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dual-wet-season regime; 4) in East Africa, from dual season to single season or multiple seasons; 5) in southern Africa, from single season to dual or multiple seasons; and 6) in central Africa, from nonseasonal humid to a single- or dual-wet-season regime. Whether more systematic transitions between climatological seasonality regimes are expected under projected climate change remains to be studied. 4. Summary and conclusions This study developed a continental-scale seasonality classification for
dual-wet-season regime; 4) in East Africa, from dual season to single season or multiple seasons; 5) in southern Africa, from single season to dual or multiple seasons; and 6) in central Africa, from nonseasonal humid to a single- or dual-wet-season regime. Whether more systematic transitions between climatological seasonality regimes are expected under projected climate change remains to be studied. 4. Summary and conclusions This study developed a continental-scale seasonality classification for
). This regionalization is different from the latest Köppen–Geiger classification used in the climate world map ( Kottek et al. 2006 ), where Vietnam has only four climatic zones: equatorial monsoon (Am), equatorial savannah with dry winter (Aw), subtropical with constant precipitation (Cfa), and subtropical with dry winter (Cwa). Recently, Polo et al. (2015a) applied cluster analysis to the continental region of Vietnam for climatic regionalization using the spatial distribution of solar irradiance
). This regionalization is different from the latest Köppen–Geiger classification used in the climate world map ( Kottek et al. 2006 ), where Vietnam has only four climatic zones: equatorial monsoon (Am), equatorial savannah with dry winter (Aw), subtropical with constant precipitation (Cfa), and subtropical with dry winter (Cwa). Recently, Polo et al. (2015a) applied cluster analysis to the continental region of Vietnam for climatic regionalization using the spatial distribution of solar irradiance
1. Introduction The climate in China differs throughout its vast territory because of differences in latitude, elevation, wind direction, and distance to oceans. A good understanding of how the climate varies by region is of great importance in a wide variety of applications. They include not only simply identifying regions with similar climate variability but also forecasting seasonal climate and applying hydrological measures, such as drought evaluation. Many climate classification schemes
1. Introduction The climate in China differs throughout its vast territory because of differences in latitude, elevation, wind direction, and distance to oceans. A good understanding of how the climate varies by region is of great importance in a wide variety of applications. They include not only simply identifying regions with similar climate variability but also forecasting seasonal climate and applying hydrological measures, such as drought evaluation. Many climate classification schemes
-scale atmospheric behavior or vice versa. This is typically investigated by employing a classification scheme, which characterizes the wide variety of atmospheric conditions for a certain location over time into a manageable sample of representative weather types or patterns ( Davis and Kalkstein 1990 ; Yarnal 1993 ). Many different strategies exist for producing classifications, but one that has emerged recently in climate literature, which is also ideally suited for connecting weather patterns to discrete
-scale atmospheric behavior or vice versa. This is typically investigated by employing a classification scheme, which characterizes the wide variety of atmospheric conditions for a certain location over time into a manageable sample of representative weather types or patterns ( Davis and Kalkstein 1990 ; Yarnal 1993 ). Many different strategies exist for producing classifications, but one that has emerged recently in climate literature, which is also ideally suited for connecting weather patterns to discrete
were found to have the greatest effect on the overall SW radiation budget in the area: an optically thick midtopped cloud regime and a very frequent low cloud regime. A similar method based on mean cloud-top properties is frequently used for model evaluation (see Williams and Webb 2009 ). Historically, climate models have tended to overestimate frontal cloud and optically thick low cloud while underrepresenting optically thin low cloud (e.g., trade cumulus) and midtopped clouds ( Webb et al. 2001
were found to have the greatest effect on the overall SW radiation budget in the area: an optically thick midtopped cloud regime and a very frequent low cloud regime. A similar method based on mean cloud-top properties is frequently used for model evaluation (see Williams and Webb 2009 ). Historically, climate models have tended to overestimate frontal cloud and optically thick low cloud while underrepresenting optically thin low cloud (e.g., trade cumulus) and midtopped clouds ( Webb et al. 2001
, H. , 1995 : Misuses of statistical analysis in climate research. Analysis of Climate Variability: Applications of Statistical Techniques, H. von Storch and A. Navarra, Eds., Springer, 11–26 . Wang , X. L. , and V. R. Swail , 2001 : Changes of extreme wave heights in Northern Hemisphere oceans and related atmospheric circulation regimes. J. Climate , 14 , 2204 – 2220 . Wheaton , E. , and Coauthors , 2005 : Lessons learned from the Canadian drought years 2001 and 2002
, H. , 1995 : Misuses of statistical analysis in climate research. Analysis of Climate Variability: Applications of Statistical Techniques, H. von Storch and A. Navarra, Eds., Springer, 11–26 . Wang , X. L. , and V. R. Swail , 2001 : Changes of extreme wave heights in Northern Hemisphere oceans and related atmospheric circulation regimes. J. Climate , 14 , 2204 – 2220 . Wheaton , E. , and Coauthors , 2005 : Lessons learned from the Canadian drought years 2001 and 2002
of years in the period 1948–75 before abrupt change, n 2 is the number of years classified as after abrupt change, and N 2 is the number of years in the period 1976–2005 after the abrupt change. The last classification parameter, ε , characterizes the ability of phase space to represent the abrupt change, that is to say, the fit to the climate factors combined into the phase space with the abrupt change in the phase space. In this study, ε is divided into four classes: when ε < 70%, the
of years in the period 1948–75 before abrupt change, n 2 is the number of years classified as after abrupt change, and N 2 is the number of years in the period 1976–2005 after the abrupt change. The last classification parameter, ε , characterizes the ability of phase space to represent the abrupt change, that is to say, the fit to the climate factors combined into the phase space with the abrupt change in the phase space. In this study, ε is divided into four classes: when ε < 70%, the
1. Introduction As population increases ( U.S. Census Bureau 2013 ) and climate changes ( Solomon et al. 2007 ), water management and sustainability policymaking in the southeast (SE) United States will be increasingly dependent upon an improved understanding of the spatial and temporal distribution of regional precipitating systems ( Robinson 2006 ). Each year the southeastern United States receives precipitation from a variety of weather systems such as midlatitude cyclones ( Curtis 2006
1. Introduction As population increases ( U.S. Census Bureau 2013 ) and climate changes ( Solomon et al. 2007 ), water management and sustainability policymaking in the southeast (SE) United States will be increasingly dependent upon an improved understanding of the spatial and temporal distribution of regional precipitating systems ( Robinson 2006 ). Each year the southeastern United States receives precipitation from a variety of weather systems such as midlatitude cyclones ( Curtis 2006
binary decision tree ( Fig. 2 ) that related basic climate variables to key snow characteristics observed in the field ( Sturm et al. 1995 ). The classification applies to all non-ice-covered terrestrial regions on Earth where snow falls (see the color shades in Fig. 3 ). As we discuss in more detail later, the primary use of the classification system and mapping has been geographic; it has been used to divide and differentiate classes of snow over regional (e.g., Derksen et al. 2010 ; Martinez
binary decision tree ( Fig. 2 ) that related basic climate variables to key snow characteristics observed in the field ( Sturm et al. 1995 ). The classification applies to all non-ice-covered terrestrial regions on Earth where snow falls (see the color shades in Fig. 3 ). As we discuss in more detail later, the primary use of the classification system and mapping has been geographic; it has been used to divide and differentiate classes of snow over regional (e.g., Derksen et al. 2010 ; Martinez
1. Introduction Blocking and the associated disruption of the usual westerly flow is among the most high-impact atmospheric regimes in middle and high latitudes, and represents key challenges in numerical weather prediction, subseasonal-to-seasonal predictions and regional projections of climate change (e.g., Woollings et al. 2018 ). There is consensus on the importance of blocking, and its driving role in a wide diversity of extreme events, including summer heatwaves, winter cold spells, poor
1. Introduction Blocking and the associated disruption of the usual westerly flow is among the most high-impact atmospheric regimes in middle and high latitudes, and represents key challenges in numerical weather prediction, subseasonal-to-seasonal predictions and regional projections of climate change (e.g., Woollings et al. 2018 ). There is consensus on the importance of blocking, and its driving role in a wide diversity of extreme events, including summer heatwaves, winter cold spells, poor
