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- Author or Editor: Ashley E. Van Beusekom x
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Abstract
Understanding near-surface atmospheric behavior in the tropics is imperative given the role of tropical energy fluxes in Earth’s climate cycles, but this area is complicated by a land–atmosphere interaction that includes rugged topography, seasonal weather drivers, and frequent environmental disturbances. This study examines variation in near-surface atmospheric behaviors in northeastern Puerto Rico using a synthesis of data from lowland and montane locations under different land covers (forest, urban, and rural) during 2008–21, when a severe drought, large hurricanes (Irma and Maria), and the COVID-19 mobility-reducing lockdown occurred. Ceilometer, weather, air quality, radiosonde, and satellite data were analyzed for annual patterns and monthly time series of data and data correlations. The results showed a system that is strongly dominated by easterly trade winds transmitting regional oceanic patterns over terrain. Environmental disturbances affected land–atmosphere interaction for short time periods after events. Events that reduce the land signature (reducing greenness: e.g., drought and hurricanes, or reducing land pollution: e.g., COVID-19 lockdown) were evidenced to strengthen the transmission of the oceanic pattern. The most variation in near-surface atmospheric behavior was seen in the mountainous areas that were influenced by both factors: trade winds, and terrain-induced orographic lifting. As an exception to the rest of the near-surface atmospheric behavior, pollutants other than ozone did not correlate positively or negatively with stronger trade winds at all sites across the region. Instead, these pollutants were hypothesized to be more anthropogenically influenced. Once COVID-19 lockdown had persisted for 3 months, urban pollution decreased and cloud base may have increased.
Abstract
Understanding near-surface atmospheric behavior in the tropics is imperative given the role of tropical energy fluxes in Earth’s climate cycles, but this area is complicated by a land–atmosphere interaction that includes rugged topography, seasonal weather drivers, and frequent environmental disturbances. This study examines variation in near-surface atmospheric behaviors in northeastern Puerto Rico using a synthesis of data from lowland and montane locations under different land covers (forest, urban, and rural) during 2008–21, when a severe drought, large hurricanes (Irma and Maria), and the COVID-19 mobility-reducing lockdown occurred. Ceilometer, weather, air quality, radiosonde, and satellite data were analyzed for annual patterns and monthly time series of data and data correlations. The results showed a system that is strongly dominated by easterly trade winds transmitting regional oceanic patterns over terrain. Environmental disturbances affected land–atmosphere interaction for short time periods after events. Events that reduce the land signature (reducing greenness: e.g., drought and hurricanes, or reducing land pollution: e.g., COVID-19 lockdown) were evidenced to strengthen the transmission of the oceanic pattern. The most variation in near-surface atmospheric behavior was seen in the mountainous areas that were influenced by both factors: trade winds, and terrain-induced orographic lifting. As an exception to the rest of the near-surface atmospheric behavior, pollutants other than ozone did not correlate positively or negatively with stronger trade winds at all sites across the region. Instead, these pollutants were hypothesized to be more anthropogenically influenced. Once COVID-19 lockdown had persisted for 3 months, urban pollution decreased and cloud base may have increased.
Abstract
As is true of many tropical regions, northeastern Puerto Rico is an ecologically sensitive area with biological life that is highly elevation dependent on precipitation and temperature. Climate change has the potential to increase the risk of losing endemic species and habitats. Consequently, it is important to explore the pattern of trends in precipitation and temperature along an elevation gradient. Statistical derivatives of a frequently sampled dataset of precipitation and temperature at 20 sites along an elevation gradient of 1000 m in northeastern Puerto Rico were examined for trends from 2001 to 2013 with nonparametric methods accounting for annual periodic variations such as yearly weather cycles. Overall daily precipitation had an increasing trend of around 0.1 mm day−1 yr−1. The driest months of the annual dry, early, and late rainfall seasons showed a small increasing trend in the precipitation (around 0.1 mm day−1 yr−1). There was strong evidence that precipitation in the driest months of each rainfall season increased faster at higher elevations (0.02 mm day−1 more increase for 100-m elevation gain) and some evidence for the same pattern in precipitation in all months of the year but at half the rate. Temperature had a positive trend in the daily minimum (around 0.02°C yr−1) and a negative trend in the daily maximum whose size is likely an order of magnitude larger than the size of the daily minimum trend. Physical mechanisms behind the trends may be related to climate change; longer-term studies will need to be undertaken in order to assess the future climatic trajectory of tropical forests.
Abstract
As is true of many tropical regions, northeastern Puerto Rico is an ecologically sensitive area with biological life that is highly elevation dependent on precipitation and temperature. Climate change has the potential to increase the risk of losing endemic species and habitats. Consequently, it is important to explore the pattern of trends in precipitation and temperature along an elevation gradient. Statistical derivatives of a frequently sampled dataset of precipitation and temperature at 20 sites along an elevation gradient of 1000 m in northeastern Puerto Rico were examined for trends from 2001 to 2013 with nonparametric methods accounting for annual periodic variations such as yearly weather cycles. Overall daily precipitation had an increasing trend of around 0.1 mm day−1 yr−1. The driest months of the annual dry, early, and late rainfall seasons showed a small increasing trend in the precipitation (around 0.1 mm day−1 yr−1). There was strong evidence that precipitation in the driest months of each rainfall season increased faster at higher elevations (0.02 mm day−1 more increase for 100-m elevation gain) and some evidence for the same pattern in precipitation in all months of the year but at half the rate. Temperature had a positive trend in the daily minimum (around 0.02°C yr−1) and a negative trend in the daily maximum whose size is likely an order of magnitude larger than the size of the daily minimum trend. Physical mechanisms behind the trends may be related to climate change; longer-term studies will need to be undertaken in order to assess the future climatic trajectory of tropical forests.
Abstract
Surface meteorological analyses are an essential input (termed “forcing”) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.
Abstract
Surface meteorological analyses are an essential input (termed “forcing”) for hydrologic modeling. This study investigated the sensitivity of different hydrologic model configurations to temporal variations of seven forcing variables (precipitation rate, air temperature, longwave radiation, specific humidity, shortwave radiation, wind speed, and air pressure). Specifically, the effects of temporally aggregating hourly forcings to hourly daily average forcings were examined. The analysis was based on 14 hydrological outputs from the Structure for Unifying Multiple Modeling Alternatives (SUMMA) model for the 671 Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) basins across the contiguous United States (CONUS). Results demonstrated that the hydrologic model sensitivity to temporally aggregating the forcing inputs varies across model output variables and model locations. We used Latin hypercube sampling to sample model parameters from eight combinations of three influential model physics choices (three model decisions with two options for each decision, i.e., eight model configurations). Results showed that the choice of model physics can change the relative influence of forcing on model outputs and the forcing importance may not be dependent on the parameter space. This allows for model output sensitivity to forcing aggregation to be tested prior to parameter calibration. More generally, this work provides a comprehensive analysis of the dependence of modeled outcomes on input forcing behavior, providing insight into the regional variability of forcing variable dominance on modeled outputs across CONUS.
