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1. Introduction Land surface processes are recognized as a potential source of climate variability and predictability at different time scales, from hours to seasons and longer ( Koster et al. 2000 ; Koster and Suarez 2003 ; Guo et al. 2011 ; Sellers et al. 1992 ; Foley et al. 2000 ). Changes in the land surface or vegetation cover can affect the way the land and the atmosphere interact at many of those time scales and can thus have an effect on climate. Changes in the surface states result
1. Introduction Land surface processes are recognized as a potential source of climate variability and predictability at different time scales, from hours to seasons and longer ( Koster et al. 2000 ; Koster and Suarez 2003 ; Guo et al. 2011 ; Sellers et al. 1992 ; Foley et al. 2000 ). Changes in the land surface or vegetation cover can affect the way the land and the atmosphere interact at many of those time scales and can thus have an effect on climate. Changes in the surface states result
the soil (e.g., Scheffer et al. 2005 ). In drylands, vegetation also has an important soil-stabilizing effect. Loss of vegetation due to climate variability, human-induced land cover change, or land degradation can result in an increase in wind erosion and lofting of dust aerosols into the atmosphere, which can inhibit precipitation by warming and stabilizing the atmosphere and by reducing the amount of solar radiation that reaches the surface. Large portions of southwest Asia are prone to dust
the soil (e.g., Scheffer et al. 2005 ). In drylands, vegetation also has an important soil-stabilizing effect. Loss of vegetation due to climate variability, human-induced land cover change, or land degradation can result in an increase in wind erosion and lofting of dust aerosols into the atmosphere, which can inhibit precipitation by warming and stabilizing the atmosphere and by reducing the amount of solar radiation that reaches the surface. Large portions of southwest Asia are prone to dust
variance along this swath, and we can speculate that the same is true in nature. We will come back to the role of the land later in our discussion of long-term trends and predictability. Fig . 5. The standard deviation of the monthly JJA 2-m temperature (°C) for the period 1980–2012: (top) MERRA, (second row) GEOS-5 AGCM simulations with interactive land, and (third row) GEOS-5 AGCM simulations with disabled land–atmosphere feedback. (bottom) The difference between the second row and the third row
variance along this swath, and we can speculate that the same is true in nature. We will come back to the role of the land later in our discussion of long-term trends and predictability. Fig . 5. The standard deviation of the monthly JJA 2-m temperature (°C) for the period 1980–2012: (top) MERRA, (second row) GEOS-5 AGCM simulations with interactive land, and (third row) GEOS-5 AGCM simulations with disabled land–atmosphere feedback. (bottom) The difference between the second row and the third row
