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minimum temperatures T min have warmed faster than daytime maximum values T max , thus decreasing the diurnal temperature range. The observed asymmetric warming at California coastal sites has been variously attributed to changes in cloud cover ( Nemani et al. 2001 ), sea surface temperatures (SSTs; Karl et al. 1993 ), upwelling ( Bakun 1990 ; Snyder et al. 2003 ; McGregor et al. 2007 ), changes in land cover/land use (LCLU; Mintz 1984 ; Zhang 1997 ; Chase et al. 2000 ; Pielke et al. 2002
minimum temperatures T min have warmed faster than daytime maximum values T max , thus decreasing the diurnal temperature range. The observed asymmetric warming at California coastal sites has been variously attributed to changes in cloud cover ( Nemani et al. 2001 ), sea surface temperatures (SSTs; Karl et al. 1993 ), upwelling ( Bakun 1990 ; Snyder et al. 2003 ; McGregor et al. 2007 ), changes in land cover/land use (LCLU; Mintz 1984 ; Zhang 1997 ; Chase et al. 2000 ; Pielke et al. 2002
explained less than 38% of the annual and winter variance. Land-use/land-cover (LULC) change has been shown to alter cloudiness and potentially precipitation ( McNider et al. 1994 ; Wetzel and Chang 1987 ) so that some part of the soil moisture and cloudiness relationship found by Rogers (2013) may be an indirect effect of LULC change. Coincident with the past century’s warming hole, the southeastern United States experienced a major LULC change. While the region was a major agricultural producer at
explained less than 38% of the annual and winter variance. Land-use/land-cover (LULC) change has been shown to alter cloudiness and potentially precipitation ( McNider et al. 1994 ; Wetzel and Chang 1987 ) so that some part of the soil moisture and cloudiness relationship found by Rogers (2013) may be an indirect effect of LULC change. Coincident with the past century’s warming hole, the southeastern United States experienced a major LULC change. While the region was a major agricultural producer at
variability driven by large-scale modes of variability ( Risbey et al. 2009 ). Land-use change (LUC) also affects the mean climate ( Pitman et al. 2009 ; Pielke et al. 2011 ; de Noblet-Ducoudré et al. 2012 ) and climate extremes (e.g., Pitman et al. 2012 ), particularly at regional scales ( Deo et al. 2009 ; Kala et al. 2011 ; Nair et al. 2011 ; Avila et al. 2012 ). The persistence of droughts and heat waves has also been linked to land processes, mostly through the soil moisture limitation of
variability driven by large-scale modes of variability ( Risbey et al. 2009 ). Land-use change (LUC) also affects the mean climate ( Pitman et al. 2009 ; Pielke et al. 2011 ; de Noblet-Ducoudré et al. 2012 ) and climate extremes (e.g., Pitman et al. 2012 ), particularly at regional scales ( Deo et al. 2009 ; Kala et al. 2011 ; Nair et al. 2011 ; Avila et al. 2012 ). The persistence of droughts and heat waves has also been linked to land processes, mostly through the soil moisture limitation of
contiguous United States; however, the impacts on precipitation were not investigated. Here, we expand on the work of Lu et al. (2015) and for the first time examine how dynamic crop growth impacts the simulated effect of irrigation on warm-season precipitation and its drivers. We used high-resolution (6.33-km model grid cell resolution) simulations of a version of the Weather Research and Forecasting (WRF) Model that is coupled to the Community Land Model version 4.0 with dynamic crop growth (WRF-CLM4
contiguous United States; however, the impacts on precipitation were not investigated. Here, we expand on the work of Lu et al. (2015) and for the first time examine how dynamic crop growth impacts the simulated effect of irrigation on warm-season precipitation and its drivers. We used high-resolution (6.33-km model grid cell resolution) simulations of a version of the Weather Research and Forecasting (WRF) Model that is coupled to the Community Land Model version 4.0 with dynamic crop growth (WRF-CLM4
1. Introduction Human activities have altered 42%–68% of the global land surface by transforming natural vegetation into crops, pastures, and woods for harvesting from the years 1700 to 2000 ( Hurtt et al. 2006 ). The biogeophysical climate impacts of human-induced land-cover change have been investigated using various general circulation models (GCM), regional climate models, and observations (e.g., Pielke et al. 2002 ; Fu 2003 ; Feddema et al. 2005 ; Bonan 2008 ). The Fifth Assessment
1. Introduction Human activities have altered 42%–68% of the global land surface by transforming natural vegetation into crops, pastures, and woods for harvesting from the years 1700 to 2000 ( Hurtt et al. 2006 ). The biogeophysical climate impacts of human-induced land-cover change have been investigated using various general circulation models (GCM), regional climate models, and observations (e.g., Pielke et al. 2002 ; Fu 2003 ; Feddema et al. 2005 ; Bonan 2008 ). The Fifth Assessment
1. Introduction Human-induced land-use change (LUC), such as the conversion of natural land cover to agriculture, transforms the land surface, altering its structure and influencing biogeophysical processes such as albedo, leaf area index (LAI), seasonality, surface roughness, and moisture fluxes. This has implications for the surface energy balance, altering shortwave radiation (SW) and the partitioning of latent and sensible heat (e.g., Brovkin et al. 2009 ; Bala et al. 2007 ; Boisier et
1. Introduction Human-induced land-use change (LUC), such as the conversion of natural land cover to agriculture, transforms the land surface, altering its structure and influencing biogeophysical processes such as albedo, leaf area index (LAI), seasonality, surface roughness, and moisture fluxes. This has implications for the surface energy balance, altering shortwave radiation (SW) and the partitioning of latent and sensible heat (e.g., Brovkin et al. 2009 ; Bala et al. 2007 ; Boisier et
results usually lack such reference values, at least for the majority of cells of a regional or global grid. More recently, analyses used data from large research networks such as FLUXNET ( http://www.fluxdata.org and http://fluxnet.ornl.gov ). The integration of such databases provides measured reference values to improve model parameterization directly and facilitates the derivation of sound estimates of the errors of fluxes from land models ( Williams et al. 2009 ; Wang and Mo 2015 ). Here, we
results usually lack such reference values, at least for the majority of cells of a regional or global grid. More recently, analyses used data from large research networks such as FLUXNET ( http://www.fluxdata.org and http://fluxnet.ornl.gov ). The integration of such databases provides measured reference values to improve model parameterization directly and facilitates the derivation of sound estimates of the errors of fluxes from land models ( Williams et al. 2009 ; Wang and Mo 2015 ). Here, we
1. Introduction Since 1950, the metropolitan region of Phoenix, Arizona, has been one of the fastest-growing urban areas in the United States ( Chow et al. 2012 ). It has undergone substantial land-use and land-cover change (LULCC) since World War II by shifting economic priorities from a mostly agrarian lifestyle to an urbanized one. The most rapid development began around 1970, when the baby boom generation reached adulthood, with a large number of job opportunities becoming available in the
1. Introduction Since 1950, the metropolitan region of Phoenix, Arizona, has been one of the fastest-growing urban areas in the United States ( Chow et al. 2012 ). It has undergone substantial land-use and land-cover change (LULCC) since World War II by shifting economic priorities from a mostly agrarian lifestyle to an urbanized one. The most rapid development began around 1970, when the baby boom generation reached adulthood, with a large number of job opportunities becoming available in the
understanding of the influencing factors for the rainfall variability over the Sahel could improve the predictive skill in rainfall forecasting, which will benefit the local people. Figure 1. The land-use and land-cover types of Africa from the MODIS land-cover dataset in 2001. The Sahel region is outlined in red. Sahel rainfall is known to be strongly influenced by sea surface temperature (SST), both globally and in oceans adjacent to the African continent ( Martin and Thorncroft 2014 ; Mohino et al. 2011
understanding of the influencing factors for the rainfall variability over the Sahel could improve the predictive skill in rainfall forecasting, which will benefit the local people. Figure 1. The land-use and land-cover types of Africa from the MODIS land-cover dataset in 2001. The Sahel region is outlined in red. Sahel rainfall is known to be strongly influenced by sea surface temperature (SST), both globally and in oceans adjacent to the African continent ( Martin and Thorncroft 2014 ; Mohino et al. 2011
climate models currently use first-generation DGVM. Various efforts have recently been made to “scale down” first-generation DGVM, usually aiming to better represent demographic processes as well as the subgrid vegetation heterogeneity caused by land-use changes ( Shevliakova et al. 2009 ), forest self-thinning, human-caused thinning, and stand-clearing harvest ( Bellassen et al. 2010 ), generic stand-clearing disturbances ( Scherstjanoi et al. 2013 ), or fire ( Haverd et al. 2013 ; Yue et al. 2013
climate models currently use first-generation DGVM. Various efforts have recently been made to “scale down” first-generation DGVM, usually aiming to better represent demographic processes as well as the subgrid vegetation heterogeneity caused by land-use changes ( Shevliakova et al. 2009 ), forest self-thinning, human-caused thinning, and stand-clearing harvest ( Bellassen et al. 2010 ), generic stand-clearing disturbances ( Scherstjanoi et al. 2013 ), or fire ( Haverd et al. 2013 ; Yue et al. 2013
