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seasonal wind stress forcing. The seasonal p B variability is examined in this paper by using GRACE gravimetric data and ocean-state estimate product ECCOv4 (release 3; Forget et al. 2015 ; Fukumori et al. 2017 ). Topographic effects are investigated by using a two-layer wind-driven model ( Yang 2015 ; Yang and Chen 2021 ). The remainder of this paper is arranged as follows: GRACE, ECCOv4 data, and the linear two-layer model are introduced in section 2 , followed by analyses of seasonal p B
seasonal wind stress forcing. The seasonal p B variability is examined in this paper by using GRACE gravimetric data and ocean-state estimate product ECCOv4 (release 3; Forget et al. 2015 ; Fukumori et al. 2017 ). Topographic effects are investigated by using a two-layer wind-driven model ( Yang 2015 ; Yang and Chen 2021 ). The remainder of this paper is arranged as follows: GRACE, ECCOv4 data, and the linear two-layer model are introduced in section 2 , followed by analyses of seasonal p B
correlation that suggests immunity acts in parallel with transmission. Ambient winter weather effects on respiratory infectiousness were mapped to show that east coasts may be at lower risk. The summer months exhibit fewer I-P deaths than winter by a factor of 3–10. Significant negative correlations between multiple climate elements and I-P mortality provide historical inferences to anticipate seasonal transitions. Multiple factors influence the spread of I-P in communities and include climatic
correlation that suggests immunity acts in parallel with transmission. Ambient winter weather effects on respiratory infectiousness were mapped to show that east coasts may be at lower risk. The summer months exhibit fewer I-P deaths than winter by a factor of 3–10. Significant negative correlations between multiple climate elements and I-P mortality provide historical inferences to anticipate seasonal transitions. Multiple factors influence the spread of I-P in communities and include climatic
1. Introduction Existing research has identified factors such as solar radiation and cloud cover as influential for diurnal temperature range (DTR) over the global land surface, but how comprehensively do they account for the observed seasonal and geographic variation? This study describes the seasonal and geographic variations in DTR using empirical regression relationships with a selection of key meteorological and surface parameters. The aim is to quantify the relationships they have with
1. Introduction Existing research has identified factors such as solar radiation and cloud cover as influential for diurnal temperature range (DTR) over the global land surface, but how comprehensively do they account for the observed seasonal and geographic variation? This study describes the seasonal and geographic variations in DTR using empirical regression relationships with a selection of key meteorological and surface parameters. The aim is to quantify the relationships they have with
2000s drought was significantly greater in magnitude and extent than during the 1950s drought. Warmer temperatures during the 2000s drought, coupled with low precipitation, seemingly drove higher vegetation water stress, increased susceptibility to insect infestations, and more plant mortality than during the relatively cooler and drier 1950s drought. d. Seasonality and effects of warmer temperatures during drought in the Southwest Although it is not clear if anthropogenic climate change has
2000s drought was significantly greater in magnitude and extent than during the 1950s drought. Warmer temperatures during the 2000s drought, coupled with low precipitation, seemingly drove higher vegetation water stress, increased susceptibility to insect infestations, and more plant mortality than during the relatively cooler and drier 1950s drought. d. Seasonality and effects of warmer temperatures during drought in the Southwest Although it is not clear if anthropogenic climate change has
Sivakumar (2003) also discuss the formation mechanism and seasonal cycle of the BL in the northern Indian Ocean. They showed that the build up of the BL during summertime becomes most prominent by February in the following year, reaching a maximum thickness of 50 m. The boreal winter is the period when the hydrological forcing generates its largest freshening effects through the river discharge and local rainfall. The BLT subsequently diminishes from February to a minimum in May before onset of the
Sivakumar (2003) also discuss the formation mechanism and seasonal cycle of the BL in the northern Indian Ocean. They showed that the build up of the BL during summertime becomes most prominent by February in the following year, reaching a maximum thickness of 50 m. The boreal winter is the period when the hydrological forcing generates its largest freshening effects through the river discharge and local rainfall. The BLT subsequently diminishes from February to a minimum in May before onset of the
simulations of local temperature, evaporation, and precipitation compared to simulations that neglect the lake effects. For example, the presence of the Great Lakes results in a phase shift in the annual cycles of latent and sensible heat fluxes, increases of the local evaporation and precipitation during the autumn and winter, and alters the meridional air temperature gradient ( Lofgren 1997 ; Bates et al. 1993 ; Hostetler et al. 1993 ; Bonan 1995 ). While most atmosphere–lake studies have focused on
simulations of local temperature, evaporation, and precipitation compared to simulations that neglect the lake effects. For example, the presence of the Great Lakes results in a phase shift in the annual cycles of latent and sensible heat fluxes, increases of the local evaporation and precipitation during the autumn and winter, and alters the meridional air temperature gradient ( Lofgren 1997 ; Bates et al. 1993 ; Hostetler et al. 1993 ; Bonan 1995 ). While most atmosphere–lake studies have focused on
explain the seasonal synchronization of ENSO events in terms of the effects of the eastern equatorial Pacific annual cycle on the stability of the equatorial Pacific coupled ocean–atmosphere system. The annual cycle of the eastern tropical Pacific can be well characterized by the seasonal movement of the intertropical convergence zone (ITCZ), which resides north of the equator and stretches across the Pacific basin. This is because the large-scale atmospheric motion of the tropical Pacific corresponds
explain the seasonal synchronization of ENSO events in terms of the effects of the eastern equatorial Pacific annual cycle on the stability of the equatorial Pacific coupled ocean–atmosphere system. The annual cycle of the eastern tropical Pacific can be well characterized by the seasonal movement of the intertropical convergence zone (ITCZ), which resides north of the equator and stretches across the Pacific basin. This is because the large-scale atmospheric motion of the tropical Pacific corresponds
global and seasonal assessment of VBP effects on the water cycle. The assessment was conducted at the global scale (i.e., is not limited to one or two regions) in recognition of the strong interconnections between regional climates, and covers all seasons (i.e., is not limited to the summer). The impact at long temporal scales, however, is not addressed in this study. VBPs include (but are not limited to) radiative transfer in the canopy, moisture exchange between soil layers and extraction by roots
global and seasonal assessment of VBP effects on the water cycle. The assessment was conducted at the global scale (i.e., is not limited to one or two regions) in recognition of the strong interconnections between regional climates, and covers all seasons (i.e., is not limited to the summer). The impact at long temporal scales, however, is not addressed in this study. VBPs include (but are not limited to) radiative transfer in the canopy, moisture exchange between soil layers and extraction by roots
disturbance term. I also estimate the equation that includes seasonal dummies, where winter is defined as December to February, spring as March to May, summer as June to August, and fall as September to November. However, the dummies are not significant at all, and the estimates and their significance of other variables are almost unchanged, so that I do not mention these results in later sections. 11 I estimate each equation with three models: ordinary least squares (OLS), a fixed effects model, and a
disturbance term. I also estimate the equation that includes seasonal dummies, where winter is defined as December to February, spring as March to May, summer as June to August, and fall as September to November. However, the dummies are not significant at all, and the estimates and their significance of other variables are almost unchanged, so that I do not mention these results in later sections. 11 I estimate each equation with three models: ordinary least squares (OLS), a fixed effects model, and a
focus in particular on seasonal variation in urban climate effects. The analysis will proceed in three parts: 1) spatial patterns and processes over time, 2) daily variation in UHI intensity, and 3) seasonal patterns and drivers of UHI intensity. 2. Methods a. Study area Madison is a city of 233 000 in the north-central United States (43°N, 89°W) with an estimated 2012 urban agglomeration population of 407 000 ( Demographia 2013 ). It has a humid-continental climate (Köppen classification: Dfa
focus in particular on seasonal variation in urban climate effects. The analysis will proceed in three parts: 1) spatial patterns and processes over time, 2) daily variation in UHI intensity, and 3) seasonal patterns and drivers of UHI intensity. 2. Methods a. Study area Madison is a city of 233 000 in the north-central United States (43°N, 89°W) with an estimated 2012 urban agglomeration population of 407 000 ( Demographia 2013 ). It has a humid-continental climate (Köppen classification: Dfa
