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Abstract
The selection of statistical methods to evaluate data depends on study questions and characteristics of available data. In climate science, some methods are more popularly used than others; however, the use of applicable alternative methods does not invalidate study findings. Regardless of limitations, some methods like Pearson ordinary correlation are widely used in all sciences including climate and by scientists at government agencies like NOAA and the USGS. In addition, the use of the robust Student’s t test is valid for near-Gaussian distributions with high sample numbers, since it is resistant to data distribution inconsistencies. We wish to put in context the citation about our article and clarify the methods and justification for using them and to educate readers about the use of some conventional statistical tools and tests.
Abstract
The selection of statistical methods to evaluate data depends on study questions and characteristics of available data. In climate science, some methods are more popularly used than others; however, the use of applicable alternative methods does not invalidate study findings. Regardless of limitations, some methods like Pearson ordinary correlation are widely used in all sciences including climate and by scientists at government agencies like NOAA and the USGS. In addition, the use of the robust Student’s t test is valid for near-Gaussian distributions with high sample numbers, since it is resistant to data distribution inconsistencies. We wish to put in context the citation about our article and clarify the methods and justification for using them and to educate readers about the use of some conventional statistical tools and tests.
Abstract
The latitudinal position of the Northern Hemisphere jet stream (NHJ) modulates the occurrence and frequency of extreme weather events. Precipitation anomalies in particular are associated with NHJ variability; the resulting floods and droughts can have considerable societal and economic impacts. This study develops a new climatology of the 300-hPa NHJ using a bottom-up approach based on seasonally explicit latitudinal NHJ positions. Four seasons with coherent NHJ patterns were identified (January–February, April–May, July–August, and October–November), along with 32 longitudinal sectors where the seasonal NHJ shows strong spatial coherence. These 32 longitudinal sectors were then used as NHJ position indices to examine the influence of seasonal NHJ position on the geographical distribution of NH precipitation and temperature variability and their link to atmospheric circulation pattern. The analyses show that the NHJ indices are related to broad-scale patterns in temperature and precipitation variability, in terrestrial vegetation productivity and spring phenology, and can be used as diagnostic/prognostic tools to link ecosystem and socioeconomic dynamics to upper-level atmospheric patterns.
Abstract
The latitudinal position of the Northern Hemisphere jet stream (NHJ) modulates the occurrence and frequency of extreme weather events. Precipitation anomalies in particular are associated with NHJ variability; the resulting floods and droughts can have considerable societal and economic impacts. This study develops a new climatology of the 300-hPa NHJ using a bottom-up approach based on seasonally explicit latitudinal NHJ positions. Four seasons with coherent NHJ patterns were identified (January–February, April–May, July–August, and October–November), along with 32 longitudinal sectors where the seasonal NHJ shows strong spatial coherence. These 32 longitudinal sectors were then used as NHJ position indices to examine the influence of seasonal NHJ position on the geographical distribution of NH precipitation and temperature variability and their link to atmospheric circulation pattern. The analyses show that the NHJ indices are related to broad-scale patterns in temperature and precipitation variability, in terrestrial vegetation productivity and spring phenology, and can be used as diagnostic/prognostic tools to link ecosystem and socioeconomic dynamics to upper-level atmospheric patterns.
Abstract
Atmospheric mineral aerosols include multiple, interrelated processes and feedbacks within the context of land–atmosphere interactions and thus are poorly understood. As the largest dust source in the world, North Africa supplies mineral dust aerosols each year to the Caribbean region and southeastern United States that alter cloud processes, ocean productivity, soil development, and the radiation budget. This study uses a suite of Earth Observation and ground-based analyses to reveal a potential novel effect of atmospheric aerosols on Pinus elliottii var. densa cambial growth during the 2010 CE growing season from the Florida Keys. Over the Florida Keys region, the Earth Observation products captured increased aerosol optical thickness with a clear geographical connection to mineral dust aerosols transported from northern Africa. The MODIS Terra and Aqua products corroborated increased Ozone Monitoring Instrument (OMI) aerosol optical thickness values. Anomalously high Aerosol Robotic Network aerosol optical depth data corresponding with low Ångstrom coefficients confirm the presence of transported mineral dust aerosols during the period circa 4–20 July 2010. The fraction of photosynthetically absorbed radiation over the region during July 2010 experienced an anomalous decrease, concurrent with reduced incoming total and direct solar radiation resulting in a reduced growth response in P. elliottii. The authors pose one of the primary mechanisms responsible for triggering growth anomalies in P. elliottii is the reduction of total photosynthetically active radiation due to a dust-derived increase in aerosol optical depth. As a rare long-lived conifer (300+ years) in a subtropical location, P. elliottii could represent a novel proxy with which to reconstruct annual or seasonal mineral dust aerosol fluxes over the Caribbean region.
Abstract
Atmospheric mineral aerosols include multiple, interrelated processes and feedbacks within the context of land–atmosphere interactions and thus are poorly understood. As the largest dust source in the world, North Africa supplies mineral dust aerosols each year to the Caribbean region and southeastern United States that alter cloud processes, ocean productivity, soil development, and the radiation budget. This study uses a suite of Earth Observation and ground-based analyses to reveal a potential novel effect of atmospheric aerosols on Pinus elliottii var. densa cambial growth during the 2010 CE growing season from the Florida Keys. Over the Florida Keys region, the Earth Observation products captured increased aerosol optical thickness with a clear geographical connection to mineral dust aerosols transported from northern Africa. The MODIS Terra and Aqua products corroborated increased Ozone Monitoring Instrument (OMI) aerosol optical thickness values. Anomalously high Aerosol Robotic Network aerosol optical depth data corresponding with low Ångstrom coefficients confirm the presence of transported mineral dust aerosols during the period circa 4–20 July 2010. The fraction of photosynthetically absorbed radiation over the region during July 2010 experienced an anomalous decrease, concurrent with reduced incoming total and direct solar radiation resulting in a reduced growth response in P. elliottii. The authors pose one of the primary mechanisms responsible for triggering growth anomalies in P. elliottii is the reduction of total photosynthetically active radiation due to a dust-derived increase in aerosol optical depth. As a rare long-lived conifer (300+ years) in a subtropical location, P. elliottii could represent a novel proxy with which to reconstruct annual or seasonal mineral dust aerosol fluxes over the Caribbean region.
Abstract
The relationship between rainfall characteristics and urbanization over the eastern United States was examined by analyzing four datasets: daily rainfall in 4593 surface stations over the last 50 years (1958–2008), a high-resolution gridded rainfall product, reanalysis wind data, and a proxy for urban land use (gridded human population data). Results indicate that summer monthly rainfall amounts show an increasing trend in urbanized regions. The frequency of heavy rainfall events has a potential positive bias toward urbanized regions. Most notably, consistent with case studies for individual cities, the climatology of rainfall amounts downwind of urban–rural boundaries shows a significant increasing trend. Analysis of heavy (90th percentile) and extreme (99.5th percentile) rainfall events indicated decreasing trends of heavy rainfall events and a possible increasing trend for extreme rainfall event frequency over urban areas. Results indicate that the urbanization impact was more pronounced in the northeastern and midwestern United States with an increase in rainfall amounts. In contrast, the southeastern United States showed a slight decrease in rainfall amounts and heavy rainfall event frequencies. Results suggest that the urbanization signature is becoming detectable in rainfall climatology as an anthropogenic influence affecting regional precipitation; however, extracting this signature is not straightforward and requires eliminating other dynamical confounding feedbacks.
Abstract
The relationship between rainfall characteristics and urbanization over the eastern United States was examined by analyzing four datasets: daily rainfall in 4593 surface stations over the last 50 years (1958–2008), a high-resolution gridded rainfall product, reanalysis wind data, and a proxy for urban land use (gridded human population data). Results indicate that summer monthly rainfall amounts show an increasing trend in urbanized regions. The frequency of heavy rainfall events has a potential positive bias toward urbanized regions. Most notably, consistent with case studies for individual cities, the climatology of rainfall amounts downwind of urban–rural boundaries shows a significant increasing trend. Analysis of heavy (90th percentile) and extreme (99.5th percentile) rainfall events indicated decreasing trends of heavy rainfall events and a possible increasing trend for extreme rainfall event frequency over urban areas. Results indicate that the urbanization impact was more pronounced in the northeastern and midwestern United States with an increase in rainfall amounts. In contrast, the southeastern United States showed a slight decrease in rainfall amounts and heavy rainfall event frequencies. Results suggest that the urbanization signature is becoming detectable in rainfall climatology as an anthropogenic influence affecting regional precipitation; however, extracting this signature is not straightforward and requires eliminating other dynamical confounding feedbacks.
Abstract
A new, high-resolution (4 km), gridded land surface dataset produced with the Land Information System (LIS) is introduced, and the first set of synthesis of key hydroclimatic variables is reported. The dataset is produced over a 33-yr time period (1980–2012) for the U.S. Midwest with the intent to aid the agricultural community in understanding hydroclimatic impacts on crop production and decision-making in operational practices. While approximately 20 hydroclimatic variables are available through the LIS dataset, the focus here is on soil water content, soil temperature, and evapotranspiration. To assess the performance of the model, the LIS dataset is compared with in situ hydrometeorological observations across the study domain and with coarse-resolution reanalysis products [NARR, MERRA, and NLDAS-2 (phase 2 of the North American Land Data Assimilation System)]. In agricultural regions such as the U.S. Midwest, finescale hydroclimatic mapping that links the regional scale to the field scale is necessary. The new dataset provides this link as an intermediate-scale product that links point observations and coarse gridded datasets. In general, the LIS dataset compares well with in situ observations and coarser gridded products in terms of both temporal and spatial patterns, but cases of strong disagreement exist particularly in areas with sandy soils. The dataset is made available to the broader research community as an effort to fill the gap in spatial hydroclimatic data availability.
Abstract
A new, high-resolution (4 km), gridded land surface dataset produced with the Land Information System (LIS) is introduced, and the first set of synthesis of key hydroclimatic variables is reported. The dataset is produced over a 33-yr time period (1980–2012) for the U.S. Midwest with the intent to aid the agricultural community in understanding hydroclimatic impacts on crop production and decision-making in operational practices. While approximately 20 hydroclimatic variables are available through the LIS dataset, the focus here is on soil water content, soil temperature, and evapotranspiration. To assess the performance of the model, the LIS dataset is compared with in situ hydrometeorological observations across the study domain and with coarse-resolution reanalysis products [NARR, MERRA, and NLDAS-2 (phase 2 of the North American Land Data Assimilation System)]. In agricultural regions such as the U.S. Midwest, finescale hydroclimatic mapping that links the regional scale to the field scale is necessary. The new dataset provides this link as an intermediate-scale product that links point observations and coarse gridded datasets. In general, the LIS dataset compares well with in situ observations and coarser gridded products in terms of both temporal and spatial patterns, but cases of strong disagreement exist particularly in areas with sandy soils. The dataset is made available to the broader research community as an effort to fill the gap in spatial hydroclimatic data availability.
Abstract
Long-term precipitation records are vital to many applications, especially the study of extreme events. The Tropical Rainfall Measuring Mission (TRMM) has served this need, but TRMM’s successor mission, Global Precipitation Measurement (GPM), does not yet provide a long-term record. Quantile mapping, the conversion of values across paired empirical distributions, offers a simple, established means to approximate such long-term statistics but only within appropriately defined domains. This method was applied to a case study in Central America, demonstrating that quantile mapping between TRMM and GPM data maintains the performance of a real-time landslide model. Use of quantile mapping could bring the benefits of the latest satellite-based precipitation dataset to existing user communities, such as those for hazard assessment, crop forecasting, numerical weather prediction, and disease tracking.
Abstract
Long-term precipitation records are vital to many applications, especially the study of extreme events. The Tropical Rainfall Measuring Mission (TRMM) has served this need, but TRMM’s successor mission, Global Precipitation Measurement (GPM), does not yet provide a long-term record. Quantile mapping, the conversion of values across paired empirical distributions, offers a simple, established means to approximate such long-term statistics but only within appropriately defined domains. This method was applied to a case study in Central America, demonstrating that quantile mapping between TRMM and GPM data maintains the performance of a real-time landslide model. Use of quantile mapping could bring the benefits of the latest satellite-based precipitation dataset to existing user communities, such as those for hazard assessment, crop forecasting, numerical weather prediction, and disease tracking.
Abstract
Half of Earth’s land surface has been altered by human activities, creating various consequences on the climate and weather systems at local to global scales, which in turn affect a myriad of land surface processes and the adaptation behaviors. This study reviews the status and major knowledge gaps in the interactions of land and atmospheric changes and present 11 grand challenge areas for the scientific research and adaptation community in the coming decade. These land-cover and land-use change (LCLUC)-related areas include 1) impacts on weather and climate, 2) carbon and other biogeochemical cycles, 3) biospheric emissions, 4) the water cycle, 5) agriculture, 6) urbanization, 7) acclimation of biogeochemical processes to climate change, 8) plant migration, 9) land-use projections, 10) model and data uncertainties, and, finally, 11) adaptation strategies. Numerous studies have demonstrated the effects of LCLUC on local to global climate and weather systems, but these putative effects vary greatly in magnitude and even sign across space, time, and scale and thus remain highly uncertain. At the same time, many challenges exist toward improved understanding of the consequences of atmospheric and climate change on land process dynamics and services. Future effort must improve the understanding of the scale-dependent, multifaceted perturbations and feedbacks between land and climate changes in both reality and models. To this end, one critical cross-disciplinary need is to systematically quantify and better understand measurement and model uncertainties. Finally, LCLUC mitigation and adaptation assessments must be strengthened to identify implementation barriers, evaluate and prioritize opportunities, and examine how decision-making processes work in specific contexts.
Abstract
Half of Earth’s land surface has been altered by human activities, creating various consequences on the climate and weather systems at local to global scales, which in turn affect a myriad of land surface processes and the adaptation behaviors. This study reviews the status and major knowledge gaps in the interactions of land and atmospheric changes and present 11 grand challenge areas for the scientific research and adaptation community in the coming decade. These land-cover and land-use change (LCLUC)-related areas include 1) impacts on weather and climate, 2) carbon and other biogeochemical cycles, 3) biospheric emissions, 4) the water cycle, 5) agriculture, 6) urbanization, 7) acclimation of biogeochemical processes to climate change, 8) plant migration, 9) land-use projections, 10) model and data uncertainties, and, finally, 11) adaptation strategies. Numerous studies have demonstrated the effects of LCLUC on local to global climate and weather systems, but these putative effects vary greatly in magnitude and even sign across space, time, and scale and thus remain highly uncertain. At the same time, many challenges exist toward improved understanding of the consequences of atmospheric and climate change on land process dynamics and services. Future effort must improve the understanding of the scale-dependent, multifaceted perturbations and feedbacks between land and climate changes in both reality and models. To this end, one critical cross-disciplinary need is to systematically quantify and better understand measurement and model uncertainties. Finally, LCLUC mitigation and adaptation assessments must be strengthened to identify implementation barriers, evaluate and prioritize opportunities, and examine how decision-making processes work in specific contexts.
Abstract
The accurate prediction of plant phenology is of significant importance for more sustainable and effective land management. This research develops a framework of phenological modeling to estimate vegetation abundance [indicated by the normalized difference vegetation index (NDVI)] 7 days into the future in the geographically diverse Upper Colorado River basin (UCRB). This framework uses phenological regions (phenoregions) as the basic units of modeling to account for the spatially variant environment–vegetation relationships. The temporal variation of the relationships is accounted for via the identification of phenological phases. The modeling technique of Multivariate Adaptive Regression Splines (MARS) is employed and tested as an approach to construct enhanced predictive phenological models in each phenoregion using a comprehensive set of environmental drivers and factors. MARS has the ability to deal with a large number of independent variables and to approximate complex relationships. The R 2 values of the models range from 91.62% to 97.22%. The root-mean-square error values of all models are close to their respective standard errors ranging from 0.016 to 0.035, as indicated by the results of cross and field validations. These demonstrate that the modeling framework ensures the accurate prediction of short-term vegetation abundance in regions with various environmental conditions.
Abstract
The accurate prediction of plant phenology is of significant importance for more sustainable and effective land management. This research develops a framework of phenological modeling to estimate vegetation abundance [indicated by the normalized difference vegetation index (NDVI)] 7 days into the future in the geographically diverse Upper Colorado River basin (UCRB). This framework uses phenological regions (phenoregions) as the basic units of modeling to account for the spatially variant environment–vegetation relationships. The temporal variation of the relationships is accounted for via the identification of phenological phases. The modeling technique of Multivariate Adaptive Regression Splines (MARS) is employed and tested as an approach to construct enhanced predictive phenological models in each phenoregion using a comprehensive set of environmental drivers and factors. MARS has the ability to deal with a large number of independent variables and to approximate complex relationships. The R 2 values of the models range from 91.62% to 97.22%. The root-mean-square error values of all models are close to their respective standard errors ranging from 0.016 to 0.035, as indicated by the results of cross and field validations. These demonstrate that the modeling framework ensures the accurate prediction of short-term vegetation abundance in regions with various environmental conditions.
Abstract
Lakes have been suggested as an indicator of climate change; however, long-term, systematic records of lake temperature are limited. Satellite remote sensing is capable of supporting lake temperature mapping with the advantage of large-area and systematic observations. The goal of this research application was to assess spatiotemporal trends in lake skin temperature for all lakes over 8 ha across northern New England for the past three decades. Nearly 10 000 Landsat scenes for July, August, and September from 1984 to 2014 were processed using MODTRAN and MERRA parameterizations to generate atmospherically corrected lake skin temperature records. Results show, on average, lakes warmed at a rate of 0.8°C decade−1, with smaller lakes warming at a faster rate. Complementing regression and space–time analyses showed similar results (R 2 = 0.63) for lake temperature trends and found lakes, on average, are warming faster than daily maximum or minimum air temperature. No major hot spots were found as lake temperature changes were heterogeneous on a local scale and evenly distributed across the region. Maximum and minimum daily temperature, lake size, and elevation were found as significant drivers of lake temperature. This effort provides the first regionally focused and comprehensive spatiotemporal assessment of thousands (n = 3955) of lakes concentrated in one geographic region. The approach is scalable and adaptable to any region for assessing lake temperature trends and potential drivers.
Abstract
Lakes have been suggested as an indicator of climate change; however, long-term, systematic records of lake temperature are limited. Satellite remote sensing is capable of supporting lake temperature mapping with the advantage of large-area and systematic observations. The goal of this research application was to assess spatiotemporal trends in lake skin temperature for all lakes over 8 ha across northern New England for the past three decades. Nearly 10 000 Landsat scenes for July, August, and September from 1984 to 2014 were processed using MODTRAN and MERRA parameterizations to generate atmospherically corrected lake skin temperature records. Results show, on average, lakes warmed at a rate of 0.8°C decade−1, with smaller lakes warming at a faster rate. Complementing regression and space–time analyses showed similar results (R 2 = 0.63) for lake temperature trends and found lakes, on average, are warming faster than daily maximum or minimum air temperature. No major hot spots were found as lake temperature changes were heterogeneous on a local scale and evenly distributed across the region. Maximum and minimum daily temperature, lake size, and elevation were found as significant drivers of lake temperature. This effort provides the first regionally focused and comprehensive spatiotemporal assessment of thousands (n = 3955) of lakes concentrated in one geographic region. The approach is scalable and adaptable to any region for assessing lake temperature trends and potential drivers.
