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Paul A. Dirmeyer, Zhichang Guo, and Xiang Gao

Abstract

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

Abstract

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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Paul A. Dirmeyer, Randal D. Koster, and Zhichang Guo

Abstract

The Global Energy and Water Cycle Experiment/Climate Variability and Predictability (GEWEX/CLIVAR) Global Land–Atmosphere Coupling Experiment (GLACE) has provided an estimate of the global distribution of land–atmosphere coupling strength during boreal summer based on the results from a dozen weather and climate models. However, there is a great deal of variation among models, attributable to a range of sensitivities in the simulation of both the terrestrial and atmospheric branches of the hydrologic cycle. It remains an open question whether any of the models, or the multimodel estimate, reflects the actual pattern and strength of land–atmosphere coupling in the earth’s hydrologic cycle. The authors attempt to diagnose this by examining the local covariability of key atmospheric and land surface variables both in models and in those few locations where comparable, relatively complete, long-term measurements exist. Most models do not encompass well the observed relationships between surface and atmospheric state variables and fluxes, suggesting that these models do not represent land–atmosphere coupling correctly. Specifically, there is evidence that systematic biases in near-surface temperature and humidity among all models may contribute to incorrect surface flux sensitivities. However, the multimodel mean generally validates better than most or all of the individual models. Regional precipitation behavior (lagged autocorrelation and predisposition toward maintenance of extremes) between models and observations is also compared. Again a great deal of variation is found among the participating models, but remarkably accurate behavior of the multimodel mean.

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Li Zhang, Paul A. Dirmeyer, Jiangfeng Wei, Zhichang Guo, and Cheng-Hsuan Lu

Abstract

The operational coupled land–atmosphere forecast model from the National Centers for Environmental Prediction (NCEP) is evaluated for the strength and characteristics of its coupling in the water cycle between land and atmosphere. Following the protocols of the Global Land–Atmosphere Coupling Experiment (GLACE) it is found that the Global Forecast System (GFS) atmospheric model coupled to the Noah land surface model exhibits extraordinarily weak land–atmosphere coupling, much as its predecessor, the GFS–Oregon State University (OSU) coupled system. The coupling strength is evaluated by the ability of subsurface soil wetness to affect locally the time series of precipitation. The surface fluxes in Noah are also found to be rather insensitive to subsurface soil wetness. Comparison to another atmospheric model coupled to Noah as well as a different land surface model show that Noah is responsible for some of the lack of sensitivity, primarily because its thick (10 cm) surface layer dominates the variability in surface latent heat fluxes. Noah is found to be as responsive as other land surface models to surface soil wetness and temperature variations, suggesting the design of the GLACE sensitivity experiment (based only on subsurface soil wetness) handicapped the Noah model. Additional experiments, in which the parameterization of evapotranspiration is altered, as well as experiments where surface soil wetness is also constrained, isolate the GFS atmospheric model as the principal source of the weak sensitivity of precipitation to land surface states.

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Stefano Materia, Paul A. Dirmeyer, Zhichang Guo, Andrea Alessandri, and Antonio Navarra

Abstract

The discharge of freshwater into oceans represents a fundamental process in the global climate system, and this flux is taken into account in simulations with general circulation models (GCMs). Moreover, the availability of realistic river routing schemes is a powerful instrument to assess the validity of land surface components, which have been recognized to be crucial for the global climate simulation. In this study, surface and subsurface runoff generated by the 13 land surface schemes (LSSs) participating in the Second Global Soil Wetness Project (GSWP-2) are used as input fields for the Hydrology Discharge (HD) routing model to simulate discharge for 30 of the world’s largest rivers. The simplest land surface models do not provide a good representation of runoff, and routed river flows using these inputs are affected by many biases. On the other hand, HD shows the best simulations when forced by two of the more sophisticated schemes. The multimodel ensemble GSWP-2 generates the best phasing of the annual cycle as well as a good representation of absolute values, although the ensemble mean tends to smooth the peaks. Finally, the intermodel comparison shows the limits and deficiencies of a velocity-constant routing model such as HD, particularly in the phase of mean annual discharge.

The second part of the study assesses the sensitivity of river discharge to the variation of external meteorological forcing. The Center for Ocean–Land–Atmosphere Studies version of the SSiB model is constrained with different meteorological fields and the resulting runoff is used as input for HD. River flow is most sensitive to precipitation variability, but changes in radiative forcing affect discharge as well, presumably because of the interaction with evaporation. Also, this analysis provides an estimate of the sensitivity of river discharge to precipitation variations. A few areas (e.g., central and eastern Asia, the Mediterranean, and much of the United States) show a magnified response of river discharge to a given percentage change in precipitation. Hence, an amplified effect of droughts as indicated by the consensus of climate change predictions may occur in places such as the Mediterranean. Conversely, increasing summer precipitation foreseen in places like southern and eastern Asia may amplify floods in these poor and heavily populated regions. Globally, a 1% fluctuation in precipitation forcing results in an average 2.3% change in discharge. These results can be used for the definition and assessment of new strategies for land use and water management in the near future.

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Sonia I. Seneviratne, Randal D. Koster, Zhichang Guo, Paul A. Dirmeyer, Eva Kowalczyk, David Lawrence, Ping Liu, David Mocko, Cheng-Hsuan Lu, Keith W. Oleson, and Diana Verseghy

Abstract

Soil moisture memory is a key aspect of land–atmosphere interaction and has major implications for seasonal forecasting. Because of a severe lack of soil moisture observations on most continents, existing analyses of global-scale soil moisture memory have relied previously on atmospheric general circulation model (AGCM) experiments, with derived conclusions that are probably model dependent. The present study is the first survey examining and contrasting global-scale (near) monthly soil moisture memory characteristics across a broad range of AGCMs. The investigated simulations, performed with eight different AGCMs, were generated as part of the Global Land–Atmosphere Coupling Experiment.

Overall, the AGCMs present relatively similar global patterns of soil moisture memory. Outliers are generally characterized by anomalous water-holding capacity or biases in radiation forcing. Water-holding capacity is highly variable among the analyzed AGCMs and is the main factor responsible for intermodel differences in soil moisture memory. Therefore, further studies on this topic should focus on the accurate characterization of this parameter for present AGCMs. Despite the range in the AGCMs’ behavior, the average soil moisture memory characteristics of the models appear realistic when compared to available in situ soil moisture observations. An analysis of the processes controlling soil moisture memory in the AGCMs demonstrates that it is mostly controlled by two effects: evaporation’s sensitivity to soil moisture, which increases with decreasing soil moisture content, and runoff’s sensitivity to soil moisture, which increases with increasing soil moisture content. Soil moisture memory is highest in regions of medium soil moisture content, where both effects are small.

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Randal D. Koster, Y. C. Sud, Zhichang Guo, Paul A. Dirmeyer, Gordon Bonan, Keith W. Oleson, Edmond Chan, Diana Verseghy, Peter Cox, Harvey Davies, Eva Kowalczyk, C. T. Gordon, Shinjiro Kanae, David Lawrence, Ping Liu, David Mocko, Cheng-Hsuan Lu, Ken Mitchell, Sergey Malyshev, Bryant McAvaney, Taikan Oki, Tomohito Yamada, Andrew Pitman, Christopher M. Taylor, Ratko Vasic, and Yongkang Xue

Abstract

The Global Land–Atmosphere Coupling Experiment (GLACE) is a model intercomparison study focusing on a typically neglected yet critical element of numerical weather and climate modeling: land–atmosphere coupling strength, or the degree to which anomalies in land surface state (e.g., soil moisture) can affect rainfall generation and other atmospheric processes. The 12 AGCM groups participating in GLACE performed a series of simple numerical experiments that allow the objective quantification of this element for boreal summer. The derived coupling strengths vary widely. Some similarity, however, is found in the spatial patterns generated by the models, with enough similarity to pinpoint multimodel “hot spots” of land–atmosphere coupling. For boreal summer, such hot spots for precipitation and temperature are found over large regions of Africa, central North America, and India; a hot spot for temperature is also found over eastern China. The design of the GLACE simulations are described in full detail so that any interested modeling group can repeat them easily and thereby place their model’s coupling strength within the broad range of those documented here.

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Zhichang Guo, Paul A. Dirmeyer, Randal D. Koster, Y. C. Sud, Gordon Bonan, Keith W. Oleson, Edmond Chan, Diana Verseghy, Peter Cox, C. T. Gordon, J. L. McGregor, Shinjiro Kanae, Eva Kowalczyk, David Lawrence, Ping Liu, David Mocko, Cheng-Hsuan Lu, Ken Mitchell, Sergey Malyshev, Bryant McAvaney, Taikan Oki, Tomohito Yamada, Andrew Pitman, Christopher M. Taylor, Ratko Vasic, and Yongkang Xue

Abstract

The 12 weather and climate models participating in the Global Land–Atmosphere Coupling Experiment (GLACE) show both a wide variation in the strength of land–atmosphere coupling and some intriguing commonalities. In this paper, the causes of variations in coupling strength—both the geographic variations within a given model and the model-to-model differences—are addressed. The ability of soil moisture to affect precipitation is examined in two stages, namely, the ability of the soil moisture to affect evaporation, and the ability of evaporation to affect precipitation. Most of the differences between the models and within a given model are found to be associated with the first stage—an evaporation rate that varies strongly and consistently with soil moisture tends to lead to a higher coupling strength. The first-stage differences reflect identifiable differences in model parameterization and model climate. Intermodel differences in the evaporation–precipitation connection, however, also play a key role.

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