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WIREs Clim Change
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Land use/land cover changes and climate: modeling analysis and observational evidence

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This article summarizes the changes in landscape structure because of human land management over the last several centuries, and using observed and modeled data, documents how these changes have altered biogeophysical and biogeochemical surface fluxes on the local, mesoscale, and regional scales. Remaining research issues are presented including whether these landscape changes alter large‐scale atmospheric circulation patterns far from where the land use and land cover changes occur. We conclude that existing climate assessments have not yet adequately factored in this climate forcing. For those regions that have undergone intensive human landscape change, or would undergo intensive change in the future, we conclude that the failure to factor in this forcing risks a misalignment of investment in climate mitigation and adaptation. WIREs Clim Change 2011, 2:828–850. doi: 10.1002/wcc.144

This article is categorized under:

  • Paleoclimates and Current Trends > Climate Forcing
Figure 1.

Long‐term historical global estimates for population, cropland, and pasture. (Reprinted with permission from Refs 29. Copyright 2010 SAGE Publications, Inc.)

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Figure 2.

Reconstructed and projected LULCC for various time periods. The scale is the relative fraction of any grid box containing the sum of pasture or crops. These data were obtained from the LULCC data downloaded from the Land Use Harmonization website at http://luh.unh.edu. Note: CIS stands for Commonwealth of Independent States, a regional organization whose participating countries are former Soviet Republics, formed during the breakup of the Soviet Union. The analysis of the type of landscape continues to undergo refinement (e.g., much of Australia is shown as pasture when a large fraction is ungrazed semiarid and arid).

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Figure 3.

Changes in the extent covered with crops and pasture between present‐day (1992) and preindustrial times (1870). Yellow and red colors are used when the extent of anthropogenic areas have increased since preindustrial times, while blue colors refer to abandoned lands. The two boxes that are drawn on the map highlight the regions that will further be used to draw Figure 11 (hereafter referred to as North America and Eurasia).

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Figure 4.

Geostationary Meteorological Satellite (GMS) visible channel imagery for January 3, 1999, 1500 LST over southwest Australia. The agricultural regions are clear, while boundary cloud formation occur over native vegetation areas. Note that the western extent of the cloud fields coincide approximately with the rabbit proof fence that demarcates the cleared areas from the regions of remnant native vegetation.

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Figure 5.

Changing patterns of 10 km averages of broadband solar albedo, contrasting (a) 1650, (b) 1850, (c) 1920, and (d) 1992. By 1920, most areas formerly covered by deciduous forests and dense native grasslands exhibited the higher peak‐season shortwave albedo characteristic of agricultural crops and pastures. Increased average albedo also characterized postharvest landscapes that resulted from removal of old‐growth conifer and mixed forests in the late 19th and early 20th centuries. (Reprinted with permission from Ref 38. Copyright 2008 American Geophysical Union)

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Figure 6.

Patterns of aerodynamic surface roughness length (cm), as 10 km characteristic values displayed using a logarithmic color scale. Maps for (a) 1650, (b) 1850, (c) 1920, and (d) 1992 time slices. Characteristic roughness lengths track changes and patterns of land use, including settlement patterns in 1850 and the fragmented distribution of recovering forests of 1992. (Reprinted with permission from Ref 38. Copyright 2008 American Geophysical Union)

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Figure 7.

Mean correlation coefficient of Normalized Difference Vegetation Index (NDVI) versus near‐surface Temperature (T) and Equivalent Temperature (TE) as a function of vegetation type. Mean correlation values and confidence interval were obtained using the ArcGIS Zonal Statistics method, which computes from a gridded dataset summary statistics for each zone (here, vegetation types). Error bars denote 95% confidence intervals at 5%. (Reprinted with permission from Ref 98. Copyright 2010 Wiley Blackwell)

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Figure 8.

Vertical profiles of several variables from Flight No. 1 over the Arkansas River based on flight altitude data measured at 95 m. Measurements over the snow‐free ground were made from 1230:00 to 1257:10 MST. Measurements over the snow were made from 1416:55 to 1435:00 MST. (a) and (b) are for potential temperature. (Reprinted with permission from Ref 110. Copyright 1991 American Meteorological Society)

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Figure 9.

Example of a radar reflectivity time sequence showing a storm approaching the Indianapolis urban area in (a) and (b). The storm then splits into two cores as it passes over the city as shown in (c); and then reintensifies downwind (d and e). (Reprinted with permission from Ref 113. Copyright 2011 American Meteorological Society)

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Figure 10.

Analysis of a decade long storm climatology using image tagging and storm vector motions showing the splitting of the storms over the urban area and the re‐emergence of bigger and reintensified downwind. (a) Frequency distribution of storm cells (reflectivity > 40 dBZ) over the downwind region (solid line) and upwind region (dashed line) from Indianapolis urban area; (b) average size of high‐echo cells with downwind distance from Indianapolis urban center. (Reprinted with permission from Ref 113. Copyright 2011 American Meteorological Society)

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Figure 11.

Box and whisker plots of the simulated changes, between the preindustrial time period and present‐day, in (a) and (b) available energy (W m−2) and (c) and (d) surface air temperature (°C) for all seasons and both selected regions (North America: a and c, Eurasia: b and d, see Figure 3 for the definition of the regions). Seven coupled atmosphere/land models were used to draw this graph (LUCID simulations; Pitman et al.21; de Noblet‐Ducoudré et al.147). All seven models undertook two sets of two simulations spanning a matrix of present day and preindustrial GHG‐concentrations/SSTs, and present day and preindustrial land cover. In these experiments the models are forced with two different vegetation distributions (representative of 1870 or 1992, Figure 3). Each model carried out at least five independent simulations for each experiment to increase the capacity to determine those changes that were robust from those that reflected internal model variability. Values used for the plot are showing the mean ensemble values of each individual model. CO2SST refer to the sole impacts of changes in atmospheric CO2, sea‐surface temperature and sea‐ice extent between present‐day and preindustrial time, while LULCC refer to the sole impact of land cover change between those same time periods. [The bottom and top of the box is the 25th and 75th percentile, and the horizontal line within each box is the 50th percentile (the median). The whiskers (straight lines) indicate the ensemble maximum and minimum values.]

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