Browse
Abstract
Classifying “urban” and “rural” environments is a challenge in understanding urban climate, specifically urban heat islands (UHIs). Stewart and Oke developed the “local climate zone” (LCZ) classification system to clarify these distinctions using 17 unique groups. This system has been applied to many areas around the world, but few studies have attempted to utilize them to detect UHI effects in smaller cities. Our aim was to use the LCZ classification system 1) to detect UHI in two small cities in Alabama and 2) to determine whether similar zones experienced similar intensity or magnitude of UHIs. For 1 week, we monitored hourly temperature in two cities, in four zones: compact low-rise, open low-rise, dense forests, and water. We found that urban zones were often warmer for overall, daytime, and nighttime temperatures relative to rural zones (from −0.1° to 2.8°C). In addition, we found that temperatures between cities in similar zones were not very similar, indicating that the LCZ system does not predict UHI intensity equally in places with similar background climates. We found that the LCZ classification system was easy to use, and we recognize its potential as a tool for urban ecologists and urban planners.
Abstract
Classifying “urban” and “rural” environments is a challenge in understanding urban climate, specifically urban heat islands (UHIs). Stewart and Oke developed the “local climate zone” (LCZ) classification system to clarify these distinctions using 17 unique groups. This system has been applied to many areas around the world, but few studies have attempted to utilize them to detect UHI effects in smaller cities. Our aim was to use the LCZ classification system 1) to detect UHI in two small cities in Alabama and 2) to determine whether similar zones experienced similar intensity or magnitude of UHIs. For 1 week, we monitored hourly temperature in two cities, in four zones: compact low-rise, open low-rise, dense forests, and water. We found that urban zones were often warmer for overall, daytime, and nighttime temperatures relative to rural zones (from −0.1° to 2.8°C). In addition, we found that temperatures between cities in similar zones were not very similar, indicating that the LCZ system does not predict UHI intensity equally in places with similar background climates. We found that the LCZ classification system was easy to use, and we recognize its potential as a tool for urban ecologists and urban planners.
Abstract
California’s winter storms produce intense rainfall capable of triggering shallow landslides, threatening lives and infrastructure. This study explores where hourly rainfall in the state meets or exceeds published values thought to trigger landslides after crossing a seasonal antecedent precipitation threshold. We answer the following questions: 1) Where in California are overthreshold events most common? 2) How are events distributed within the cool season (October–May) and interannually? 3) Are these events related to atmospheric rivers? To do this, we compile and quality control hourly precipitation data over a 22-yr period for 147 Remote Automated Weather Stations (RAWS). Stations in the Transverse and Coast Ranges and portions of the northwestern Sierra Nevada have the greatest number of rainfall events exceeding thresholds. Atmospheric rivers coincide with 60%–90% of these events. Overthreshold events tend to occur in the climatological wettest month of the year, and they commonly occur multiple times within a storm. These statewide maps depict where to expect intense rainfalls that have historically triggered shallow landslides. They predict that some areas of California are less susceptible to storm-driven landslides solely because high-intensity rainfall is unlikely.
Abstract
California’s winter storms produce intense rainfall capable of triggering shallow landslides, threatening lives and infrastructure. This study explores where hourly rainfall in the state meets or exceeds published values thought to trigger landslides after crossing a seasonal antecedent precipitation threshold. We answer the following questions: 1) Where in California are overthreshold events most common? 2) How are events distributed within the cool season (October–May) and interannually? 3) Are these events related to atmospheric rivers? To do this, we compile and quality control hourly precipitation data over a 22-yr period for 147 Remote Automated Weather Stations (RAWS). Stations in the Transverse and Coast Ranges and portions of the northwestern Sierra Nevada have the greatest number of rainfall events exceeding thresholds. Atmospheric rivers coincide with 60%–90% of these events. Overthreshold events tend to occur in the climatological wettest month of the year, and they commonly occur multiple times within a storm. These statewide maps depict where to expect intense rainfalls that have historically triggered shallow landslides. They predict that some areas of California are less susceptible to storm-driven landslides solely because high-intensity rainfall is unlikely.
Abstract
Better quantification of the spatiotemporal distribution of soil moisture across different spatial scales contributes significantly to the understanding of land surface processes on the Earth as an integrated system. While observational data for root-zone soil moisture (RZSM) often have sparse spatial coverage, model-simulated soil moisture may provide a useful alternative. TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) has been widely studied and actively modified in recent years, while a detailed regional application with evaluation currently is still lacking. Thus, TOPLATS was used to generate high-resolution (30 arc s) RZSM based on coarse-scale (0.125°) forcing data over part of the Arkansas–Red River basin. First, the simulated RZSM was resampled to coarse scale to compare with the results of Mosaic, Noah, and VIC from NLDAS. Second, TOPLATS performance was assessed based on the spatial absolute difference among the models. The comparison shows that TOPLATS performance is similar to VIC, but different from Mosaic and Noah. Last, the simulated RZSM was compared with in situ observations of 16 stations in the study area. The results suggest that the simulated spatial distribution of RZSM is largely consistent with the distribution of topographic index (TI) in most instances, as topography was traditionally considered a major, but not the only, factor in horizontal redistribution of soil moisture. In addition, the finer-resolution RZSM can reflect the in situ soil moisture change at most local sites to a certain degree. The evaluation confirms that TOPLATS is a useful tool to estimate high-resolution soil moisture and has great potential to provide regional soil moisture estimates.
Abstract
Better quantification of the spatiotemporal distribution of soil moisture across different spatial scales contributes significantly to the understanding of land surface processes on the Earth as an integrated system. While observational data for root-zone soil moisture (RZSM) often have sparse spatial coverage, model-simulated soil moisture may provide a useful alternative. TOPMODEL-Based Land Surface–Atmosphere Transfer Scheme (TOPLATS) has been widely studied and actively modified in recent years, while a detailed regional application with evaluation currently is still lacking. Thus, TOPLATS was used to generate high-resolution (30 arc s) RZSM based on coarse-scale (0.125°) forcing data over part of the Arkansas–Red River basin. First, the simulated RZSM was resampled to coarse scale to compare with the results of Mosaic, Noah, and VIC from NLDAS. Second, TOPLATS performance was assessed based on the spatial absolute difference among the models. The comparison shows that TOPLATS performance is similar to VIC, but different from Mosaic and Noah. Last, the simulated RZSM was compared with in situ observations of 16 stations in the study area. The results suggest that the simulated spatial distribution of RZSM is largely consistent with the distribution of topographic index (TI) in most instances, as topography was traditionally considered a major, but not the only, factor in horizontal redistribution of soil moisture. In addition, the finer-resolution RZSM can reflect the in situ soil moisture change at most local sites to a certain degree. The evaluation confirms that TOPLATS is a useful tool to estimate high-resolution soil moisture and has great potential to provide regional soil moisture estimates.
Abstract
The concentration of trace gases and aerosols in Ethiopia is poorly characterized due to a limited history of surface measurements. Here, satellite measurements and model estimates of atmospheric composition are employed to understand space–time distributions in the period 2000–16. Methane (CH4) and carbon monoxide (CO) display high concentrations over the highlands and provide a focus for analysis of monthly and daily data. CH4 emissions from livestock peak at the beginning of the dry season, while CO from biomass burning rises at the end of the dry season. The seasonal cycle of dust, aerosol optical depth (AOD), and CO2 is inversely related with CH4, while CO closely follows sensible heat flux, thus linking drying and rural biomass burning. Stable easterly flow in the dry season accumulates local emissions, so near-surface concentrations of CO and CH4 are high then. The weather pattern underlying an episode of high nitrogen dioxide (NO2) concentrations was studied. In addition to a stable lapse rate and dry anticyclonic weather, midtropospheric subsidence was related to intrusion of the northern subtropical jet stream on 24–26 December 2010. The wind shadow was cast by the Rift Escarpment limited dispersion, particularly with the dry, stable weather conditions. A key outcome of this work is that CH4 concentrations over Ethiopia are high in global context and have increased >0.1 ppm from 2002 to 2016; hence, there is a need to improve livestock management and production efficiency.
Abstract
The concentration of trace gases and aerosols in Ethiopia is poorly characterized due to a limited history of surface measurements. Here, satellite measurements and model estimates of atmospheric composition are employed to understand space–time distributions in the period 2000–16. Methane (CH4) and carbon monoxide (CO) display high concentrations over the highlands and provide a focus for analysis of monthly and daily data. CH4 emissions from livestock peak at the beginning of the dry season, while CO from biomass burning rises at the end of the dry season. The seasonal cycle of dust, aerosol optical depth (AOD), and CO2 is inversely related with CH4, while CO closely follows sensible heat flux, thus linking drying and rural biomass burning. Stable easterly flow in the dry season accumulates local emissions, so near-surface concentrations of CO and CH4 are high then. The weather pattern underlying an episode of high nitrogen dioxide (NO2) concentrations was studied. In addition to a stable lapse rate and dry anticyclonic weather, midtropospheric subsidence was related to intrusion of the northern subtropical jet stream on 24–26 December 2010. The wind shadow was cast by the Rift Escarpment limited dispersion, particularly with the dry, stable weather conditions. A key outcome of this work is that CH4 concentrations over Ethiopia are high in global context and have increased >0.1 ppm from 2002 to 2016; hence, there is a need to improve livestock management and production efficiency.
Abstract
Climate change is likely to alter the quantity and quality of urban stormwater, presenting a risk to water quality and public health. How might stormwater management practices need to change to address future climate? Answering requires understanding how management practices respond to climate forcing. Traditional “gray” stormwater design employs engineered structures, sized based on assumptions about future rainfall, which have limited flexibility once built. Green infrastructure (GI) uses vegetation, soil, and distributed structures to manage rainwater where it falls and may provide greater flexibility for adaptation. There is, however, uncertainty about how a warmer climate may affect performance of different types of GI. This study uses the hydrologic and biogeochemical watershed model, Regional Hydro-Ecologic Simulation System (RHESSys), to investigate sensitivity of different GI practices to climate. Simulations examine 36 urban “archetypes” representing different development patterns (at the city block scale) of typical U.S. cities, 11 regional climatic settings, and a range of mid-twenty-first-century scenarios based on downscaled climate model output. Results suggest regionally variable effects of climate change on the performance of GI practices for water quantity, water quality, and carbon sequestration. GI is able to mitigate most projected future increases in surface runoff, while bioretention can mitigate increased nitrogen yield at nine of 11 sites. Simulated changes in carbon balance are small, while local evaporative cooling can be substantial. Given uncertainty in the local expression of future climate, infrastructure design should emphasize flexibility and robustness to a range of future conditions.
Abstract
Climate change is likely to alter the quantity and quality of urban stormwater, presenting a risk to water quality and public health. How might stormwater management practices need to change to address future climate? Answering requires understanding how management practices respond to climate forcing. Traditional “gray” stormwater design employs engineered structures, sized based on assumptions about future rainfall, which have limited flexibility once built. Green infrastructure (GI) uses vegetation, soil, and distributed structures to manage rainwater where it falls and may provide greater flexibility for adaptation. There is, however, uncertainty about how a warmer climate may affect performance of different types of GI. This study uses the hydrologic and biogeochemical watershed model, Regional Hydro-Ecologic Simulation System (RHESSys), to investigate sensitivity of different GI practices to climate. Simulations examine 36 urban “archetypes” representing different development patterns (at the city block scale) of typical U.S. cities, 11 regional climatic settings, and a range of mid-twenty-first-century scenarios based on downscaled climate model output. Results suggest regionally variable effects of climate change on the performance of GI practices for water quantity, water quality, and carbon sequestration. GI is able to mitigate most projected future increases in surface runoff, while bioretention can mitigate increased nitrogen yield at nine of 11 sites. Simulated changes in carbon balance are small, while local evaporative cooling can be substantial. Given uncertainty in the local expression of future climate, infrastructure design should emphasize flexibility and robustness to a range of future conditions.
Abstract
The accuracy of statistically downscaled (SD) general circulation model (GCM) simulations of monthly surface climate for historical conditions (1950–2005) was assessed for the conterminous United States (CONUS). The SD monthly precipitation (PPT) and temperature (TAVE) from 95 GCMs from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) were used as inputs to a monthly water balance model (MWBM). Distributions of MWBM input (PPT and TAVE) and output [runoff (RUN)] variables derived from gridded station data (GSD) and historical SD climate were compared using the Kolmogorov–Smirnov (KS) test For all three variables considered, the KS test results showed that variables simulated using CMIP5 generally are more reliable than those derived from CMIP3, likely due to improvements in PPT simulations. At most locations across the CONUS, the largest differences between GSD and SD PPT and RUN occurred in the lowest part of the distributions (i.e., low-flow RUN and low-magnitude PPT). Results indicate that for the majority of the CONUS, there are downscaled GCMs that can reliably simulate historical climatic conditions. But, in some geographic locations, none of the SD GCMs replicated historical conditions for two of the three variables (PPT and RUN) based on the KS test, with a significance level of 0.05. In these locations, improved GCM simulations of PPT are needed to more reliably estimate components of the hydrologic cycle. Simple metrics and statistical tests, such as those described here, can provide an initial set of criteria to help simplify GCM selection.
Abstract
The accuracy of statistically downscaled (SD) general circulation model (GCM) simulations of monthly surface climate for historical conditions (1950–2005) was assessed for the conterminous United States (CONUS). The SD monthly precipitation (PPT) and temperature (TAVE) from 95 GCMs from phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) were used as inputs to a monthly water balance model (MWBM). Distributions of MWBM input (PPT and TAVE) and output [runoff (RUN)] variables derived from gridded station data (GSD) and historical SD climate were compared using the Kolmogorov–Smirnov (KS) test For all three variables considered, the KS test results showed that variables simulated using CMIP5 generally are more reliable than those derived from CMIP3, likely due to improvements in PPT simulations. At most locations across the CONUS, the largest differences between GSD and SD PPT and RUN occurred in the lowest part of the distributions (i.e., low-flow RUN and low-magnitude PPT). Results indicate that for the majority of the CONUS, there are downscaled GCMs that can reliably simulate historical climatic conditions. But, in some geographic locations, none of the SD GCMs replicated historical conditions for two of the three variables (PPT and RUN) based on the KS test, with a significance level of 0.05. In these locations, improved GCM simulations of PPT are needed to more reliably estimate components of the hydrologic cycle. Simple metrics and statistical tests, such as those described here, can provide an initial set of criteria to help simplify GCM selection.
Abstract
Because of Earth’s motion, periodic temperature fluctuations occur in soil on daily and yearly time scales, which inevitably lead to pulsation of the air pressure in the soil. The Earth–air pressure monitoring technique called “hou-qi” is the basis of a calendar formulated by the ancient Chinese. However, the daily/yearly variation of air pressure in the soil is very weak, according to practical monitoring experiments, so hou-qi has long been considered a pseudoscience. To determine the potential maximum change of Earth–air pressure, identify what is causing the underlying Earth–air pressure variation, and reveal the mechanism, we use a closed system, which is a more appropriate system test of the possible validity of hou-qi, to monitor Earth–air pressure variation in this paper. The results show that there are potential air pressure fluctuations of about 120–190 hPa in closed soil over the daily/yearly temperature range of 5°–40°C. This provides ample magnitude for hou-qi. The largest contribution made to Earth–air pressure variation was pressurization due to air warming, with vapor due to water evaporation and desorbed gas having smaller effects. The soil’s water content has a significant effect on Earth–air pressure amplitude. Dry soil contributes almost no water vapor, but the adsorbed gases from dry soil have up to a 38-hPa influence on air pressure. The soil’s salt content also has an important regulating role on Earth–air pressure and can reduce the influence of vapor pressure. This paper provides a scientific basis for the ancient Earth–air monitoring system of hou-qi and also has important significance for hydrometeorology and research on the ancient Chinese civilization.
Abstract
Because of Earth’s motion, periodic temperature fluctuations occur in soil on daily and yearly time scales, which inevitably lead to pulsation of the air pressure in the soil. The Earth–air pressure monitoring technique called “hou-qi” is the basis of a calendar formulated by the ancient Chinese. However, the daily/yearly variation of air pressure in the soil is very weak, according to practical monitoring experiments, so hou-qi has long been considered a pseudoscience. To determine the potential maximum change of Earth–air pressure, identify what is causing the underlying Earth–air pressure variation, and reveal the mechanism, we use a closed system, which is a more appropriate system test of the possible validity of hou-qi, to monitor Earth–air pressure variation in this paper. The results show that there are potential air pressure fluctuations of about 120–190 hPa in closed soil over the daily/yearly temperature range of 5°–40°C. This provides ample magnitude for hou-qi. The largest contribution made to Earth–air pressure variation was pressurization due to air warming, with vapor due to water evaporation and desorbed gas having smaller effects. The soil’s water content has a significant effect on Earth–air pressure amplitude. Dry soil contributes almost no water vapor, but the adsorbed gases from dry soil have up to a 38-hPa influence on air pressure. The soil’s salt content also has an important regulating role on Earth–air pressure and can reduce the influence of vapor pressure. This paper provides a scientific basis for the ancient Earth–air monitoring system of hou-qi and also has important significance for hydrometeorology and research on the ancient Chinese civilization.
Abstract
Climate warming exhibits asymmetric patterns over a diel time, with the trend of nighttime warming exceeding that of daytime warming, a phenomenon commonly known as asymmetric warming. Recently, increasing studies have documented the significant instantaneous impacts of asymmetric warming on terrestrial vegetation growth, but the indirect effects of asymmetric warming carrying over vegetation growth (referred to here as time-lag effects) remain unknown. Here, we quantitatively studied the time-lag effects (within 1 year) of asymmetric warming on global plant biomes by using terrestrial vegetation net primary production (NPP) derived by the Carnegie–Ames–Stanford Approach (CASA) model and accumulated daytime and nighttime temperature (ATmax and ATmin) from 1982 to 2013. Partial correlation and time-lag analyses were conducted at a monthly scale to obtain the partial correlation coefficients between NPP and ATmax/ATmin and the lagged durations of NPP responses to ATmax/ATmin. The results showed that (i) asymmetric warming has nonuniform time-lag effects on single plant biomes, and distinguishing correlations exist in different vegetation biomes’ associations to asymmetric warming; (ii) terrestrial biomes respond to ATmax (4.63 ± 3.92 months) with a shorter protracted duration than to ATmin (6.06 ± 4.27 months); (iii) forest biomes exhibit longer prolonged duration in responding to asymmetric warming than nonforest biomes do; (iv) mosses and lichens (Mosses), evergreen needleleaf forests (ENF), deciduous needleleaf forests (DNF), and mixed forests (MF) tend to positively correlate with ATmax, whereas the other biomes associate with ATmax with near-equal splits of positive and negative correlation; and (v) ATmin has a predominantly positive influence on terrestrial biomes, except for Mosses and DNF. This study provides a new perspective on terrestrial ecosystem responses to asymmetric warming and highlights the importance of including such nonuniform time-lag effects into currently used terrestrial ecosystem models during future investigations of vegetation–climate interactions.
Abstract
Climate warming exhibits asymmetric patterns over a diel time, with the trend of nighttime warming exceeding that of daytime warming, a phenomenon commonly known as asymmetric warming. Recently, increasing studies have documented the significant instantaneous impacts of asymmetric warming on terrestrial vegetation growth, but the indirect effects of asymmetric warming carrying over vegetation growth (referred to here as time-lag effects) remain unknown. Here, we quantitatively studied the time-lag effects (within 1 year) of asymmetric warming on global plant biomes by using terrestrial vegetation net primary production (NPP) derived by the Carnegie–Ames–Stanford Approach (CASA) model and accumulated daytime and nighttime temperature (ATmax and ATmin) from 1982 to 2013. Partial correlation and time-lag analyses were conducted at a monthly scale to obtain the partial correlation coefficients between NPP and ATmax/ATmin and the lagged durations of NPP responses to ATmax/ATmin. The results showed that (i) asymmetric warming has nonuniform time-lag effects on single plant biomes, and distinguishing correlations exist in different vegetation biomes’ associations to asymmetric warming; (ii) terrestrial biomes respond to ATmax (4.63 ± 3.92 months) with a shorter protracted duration than to ATmin (6.06 ± 4.27 months); (iii) forest biomes exhibit longer prolonged duration in responding to asymmetric warming than nonforest biomes do; (iv) mosses and lichens (Mosses), evergreen needleleaf forests (ENF), deciduous needleleaf forests (DNF), and mixed forests (MF) tend to positively correlate with ATmax, whereas the other biomes associate with ATmax with near-equal splits of positive and negative correlation; and (v) ATmin has a predominantly positive influence on terrestrial biomes, except for Mosses and DNF. This study provides a new perspective on terrestrial ecosystem responses to asymmetric warming and highlights the importance of including such nonuniform time-lag effects into currently used terrestrial ecosystem models during future investigations of vegetation–climate interactions.
Abstract
While coupling carbon and nitrogen processes is critical for Earth system models to accurately predict future climate and land biogeochemistry feedbacks, it has not yet been analyzed how numerical methods that land biogeochemical models apply to couple soil mineral nitrogen mobilizing and immobilizing processes affect predicted ecosystem carbon and nitrogen cycling. These effects were investigated here by using the E3SM land model and an evaluation of three plausible and widely used numerical couplings: 1) the mineral nitrogen–based limitation scheme, 2) the net uptake–based limitation scheme, and 3) the proportional nitrogen flux–based limitation scheme. It was found that these three schemes resulted in large differences (with a range of 316 PgC) in predicted cumulative land–atmosphere carbon exchanges under the RCP4.5 atmospheric CO2 simulations. This large uncertainty is without accounting for the different representations of the many land biogeochemical processes, but is about 73% of the range (434 PgC) reported for CMIP5 RCP4.5 simulations. These results help explain the large uncertainty found in various model intercomparison experiments and suggest that more robust numerical implementations are needed to improve carbon–nutrient cycle coupling in terrestrial ecosystem models.
Abstract
While coupling carbon and nitrogen processes is critical for Earth system models to accurately predict future climate and land biogeochemistry feedbacks, it has not yet been analyzed how numerical methods that land biogeochemical models apply to couple soil mineral nitrogen mobilizing and immobilizing processes affect predicted ecosystem carbon and nitrogen cycling. These effects were investigated here by using the E3SM land model and an evaluation of three plausible and widely used numerical couplings: 1) the mineral nitrogen–based limitation scheme, 2) the net uptake–based limitation scheme, and 3) the proportional nitrogen flux–based limitation scheme. It was found that these three schemes resulted in large differences (with a range of 316 PgC) in predicted cumulative land–atmosphere carbon exchanges under the RCP4.5 atmospheric CO2 simulations. This large uncertainty is without accounting for the different representations of the many land biogeochemical processes, but is about 73% of the range (434 PgC) reported for CMIP5 RCP4.5 simulations. These results help explain the large uncertainty found in various model intercomparison experiments and suggest that more robust numerical implementations are needed to improve carbon–nutrient cycle coupling in terrestrial ecosystem models.
