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ultimately begins with meteorological and/or agricultural drought ( Kiem et al. 2016 ). b. Quantifying drought Together with this lack of universal definition, many questions remain about how best to measure and quantify drought. Droughts are typically quantified by type and in terms of severity, duration, spatial distribution, frequency, magnitude, and predictability ( Zargar et al. 2011 ). The most prevalent method of drought quantification is the use of drought indices. Indices are practical, useful
ultimately begins with meteorological and/or agricultural drought ( Kiem et al. 2016 ). b. Quantifying drought Together with this lack of universal definition, many questions remain about how best to measure and quantify drought. Droughts are typically quantified by type and in terms of severity, duration, spatial distribution, frequency, magnitude, and predictability ( Zargar et al. 2011 ). The most prevalent method of drought quantification is the use of drought indices. Indices are practical, useful
way to monitor drought is to use drought indices. Two indices commonly used to monitor drought are the standardized precipitation index (SPI) and the Palmer drought severity index (PDSI). The SPI is based on precipitation ( P ) alone ( Hayes et al. 1999 ; McKee et al. 1993 , 1995 ). It measures the P deficits, but it does not take into account of water supply. The PDSI is based on the water balance between soil moisture supply and demand ( Palmer 1965 ). There are many shortcomings of the PDSI
way to monitor drought is to use drought indices. Two indices commonly used to monitor drought are the standardized precipitation index (SPI) and the Palmer drought severity index (PDSI). The SPI is based on precipitation ( P ) alone ( Hayes et al. 1999 ; McKee et al. 1993 , 1995 ). It measures the P deficits, but it does not take into account of water supply. The PDSI is based on the water balance between soil moisture supply and demand ( Palmer 1965 ). There are many shortcomings of the PDSI
1. Introduction Complexity in climate systems makes prediction difficult. One way to simplify the climate systems is to represent low-frequency variability of atmospheric circulations by teleconnection patterns, such as the Arctic Oscillation (AO), Antarctic Oscillation (AAO), North Atlantic Oscillation (NAO), Pacific–North American pattern (PNA), and Southern Oscillation (SO). Temporally varying indices, s ( t ), were calculated for these patterns, where t denotes time. Among them, the SO
1. Introduction Complexity in climate systems makes prediction difficult. One way to simplify the climate systems is to represent low-frequency variability of atmospheric circulations by teleconnection patterns, such as the Arctic Oscillation (AO), Antarctic Oscillation (AAO), North Atlantic Oscillation (NAO), Pacific–North American pattern (PNA), and Southern Oscillation (SO). Temporally varying indices, s ( t ), were calculated for these patterns, where t denotes time. Among them, the SO
to identify the most severe parts of a convective cloud system ( Setvák and Rabin 2005 ; Rosenfeld and Lensky 2006 ). The MSG infrared channel selection, however, makes it also possible to assess the air stability in preconvective, that is, still cloud-free, conditions. Air instability indices as single-valued numbers have a long history of evaluating the convective potential of the atmosphere. A comprehensive summary can be found in Peppler (1988) . They are usually used such that some
to identify the most severe parts of a convective cloud system ( Setvák and Rabin 2005 ; Rosenfeld and Lensky 2006 ). The MSG infrared channel selection, however, makes it also possible to assess the air stability in preconvective, that is, still cloud-free, conditions. Air instability indices as single-valued numbers have a long history of evaluating the convective potential of the atmosphere. A comprehensive summary can be found in Peppler (1988) . They are usually used such that some
human comfort indices) to assess the vulnerability of populations to heat stress (e.g., Höppe 1999 ; Spagnolo and de Dear 2003 ; Nicholls et al. 2008 ; Barnett et al. 2010 ). An essential requirement for normal body function is that the human body constantly regulates its internal temperature with the surrounding environment through several mechanisms of heat exchange. When the body reaches thermal equilibrium with the surrounding environment, thermal comfort occurs ( Kerslake 1972 ). According
human comfort indices) to assess the vulnerability of populations to heat stress (e.g., Höppe 1999 ; Spagnolo and de Dear 2003 ; Nicholls et al. 2008 ; Barnett et al. 2010 ). An essential requirement for normal body function is that the human body constantly regulates its internal temperature with the surrounding environment through several mechanisms of heat exchange. When the body reaches thermal equilibrium with the surrounding environment, thermal comfort occurs ( Kerslake 1972 ). According
1. Introduction An understanding of long-term change of extreme temperature events is of importance to the detection and attribution of climate change and to the assessment of climate change impacts on natural and human systems. It is unclear in the present, however, whether or to what extent the urbanization has affected the long-term trends of the extreme temperature indices series constructed based on the frequently used observational datasets on a subcontinental to global scale. Many
1. Introduction An understanding of long-term change of extreme temperature events is of importance to the detection and attribution of climate change and to the assessment of climate change impacts on natural and human systems. It is unclear in the present, however, whether or to what extent the urbanization has affected the long-term trends of the extreme temperature indices series constructed based on the frequently used observational datasets on a subcontinental to global scale. Many
superior to the skill scores of the Renick and Maxwell (1977) method when applied to the same dataset. Apart from these physical methods, purely statistical models were also developed. An example is the multivariate statistical approach given by López et al. (2007) , which developed a short-term hail occurrence forecast from sounding-derived indices, using a logistic-regression approach. Starting from 22 candidate indices, López et al. selected a set of seven variables, including instability (total
superior to the skill scores of the Renick and Maxwell (1977) method when applied to the same dataset. Apart from these physical methods, purely statistical models were also developed. An example is the multivariate statistical approach given by López et al. (2007) , which developed a short-term hail occurrence forecast from sounding-derived indices, using a logistic-regression approach. Starting from 22 candidate indices, López et al. selected a set of seven variables, including instability (total
frequency and large-scale climate conditions is an essential step in order to improve our predictive and explanatory understanding of TS variations. Multiple studies have associated tropical storm activity with different climate indices, such as Atlantic (e.g., Shapiro and Goldenberg 1998 ; Landsea et al. 1999 ; Vitart and Anderson 2001 ; Emanuel 2005 ; Jagger and Elsner 2006 ; Bell and Chelliah 2006 ; Hoyos et al. 2006 ; Saunders and Lea 2008 ) and tropical (e.g., Latif et al. 2007 ; Vecchi
frequency and large-scale climate conditions is an essential step in order to improve our predictive and explanatory understanding of TS variations. Multiple studies have associated tropical storm activity with different climate indices, such as Atlantic (e.g., Shapiro and Goldenberg 1998 ; Landsea et al. 1999 ; Vitart and Anderson 2001 ; Emanuel 2005 ; Jagger and Elsner 2006 ; Bell and Chelliah 2006 ; Hoyos et al. 2006 ; Saunders and Lea 2008 ) and tropical (e.g., Latif et al. 2007 ; Vecchi
potentially improve the projections of climate change effects on agricultural production is to calculate climate-based indices such as the annual frost days, or consecutive days without precipitation; measures that are strongly correlated with biomass production in agro-ecosystems ( Tollenaar and Hunter 1983 ; Muchow et al. 1990 ; Wilhelm et al. 1999 ). The three most common temperature-based agro-climate indices measure heat stress (heat stress index), cold stress (frost days or growing season length
potentially improve the projections of climate change effects on agricultural production is to calculate climate-based indices such as the annual frost days, or consecutive days without precipitation; measures that are strongly correlated with biomass production in agro-ecosystems ( Tollenaar and Hunter 1983 ; Muchow et al. 1990 ; Wilhelm et al. 1999 ). The three most common temperature-based agro-climate indices measure heat stress (heat stress index), cold stress (frost days or growing season length
suggest a link between dropouts and large-scale flow patterns, the results show strong case dependence. With these points in mind, this work seeks to build on the existing dropout literature by presenting an inter-model comparison of dropouts, and by further investigating the statistical links between dropouts and large-scale flow indices. The manuscript proceeds as follows: section 2 describes the data and methodology. In section 3 , we use a short-term climatology to characterize the seasonality
suggest a link between dropouts and large-scale flow patterns, the results show strong case dependence. With these points in mind, this work seeks to build on the existing dropout literature by presenting an inter-model comparison of dropouts, and by further investigating the statistical links between dropouts and large-scale flow indices. The manuscript proceeds as follows: section 2 describes the data and methodology. In section 3 , we use a short-term climatology to characterize the seasonality
