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C. Adam Schlosser and Xiang Gao

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This study assesses the simulations of global-scale evapotranspiration from the second Global Soil Wetness Project (GSWP-2) within a global water budget framework. The scatter in the GSWP-2 global evapotranspiration estimates from various land surface models can constrain the global annual water budget fluxes to within ±2.5% and, by using estimates of global precipitation, the residual ocean evaporation estimate falls within the range of other independently derived bulk estimates. The GSWP-2 scatter, however, cannot entirely explain the imbalance of the annual fluxes from a modern-era, observationally based global water budget assessment. Inconsistencies in the magnitude and timing of seasonal variations between the global water budget terms are also found. Intermodel inconsistencies in evapotranspiration are largest for high-latitude interannual variability as well as for interseasonal variations in the tropics, and analyses with field-scale data also highlight model disparity at estimating evapotranspiration in high-latitude regions. Analyses of the sensitivity simulations that replace uncertain forcings (i.e., radiation, precipitation, and meteorological variables) indicate that global (land) evapotranspiration is slightly more sensitive to precipitation than net radiation perturbations, and the majority of the GSWP-2 models, at a global scale, fall in a marginally moisture-limited evaporative condition. Lastly, the range of global evapotranspiration estimates among the models is larger than any bias caused by uncertainties in the GSWP-2 atmospheric forcing, indicating that model structure plays a more important role toward improving global land evaporation estimates (as opposed to improved atmospheric forcing).

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C. Adam Schlosser and P. C. D. Milly

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Soil moisture predictability and the associated predictability of continental climate are explored as an initial-value problem, using a coupled land–atmosphere model with prescribed ocean surface temperatures. Ensemble simulations are designed to assess the extent to which initial soil moisture fields explain variance of future predictands (soil moisture, near-surface air temperature, and precipitation). For soil moisture, the decrease of explained variance with lead time can be characterized as a first-order decay process, and a predictability timescale is introduced as the lead time at which this decay reaches e−1. The predictability timescale ranges from about 2 weeks or less (in midlatitudes during summer, and in the Tropics and subtropics) to 2–6 months (in mid- to high latitudes for simulations that start in late fall and early winter). The predictability timescale of the modeled soil moisture is directly related to the soil moisture's autocorrelation timescale. The degree of translation of soil moisture predictability to predictability of any atmospheric variable can be characterized by the ratio of the fraction of explained variance of the atmospheric variable to the fraction of explained soil moisture variance. By this measure, regions with the highest associated predictability of 30-day-mean near-surface air temperature (ratio greater than 0.5) are, generally speaking, coincident with regions and seasons of the smallest soil moisture predictability timescales. High associated temperature predictability is found where strong variability of soil moisture stress on evapotranspiration and abundant net radiation at the continental surface coincide. The associated predictability of 30-day-mean precipitation, in contrast, is very low.

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C. Adam Schlosser and Paul R. Houser

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The capability of a global data compilation, largely satellite based, is assessed to depict the global atmospheric water cycle’s mean state and variability. Monthly global precipitation estimates from the Global Precipitation Climatology Project (GPCP) and the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) span from 1979 to 1999. Monthly global Special Sensor Microwave Imager (SSM/I)-based bulk aerodynamic ocean evaporation estimates span from June 1987 to December 1999. Global terrestrial evapotranspiration rates are estimated over a multidecade period (1975–99) using a global land model simulation forced by bias-corrected reanalysis data. Monthly total precipitable water (TPW) from the NASA Global Water Vapor Project (NVAP) spans from 1988 to 1999.

The averaged annual global precipitation (P) and evaporation (E) estimates are out of balance by 5% or 24 000 (metric) gigatons (Gton) of water, which exceeds the uncertainty of global mean annual precipitation (∼±1%). For any given year, the annual flux imbalance can be on the order of 10% (48 000 Gton of water). However, observed global TPW interannual variations suggest a water flux imbalance on the order of 0.01% (48 Gton of water)—a finding consistent with a general circulation model (GCM) simulation. Variations in observationally based global P and E rates show weak monthly and interannual consistency, and depending on the choice of ocean evaporation data, the mean annual cycle of global EP can be up to 5 times larger to that of TPW. The global ocean annual evaporation rates have as much as a ∼1% yr−1 increase during the period analyzed (1988–99), which is consistent in sign with most transient CO2 GCM simulations, but at least an order of magnitude larger. The ocean evaporation trends are driven by trends in SSM/I-retrieved near-surface atmospheric humidity and wind speed, and the largest year-to-year changes are coincident with transitions in the SSM/I fleet.

In light of (potential) global water cycle changes in GCM projections, the ability to consistently detect or verify these changes in nature rests upon one or more of the following: quantification of global evaporation uncertainty, at least a twofold improvement in consistency between the observationally based global precipitation and evaporation variations, a two order of magnitude rectification between annual variations of EP and precipitable water as well as substantial improvements in the consistency of their seasonal cycles, a critical reevaluation of intersatellite calibration for the relevant geophysical quantities used for ocean evaporation estimates, and the continuation of a dedicated calibration in this regard for future satellite transitions.

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Paul A. Dirmeyer, C. Adam Schlosser, and Kaye L. Brubaker

Abstract

A synthesis of several approaches to quantifying land–atmosphere interactions is presented. These approaches use data from observations or atmospheric reanalyses applied to atmospheric tracer models and stand-alone land surface schemes. None of these approaches relies on the results of general circulation model simulations. A high degree of correlation is found among these independent approaches, and constructed here is a composite assessment of global land–atmosphere feedback strength as a function of season. The composite combines the characteristics of persistence of soil moisture anomalies, strong soil moisture regulation of evaporation rates, and reinforcement of water cycle anomalies through recycling. The regions and seasons that have a strong composite signal predominate in both summer and winter monsoon regions in the period after the rainy season wanes. However, there are exceptions to this pattern, most notably over the Great Plains of North America and the Pampas/Pantanal of South America, where there are signs of land–atmosphere feedback throughout most of the year. Soil moisture memory in many of these regions is long enough to suggest that real-time monitoring and accurate initialization of the land surface in forecast models could lead to improvements in medium-range weather to subseasonal climate forecasts.

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Alan Robock, Konstantin Ya Vinnikov, and C. Adam Schlosser
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Xiang Gao, Alexander Avramov, Eri Saikawa, and C. Adam Schlosser

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Land surface models (LSMs) are limited in their ability to reproduce observed soil moisture partially due to uncertainties in model parameters. Here we conduct a variance-based sensitivity analysis to quantify the relative contribution of different model parameters and their interactions to the uncertainty in the surface and root-zone soil moisture in the Community Land Model 5.0 (CLM5). We focus on soil-texture-related parameters (porosity, saturated matric potential, saturated hydraulic conductivity, shape parameter of soil-water retention model) and organic matter fraction. A Gaussian process emulator is constructed based on CLM5 simulations and used to estimate soil moisture across the five-dimensional parameter space for sensitivity analysis. The procedure is demonstrated for four seasons across various U.S. sites of distinct soil and vegetation types. We find that the emulator captures well the CLM5 behavior across the parameter space for different soil textures and seasons. The uncertainties of surface and root-zone soil moisture are dominated by the uncertainties in porosity and shape parameter with negligible parametric interactions. However, relative importance of porosity versus shape parameter varies with soil textures (sites), depths (surface versus root zone), and seasons. At most of the sites, surface soil moisture uncertainty is attributed largely to shape parameter uncertainty, while porosity uncertainty is more important for the root-zone soil moisture uncertainty. All individual parameter and interaction effects demonstrate less variability across different soil textures and seasons for root zone than for surface soil moisture. These results provide scientific guidance to prioritize reducing the uncertainty of sensitive parameters for improving soil moisture modeling with CLM.

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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

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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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C. Adam Schlosser, Xiang Gao, Kenneth Strzepek, Andrei Sokolov, Chris E. Forest, Sirein Awadalla, and William Farmer

Abstract

The growing need for risk-based assessments of impacts and adaptation to climate change calls for increased capability in climate projections: specifically, the quantification of the likelihood of regional outcomes and the representation of their uncertainty. Herein, the authors present a technique that extends the latitudinal projections of the 2D atmospheric model of the Massachusetts Institute of Technology (MIT) Integrated Global System Model (IGSM) by applying longitudinally resolved patterns from observations, and from climate model projections archived from exercises carried out for the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC). The method maps the IGSM zonal means across longitude using a set of transformation coefficients, and this approach is demonstrated in application to near-surface air temperature and precipitation, for which high-quality observational datasets and model simulations of climate change are available. The current climatology of the transformation coefficients is observationally based. To estimate how these coefficients may alter with climate, the authors characterize the climate models’ spatial responses, relative to their zonal mean, from transient increases in trace-gas concentrations and then normalize these responses against their corresponding transient global temperature responses. This procedure allows for the construction of metaensembles of regional climate outcomes, combining the ensembles of the MIT IGSM—which produce global and latitudinal climate projections, with uncertainty, under different global climate policy scenarios—with regionally resolved patterns from the archived IPCC climate model projections. This hybridization of the climate model longitudinal projections with the global and latitudinal patterns projected by the IGSM can, in principle, be applied to any given state or flux variable that has the sufficient observational and model-based information.

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