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lesser extent, warm tropical North Atlantic sea surface temperature (SST) anomalies ( Schubert et al. 2004a , b ; Seager et al. 2005b ; Herweijer et al. 2006 ; Seager 2007 ). This most recent drought also coincided with a La Niña event. Indeed, a recent study ( Hoerling et al. 2013 ) has concluded that the precipitation reduction over Texas in the summer of 2011 was within the range of natural variability of the atmosphere–ocean–land surface system and made much more likely by the La Niña of 2010
lesser extent, warm tropical North Atlantic sea surface temperature (SST) anomalies ( Schubert et al. 2004a , b ; Seager et al. 2005b ; Herweijer et al. 2006 ; Seager 2007 ). This most recent drought also coincided with a La Niña event. Indeed, a recent study ( Hoerling et al. 2013 ) has concluded that the precipitation reduction over Texas in the summer of 2011 was within the range of natural variability of the atmosphere–ocean–land surface system and made much more likely by the La Niña of 2010
1. Introduction Rain or snow falling over any particular location is composed of condensed water vapor that entered the atmosphere as surface evaporation from a range of upstream locations. Surface and atmospheric conditions along the paths of moisture advection determine the ultimate sources of evaporative moisture, which generally have a combination of oceanic and terrestrial origins. Knowledge of the sources of moisture supplying precipitation over a particular location could be used to
1. Introduction Rain or snow falling over any particular location is composed of condensed water vapor that entered the atmosphere as surface evaporation from a range of upstream locations. Surface and atmospheric conditions along the paths of moisture advection determine the ultimate sources of evaporative moisture, which generally have a combination of oceanic and terrestrial origins. Knowledge of the sources of moisture supplying precipitation over a particular location could be used to
( Ropelewski and Halpert 1986 , 1987 ; Mo and Schemm 2008a , b ; Seager et al. 2009 ). In contrast to Mo and Schemm (2008a) , Seager et al. (2009) concluded that rainfall is more closely related to internal atmospheric variability, particularly in summer. Noise or internal atmospheric variability, for the purposes of this paper, is unpredictable, that is, we cannot relate it to specific forcing or feedback (ocean–atmosphere or atmosphere–land). This unpredictable variability is due to internal
( Ropelewski and Halpert 1986 , 1987 ; Mo and Schemm 2008a , b ; Seager et al. 2009 ). In contrast to Mo and Schemm (2008a) , Seager et al. (2009) concluded that rainfall is more closely related to internal atmospheric variability, particularly in summer. Noise or internal atmospheric variability, for the purposes of this paper, is unpredictable, that is, we cannot relate it to specific forcing or feedback (ocean–atmosphere or atmosphere–land). This unpredictable variability is due to internal
drought and determining the timing of the drought, sustaining and/or amplifying droughts over the United States involves other factors such as local soil moisture feedback and random atmospheric internal variability (e.g., Koster et al. 2003 ; Ferguson et al. 2010 ). For example, a month with low precipitation leads to a drier-than-average soil, which in turn can lead to lower-than-average evaporation, which may lead to continued low precipitation. Such feedback between the land and atmosphere plays
drought and determining the timing of the drought, sustaining and/or amplifying droughts over the United States involves other factors such as local soil moisture feedback and random atmospheric internal variability (e.g., Koster et al. 2003 ; Ferguson et al. 2010 ). For example, a month with low precipitation leads to a drier-than-average soil, which in turn can lead to lower-than-average evaporation, which may lead to continued low precipitation. Such feedback between the land and atmosphere plays
May and June ( Fig. 5 , top panels), a zonal ridge of high pressure anomalies inhibited the typical southward push of cold fronts from Canada that often serve to organize widespread rains. July (bottom left) saw a somewhat different pattern, though no less effective in inhibiting rainfall. An intense anticyclone was centered over the northern plains region, preventing frontal incursions while also stabilizing the atmosphere and inhibiting deep convection that typically contributes appreciably to
May and June ( Fig. 5 , top panels), a zonal ridge of high pressure anomalies inhibited the typical southward push of cold fronts from Canada that often serve to organize widespread rains. July (bottom left) saw a somewhat different pattern, though no less effective in inhibiting rainfall. An intense anticyclone was centered over the northern plains region, preventing frontal incursions while also stabilizing the atmosphere and inhibiting deep convection that typically contributes appreciably to
several factors, including errors related to variations in snow physical properties, surface, and the atmosphere ( Foster et al. 2005 ; Markus et al. 2006 ; Tedesco and Narvekar 2010 ). The snow retrievals are also prone to large errors in the presence of dense vegetation and water bodies ( Foster et al. 2005 ). Moreover, they have been shown to be less sensitive to thin snow packs (SWE less than around 10 mm) and to saturate for thick snowpacks (SWE above ground 200 mm; Dong et al. 2005 ). The
several factors, including errors related to variations in snow physical properties, surface, and the atmosphere ( Foster et al. 2005 ; Markus et al. 2006 ; Tedesco and Narvekar 2010 ). The snow retrievals are also prone to large errors in the presence of dense vegetation and water bodies ( Foster et al. 2005 ). Moreover, they have been shown to be less sensitive to thin snow packs (SWE less than around 10 mm) and to saturate for thick snowpacks (SWE above ground 200 mm; Dong et al. 2005 ). The
potential ET (PET) has been used as the normalization factor to compute the ESI. Here, the performance of several forms of scaling flux is examined, as well as a benchmark case using no scaling flux, sampling anomalies in ET itself. ET estimates employed in the ESI are obtained from the TIR-based remote sensing Atmosphere–Land Exchange Inverse (ALEXI) model ( Anderson et al. 1997 ; Mecikalski et al. 1999 ; Anderson et al. 2007a ). ALEXI uses measurements of the morning LST rise, provided by
potential ET (PET) has been used as the normalization factor to compute the ESI. Here, the performance of several forms of scaling flux is examined, as well as a benchmark case using no scaling flux, sampling anomalies in ET itself. ET estimates employed in the ESI are obtained from the TIR-based remote sensing Atmosphere–Land Exchange Inverse (ALEXI) model ( Anderson et al. 1997 ; Mecikalski et al. 1999 ; Anderson et al. 2007a ). ALEXI uses measurements of the morning LST rise, provided by
