scholarly journals A climate-dependent global model of ammonia emissions from chicken farming

2020 ◽  
Author(s):  
Jize Jiang ◽  
David S. Stevenson ◽  
Aimable Uwizeye ◽  
Giuseppe Tempio ◽  
Mark A. Sutton
Keyword(s):  
Climate Change ◽  
Temporal Variations ◽  
Global Scale ◽  
Agricultural Practice ◽  
Ammonia Emissions ◽  
Partial Control ◽  
Dry Tropics ◽  
Emissions Inventories ◽  
Emission Fraction ◽  
Global Data

Abstract. Ammonia (NH3) has significant impacts on the environment, which can influence climate and air quality, and cause acidification and eutrophication in terrestrial and aquatic ecosystems. Agricultural activities are the main sources of NH3 emissions globally. Emissions of NH3 from chicken farming are highly dependent on climate, affecting their environmental footprint and impact. In order to investigate the effects of meteorological factors and to quantify how climate change affect these emissions, a process-based model, AMmonia-CLIMate-Poultry (AMCLIM-Poultry) has been developed to simulate and predict temporal variations in NH3 emissions from poultry excretion, here focusing on chicken farms and manure spreading. The model simulates the decomposition of uric acid to form total ammoniacal nitrogen which then partitions into gaseous NH3 that is released to the atmosphere at hourly to daily resolution. Ammonia emissions are simulated by calculating nitrogen and moisture budgets within poultry excretion, including a dependence on environmental variables. By applying the model with global data for livestock, agricultural practice and meteorology, we calculate NH3 emissions from chicken farming at global scale (0.5° resolution). Based on 2010 data, the AMCLIM-Poultry model estimates NH3 emissions from global chicken farming of 5.5 Tg N yr−1, about 13 % of the agriculture-derived NH3 emissions. Taking account of partial control of the ambient environment for housed chicken (layers and broilers), the fraction of excreted nitrogen emitted as NH3 is found to be up to three times larger in humid tropical locations than in cold or dry locations. For spreading of manure to land, rain becomes a critical driver affecting emissions in addition to temperature, with the emission fraction being up to five times larger in the semi-dry tropics than in cold, wet climates. The results highlight the importance of incorporating climate effects into global NH3 emissions inventories for agricultural sources. The model shows increased emissions under warm and wet conditions, indicating that climate change will tend to increase NH3 emissions over the coming century.

scholarly journals A climate-dependent global model of ammonia emissions from chicken farming

2021 ◽  
Vol 18 (1) ◽  
pp. 135-158
Author(s):  
Jize Jiang ◽  
David S. Stevenson ◽  
Aimable Uwizeye ◽  
Giuseppe Tempio ◽  
Mark A. Sutton
Keyword(s):  
Climate Change ◽  
Temporal Variations ◽  
Global Scale ◽  
Agricultural Practice ◽  
Ammonia Emissions ◽  
Partial Control ◽  
Dry Tropics ◽  
Emissions Inventories ◽  
Emission Fraction ◽  
Global Data

Abstract. Ammonia (NH3) has significant impacts on the environment, which can influence climate and air quality and cause acidification and eutrophication in terrestrial and aquatic ecosystems. Agricultural activities are the main sources of NH3 emissions globally. Emissions of NH3 from chicken farming are highly dependent on climate, affecting their environmental footprint and impact. In order to investigate the effects of meteorological factors and to quantify how climate change affects these emissions, a process-based model, AMmonia–CLIMate–Poultry (AMCLIM–Poultry), has been developed to simulate and predict temporal variations in NH3 emissions from poultry excretion, here focusing on chicken farms and manure spreading. The model simulates the decomposition of uric acid to form total ammoniacal nitrogen, which then partitions into gaseous NH3 that is released to the atmosphere at an hourly to daily resolution. Ammonia emissions are simulated by calculating nitrogen and moisture budgets within poultry excretion, including a dependence on environmental variables. By applying the model with global data for livestock, agricultural practice and meteorology, we calculate NH3 emissions from chicken farming on a global scale (0.5∘ resolution). Based on 2010 data, the AMCLIM–Poultry model estimates NH3 emissions from global chicken farming of 5.5 ± 1.2 Tg N yr−1, about 13 % of the agriculture-derived NH3 emissions. Taking account of partial control of the ambient environment for housed chicken (layers and broilers), the fraction of excreted nitrogen emitted as NH3 is found to be up to 3 times larger in humid tropical locations than in cold or dry locations. For spreading of manure to land, rain becomes a critical driver affecting emissions in addition to temperature, with the emission fraction being up to 5 times larger in the semi-dry tropics than in cold, wet climates. The results highlight the importance of incorporating climate effects into global NH3 emissions inventories for agricultural sources. The model shows increased emissions under warm and wet conditions, indicating that climate change will tend to increase NH3 emissions over the coming century.

DEVELOPMENT AND EVALUATION OF EMULATORS FOR THE ASSESSMENT OF THE GLOBAL-SCALE ECONOMIC IMPACT OF CLIMATE CHANGE

2019 ◽  
Vol 75 (5) ◽  
pp. I_73-I_80
Author(s):  
Jun’ya TAKAKURA ◽  
Shinichiro FUJIMORI ◽  
Kiyoshi TAKAHASHI ◽  
Qian ZHOU ◽  
Naota HANASAKI ◽  
...  
Keyword(s):  
Climate Change ◽  
Economic Impact ◽  
Global Scale ◽  
Impact Of Climate Change

scholarly journals Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1109
Author(s):  
Nobuaki Kimura ◽  
Kei Ishida ◽  
Daichi Baba
Keyword(s):  
Climate Change ◽  
Surface Water ◽  
Water Temperature ◽  
Transfer Learning ◽  
Air Temperature ◽  
Climate Models ◽  
Temporal Variations ◽  
Training Data ◽  
Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

scholarly journals Shared socioeconomic pathways for climate change research in Finland: co-developing extended SSP narratives for agriculture

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Heikki S. Lehtonen ◽  
Jyrki Aakkula ◽  
Stefan Fronzek ◽  
Janne Helin ◽  
Mikael Hildén ◽  
...  
Keyword(s):  
Climate Change ◽  
National Level ◽  
Global Scale ◽  
Change Scenario ◽  
Policy Makers ◽  
Climate Change Research ◽  
Food And Agriculture ◽  
Agriculture And Food ◽  
Farm Subsidies

AbstractShared socioeconomic pathways (SSPs), developed at global scale, comprise narrative descriptions and quantifications of future world developments that are intended for climate change scenario analysis. However, their extension to national and regional scales can be challenging. Here, we present SSP narratives co-developed with stakeholders for the agriculture and food sector in Finland. These are derived from intensive discussions at a workshop attended by approximately 39 participants offering a range of sectoral perspectives. Using general background descriptions of the SSPs for Europe, facilitated discussions were held in parallel for each of four SSPs reflecting very different contexts for the development of the sector up to 2050 and beyond. Discussions focused on five themes from the perspectives of consumers, producers and policy-makers, included a joint final session and allowed for post-workshop feedback. Results reflect careful sector-based, national-level interpretations of the global SSPs from which we have constructed consensus narratives. Our results also show important critical remarks and minority viewpoints. Interesting features of the Finnish narratives compared to the global SSP narratives include greater emphasis on environmental quality; significant land abandonment in SSPs with reduced livestock production and increased plant-based diets; continued need for some farm subsidies across all SSPs and opportunities for diversifying domestic production under scenarios of restricted trade. Our results can contribute to the development of more detailed national long-term scenarios for food and agriculture that are both relevant for local stakeholders and researchers as well as being consistent with global scenarios being applied internationally.

scholarly journals Reduced resilience of terrestrial ecosystems locally is not reflected on a global scale

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Yuhao Feng ◽  
Haojie Su ◽  
Zhiyao Tang ◽  
Shaopeng Wang ◽  
Xia Zhao ◽  
...  
Keyword(s):  
Climate Change ◽  
Global Climate Change ◽  
Vegetation Index ◽  
Global Climate ◽  
Terrestrial Ecosystem ◽  
Terrestrial Ecosystems ◽  
Global Scale ◽  
Ecological Resilience ◽  
Terrestrial Vegetation ◽  
Ecosystem Resilience

AbstractGlobal climate change likely alters the structure and function of vegetation and the stability of terrestrial ecosystems. It is therefore important to assess the factors controlling ecosystem resilience from local to global scales. Here we assess terrestrial vegetation resilience over the past 35 years using early warning indicators calculated from normalized difference vegetation index data. On a local scale we find that climate change reduced the resilience of ecosystems in 64.5% of the global terrestrial vegetated area. Temperature had a greater influence on vegetation resilience than precipitation, while climate mean state had a greater influence than climate variability. However, there is no evidence for decreased ecological resilience on larger scales. Instead, climate warming increased spatial asynchrony of vegetation which buffered the global-scale impacts on resilience. We suggest that the response of terrestrial ecosystem resilience to global climate change is scale-dependent and influenced by spatial asynchrony on the global scale.

Contrasting wheat phenological responses to climate change in global scale

2019 ◽  
Vol 665 ◽  
pp. 620-631 ◽  
Author(s):  
Shilong Ren ◽  
Qiming Qin ◽  
Huazhong Ren
Keyword(s):  
Climate Change ◽  
Global Scale

scholarly journals Reconstruction of recent climate change in Alaska from the Aurora Peak ice core, central Alaska

2015 ◽  
Vol 11 (2) ◽  
pp. 217-226 ◽  
Author(s):  
A. Tsushima ◽  
S. Matoba ◽  
T. Shiraiwa ◽  
S. Okamoto ◽  
H. Sasaki ◽  
...  
Keyword(s):  
Climate Change ◽  
Accumulation Rate ◽  
Ice Core ◽  
Chemical Species ◽  
Volcanic Eruptions ◽  
Gulf Of Alaska ◽  
Temporal Variations ◽  
Storm Activity ◽  
Alaska Range ◽  
Recent Climate

Abstract. A 180.17 m ice core was drilled at Aurora Peak in the central part of the Alaska Range, Alaska, in 2008 to allow reconstruction of centennial-scale climate change in the northern North Pacific. The 10 m depth temperature in the borehole was −2.2 °C, which corresponded to the annual mean air temperature at the drilling site. In this ice core, there were many melt–refreeze layers due to high temperature and/or strong insolation during summer seasons. We analyzed stable hydrogen isotopes (δD) and chemical species in the ice core. The ice core age was determined by annual counts of δD and seasonal cycles of Na+, and we used reference horizons of tritium peaks in 1963 and 1964, major volcanic eruptions of Mount Spurr in 1992 and Mount Katmai in 1912, and a large forest fire in 2004 as age controls. Here, we show that the chronology of the Aurora Peak ice core from 95.61 m to the top corresponds to the period from 1900 to the summer season of 2008, with a dating error of ± 3 years. We estimated that the mean accumulation rate from 1997 to 2007 (except for 2004) was 2.04 m w.eq. yr-1. Our results suggest that temporal variations in δD and annual accumulation rates are strongly related to shifts in the Pacific Decadal Oscillation index (PDOI). The remarkable increase in annual precipitation since the 1970s has likely been the result of enhanced storm activity associated with shifts in the PDOI during winter in the Gulf of Alaska.

scholarly journals Temporal evolution of flow-like landslide hazard for a road infrastructure in the municipality of Nocera Inferiore (southern Italy) under the effect of climate change

2018 ◽  
Vol 18 (11) ◽  
pp. 3019-3035 ◽  
Author(s):  
Marco Uzielli ◽  
Guido Rianna ◽  
Fabio Ciervo ◽  
Paola Mercogliano ◽  
Unni K. Eidsvig
Keyword(s):  
Climate Change ◽  
Southern Italy ◽  
Spatial Location ◽  
Temporal Variations ◽  
Landslide Hazard ◽  
Climate Projections ◽  
Atmospheric Concentration ◽  
Campania Region ◽  
Event Occurrence

Abstract. In recent years, flow-like landslides have extensively affected pyroclastic covers in the Campania region in southern Italy, causing human suffering and conspicuous economic damages. Due to the high criticality of the area, a proper assessment of future variations in event occurrences due to expected climate changes is crucial. The study assesses the temporal variation in flow-like landslide hazard for a section of the A3 “Salerno–Napoli” motorway, which runs across the toe of the Monte Albino relief in the Nocera Inferiore municipality. Hazard is estimated spatially depending on (1) the likelihood of rainfall-induced event occurrence within the study area and (2) the probability that the any specific location in the study area will be affected during the runout. The probability of occurrence of an event is calculated through the application of Bayesian theory. Temporal variations due to climate change are estimated up to the year 2100 through an ensemble of high-resolution climate projections, accounting for current uncertainties in the characterization of variations in rainfall patterns. Reach probability, or defining the probability that a given spatial location is affected by flow-like landslides, is calculated spatially based on a distributed empirical model. The outputs of the study predict substantial increases in occurrence probability over time for two different scenarios of future socioeconomic growth and atmospheric concentration of greenhouse gases.

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