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Abstract
The mosquito virus vector Aedes (Ae.) aegypti exploits a wide range of containers as sites for egg laying and development of the immature life stages, yet the approaches for modeling meteorologically sensitive container water dynamics have been limited. This study introduces the Water Height and Temperature in Container Habitats Energy Model (WHATCH’EM), a state-of-the-science, physically based energy balance model of water height and temperature in containers that may serve as development sites for mosquitoes. The authors employ WHATCH’EM to model container water dynamics in three cities along a climatic gradient in México ranging from sea level, where Ae. aegypti is highly abundant, to ~2100 m, where Ae. aegypti is rarely found. When compared with measurements from a 1-month field experiment in two of these cities during summer 2013, WHATCH’EM realistically simulates the daily mean and range of water temperature for a variety of containers. To examine container dynamics for an entire season, WHATCH’EM is also driven with field-derived meteorological data from May to September 2011 and evaluated for three commonly encountered container types. WHATCH’EM simulates the highly nonlinear manner in which air temperature, humidity, rainfall, clouds, and container characteristics (shape, size, and color) determine water temperature and height. Sunlight exposure, modulated by clouds and shading from nearby objects, plays a first-order role. In general, simulated water temperatures are higher for containers that are larger, darker, and receive more sunlight. WHATCH’EM simulations will be helpful in understanding the limiting meteorological and container-related factors for proliferation of Ae. aegypti and may be useful for informing weather-driven early warning systems for viruses transmitted by Ae. aegypti.
Abstract
The mosquito virus vector Aedes (Ae.) aegypti exploits a wide range of containers as sites for egg laying and development of the immature life stages, yet the approaches for modeling meteorologically sensitive container water dynamics have been limited. This study introduces the Water Height and Temperature in Container Habitats Energy Model (WHATCH’EM), a state-of-the-science, physically based energy balance model of water height and temperature in containers that may serve as development sites for mosquitoes. The authors employ WHATCH’EM to model container water dynamics in three cities along a climatic gradient in México ranging from sea level, where Ae. aegypti is highly abundant, to ~2100 m, where Ae. aegypti is rarely found. When compared with measurements from a 1-month field experiment in two of these cities during summer 2013, WHATCH’EM realistically simulates the daily mean and range of water temperature for a variety of containers. To examine container dynamics for an entire season, WHATCH’EM is also driven with field-derived meteorological data from May to September 2011 and evaluated for three commonly encountered container types. WHATCH’EM simulates the highly nonlinear manner in which air temperature, humidity, rainfall, clouds, and container characteristics (shape, size, and color) determine water temperature and height. Sunlight exposure, modulated by clouds and shading from nearby objects, plays a first-order role. In general, simulated water temperatures are higher for containers that are larger, darker, and receive more sunlight. WHATCH’EM simulations will be helpful in understanding the limiting meteorological and container-related factors for proliferation of Ae. aegypti and may be useful for informing weather-driven early warning systems for viruses transmitted by Ae. aegypti.
Abstract
The vast forests and natural areas of the Pacific Northwest compose one of the most productive ecosystems in the Northern Hemisphere. The heterogeneous landscape of Oregon poses a particular challenge to ecosystem models. This study presents a framework using a scaling factor Bayesian inversion to improve the modeled atmosphere–biosphere exchange of CO2. Observations from five CO/CO2 towers, eddy covariance towers, and airborne campaigns were used to constrain the Community Land Model, version 4.5 (CLM4.5), simulated terrestrial CO2 exchange at a high spatial and temporal resolution (1/24°; 3 hourly). To balance aggregation errors and the degrees of freedom in the inverse modeling system, the authors applied an unsupervised clustering approach for the spatial structuring of the model domain. Data from flight campaigns were used to quantify the uncertainty introduced by the Lagrangian particle dispersion model that was applied for the inversions. The average annual statewide net ecosystem productivity (NEP) was increased by 32% to 29.7 TgC yr−1 by assimilating the tropospheric mixing ratio data. The associated uncertainty was decreased by 28.4%–29% on average over the entire Oregon model domain with the lowest uncertainties of 11% in western Oregon. The largest differences between posterior and prior CO2 fluxes were found for the Coast Range ecoregion of Oregon that also exhibits the highest availability of atmospheric observations and associated footprints. In this area, covered by highly productive Douglas fir forest, the differences between the prior and posterior estimate of NEP averaged 3.84 TgC yr−1 during the study period from 2012 through 2014.
Abstract
The vast forests and natural areas of the Pacific Northwest compose one of the most productive ecosystems in the Northern Hemisphere. The heterogeneous landscape of Oregon poses a particular challenge to ecosystem models. This study presents a framework using a scaling factor Bayesian inversion to improve the modeled atmosphere–biosphere exchange of CO2. Observations from five CO/CO2 towers, eddy covariance towers, and airborne campaigns were used to constrain the Community Land Model, version 4.5 (CLM4.5), simulated terrestrial CO2 exchange at a high spatial and temporal resolution (1/24°; 3 hourly). To balance aggregation errors and the degrees of freedom in the inverse modeling system, the authors applied an unsupervised clustering approach for the spatial structuring of the model domain. Data from flight campaigns were used to quantify the uncertainty introduced by the Lagrangian particle dispersion model that was applied for the inversions. The average annual statewide net ecosystem productivity (NEP) was increased by 32% to 29.7 TgC yr−1 by assimilating the tropospheric mixing ratio data. The associated uncertainty was decreased by 28.4%–29% on average over the entire Oregon model domain with the lowest uncertainties of 11% in western Oregon. The largest differences between posterior and prior CO2 fluxes were found for the Coast Range ecoregion of Oregon that also exhibits the highest availability of atmospheric observations and associated footprints. In this area, covered by highly productive Douglas fir forest, the differences between the prior and posterior estimate of NEP averaged 3.84 TgC yr−1 during the study period from 2012 through 2014.
Abstract
This study analyzes a convective outbreak over the Red Sea on 25 August 2009 that generated easterly waves over the Sahel, floods in Ouagadougou, and a hurricane in the east Atlantic. The convective outbreak occurred on the equatorward flank of the African easterly jet 18°–22°N and associated meridional heating gradients over the Arabian Peninsula. The Rift Valley mountains induced a vertical orographic undulation and cyclonic perturbation. Two thunderstorm clusters over the southern Red Sea received moist inflow from the Ethiopian highlands and northern Red Sea. This group of three easterly waves intensified downstream over the Sahel. One of the convective triggers was enhancement of the Arabian Ridge by the northern subtropical jet. Statistical analyses indicate that African easterly waves and subsequent tropical storms are more influenced by upstream kinematic shear than thermodynamic energy. The work offers new insights on the formation of easterly waves over the northern Rift Valley.
Abstract
This study analyzes a convective outbreak over the Red Sea on 25 August 2009 that generated easterly waves over the Sahel, floods in Ouagadougou, and a hurricane in the east Atlantic. The convective outbreak occurred on the equatorward flank of the African easterly jet 18°–22°N and associated meridional heating gradients over the Arabian Peninsula. The Rift Valley mountains induced a vertical orographic undulation and cyclonic perturbation. Two thunderstorm clusters over the southern Red Sea received moist inflow from the Ethiopian highlands and northern Red Sea. This group of three easterly waves intensified downstream over the Sahel. One of the convective triggers was enhancement of the Arabian Ridge by the northern subtropical jet. Statistical analyses indicate that African easterly waves and subsequent tropical storms are more influenced by upstream kinematic shear than thermodynamic energy. The work offers new insights on the formation of easterly waves over the northern Rift Valley.
Abstract
Land-use and land-cover change (LULCC) due to urban expansion alter the surface albedo, heat capacity, and thermal conductivity of the surface. Consequently, the energy balance in urban regions is different from that of natural surfaces. To evaluate the changes in regional climate that could arise because of projected urbanization in the Phoenix–Tucson corridor, Arizona, this study applied the coupled WRF Model–Noah–Urban Canopy Model (UCM; which includes a detailed urban radiation scheme) to this region. Land-cover changes were represented using land-cover data for 2005 and projections to 2050, and historical North American Regional Reanalysis (NARR) data were used to specify the lateral boundary conditions. Results suggest that temperature changes will be well defined, reflecting the urban heat island (UHI) effect within areas experiencing LULCC. Changes in precipitation are less robust but seem to indicate reductions in precipitation over the mountainous regions northeast of Phoenix and decreased evening precipitation over the newly urbanized area.
Abstract
Land-use and land-cover change (LULCC) due to urban expansion alter the surface albedo, heat capacity, and thermal conductivity of the surface. Consequently, the energy balance in urban regions is different from that of natural surfaces. To evaluate the changes in regional climate that could arise because of projected urbanization in the Phoenix–Tucson corridor, Arizona, this study applied the coupled WRF Model–Noah–Urban Canopy Model (UCM; which includes a detailed urban radiation scheme) to this region. Land-cover changes were represented using land-cover data for 2005 and projections to 2050, and historical North American Regional Reanalysis (NARR) data were used to specify the lateral boundary conditions. Results suggest that temperature changes will be well defined, reflecting the urban heat island (UHI) effect within areas experiencing LULCC. Changes in precipitation are less robust but seem to indicate reductions in precipitation over the mountainous regions northeast of Phoenix and decreased evening precipitation over the newly urbanized area.
Abstract
Forest degradation is the long-term and gradual reduction of canopy cover due to forest fire and unsustainable logging. A critical consequence of this process is increased atmospheric carbon emissions. Although this issue is gaining attention, forest degradation in the Brazilian Amazon has not yet been properly addressed. The claim here is that this process is not constant throughout Amazonia and varies according to colonization frontiers. Moreover, the accurate characterization of degradation requires lengthy observation periods to track gradual forest changes. The forest degradation process, the associated timeframe, spatial patterns, trajectories, and extent were characterized in the context of the Amazon frontiers of the 1990s using 28 years (1984–2011) of annual Landsat images. Given the large database and the characteristic of logging and burning, this study used data mining techniques and cell approach classification to analyze the spatial patterns and to construct associated trajectories. Multitemporal analysis indicated that forest degradation in the last two decades has caused as many interannual landscape changes as have clear-cuts. In addition, selective logging, as a major aspect of forest degradation, affected a larger amount of forest land than did forest fire. Although a large proportion of logged forest was deforested in the following years, selective logging did not always precede complete deforestation. Instead, the results indicate that logged forests were abandoned for approximately 4 years before clearance. Throughout the forest degradation process, there were no recurrent forest fires, and loggers did not revisit the forest. Forest degradation mostly occurred as a result of a single selective logging event and was associated with low-intensity forest damage.
Abstract
Forest degradation is the long-term and gradual reduction of canopy cover due to forest fire and unsustainable logging. A critical consequence of this process is increased atmospheric carbon emissions. Although this issue is gaining attention, forest degradation in the Brazilian Amazon has not yet been properly addressed. The claim here is that this process is not constant throughout Amazonia and varies according to colonization frontiers. Moreover, the accurate characterization of degradation requires lengthy observation periods to track gradual forest changes. The forest degradation process, the associated timeframe, spatial patterns, trajectories, and extent were characterized in the context of the Amazon frontiers of the 1990s using 28 years (1984–2011) of annual Landsat images. Given the large database and the characteristic of logging and burning, this study used data mining techniques and cell approach classification to analyze the spatial patterns and to construct associated trajectories. Multitemporal analysis indicated that forest degradation in the last two decades has caused as many interannual landscape changes as have clear-cuts. In addition, selective logging, as a major aspect of forest degradation, affected a larger amount of forest land than did forest fire. Although a large proportion of logged forest was deforested in the following years, selective logging did not always precede complete deforestation. Instead, the results indicate that logged forests were abandoned for approximately 4 years before clearance. Throughout the forest degradation process, there were no recurrent forest fires, and loggers did not revisit the forest. Forest degradation mostly occurred as a result of a single selective logging event and was associated with low-intensity forest damage.
Abstract
This paper explores the link between the anomalous warming hole in the southeastern United States and a major land-use/land-cover (LULC) change in the region. Land surface and satellite observations were analyzed to estimate the net radiative forcing due to LULC change. Albedo and latent energy were specifically addressed for the dominant LULC change of agriculture to forests. It was assumed that in the energy-limited environment of the region, the partition of changes in available energy due to albedo will mostly impact the sensible heat. The results show that in the southeastern United States, for the period of 1920 to 1992, the changes in sensible (as a result of albedo) and latent energies are in direct competition with each other. In the spring and early summer months, the croplands are in peak production and the latent energy associated with their evapotranspiration (ET) is comparable to that of the forests so the decrease in radiation due to albedo dominates the signal. However, during the late summer and fall months, most major crops have matured, thus reducing their transpiration rate while forests (particularly evergreens) maintain their foliage and with their deep roots are able to continue to transpire as long as atmospheric conditions are favorable. This later influence of latent energy appears to more than offset the increased radiative forcing from the spring and early summer. Overall, a mean annual net radiative forcing resulting from a LULC change from cropland to forests was estimated to be −1.06 W m−2 and thus a probable contribution to the “warming hole” over the Southeast during the majority of the twentieth century.
Abstract
This paper explores the link between the anomalous warming hole in the southeastern United States and a major land-use/land-cover (LULC) change in the region. Land surface and satellite observations were analyzed to estimate the net radiative forcing due to LULC change. Albedo and latent energy were specifically addressed for the dominant LULC change of agriculture to forests. It was assumed that in the energy-limited environment of the region, the partition of changes in available energy due to albedo will mostly impact the sensible heat. The results show that in the southeastern United States, for the period of 1920 to 1992, the changes in sensible (as a result of albedo) and latent energies are in direct competition with each other. In the spring and early summer months, the croplands are in peak production and the latent energy associated with their evapotranspiration (ET) is comparable to that of the forests so the decrease in radiation due to albedo dominates the signal. However, during the late summer and fall months, most major crops have matured, thus reducing their transpiration rate while forests (particularly evergreens) maintain their foliage and with their deep roots are able to continue to transpire as long as atmospheric conditions are favorable. This later influence of latent energy appears to more than offset the increased radiative forcing from the spring and early summer. Overall, a mean annual net radiative forcing resulting from a LULC change from cropland to forests was estimated to be −1.06 W m−2 and thus a probable contribution to the “warming hole” over the Southeast during the majority of the twentieth century.
Abstract
Understanding the interactions between large-scale atmospheric and oceanic circulation patterns and changes in land cover and land use (LCLU) due to urbanization is a relevant subject in many coastal climates. Recent studies by Lebassi et al. found that the average maximum air temperatures during the summer in two populated California coastal areas decreased at low-elevation areas open to marine air penetration during the period of 1970–2005. This coastal cooling was attributed to an increase in sea-breeze activity.
The aims of this work are to better understand the coastal flow patterns and sea–land thermal gradient by improving the land-cover classification scheme in the region using updated airborne remote sensing data and to assess the suitability of the updated regional atmospheric modeling system for representing maritime flows in this region. This study uses high-resolution airborne data from the NASA Hyperspectral Infrared Imager (HyspIRI) mission preparatory flight campaign over Southern California and surface ground stations to compare observations against model estimations.
Five new urban land classes were created using broadband albedo derived from the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) sensor and then assimilated into the Weather Research and Forecasting (WRF) Model. The updated model captures the diurnal spatial and temporal sea-breeze patterns in the region. Results show notable improvements of simulated daytime surface temperature and coastal winds using the HyspIRI-derived products in the model against the default land classification, reaffirming the importance of accounting for heterogeneity of urban surface properties.
Abstract
Understanding the interactions between large-scale atmospheric and oceanic circulation patterns and changes in land cover and land use (LCLU) due to urbanization is a relevant subject in many coastal climates. Recent studies by Lebassi et al. found that the average maximum air temperatures during the summer in two populated California coastal areas decreased at low-elevation areas open to marine air penetration during the period of 1970–2005. This coastal cooling was attributed to an increase in sea-breeze activity.
The aims of this work are to better understand the coastal flow patterns and sea–land thermal gradient by improving the land-cover classification scheme in the region using updated airborne remote sensing data and to assess the suitability of the updated regional atmospheric modeling system for representing maritime flows in this region. This study uses high-resolution airborne data from the NASA Hyperspectral Infrared Imager (HyspIRI) mission preparatory flight campaign over Southern California and surface ground stations to compare observations against model estimations.
Five new urban land classes were created using broadband albedo derived from the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) sensor and then assimilated into the Weather Research and Forecasting (WRF) Model. The updated model captures the diurnal spatial and temporal sea-breeze patterns in the region. Results show notable improvements of simulated daytime surface temperature and coastal winds using the HyspIRI-derived products in the model against the default land classification, reaffirming the importance of accounting for heterogeneity of urban surface properties.
Abstract
The presence of low-lying stratocumulus clouds and fog has been known to modify biophysical and ecological properties in coastal California where forests are frequently shaded by low-lying clouds or immersed in fog during otherwise warm and dry summer months. Summer fog and stratus can ameliorate summer drought stress and enhance soil water budgets and often have different spatial and temporal patterns. Here, this study uses remote sensing datasets to characterize the spatial and temporal patterns of cloud cover over California’s northern Channel Islands. The authors found marine stratus to be persistent from May to September across the years 2001–12. Stratus clouds were both most frequent and had the greatest spatial extent in July. Clouds typically formed in the evening and dissipated by the following early afternoon. This study presents a novel method to downscale satellite imagery using atmospheric observations and discriminate patterns of fog from those of stratus and help explain patterns of fog deposition previously studied on the islands. The outcomes of this study contribute significantly to the ability to quantify the occurrence of coastal fog at biologically meaningful spatial and temporal scales that can improve the understanding of cloud–ecosystem interactions, species distributions, and coastal ecohydrology.
Abstract
The presence of low-lying stratocumulus clouds and fog has been known to modify biophysical and ecological properties in coastal California where forests are frequently shaded by low-lying clouds or immersed in fog during otherwise warm and dry summer months. Summer fog and stratus can ameliorate summer drought stress and enhance soil water budgets and often have different spatial and temporal patterns. Here, this study uses remote sensing datasets to characterize the spatial and temporal patterns of cloud cover over California’s northern Channel Islands. The authors found marine stratus to be persistent from May to September across the years 2001–12. Stratus clouds were both most frequent and had the greatest spatial extent in July. Clouds typically formed in the evening and dissipated by the following early afternoon. This study presents a novel method to downscale satellite imagery using atmospheric observations and discriminate patterns of fog from those of stratus and help explain patterns of fog deposition previously studied on the islands. The outcomes of this study contribute significantly to the ability to quantify the occurrence of coastal fog at biologically meaningful spatial and temporal scales that can improve the understanding of cloud–ecosystem interactions, species distributions, and coastal ecohydrology.
Abstract
Simulations of future climate change impacts on water resources are subject to multiple and cascading uncertainties associated with different modeling and methodological choices. A key facet of this uncertainty is the coarse spatial resolution of GCM output compared to the finer-resolution information needed by water managers. To address this issue, it is now common practice to apply spatial downscaling techniques, using either higher-resolution regional climate models or statistical approaches applied to GCM output, to develop finer-resolution information. Downscaling, however, can also introduce its own uncertainties into water resources’ impact assessments. This study uses watershed simulations in five U.S. basins to quantify the sources of variability in streamflow, nitrogen, phosphorus, and sediment loads associated with the underlying GCM compared to the choice of downscaling method (both statistically and dynamically downscaled GCM output). This study also assesses the specific, incremental effects of downscaling by comparing watershed simulations based on downscaled and nondownscaled GCM model output. Results show that the underlying GCM and the downscaling method each contribute to the variability of simulated watershed responses. The relative contribution of GCM and downscaling method to the variability of simulated responses varies by watershed and season of the year. Results illustrate the potential implications of one key methodological choice in conducting climate change impact assessments for water—the selection of downscaled climate change information.
Abstract
Simulations of future climate change impacts on water resources are subject to multiple and cascading uncertainties associated with different modeling and methodological choices. A key facet of this uncertainty is the coarse spatial resolution of GCM output compared to the finer-resolution information needed by water managers. To address this issue, it is now common practice to apply spatial downscaling techniques, using either higher-resolution regional climate models or statistical approaches applied to GCM output, to develop finer-resolution information. Downscaling, however, can also introduce its own uncertainties into water resources’ impact assessments. This study uses watershed simulations in five U.S. basins to quantify the sources of variability in streamflow, nitrogen, phosphorus, and sediment loads associated with the underlying GCM compared to the choice of downscaling method (both statistically and dynamically downscaled GCM output). This study also assesses the specific, incremental effects of downscaling by comparing watershed simulations based on downscaled and nondownscaled GCM model output. Results show that the underlying GCM and the downscaling method each contribute to the variability of simulated watershed responses. The relative contribution of GCM and downscaling method to the variability of simulated responses varies by watershed and season of the year. Results illustrate the potential implications of one key methodological choice in conducting climate change impact assessments for water—the selection of downscaled climate change information.
