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Climate, Volume 7, Issue 3 (March 2019)

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Cover Story (view full-size image) Green infrastructure (GI) is a popular tool for adapting to and mitigating climate change and [...] Read more.
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Open AccessArticle Communicating Climate Mitigation and Adaptation Efforts in American Cities
Climate 2019, 7(3), 45; https://doi.org/10.3390/cli7030045
Received: 25 February 2019 / Revised: 13 March 2019 / Accepted: 16 March 2019 / Published: 24 March 2019
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Abstract
City governments have a large role to play in climate change mitigation and adaptation policies, given that urban locales are responsible for disproportionately high levels of greenhouse gas (GHG) emissions and are on the “front lines” of observed and anticipated climate change impacts. [...] Read more.
City governments have a large role to play in climate change mitigation and adaptation policies, given that urban locales are responsible for disproportionately high levels of greenhouse gas (GHG) emissions and are on the “front lines” of observed and anticipated climate change impacts. This study examines how US mayors prioritize climate policies within the context of the city agenda. Employing a computer-assisted content analysis of over 2886 mayoral press releases related to climate change from 82 major American cities for the period 2010–2016, we describe and explain the extent to which city governments discuss mitigation and adaptation policies in official communications. Specifically, we rely on a semi-supervised topic model to measure key climate policy themes in city press releases and examine their correlates using a multilevel statistical model. Our results suggest that while mitigation policies tend to dominate the city agenda on climate policy, discussion of adaptation efforts has risen dramatically in the past few years. Further, our statistical analysis indicates that partisanship influences city discussion on a range of climate policy areas—including emissions, land use policy, and climate resiliency—while projected vulnerability to climatic risks only influences discussion of climate resiliency and adaptation efforts. Full article
(This article belongs to the Special Issue Climate Change Resilience and Urban Sustainability)
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Open AccessReview Sensor Applications in Agrifood Systems: Current Trends and Opportunities for Water Stewardship
Climate 2019, 7(3), 44; https://doi.org/10.3390/cli7030044
Received: 6 February 2019 / Revised: 12 March 2019 / Accepted: 16 March 2019 / Published: 20 March 2019
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Abstract
Growing global food demand and security concerns dictate the need for state-of-the-art food production technologies to increase farming efficiency. Concurrently, freshwater overexploitation in agriculture, especially in arid and water-scarce areas, emphasises the vital role of appropriate water-saving irrigations techniques to ensure natural resources [...] Read more.
Growing global food demand and security concerns dictate the need for state-of-the-art food production technologies to increase farming efficiency. Concurrently, freshwater overexploitation in agriculture, especially in arid and water-scarce areas, emphasises the vital role of appropriate water-saving irrigations techniques to ensure natural resources sustainability in food supply networks. In line with the development of automated systems, the use of sensors for water monitoring, indicatively in the cases of smart farming or precision agriculture, could further promote the preservation of freshwater resources. To this end, this research first provides a review of sensor applications for improving sustainability in agrifood systems. We then focus on digital technologies applied for monitoring and assessing freshwater utilisation in the food commodities sector based on academic literature and real-world business evidence. A contextual map is developed for capturing the main technical, environmental and economic factors affecting the selection of sensors for water monitoring and stewardship during agricultural production. This first-effort framework, in terms of sensor-based freshwater monitoring, aims at supporting the agrifood system’s decision makers to identify the optimal sensor applications for improving sustainability and water efficiency in agricultural operations. Full article
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Open AccessArticle The North Atlantic Oscillations: Cycle Times for the NAO, the AMO and the AMOC
Climate 2019, 7(3), 43; https://doi.org/10.3390/cli7030043
Received: 22 January 2019 / Revised: 26 February 2019 / Accepted: 13 March 2019 / Published: 19 March 2019
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Abstract
We show that oceanic cycle lengths persist across oceanic cyclic time-series by comparing cycles in series that come from “sister” measurements in the North Atlantic Ocean. These are the North Atlantic oscillation (NAO), the Atlantic multidecadal oscillation (AMO) and the Atlantic meridional overturning [...] Read more.
We show that oceanic cycle lengths persist across oceanic cyclic time-series by comparing cycles in series that come from “sister” measurements in the North Atlantic Ocean. These are the North Atlantic oscillation (NAO), the Atlantic multidecadal oscillation (AMO) and the Atlantic meridional overturning circulation (AMOC). The raw NAO series, which is an extremely noisy series in its raw format, showed cycles at 7, 13, 20, 26 and 34 years that were common with, or overlapped, the other two series, and across increasing degrees of smoothing of the NAO series. At the 1960 midpoint of the hiatus period 1943–1975, NAO was leading time-series to AMOC and AMO and AMO was a leading time-series to AMOC, but in 1975, at the end of the hiatus period, the leading relations were reversed. Full article
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Open AccessArticle A New Wetness Index to Evaluate the Soil Water Availability Influence on Gross Primary Production of European Forests
Climate 2019, 7(3), 42; https://doi.org/10.3390/cli7030042
Received: 19 February 2019 / Revised: 11 March 2019 / Accepted: 14 March 2019 / Published: 19 March 2019
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Abstract
Rising temperature, drought and more-frequent extreme climatic events have been predicted for the next decades in many regions around the globe. In this framework, soil water availability plays a pivotal role in affecting vegetation productivity, especially in arid or semi-arid environments. However, direct [...] Read more.
Rising temperature, drought and more-frequent extreme climatic events have been predicted for the next decades in many regions around the globe. In this framework, soil water availability plays a pivotal role in affecting vegetation productivity, especially in arid or semi-arid environments. However, direct measurements of soil moisture are scarce, and modeling estimations are still subject to biases. Further investigation on the effect of soil moisture on plant productivity is required. This study aims at analyzing spatio-temporal variations of a modified temperature vegetation wetness index (mTVWI), a proxy of soil moisture, and evaluating its effect on gross primary production (GPP) in forests. The study was carried out in Europe on 19 representative tree species during the 2000–2010 time period. Results outline a north–south gradient of mTVWI with minimum values (low soil water availability) in Southern Europe and maximum values (high soil water availability) in Northeastern Europe. A low soil water availability negatively affected GPP from 20 to 80%, as a function of site location, tree species, and weather conditions. Such a wetness index improves our understanding of water stress impacts, which is crucial for predicting the response of forest carbon cycling to drought and aridity. Full article
(This article belongs to the Special Issue Air Pollution and Plant Ecosystems)
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Open AccessFeature PaperArticle A Lagrangian Ocean Model for Climate Studies
Climate 2019, 7(3), 41; https://doi.org/10.3390/cli7030041
Received: 1 February 2019 / Revised: 7 March 2019 / Accepted: 9 March 2019 / Published: 15 March 2019
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Abstract
Most weather and climate models simulate circulations by numerically approximating a complex system of partial differential equations that describe fluid flow. These models also typically use one of a few standard methods to parameterize the effects of smaller-scale circulations such as convective plumes. [...] Read more.
Most weather and climate models simulate circulations by numerically approximating a complex system of partial differential equations that describe fluid flow. These models also typically use one of a few standard methods to parameterize the effects of smaller-scale circulations such as convective plumes. This paper discusses the continued development of a radically different modeling approach. Rather than solving partial differential equations, the author’s Lagrangian models predict the motions of individual fluid parcels using ordinary differential equations. They also use a unique convective parameterization, in which the vertical positions of fluid parcels are rearranged to remove convective instability. Previously, a global atmospheric model and basin-scale ocean models were developed with this approach. In the present study, components of these models are combined to create a new global Lagrangian ocean model (GLOM), which will soon be coupled to a Lagrangian atmospheric model. The first simulations conducted with the GLOM examine the contribution of interior tracer mixing to ocean circulation, stratification, and water mass distributions, and they highlight several special model capabilities: (1) simulating ocean circulations without numerical diffusion of tracers; (2) modeling deep convective transports at low resolution; and (3) identifying the formation location of ocean water masses and water pathways. Full article
(This article belongs to the Special Issue Climate and Atmospheric Dynamics and Predictability)
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Open AccessArticle Impact of Climate Change on Twenty-First Century Crop Yields in the U.S.
Climate 2019, 7(3), 40; https://doi.org/10.3390/cli7030040
Received: 3 February 2019 / Revised: 8 March 2019 / Accepted: 8 March 2019 / Published: 14 March 2019
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Abstract
Crop yields are strongly dependent on the average climate, extreme temperatures, and carbon dioxide concentrations, all of which are projected to increase in the coming century. In this study, a statistical model was created to predict US yields to 2100 for three crops [...] Read more.
Crop yields are strongly dependent on the average climate, extreme temperatures, and carbon dioxide concentrations, all of which are projected to increase in the coming century. In this study, a statistical model was created to predict US yields to 2100 for three crops using low and high-emissions future scenarios (RCP 4.5 and 8.5). The model is based on linear regressions between historical crop yields and daily weather observations since 1970 for every county in the US. Yields were found to be most strongly dependent on heat waves, summer average temperatures, and killing degree days; these relationships were hence used to predict future yields. The model shows that warming temperatures will significantly decrease corn and soybean yields, but will not have as strong of an influence on rice. Before accounting for CO2 fertilization, crops in the high-emissions scenario are predicted to produce 77%, 85%, and 96% of their expected yield without climate change for corn, soybeans, and rice, respectively. When a simple CO2 fertilization factor is included, corn, a C4 plant, increases slightly, while the yields of the C3 plants (soybeans and rice) are actually predicted to increase compared to today’s yields. This study exhibits the wide range of possible impacts of climate change on crop yields in the coming century, and emphasizes the need for field research on the combined effects of CO2 fertilization and heat extremes. Full article
(This article belongs to the Special Issue Sustainable Agriculture for Climate Change Adaptation)
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Open AccessArticle Green Infrastructure Financing as an Imperative to Achieve Green Goals
Climate 2019, 7(3), 39; https://doi.org/10.3390/cli7030039
Received: 31 January 2019 / Revised: 2 March 2019 / Accepted: 3 March 2019 / Published: 9 March 2019
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Abstract
Green infrastructure (GI) has increasingly gained popularity for achieving adaptation and mitigation goals associated with climate change and extreme weather events. To continue implementing GI, financial tools are needed for upfront project capital or development costs and later for maintenance. This study’s purpose [...] Read more.
Green infrastructure (GI) has increasingly gained popularity for achieving adaptation and mitigation goals associated with climate change and extreme weather events. To continue implementing GI, financial tools are needed for upfront project capital or development costs and later for maintenance. This study’s purpose is to evaluate financing tools used in a selected GI dataset and to assess how those tools are linked to various GI technologies and other GI project characteristics like cost and size. The dataset includes over 400 GI U.S. projects, comprising a convenience sample, from the American Society of Landscape Architects (ASLA). GI project characteristics were organized to answer a number of research questions using descriptive statistics. Results indicated that the number of projects and overall cost shares were mostly located in a few states. Grants were the most common financial tool with about two-thirds of the projects reporting information on financial tools receiving grant funding. Most projects reported financing from only one tool with a maximum of three tools. Projects primarily included multiple GI technologies averaging three and a maximum of nine. The most common GI technologies were bioswales, retention, rain gardens, and porous pavements. These findings are useful for decision-makers evaluating funding support for GI. Full article
(This article belongs to the Special Issue Climate Change Resilience and Urban Sustainability)
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Open AccessArticle The Indian Ocean Dipole: A Missing Link between El Niño Modokiand Tropical Cyclone Intensity in the North Indian Ocean
Climate 2019, 7(3), 38; https://doi.org/10.3390/cli7030038
Received: 14 December 2018 / Revised: 11 February 2019 / Accepted: 22 February 2019 / Published: 1 March 2019
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Abstract
This study is set out to understand the impact of El Niño Modoki and the Tropical Cyclone Potential Intensity (TCPI) in the North Indian Ocean. We also hypothesized and tested if the Indian Ocean Dipole (IOD) reveals a likely connection between the two [...] Read more.
This study is set out to understand the impact of El Niño Modoki and the Tropical Cyclone Potential Intensity (TCPI) in the North Indian Ocean. We also hypothesized and tested if the Indian Ocean Dipole (IOD) reveals a likely connection between the two phenomena. An advanced mathematical tool namely the Empirical Mode Decomposition (EMD) is employed for the analysis. A major advantage of using EMD is its adaptability approach to deal with the non-linear and non-stationary signals which are similar to the signals used in this study and are also common in both atmospheric and oceanic sciences. This study has identified IOD as a likely missing link to explain the connection between El Niño Modoki and TCPI. This lays the groundwork for future research into this connection and its possible applications in meteorology. Full article
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Open AccessArticle Not so Normal Normals: Species Distribution Model Results are Sensitive to Choice of Climate Normals and Model Type
Climate 2019, 7(3), 37; https://doi.org/10.3390/cli7030037
Received: 13 February 2019 / Accepted: 23 February 2019 / Published: 28 February 2019
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Abstract
Species distribution models have many applications in conservation and ecology, and climate data are frequently a key driver of these models. Often, correlative modeling approaches are developed with readily available climate data; however, the impacts of the choice of climate normals is rarely [...] Read more.
Species distribution models have many applications in conservation and ecology, and climate data are frequently a key driver of these models. Often, correlative modeling approaches are developed with readily available climate data; however, the impacts of the choice of climate normals is rarely considered. Here, we produced species distribution models for five disparate species using four different modeling algorithms and compared results between two different, but overlapping, climate normals time periods. Although the correlation structure among climate predictors did not change between the time periods, model results were sensitive to both baseline climate period and model method, even with model parameters specifically tuned to a species. Each species and each model type had at least one difference in variable retention or relative ranking with the change in climate time period. Pairwise comparisons of spatial predictions were also different, ranging from a low of 1.6% for climate period differences to a high of 25% for algorithm differences. While uncertainty from model algorithm selection is recognized as an important source of uncertainty, the impact of climate period is not commonly assessed. These uncertainties may affect conservation decisions, especially when projecting to future climates, and should be evaluated during model development. Full article
(This article belongs to the Special Issue Climate and Climate Niche Models)
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Open AccessReview Observed Spatiotemporal Trends in Intense Precipitation Events across United States: Applications for Stochastic Weather Generation
Climate 2019, 7(3), 36; https://doi.org/10.3390/cli7030036
Received: 31 January 2019 / Revised: 20 February 2019 / Accepted: 23 February 2019 / Published: 27 February 2019
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Abstract
An increasing focus of climate change studies is the projection of storm events characterized by heavy, very heavy, extreme, and/or intense precipitation. Projected changes in the spatiotemporal distributions of such intense precipitation events remain uncertain due to large measures of variability in both [...] Read more.
An increasing focus of climate change studies is the projection of storm events characterized by heavy, very heavy, extreme, and/or intense precipitation. Projected changes in the spatiotemporal distributions of such intense precipitation events remain uncertain due to large measures of variability in both the definition and evidence of increased intensity in the upper percentile range of observed daily precipitation distributions, particularly on a regional basis. As a result, projecting changes in future precipitation at the upper tail of the distribution (i.e., the heavy to heaviest events), such as through the use of stochastic weather generator programs, remains challenging. One approach to address this challenge is to better define what constitutes intense precipitation events and the degree of location-specific adjustment needed for the weather generator programs to appropriately account for potential increases in precipitation intensity due to climate change. In this study, we synthesized information on categories of intense precipitation events and assessed reported trends in the categories at national and regional scales within the context of applying this information to stochastic weather generation. Investigations of adjusting weather generation models to include long-term regional trends in intense precipitation events are limited, and modeling trends in site-specific future precipitation distributions forecasted by weather generator programs remains challenging. Probability exceedance curves and variations between simulated and observed distributions can help in modeling and assessment of trends in future extreme precipitation events that reflect changes in precipitation intensity due to climate change. Full article
Open AccessArticle Past, Present and Future Climate Trends Under Varied Representative Concentration Pathways for a Sub-Humid Region in Uganda
Climate 2019, 7(3), 35; https://doi.org/10.3390/cli7030035
Received: 12 December 2018 / Revised: 26 January 2019 / Accepted: 28 January 2019 / Published: 26 February 2019
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Abstract
Long-term trend analysis at local scale for rainfall and temperature is critical for detecting climate change patterns. This study analysed historical (1980–2009), near future (2010–2039), mid- (1940–2069) and end-century (2070–2099) rainfall and temperature over Karamoja sub-region. The Modern Era-Retrospective Analysis for Research and [...] Read more.
Long-term trend analysis at local scale for rainfall and temperature is critical for detecting climate change patterns. This study analysed historical (1980–2009), near future (2010–2039), mid- (1940–2069) and end-century (2070–2099) rainfall and temperature over Karamoja sub-region. The Modern Era-Retrospective Analysis for Research and Applications (MERRA) daily climate data provided by the Agricultural Model Inter-comparison and Improvement Project (AgMIP) was used. The AgMIP delta method analysis protocol was used for an ensemble of 20 models under two representative concentration pathways (RCPs 4.5 and 8.5). Historical mean rainfall was 920.1 ± 118.9 mm and minimum, maximum and mean temperature were 16.8 ± 0.5 °C, 30.6 ± 0.4 °C and 32.0 ± 0.7 °C, respectively. Minimum temperature over the historical period significantly rose between 2000 and 2008. Near future rainfall varied by scenario with 1012.9 ± 146.3 mm and 997.5 ± 144.7 mm for RCP4.5 and RCP8.5 respectively; with a sharp rise predicted in 2017. In the mid-century, mean annual rainfall will be 1084.7 ± 137.4 mm and 1205.5 ± 164.9 mm under RCP4.5 and RCP8.5 respectively. The districts of Kaabong and Kotido are projected to experience low rainfall total under RCP4.5 (mid-century) and RCP8.5 (end-century). The minimum temperature is projected to increase by 1.8 °C (RCP4.5) and 2.1 °C (RCP8.5) in mid-century, and by 2.2 °C (RCP4.5) and 4.0 °C (RCP8.5) in end-century. Full article
(This article belongs to the Special Issue Climate Services for Local Disaster Risk Reduction in Africa)
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