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Dagang Wang and Guiling Wang

Abstract

Representation of the canopy hydrological processes has been challenging in land surface modeling due to the subgrid heterogeneity in both precipitation and surface characteristics. The Shuttleworth dynamic–statistical method is widely used to represent the impact of the precipitation subgrid variability on canopy hydrological processes but shows unwanted sensitivity to temporal resolution when implemented into land surface models. This paper presents a canopy hydrology scheme that is robust at different temporal resolutions. This scheme is devised by applying two physically based treatments to the Shuttleworth scheme: 1) the canopy hydrological processes within the rain-covered area are treated separately from those within the nonrain area, and the scheme tracks the relative rain location between adjacent time steps; and 2) within the rain-covered area, the canopy interception is so determined as to sustain the potential evaporation from the wetted canopy or is equal to precipitation, whichever is less, to maintain somewhat wet canopy during any rainy time step. When applied to the Amazon region, the new scheme establishes interception loss ratios of 0.3 at a 10-min time step and 0.23 at a 2-h time step. Compared to interception loss ratios of 0.45 and 0.09 at the corresponding time steps established by the original Shuttleworth scheme, the new scheme is much more stable under different temporal resolutions.

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Guiling Wang, Yeonjoo Kim, and Dagang Wang

Abstract

This paper presents a new index to quantify the strength of soil moisture–precipitation coupling in AGCMs and explores how the soil moisture–precipitation coupling in Community Atmosphere Model version 3 (CAM3)–Community Land Model version 3 (CAM3–CLM3) responds to parameterization-induced surface water budget changes. Specifically, this study (a) compares the regions of strong coupling identified by the newly proposed index and the index currently used in the Global Land–Atmosphere Coupling Experiment (GLACE); (b) examines how the surface water budget changes influence the strength of soil moisture–precipitation coupling as measured by the two indexes, respectively; and (c) examines how these changes influence the memory of the coupled land–atmosphere system as measured by the correlation between soil moisture and subsequent precipitation. The new index and the GLACE index are consistent in identifying central North America and West Africa as major regions of strong coupling during June–August (JJA). However, in some areas of western Europe and of subtropical South America where the GLACE index is low, the new index suggests a modest significant coupling during JJA. In response to the surface water budget changes that presumably favor a stronger soil moisture–precipitation coupling, the new index increases, but the GLACE index decreases in a majority of the regions of modest-to-strong coupling, although both show some mixed response. Changes in the land–atmosphere system memory suggest an increase of coupling strength, consistent with results from the new index. The strong dependence of the GLACE index on the relative importance of atmospheric internal variability is identified as a potential cause for the differences between the two indexes. The two indexes emphasize different aspects of soil moisture–precipitation coupling, and one might be more suitable than the other depending on the purpose of individual studies.

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Dagang Wang, Guiling Wang, and Emmanouil N. Anagnostou

Abstract

Precipitation exhibits significant spatial variability at scales much smaller than the typical size of climate model grid cells. Neglecting such subgrid-scale variability in climate models causes unrealistic representation of land–atmosphere flux exchanges. It is especially problematic over densely vegetated land. This paper addresses this issue by incorporating satellite-based precipitation observations into the representation of canopy interception processes in land surface models. Rainfall data derived from passive microwave (PM) observations are used to obtain realistic estimates of 1) conditional mean rain rates, which together with the modeled rain rate are used to estimate the rainfall coverage fraction at each model grid cell in this study, and 2) the probability density function (pdf) of rain rates within the rain-covered areas. Both of these properties significantly impact the land–atmosphere water vapor exchanges. Based on the above information, a statistical–dynamical approach is taken to incorporate the representation of precipitation subgrid variability into canopy interception processes in land surface models. The results reveal that incorporation of precipitation subgrid variability significantly alters the partitioning between runoff and total evapotranspiration as well as the partitioning among the three components of evapotranspiration (i.e., canopy interception loss, ground evaporation, and plant transpiration). This further influences soil water, surface temperature, and surface heat fluxes. It is shown that the choice of the rain-rate pdf within rain-covered areas has an effect on the model simulation of land–atmosphere flux exchanges. This study demonstrates that land surface and climate models can substantially benefit from the fine-resolution remotely sensed rainfall observations.

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Dashan Wang, Xianwei Wang, Lin Liu, Dagang Wang, and Zhenzhong Zeng

Abstract

Urban areas demonstrate great influence on precipitation, yet the spatial clustering features of precipitation are still unclear over urban areas. This study quantitatively examines the spatial clustering of precipitation intensity in 130 urban-affected regions over mainland China during 2008–15 using a high-resolution merged precipitation product. Results show that the spatial heterogeneity patterns display diverse distribution and vary with precipitation intensity and urban sizes. Extreme and heavy precipitation has higher spatial heterogeneity than light precipitation over the urban-affected regions of grade 1 cities, and their mean Moran’s I are 0.49, 0.47, and 0.37 for the intensity percentiles of ≥95%, 75%–95%, and <75%, respectively. The urban signatures in the spatial clustering of precipitation extremes are observed in 37 cities (28%), mainly occurring in the Haihe River basin, the Yangtze River basin, and the Pearl River basin. The spatial clustering patterns of precipitation extremes are affected by the local dominant synoptic conditions, such as the heavy storms of convective precipitation in Beijing (Moran’s I = 0.47) and the cold frontal system in the Pearl River delta (Moran’s I = 0.78), resulting in large regional variability. The role of urban environments for the spatial clustering is more evident in wetter conditions [e.g., relative humidity (RH) > 75% over Beijing and RH > 85% over the Pearl River delta] and warmer conditions (T > 25°C over Beijing and T > 28°C over the Pearl River delta). This study highlights the urban modification on the spatial clustering of some precipitation extremes, and calls for precautions and adaptation strategies to mitigate the adverse effect of the highly clustered extreme rainfall events.

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Weilin Liao, Xiaoping Liu, Elizabeth Burakowski, Dagang Wang, Linying Wang, and Dan Li

Abstract

While the significance of quantifying the biophysical effects of deforestation is rarely disputed, the sensitivities of land surface temperature (LST) to deforestation-induced changes in different biophysical factors (e.g., albedo, aerodynamic resistance, and surface resistance) and the relative importance of those biophysical changes remain elusive. Based on the subgrid-scale outputs from two global Earth system models (ESMs, i.e., the Geophysical Fluid Dynamics Laboratory Earth System Model and the Community Earth System Model) and an improved attribution framework, the sensitivities and responses of LST to deforestation are examined. Both models show that changes in aerodynamic resistance are the most important factor responsible for LST changes, with other factors such as albedo and surface resistance playing secondary but important roles. However, the magnitude of the contributions from different biophysical factors to LST changes is quite different for the two ESMs. We find that the differences between the two models in terms of the sensitivities are smaller than those of the corresponding biophysical changes, indicating that the dissimilarity between the two models in terms of LST responses to deforestation is more related to the magnitude of biophysical changes. It is the first time that the attribution of subgrid surface temperature variability is comprehensively compared based on simulations with two commonly used global ESMs. This study yields new insights into the similarity and dissimilarity in terms of how the biophysical processes are represented in different ESMs and further improves our understanding of how deforestation impacts on the local surface climate.

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Youlong Xia, David M. Mocko, Shugong Wang, Ming Pan, Sujay V. Kumar, Christa D. Peters-Lidard, Helin Wei, Dagang Wang, and Michael B. Ek

Abstract

Since the second phase of the North American Land Data Assimilation System (NLDAS-2) was operationally implemented at NOAA/NCEP as part of the production suite in August 2014, developing the next phase of NLDAS has been a key focus of the NCEP and NASA NLDAS teams. The Variable Infiltration Capacity (VIC) model is one of the four land surface models of the NLDAS system. The current operational NLDAS-2 uses version 4.0.3 (VIC403), the research NLDAS-2 used version 4.0.5 (VIC405), and the NASA Land Information System (LIS)-based NLDAS uses version 4.1.2.l (VIC412). The purpose of this study is to evaluate VIC403 and VIC412 and check if the latter version has better performance for the next phase of NLDAS. Toward this, a comprehensive evaluation was conducted, targeting multiple variables and using multiple metrics to assess the performance of different model versions. The evaluation results show large and significant improvements in VIC412 over the southeastern United States when compared with VIC403 and VIC405. In other regions, there are very limited improvements or even deterioration to some degree. This is partially due to 1) the sparseness of USGS streamflow observations for model parameter calibration and 2) a deterioration of VIC model performance in the Great Plains (GP) region after a model upgrade to a newer version. Overall, the model upgrade enhances model performance and skill scores for most parts of the continental United States; exceptions include the GP and western mountainous regions, as well as the daily soil moisture simulation skill, suggesting that VIC model development is on the right path. Further efforts are needed for scientific understanding of land surface physical processes in the GP, and a recalibration of VIC412 using reasonable reference datasets is recommended.

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