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1. Introduction Higher levels of atmospheric carbon dioxide and associated elevated global temperatures have led to an acceleration of the global hydrologic cycle ( Held and Soden 2006 ) that has resulted in changes in the occurrence of both extreme climate events and interannual variability in precipitation ( O’Gorman 2012 ; Allan and Soden 2008 ; Sun et al. 2012 ; Polade et al. 2014 ; Portmann et al. 2009 ). Accompanying these climatic shifts are observed changes in the intensity of
1. Introduction Higher levels of atmospheric carbon dioxide and associated elevated global temperatures have led to an acceleration of the global hydrologic cycle ( Held and Soden 2006 ) that has resulted in changes in the occurrence of both extreme climate events and interannual variability in precipitation ( O’Gorman 2012 ; Allan and Soden 2008 ; Sun et al. 2012 ; Polade et al. 2014 ; Portmann et al. 2009 ). Accompanying these climatic shifts are observed changes in the intensity of
, the mechanisms driving the climatological Hadley cell and its interannual variability are not necessarily the same. For instance, it would be possible for the variability of a thermally forced Hadley cell to be driven by the eddy momentum fluxes if these fluxes were much more variable than the tropical heating. Using reanalysis data, C07 studied the impact of eddy forcing on the Hadley cell during boreal winter, finding significant driving of the northern (southern) Hadley cell variability by
, the mechanisms driving the climatological Hadley cell and its interannual variability are not necessarily the same. For instance, it would be possible for the variability of a thermally forced Hadley cell to be driven by the eddy momentum fluxes if these fluxes were much more variable than the tropical heating. Using reanalysis data, C07 studied the impact of eddy forcing on the Hadley cell during boreal winter, finding significant driving of the northern (southern) Hadley cell variability by
Dirmeyer 2010 ; Zeng et al. 2010 ; Zhang et al. 2011 ). Previous studies mainly focused on the spatial distribution of land–atmosphere coupling and identified the “hot spot” regions with strong soil moisture–precipitation interaction. They found that these regions are mainly located in the transition zones with intermediate soil wetness between dry and wet climate regimes. However, less attention has been paid to the interannual variability of land–atmosphere coupling. In fact some studies do imply
Dirmeyer 2010 ; Zeng et al. 2010 ; Zhang et al. 2011 ). Previous studies mainly focused on the spatial distribution of land–atmosphere coupling and identified the “hot spot” regions with strong soil moisture–precipitation interaction. They found that these regions are mainly located in the transition zones with intermediate soil wetness between dry and wet climate regimes. However, less attention has been paid to the interannual variability of land–atmosphere coupling. In fact some studies do imply
1. Introduction Identifying the nature and patterns of the interannual variability of precipitation can be crucial because these fluctuations exert a long-term control on water resources, affect plant growth and the biogeochemical cycle, and modulate extreme events, such as floods and prolonged dry periods. For instance, several studies suggested that the variability of annual precipitation can be important for the temporal dynamics of aboveground primary production and thus for global
1. Introduction Identifying the nature and patterns of the interannual variability of precipitation can be crucial because these fluctuations exert a long-term control on water resources, affect plant growth and the biogeochemical cycle, and modulate extreme events, such as floods and prolonged dry periods. For instance, several studies suggested that the variability of annual precipitation can be important for the temporal dynamics of aboveground primary production and thus for global
summer is found. The CIO mode and related processes have been diagnosed in Zhou et al. (2017) at intraseasonal time scales. As can be expected of this multiscale system, the CIO mode also has distinct features at seasonal–interannual time scales. Such low-frequency variability of the CIO mode and the driving mechanism are analyzed in this study. Decadal and multidecadal time scales and trends under global climate change will be diagnosed in a separate study especially in the context of the negative
summer is found. The CIO mode and related processes have been diagnosed in Zhou et al. (2017) at intraseasonal time scales. As can be expected of this multiscale system, the CIO mode also has distinct features at seasonal–interannual time scales. Such low-frequency variability of the CIO mode and the driving mechanism are analyzed in this study. Decadal and multidecadal time scales and trends under global climate change will be diagnosed in a separate study especially in the context of the negative
1. Introduction Water resources of the western United States depend upon winter snowpack as a natural reservoir and are sensitive to an array of atmospheric drivers ( McCabe and Dettinger 2002 ; Clark 2010 ). Large interannual variability in winter precipitation across the western United States, where the majority of precipitation falls during the winter months, coupled with increasing water demand make the region susceptible to water scarcity ( Wilhite et al. 2007 ). Widespread observations
1. Introduction Water resources of the western United States depend upon winter snowpack as a natural reservoir and are sensitive to an array of atmospheric drivers ( McCabe and Dettinger 2002 ; Clark 2010 ). Large interannual variability in winter precipitation across the western United States, where the majority of precipitation falls during the winter months, coupled with increasing water demand make the region susceptible to water scarcity ( Wilhite et al. 2007 ). Widespread observations
1. Introduction The dependence of agriculture, drinking water, and energy production on the Indian summer monsoon (ISM) rainfall makes it the lifeline for a large fraction of the world’s population. The economy, life, and property in the region are vulnerable to significant variability of the ISM on intraseasonal, interannual, and interdecadal time scales ( Webster et al. 1998 ; Krishnamurthy and Goswami 2000 ; Goswami et al. 2006b ). Hence, predicting the seasonal mean ISM rainfall is of
1. Introduction The dependence of agriculture, drinking water, and energy production on the Indian summer monsoon (ISM) rainfall makes it the lifeline for a large fraction of the world’s population. The economy, life, and property in the region are vulnerable to significant variability of the ISM on intraseasonal, interannual, and interdecadal time scales ( Webster et al. 1998 ; Krishnamurthy and Goswami 2000 ; Goswami et al. 2006b ). Hence, predicting the seasonal mean ISM rainfall is of
rainfall over South China ( Xin et al. 2006 ). In contrast to sufficient studies of interdecadal variability, less effort has been devoted to the interannual variability of tropospheric temperature over East Asia. There have been many studies on the interannual variations of tropical tropospheric temperature (TT) associated with ENSO. As a dynamical response to equatorial diabatic heating, the warm phase of ENSO is characterized by an overall warming of tropical TT superimposed upon a distinctive
rainfall over South China ( Xin et al. 2006 ). In contrast to sufficient studies of interdecadal variability, less effort has been devoted to the interannual variability of tropospheric temperature over East Asia. There have been many studies on the interannual variations of tropical tropospheric temperature (TT) associated with ENSO. As a dynamical response to equatorial diabatic heating, the warm phase of ENSO is characterized by an overall warming of tropical TT superimposed upon a distinctive
surface heat flux to the tropical Indian Ocean ( Vranes et al. 2002 ). The ITF net volume transport exhibits interannual variability according to the El Niño/La Niña cycles [El Niño–Southern Oscillation (ENSO); Meyers 1996 ; England and Huang 2005 ]. Generally an El Niño event is followed by a weak ITF volume transport. The negative anomalies of the ITF volume transport lag El Niño by approximately 8 months. During El Niño years, the anomalous westerlies in the central equatorial Pacific and the
surface heat flux to the tropical Indian Ocean ( Vranes et al. 2002 ). The ITF net volume transport exhibits interannual variability according to the El Niño/La Niña cycles [El Niño–Southern Oscillation (ENSO); Meyers 1996 ; England and Huang 2005 ]. Generally an El Niño event is followed by a weak ITF volume transport. The negative anomalies of the ITF volume transport lag El Niño by approximately 8 months. During El Niño years, the anomalous westerlies in the central equatorial Pacific and the
identified as a critical factor for the formation and variability of the Indian summer monsoon (e.g., Wu et al. 2007 ). Conversely, some studies also demonstrated that the Indian summer monsoon can affect rainfall over the TP on a wide range of time scales (e.g., Yao et al. 2013 ; Zhou et al. 2015 ). Thus, the TP and the Indian summer monsoon should be considered as an interactive system. Indeed, Jiang and Ting (2017) found that interannual variability of the July–August rainfall anomaly across the
identified as a critical factor for the formation and variability of the Indian summer monsoon (e.g., Wu et al. 2007 ). Conversely, some studies also demonstrated that the Indian summer monsoon can affect rainfall over the TP on a wide range of time scales (e.g., Yao et al. 2013 ; Zhou et al. 2015 ). Thus, the TP and the Indian summer monsoon should be considered as an interactive system. Indeed, Jiang and Ting (2017) found that interannual variability of the July–August rainfall anomaly across the
