[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Next Issue
Volume 7, January
Previous Issue
Volume 6, September
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.0
Journals
Climate
Volume 6
Issue 4
share announcement

Climate, Volume 6, Issue 4 (December 2018) – 23 articles

Cover Story (view full-size image): We present a proactive decision support to compute the impacts of different possible scenarios for territories impacted by mountain risks. The objective of this work was to develop and test various hazard and risk assessment methods, and to implement them into a web-application platform showing possible risks induced by global change on ecosystems and society. This web-tool, dedicated to stakeholders, has proven its usefulness to test various socio-economical pathways as multiple scenarios—considered as probable in inhabited valleys—can be benchmarked, analyzed, and compared. View this paper.
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
21 pages, 3221 KiB  
Article
Multi-Model Forecasts of Very-Large Fire Occurences during the End of the 21st Century
by Harry R. Podschwit, Narasimhan K. Larkin, E. Ashley Steel, Alison Cullen and Ernesto Alvarado
Climate 2018, 6(4), 100; https://doi.org/10.3390/cli6040100 - 19 Dec 2018
Cited by 23 | Viewed by 6011
Abstract
Climate change is anticipated to influence future wildfire activity in complicated, and potentially unexpected ways. Specifically, the probability distribution of wildfire size may change so that incidents that were historically rare become more frequent. Given that fires in the upper tails of the [...] Read more.
Climate change is anticipated to influence future wildfire activity in complicated, and potentially unexpected ways. Specifically, the probability distribution of wildfire size may change so that incidents that were historically rare become more frequent. Given that fires in the upper tails of the size distribution are associated with serious economic, public health, and environmental impacts, it is important for decision-makers to plan for these anticipated changes. However, at least two kinds of structural uncertainties hinder reliable estimation of these quantities—those associated with the future climate and those associated with the impacts. In this paper, we incorporate these structural uncertainties into projections of very-large fire (VLF)—those in the upper 95th percentile of the regional size distribution—frequencies in the Continental United States during the last half of the 21st century by using Bayesian model averaging. Under both moderate and high carbon emission scenarios, large increases in VLF frequency are predicted, with larger increases typically observed under the highest carbon emission scenarios. We also report other changes to future wildfire characteristics such as large fire frequency, seasonality, and the conditional likelihood of very-large fire events. Full article
(This article belongs to the Special Issue Climate Variability and Change in the 21th Century)
►▼ Show Figures

Figure 1

Figure 1
<p>Map of multi-model mean temperature changes between 1955–2005 and 2050–2099 over the relevant Bailey’s divisions under the representative concentration pathway (RCP) 4.5 (top) and RCP 8.5 emission scenarios (bottom). Regions with insufficient fire occurrence data for analysis are colored white.</p> Full article ">Figure 2
<p>Workflow of very-large fire probability calculations. In the prediction phase, probability estimates are calculated for every combination pair of climate model and probability estimation tree. The model-averaging phase combines these predictions into probability estimates using either a weighted or unweighted averages. The final phase uses Bayes rule to calculate the very-large fire probability as the product of both components from the model-averaging phase.</p> Full article ">Figure 3
<p>Summary statistics of each forest of probability estimation trees. The top panels show the percentage of probability estimation trees (PETs) in each forest that uses a particular weather predictor for at least one split. The bottom panels show the percentage of PETs for which a weather predictor is selected as the optimal splitting criterion. The relative contribution of each first-split variable under the unweighted (left) and weighted (right) averaging methods are displayed side-by-side.</p> Full article ">Figure 4
<p>Kernel density estimates of the posterior and mean of the number of additional very-large fires per year relative to the 1956–2005 reference period per year by ecoregion under the representative concentration pathway (RCP) 4.5 (blue) and RCP 8.5 (red) scenarios arranged by magnitude of change. The excess very-large fire frequency is calculated by randomly sampling (<math display="inline"><semantics> <mrow> <mi>n</mi> <mo>=</mo> <msup> <mn>10</mn> <mn>6</mn> </msup> </mrow> </semantics></math>) from the posterior of historical (1956–2005) and future (2050–2099) multi-model averages and calculating the difference.</p> Full article ">Figure 5
<p>Predicted intra-annual changes in very-large fire frequency across sixteen biogeographical regions within the Continental United States under the RCP 4.5 (blue) and RCP 8.5 (red). The central 90th percentile and mean of the excess very-large fires are based on 1,000,000 random samples of the posterior multi-model average very-large fire probabilities from the historical (1956–2005) and future (2050–2099) scenarios.</p> Full article ">Figure 6
<p>Simulated change in monthly multi-model large-fire and conditional very-large fire probability estimates across biogeographical divisions of the continental United States. The point cloud is a sample of 100,000 differences in average posterior probability components under the historical (1956–2005) and future scenarios (2050–2099); with the RCP 8.5 scenario colored red and RCP 4.5 colored blue. The solid black lines represent the central 90th percentile and the dashed lines are horizontal and vertical lines passing through the origin.</p> Full article ">Figure 7
<p>Simulated and actual very-large fire month counts for each region and three time periods: 1984–2005, 2006–2015, and 2016. A sample of 100,000 simulated very-large fire (VLF) counts are produced under historical (grey), RCP 4.5 (blue), and RCP 8.5 (red) scenarios by randomly selecting a VLF probability time series from the posterior and randomly generating a VLF occurrence time series. The observed VLF counts are represented with arrows.</p> Full article ">
28 pages, 3720 KiB  
Article
Analysis of the Impact of Values and Perception on Climate Change Skepticism and Its Implication for Public Policy
by Jaesun Wang and Seoyong Kim
Climate 2018, 6(4), 99; https://doi.org/10.3390/cli6040099 - 14 Dec 2018
Cited by 43 | Viewed by 14097
Abstract
Climate change is an unprecedented risk that humans have not previously experienced. It is accepted that people are generally worried about global warming. However, it is also a fact that there is a small but increasing number of climate change skeptics. These skeptics [...] Read more.
Climate change is an unprecedented risk that humans have not previously experienced. It is accepted that people are generally worried about global warming. However, it is also a fact that there is a small but increasing number of climate change skeptics. These skeptics do not believe that there is any risk, nor are they concerned with other worrisome facts related to climate change. Skeptics regard the present scientific findings supporting climate change as false artefacts. Our study aimed to explore the factors that influence climate skepticism. In this work, to make a regression model, we established environmental skepticism as a dependent variable and included sociodemographic factors, values, and perception factors as the three independent variables. Also, to examine their roles indirectly, we regarded values as moderators. The results show that, in terms of values, ideology, environmentalism, religiosity, two kinds of cultural biases, and science and technology (S&T) optimism influence skepticism at the individual level, whereas, in terms of perception factors, perceived risk, perceived benefit, and negative affect have an impact. Also, values such as ideology, religiosity, environmentalism, and cultural biases play a moderating role that facilitates, buffers, or changes the effect of psychometric variables on an individual’s skepticism. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Research framework and hypotheses.</p> Full article ">Figure 2
<p>Attitudes toward climate change.</p> Full article ">Figure 3
<p>Mean by sociodemographic variables.</p> Full article ">Figure 4
<p>Perceived risk (IV) × religiosity (M) = skepticism (DV). Note: IV (independent variable), M (moderator), DV (dependent variable).</p> Full article ">Figure 5
<p>Perceived risk (IV) × individualism (M) = skepticism (DV).</p> Full article ">Figure 6
<p>Perceived benefit (IV) × individualism (M) = skepticism (DV).</p> Full article ">Figure 7
<p>Trust (IV) × ideology (M) = skepticism (DV).</p> Full article ">Figure 8
<p>Trust (IV) × environmentalism (M) = skepticism (DV).</p> Full article ">Figure 9
<p>Negative affect (IV) × egalitarianism (M) = skepticism (DV).</p> Full article ">Figure 10
<p>Knowledge (IV) × religiosity (M) = skepticism (DV).</p> Full article ">Figure 11
<p>Knowledge (IV) × hierarchy (M) = skepticism (DV).</p> Full article ">Figure 12
<p>Knowledge (IV) × individualism (M) = skepticism (DV).</p> Full article ">
19 pages, 10541 KiB  
Article
Observational Evidence of Neighborhood Scale Reductions in Air Temperature Associated with Increases in Roof Albedo
by Arash Mohegh, Ronnen Levinson, Haider Taha, Haley Gilbert, Jiachen Zhang, Yun Li, Tianbo Tang and George A. Ban-Weiss
Climate 2018, 6(4), 98; https://doi.org/10.3390/cli6040098 - 12 Dec 2018
Cited by 16 | Viewed by 4855
Abstract
The effects of neighborhood-scale land use and land cover (LULC) properties on observed air temperatures are investigated in two regions within Los Angeles County: Central Los Angeles and the San Fernando Valley (SFV). LULC properties of particular interest in this study are albedo [...] Read more.
The effects of neighborhood-scale land use and land cover (LULC) properties on observed air temperatures are investigated in two regions within Los Angeles County: Central Los Angeles and the San Fernando Valley (SFV). LULC properties of particular interest in this study are albedo and tree fraction. High spatial density meteorological observations are obtained from 76 personal weather-stations. Observed air temperatures were then related to the spatial mean of each LULC parameter within a 500 m radius “neighborhood” of each weather station, using robust regression for each hour of July 2015. For the neighborhoods under investigation, increases in roof albedo are associated with decreases in air temperature, with the strongest sensitivities occurring in the afternoon. Air temperatures at 14:00–15:00 local daylight time are reduced by 0.31 °C and 0.49 °C per 1 MW increase in daily average solar power reflected from roofs per neighborhood in SFV and Central Los Angeles, respectively. Per 0.10 increase in neighborhood average albedo, daily average air temperatures were reduced by 0.25 °C and 1.84 °C. While roof albedo effects on air temperature seem to exceed tree fraction effects during the day in these two regions, increases in tree fraction are associated with reduced air temperatures at night. Full article
(This article belongs to the Special Issue Climate Change Resilience and Urban Sustainability)
►▼ Show Figures

Figure 1

Figure 1
<p>The aggregation area, or “neighborhood” (green circle of radius 500 m) around an example weather station (red dot) in SFV. The underlying imagery shows the building footprint dataset.</p> Full article ">Figure 2
<p>Location of selected personal weather stations in the Los Angeles basin. Stations are color coded to identify study regions. Note that the dots are drawn to scale to indicate the size of each 500 m radius neighborhood.</p> Full article ">Figure 3
<p>Afternoon (14:00–15:00 LDT) temperature versus daily average reflected solar power from roofs per neighborhood (500 m radius circle around each station) in Central Los Angeles for each day during July 2015. Each subpanel represents one day, and each point represents a single weather station and associated LULC parameter. Slopes from least squares regressions are used to obtain daily sensitivities of the temperature to the LULC parameter under investigation. The mean irradiance (W/m<sup>2</sup>) at 14:00–15:00 LDT is shown above each subpanel. Red dots are removed from regressions as outliers. The red dotted regression line corresponds to linear regressions using all points (including outliers) and the black line corresponds to those using only the black squares (non-outliers). The size of each point (area) is proportional to its influence.</p> Full article ">Figure 4
<p>Boxplots for the diurnal cycle of sensitivity of temperature to (<b>a</b>,<b>b</b>) daily average solar power reflected by roofs, and (<b>c</b>,<b>d</b>) tree fraction. Panels (<b>a</b>,<b>c</b>) are for Central Los Angeles, and panels (<b>b</b>,<b>d</b>) are for San Fernando Valley (SFV). Each box contains the sensitivities per hour for the entire month (July 2015). The hours with statistically insignificant sensitivities (see Methodology section for details) have red hatching. Boxes show the inner-quartile range (IQR); whiskers show [(Q1 − 1.5 IQR), (Q3 + 1.5 IQR)], and the black line within the box represents the median. Hour of day 1 = 00:00 to 01:00 LDT.</p> Full article ">Figure 4 Cont.
<p>Boxplots for the diurnal cycle of sensitivity of temperature to (<b>a</b>,<b>b</b>) daily average solar power reflected by roofs, and (<b>c</b>,<b>d</b>) tree fraction. Panels (<b>a</b>,<b>c</b>) are for Central Los Angeles, and panels (<b>b</b>,<b>d</b>) are for San Fernando Valley (SFV). Each box contains the sensitivities per hour for the entire month (July 2015). The hours with statistically insignificant sensitivities (see Methodology section for details) have red hatching. Boxes show the inner-quartile range (IQR); whiskers show [(Q1 − 1.5 IQR), (Q3 + 1.5 IQR)], and the black line within the box represents the median. Hour of day 1 = 00:00 to 01:00 LDT.</p> Full article ">Figure 5
<p>Daily average solar power reflected by roofs vs. tree fraction for each region. Each point represents a different neighborhood (500 m radius around a weather station). Least squares linear regressions are also shown for SFV (black line) and Central Los Angeles (red line).</p> Full article ">Figure 6
<p>Comparison of daily average solar power reflected from (<b>a</b>) roof and (<b>b</b>) non-roof surfaces versus daily average solar power reflected from all surfaces in each corresponding neighborhood. Least squares linear regressions are also shown separately for the two areas (i.e., SFV and Central Los Angeles). The higher coefficients of determination (<span class="html-italic">R</span><sup>2</sup>) in panel (<b>a</b>) versus (<b>b</b>) suggest that variations in roof albedo are responsible for the majority of variations in neighborhood albedo.</p> Full article ">
20 pages, 919 KiB  
Review
Geographic Information and Communication Technologies for Supporting Smallholder Agriculture and Climate Resilience
by Billy Tusker Haworth, Eloise Biggs, John Duncan, Nathan Wales, Bryan Boruff and Eleanor Bruce
Climate 2018, 6(4), 97; https://doi.org/10.3390/cli6040097 - 10 Dec 2018
Cited by 23 | Viewed by 8372
Abstract
Multiple factors constrain smallholder agriculture and farmers’ adaptive capacities under changing climates, including access to information to support context appropriate farm decision-making. Current approaches to geographic information dissemination to smallholders, such as the rural extension model, are limited, yet advancements in internet and [...] Read more.
Multiple factors constrain smallholder agriculture and farmers’ adaptive capacities under changing climates, including access to information to support context appropriate farm decision-making. Current approaches to geographic information dissemination to smallholders, such as the rural extension model, are limited, yet advancements in internet and communication technologies (ICTs) could help augment these processes through the provision of agricultural geographic information (AGI) directly to farmers. We analysed recent ICT initiatives for communicating climate and agriculture-related information to smallholders for improved livelihoods and climate change adaptation. Through the critical analysis of initiatives, we identified opportunities for the success of future AGI developments. We systematically examined 27 AGI initiatives reported in academic and grey literature (e.g., organisational databases). Important factors identified for the success of initiatives include affordability, language(s), community partnerships, user collaboration, high quality and locally-relevant information through low-tech platforms, organisational trust, clear business models, and adaptability. We propose initiatives should be better-targeted to deliver AGI to regions in most need of climate adaptation assistance, including SE Asia, the Pacific, and the Caribbean. Further assessment of the most effective technological approaches is needed. Initiatives should be independently assessed for evaluation of their uptake and success, and local communities should be better-incorporated into the development of AGI initiatives. Full article
(This article belongs to the Special Issue Sustainable Agriculture for Climate Change Adaptation)
►▼ Show Figures

Figure 1

Figure 1
<p>Flow diagram for the academic literature search resulting in 11 relevant papers (12 agricultural geographic information (AGI) initiatives) for analysis (see <a href="#climate-06-00097-t001" class="html-table">Table 1</a> for sources). * One paper described multiple initiatives.</p> Full article ">Figure 2
<p>Technologies featured in reviewed AGI initiatives.</p> Full article ">
18 pages, 4425 KiB  
Article
Objective Definition of Climatologically Homogeneous Areas in the Southern Balkans Based on the ERA5 Data Set
by Christos J. Lolis, Georgios Kotsias and Aristides Bartzokas
Climate 2018, 6(4), 96; https://doi.org/10.3390/cli6040096 - 7 Dec 2018
Cited by 4 | Viewed by 4018
Abstract
An objective definition of climatologically homogeneous areas in the southern Balkans is attempted with the use of daily 0.25° × 0.25° ERA5 meteorological data of air temperature, dew point, zonal and meridional wind components, Convective Available Potential Energy, Convective Inhibition, and total cloud [...] Read more.
An objective definition of climatologically homogeneous areas in the southern Balkans is attempted with the use of daily 0.25° × 0.25° ERA5 meteorological data of air temperature, dew point, zonal and meridional wind components, Convective Available Potential Energy, Convective Inhibition, and total cloud cover. The classification of the various grid points into climatologically homogeneous areas is carried out by applying Principal Component Analysis and K-means Cluster Analysis on the mean spatial anomaly patterns of the above parameters for the 10-year period of 2008 to 2017. According to the results, 12 climatologically homogenous areas are found. From these areas, eight are mainly over the sea and four are mainly over the land. The mean intra-annual variations of the spatial anomalies of the above parameters reveal the main climatic characteristics of these areas for the above period. These characteristics refer, for example, to how much warmer or cloudy the climate of a specific area is in a specific season relatively to the rest of the geographical domain. The continentality, the latitude, the altitude, the orientation, and the seasonal variability of the thermal and dynamic factors affecting the Mediterranean region are responsible for the climate characteristics of the 12 areas and the differences among them. Full article
(This article belongs to the Special Issue Climate Variability and Change in the 21th Century)
►▼ Show Figures

Figure 1

Figure 1
<p>The geographical domain used.</p> Full article ">Figure 2
<p>The methodology scheme used in the present study.</p> Full article ">Figure 3
<p>The spatial distribution of the 12 clusters.</p> Full article ">Figure 4
<p>Mean intra-annual variations of the spatial anomalies of the meteorological parameters for the areas of clusters 1 and 2. Continuous and dashed lines correspond to 12UTC and 00UTC respectively.</p> Full article ">Figure 5
<p>As in <a href="#climate-06-00096-f004" class="html-fig">Figure 4</a>, but for clusters 3 and 4.</p> Full article ">Figure 6
<p>As in <a href="#climate-06-00096-f004" class="html-fig">Figure 4</a>, but for clusters 5 and 6.</p> Full article ">Figure 7
<p>As in <a href="#climate-06-00096-f004" class="html-fig">Figure 4</a>, but for clusters 7 and 8.</p> Full article ">Figure 8
<p>As in <a href="#climate-06-00096-f004" class="html-fig">Figure 4</a>, but for clusters 9 and 10.</p> Full article ">Figure 9
<p>As in <a href="#climate-06-00096-f004" class="html-fig">Figure 4</a>, but for clusters 11 and 12.</p> Full article ">Figure 10
<p>Scatterplots of the spatial mean values of ERA-Interim and ERA5 air temperature and total cloud cover for the areas of clusters 7 and 10–12 (land areas).</p> Full article ">Figure 11
<p>As in <a href="#climate-06-00096-f010" class="html-fig">Figure 10</a>, but for clusters 1–6 and 8–9 (sea areas).</p> Full article ">Figure 12
<p>The mean intra-annual variations of the spatial anomalies of air temperature and total cloud cover for clusters 1 and 11. Black lines represent the ERA5 dataset while red lines represent the ERA-Interim dataset. Solid and dashed lines correspond to 12UTC and 00UTC, respectively.</p> Full article ">
24 pages, 8317 KiB  
Article
Time Series Analysis of MODIS-Derived NDVI for the Hluhluwe-Imfolozi Park, South Africa: Impact of Recent Intense Drought
by Nkanyiso Mbatha and Sifiso Xulu
Climate 2018, 6(4), 95; https://doi.org/10.3390/cli6040095 - 30 Nov 2018
Cited by 50 | Viewed by 10374
Abstract
The variability of temperature and precipitation influenced by El Niño-Southern Oscillation (ENSO) is potentially one of key factors contributing to vegetation product in southern Africa. Thus, understanding large-scale ocean–atmospheric phenomena like the ENSO and Indian Ocean Dipole/Dipole Mode Index (DMI) is important. In [...] Read more.
The variability of temperature and precipitation influenced by El Niño-Southern Oscillation (ENSO) is potentially one of key factors contributing to vegetation product in southern Africa. Thus, understanding large-scale ocean–atmospheric phenomena like the ENSO and Indian Ocean Dipole/Dipole Mode Index (DMI) is important. In this study, 16 years (2002–2017) of Moderate Resolution Imaging Spectroradiometer (MODIS) Terra/Aqua 16-day normalized difference vegetation index (NDVI), extracted and processed using JavaScript code editor in the Google Earth Engine (GEE) platform was used to analyze the vegetation response pattern of the oldest proclaimed nature reserve in Africa, the Hluhluwe-iMfolozi Park (HiP) to climatic variability. The MODIS enhanced vegetation index (EVI), burned area index (BAI), and normalized difference infrared index (NDII) were also analyzed. The study used the Modern Retrospective Analysis for the Research Application (MERRA) model monthly mean soil temperature and precipitations. The Global Land Data Assimilation System (GLDAS) evapotranspiration (ET) data were used to investigate the HiP vegetation water stress. The region in the southern part of the HiP which has land cover dominated by savanna experienced the most impact of the strong El Niño. Both the HiP NDVI inter-annual Mann–Kendal trend test and sequential Mann–Kendall (SQ-MK) test indicated a significant downward trend during the El Niño years of 2003 and 2014–2015. The SQ-MK significant trend turning point which was thought to be associated with the 2014–2015 El Niño periods begun in November 2012. The wavelet coherence and coherence phase indicated a positive teleconnection/correlation between soil temperatures, precipitation, soil moisture (NDII), and ET. This was explained by a dominant in-phase relationship between the NDVI and climatic parameters especially at a period band of 8–16 months. Full article
(This article belongs to the Special Issue Climate Variability and Change in the 21th Century)
►▼ Show Figures

Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>The study area showing the Hluhluwe-iMfolozi Park in the north-eastern part of South Africa.</p> Full article ">Figure 2
<p>The standardized monthly Niño3.4 (<b>a</b>) and dipole mode index (DMI) (<b>b</b>) time series for the period from 1980 to 2017.</p> Full article ">Figure 3
<p>The spatiotemporal variability of normalized difference vegetation index (NDVI) at the Hluhluwe-iMfolozi Park for the period from 2002 to 2017. The scale represents the range of NDVI values from 0 to 1.</p> Full article ">Figure 4
<p>(<b>a</b>) The deseasonalized monthly mean NDVI time series for HiP. The continuous red line indicates the trend estimate and the dashed red lines show the 95% confidence interval for the trend based on resampling methods. (<b>b</b>,<b>c</b>) show the histogram and yearly mean time series, respectively.</p> Full article ">Figure 5
<p>The monthly mean time series values of (<b>a</b>) NDVI, (<b>b</b>) Enhanced Vegetation Index (EVI), (<b>c</b>) Burned Area (BAI), and Modern Retrospective Analysis for the Research Application (MERRA-2) model soil temperature (<b>d</b>) and precipitation (<b>e</b>), Global Land Data Assimilation System (GILDAS) evapotranspiration (<b>f</b>) and Normalized Difference Infrared Index (NDII) (<b>g</b>). The dotted lines represent the 12-month smooth trends.</p> Full article ">Figure 6
<p>The (<b>a</b>,<b>b</b>) NDVI, (<b>c</b>,<b>d</b>) EVI and (<b>e</b>,<b>f</b>) BAI (blue dashed line) 12-month smooth trends versus Niño3.4 (left panels) and DMI (right panels) for the period from 2002 to 2017 for the HiP.</p> Full article ">Figure 7
<p>The heat map of Pearson correlation coefficients for NDVI, NDII, precipitation (Prec), soil temperature (Soil.Temo), ET, BAI, Niño3.4, and DMI.</p> Full article ">Figure 8
<p>The inter-annual variability of linear correlations between NDVI and BAI, Soil Temp, Prec, Niño3.4, DMI, ET, and NDII for the period 2002 to 2017.</p> Full article ">Figure 9
<p>(<b>a</b>) The inter-annual variation of Mann–Kendall <span class="html-italic">z</span>-scores (α = 0.05, <span class="html-italic">Z</span>1 = –1.96, <span class="html-italic">Z</span>2 = 1.96) for the HiP from 2002 to 2017. (<b>b</b>) Sequential statistics values of progressive (Prog) <math display="inline"><semantics> <mrow> <mi>u</mi> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (solid red line) and retrograde <math display="inline"><semantics> <mrow> <msup> <mi>u</mi> <mo>′</mo> </msup> <mrow> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </mrow> </semantics></math> (black solid line) obtained by Sequential Mann-Kendall (SQ-MK) test for HiP monthly mean NDVI data for the period from 2002 to 2017.</p> Full article ">Figure 10
<p>The normalized wavelet power spectra of monthly mean (<b>a</b>) NDVI, (<b>b</b>) precipitation, (<b>c</b>) Soil temperature, (<b>d</b>) DMI, (<b>e</b>) Niño3, (<b>f</b>) NDII, and (<b>g</b>) ET, plotted for the period from 2002 to 2017. The black lines which encircle the yellowish colors indicate the areas of significance at the 95% confidence level using the red noise model.</p> Full article ">Figure 11
<p>The squared cross-wavelet power spectra for NDVI–Niño3.4, NDVI–DMI, NDVI-precipitation, NDVI–soil temperature, NDVI–NDII, and NDVI–ET. The continuous black lines demarcate the areas of significance at the 95% confidence level using the red noise model. The arrows are vectors indicating the phase difference between the cross-wavelet parameters (see the legend in the bottom left corner).</p> Full article ">Figure A1
<p>The Google Earth Engine interactive development environment.</p> Full article ">
11 pages, 2737 KiB  
Article
Field Measurements and Satellite Remote Sensing of Daily Soil Surface Temperature Variations in the Lower Colorado Desert of California
by Dana Coppernoll-Houston and Christopher Potter
Climate 2018, 6(4), 94; https://doi.org/10.3390/cli6040094 - 30 Nov 2018
Cited by 9 | Viewed by 4106
Abstract
The purpose of this study was to better understand the relationships between diurnal variations of air temperature measured hourly at the soil surface, compared with the thermal infra-red (TIR) emission properties of soil surfaces located in the Lower Colorado Desert of California, eastern [...] Read more.
The purpose of this study was to better understand the relationships between diurnal variations of air temperature measured hourly at the soil surface, compared with the thermal infra-red (TIR) emission properties of soil surfaces located in the Lower Colorado Desert of California, eastern Riverside County. Fifty air temperature loggers were deployed in January of 2017 on wooden stakes that were driven into the sandy or rocky desert soils at both Ford Dry Lake and the southern McCoy Mountains wash. The land surface temperature (LST) derived from Landsat satellite images was compared to measured air temperatures at 1 m and at the soil surface on 14 separate dates, until mid-September, 2017. Results showed that it is feasible to derive estimated temperatures at the soil surface from hourly air temperatures, recorded at 1 m above the surface (ambient). The study further correlated Landsat LST closely with site measurements of air and surface temperatures in these solar energy development zones of southern California, allowing inter-conversion with ground-based measurements for use in ecosystem change and animal population biology studies. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Transect locations of temperature measurement stakes (black dots within circles) in eastern Riverside County, California. The southern McCoy Mountains are to the right center on the true-color satellite image, while Ford Dry Lake is on the western-most section of the study area map.</p> Full article ">Figure 2
<p>Climatology from the Ford Dry Lake SCAN station in 2017.</p> Full article ">Figure 3
<p>Diurnal T<sub>surface</sub> and T<sub>air</sub> for a selected measurement location (27) for all available dates of Landsat image acquisition.</p> Full article ">Figure 4
<p>Scatterplot of T<sub>air</sub> and 2017 Landsat LST image data (for measurement location 1) in Ford Dry Lake.</p> Full article ">Figure 5
<p>Slope and <span class="html-italic">R</span><sup>2</sup> for the linear best-fit line between hourly T<sub>air</sub> measurements and LST from the Landsat imagery. The “best-fit” 1:1 ratio level is show as a dashed horizonal line.</p> Full article ">Figure 6
<p>Comparison of the T<sub>surface</sub>, T<sub>air</sub>, and LST at the hour of the Landsat fly-over for a selected measurement location (13).</p> Full article ">Figure 7
<p>June 2016 Landsat LST gray-scale image, centered on the Palen and McCoy Wilderness Area in eastern Riverside County and the solar energy development zones (shown in white outlines). LST ranges from &lt;40 °C in black shades to &gt;50 °C in light gray shades.</p> Full article ">
15 pages, 714 KiB  
Article
Emergent Scale Invariance and Climate Sensitivity
by Martin Rypdal, Hege-Beate Fredriksen, Eirik Myrvoll-Nilsen, Kristoffer Rypdal and Sigrunn H. Sørbye
Climate 2018, 6(4), 93; https://doi.org/10.3390/cli6040093 - 28 Nov 2018
Cited by 9 | Viewed by 5089
Abstract
Earth’s global surface temperature shows variability on an extended range of temporal scales and satisfies an emergent scaling symmetry. Recent studies indicate that scale invariance is not only a feature of the observed temperature fluctuations, but an inherent property of the temperature response [...] Read more.
Earth’s global surface temperature shows variability on an extended range of temporal scales and satisfies an emergent scaling symmetry. Recent studies indicate that scale invariance is not only a feature of the observed temperature fluctuations, but an inherent property of the temperature response to radiative forcing, and a principle that links the fast and slow climate responses. It provides a bridge between the decadal- and centennial-scale fluctuations in the instrumental temperature record, and the millennial-scale equilibration following perturbations in the radiative balance. In particular, the emergent scale invariance makes it possible to infer equilibrium climate sensitivity (ECS) from the observed relation between radiative forcing and global temperature in the instrumental era. This is verified in ensembles of Earth system models (ESMs), where the inferred values of ECS correlate strongly to estimates from idealized model runs. For the range of forcing data explored in this paper, the method gives best estimates of ECS between 1.8 and 3.7 K, but statistical uncertainties in the best estimates themselves will provide a wider likely range of the ECS. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>(<b>a</b>) The red curve is the adjusted forcing for the NorESM1-M model provided by Forster et al. [<a href="#B15-climate-06-00093" class="html-bibr">15</a>]. The black curve is the forcing data of Hansen et al. [<a href="#B16-climate-06-00093" class="html-bibr">16</a>] modified so that its 17-year moving average equals the 17-year moving average of the red curve. (<b>b</b>) The black curve is the global surface temperature in the historical run of the NorESM1-M model, and the blue curve is the response to the modified Hansen forcing (the black curve in (<b>a</b>)) for the model given by Equation (<a href="#FD5-climate-06-00093" class="html-disp-formula">5</a>). Parameters are estimated as <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.67</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>7.8</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> y<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math>.</p> Full article ">Figure 2
<p>(<b>a</b>) The sloping lines are double-logarithmic plots of the scale dependent sensitivity <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> </semantics></math> for each ESM in the ensemble. The different slopes correspond to different <math display="inline"><semantics> <mi>β</mi> </semantics></math>-estimates. The horizontal lines indicate the ECS of the ESMs obtained from the Gregory plots and reported in [<a href="#B1-climate-06-00093" class="html-bibr">1</a>], and the black dots indicate for which frequency <span class="html-italic">f</span> we have <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>(</mo> <mi>f</mi> <mo>)</mo> <mo>=</mo> <mi>ECS</mi> </mrow> </semantics></math> for each model. (<b>b</b>) Correlation (over the ensemble of ESMs) between the scale-dependent sensitivity <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> </semantics></math> and the Gregory estimate of the ECS. The correlation coefficient is plotted as a function of the frequency <span class="html-italic">f</span>.</p> Full article ">Figure 3
<p>(<b>a</b>) The letters (see the legend inserted in panel (<b>b</b>)) show the Gregory estimate of ECS versus R(f)m evaluated at <math display="inline"><semantics> <mrow> <mi>f</mi> <mo>=</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> y<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> for each model in the ensemble. The contour plot shows the conditional probability density function (PDF) <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>(</mo> <mi>ECS</mi> <mo>|</mo> <mi>R</mi> <mo>)</mo> </mrow> </semantics></math>. The vertical black line is <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>=</mo> <mn>2.9</mn> </mrow> </semantics></math> K, which is obtained from the parameters <math display="inline"><semantics> <mrow> <mi>β</mi> <mo>=</mo> <mn>0.66</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mi>μ</mi> <mo>=</mo> <mn>11.9</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>−</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics></math> y<math display="inline"><semantics> <msup> <mrow/> <mrow> <mo>−</mo> <mn>1</mn> </mrow> </msup> </semantics></math> estimated from the instrumental temperature record using the Hansen forcing. The thick, black curve is the estimated PDF of <math display="inline"><semantics> <mrow> <mi>R</mi> <mo>(</mo> <mi>f</mi> <mo>)</mo> </mrow> </semantics></math>. (<b>b</b>) The full curve shows the PDF for ECS computed from Equation (<a href="#FD14-climate-06-00093" class="html-disp-formula">14</a>), where <math display="inline"><semantics> <mrow> <mi>p</mi> <mo>(</mo> <mi>R</mi> <mo>)</mo> </mrow> </semantics></math> is the PDF shown in (<b>a</b>). The histogram is the distribution of ECS in the model ensemble, and the dotted curve is a Gaussian fit to the histogram.</p> Full article ">Figure 4
<p>(<b>a</b>) The points in the scatter plot are the same as (the letters) in <a href="#climate-06-00093-f003" class="html-fig">Figure 3</a>a. The line is the least-square fit of the model <math display="inline"><semantics> <mrow> <mi>ECS</mi> <mo>=</mo> <mi>a</mi> <mi>R</mi> <mo>+</mo> <mi>b</mi> </mrow> </semantics></math>. The vertical lines correspond to the <span class="html-italic">R</span>-values estimated from the instrumental temperature record for the different modified Hansen forcing time series. The horizontal lines show how these <span class="html-italic">R</span>-values are mapped to ECS-values by the linear model. (<b>b</b>) As in (<b>a</b>), but prior to the analysis the volcanic forcing is reduced to half of its original values. (<b>c</b>) As in (<b>b</b>), but using a linear model <math display="inline"><semantics> <mrow> <mi>ECS</mi> <mo>=</mo> <mi>a</mi> <mi>R</mi> </mrow> </semantics></math> with zero intercept to map <span class="html-italic">R</span>-values to ECS-values.</p> Full article ">
14 pages, 8404 KiB  
Article
A Novel Multi-Risk Assessment Web-Tool for Evaluating Future Impacts of Global Change in Mountainous Areas
by Gilles Grandjean, Loïc Thomas, Séverine Bernardie and The SAMCO Team
Climate 2018, 6(4), 92; https://doi.org/10.3390/cli6040092 - 21 Nov 2018
Cited by 9 | Viewed by 6506
Abstract
In our study, we present a proactive decision support tool able to compute the impacts of different possible scenarios for territories impacted by mountain risks. The objective of this work was to develop and test various hazard and risk assessment methods, and to [...] Read more.
In our study, we present a proactive decision support tool able to compute the impacts of different possible scenarios for territories impacted by mountain risks. The objective of this work was to develop and test various hazard and risk assessment methods, and to implement them into a web-application platform able to show possible risks induced by global change on ecosystems and society. Four case studies were selected for their representativeness: One located in a Pyrenean valley, others in the French Alps. Methodology addressed several points. The first one was on the identification of the impacts of global environmental changes (climatic situations, land use, and socio-economic systems) on identified hazards. The second one was on the analysis of these impacts in terms of vulnerability (e.g., the places and the physical modifications of impacted stakes, as well as levels of perturbation). The third one was on the integration of developed methodologies in a single coherent framework in order to investigate and map indicators of vulnerability. The last one was on the development of a demonstration platform with GIS (Geographic Information System) capabilities and usable on the web. The architecture and the main features of the web-platform are detailed within several cases for which hazard and impact assessments are evaluated for not only past and present, but also future periods. This web-tool, mostly dedicated to stakeholders, has proven its usefulness to test various socio-economical pathways, because multiple scenarios, considered as probable in inhabited valleys, can be benchmarked, analyzed, and compared. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Conceptual approach of the SAMCO (Society Adaptation for coping with Mountain risks in a global change COntext) project. Three aspects are considered (multi-hazards, exposure/vulnerability and resilience) as well as forcing factors like climate change (CC) and land use/cover changes (LUCC), all of them being assessed by the mean of maps socio-economic functions evolution.</p> Full article ">Figure 2
<p>Illustration of the SAMCO methodology: Scenarios are first set up in order to assess the evolution of climate and economics in time. Parameters such as landcover, rainfalls, DEM, lithology, and stakes at risk are considered in different modelling tools to compute multi-hazards maps and impacts.</p> Full article ">Figure 3
<p>(<b>a</b>) Location of the Cauterets site; (<b>b</b>) radiative forcing over years for RCP4.5 (grey) and RCP8.5 (black) scenarios; and (<b>c</b>) Landslides hazard maps of the Cauterets site corresponding to a socio-economic scenario “sheeps and woods” at present days (<b>d</b>) and for a climatic evolution according to the RCP8.5 scenario at horizon 2100.</p> Full article ">Figure 4
<p>The SAMCO platform conceptual architecture.</p> Full article ">Figure 5
<p>Data storage example used in the SAMCO simulations.</p> Full article ">Figure 6
<p>“Print screen” of the SAMCO Web-platform showing the landslide hazard map for the scenario: Site = Cauterets, Period = 2040, Hazard = Landslides, Economy = Green actions, climate = RCP4.5 (not visible on the screen).</p> Full article ">Figure 7
<p>“Print screen” of the SAMCO Web-platform showing the landslide hazard map for the scenario: Site = Cauterets, Period = 2040, Hazard = Landslides, Economy = Green actions, climate = RCP8.5 at present days (<b>left</b>) and at horizon 2040 (<b>right</b>). The locations where the landslide hazard level goes from low to moderate are clearly visible.</p> Full article ">
19 pages, 6919 KiB  
Article
Decadal Ocean Heat Redistribution Since the Late 1990s and Its Association with Key Climate Modes
by Lijing Cheng, Gongjie Wang, John P. Abraham and Gang Huang
Climate 2018, 6(4), 91; https://doi.org/10.3390/cli6040091 - 19 Nov 2018
Cited by 21 | Viewed by 10164
Abstract
Ocean heat content (OHC) is the major component of the earth’s energy imbalance. Its decadal scale variability has been heavily debated in the research interest of the so-called “surface warming slowdown” (SWS) that occurred during the 1998–2013 period. Here, we first clarify that [...] Read more.
Ocean heat content (OHC) is the major component of the earth’s energy imbalance. Its decadal scale variability has been heavily debated in the research interest of the so-called “surface warming slowdown” (SWS) that occurred during the 1998–2013 period. Here, we first clarify that OHC has accelerated since the late 1990s. This finding refutes the concept of a slowdown of the human-induced global warming. This study also addresses the question of how heat is redistributed within the global ocean and provides some explanation of the underlying physical phenomena. Previous efforts to answer this question end with contradictory conclusions; we show that the systematic errors in some OHC datasets are partly responsible for these contradictions. Using an improved OHC product, the three-dimensional OHC changes during the SWS period are depicted, related to a reference period of 1982–1997. Several “hot spots” and “cold spots” are identified, showing a significant decadal-scale redistribution of ocean heat, which is distinct from the long-term ocean-warming pattern. To provide clues for the potential drivers of the OHC changes during the SWS period, we examine the OHC changes related to the key climate modes by regressing the Pacific Decadal Oscillation (PDO), El Niño-Southern Oscillation (ENSO), and Atlantic Multi-decadal Oscillation (AMO) indices onto the de-trended gridded OHC anomalies. We find that no single mode can fully explain the OHC change patterns during the SWS period, suggesting that there is not a single “pacemaker” for the recent SWS. Our observation-based analyses provide a basis for further understanding the mechanisms of the decadal ocean heat uptake and evaluating the climate models. Full article
(This article belongs to the Special Issue Postmortem of the Global Warming Hiatus)
►▼ Show Figures

Figure 1

Figure 1
<p>Global mean surface temperature (GMST) (green solid) and OHC (0–2000 m, purple solid) time series since 1955 with monthly time series in light lines and 12-month running means in thick lines. The surface warming slowdown (SWS) period (16 years from 1998 to 2013) is shaded in red and the previous 16-year period (1982–1997) is marked by blue shading. The linear trends for the two records during the two periods are shown in dashed lines. GMST data is Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP) available at (<a href="https://data.giss.nasa.gov/gistemp/" target="_blank">https://data.giss.nasa.gov/gistemp/</a>).</p> Full article ">Figure 2
<p>Ocean heat content changes in the upper 2000-m (1966–2016) with three superior XBT bias correction schemes in solid lines: Levitus et al. [<a href="#B71-climate-06-00091" class="html-bibr">71</a>] (L09), Gouretski and Reseghetii [<a href="#B72-climate-06-00091" class="html-bibr">72</a>] (GR10), and CH14 based on IAP mapping. For comparison, time series for Ishii-old, Ishii-new, and EN4 are attached in dashed curves. Ishii-old data is for 0–1500 m and all other data are for 0–2000 m (note that OHC change within 1500–2000 m is very small and is not responsible for the distinction between Ishii-old and IAP). 12-month running means are applied to all time series.</p> Full article ">Figure 3
<p>Temperature difference between August 1971 and August 1994 revealed by Ishii-old, Ishii-new, EN4, and IAP data at the depth of 600 m. Distribution of the temperature observations at 600 m in August 1971 (purple) and August 1994 (grey) are shown in dots.</p> Full article ">Figure 4
<p>Trend difference of OHC over the global ocean and four major ocean basins during the SWS period related to the reference period (1982–1997). IAP data is shown in red and Ishii-old is shown in blue. The Southern oceans represent ocean areas within 70° S to 30° S.</p> Full article ">Figure 5
<p>Trend difference between the surface slowdown period (1998–2013) and the 1982–1997 period. The front section shows the meridional mean temperature trend over the global ocean, the right section shows the zonal mean temperature trend. The upper map shows the trend of average temperature in the upper 2000 m range. IAP data is used. The units are K/yr.</p> Full article ">Figure 6
<p>Long-term ocean temperature trend since 1955. The front section shows the meridional mean temperature trend over the global ocean, the right section shows the zonal mean temperature trend. The upper map shows the trend of average temperature in the upper 2000 m range. IAP data is used. The units are K/yr.</p> Full article ">Figure 7
<p>Key indices of climate variability. El Niño-Southern Oscillation (ENSO) index: sea surface temperature in Nino 3.4 region, shown in as Oceanic Niño Index (ONI)—Niño (<a href="http://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php" target="_blank">http://origin.cpc.ncep.noaa.gov/products/analysis_monitoring/ensostuff/ONI_v5.php</a>); Pacific Decadal Oscillation (PDO) (<a href="https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/PDO/" target="_blank">https://www.esrl.noaa.gov/psd/gcos_wgsp/Timeseries/PDO/</a>); and Atlantic Multi-decadal Oscillation (AMO) (<a href="https://www.esrl.noaa.gov/psd/data/timeseries/AMO/" target="_blank">https://www.esrl.noaa.gov/psd/data/timeseries/AMO/</a>).</p> Full article ">Figure 8
<p>OHC 0–2000 m trend difference between the SWS period and reference period (<b>a</b>), similar to the top panel in <a href="#climate-06-00091-f005" class="html-fig">Figure 5</a>. OHC changes regressed onto the ENSO index (ONI) (<b>b</b>), PDO index (<b>c</b>), and AMO index (<b>d</b>). For ENSO/PDO, the negative regression coefficients are shown, because both ENSO and PDO are shifted to the negative phase (more La Nina episode for ENSO) during the SWS period. For OHC, a linear trend is removed before the regression is calculated, and the insignificant regression coefficients (at 95% confidence interval) are stippled.</p> Full article ">
34 pages, 4651 KiB  
Article
Climate and the Decline and Fall of the Western Roman Empire: A Bibliometric View on an Interdisciplinary Approach to Answer a Most Classic Historical Question
by Werner Marx, Robin Haunschild and Lutz Bornmann
Climate 2018, 6(4), 90; https://doi.org/10.3390/cli6040090 - 15 Nov 2018
Cited by 20 | Viewed by 17214
Abstract
This bibliometric analysis deals with research on the decline and fall of the Western Roman Empire in connection with climate change. Based on the Web of Science (WoS) database, we applied a combination of three different search queries for retrieving the relevant literature: [...] Read more.
This bibliometric analysis deals with research on the decline and fall of the Western Roman Empire in connection with climate change. Based on the Web of Science (WoS) database, we applied a combination of three different search queries for retrieving the relevant literature: (1) on the decline and fall of the Roman Empire in general, (2) more specifically on the downfall in connection with a changing climate, and (3) on paleoclimatic research in combination with the time period of the Roman Empire and Late Antiquity. Additionally, we considered all references cited by an ensemble of selected key papers and all citing papers of these key papers, whereby we retrieved additional publications (in particular, books and book chapters). We merged the literature retrieved, receiving a final publication set of 85 publications. We analyzed this publication set by applying a toolset of bibliometric methods and visualization programs. A co-authorship map of all authors, a keyword map for a rough content analysis, and a citation network based on the publication set of 85 papers are presented. We also considered news mentions in this study to identify papers with impacts beyond science. According to the literature retrieved, a multitude of paleoclimatic data from various geographical sites for the time of late antiquity indicate a climatic shift away from the stability of previous centuries. Recently, some scholars have argued that drought in Central Asia and the onset of a cooler climate in North-West Eurasia may have put Germanic tribes, Goths, and Huns on the move into the Roman Empire, provoking the Migration Period and eventually leading to the downfall of the Western Roman Empire. However, climate is only one variable at play; a combination of many factors interacting with each other is a possible explanation for the pattern of long-lasting decline and final collapse. Currently, the number of records from different locations, the toolbox of suitable analytic methods, and the precision of dating are evolving rapidly, contributing to an answer for one of the most classic of all historical questions. However, these studies still lack the inevitable collaboration of the major disciplines involved: archeology, history, and climatology. The articles of the publication set analyzed mainly result from research in the geosciences. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Flow diagram of the search and selection steps.</p> Full article ">Figure 2
<p>Co-authorship map of all authors (435) from the 85 papers dealing with the fall of Rome or the time period of the Roman Empire in connection with climate change (the minimum number of papers containing a specific author is one). Readers interested in an in-depth analysis of our publication set can use VOSviewer interactively and zoom into the clusters via the following URL: <a href="https://tinyurl.com/y87aojfx" target="_blank">https://tinyurl.com/y87aojfx</a>. Source: WoS, VOSviewer.</p> Full article ">Figure 3
<p>Co-occurrence map of “keywords plus” (95 out of 439 keywords) from the 85 papers dealing with the fall of Rome or the time period of the Roman Empire in connection with climate change for a rough content analysis (the minimum number of co-occurrences of a keyword is two). Readers interested in an in-depth analysis can use VOSviewer interactively and zoom into the clusters via the following URL: <a href="https://tinyurl.com/ybrbmqc6" target="_blank">https://tinyurl.com/ybrbmqc6</a>. Source: WoS, VOSviewer.</p> Full article ">Figure 4
<p>(<b>a</b>) Citation network (137 citation links) of the 85 papers dealing with the fall of Rome or the time period of the Roman Empire in connection with climate change (excluding the non-matching cited references). A crop of the network of the papers at center bottom has been inserted for showing all author names. Orange: 10 key papers. The coloring has been performed manually within CitNetExplorer. Source: WoS, CitNetExplorer. (<b>b</b>) Screenshot of the citations of Decker [<a href="#B17-climate-06-00090" class="html-bibr">17</a>]: Decker cites 11 other papers out of our publication set of 85 papers (four other key papers).</p> Full article ">Figure 4 Cont.
<p>(<b>a</b>) Citation network (137 citation links) of the 85 papers dealing with the fall of Rome or the time period of the Roman Empire in connection with climate change (excluding the non-matching cited references). A crop of the network of the papers at center bottom has been inserted for showing all author names. Orange: 10 key papers. The coloring has been performed manually within CitNetExplorer. Source: WoS, CitNetExplorer. (<b>b</b>) Screenshot of the citations of Decker [<a href="#B17-climate-06-00090" class="html-bibr">17</a>]: Decker cites 11 other papers out of our publication set of 85 papers (four other key papers).</p> Full article ">
24 pages, 6935 KiB  
Article
Selecting and Downscaling a Set of Climate Models for Projecting Climatic Change for Impact Assessment in the Upper Indus Basin (UIB)
by Asim Jahangir Khan and Manfred Koch
Climate 2018, 6(4), 89; https://doi.org/10.3390/cli6040089 - 14 Nov 2018
Cited by 26 | Viewed by 7333
Abstract
This study focusses on identifying a set of representative climate model projections for the Upper Indus Basin (UIB). Although a large number of General Circulation Models (GCM) predictor sets are available nowadays in the CMIP5 archive, the issue of their reliability for specific [...] Read more.
This study focusses on identifying a set of representative climate model projections for the Upper Indus Basin (UIB). Although a large number of General Circulation Models (GCM) predictor sets are available nowadays in the CMIP5 archive, the issue of their reliability for specific regions must still be confronted. This situation makes it imperative to sort out the most appropriate single or small-ensemble set of GCMs for the assessment of climate change impacts in a region. Here a set of different approaches is adopted and applied for the step-wise shortlisting and selection of appropriate climate models for the UIB under two RCPs: RCP 4.5 and RCP 8.5, based on: (a) range of projected mean changes, (b) range of projected extreme changes, and (c) skill in reproducing the past climate. Furthermore, because of higher uncertainties in climate projection for high mountainous regions like the UIB, a wider range of future GCM climate projections is considered by using all possible extreme future scenarios (wet-warm, wet-cold, dry-warm, dry-cold). Based on this two-fold procedure, a limited number of climate models is pre-selected, from of which the final selection is done by assigning ranks to the weighted score for each of the mentioned selection criteria. The dynamically downscaled climate projections from the Coordinated Regional Downscaling Experiment (CORDEX) available for the top-ranked GCMs are further statistically downscaled (bias-corrected) over the UIB. The downscaled projections up to the year 2100 indicate temperature increases ranging between 2.3 °C and 9.0 °C and precipitation changes that range from a slight annual increase of 2.2% under the drier scenarios to as high as 15.9% in the wet scenarios. Moreover, for all scenarios, future precipitation will be more extreme, as the probability of wet days will decrease, while, at the same time, precipitation intensities will increase. The spatial distribution of the downscaled predictors across the UIB also shows similar patterns for all scenarios, with a distinct precipitation decrease over the south-eastern parts of the basin, but an increase in the northeastern parts. These two features are particularly intense for the “Dry-Warm” and the “Median” scenarios over the late 21st century. Full article
(This article belongs to the Special Issue Climate Variability and Change in the 21th Century)
►▼ Show Figures

Figure 1

Figure 1
<p>Upper Indus Basin (UIB): Main catchments, meteorological stations, streams and tributaries.</p> Full article ">Figure 2
<p>Reference climate data: (<b>a</b>) Mean annual precipitation (mm), (<b>b</b>) mean temperature- maximum (°C), and (<b>c</b>) mean temperature- minimum (°C).</p> Full article ">Figure 3
<p>Projected changes in mean air temperature (Δ<span class="html-italic">T)</span> and annual precipitation sum (Δ<span class="html-italic">P)</span> between 2071 and 2100 and 1971 and 2000 for all included RCP4.5 GCM runs. Blue crosses indicate the 10th, 50th and 90th percentile values for Δ<span class="html-italic">T</span> and Δ<span class="html-italic">P</span>. The model runs shortlisted during this step are indicated in red color.</p> Full article ">Figure 4
<p>Similar to <a href="#climate-06-00089-f003" class="html-fig">Figure 3</a>, but for RCP8.5 GCM runs.</p> Full article ">Figure 5
<p>Spatial distribution of projected precipitation change across the UIB over the mid (2014–2070) and the late- (2071–2100) 21st century for 5 models and 2 RCPs. The figure is arranged in a tabular form where the 1st and 2nd column represent projected change in precipitation for RCP 4.5, for the mid-century (2014–2070) and the late-century (2071–2100), respectively, while the 3rd and 4th columns show the projected change in the mid-century and the late-century precipitation for RCP 8.5, respectively. The rows represent the climate models used.</p> Full article ">Figure 6
<p>Similar to <a href="#climate-06-00089-f005" class="html-fig">Figure 5</a>, but for temperature changes.</p> Full article ">
12 pages, 2049 KiB  
Article
Are Energy Security Concerns Dominating Environmental Concerns? Evidence from Stakeholder Participation Processes on Energy Transition in Jordan
by Nadejda Komendantova, Love Ekenberg, Leena Marashdeh, Ahmed Al Salaymeh, Mats Danielson and Joanne Linnerooth-Bayer
Climate 2018, 6(4), 88; https://doi.org/10.3390/cli6040088 - 1 Nov 2018
Cited by 23 | Viewed by 5622
Abstract
To satisfy Jordan’s growing demand for electricity and to diversify its energy mix, the Jordanian government is considering a number of electricity-generation technologies that would allow for locally available resources to be used alongside imported energy. Energy policy in Jordan aims to address [...] Read more.
To satisfy Jordan’s growing demand for electricity and to diversify its energy mix, the Jordanian government is considering a number of electricity-generation technologies that would allow for locally available resources to be used alongside imported energy. Energy policy in Jordan aims to address both climate change mitigation and energy security by increasing the share of low-carbon technologies and domestically available resources in the Jordanian electricity mix. Existing technological alternatives include the scaling up of renewable energy sources, such as solar and wind; the deployment of nuclear energy; and shale oil exploration. However, the views, perceptions, and opinions regarding these technologies—their benefits, risks, and costs—vary significantly among different social groups both inside and outside the country. Considering the large-scale policy intervention that would be needed to deploy these technologies, a compromise solution must be reached. This paper is based on the results of a four-year research project that included extensive stakeholder processes in Jordan, involving several social groups and the application of various methods of participatory governance research, such as multi-criteria decision-making. The results show the variety of opinions expressed and provide insights into each type of electricity-generation technology and its relevance for each stakeholder group. There is a strong prevalence of economic rationality in the results, given that electricity-system costs are prioritized by almost all stakeholder groups. Full article
(This article belongs to the Special Issue Climate Change, Carbon Budget and Energy Policy)
►▼ Show Figures

Figure 1

Figure 1
<p>Results for the second round of the final workshop.</p> Full article ">
17 pages, 6151 KiB  
Article
Spatio-Temporal Trend Analysis of Rainfall and Temperature Extremes in the Vea Catchment, Ghana
by Isaac Larbi, Fabien C. C. Hountondji, Thompson Annor, Wilson Agyei Agyare, John Mwangi Gathenya and Joshua Amuzu
Climate 2018, 6(4), 87; https://doi.org/10.3390/cli6040087 - 31 Oct 2018
Cited by 49 | Viewed by 7953
Abstract
This study examined the trends in annual rainfall and temperature extremes over the Vea catchment for the period 1985–2016, using quality-controlled stations and a high resolution (5 km) Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. The CHIRPS gridded precipitation data’s ability [...] Read more.
This study examined the trends in annual rainfall and temperature extremes over the Vea catchment for the period 1985–2016, using quality-controlled stations and a high resolution (5 km) Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) data. The CHIRPS gridded precipitation data’s ability in reproducing the climatology of the catchment was evaluated. The extreme rainfall and temperature indices were computed using a RClimdex package by considering seventeen (17) climate change indices from the Expert Team on Climate Change Detection Monitoring Indices (ETCCDMI). Trend detection and quantification in the rainfall (frequency and intensity) and temperature extreme indices were analyzed using the non-parametric Mann–Kendall (MK) test and Sen’s slope estimator. The results show a very high seasonal correlation coefficient (r = 0.99), Nash–Sutcliff efficiency (0.98) and percentage bias (4.4% and −8.1%) between the stations and the gridded data. An investigation of dry and wet years using Standardized Anomaly Index shows 45.5% frequency of drier than normal periods compared to 54.5% wetter than normal periods in the catchment with 1999 and 2003 been extremely wet years while the year 1990 and 2013 were extremely dry. The intensity and magnitude of extreme rainfall indices show a decreasing trend for more than 78% of the rainfall locations while positive trends were observed in the frequency of extreme rainfall indices (R10mm, R20mm, and CDD) with the exception of consecutive wet days (CWD) that shows a decreasing trend. A general warming trend over the catchment was observed through the increase in the annual number of warm days (TX90p), warm nights (TN90p) and warm spells (WSDI). The spatial distribution analysis shows a high frequency and intensity of extremes rainfall indices in the south of the catchment compared to the middle and northern of part of the catchment, while temperature extremes were uniformly distributed over the catchment. Full article
►▼ Show Figures

Figure 1

Figure 1
<p>Map of the Vea catchment showing station and gridded precipitation locations within a 5 km grid.</p> Full article ">Figure 2
<p>Performance evaluation of Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) based on mean monthly distribution (<b>A</b>,<b>B</b>) and annual distribution (<b>C</b>,<b>D</b>) of rainfall from 1990 to 2016 for the Vea and Bolgatanga locations.</p> Full article ">Figure 3
<p>Mean Annual Standardized Anomaly Index from 1985 to 2016 for the individual locations (<b>A</b>–<b>N</b>) and Catchment Scale (<b>O</b>).</p> Full article ">Figure 3 Cont.
<p>Mean Annual Standardized Anomaly Index from 1985 to 2016 for the individual locations (<b>A</b>–<b>N</b>) and Catchment Scale (<b>O</b>).</p> Full article ">Figure 3 Cont.
<p>Mean Annual Standardized Anomaly Index from 1985 to 2016 for the individual locations (<b>A</b>–<b>N</b>) and Catchment Scale (<b>O</b>).</p> Full article ">Figure 4
<p>Spatial distribution of annual extreme rainfall intensity Indices.</p> Full article ">Figure 5
<p>Spatial distribution of frequency of extreme rainfall Indices.</p> Full article ">Figure 6
<p>Spatial distribution of Temperature extreme Indices from 1985–2016.</p> Full article ">
24 pages, 2911 KiB  
Article
The Nexus of Weather Extremes to Agriculture Production Indexes and the Future Risk in Ghana
by Abdul-Aziz Ibn Musah, Jianguo Du, Thomas Bilaliib Udimal and Mohammed Abubakari Sadick
Climate 2018, 6(4), 86; https://doi.org/10.3390/cli6040086 - 31 Oct 2018
Cited by 12 | Viewed by 8831
Abstract
The agricultural industry employs a large workforce in Ghana and remains the primary source of food security and income. The consequences of extreme weather in this sector can be catastrophic. A consistent picture of meteorological risk and adaptation patterns can lead to useful [...] Read more.
The agricultural industry employs a large workforce in Ghana and remains the primary source of food security and income. The consequences of extreme weather in this sector can be catastrophic. A consistent picture of meteorological risk and adaptation patterns can lead to useful information, which can help local farmers make informed decisions to advance their livelihoods. We modelled historical data using extreme value theory and structural equation modelling. Subsequently, we studied extreme weather variability and its relationship to composite indicators of agricultural production and the long-term trend of weather risk. Minimum and maximum annual temperatures have negligible heterogeneity in their trends, while the annual maximum rainfall is homogenous in trend. Severe rainfall affects cereals and cocoa production, resulting in reduced yields. Cereals and cocoa grow well when there is even distribution of rainfall. The return levels for the next 20–100 years are gradually increasing with the long-term prediction of extreme weather. Also, heavy rains affect cereals and cocoa production negatively. All indicators of agriculture had a positive relationship with maximum extreme weather. Full article
(This article belongs to the Special Issue Sustainable Agriculture for Climate Change Adaptation)
►▼ Show Figures

Figure 1

Figure 1
<p>The Monthly trend of temperature and rainfall in Ghana.</p> Full article ">Figure 2
<p>Location Map of Ghana.</p> Full article ">Figure 3
<p>Major food crops calendar in Ghana.</p> Full article ">Figure 4
<p>Diagnostic annual maximum rainfall plots.</p> Full article ">Figure 5
<p>Diagnostic annual maximum temperature plots.</p> Full article ">Figure 6
<p>Diagnostic annual minimum temperature plots.</p> Full article ">Figure 7
<p>The Conceptual frame of the relationship of extreme weather on agriculture production indexes.</p> Full article ">
16 pages, 3611 KiB  
Article
Estimating the Impact of Artificially Injected Stratospheric Aerosols on the Global Mean Surface Temperature in the 21th Century
by Sergei A. Soldatenko
Climate 2018, 6(4), 85; https://doi.org/10.3390/cli6040085 - 28 Oct 2018
Cited by 5 | Viewed by 5389
Abstract
In this paper, we apply the optimal control theory to obtain the analytic solutions of the two-component globally averaged energy balance model in order to estimate the influence of solar radiation management (SRM) operations on the global mean surface temperature in the 21st [...] Read more.
In this paper, we apply the optimal control theory to obtain the analytic solutions of the two-component globally averaged energy balance model in order to estimate the influence of solar radiation management (SRM) operations on the global mean surface temperature in the 21st century. It is assumed that SRM is executed via injection of sulfur aerosols into the stratosphere to limit the global temperature increase in the year 2100 by 1.5 °C and keeping global temperature over the specified period (2020–2100) within 2 °C as required by the Paris climate agreement. The radiative forcing produced by the rise in the atmospheric concentrations of greenhouse gases is defined by the Representative Concentration Pathways and the 1pctCO2 (1% per year CO2 increase) scenario. The goal of SRM is formulated in terms of extremal problem, which entails finding a control function (the albedo of aerosol layer) that minimizes the amount of aerosols injected into the upper atmosphere to satisfy the Paris climate target. For each climate change scenario, the optimal albedo of the aerosol layer and the corresponding global mean surface temperature changes were obtained. In addition, the aerosol emission rates required to create an aerosol cloud with optimal optical properties were calculated. Full article
(This article belongs to the Special Issue Climate Variability and Change in the 21th Century)
►▼ Show Figures

Figure 1

Figure 1
<p>Changes in (<b>a</b>) global mean surface temperature and (<b>b</b>) deep ocean temperature calculated for different climate change scenarios in the absence of climate engineering interventions.</p> Full article ">Figure 2
<p>Results for the RCP8.5 pathway: (<b>a</b>) optimal albedo of aerosol layer <math display="inline"><semantics> <mrow> <msubsup> <mi>α</mi> <mi>A</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>; (<b>b</b>) the corresponding temperature anomaly <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>∗</mo> </msup> </mrow> </semantics></math>; (<b>c</b>) total mass of aerosols <math display="inline"><semantics> <mrow> <msubsup> <mi>M</mi> <mi>S</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>; and (<b>d</b>) the optimal emission rate <math display="inline"><semantics> <mrow> <msubsup> <mi>E</mi> <mi>S</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>Results for the 1pctCO<sub>2</sub> scenario: (<b>a</b>) optimal albedo of aerosol layer <math display="inline"><semantics> <mrow> <msubsup> <mi>α</mi> <mi>A</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>; (<b>b</b>) the corresponding temperature anomaly <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>∗</mo> </msup> </mrow> </semantics></math>; (<b>c</b>) total aerosol mass <math display="inline"><semantics> <mrow> <msubsup> <mi>M</mi> <mi>S</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>; and (<b>d</b>) the optimal emission rate <math display="inline"><semantics> <mrow> <msubsup> <mi>E</mi> <mi>S</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>.</p> Full article ">Figure 4
<p>Results for the RCP6.0 pathway: (<b>a</b>) optimal albedo of aerosol layer <math display="inline"><semantics> <mrow> <msubsup> <mi>α</mi> <mi>A</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>; (<b>b</b>) the corresponding temperature anomaly <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>∗</mo> </msup> </mrow> </semantics></math>; (<b>c</b>) total mass of aerosols <math display="inline"><semantics> <mrow> <msubsup> <mi>M</mi> <mi>S</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>; and (<b>d</b>) the optimal emission rate <math display="inline"><semantics> <mrow> <msubsup> <mi>E</mi> <mi>S</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>.</p> Full article ">Figure 4 Cont.
<p>Results for the RCP6.0 pathway: (<b>a</b>) optimal albedo of aerosol layer <math display="inline"><semantics> <mrow> <msubsup> <mi>α</mi> <mi>A</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>; (<b>b</b>) the corresponding temperature anomaly <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>∗</mo> </msup> </mrow> </semantics></math>; (<b>c</b>) total mass of aerosols <math display="inline"><semantics> <mrow> <msubsup> <mi>M</mi> <mi>S</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>; and (<b>d</b>) the optimal emission rate <math display="inline"><semantics> <mrow> <msubsup> <mi>E</mi> <mi>S</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>Results for the RCP4.5 pathway: (<b>a</b>) optimal albedo of aerosol layer <math display="inline"><semantics> <mrow> <msubsup> <mi>α</mi> <mi>A</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>; (<b>b</b>) the corresponding temperature anomaly <math display="inline"><semantics> <mrow> <msup> <mi>T</mi> <mo>∗</mo> </msup> </mrow> </semantics></math>; (<b>c</b>) total mass of aerosols <math display="inline"><semantics> <mrow> <msubsup> <mi>M</mi> <mi>S</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>; and (<b>d</b>) the optimal emission rate <math display="inline"><semantics> <mrow> <msubsup> <mi>E</mi> <mi>S</mi> <mo>∗</mo> </msubsup> </mrow> </semantics></math>.</p> Full article ">
13 pages, 6615 KiB  
Article
Relationship between City Size, Coastal Land Use, and Summer Daytime Air Temperature Rise with Distance from Coast
by Hideki Takebayashi, Takahiro Tanaka, Masakazu Moriyama, Hironori Watanabe, Hiroshi Miyazaki and Kosuke Kittaka
Climate 2018, 6(4), 84; https://doi.org/10.3390/cli6040084 - 27 Oct 2018
Cited by 5 | Viewed by 4794
Abstract
The relationship between city size, coastal land use, and air temperature rise with distance from coast during summer day is analyzed using the meso-scale weather research and forecasting (WRF) model in five coastal cities in Japan with different sizes and coastal land use [...] Read more.
The relationship between city size, coastal land use, and air temperature rise with distance from coast during summer day is analyzed using the meso-scale weather research and forecasting (WRF) model in five coastal cities in Japan with different sizes and coastal land use (Tokyo, Osaka, Nagoya, Hiroshima, and Sendai) and inland cities in Germany (Berlin, Essen, and Karlsruhe). Air temperature increased as distance from the coast increased, reached its maximum, and then decreased slightly. In Nagoya and Sendai, the amount of urban land use in coastal areas is less than the other three cities, where air temperature is a little lower. As a result, air temperature difference between coastal and inland urban area is small and the curve of air temperature rise is smaller than those in Tokyo and Osaka. In Sendai, air temperature in the inland urban area is the same as in the other cities, but air temperature in the coastal urban area is a little lower than the other cities, due to an approximate one degree lower sea surface temperature being influenced by the latitude. In three German cities, the urban boundary layer may not develop sufficiently because the fetch distance is not enough. Full article
(This article belongs to the Special Issue Climate Change Resilience and Urban Sustainability)
►▼ Show Figures

Figure 1

Figure 1
<p>Objective study areas in Tokyo, Osaka, Nagoya, Hiroshima, and Sendai (Domain 1: 3 km grid, 360 km square, Domain 2: 1 km grid, 103 km square).</p> Full article ">Figure 2
<p>Objective study areas in Berlin, Essen, and Karlsruhe (Domain 1: 3 km grid, 360 km square, Domain 2: 1 km grid, 103 km square).</p> Full article ">Figure 3
<p>Land use conditions and number of urban meshes in (<b>a</b>) Tokyo, (<b>b</b>) Osaka, (<b>c</b>) Nagoya, (<b>d</b>) Hiroshima, and (<b>e</b>) Sendai (1 km grid, 103 km square).</p> Full article ">Figure 4
<p>Land use conditions and number of urban meshes in (<b>a</b>) Berlin, (<b>b</b>) Essen, and (<b>c</b>) Karlsruhe (1 km grid, 103 km square).</p> Full article ">Figure 5
<p>Frequency of urban land use at each distance point from the coast.</p> Full article ">Figure 6
<p>Position of measurement sites in (<b>a</b>) Tokyo, (<b>b</b>) Osaka, (<b>c</b>) Nagoya, (<b>d</b>) Hiroshima, and (<b>e</b>) Sendai (103 km square).</p> Full article ">Figure 7
<p>Position of measurement sites in (<b>a</b>) Berlin, (<b>b</b>) Essen, and (<b>c</b>) Karlsruhe (103 km square).</p> Full article ">Figure 8
<p>Comparison of observed and calculated period average air temperatures at the (<b>a</b>) Tokyo, (<b>b</b>) Osaka, (<b>c</b>) Nagoya, (<b>d</b>) Hiroshima, and (<b>e</b>) Sendai observatories.</p> Full article ">Figure 9
<p>Comparison of observed and calculated period average air temperatures at the (<b>a</b>) Berlin, (<b>b</b>) Essen, and (<b>c</b>) Karlsruhe observatories.</p> Full article ">Figure 10
<p>Air temperature distribution at 2 m height in Japanese cities (103 km square). (<b>a</b>) Tokyo, (<b>b</b>) Osaka, (<b>c</b>) Nagoya, (<b>d</b>) Hiroshima, and (<b>e</b>) Sendai.</p> Full article ">Figure 11
<p>Air temperature distribution at 2 m height in German cities (103 km square). (<b>a</b>) Berlin, (<b>b</b>) Essen, and (<b>c</b>) Karlsruhe.</p> Full article ">Figure 12
<p>Relationship between distance from the coast and air temperature in Japanese cities. (<b>a</b>) Tokyo, (<b>b</b>) Osaka, (<b>c</b>) Nagoya, (<b>d</b>) Hiroshima, and (<b>e</b>) Sendai.</p> Full article ">Figure 13
<p>Relationship between distance from the suburb and air temperature in German cities. (<b>a</b>) Berlin, (<b>b</b>) Essen, and (<b>c</b>) Karlsruhe.</p> Full article ">Figure 14
<p>Relationship between distance from the coast and air temperature at 2:00 p.m. averaged in fine weather condition for Japanese cities.</p> Full article ">Figure 15
<p>Frequencies of air temperature at 14:00 on August 25 (left) and 7 (right), 2010.</p> Full article ">Figure 16
<p>Distance from boundary and air temperature rise in Tokyo, Osaka, Nagoya, Berlin, Essen, and Karlsruhe.</p> Full article ">Figure 17
<p>Approximate curves of air temperature rise of each day in fine days in German cities. (<b>a</b>) Berlin, (<b>b</b>) Essen, and (<b>c</b>) Karlsruhe.</p> Full article ">
14 pages, 1220 KiB  
Article
Patterns in Indices of Daily and Seasonal Rainfall Extremes: Southwest Florida Gulf Coastal Zone
by Margaret W. Gitau
Climate 2018, 6(4), 83; https://doi.org/10.3390/cli6040083 - 25 Oct 2018
Cited by 5 | Viewed by 3240
Abstract
Extreme events have the most adverse impacts on society and infrastructure, and present the greatest challenges with respect to impacts. Information on the status and trends of these events is, thus, important for system design, management, and policy decision-making. In this study, variations [...] Read more.
Extreme events have the most adverse impacts on society and infrastructure, and present the greatest challenges with respect to impacts. Information on the status and trends of these events is, thus, important for system design, management, and policy decision-making. In this study, variations in daily and seasonal rainfall extremes were explored with a focus on the southwest Florida Gulf coastal zone for the period 1950–2016. Rainfall occurring on very wet days accounted for about 50% of the seasonal rainfall in the area (regardless of the season), while about 25% of the seasonal rainfall came from extremely wet days except in the period between October and December for which this latter value was about 40%. No significant changes were seen in the maximum one-day rainfall at any of the stations regardless of the time scale. However, there was a significant increase in the number of wet days in the rainy season at Myakka River (p = 0.0062) and Naples (p = 0.0027) and during October–December at Myakka River (p = 0.0204). These two stations also experienced significant increases in the number of wet days in a year. Significant increases in the contribution to rainy season rainfall from very wet days (rainfall > 25.4 mm, 1 in) were seen at Arcadia (p = 0.0055). Regional results point to an increasingly wetter climate with increasing contributions from extreme events in some areas, both of which have implications for design and management decision making. Full article
(This article belongs to the Special Issue Decadal Variability and Predictability of Climate)
►▼ Show Figures

Figure 1

Figure 1
<p>Southwest Florida region showing weather station locations. Rivers from northwest to southeast: Mykka River, Peace River, and Caloosahatchee River.</p> Full article ">Figure 2
<p>Time series of annual one-day maximum rainfall at stations in the southwest Florida Gulf coastal region. Dashed line shows variations based on the LOWESS smoother (span = 0.75).</p> Full article ">Figure 3
<p>Time series of the number of wet days in a year at stations in the southwest Florida Gulf coastal region. Dashed line shows variations based on the LOWESS smoother (span = 0.75).</p> Full article ">Figure 4
<p>Percentage of Rainy season rainfall contributed by rainfall from days with greater than the 95th percentile rainfall amount (25.4 mm, 1 in) and the 99th percentile rainfall amount (50.8 mm, 2 in) in the southwest Florida Gulf coastal region. Dashed line shows variations based on the LOWESS smoother (span = 0.75).</p> Full article ">Figure 5
<p>Time series of total seasonal rainfall at Naples and Myakka River stations showing patterns for the Rainy season (<b>top</b>) and Dry2 (<b>bottom</b>).</p> Full article ">Figure 6
<p>Time evolution of Rainy season rainfall characteristics showing decadal variations across the region. Short horizontal lines: Medians for the respective decades. Solid line represents the median of the data.</p> Full article ">
19 pages, 4070 KiB  
Review
Understanding the Recent Global Surface Warming Slowdown: A Review
by Ka-Kit Tung and Xianyao Chen
Climate 2018, 6(4), 82; https://doi.org/10.3390/cli6040082 - 24 Oct 2018
Cited by 24 | Viewed by 8157
Abstract
The Intergovernmental Panel on Climate Change (IPCC) noted a recent 15-year period (1998–2012) when the rate of surface global warming was a factor of 4 smaller than the mean of the state-of-art climate model projections and than that observed in the previous three [...] Read more.
The Intergovernmental Panel on Climate Change (IPCC) noted a recent 15-year period (1998–2012) when the rate of surface global warming was a factor of 4 smaller than the mean of the state-of-art climate model projections and than that observed in the previous three decades. When updated to include 2014 by Karl et al. using the new version of NOAA data, the observed warming trend is higher, but is still half or less, depending on dataset used, that of previous decades and the multi-model mean projections. This period is called a surface warming slowdown. Intense community efforts devoted to understanding this puzzling phenomenon—puzzling because atmospheric greenhouse gas accumulation has not abated while surface warming slowed—have yielded insights on our climate system, and this may be an opportune time to take stock of what we have learned. Proposed explanations differ on whether it is forced by counteracting agents (such as volcanic and pollution aerosols and stratospheric water vapor) or is an internal variability, and if the latter, on which ocean basin is responsible (Pacific, Indian, or Atlantic Ocean). Here we critically review the observational records, their analyses and interpretations, and offer interpretations of model simulations, with emphasis on sorting through the rather confusing signals at the ocean’s surface, and reconciling them with the subsurface signals. Full article
(This article belongs to the Special Issue Postmortem of the Global Warming Hiatus)
►▼ Show Figures

Figure 1

Figure 1
<p>Modeled and observed annual-mean global-mean surface temperature relative to 1986–2005 mean. Adapted from IPCC AR5 Figure 11.9 and updated to the end of 2017. The observations from HadCRUT4.3 [<a href="#B4-climate-06-00082" class="html-bibr">4</a>], GISTEMP [<a href="#B5-climate-06-00082" class="html-bibr">5</a>] and ERA-Interim [<a href="#B6-climate-06-00082" class="html-bibr">6</a>] are in black lines and NCDC (ERSST.v4 land + ocean) is in green. GISTEMP uses ERSST.v4 SST [<a href="#B2-climate-06-00082" class="html-bibr">2</a>] for its sea-surface temperature (SST). The red line is the one standard deviation of combined observational uncertainty due to measurement, sampling and coverage. The shading shows the 5–95% range of 42 models, with grey for the historical simulation and blue for the projection under the RCP4.5 scenario. The white line is for the multi-model mean. The grey curves are the maximum and minimum envelopes of 107 ensemble members.</p> Full article ">Figure 2
<p>Coincidence of the three AMOC phases with global warming slowdown and acceleration: (<b>a</b>) Global mean surface temperature; (<b>b</b>) OHC north of 45° N in the Atlantic; (<b>c</b>): Salinity north of 45° N in the Atlantic. Adapted and updated to the end of 2017 from Chen and Tung [<a href="#B12-climate-06-00082" class="html-bibr">12</a>].</p> Full article ">Figure 3
<p>Decomposition of the annual-mean global-mean temperature (relative to 1961–1990 mean): One hundred ensemble members in the HadCRUT4.3 dataset, sampling systematic components of observational uncertainty, are shown in light grey. Their ensemble mean is in black. (<b>a</b>) Decomposition of each of the ensemble members of the HadCRUT4.3 global-mean surface (land+ocean) temperature into its high-frequency (shorter than decadal; in green) and low-frequency (longer than decadal, in red) components. The secular trend is indicated with the brown line. The blue lines, which are the sum of the red and the brown lines, are seen as a good smoothed version of the original time series. The darker color line denotes the ensemble mean of the light color lines. The method used in the decomposition is Ensemble Empirical Mode Decomposition [<a href="#B19-climate-06-00082" class="html-bibr">19</a>] (EEMD), which has the advantage that it is lossless (the three components add up to the original time series perfectly) and that the high and low frequency components are constructed to be orthogonal (for all practical purposes in the implementation). The high-frequency component can also be seen as the difference between the original data and the smoothed version. Bottom panels: Spatial patterns obtained by regressing the global land+ocean surface temperature for the period 1910–2014 onto the secular trend (brown curve) (<b>b</b>) onto the low-frequency component (the red curve) (<b>c</b>) and the high-frequency component (the green curve) (<b>d</b>) after the low and high frequency time series are first normalized to unit standard deviation, so that the color shows the amplitude in degrees C. The left color scale is for the secular trend, while the right color scale is for the low and high frequency oscillations. Adapted and updated from Chen and Tung [<a href="#B7-climate-06-00082" class="html-bibr">7</a>] to the end of 2017.</p> Full article ">Figure 4
<p>East vs. West Pacific SST (relative to 1961–1990 mean): The mean SST of the eastern tropical Pacific in blue curve (with weight = 1), and the mean SST of the rest of the Pacific (area weighted by 1.86). The mean SST for the Pacific basin as a whole is in green. The regions used are depicted in the inset. The blue region in the eastern Pacific is the same as that used in Kosaka and Xie [<a href="#B38-climate-06-00082" class="html-bibr">38</a>] and is 35% of the Pacific basin. The SST time series have been smoothed using 27-month running mean. The linear trend, using the same color as the SST for each region, is objectively determined by least-square linear regression.</p> Full article ">Figure 5
<p>Global mean SST and OHC relative to 1998: SST from ERSST.v4 is in black and 0–200 m OHC in light orange curve, showing that they co-vary and both are in a warming slowdown, while the “total” OHC, as approximated by the 0–1500 m OHC in red curve, is increasing at the regressed linear rate of 0.42 W·m<sup>−2</sup> (red dashed straight line). The orange shaded region is the amount of heat stored in the 200–1500 m layer, and it is about 89 zetajoules for the period 2000–2014. The data were adjusted by us for the Southern Ocean to remove a possible artifact due to the rapid transition from no-Argo to Argo observing platform around 2002–2003, as pointed out by Cheng and Zhu [<a href="#B65-climate-06-00082" class="html-bibr">65</a>]. The original data [<a href="#B66-climate-06-00082" class="html-bibr">66</a>] for the unadjusted 0–1500 m OHC are shown in grey. The inset shows the division of the 89 zetajoules of the global ocean increase in heat storage in 200–1500 m (linear trend multiplied by the number of years) into the four ocean basins. Note that the definition of the Southern Ocean in oceanography and here is south of 35S. The tip of the Drake Passage is at 38S. The error bars are from the linear trend regression. For the subsurface ocean heat content we use Ishii data [<a href="#B66-climate-06-00082" class="html-bibr">66</a>], which is available up to 2012. Most of the recent measurements are from the Argo floats [<a href="#B62-climate-06-00082" class="html-bibr">62</a>].</p> Full article ">Figure 6
<p>Linear trend of OHC cross-sections: (<b>a</b>,<b>b</b>) linear trends in the two periods indicated, zonally integrated (not averaged) over the longitude of the Atlantic basin as a function of latitude. South of 35S, the zonal integration is circumpolar (skipping over the land). The thin black line at 35S separates the two latitude regions with very different zonal extents. (<b>c</b>,<b>d</b>) OHC linear trends in the two periods indicated, as a function of longitude in the Indo-Pacific sector, meridionally integrated (not averaged) from 35S to the northern boundary of the ocean basins. The topography in the Indonesia Through Flow is 8S–8N averaged and shown in black. Top two panels are linear trends over 1995–2004. Bottom two panels are the linear trends over 2005–2014. Each period is 10 years. The Southern Ocean part is blanked out in (<b>a</b>) due to poor data quality. Regions where signal is statistically significant above 95% confidence level are stippled.</p> Full article ">Figure 7
<p>The composition of the IPO. IPO index in black. The composition of the IPO as reconstructed using the sum of its four leading components in blue. The individual component is also shown. It is dominated by the AMO. IPO is here defined as the second Principle Component of 13-year low-pass filtered SST. Reproduced from Chen and Tung [<a href="#B7-climate-06-00082" class="html-bibr">7</a>].</p> Full article ">Figure 8
<p>The global-mean surface temperature (GST). Shown are the four orthogonal components of the GST of unfiltered data as a function of time. Each curve has been offset vertically by 0.2. From top to bottom, GST and the sum of the first 4 components. They are the trend mode (TR), the ENSO-cycle mode (ENSO), the global PDO mode (PDO), and the global AMO mode. Reproduced from Chen and Tung [<a href="#B7-climate-06-00082" class="html-bibr">7</a>].</p> Full article ">
20 pages, 3682 KiB  
Article
Solving Multi-Objective Problems for Multifunctional and Sustainable Management in Maritime Pine Forest Landscapes
by Fernando Pérez-Rodríguez, Luís Nunes and João C. Azevedo
Climate 2018, 6(4), 81; https://doi.org/10.3390/cli6040081 - 15 Oct 2018
Cited by 4 | Viewed by 4981
Abstract
Forest management based on sustainability and multifunctionality requires reliable and user-friendly tools to address several objectives simultaneously. In this work we present FlorNExT Pro®, a multiple-criteria landscape-scale forest planning and management computer tool, and apply it in a region in the [...] Read more.
Forest management based on sustainability and multifunctionality requires reliable and user-friendly tools to address several objectives simultaneously. In this work we present FlorNExT Pro®, a multiple-criteria landscape-scale forest planning and management computer tool, and apply it in a region in the north of Portugal to find optimized management solutions according to objectives such as maximization of net present value (NPV), volume growth, and carbon storage, and minimization of losses due to fire. Comparisons made among single- and multi-objective solutions were made to explore the range of possible indicators provided by the tool such as carbon sequestered, volume growth, probability of fire occurrence, volume of wood extracted, and evenness of harvesting in the management period. Results show that FlorNExT Pro® is a reliable, flexible, and useful tool to incorporate multiple criteria and objectives into spatially explicit complex management problems and to prepare sustainable and multifunctional forest management plans at the landscape level. FlorNExT Pro® is also suited to guiding and adapting forest management for uncertainty scenarios for the assessment of ecosystem services and fire risk, therefore playing an important role in the maintenance of sustainable landscapes in the south of Europe. Full article
(This article belongs to the Special Issue Social-Ecological Systems, Climate and Global Change Impacts)
►▼ Show Figures

Figure 1

Figure 1
<p>Simplification of the general management optimization procedure of FlorNExT Pro<sup>®</sup>.</p> Full article ">Figure 2
<p>FlorNExT Pro<sup>®</sup> interface (in Portuguese) of the general restrictions (<b>a</b>) and management alternatives generator (<b>b</b>). General restrictions (<span class="html-italic">Restrições gerais</span>) include: minimum age for felling (<span class="html-italic">Corte final: Idade mínima</span>), minimum and maximum age for thinning (<span class="html-italic">Desbastes: Idade mínima; Idade máxima</span>), and number of days with more than 1 mm of rain for thinning (<span class="html-italic">Desbastes: Dias com mais de 1mm de chuva ao ano</span>). The management alternatives generator (<span class="html-italic">Configuração de alternativas</span>) requires definition of: number of periods (<span class="html-italic">Número de períodos</span>), maximum number of thinnings (<span class="html-italic">Número máximo de desbastes</span>), interval between operations (<span class="html-italic">Intervalo entre operações</span>), starting year (<span class="html-italic">Início</span>), and amplitude.</p> Full article ">Figure 3
<p>Example of spatial data manipulation capacities in FlorNExT Pro<sup>®</sup>: polygon editing based on photointerpretation of an aerial photograph in the region showing the location of polygon vertices that can be moved or deleted by the user (<b>above</b>) and tabular identification of these vertices (<b>below</b>). Translation: <span class="html-italic">Pontos do polígono em edição—</span>points of the edited polygon.</p> Full article ">Figure 4
<p>Section of the FlorNExT Pro<sup>®</sup> interface (in Portuguese) showing the choices of criteria available to be addressed in the multi-objective problem formulation. In this example, the objectives “maximize volume growth in stands”, “maximize carbon fixation” and “minimize losses due to fire” have been selected with associated weights of 1, 0.9, and 1, respectively. Translations (in the main folder): <span class="html-italic">Otimização—</span>optimization; <span class="html-italic">Maximizar o VAL—</span>maximize net present value (NPV); <span class="html-italic">Preço médio em desbastes—</span>mean price from thinning; <span class="html-italic">Preço médio em corte final—</span>mean price from felling; <span class="html-italic">Taxa de desconto—</span>discount rate; <span class="html-italic">Maximizar o crescimento de volume nas parcelas—</span>maximize volume growth in stands; <span class="html-italic">Maximizar a fixação de carbono—</span>maximize carbon fixation; <span class="html-italic">Minimizar perdas por incêndios—</span>minimize losses due to fire.</p> Full article ">Figure 5
<p>General structure of the forest management optimization tool FlorNExT Pro<sup>®</sup>.</p> Full article ">Figure 6
<p>Volume extracted and average probability of fire occurrence (<b>top</b>) and volume extracted by thinning and harvesting (<b>bottom</b>) for each of the 28 management objectives scenarios tested in the Lomba ZIF, Portugal.</p> Full article ">Figure 6 Cont.
<p>Volume extracted and average probability of fire occurrence (<b>top</b>) and volume extracted by thinning and harvesting (<b>bottom</b>) for each of the 28 management objectives scenarios tested in the Lomba ZIF, Portugal.</p> Full article ">Figure 7
<p>Net present value in million Euro (M€) for maritime pine stands the overall area under management for the 28 management objective scenarios tested in the Lomba ZIF, Portugal.</p> Full article ">Figure 8
<p>Mean volume growth rate (m<sup>3</sup>/ha/year) for maritime pine stands in the overall area under management according to each of the 28 management objective scenarios tested in the Lomba ZIF, Portugal.</p> Full article ">Figure 9
<p>Average carbon storage (Mg) in maritime pine stands in the Lomba ZIF, Portugal, in each of the 28 management objective scenarios tested.</p> Full article ">Figure 10
<p>Temporal patterns of wood extraction for contrasting scenarios: (<b>a</b>) Scenario 1; (<b>b</b>) Scenario 14; (<b>c</b>) Scenario 17; (<b>d</b>) Scenario 20; (<b>e</b>) Scenario 23; and (<b>f</b>) Scenario 27. Bars indicate level of extraction in m<sup>3</sup> for time intervals 0 to 10.</p> Full article ">
17 pages, 1607 KiB  
Perspective
New Breeding Techniques for Greenhouse Gas (GHG) Mitigation: Plants May Express Nitrous Oxide Reductase
by Jordan J. Demone, Shen Wan, Maryam Nourimand, Asbjörn Erik Hansen, Qing-yao Shu and Illimar Altosaar
Climate 2018, 6(4), 80; https://doi.org/10.3390/cli6040080 - 27 Sep 2018
Cited by 5 | Viewed by 6618
Abstract
Nitrous oxide (N2O) is a potent greenhouse gas (GHG). Although it comprises only 0.03% of total GHGs produced, N2O makes a marked contribution to global warming. Much of the N2O in the atmosphere issues from incomplete bacterial [...] Read more.
Nitrous oxide (N2O) is a potent greenhouse gas (GHG). Although it comprises only 0.03% of total GHGs produced, N2O makes a marked contribution to global warming. Much of the N2O in the atmosphere issues from incomplete bacterial denitrification processes acting on high levels of nitrogen (N) in the soil due to fertilizer usage. Using less fertilizer is the obvious solution for denitrification mitigation, but there is a significant drawback (especially where not enough N is available for the crop via N deposition, irrigation water, mineral soil N, or mineralization of organic matter): some crops require high-N fertilizer to produce the yields necessary to help feed the world’s increasing population. Alternatives for denitrification have considerable caveats. The long-standing promise of genetic modification for N fixation may be expanded now to enhance dissimilatory denitrification via genetic engineering. Biotechnology may solve what is thought to be a pivotal environmental challenge of the 21st century, reducing GHGs. Current approaches towards N2O mitigation are examined here, revealing an innovative solution for producing staple crops that can ‘crack’ N2O. The transfer of the bacterial nitrous oxide reductase gene (nosZ) into plants may herald the development of plants that express the nitrous oxide reductase enzyme (N2OR). This tactic would parallel the precedents of using the molecular toolkit innately offered by the soil microflora to reduce the environmental footprint of agriculture. Full article
(This article belongs to the Special Issue Sustainable Agriculture for Climate Change Adaptation)
►▼ Show Figures

Figure 1

Figure 1
<p>GHG levels since 1850. The green line represents the increase in CO<sub>2</sub> concentration since 1850; the orange line represents the increase in CH<sub>4</sub> concentration since 1850; lastly, the red line represents the increase in N<sub>2</sub>O since 1850 [<a href="#B19-climate-06-00080" class="html-bibr">19</a>].</p> Full article ">Figure 2
<p>Nitrification-denitrification pathway and overview of current N<sub>2</sub>O mitigation strategies. Orange arrows and lines show eight N<sub>2</sub>O mitigation strategies described in <a href="#climate-06-00080-t001" class="html-table">Table 1</a>. Green arrows show nitrification and purple arrows represent denitrification reactions. BDI, biological denitrification inhibitor; BMPs, best management practices; BNI, biological nitrification inhibitor; EENFs, enhanced efficiency nitrogen fertilizers; SDI, synthetic denitrification inhibitor; SNI, synthetic nitrification inhibitor; UI, urease inhibitor. <b>O</b> Encircled numbers refer to <a href="#climate-06-00080-t001" class="html-table">Table 1</a> strategies.</p> Full article ">Figure 3
<p>Structure of <span class="html-italic">Marinobacter hydrocarbonoclasticus</span> nitrous oxide reductase (N<sub>2</sub>OR) homodimer. N<sub>2</sub>OR is organized as a head-to-tail homodimer. Monomers are coloured differently so that they can be distinguished. In both monomers, the N-terminal domain is dark-coloured. The N-terminal domain forms a seven-bladed β-propeller fold that coordinates the catalytic tetranuclear active site Cu<sub>Z</sub> through seven histidine residues at its hub. The C-terminal domain forms a cupredoxin fold and binds the dinuclear mixed-valent Cu<sub>A</sub> centre [<a href="#B104-climate-06-00080" class="html-bibr">104</a>].</p> Full article ">Figure 4
<p>Nitrous oxide reductase-related publications released since 1990 on Web of Science (Clarivate Analytics). Publications by key word vs “nitrous oxide reductase” from 1990 to 2018. The orange line indicates “nitrous oxide reductase” + “microb*”; green: “nitrous oxide reductase” + “plant”; blue: “nitrous oxide reductase” + “climat*”.</p> Full article ">
16 pages, 1936 KiB  
Article
A Proposal to Evaluate Drought Characteristics Using Multiple Climate Models for Multiple Timescales
by Nguyen Tien Thanh
Climate 2018, 6(4), 79; https://doi.org/10.3390/cli6040079 - 26 Sep 2018
Cited by 6 | Viewed by 5404
Abstract
This study presents a method to investigate meteorological drought characteristics using multiple climate models for multiple timescales under two representative concentration pathway (RCP) scenarios, RCP4.5 and RCP8.5, during 2021–2050. The methods of delta change factor, unequal weights, standardized precipitation index, Mann–Kendall and Sen’s [...] Read more.
This study presents a method to investigate meteorological drought characteristics using multiple climate models for multiple timescales under two representative concentration pathway (RCP) scenarios, RCP4.5 and RCP8.5, during 2021–2050. The methods of delta change factor, unequal weights, standardized precipitation index, Mann–Kendall and Sen’s slope are proposed and applied with the main purpose of reducing uncertainty in climate projections and detection of the projection trends in meteorological drought. Climate simulations of three regional climate models driven by four global climate models are used to estimate weights for each run on the basic of rank sum. The reliability is then assessed by comparing a weighted ensemble climate output with observations during 1989–2008. Timescales of 1, 3, 6, 9, 12, and 24 months are considered to calculate the standardized precipitation index, taking the Vu Gia-Thu Bon (VG-TB) as a pilot basin. The results show efficient precipitation simulations using unequal weights. In the same timescales, the occurrence of moderately wet events is smaller than that of moderately dry events under the RCP4.5 scenario during 2021–2050. Events classified as “extremely wet”, “extremely dry”, “very wet” and “severely dry” are expected to rarely occur under the RCP8.5 scenario. Full article
(This article belongs to the Special Issue Climate Variability and Change in the 21th Century)
►▼ Show Figures

Figure 1

Figure 1
<p>Network of hydro-meteorological stations at Vu Gia-Thu Bon (VG-TB) basin.</p> Full article ">Figure 2
<p>The probability transformation from fitted gamma distribution to the standard normal distribution.</p> Full article ">Figure 3
<p>Scatter plot ofthe precipitation simulations with equal and unequal weights minus observations over the whole VG-TB during 1989–2008 (mm/month).</p> Full article ">Figure 4
<p>Standardized precipitation index (SPI)for multiple timescales under RCP4.5 scenario (2021–2050) for Danang station.</p> Full article ">Figure 5
<p>SPI for multiple timescales under RCP8.5 scenario (2021–2050) for Danang station.</p> Full article ">Figure 6
<p>Spatial distribution of Sen’s slope in February (<b>a</b>) and in November (<b>b</b>) for SPI-1 index.</p> Full article ">
14 pages, 1947 KiB  
Article
Possible Scenarios of Winter Wheat Yield Reduction of Dryland Qazvin Province, Iran, Based on Prediction of Temperature and Precipitation Till the End of the Century
by Behnam Mirgol and Meisam Nazari
Climate 2018, 6(4), 78; https://doi.org/10.3390/cli6040078 - 23 Sep 2018
Cited by 11 | Viewed by 4627
Abstract
The climate of the Earth is changing. The Earth’s temperature is projected to maintain its upward trend in the next few decades. Temperature and precipitation are two very important factors affecting crop yields, especially in arid and semi-arid regions. There is a need [...] Read more.
The climate of the Earth is changing. The Earth’s temperature is projected to maintain its upward trend in the next few decades. Temperature and precipitation are two very important factors affecting crop yields, especially in arid and semi-arid regions. There is a need for future climate predictions to protect vulnerable sectors like agriculture in drylands. In this study, the downscaling of two important climatic variables—temperature and precipitation—was done by the CanESM2 and HadCM3 models under five different scenarios for the semi-arid province of Qazvin, located in Iran. The most efficient scenario was selected to predict the dryland winter wheat yield of the province for the three periods: 2010–2039, 2040–2069, and 2070–2099. The results showed that the models are able to satisfactorily predict the daily mean temperature and annual precipitation for the three mentioned periods. Generally, the daily mean temperature and annual precipitation tended to decrease in these periods when compared to the current reference values. However, the scenarios rcp2.6 and B2, respectively, predicted that the precipitation will fall less or even increase in the period 2070–2099. The scenario rcp2.6 seemed to be the most efficient to predict the dryland winter wheat yield of the province for the next few decades. The grain yield is projected to drop considerably over the three periods, especially in the last period, mainly due to the reduction in precipitation in March. This leads us to devise some adaptive strategies to prevent the detrimental impacts of climate change on the dryland winter wheat yield of the province. Full article
(This article belongs to the Special Issue Sustainable Agriculture for Climate Change Adaptation)
►▼ Show Figures

Figure 1

Figure 1
<p>Map of the studied area.</p> Full article ">Figure 2
<p>Results of the comparison between the observed and simulated monthly mean temperature values (2006–2015).</p> Full article ">Figure 3
<p>Results of the comparison between the observed precipitation values (2006–2015) and the simulated precipitation values. I = ± SD: standard deviation, the overlapping bars show no significant differences.</p> Full article ">Figure 4
<p>Relationship between the yield reduction and rcp2.6-induced precipitation of March in the three future periods.</p> Full article ">
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-23
Previous Issue
Next Issue
Back to TopTop