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Volume 8
Issue 4
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Climate, Volume 8, Issue 4 (April 2020) – 11 articles

Cover Story (view full-size image): There is considerable concern worldwide over the potential impacts of plant invasions in protected areas (PAs). To understand the potential risks of plant invasions in PAs in Sri Lanka, we conducted an analysis using the maximum entropy (MaxEnt) approach and tested how species invasion may change in PAs under climate change. We evaluated how the climate suitability of 14 invasive alien plant species (IAPS) will vary within PAs and outside PAs by 2050 under climate change scenarios. Our findings suggest there will be increased risks from multiple IAPS inside PAs and outside PAs in Sri Lanka; however, the potential risk is comparatively lower in PAs. The findings highlight important implications for the strategic management of plant invasions in PAs in order to safeguard biodiversity, with special reference to vertebrates. View this paper
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17 pages, 966 KiB  
Review
Understanding the Knowledge and Data Landscape of Climate Change Impacts and Adaptation in the Chesapeake Bay Region: A Systematic Review
by Jose Daniel Teodoro and Bruce Nairn
Climate 2020, 8(4), 58; https://doi.org/10.3390/cli8040058 - 17 Apr 2020
Cited by 13 | Viewed by 4536
Abstract
Climate change is increasingly threatening coastal communities around the world. This article reviews the literature on climate change impacts and adaptation in the Chesapeake Bay region (USA). We reviewed both climate impacts and adaptation literature (n = 283) published in the period 2007–2018 [...] Read more.
Climate change is increasingly threatening coastal communities around the world. This article reviews the literature on climate change impacts and adaptation in the Chesapeake Bay region (USA). We reviewed both climate impacts and adaptation literature (n = 283) published in the period 2007–2018 to answer the questions: (i) how are indicators of climate impacts measured and reported by different types of authors (e.g., scientists, government, and NGOs), document types (e.g., academic articles or reports), and geographic focus (e.g., State, region, county, or municipal level)? (ii) what are the current approaches for measuring the most pressing climate impacts in the Chesapeake Bay? We found that scientists produce the most amount of data but are increasingly shifting towards engaging with practitioners through reports and online resources. Most indicators focus on the Chesapeake Bay scale, but data is most needed at the local level where adaptive policies are implemented. Our analysis shows emerging approaches to monitoring climate hazards and areas where synergies between types of authors are likely to increase resilience in the 21st century. This review expands the understanding of the information network in the Chesapeake Bay and explores the institutional landscape of stakeholders involved in the production and consumption of environmental and social change data. The analysis and insights of this review may be extended to similar regions around the planet experiencing or anticipating similar climate hazards to the Chesapeake Bay. Full article
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<p>Document selection process for systematic review.</p> Full article ">Figure 2
<p>Descriptive distribution of: (<b>a</b>) type of author, (<b>b</b>) type of document (i.e., Science article, report, presentation, or online resource), and (<b>c</b>) geographic focus of the document.</p> Full article ">Figure 3
<p>Year of publication of sampled documents by (<b>a</b>) type of author, (<b>b</b>) type of document, and (<b>c</b>) geographic focus of document.</p> Full article ">Figure 4
<p>Number of documents that contained different climate change impact indicators.</p> Full article ">
25 pages, 51633 KiB  
Article
Effects of Climatic Warming and Wildfires on Recent Vegetation Changes in the Lake Baikal Basin
by Alexander N. Safronov
Climate 2020, 8(4), 57; https://doi.org/10.3390/cli8040057 - 16 Apr 2020
Cited by 14 | Viewed by 4183
Abstract
The vegetation changes in the area of the Russian part of the Lake Baikal water basin for the period 2010–2018 were investigated using MCD12C1 land cover. The decline in swamp systems area began in 2012 and continued until 2015, after which it partially [...] Read more.
The vegetation changes in the area of the Russian part of the Lake Baikal water basin for the period 2010–2018 were investigated using MCD12C1 land cover. The decline in swamp systems area began in 2012 and continued until 2015, after which it partially recovered during the heavy rain season in 2018. During the period of 2010–2018, the area covered by forests did not exceed 20.3% of the Baikal basin of the total portion of the Baikal basin under study. Deforestation began in 2013 and continued until 2017. Over 2013–2018, the forest level decreased by 12.1% compared to the forest state in 2013. The analysis of summer rainfalls and aridity indexes was performed by using CRU TS and GPCC climatic datasets. It is shown that the interannual variations of precipitation and aridity changes are determined by the variability of the global circulation of moist air masses. The MCD64A1 (burned area) and MCD14ML (active fires) MODIS products were used for investigation of the influence of wildfires on vegetation changes. The spatial hotspot distributions and burned areas in general correspond to aridity zones, but they cannot explain the 20-fold increase in the number of wildfires. Most of the hotspot locations are away from settlements, roads, and loggings, in difficult-to-access mountainous areas, as well as in the low-inhabited areas of Siberia. We assume that the nature of such ignitions includes dry thunderstorms, pyrocumulus lightning, or remote impact. Full article
(This article belongs to the Special Issue Landscape and Climate Change)
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<p>Overview of the Lake Baikal basin region; Russian and Mongolian parts are marked as blue and gray areas, respectively. The country boundary is shown as a bold gray line.</p> Full article ">Figure 2
<p>(<b>a</b>) MCD12C1 land-cover map nearby Lake Baikal in 2018. The spatial resolution of the map is 0.05°. The vegetation legends correspond to the International Geosphere-Biosphere Program (IGBP) classes. (<b>b</b>) The Russian Forest map in 2004.</p> Full article ">Figure 3
<p>The interannual variability of areas occupied by forests (<b>a</b>) and by swamp systems (<b>b</b>), as a percentage of the Russian basin of the Lake Baikal as a function of time for the period 2010–2018, considered in this paper. The area of water surface is presented for comparison. The vegetation types in MCD12C1 correspond to IGBP classes.</p> Full article ">Figure 4
<p>The interannual variability of areas occupied by evergreen needleleaf, deciduous needleleaf, deciduous broadleaf, and mixed forests as a percentage of the Russian basin of Lake Baikal, is presented as a function of time for the period 2010–2018, considered in this paper. The vegetation types in MCD12C1 correspond to IGBP classes.</p> Full article ">Figure 5
<p>The inter-annual variability (as a percentage of the Russian sector of Lake Baikal basin) of areas occupied by open and closed shrublands (<b>a</b>) and forest-steppe/steppe/grassland (<b>b</b>), as a function of time. The vegetation types in MCD12C1 correspond to IGBP classes.</p> Full article ">Figure 6
<p>Changes in forest structure nearby Lake Baikal aimed at comparing the MCD12C1 forest distribution in 2010 and 2018. The forest cover was determined from the original MCD12Q1 maps, with 100% pixel coverage. The spatial resolution of the MCD12C1 map is 1 km. The red-colored pixels indicate areas where forests, grown in 2010, completely disappeared or their structure was significantly changed in 2010–2018. In addition, the spatial distribution of forests in 2010 is shown as green-colored sectors.</p> Full article ">Figure 7
<p>The comparison between the spatial distribution of CRU TS (Climatic Research Unit) monthly precipitation (mm/month) in the relative wet 2010 summer (<b>a</b>,<b>c</b>,<b>e</b>) and in the abnormally dry 2015 summer (<b>b</b>,<b>d</b>,<b>f</b>) is shown. Panels (<b>a,b</b>) correspond to June, (<b>c</b>,<b>d</b>) correspond to July, and (<b>e</b>,<b>f</b>) correspond to August.</p> Full article ">Figure 8
<p>The comparison between the spatial distribution of Global Precipitation Climatology Center (GPCC), Version 2018 climatic precipitation dataset monthly precipitation (mm/month) in the relative wet 2010 summer (<b>a</b>,<b>c</b>,<b>e</b>) and in the abnormally dry 2015 summer (<b>b</b>,<b>d</b>,<b>f</b>) is shown. Panels (<b>a,b</b>) correspond to June, (<b>c</b>,<b>d</b>) correspond to July, and (<b>e</b>,<b>f</b>) correspond to August.</p> Full article ">Figure 9
<p>The monthly total precipitation (mm/month), spatially averaged over the territory of the Russian (<b>a</b>,<b>b</b>) and the Mongolian parts (<b>c</b>,<b>d</b>) of the Baikal basin, and temporally averaged over the wet period (July–August, blue lines), the dry period (June, brown lines), and the whole summer (June–August, gray dashed lines). The CRU TS precipitation datasets (2010–2018) are presented in panels (<b>a</b>,<b>c</b>); the GPCC precipitation datasets (2010–2016) are presented in panels (<b>b</b>,<b>d</b>).</p> Full article ">Figure 10
<p>The comparison between the spatial distribution of aridity index in the relatively wet 2010 summer (<b>a</b>,<b>c</b>,<b>e</b>) and in the abnormally dry 2015 summer (<b>b</b>,<b>d</b>,<b>f</b>). Panels (<b>a</b>,<b>b</b>) correspond to June, (<b>c</b>,<b>d</b>) correspond to July, and (<b>e</b>,<b>f</b>) correspond to August. The aridity index was calculated based on the CRU TS climatic dataset.</p> Full article ">Figure 11
<p>The aridity indexes, spatial averaged over the territory of the Russian (<b>a</b>) and the Mongolian parts (<b>b</b>) of the Baikal basin, for the wet period (July–August, blue lines), dry period (June, brown lines), and the whole summer (June–August, gray dashed lines). The aridity index was calculated using the CRU TS climatic dataset.</p> Full article ">Figure 12
<p>The ratios of total precipitations, in the Russian and in the Mongolian parts of the Baikal basin, during the wet period (July–August) are presented. The calculations were done using the CRU TS (black line) and by the GPCC (blue line) precipitation datasets.</p> Full article ">Figure 13
<p>The comparison between spatial distributions of July–August wildfires (MCD14ML active fires, AF) in the relatively wet 2010 summer (<b>a</b>) and in the abnormally dry 2015 summer (<b>b</b>) is presented.</p> Full article ">Figure 14
<p>The total burned area, damaged by wildfires from June–August in 2010–2018, is shown. The burned area was obtained from the MCD64A1 (burned area, BA) dataset. The MCD12C1 land-cover map (2010) is presented as a background.</p> Full article ">Figure 15
<p>The image of fires and thermal anomalies (red) created by the Terra and Aqua satellite products (MOD14, MYD14, red color) on 22 August 2015, obtained using the World View of Global Imagery Browse Services (GIBS), is presented. The MODIS corrected reflectance imagery (true color: red = Band 1, green = Band 4, blue = Band 3) is presented as a background satellite image. The smoke from the wildfires are clearly visible.</p> Full article ">
11 pages, 535 KiB  
Communication
Environmental Concerns of Russian Businesses: Top Company Missions and Climate Change Agenda
by Tatyana K. Molchanova, Natalia N. Yashalova and Dmitry A. Ruban
Climate 2020, 8(4), 56; https://doi.org/10.3390/cli8040056 - 13 Apr 2020
Cited by 11 | Viewed by 4683
Abstract
Climate change is on the national agenda of Russia due to this country’s contribution to greenhouse gas emissions and the expected degree of warming and precipitation increase in its territory. A content analysis of the mission statements of the 100 biggest Russian companies [...] Read more.
Climate change is on the national agenda of Russia due to this country’s contribution to greenhouse gas emissions and the expected degree of warming and precipitation increase in its territory. A content analysis of the mission statements of the 100 biggest Russian companies shows that 18.5% of them deal with environmental issues. About half of the companies that declare pro-environmental behavior belong to the energy production and transmission industry. It also is found that more than 30% of all leading hydrocarbon, chemical, and mining companies express environmental concerns in their mission statements. The main environmental priorities declared by the top Russian companies include caring for nature, production ecologization, energy efficiency, and ecological standards. These priorities are related to climate-friendly behavior, but the latter is not stated directly. Direct consideration of climate change in the mission statements of Russian companies is recommended. Full article
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<p>Conceptualization of some influence and feedback relevant to pro-environmental behavior.</p> Full article ">Figure 2
<p>Distribution of registered environmental concerns by industry.</p> Full article ">
33 pages, 13148 KiB  
Article
Analysis of Anomalies and Trends of Climate Change Indices in Zacatecas, Mexico
by Oscar Pita-Díaz and David Ortega-Gaucin
Climate 2020, 8(4), 55; https://doi.org/10.3390/cli8040055 - 11 Apr 2020
Cited by 13 | Viewed by 8129
Abstract
Sufficient evidence is currently available to demonstrate the reality of the warming of our planet’s climate system. Global warming has different effects on climate at the regional and local levels. The detection of changes in extreme events using instrumental data provides further evidence [...] Read more.
Sufficient evidence is currently available to demonstrate the reality of the warming of our planet’s climate system. Global warming has different effects on climate at the regional and local levels. The detection of changes in extreme events using instrumental data provides further evidence of such warming and allows for the characterization of its local manifestations. The present study analyzes changes in temperature and precipitation extremes in the Mexican state of Zacatecas using climate change indices developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). We studied a 40-year period (1976–2015) using annual and seasonal time series. Maximum and minimum temperature data were used, as well as precipitation statistics from the Mexican climatology database (CLICOM) provided by the Mexican Meteorological Service. Weather stations with at least 80% of data availability for the selected study period were selected; these databases were subjected to quality control, homogenization, and data filling using Climatol, which runs in the R programming language. These homogenized series were used to obtain daily grids of the three variables at a resolution of 1.3 km. Results reveal important changes in temperature-related indices, such as the increase in maximum temperature and the decrease in minimum temperature. Irregular variability was observed in the case of precipitation, which could be associated with low-frequency oscillations such as the Pacific Decadal Oscillation and the El Niño–Southern Oscillation. The possible impact of these changes in temperature and the increased irregularity of precipitation could have a negative impact on the agricultural sector, especially given that the state of Zacatecas is the largest national bean producer. The most important problems in the short term will be related to the difficulty of adapting to these rapid changes and the new climate scenario, which will pose new challenges in the future. Full article
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Full article ">Figure 1
<p>Maps of the state of Zacatecas: (<b>a</b>) geographical location; (<b>b</b>) relief and borders; (<b>c</b>) types of climates; (<b>d</b>) hydrological regions; (<b>e</b>) soil use and vegetation. Source: prepared by the authors using data from [<a href="#B43-climate-08-00055" class="html-bibr">43</a>,<a href="#B44-climate-08-00055" class="html-bibr">44</a>,<a href="#B45-climate-08-00055" class="html-bibr">45</a>,<a href="#B46-climate-08-00055" class="html-bibr">46</a>].</p> Full article ">Figure 2
<p>Location of weather stations and reanalysis stations used for data homogenization: (<b>a</b>) maximum temperature, (<b>b</b>) minimum temperature, and (<b>c</b>) precipitation.</p> Full article ">Figure 3
<p>Availability of daily data (orange strips) of (<b>a</b>) maximum temperature, (<b>b</b>) minimum temperature, and (<b>c</b>) precipitation for weather stations located 40 km within Zacatecas’s state limits (1976–2015). Identifiers for each station are placed on the ordinate axis.</p> Full article ">Figure 4
<p>(<b>a</b>) comparison of the maximum temperature grid generated by Climatol, at a 0.2° resolution; and (<b>b</b>) the grid generated using the base level temperature, at a 1.3-km resolution.</p> Full article ">Figure 5
<p>Maximum temperature, minimum temperature, and precipitation climograph for the state of Zacatecas from 1976 to 2015.</p> Full article ">Figure 6
<p>Geographic distribution of seasonal mean of maximum temperature (<b>a</b>) and minimum temperature (<b>b</b>) in the state of Zacatecas.</p> Full article ">Figure 7
<p>Geographic distribution of the seasonal mean of precipitation in the state of Zacatecas.</p> Full article ">Figure 8
<p>Maps and diagrams of the summer days (SU) index in the state of Zacatecas: (<b>a</b>) average number of summer days; (<b>b</b>) Hovmöller diagram of index; (<b>c</b>) Hovmöller diagram of standardized anomaly; (<b>d</b>) time series with linear trend; (<b>e</b>) spatial trend of index; and (<b>f</b>) spatial statistical significance of index.</p> Full article ">Figure 9
<p>Maps and diagrams of the frost days (FD) index in the state of Zacatecas: (<b>a</b>) average number of frost days; (<b>b</b>) Hovmöller diagram of index; (<b>c</b>) Hovmöller diagram of standardized anomaly; (<b>d</b>) time series with linear trend; (<b>e</b>) spatial trend of index; and (<b>f</b>) spatial statistical significance of index.</p> Full article ">Figure 10
<p>Seasonal maps and diagrams of the extreme maximum temperature (TXx) index in the state of Zacatecas: (<b>a</b>) seasonal maps of average TXx; (<b>b</b>) Hovmöller diagram of the index; (<b>c</b>) time series (line) and seasonal standardized anomaly (bars); (<b>d</b>) seasonal time series with linear trend; (<b>e</b>) seasonal spatial trend; and (<b>f</b>) spatial statistical significance.</p> Full article ">Figure 11
<p>Maps and seasonal diagrams of the extreme minimum temperature (TNn) index in the state of Zacatecas: (<b>a</b>) seasonal maps of average TNn; (<b>b</b>) Hovmöller diagram of the index; (<b>c</b>) time series (line) and seasonal standardized anomaly (bars); (<b>d</b>) seasonal time series with linear trend; (<b>e</b>) seasonal spatial trend; and (<b>f</b>) spatial statistical significance.</p> Full article ">Figure 12
<p>Maps and diagrams of the index of total precipitation on humid days (PRCPTOT) in the state of Zacatecas: (<b>a</b>) average total precipitation; (<b>b</b>) Hovmöller diagram of index; (<b>c</b>) Hovmöller diagram of standardized anomaly; (<b>d</b>) time series with linear trend; (<b>e</b>) spatial trends; (<b>f</b>) spatial statistical significance of index.</p> Full article ">Figure 13
<p>Maps and diagrams of the consecutive dry days (CDD) index in the state of Zacatecas: (<b>a</b>) average consecutive dry days; (<b>b</b>) Hovmöller diagram of index; (<b>c</b>) Hovmöller diagram of standardized anomaly; (<b>d</b>) time series with linear trend; (<b>e</b>) spatial trends; (<b>f</b>) spatial statistical significance of index.</p> Full article ">Figure 14
<p>Maps and diagrams of the daily maximum precipitation index (Rx1day) in the state of Zacatecas: (<b>a</b>) seasonal maps of average index; (<b>b</b>) Hovmöller diagram of index; (<b>c</b>) time series (line) and seasonal standardized anomaly (bars); (<b>d</b>) seasonal time series with linear trend; (<b>e</b>) seasonal spatial trend; and (<b>f</b>) spatial statistical significance.</p> Full article ">Figure 15
<p>Maps and diagrams of the maximum consecutive 5-day precipitation index (Rx5day) in the state of Zacatecas: (<b>a</b>) seasonal maps of average index; (<b>b</b>) Hovmöller diagram of index; (<b>c</b>) time series (line) and seasonal standardized anomaly (bars); (<b>d</b>) seasonal time series with linear trend; (<b>e</b>) seasonal spatial trend; (<b>f</b>) spatial statistical significance of index.</p> Full article ">
11 pages, 1080 KiB  
Article
Adaptation of Mediterranean Olive Groves to Climate Change through Sustainable Cultivation Practices
by G. Michalopoulos, K. A. Kasapi, G. Koubouris, G. Psarras, G. Arampatzis, E. Hatzigiannakis, V. Kavvadias, C. Xiloyannis, G. Montanaro, S. Malliaraki, A. Angelaki, C. Manolaraki, G. Giakoumaki, S. Reppas, N. Kourgialas and G. Kokkinos
Climate 2020, 8(4), 54; https://doi.org/10.3390/cli8040054 - 11 Apr 2020
Cited by 52 | Viewed by 9134
Abstract
Olive cultivation is considered as one of the most significant agricultural activities in Greece, from a financial, social, and ecological point of view. Intensive cultivation practices in combination with the Mediterranean climate, lead to depletion of soil organic matter, erosion, desertification, and degradation [...] Read more.
Olive cultivation is considered as one of the most significant agricultural activities in Greece, from a financial, social, and ecological point of view. Intensive cultivation practices in combination with the Mediterranean climate, lead to depletion of soil organic matter, erosion, desertification, and degradation of water resources. This paper describes sustainable olive crop management practices that were comparatively applied in 120 olive groves in Greece for 5 years with the participation of three farmers groups. Organic materials recycled in the olive groves during the present study were valuable sources of carbon, nitrogen, phosphorus, and potassium. Carbon content was highest in pruning residue (53.8–54.2%) while all materials studied were considered rich in C ranging between 41.9–46.2% (compost) and 34.9–42.5% (three-phase olive mill waste-OMW). The highest content in nitrogen was detected in compost (2–2.45%) followed by pruning residue (0.93–0.99%) and OMW (0.03–0.1%). Compost was considered a good source of phosphorus (0.3–0.6%) followed by pruning residue (0.08–0.13%) and OMW (0.01–0.3%). Potassium was also considerable in the organic materials recycled ranging 0.5–1.5% in compost followed by pruning residue (0.5–0.7%) and OMW (0.3–1.1%). Adoption of modified pruning also had important contribution toward sustainable management of olive trees. Sustainable pruning resulted in a well-balanced ratio between vegetative growth and fruiting (balanced, every year, in order to eradicate biennial bearing). Significant fluctuation in olive yields was observed in the first years of the project while yields were gradually stabilised by applying sustainable crop management. In parallel, yield increase without additional inputs, lowers the carbon—environmental footprint of the product regarding several environmental impact categories. Results can be integrated in the national agricultural and environmental policy in Mediterranean countries toward the achievement of a circular economy. Full article
(This article belongs to the Special Issue The Water Security and Management under Climate Change)
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<p>(<b>a</b>) The three study areas in Greece (Nileas, Peza, and Mirabello) and (<b>b</b>) olive grove acreage (Ha) in each study area. CR: control rainfed, CI: control irrigated, TR: treatment rainfed, TI: treatment irrigated. Bars present means of ten replicates with standard errors. Bars with different letters have statistically significant difference (Tukey test, 0.05).</p> Full article ">Figure 2
<p>Content of organic materials (compost, pruning residue, three phase olive mill waste—OMW) recycled in olive groves in (<b>a</b>) carbon, (<b>b</b>) nitrogen, (<b>c</b>) phosphorus, and (<b>d</b>) potassium in the three study areas in Greece (Nileas, Peza, and Mirabello). Bars present means with standard errors. Bars with different letters have statistically significant difference (Tukey test, 0.05). The number of samples used in the graphs were 180 for C, 179 for N, 177 for P and 167 for K. In Nileas, olive mill waste (OMW) from three-phase mill was not available because all olive mills were two-phased.</p> Full article ">Figure 3
<p>Evolution of olive oil production (Kg Ha<sup>−1</sup>) per year during 2011–2016 in the three study areas in Greece (Nileas, Peza, and Mirabello). Bars present means of 60 replicates with standard errors. The means of the three study areas were analysed. Bars with different letters have statistically significant difference (LSD test, 0.05).</p> Full article ">
20 pages, 5077 KiB  
Article
Prediction of Autumn Precipitation over the Middle and Lower Reaches of the Yangtze River Basin Based on Climate Indices
by Heng Qian and Shi-Bin Xu
Climate 2020, 8(4), 53; https://doi.org/10.3390/cli8040053 - 9 Apr 2020
Cited by 5 | Viewed by 3096
Abstract
Autumn precipitation (AP) has important impacts on agricultural production, water conservation, and water transportation in the middle and lower reaches of the Yangtze River Basin (MLYRB; 25°–35° N and 105°–122° E). We obtain the main empirical orthogonal function (EOF) modes of the interannual [...] Read more.
Autumn precipitation (AP) has important impacts on agricultural production, water conservation, and water transportation in the middle and lower reaches of the Yangtze River Basin (MLYRB; 25°–35° N and 105°–122° E). We obtain the main empirical orthogonal function (EOF) modes of the interannual variation in AP based on daily precipitation data from 97 stations throughout the MLYRB during 1980–2015. The results show that the first leading EOF mode accounts for 30.83% of the total variation. The spatial pattern shows uniform change over the whole region. The variance contribution of the second mode is 16.13%, and its spatial distribution function shows a north-south phase inversion. Based on previous research and the physical considerations discussed herein, we include 13 climate indices to reveal the major predictors. To obtain an acceptable prediction performance, we comprehensively rank the climate indices, which are sorted according to the values of the new standardized algorithm of information flow (NIF, a causality-based approach) and correlation coefficient (a traditional climate diagnostic tool). Finally, Tropical Indian Ocean Dipole (TIOD), Arctic Oscillation (AO), and other four indicators are chosen as the final predictors affecting the first mode of AP over the MLYRB; NINO3.4 SSTA (NINO3.4), Atlantic-European Circulation E Pattern (AECE), and other four indicators are the major predictors for the second mode. In the final prediction experiment, considering the time series prediction of principal components (PCs) to be a small-sample problem, the Bayesian linear regression (BLR) model is used for the prediction. The experimental results reveal that the BLR model can effectively capture the time series trends of the first two modes (the correlation coefficients are greater than 0.5), and the overall performance is significantly better than that of the multiple linear regression (MLR) model. The prediction factors and precipitation prediction results identified in this study can be referenced to rapidly obtain climatological information for AP over the MLYRB and improve the regional prediction of AP elsewhere, which will also help policymakers prepare appropriate adaptation and mitigation measures for future climate change. Full article
(This article belongs to the Special Issue Precipitation: Forecasting and Climate Projections)
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Full article ">Figure 1
<p>Location of the 97 meteorological stations over the MLYRB.</p> Full article ">Figure 2
<p>First leading mode.</p> Full article ">Figure 3
<p>Second leading mode.</p> Full article ">Figure 4
<p>Regression maps based on EWI (the marked areas indicate statistically significant values at the 5% level).</p> Full article ">Figure 4 Cont.
<p>Regression maps based on EWI (the marked areas indicate statistically significant values at the 5% level).</p> Full article ">Figure 5
<p>Regression maps based on TIOD (the marked areas indicate statistically significant values at the 5% level).</p> Full article ">Figure 5 Cont.
<p>Regression maps based on TIOD (the marked areas indicate statistically significant values at the 5% level).</p> Full article ">Figure 6
<p>Regression maps based on AECC (the marked areas indicate statistically significant values at the 5% level).</p> Full article ">Figure 6 Cont.
<p>Regression maps based on AECC (the marked areas indicate statistically significant values at the 5% level).</p> Full article ">Figure 7
<p>Histograms of the Bayesian linear regression (BLR) parameters of PC1 that map all uncertainty onto the parameter space ((<b>a</b>–<b>g</b>) refer to each regression coefficient).</p> Full article ">Figure 8
<p><a href="#climate-08-00053-f007" class="html-fig">Figure 7</a> Histograms of the BLR parameters of PC2 that map all uncertainty onto the parameter space ((<b>a</b>–<b>g</b>) refer to each regression coefficient).</p> Full article ">Figure 9
<p>Comparison among the BLR-predicted results, the MLR sequence and PC1 (the red area represents a 90% confidence interval of the BLR model, and the thick green solid line is the average of the confidence interval for BLR).</p> Full article ">Figure 10
<p>Comparison among the BLR-predicted results, the multiple linear regression (MLR) sequence and PC2 (the red area represents the 90% confidence interval of the BLR model, and the green thick solid line is the average of the confidence interval for BLR).</p> Full article ">
12 pages, 1737 KiB  
Article
Modelling of Regional Economic Metabolism
by Afonso Silva, Bruno Augusto, Sandra Rafael, Johnny Reis, Myriam Lopes, Sérgio Costa and Carlos Borrego
Climate 2020, 8(4), 52; https://doi.org/10.3390/cli8040052 - 2 Apr 2020
Cited by 1 | Viewed by 3039
Abstract
The current linear economic system has led Europe to unsustainable development, aggravating several issues, such as climate change, limitation of resources, and pollution. As a sustainable alternative, circular economy (CE) has been promoted around the world. This economic system allows for the maximization [...] Read more.
The current linear economic system has led Europe to unsustainable development, aggravating several issues, such as climate change, limitation of resources, and pollution. As a sustainable alternative, circular economy (CE) has been promoted around the world. This economic system allows for the maximization of a product’s life, thus decreasing its environmental impact and increasing its value. The main goal of this work is to scrutinise the concepts of CE over time, from the beginning of the concept, to its implementation in Europe and its application in Portugal. In addition, the requirement for strategies that led to studies on regional urban metabolism are addressed. Another goal is to examine Portugal and see how the country is dealing with the implementation of strategies for CE, moving from concept to practice. This part of the work resulted in the creation of the REMET-UA model, a tool to assess the regional economic metabolism, which also has the potential to evaluate synergies of materials in terms of fluxes between regions, maximizing the amount of information available at this scale for municipalities and enterprises to be used, having taken into account the purpose of circular economy. The results showed that REMET-UA is fully operational and corresponds to the goal for which the model was made. Future developments have been identified and are underway to improve the model and bring it as close to reality as possible. Full article
(This article belongs to the Special Issue Fighting Climate Change with Circular Economy)
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<p>Overview of circular economy strategies (planned, updated and on-going) in Europe (Salvatori et al., 2019) [<a href="#B19-climate-08-00052" class="html-bibr">19</a>].</p> Full article ">Figure 2
<p>Conceptual scheme of material flows considered in the REMET-UA model [<a href="#B23-climate-08-00052" class="html-bibr">23</a>].</p> Full article ">Figure 3
<p>Examples of graphic representations produced by REMET-UA model [<a href="#B23-climate-08-00052" class="html-bibr">23</a>].</p> Full article ">Figure 4
<p>Graphical example of the international imports calculated by REMET-UA mode [<a href="#B23-climate-08-00052" class="html-bibr">23</a>].</p> Full article ">
20 pages, 1660 KiB  
Article
Potential Risks of Plant Invasions in Protected Areas of Sri Lanka under Climate Change with Special Reference to Threatened Vertebrates
by Champika S. Kariyawasam, Lalit Kumar and Sujith S. Ratnayake
Climate 2020, 8(4), 51; https://doi.org/10.3390/cli8040051 - 1 Apr 2020
Cited by 14 | Viewed by 6864
Abstract
There is substantial global concern over the potential impacts of plant invasions on native biodiversity in protected areas (PAs). Protected areas in tropical island countries that host rich biodiversity face an imminent risk from the potential spread of invasive alien plant species. Thus, [...] Read more.
There is substantial global concern over the potential impacts of plant invasions on native biodiversity in protected areas (PAs). Protected areas in tropical island countries that host rich biodiversity face an imminent risk from the potential spread of invasive alien plant species. Thus, the aim of this study was to gain a general understanding of the potential risks of multiple plant invasions in PAs located in the tropical island of Sri Lanka under projected climate change. We conducted a further analysis of a multi-species climate suitability assessment, based on a previous study using the Maximum Entropy (MaxEnt) modeling approach, and tested how species invasion may change in protected areas under climate change. We evaluated how the climate suitability of 14 nationally recognized invasive alien plant species (IAPS) will vary within PAs and outside PAs by 2050 under two climate change scenarios, representative concentration pathways (RCP) 4.5 and 8.5. Our findings suggest that there will be increased risks from multiple IAPS inside PAs and outside PAs in Sri Lanka in the future; however, the potential risk is comparatively less in PAs. We provide an overview of the species richness of selected threatened vertebrate groups, which can be potentially impacted by IAPS in PAs. The findings of this study highlight important implications for the strategic management of plant invasions in PAs in order to safeguard native biodiversity, with special reference to vertebrates. Full article
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<p>Protected areas overlaid on projected climate suitability for 14 nationally prioritized invasive alien plant species in Sri Lanka under the current climate and Model for Interdisciplinary Research on Climate (MIROC5) representative concentration pathway (RCP) 4.5 and 8.5 for 2050.</p> Full article ">Figure 2
<p>Representation of five climate suitability classes in the wildlife protected areas of Sri Lanka under the current climate and MIROC5 RCP 4.5 and 8.5 for 2050.</p> Full article ">Figure 3
<p>Representation of five climate suitability classes in the forest protected areas of Sri Lanka under the current climate and MIROC5 RCP 4.5 and 8.5 for 2050.</p> Full article ">Figure 4
<p>Representation of five climate suitability classes outside protected areas of Sri Lanka under the current climate and MIROC5 RCP 4.5 and 8.5 for 2050.</p> Full article ">Figure 5
<p>Occurrences of critically endangered amphibians, reptiles, and mammals (available at GBIF on September 21, 2019) overlaid on climate suitability for invasive alien plant species under the current climate and MIROC5 RCP 4.5 and 8.5 for 2050.</p> Full article ">Figure 6
<p>Occurrences of critically endangered birds (available at GBIF on September 21, 2019) overlaid on climate suitability for invasive alien plant species establishment under the current climate and MIROC5 RCP 4.5 and 8.5 for 2050.</p> Full article ">
14 pages, 4206 KiB  
Article
Assessment of Climate Change Impacts on Sea Surface Temperatures and Sea Level Rise—The Arabian Gulf
by Mohamed E. Hereher
Climate 2020, 8(4), 50; https://doi.org/10.3390/cli8040050 - 30 Mar 2020
Cited by 43 | Viewed by 16283
Abstract
The Arabian Gulf is one of the regions in the world experiencing major changes due to increased economic growth rates and development practices. As a shallow water body within a hot desert, the Gulf is exposed to obvious warming in the sea surface [...] Read more.
The Arabian Gulf is one of the regions in the world experiencing major changes due to increased economic growth rates and development practices. As a shallow water body within a hot desert, the Gulf is exposed to obvious warming in the sea surface temperatures (SST). Remotely sensed SST data were utilized to estimate decadal change in SST with a focus on coral reef locations. There is a positive trend in monthly time series SSTs, with a maximum value of about 0.7 °C/decade for the western side of the Gulf. This high trend of SST is associated with significant coral reef bleaching and it coincides with major climate/ocean interactions. Most of the Arabian countries along the Gulf have coastal developments at low-land areas of high vulnerability to sea level rise. Digital elevation models showed that there are more than 3100 km2 of coastal areas that occur at 1 m level along the Arabian countries of the Gulf. Coastal protection and conservation measures are crucial to protect low-lying coasts of urban use. Full article
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<p>Regional satellite image from Moderate Resolution Imaging Spectroradiometer (MODIS) showing the Arabian Gulf region.</p> Full article ">Figure 2
<p>A bathymetry map of the Arabian Gulf. Note the shallow water depths for the Gulf.</p> Full article ">Figure 3
<p>Monthly maps of the sea surface temperatures (SST) in the Arabian Gulf. Note the variation from the west to east.</p> Full article ">Figure 4
<p>The mean annual distribution of the SST in the Arabian Gulf.</p> Full article ">Figure 5
<p>The distribution of corals and bleached corals in the Gulf. Note that there are seven coral assemblages mostly occurring along the southern side of the Gulf. Source: <a href="http://www.reefbase.org" target="_blank">http://www.reefbase.org</a>.</p> Full article ">Figure 6
<p>The time-series monthly SST trends for the seven locations of the coral assemblages. The X and Y axes refer to the degree Celsius and months, respectively.</p> Full article ">Figure 7
<p>The landscape change in United Arab Emirates (UAE) (left) and Bahrain (right) as revealed by Google Earth maps in 1985 and 2016.</p> Full article ">Figure 8
<p>The distribution of the low-lands along the Arabian countries, (<b>a</b>) is for southern Iraq, (<b>b</b>) northern UAE, (<b>c</b>) eastern Kuwait, (<b>d</b>) northern Bahrain, (<b>e</b>) Qatar and Bahrain, (<b>f</b>) eastern Kingdom of Saudi Arabia (KSA), and (<b>g</b>) Musandam Peninsula of Oman.</p> Full article ">
9 pages, 191 KiB  
Review
A Review of Ocean Dynamics in the North Atlantic: Achievements and Challenges
by Knut Lehre Seip
Climate 2020, 8(4), 49; https://doi.org/10.3390/cli8040049 - 30 Mar 2020
Viewed by 3354
Abstract
I address 12 issues related to the study of ocean dynamics and its impact on global temperature change, regional and local climate change, and on the North Atlantic ecosystem. I outline the present achievements and challenges that lie ahead. I start with observations [...] Read more.
I address 12 issues related to the study of ocean dynamics and its impact on global temperature change, regional and local climate change, and on the North Atlantic ecosystem. I outline the present achievements and challenges that lie ahead. I start with observations and methods to extend the observations of ocean oscillations over time and end with challenges to find connections between ocean dynamics in the North Atlantic and dynamics in other parts of the globe. Full article
(This article belongs to the Special Issue The North Atlantic Ocean Dynamics and Climate Change)
28 pages, 4403 KiB  
Review
Human–Environment Natural Disasters Interconnection in China: A Review
by Rawshan Ali, Alban Kuriqi and Ozgur Kisi
Climate 2020, 8(4), 48; https://doi.org/10.3390/cli8040048 - 26 Mar 2020
Cited by 78 | Viewed by 9455
Abstract
This study aimed to assess the interrelationship among extreme natural events and their impacts on environments and humans through a systematic and quantitative review based on the up-to-date scientific literature. Namely, the main goal was to add additional knowledge to the existing evidence [...] Read more.
This study aimed to assess the interrelationship among extreme natural events and their impacts on environments and humans through a systematic and quantitative review based on the up-to-date scientific literature. Namely, the main goal was to add additional knowledge to the existing evidence of the impacts related to floods, droughts, and landslides on humans and the environment in China; this in order to identify knowledge gaps in research and practice to aid in improving the adaptation and mitigation measures against extreme natural events in China. In this study, 110 documents were analyzed in the evaluation of several impacts triggered by extreme events. Records were obtained from Scopus and Web of Science and examined with a text mining instrument to assess the pattern of publications over the years; the problems linked to extreme weather events were investigated, and the study gaps were discussed. This paper extends work by systematically reviewing recent evidence related to floods, droughts, and landslides in China. We listed the critical studies that focused on the impact of extreme events on both humans and the environment described in current reviews. The findings revealed that goods safety, social safety, and financial losses are of significant concern to the scientific community due to extreme natural events, which from our analysis resulted in being more frequent and intense. It is still underdeveloped to implement distant sensing and imaging methods to monitor and detect the impact of severe weather occurrences. There are still significant study gaps in the fields of the effects of extreme weather events. The analysis result shows that extreme events are increased during the time, so more in-depth investigation and efforts on adaptation, mitigation measures, and strategical governance plans are desperately required. Full article
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<p>The central panel shows the flood hazard index over China (after [<a href="#B25-climate-08-00048" class="html-bibr">25</a>]). (<b>a</b>) Submerged neighborhood in Jiangxi province (<span class="html-italic">credits:</span> REUTERS); (<b>b</b>) high water level rise in Changsha, Hunan province (<span class="html-italic">credits:</span> Xinhua); and (<b>c</b>) Dajing city, Zhejiang province under floods, August 2019 (<span class="html-italic">credits:</span> VCG).</p> Full article ">Figure 2
<p>The central panel shows spatial distribution of drought frequency in China (after [<a href="#B44-climate-08-00048" class="html-bibr">44</a>]. (<b>a</b>) Massive dead fish in a dried lake located in inner Mongolia (<span class="html-italic">credits:</span> <a href="http://earth-chronicles.com" target="_blank">http://earth-chronicles.com</a>); (<b>b</b>) rice cultivated land, lack of soil moister and deep cracks in Xinqiao Township, southwest China’s Yunnan Province, July 8, 2015 (<span class="html-italic">credits:</span> Xinhua/Yang Zongyou); and (<b>c</b>) Former Shima reservoir at Shima Village of Shaoyang County, central China’s Hunan Province, July 27, 2013, completely dried-up (<span class="html-italic">credits:</span> Xinhua/Yang Zongyou).</p> Full article ">Figure 3
<p>The central panel shows hazard landslides map over China. (<b>a</b>) Massive landslide in Zhouqu on August 12, 2010, in northwest China’s Gansu province destroyed several houses and many people died (<span class="html-italic">credits:</span> FREDERIC J. BROWN/AFP/Getty Images); (<b>b</b>) residential buildings collapsed during a landslide in Xiangning county in Shanxi province (<span class="html-italic">credits:</span> Handout); and (<b>c</b>) buildings collapse during a landslide in Shenzhen, Guangdong province (<span class="html-italic">credits</span>: REUTERS).</p> Full article ">Figure 4
<p>Flow chart of the article selection process. “n” stands for the number of publications.</p> Full article ">Figure 5
<p>The relationship between year and number of papers for three events.</p> Full article ">Figure 6
<p>The relationship between year and number of events.</p> Full article ">Figure 7
<p>Qualitative representation of the climate extremes evolution modified from [<a href="#B6-climate-08-00048" class="html-bibr">6</a>].</p> Full article ">
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