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Pedro Sequera, Jorge E. González, Kyle McDonald, Steve LaDochy, and Daniel Comarazamy

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.

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Bharat Rastogi, A. Park Williams, Douglas T. Fischer, Sam F. Iacobellis, Kathryn McEachern, Leila Carvalho, Charles Jones, Sara A. Baguskas, and Christopher J. Still

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.

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Ashley E. Van Beusekom, Grizelle González, and Maria M. Rivera
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D. M. Nover, J. W. Witt, J. B. Butcher, T. E. Johnson, and C. P. Weaver

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.

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Zhijuan Liu, Xiaoguang Yang, Xiaomao Lin, Kenneth G. Hubbard, Shuo Lv, and Jing Wang

Abstract

Northeast China (NEC) is one of the major agricultural production areas in China, producing about 30% of China’s total maize output. In the past five decades, maize yields in NEC increased rapidly. However, farmer yields still have potential to be increased. Therefore, it is important to quantify the impacts of agronomic factors, including soil physical properties, cultivar selections, and management practices on yield gaps of maize under the changing climate in NEC in order to provide reliable recommendations to narrow down the yield gaps. In this study, the Agricultural Production Systems Simulator (APSIM)-Maize model was used to separate the contributions of soil physical properties, cultivar selections, and management practices to maize yield gaps. The results indicate that approximately 5%, 12%, and 18% of potential yield loss of maize is attributable to soil physical properties, cultivar selection, and management practices. Simulation analyses showed that potential ascensions of yield of maize by improving soil physical properties PAYs, changing to cultivar with longer maturity PAYc, and improving management practices PAYm for the entire region were 0.6, 1.5, and 2.2 ton ha−1 or 9%, 23%, and 34% increases, respectively, in NEC. In addition, PAYc and PAYm varied considerably from location to location (0.4 to 2.2 and 0.9 to 4.5 ton ha−1 respectively), which may be associated with the spatial variation of growing season temperature and precipitation among climate zones in NEC. Therefore, changing to cultivars with longer growing season requirement and improving management practices are the top strategies for improving yield of maize in NEC, especially for the north and west areas.

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Madhavi Jain, A. P. Dimri, and D. Niyogi

Abstract

Recent decades have witnessed rapid urbanization and urban population growth resulting in urban sprawl of cities. This paper analyzes the spatiotemporal dynamics of the urbanization process (using remote sensing and spatial metrics) that has occurred in Delhi, the capital city of India, which is divided into nine districts. The urban patterns and processes within the nine administrative districts of the city based on raw satellite data have been taken into consideration. Area, population, patch, edge, and shape metrics along with Pearson’s chi statistics and Shannon’s entropy have been calculated. Three types of urban patterns exist in the city: 1) highly sprawled districts, namely, West, North, North East, and East; 2) medium sprawled districts, namely, North West, South, and South West; and 3) least sprawled districts—Central and New Delhi. Relative entropy, which scales Shannon’s entropy values from 0 to 1, is calculated for the districts and time spans. Its values are 0.80, 0.92, and 0.50 from 1977 to 1993, 1993 to 2006, and 2006 to 2014, respectively, indicating a high degree of urban sprawl. Parametric and nonparametric correlation tests suggest the existence of associations between built-up density and population density, area-weighted mean patch fractal dimension (AWMPFD) and area-weighted mean shape index (AWMSI), compactness index and edge density, normalized compactness index and number of patches, and AWMPFD and built-up density.

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Xiaosong Li and Jin Zhang

Abstract

The green vegetation fraction Fg, which represents the horizontal density of live vegetation, is an important parameter for the study of global energy, carbon, hydrological, and biogeochemical cycling. A common method of calculating Fg is to create a simple linear mixing model between two NDVI endmembers: bare soil NDVI, , and full vegetation NDVI, . However, many uncertainties exist for the determination of these parameters at large scales. The present study investigates how and determination can impact Fg calculations for all of China, based on different land-cover datasets, hyperspectral data, and soil type classification maps. The results show the following: 1) The regional ChinaCover dataset, with higher accuracy and more detailed classification, is preferable for calculating Fg in China, compared with the most commonly used MOD12Q1 dataset, although it would not lead to too much difference in values. 2) The soil NDVI from Hyperion datasets shows that soils have highly variable NDVI values (0.006–0.2), and 79.36% of the area studied has a much larger NDVI value than the commonly used value of 0.05. Therefore, the dynamic values with different soil types are much better for Fg calculation than the invariant value (0.05), which would yield a significant overestimation of Fg, especially for areas with low vegetation coverage. 3) A high-quality Fg dataset for China from 2000 to 2010 was established with and parameters based on MOD13Q1 250-m NDVI data.

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Weiyue Zhang, Zhongfeng Xu, and Weidong Guo

Abstract

The impacts of land-use and land-cover change (LULCC) on tropospheric temperatures are investigated in this study using the fully coupled Community Earth System Model. Two simulations are performed using potential and current vegetation cover. The results show that LULCC can induce detectable changes in the tropospheric air temperature. Although the influence of LULCC on tropospheric temperature is weak, a significant influence can still be found below 300 hPa in summer over land. Compared to the global-mean temperature change, LULCC-induced changes in the regional-mean air temperature can be 2–3 times larger in the middle–upper troposphere and approximately 8 times larger in the lower troposphere. In East Asia and South Asia, LULCC is shown to produce significant decreases (0.2° to 0.4°C) in air temperature in the middle–upper troposphere in spring and autumn due to the largest decrease in the latent heat release from precipitation. In Europe and North America, the most significant tropospheric cooling occurs in summer, which can be attributed to the significant decrease in the absorbed solar radiation and sensible heat flux during this season. In addition to local effects, LULCC also induces nonlocal responses in the tropospheric air temperature that are characterized by significant decreases over the leeward sides of LULCC regions, which include East Asia–western North Pacific Ocean, Mediterranean Sea–North Africa, North America–Atlantic Ocean, and North America–eastern Pacific. Cooling in the leeward sides of LULCC regions is primarily caused by an enhanced cold advection induced by LULCC.

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Avijit Gangopadhyay, Ayan H. Chaudhuri, and Arnold H. Taylor

Abstract

The response of the Gulf Stream (GS) system to atmospheric forcing is generally linked either to the basin-scale winds on the subtropical gyre or to the buoyancy forcing from the Labrador Sea. This study presents a multiscale synergistic perspective to describe the low-frequency response of the GS system. The authors identify dominant temporal variability in the North Atlantic Oscillation (NAO), in known indices of the GS path, and in the observed GS latitudes along its path derived from sea surface height (SSH) contours over the period 1993–2013. The analysis suggests that the signature of interannual variability changes along the stream’s path from 75° to 55°W. From its separation at Cape Hatteras to the west of 65°W, the variability of the GS is mainly in the near-decadal (7–10 years) band, which is missing to the east of 60°W, where a new interannual (4–5 years) band peaks. The latter peak (4–5 years) was missing to the west of 65°W. The region between 65° and 60°W seems to be a transition region. A 2–3-yr secondary peak was pervasive in all time series, including that for the NAO. This multiscale response of the GS system is supported by results from a basin-scale North Atlantic model. The near-decadal response can be attributed to similar forcing periods in the NAO signal; however, the interannual variability of 4–5 years in the eastern segment of the GS path is as yet unexplained. More numerical and observational studies are warranted to understand such causality.

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Edward Armstrong, Paul Valdes, Jo House, and Joy Singarayer

Abstract

Human-induced land-use change (LUC) alters the biogeophysical characteristics of the land surface influencing the surface energy balance. The level of atmospheric CO2 is expected to increase in the coming century and beyond, modifying temperature and precipitation patterns and altering the distribution and physiology of natural vegetation. It is important to constrain how CO2-induced climate and vegetation change may influence the regional extent to which LUC alters climate. This sensitivity study uses the HadCM3 coupled climate model under a range of equilibrium forcings to show that the impact of LUC declines under increasing atmospheric CO2, specifically in temperate and boreal regions. A surface energy balance analysis is used to diagnose how these changes occur. In Northern Hemisphere winter this pattern is attributed in part to the decline in winter snow cover and in the summer due to a reduction in latent cooling with higher levels of CO2. The CO2-induced change in natural vegetation distribution is also shown to play a significant role. Simulations run at elevated CO2, yet present-day vegetation show a significantly increased sensitivity to LUC, driven in part by an increase in latent cooling. This study shows that modeling the impact of LUC needs to accurately simulate CO2-driven changes in precipitation and snowfall and incorporate accurate, dynamic vegetation distribution.

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