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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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Xiang Gao and Paul A. Dirmeyer

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Multimodel ensemble forecasting has been shown to offer a systematic improvement in the skill of climate prediction with atmosphere and ocean circulation models. However, little such work has been done for the land surface component, an important lower boundary for weather and climate forecast models. In this study, the authors examine and evaluate several methods of combining individual global soil wetness products from uncoupled land surface model calculations and coupled land–atmosphere model reanalyses to produce an ensemble analysis. Analyses are verified against observations from the Global Soil Moisture Data Bank (GSMDB) with skill measured by correlation coefficient and root-mean-square error (RMSE). A preliminary transferability study is conducted as well for investigating the feasibility of transferring ensemble regression parameters within two specific regions (Illinois and east-central China) and between these two regions of similar climate and land use. The results show that when sufficient validation data are available, one can use a seasonally dependent linear regression to improve the skill of any individual model simulation of soil wetness. Further improvements in skill can be achieved with more sophisticated ensembling methods, such as the regression-adjusted multimodel ensemble mean analysis and regression-adjusted multimodel analysis. However, all the ensembling schemes involving regression usually do not help improve the skill scores as far as the simulation of anomalies of soil wetness is concerned. In the absence of calibration data, the simple arithmetic ensemble mean across multiple soil wetness products generally does as well or better than the best individual model at any location in the representation of both soil wetness and its anomaly. Transferability from one subset of stations from the Illinois or east-central China dataset to another gives satisfactory results. However, results are poor when transferring regression weights between different regions, even with similar climate regimes and land cover. Such an exercise helps us to understand better the virtues and limitations of various ensembling techniques and enables progress toward creating an optimum, model-independent analysis from a practical point of view.

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Paul A. Dirmeyer, Zhichang Guo, and Xiang Gao

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The characteristics of eight global soil wetness products, three produced by land surface model calculations, three from coupled land–atmosphere model reanalyses, and two from microwave remote sensing estimates, have been examined. The goal of this study is to determine whether there exists an optimal dataset for the initialization of the land surface component of global weather and climate forecast models. Their abilities to simulate the phasing of the annual cycle and to accurately represent interannual variability in soil wetness by comparing to available in situ measurements are validated. Because soil wetness climatologies vary greatly among land surface models, and models have different operating ranges for soil wetness (i.e., very different mean values, variances, and hydrologically critical thresholds such as the point where evaporation occurs at the potential rate or where surface runoff begins), one cannot simply take the soil wetness field from one product and apply it to an arbitrary land surface scheme (LSS) as an initial condition without experiencing some sort of initialization shock. A means of renormalizing soil wetness is proposed based on the local statistical properties of this field in the source and target models, to allow a large number of climate models to apply the same initialization in multimodel studies or intercomparisons. As a test of feasibility, renormalization among the model-derived products is applied to see how it alters the character of the soil wetness climatologies.

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Xiang Gao, Paul A. Dirmeyer, Zhichang Guo, and Mei Zhao

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A coupled land–atmosphere climate model is used to investigate the impact of vegetation parameters (leaf area index, absorbed radiation, and greenness fraction) on the simulation of surface fluxes and their potential role in improving climate forecasts. Ensemble simulations for 1986–95 have been conducted with specified observed sea surface temperatures. The vegetation impact is analyzed by comparing integrations with two different ways of specifying vegetation boundary conditions: observed interannually varying vegetation versus the climatological annual cycle. Parallel integrations are also implemented and analyzed for the land surface model in an uncoupled mode within the framework of the Second Global Soil Wetness Project (GSWP-2) for the same period. The sensitivity to vegetation anomalies in the coupled simulations appears to be relatively small. There appears to be only episodic and localized favorable impacts of vegetation variations on the skill of precipitation and temperature simulations. Impacts are sometimes manifested strictly through changes in land surface fluxes, and in other cases involve clear interactions with atmospheric processes. In general, interannual variations of vegetation tend to increase the temporal variability of radiation fluxes, soil evaporation, and canopy interception loss in terms of both spatial frequency and global mean. Over cohesive regions of significant and persistent vegetation anomalies, cumulative statistics do show a net response of surface fluxes, temperature, and precipitation with vegetation anomalies of ±20% corresponding to a precipitation response of about ±6%. However, in about half of these cases no significant response was found. The results presented here suggest that vegetation may be a useful element of the land surface for enhancing seasonal predictability, but its role in this model appears to be relatively minor. Improvement does not occur in all circumstances, and strong anomalies have the best chance of a positive impact on the simulation.

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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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Feng Gao, Xiaoyan Zhang, Neil A. Jacobs, Xiang-Yu Huang, Xin Zhang, and Peter P. Childs

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Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observations are becoming a major data source for numerical weather prediction (NWP) because of the advantages of their high spatiotemporal resolution and humidity measurements. In this study, the estimation of TAMDAR observational errors, and the impacts of TAMDAR observations with new error statistics on short-term forecasts are presented. The observational errors are estimated by a three-way collocated statistical comparison. This method employs collocated meteorological reports from three data sources: TAMDAR, radiosondes, and the 6-h forecast from a Weather Research and Forecasting Model (WRF). The performance of TAMDAR observations with the new error statistics was then evaluated based on this model, and the WRF Data Assimilation (WRFDA) three-dimensional variational data assimilation (3DVAR) system. The analysis was conducted for both January and June of 2010. The experiments assimilate TAMDAR, as well as other conventional data with the exception of non-TAMDAR aircraft observations, every 6 h, and a 24-h forecast is produced. The standard deviation of the observational error of TAMDAR, which has relatively stable values regardless of season, is comparable to radiosondes for temperature, and slightly smaller than that of a radiosonde for relative humidity. The observational errors in wind direction significantly depend on wind speeds. In general, at low wind speeds, the error in TAMDAR is greater than that of radiosondes; however, the opposite is true for higher wind speeds. The impact of TAMDAR observations on both the 6- and 24-h WRF forecasts during the studied period is positive when using the default observational aircraft weather report (AIREP) error statistics. The new TAMDAR error statistics presented here bring additional improvement over the default error.

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Paul A. Dirmeyer, Xiang Gao, Mei Zhao, Zhichang Guo, Taikan Oki, and Naota Hanasaki
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Paul A. Dirmeyer, Xiang Gao, Mei Zhao, Zhichang Guo, Taikan Oki, and Naota Hanasaki

Quantification of sources and sinks of carbon at global and regional scales requires not only a good description of the land sources and sinks of carbon, but also of the synoptic and mesoscale meteorology. An experiment was performed in Les Landes, southwest France, during May–June 2005, to determine the variability in concentration gradients and fluxes of CO2 The CarboEurope Regional Experiment Strategy (CERES; see also http://carboregional.mediasfrance.org/index) aimed to produce aggregated estimates of the carbon balance of a region that can be meaningfully compared to those obtained from the smallest downscaled information of atmospheric measurements and continental-scale inversions. We deployed several aircraft to sample the CO2 concentration and fluxes over the whole area, while fixed stations observed the fluxes and concentrations at high accuracy. Several (mesoscale) meteorological modeling tools were used to plan the experiment and flight patterns.

Results show that at regional scale the relation between profiles and fluxes is not obvious, and is strongly influenced by airmass history and mesoscale flow patterns. In particular, we show from an analysis of data for a single day that taking either the concentration at several locations as representative of local fluxes or taking the flux measurements at those sites as representative of larger regions would lead to incorrect conclusions about the distribution of sources and sinks of carbon. Joint consideration of the synoptic and regional flow, fluxes, and land surface is required for a correct interpretation. This calls for an experimental and modeling strategy that takes into account the large spatial gradients in concentrations and the variability in sources and sinks that arise from different land use types. We briefly describe how such an analysis can be performed and evaluate the usefulness of the data for planning of future networks or longer campaigns with reduced experimental efforts.

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

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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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Xiang Gao, C. Adam Schlosser, Pingping Xie, Erwan Monier, and Dara Entekhabi

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An analogue method is presented to detect the occurrence of heavy precipitation events without relying on modeled precipitation. The approach is based on using composites to identify distinct large-scale atmospheric conditions associated with widespread heavy precipitation events across local scales. These composites, exemplified in the south-central, midwestern, and western United States, are derived through the analysis of 27-yr (1979–2005) Climate Prediction Center (CPC) gridded station data and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). Circulation features and moisture plumes associated with heavy precipitation events are examined. The analogues are evaluated against the relevant daily meteorological fields from the MERRA reanalysis and achieve a success rate of around 80% in detecting observed heavy events within one or two days. The method also captures the observed interannual variations of seasonal heavy events with higher correlation and smaller RMSE than MERRA precipitation. When applied to the same 27-yr twentieth-century climate model simulations from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), the analogue method produces a more consistent and less uncertain number of seasonal heavy precipitation events with observation as opposed to using model-simulated precipitation. The analogue method also performs better than model-based precipitation in characterizing the statistics (minimum, lower and upper quartile, median, and maximum) of year-to-year seasonal heavy precipitation days. These results indicate the capability of CMIP5 models to realistically simulate large-scale atmospheric conditions associated with widespread local-scale heavy precipitation events with a credible frequency. Overall, the presented analyses highlight the improved diagnoses of the analogue method against an evaluation that considers modeled precipitation alone to assess heavy precipitation frequency.

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