Nina A. Speranskaya

The Global Soil Moisture Data Bank

Abstract 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 stat...

Scales of temporal and spatial variability of midlatitude soil moisture

Soil moisture observations from direct gravimetric measurements in Russia are used to study the relationship between soil moisture, runoff, and water table depth for catchments with different vegetation types, and to estimate the spatial and temporal correlation functions of soil moisture for different soil layers. For three catchments at Valdai, Russia, one with a grassland, one with an old forest, and one with a growing forest, the interannual soil moisture variations are virtually the same for the 31-year period, 1960–1990. The runoff is higher for the grassland than for the old forest, and the water table depth is not as deep. The runoff and water table for the growing forest vary from grassland-like during the first decade, when the trees are small, to old forest-like at the end of the period. The seasonal cycle of soil moisture is similar at all three catchments, but the snowmelt and summer drying begin a month earlier at the grassland than in the forests. A statistical model of both temporal and spatial variations in soil moisture is developed that partitions the variations into red noise and white noise components. For flat homogeneous plots, the white noise component is relatively small and represents solely random errors of measurement. For natural landscapes with variable vegetation and soil types, and complicated topography, this component is responsible for most of the temporal or spatial variance. The red noise component of temporal variability is in good agreement with theory. The timescale of this component is equal to the ratio of field capacity of soil to potential evapotranspiration, approximately 3 months. The red noise component of spatial variability reflects the statistical properties of the monthly averaged precipitation field. The scale of spatial correlation of this component is about 500 km. The estimates of scales of temporal and spatial correlation do not differ significantly for water content in the top 20-cm and 1-m layers of soil. These results have important implications for both remote sensing of soil moisture and soil moisture parameterization in climate models.

Use of Midlatitude Soil Moisture and Meteorological Observations to Validate Soil Moisture Simulations with Biosphere and Bucket Models

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 earths 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 statio...

Evaporation changes over the contiguous United States and the former USSR: A reassessment

Observed decreases in pan evaporation over most of the United States and the former USSR during the post-WWII period, if interpreted as a decrease in actual evaporation, are at odds with increases in temperature and precipitation over many regions of these two countries. Using parallel observations of actual and pan evaporation at six Russian, one Latvian, and one U.S. experimental sites, we recalibrate trends in pan evaporation to make them more representative of actual evaporation changes. After applying this transformation, pan evaporation time series over southern Russia and most of the United States reveal an increasing trend in actual evaporation during the past forty years.

Simulations of a Boreal Grassland Hydrology at Valdai, Russia: PILPS Phase 2(d)

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.

Evaluation of the AMIP soil moisture simulations

Abstract The Atmospheric Model Intercomparison Project (AMIP) conducted simulations by 30 different atmospheric general circulation models forced by observed sea surface temperatures for the 10-year period, 1979–1988. These models include a variety of different soil moisture parameterizations which influence their simulations of the entire land surface hydrology, including evaporation, soil moisture, and runoff, and their simulations of the energy balance at the surface. Here we compare these parameterizations, and evaluate their simulations of soil moisture by comparing them with actual observations of soil moisture, literally ground truth. We compared model-generated `data sets and simulations of soil moisture with observations from 150 stations in the former Soviet Union for 1979–1985 and Illinois for 1981–1988. The spatial patterns, mean annual cycles, and interannual variations were compared to plant-available soil moisture in the upper 1 m of soil. The model-generated `data sets are quite different from the observations, and from each other in many regions, even though they use the same bucket model calculation method. The AMIP model simulations are also quite different from each other, especially in the tropics. Models with 15-cm field capacities do not capture the observed large high latitude values of soil moisture. In addition, none of the models properly simulate winter soil moisture variations in high latitudes, keeping soil moisture constant, while observations show that soil moisture varies in the winter as much as in other seasons. The observed interannual variations of soil moisture were not captured by any of the AMIP models. Several models have large soil moisture trends during the first year or two of the AMIP simulations, with potentially large impacts on global hydrological cycle trends and on other climate elements. This is because the simulations were begun without spinning up the soil moisture to the model climatology. The length of time it took for each to reach equilibrium depended on the particular parameterization. Although observed temporal autocorrelation time scales are a few months, some models had much longer time scales than that. In particular, the three parameterizations based on the Simple Biosphere model (SiB) had trends in some regions for virtually the entire AMIP simulation period.

18-Year Land-Surface Hydrology Model Simulations for a Midlatitude Grassland Catchment in Valdai, Russia

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.

Climatically driven loss of calcium in steppe soil as a sink for atmospheric carbon

[1]xa0During the last several thousand years the semi-arid, cold climate of the Russian steppe formed highly fertile soils rich in organic carbon and calcium (classified as Chernozems in the Russian system). Analysis of archived soil samples collected in Kemannaya Steppe Preserve in 1920, 1947, 1970, and fresh samples collected in 1998 indicated that the native steppe Chernozems, however, lost 17–28 kg m−2 of calcium in the form of carbonates in 1970–1998. Here we demonstrate that the loss of calcium was caused by fundamental shift in the steppe hydrologic balance. Previously unleached soils where precipitation was less than potential evapotranspiration are now being leached due to increased precipitation and, possibly, due to decreased actual evapotranspiration. Because this region receives low levels of acidic deposition, the dissolution of carbonates involves the consumption of atmospheric CO2. Our estimates indicate that this climatically driven terrestrial sink of atmospheric CO2 is ∼2.1–7.4 g C m−2 a−1. In addition to the net sink of atmospheric carbon, leaching of pedogenic carbonates significantly amplified seasonal amplitude of CO2 exchange between atmosphere and steppe soil.

Climate Changes in Siberia

This chapter provides observational evidence of climatic variations in Siberia for three time scales: during the past 10,000 years, during the past millennium prior to instrumental observations, and for the past 130 years during the period of large-scale meteorological observations. The observational evidence is appended with the global climate model projections for the twenty-first century based on the most probable scenarios of the future dynamics of the major anthropogenic and natural factors responsible for contemporary climatic changes. Historically, climate of Siberia varied broadly. It was both warmer and colder than the present. However, during the past century, it became much warmer; the cold season precipitation north of 55°N increased, but no rainfall increase over most of Siberia has occurred. This led to drier summer conditions and to increased possibility of droughts and fire weather. Projections of the future climate indicate the further temperature increases, more in the cold season and less in the warm season, significant changes in the hydrological cycle in Central and southern Siberia (summer dryness), ecosystems’ shifts, and changes in the permafrost distribution and stability. Observed and projected frequencies of various extreme events have increased recently and are projected to further increase. While in the north of Siberia, contemporary models predict warmer winters at the end of the twenty-first century and paleoreconstructions hint to warmer summers compared to the present warming observed during the period of instrumental observations. These three groups of estimates are broadly consistent with each other.

Northern Eurasia Future Initiative (NEFI): facing the challenges and pathways of global change in the twenty-first century

AbstractDuring the past several decades, the Earth system has changed significantly, especially across Northern Eurasia. Changes in the socio-economic conditions of the larger countries in the region have also resulted in a variety of regional environmental changes that can have global consequences. The Northern Eurasia Future Initiative (NEFI) has been designed as an essential continuation of the Northern Eurasia Earth Science Partnership Initiative (NEESPI), which was launched in 2004. NEESPI sought to elucidate all aspects of ongoing environmental change, to inform societies and, thus, to better prepare societies for future developments. A key principle of NEFI is that these developments must now be secured through science-based strategies co-designed with regional decision-makers to lead their societies to prosperity in the face of environmental and institutional challenges. NEESPI scientific research, data, and models have created a solid knowledge base to support the NEFI program. This paper presents the NEFI research vision consensus based on that knowledge. It provides the reader with samples of recent accomplishments in regional studies and formulates new NEFI science questions. To address these questions, nine research foci are identified and their selections are briefly justified. These foci include warming of the Arctic; changing frequency, pattern, and intensity of extreme and inclement environmental conditions; retreat of the cryosphere; changes in terrestrial water cycles; changes in the biosphere; pressures on land use; changes in infrastructure; societal actions in response to environmental change; and quantification of Northern Eurasia’s role in the global Earth system. Powerful feedbacks between the Earth and human systems in Northern Eurasia (e.g., mega-fires, droughts, depletion of the cryosphere essential for water supply, retreat of sea ice) result from past and current human activities (e.g., large-scale water withdrawals, land use, and governance change) and potentially restrict or provide new opportunities for future human activities. Therefore, we propose that integrated assessment models are needed as the final stage of global change assessment. The overarching goal of this NEFI modeling effort will enable evaluation of economic decisions in response to changing environmental conditions and justification of mitigation and adaptation efforts.n