very different surface latent and sensible beat fluxes, which would have large effects on GCM simulations.

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Alan Robock
,
Konstantin Y. Vinnikov
,
Govindarajalu Srinivasan
,
Jared K. Entin
,
Steven E. Hollinger
,
Nina A. Speranskaya
,
Suxia Liu
, and
A. Namkhai

Soil moisture is an important variable in the climate system. Understanding and predicting variations of surface temperature, drought, and flood depend critically on knowledge of soil moisture variations, as do impacts of climate change and weather forecasting. An observational dataset of actual in situ measurements is crucial for climatological analysis, for model development and evaluation, and as ground truth for remote sensing. To that end, the Global Soil Moisture Data Bank, a Web site (http://climate.envsci.rutgers.edu/soil_moisture) dedicated to collection, dissemination, and analysis of soil moisture data from around the globe, is described. The data bank currently has soil moisture observations for over 600 stations from a large variety of global climates, including the former Soviet Union, China, Mongolia, India, and the United States. Most of the data are in situ gravimetric observations of soil moisture; all extend for at least 6 years and most for more than 15 years. Most of the stations have grass vegetation, and some are agricultural. The observations have been used to examine the temporal and spatial scales of soil moisture variations, to evaluate Atmospheric Model Intercomparison Project, Project for Intercomparison of Land-Surface Parameterization Schemes, and Global Soil Wetness Project simulations of soil moisture, for remote sensing of soil moisture, for designing new soil moisture observational networks, and to examine soil moisture trends. For the top 1-m soil layers, the temporal scale of soil moisture variation at all midlatitude sites is 1.5 to 2 months and the spatial scale is about 500 km. Land surface models, in general, do not capture the observed soil moisture variations when forced with either model-generated or observed meteorology. In contrast to predictions of summer desiccation with increasing temperatures, for the stations with the longest records summer soil moisture in the top 1 m has increased while temperatures have risen. The increasing trend in precipitation more than compensated for the enhanced evaporation.

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C. Adam Schlosser
,
Andrew G. Slater
,
Alan Robock
,
Andrew J. Pitman
,
Konstantin Ya. Vinnikov
,
Ann Henderson-Sellers
,
Nina A. Speranskaya
,
Ken Mitchell
, and
The PILPS 2(D) Contributors

Abstract

The Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) aims to improve understanding and modeling of land surface processes. PILPS phase 2(d) uses a set of meteorological and hydrological data spanning 18 yr (1966–83) from a grassland catchment at the Valdai water-balance research site in Russia. A suite of stand-alone simulations is performed by 21 land surface schemes (LSSs) to explore the LSSs’ sensitivity to downward longwave radiative forcing, timescales of simulated hydrologic variability, and biases resulting from single-year simulations that use recursive spinup. These simulations are the first in PILPS to investigate the performance of LSSs at a site with a well-defined seasonal snow cover and frozen soil. Considerable model scatter for the control simulations exists. However, nearly all the LSS scatter in simulated root-zone soil moisture is contained within the spatial variability observed inside the catchment. In addition, all models show a considerable sensitivity to longwave forcing for the simulation of the snowpack, which during the spring melt affects runoff, meltwater infiltration, and subsequent evapotranspiration. A greater sensitivity of the ablation, compared to the accumulation, of the winter snowpack to the choice of snow parameterization is found. Sensitivity simulations starting at prescribed conditions with no spinup demonstrate that the treatment of frozen soil (moisture) processes can affect the long-term variability of the models. The single-year recursive runs show large biases, compared to the corresponding year of the control run, that can persist through the entire year and underscore the importance of performing multiyear simulations.

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Mark P. Baldwin
,
Thomas Birner
,
Guy Brasseur
,
John Burrows
,
Neal Butchart
,
Rolando Garcia
,
Marvin Geller
,
Lesley Gray
,
Kevin Hamilton
,
Nili Harnik
,
Michaela I. Hegglin
,
Ulrike Langematz
,
Alan Robock
,
Kaoru Sato
, and
Adam A. Scaife

Abstract

The stratosphere contains ~17% of Earth’s atmospheric mass, but its existence was unknown until 1902. In the following decades our knowledge grew gradually as more observations of the stratosphere were made. In 1913 the ozone layer, which protects life from harmful ultraviolet radiation, was discovered. From ozone and water vapor observations, a first basic idea of a stratospheric general circulation was put forward. Since the 1950s our knowledge of the stratosphere and mesosphere has expanded rapidly, and the importance of this region in the climate system has become clear. With more observations, several new stratospheric phenomena have been discovered: the quasi-biennial oscillation, sudden stratospheric warmings, the Southern Hemisphere ozone hole, and surface weather impacts of stratospheric variability. None of these phenomena were anticipated by theory. Advances in theory have more often than not been prompted by unexplained phenomena seen in new stratospheric observations. From the 1960s onward, the importance of dynamical processes and the coupled stratosphere–troposphere circulation was realized. Since approximately 2000, better representations of the stratosphere—and even the mesosphere—have been included in climate and weather forecasting models. We now know that in order to produce accurate seasonal weather forecasts, and to predict long-term changes in climate and the future evolution of the ozone layer, models with a well-resolved stratosphere with realistic dynamics and chemistry are necessary.

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A. G. Slater
,
C. A. Schlosser
,
C. E. Desborough
,
A. J. Pitman
,
A. Henderson-Sellers
,
A. Robock
,
K. Ya Vinnikov
,
J. Entin
,
K. Mitchell
,
F. Chen
,
A. Boone
,
P. Etchevers
,
F. Habets
,
J. Noilhan
,
H. Braden
,
P. M. Cox
,
P. de Rosnay
,
R. E. Dickinson
,
Z-L. Yang
,
Y-J. Dai
,
Q. Zeng
,
Q. Duan
,
V. Koren
,
S. Schaake
,
N. Gedney
,
Ye M. Gusev
,
O. N. Nasonova
,
J. Kim
,
E. A. Kowalczyk
,
A. B. Shmakin
,
T. G. Smirnova
,
D. Verseghy
,
P. Wetzel
, and
Y. Xue

Abstract

Twenty-one land surface schemes (LSSs) performed simulations forced by 18 yr of observed meteorological data from a grassland catchment at Valdai, Russia, as part of the Project for the Intercomparison of Land-Surface Parameterization Schemes (PILPS) Phase 2(d). In this paper the authors examine the simulation of snow. In comparison with observations, the models are able to capture the broad features of the snow regime on both an intra- and interannual basis. However, weaknesses in the simulations exist, and early season ablation events are a significant source of model scatter. Over the 18-yr simulation, systematic differences between the models’ snow simulations are evident and reveal specific aspects of snow model parameterization and design as being responsible. Vapor exchange at the snow surface varies widely among the models, ranging from a large net loss to a small net source for the snow season. Snow albedo, fractional snow cover, and their interplay have a large effect on energy available for ablation, with differences among models most evident at low snow depths. The incorporation of the snowpack within an LSS structure affects the method by which snow accesses, as well as utilizes, available energy for ablation. The sensitivity of some models to longwave radiation, the dominant winter radiative flux, is partly due to a stability-induced feedback and the differing abilities of models to exchange turbulent energy with the atmosphere. Results presented in this paper suggest where weaknesses in macroscale snow modeling lie and where both theoretical and observational work should be focused to address these weaknesses.

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Weiqing Qu
,
A. Henderson-Sellers
,
A. J. Pitman
,
T. H. Chen
,
F. Abramopoulos
,
A. Boone
,
S. Chang
,
F. Chen
,
Y. Dai
,
R. E. Dickinson
,
L. Dümenil
,
M. Ek
,
N. Gedney
,
Y. M. Gusev
,
J. Kim
,
R. Koster
,
E. A. Kowalczyk
,
J. Lean
,
D. Lettenmaier
,
X. Liang
,
J.-F. Mahfouf
,
H.-T. Mengelkamp
,
K. Mitchell
,
O. N. Nasonova
,
J. Noilhan
,
A. Robock
,
C. Rosenzweig
,
J. Schaake
,
C. A. Schlosser
,
J.-P. Schulz
,
A. B. Shmakin
,
D. L. Verseghy
,
P. Wetzel
,
E. F. Wood
,
Z.-L. Yang
, and
Q. Zeng

Abstract

In the PILPS Phase 2a experiment, 23 land-surface schemes were compared in an off-line control experiment using observed meteorological data from Cabauw, the Netherlands. Two simple sensitivity experiments were also undertaken in which the observed surface air temperature was artificially increased or decreased by 2 K while all other factors remained as observed. On the annual timescale, all schemes show similar responses to these perturbations in latent, sensible heat flux, and other key variables. For the 2-K increase in temperature, surface temperatures and latent heat fluxes all increase while net radiation, sensible heat fluxes, and soil moistures all decrease. The results are reversed for a 2-K temperature decrease. The changes in sensible heat fluxes and, especially, the changes in the latent heat fluxes are not linearly related to the change of temperature. Theoretically, the nonlinear relationship between air temperature and the latent heat flux is evident and due to the convex relationship between air temperature and saturation vapor pressure. A simple test shows that, the effect of the change of air temperature on the atmospheric stratification aside, this nonlinear relationship is shown in the form that the increase of the latent heat flux for a 2-K temperature increase is larger than its decrease for a 2-K temperature decrease. However, the results from the Cabauw sensitivity experiments show that the increase of the latent heat flux in the +2-K experiment is smaller than the decrease of the latent heat flux in the −2-K experiment (we refer to this as the asymmetry). The analysis in this paper shows that this inconsistency between the theoretical relationship and the Cabauw sensitivity experiments results (or the asymmetry) is due to (i) the involvement of the β g formulation, which is a function of a series stress factors that limited the evaporation and whose values change in the ±2-K experiments, leading to strong modifications of the latent heat flux; (ii) the change of the drag coefficient induced by the changes in stratification due to the imposed air temperature changes (±2 K) in parameterizations of latent heat flux common in current land-surface schemes. Among all stress factors involved in the β g formulation, the soil moisture stress in the +2-K experiment induced by the increased evaporation is the main factor that contributes to the asymmetry.

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T. H. Chen
,
A. Henderson-Sellers
,
P. C. D. Milly
,
A. J. Pitman
,
A. C. M. Beljaars
,
J. Polcher
,
F. Abramopoulos
,
A. Boone
,
S. Chang
,
F. Chen
,
Y. Dai
,
C. E. Desborough
,
R. E. Dickinson
,
L. Dümenil
,
M. Ek
,
J. R. Garratt
,
N. Gedney
,
Y. M. Gusev
,
J. Kim
,
R. Koster
,
E. A. Kowalczyk
,
K. Laval
,
J. Lean
,
D. Lettenmaier
,
X. Liang
,
J.-F. Mahfouf
,
H.-T. Mengelkamp
,
K. Mitchell
,
O. N. Nasonova
,
J. Noilhan
,
A. Robock
,
C. Rosenzweig
,
J. Schaake
,
C. A. Schlosser
,
J.-P. Schulz
,
Y. Shao
,
A. B. Shmakin
,
D. L. Verseghy
,
P. Wetzel
,
E. F. Wood
,
Y. Xue
,
Z.-L. Yang
, and
Q. Zeng

Abstract

In the Project for Intercomparison of Land-Surface Parameterization Schemes phase 2a experiment, meteorological data for the year 1987 from Cabauw, the Netherlands, were used as inputs to 23 land-surface flux schemes designed for use in climate and weather models. Schemes were evaluated by comparing their outputs with long-term measurements of surface sensible heat fluxes into the atmosphere and the ground, and of upward longwave radiation and total net radiative fluxes, and also comparing them with latent heat fluxes derived from a surface energy balance. Tuning of schemes by use of the observed flux data was not permitted. On an annual basis, the predicted surface radiative temperature exhibits a range of 2 K across schemes, consistent with the range of about 10 W m−2 in predicted surface net radiation. Most modeled values of monthly net radiation differ from the observations by less than the estimated maximum monthly observational error (±10 W m−2). However, modeled radiative surface temperature appears to have a systematic positive bias in most schemes; this might be explained by an error in assumed emissivity and by models’ neglect of canopy thermal heterogeneity. Annual means of sensible and latent heat fluxes, into which net radiation is partitioned, have ranges across schemes of30 W m−2 and 25 W m−2, respectively. Annual totals of evapotranspiration and runoff, into which the precipitation is partitioned, both have ranges of 315 mm. These ranges in annual heat and water fluxes were approximately halved upon exclusion of the three schemes that have no stomatal resistance under non-water-stressed conditions. Many schemes tend to underestimate latent heat flux and overestimate sensible heat flux in summer, with a reverse tendency in winter. For six schemes, root-mean-square deviations of predictions from monthly observations are less than the estimated upper bounds on observation errors (5 W m−2 for sensible heat flux and 10 W m−2 for latent heat flux). Actual runoff at the site is believed to be dominated by vertical drainage to groundwater, but several schemes produced significant amounts of runoff as overland flow or interflow. There is a range across schemes of 184 mm (40% of total pore volume) in the simulated annual mean root-zone soil moisture. Unfortunately, no measurements of soil moisture were available for model evaluation. A theoretical analysis suggested that differences in boundary conditions used in various schemes are not sufficient to explain the large variance in soil moisture. However, many of the extreme values of soil moisture could be explained in terms of the particulars of experimental setup or excessive evapotranspiration.

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