The response of drought and precipitation variability to regional climate forcing in northeast Puerto Rico

Northeast Puerto Rico is home to a wide range of terrestrial and aquatic climate-sensitive ecosystems. Climate disturbances such as extreme events (e.g. hurricanes) and drought have cascading impacts on the biota. The biota responds to changes in precipitation variability on daily and sub-daily time...

Full description

Bibliographic Details
Main Author: Ramseyer, Craig Allen
Format: Doctoral or Postdoctoral Thesis
Language:English
Published: uga 2016
Subjects:
Online Access:http://hdl.handle.net/10724/36317
http://purl.galileo.usg.edu/uga_etd/ramseyer_craig_a_201605_phd
id ftunivgeorgia:oai:athenaeum.libs.uga.edu:10724/36317
record_format openpolar
spelling ftunivgeorgia:oai:athenaeum.libs.uga.edu:10724/36317 2023-05-15T17:32:56+02:00 The response of drought and precipitation variability to regional climate forcing in northeast Puerto Rico Ramseyer, Craig Allen 2016-05 http://hdl.handle.net/10724/36317 http://purl.galileo.usg.edu/uga_etd/ramseyer_craig_a_201605_phd eng eng uga ramseyer_craig_a_201605_phd http://purl.galileo.usg.edu/uga_etd/ramseyer_craig_a_201605_phd http://hdl.handle.net/10724/36317 On Campus Only Until 2018-05-01 Tropical Climatology Climate Modeling Precipitation Variability Puerto Rico Climate Climate Change Artificial Neural Networks Dissertation 2016 ftunivgeorgia 2020-09-24T10:07:27Z Northeast Puerto Rico is home to a wide range of terrestrial and aquatic climate-sensitive ecosystems. Climate disturbances such as extreme events (e.g. hurricanes) and drought have cascading impacts on the biota. The biota responds to changes in precipitation variability on daily and sub-daily time scales. As a result, high temporal and spatial resolution climate data are needed to adequately assess climate impacts on the ecological process occurring in the region. This dissertation analyzes past, present, and future precipitation variability at a highly localized scale in northeast Puerto Rico. Additionally, a more comprehensive understanding of the regional climate forcing on precipitation variability is achieved. Artificial neural networks are used to downscale synoptic scale atmospheric variables to precipitation. These tools allow for modeling precipitation and determining the atmospheric processes driving precipitation variability This dissertation finds that precipitation throughout Puerto Rico is driven primarily by variability in specific humidity and wind shear in the low-troposphere. The driest daily precipitation in northeast Puerto Rico is observed in synoptic environments with high wind shear and low moisture at 700 hPa. Both of these atmospheric variables are driven by changes in the north Atlantic sea-surface temperature, the Saharan Air Layer, and the North Atlantic Subtropical High. The historical record shows little linear trend in total precipitation, however, precipitation variability is shown to be changing especially during the early rainfall season. It is posited that this increase in variability could be in part due to changes in the mechanisms driving the Caribbean Mid-Summer Drought. Future precipitation in northeast Puerto Rico is likely to be more variable with an overall drying trend. The highest magnitude changes are expected to occur in the early rainfall season as the trade wind inversion strengthens and wind shear across the region increases. These changes will cause disruptions to precipitation processes across several scales of motion, from tropical storm development to deep, moist convection. These trends in precipitation will likely cause significant impacts to the ecosystems of northeast Puerto Rico. PhD Geography Geography Thomas Mote Thomas Mote J. Marshall Shepherd Andrew Grundstein Douglas Gamble Alan Covich Doctoral or Postdoctoral Thesis North Atlantic University of Georgia: Athenaeum@UGA
institution Open Polar
collection University of Georgia: Athenaeum@UGA
op_collection_id ftunivgeorgia
language English
topic Tropical Climatology
Climate Modeling
Precipitation Variability
Puerto Rico Climate
Climate Change
Artificial Neural Networks
spellingShingle Tropical Climatology
Climate Modeling
Precipitation Variability
Puerto Rico Climate
Climate Change
Artificial Neural Networks
Ramseyer, Craig Allen
The response of drought and precipitation variability to regional climate forcing in northeast Puerto Rico
topic_facet Tropical Climatology
Climate Modeling
Precipitation Variability
Puerto Rico Climate
Climate Change
Artificial Neural Networks
description Northeast Puerto Rico is home to a wide range of terrestrial and aquatic climate-sensitive ecosystems. Climate disturbances such as extreme events (e.g. hurricanes) and drought have cascading impacts on the biota. The biota responds to changes in precipitation variability on daily and sub-daily time scales. As a result, high temporal and spatial resolution climate data are needed to adequately assess climate impacts on the ecological process occurring in the region. This dissertation analyzes past, present, and future precipitation variability at a highly localized scale in northeast Puerto Rico. Additionally, a more comprehensive understanding of the regional climate forcing on precipitation variability is achieved. Artificial neural networks are used to downscale synoptic scale atmospheric variables to precipitation. These tools allow for modeling precipitation and determining the atmospheric processes driving precipitation variability This dissertation finds that precipitation throughout Puerto Rico is driven primarily by variability in specific humidity and wind shear in the low-troposphere. The driest daily precipitation in northeast Puerto Rico is observed in synoptic environments with high wind shear and low moisture at 700 hPa. Both of these atmospheric variables are driven by changes in the north Atlantic sea-surface temperature, the Saharan Air Layer, and the North Atlantic Subtropical High. The historical record shows little linear trend in total precipitation, however, precipitation variability is shown to be changing especially during the early rainfall season. It is posited that this increase in variability could be in part due to changes in the mechanisms driving the Caribbean Mid-Summer Drought. Future precipitation in northeast Puerto Rico is likely to be more variable with an overall drying trend. The highest magnitude changes are expected to occur in the early rainfall season as the trade wind inversion strengthens and wind shear across the region increases. These changes will cause disruptions to precipitation processes across several scales of motion, from tropical storm development to deep, moist convection. These trends in precipitation will likely cause significant impacts to the ecosystems of northeast Puerto Rico. PhD Geography Geography Thomas Mote Thomas Mote J. Marshall Shepherd Andrew Grundstein Douglas Gamble Alan Covich
format Doctoral or Postdoctoral Thesis
author Ramseyer, Craig Allen
author_facet Ramseyer, Craig Allen
author_sort Ramseyer, Craig Allen
title The response of drought and precipitation variability to regional climate forcing in northeast Puerto Rico
title_short The response of drought and precipitation variability to regional climate forcing in northeast Puerto Rico
title_full The response of drought and precipitation variability to regional climate forcing in northeast Puerto Rico
title_fullStr The response of drought and precipitation variability to regional climate forcing in northeast Puerto Rico
title_full_unstemmed The response of drought and precipitation variability to regional climate forcing in northeast Puerto Rico
title_sort response of drought and precipitation variability to regional climate forcing in northeast puerto rico
publisher uga
publishDate 2016
url http://hdl.handle.net/10724/36317
http://purl.galileo.usg.edu/uga_etd/ramseyer_craig_a_201605_phd
genre North Atlantic
genre_facet North Atlantic
op_relation ramseyer_craig_a_201605_phd
http://purl.galileo.usg.edu/uga_etd/ramseyer_craig_a_201605_phd
http://hdl.handle.net/10724/36317
op_rights On Campus Only Until 2018-05-01
_version_ 1766131269909872640