Comments on “Atmospheric Disturbance Caused by Human Modification of the Landscape”

Doran, J. C.; Zhong, Shiyuan

doi:10.1175/1520-0477(2002)083<0279:coaecb>2.3.co;2

Cited by 10 publications

(3 citation statements)

References 9 publications

(10 reference statements)

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“…This hypothesis is in agreement with the IPCC-reported observation of increased precipitation in all tropical forest regions in the last three decades (Folland et al 2001), although this may equally be due to natural variability or global warming. Furthermore, we recognize that there is still significant debate about the extent to which mesoscale circulation changes might affect larger-scale patterns (Doran and Zhong 2002;cf. Weaver and Avissar 2002).…”

Section: R Ecent Intensification Of Thementioning

confidence: 97%

Comments

2002

Bull. Amer. Meteor. Soc.

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Section: R Ecent Intensification Of Thementioning

confidence: 97%

Comments

2002

Bull. Amer. Meteor. Soc.

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“…This is particularly interesting given the ongoing discussion regarding whether mesoscale circulations in the SGP are a function of vegetation cover (WEAVER and AVISSAR, 2001) or topography (DORAN and ZHONG, 2001). Using 20 m elevation slices spanning the WRW, the percent coverage of each LULC classification was determined.…”

Section: Relationships Between Surface Characteristics and Land Use/lmentioning

confidence: 99%

A Simple Reclassification Method for Correcting Uncertainty in Land Use/Land Cover Data Sets Used with Land Surface Models

Alfieri

Niyogi

LeMone

et al. 2007

Pure appl. geophys.

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With increasing computational resources, environmental models are run at finer grid spacing to resolve the land surface characteristics. The land use/land cover (LULC) data sets input into land surface models are used to assign various default parameters from a look-up tables. The objective of this study is to assess the potential uncertainty in the LULC data and to present a reclassification method for improving the accuracy of LULC data sets. The study focuses on the Southern Great Plains and specifically the Walnut River Watershed in southeastern Kansas, USA. The uncertainty analysis is conducted using two data sets: The National Land Cover Dataset 1992 (NLCD 92) and the Gap Analysis Program (GAP) data set, and a reclassification logic tree. A comparison of these data sets showed that they do not agree for approximately 27% of the watershed. Moreover, an accuracy assessment of these two data sets indicated that neither had an overall accuracy as high as 80%. Using the relationships between land-surface characteristics and LULC, a reclassification of the watershed was conducted using a logical model. This model iteratively reclassified the uncertain pixels according to their surface characteristics. The model utilized normalized difference vegetation index (NDVI) measurements during April and July 2003, elevation, and slope. The reclassification yielded a revised LULC dataset that was substantially improved. The overall accuracy of the revised data set was nearly 93%. The study results suggest: (i) as models adopt finer grid spacings, the uncertainty in the LULC data will become significant; (ii) assimilating NDVI into the land-surface models can reduce the uncertainty due to LULC assignment; (iii) the standard LULC data sets must be used with caution when the focus is on local scale; and (iv) reclassification is a valuable means of improving the accuracy of LULC data sets prior to applying them to local issues or phenomena.

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“…However, there have been debates regarding the importance and spatial scales of secondary circulations induced by land surface heterogeneities, particularly those of vertical velocity and turbulent fluxes (Doran & Zhong, 2002; Kang & Davis, 2008, 2009; Weaver, 2009; Weaver & Avissar, 2001). Zhong and Doran (1998) studied mesoscale model simulations over the Southern Great Plains (SGP) using observation‐based surface fluxes and large‐scale winds for boundary conditions and concluded that mesoscale secondary circulations and their influence on the mean properties over a large domain (e.g., a few hundreds of km) are substantially smaller than those found with idealized heterogeneities and zero background winds.…”

Section: Introductionmentioning

confidence: 99%

Determining Spatial Scales of Soil Moisture—Cloud Coupling Pathways Using Semi‐Idealized Simulations

et al. 2022

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Soil moisture (SM) is an important component of the land-atmosphere (L-A) system. SM changes how available energy at the land surface is partitioned between sensible and latent heat fluxes, which in turn affects the buoyancy of the near-surface air parcels and the growth of the atmospheric boundary layer (BL). For subtropical and mid-latitude continents in the warm season, surface evapotranspiration originating from SM can be an important source for atmospheric moisture. Both the BL growth and atmospheric moisture affect cloud development and precipitation, which then changes the water and energy fluxes into the surface, forming a feedback loop (Betts, 2004;D'Odorico & Porporato, 2004;Koster et al., 2003).Spatial heterogeneity of surface characteristics can induce thermally forced secondary circulations in the BL (e.g., Pielke et al., 1991;Segal & Arritt, 1992;Taylor et al., 2012). The associated upward motions transport moisture to the free troposphere to facilitate cloud formation. Although background winds or other surface forcing can dominate over such secondary circulations (e.g., Avissar & Schmidt, 1998;Lee et al., 2019;Shaw & Doran, 2001), given the very limited predictability of turbulent flow and moist convection, spatiotemporal distributions of surface forcing are still valuable to help constrain the initiation and development of convective systems (Yano et al., 2018). Several studies demonstrated the potential of the knowledge of SM distributions to improve skills of weather and seasonal forecasts (Beljaars et al., 1996;Koster et al., 2010;Xue et al., 2001). Because of the feedback through the hydrological and energy cycles, SM plays an important role in the climate time scale as well, including large-scale monsoon circulations (e.g.,

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Comments on “Atmospheric Disturbance Caused by Human Modification of the Landscape”

Cited by 10 publications

References 9 publications

Comments

Comments

A Simple Reclassification Method for Correcting Uncertainty in Land Use/Land Cover Data Sets Used with Land Surface Models

Determining Spatial Scales of Soil Moisture—Cloud Coupling Pathways Using Semi‐Idealized Simulations

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