Special Issue "Climate Variability and Change in the 21th Century"

A special issue of Climate (ISSN 2225-1154).

Deadline for manuscript submissions: 31 January 2019

Special Issue Editors

Guest Editor
Dr. Stefanos Stefanidis

Laboratory of Mountainous Water Management and Control, Faculty of Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloníki, Greece
Website | E-Mail
Interests: soil erosion and mountainous catchment degrafation; landslide management and control; cause and mechanism of debris and mud flow phenomena; torrent control works; check dams design and dimmensioning; sediment sources areas; flash floods phenomena; forest hydrology
Guest Editor
Dr. Konstantia Tolika

Department of Meteorology and Climatology, School of Geology, Aristotle University of Thessaloniki,University Campus, 54124, Thessaloniki, Greece
Website | E-Mail
Interests: climate change, climate variability, regional climate models, extreme events, atmospheric circulation, artificial neural networks

Special Issue Information

Dear Colleagues,

During the last decades, there is a growing interest in climate variability and change by scientific community, due to their economic, social and ecological impacts. Knowledge of spatial and temporal climate distribution is fundamental to several disciplines such as geography, hydrology, forest management, agriculture, ecology and others. Moreover future climate projections is essential for rural development and planning and infrastructure works programming.

Topics of interest include, but are not limited to:

  • Regional Climate Models
  • Climate Variability
  • Climate Change Impacts
  • Dynamic and Statistical Downscaling Techniques
  • Spatial Mapping of Meteorological Parameters

Mr. Stefanos Stefanidis
Dr. Konstantia Tolika
Guest Editors

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Keywords

  • climate change
  • spatial and temporal distribution

Published Papers (6 papers)

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Research

Open AccessArticle Selecting and Downscaling a Set of Climate Models for Projecting Climatic Change for Impact Assessment in the Upper Indus Basin (UIB)
Climate 2018, 6(4), 89; https://doi.org/10.3390/cli6040089 (registering DOI)
Received: 26 September 2018 / Revised: 26 October 2018 / Accepted: 10 November 2018 / Published: 14 November 2018
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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)
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Open AccessArticle Estimating the Impact of Artificially Injected Stratospheric Aerosols on the Global Mean Surface Temperature in the 21th Century
Climate 2018, 6(4), 85; https://doi.org/10.3390/cli6040085
Received: 28 September 2018 / Revised: 24 October 2018 / Accepted: 26 October 2018 / Published: 28 October 2018
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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
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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)
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Open AccessArticle A Proposal to Evaluate Drought Characteristics Using Multiple Climate Models for Multiple Timescales
Climate 2018, 6(4), 79; https://doi.org/10.3390/cli6040079
Received: 23 August 2018 / Revised: 16 September 2018 / Accepted: 18 September 2018 / Published: 26 September 2018
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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
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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)
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Open AccessArticle Spatial and Temporal Rainfall Variability over the Mountainous Central Pindus (Greece)
Climate 2018, 6(3), 75; https://doi.org/10.3390/cli6030075
Received: 19 July 2018 / Revised: 2 September 2018 / Accepted: 5 September 2018 / Published: 6 September 2018
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Abstract
In this study, the authors evaluated the spatial and temporal variability of rainfall over the central Pindus mountain range. To accomplish this, long-term (1961–2016) monthly rainfall data from nine rain gauges were collected and analyzed. Seasonal and annual rainfall data were subjected to
[...] Read more.
In this study, the authors evaluated the spatial and temporal variability of rainfall over the central Pindus mountain range. To accomplish this, long-term (1961–2016) monthly rainfall data from nine rain gauges were collected and analyzed. Seasonal and annual rainfall data were subjected to Mann–Kendall tests to assess the possible upward or downward statistically significant trends and to change-point analyses to detect whether a change in the rainfall time series mean had taken place. Additionally, Sen’s slope method was used to estimate the trend magnitude, whereas multiple regression models were developed to determine the relationship between rainfall and geomorphological factors. The results showed decreasing trends in annual, winter, and spring rainfalls and increasing trends in autumn and summer rainfalls, both not statistically significant, for most stations. Rainfall non-stationarity started to occur in the middle of the 1960s for the annual, autumn, spring, and summer rainfalls and in the early 1970s for the winter rainfall in most of the stations. In addition, the average magnitude trend per decade is approximately −1.9%, −3.2%, +0.7%, +0.2%, and +2.4% for annual, winter, autumn, spring, and summer rainfalls, respectively. The multiple regression model can explain 62.2% of the spatial variability in annual rainfall, 58.9% of variability in winter, 75.9% of variability in autumn, 55.1% of variability in spring, and 32.2% of variability in summer. Moreover, rainfall spatial distribution maps were produced using the ordinary kriging method, through GIS software, representing the major rainfall range within the mountainous catchment of the study area. Full article
(This article belongs to the Special Issue Climate Variability and Change in the 21th Century)
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Open AccessArticle Intercomparison of Univariate and Joint Bias Correction Methods in Changing Climate From a Hydrological Perspective
Climate 2018, 6(2), 33; https://doi.org/10.3390/cli6020033
Received: 26 February 2018 / Revised: 20 April 2018 / Accepted: 23 April 2018 / Published: 26 April 2018
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Abstract
In this paper, the ability of two joint bias correction algorithms to adjust biases in daily mean temperature and precipitation is compared against two univariate quantile mapping methods when constructing projections from years 1981–2010 to early (2011–2040) and late (2061–2090) 21st century periods.
[...] Read more.
In this paper, the ability of two joint bias correction algorithms to adjust biases in daily mean temperature and precipitation is compared against two univariate quantile mapping methods when constructing projections from years 1981–2010 to early (2011–2040) and late (2061–2090) 21st century periods. Using both climate model simulations and the corresponding hydrological model simulations as proxies for the future in a pseudo-reality framework, these methods are inter-compared in a cross-validation manner in order to assess to what extent the more sophisticated methods have added value, particularly from the hydrological modeling perspective. By design, bi-variate bias correction methods improve the inter-variable relationships in the baseline period. Cross-validation results show, however, that both in the early and late 21st century conditions the additional benefit of using bi-variate bias correction methods is not obvious, as univariate methods have a comparable performance. From the evaluated hydrological variables, the added value is most clearly seen in the simulated snow water equivalent. Although not having the best performance in adjusting the temperature and precipitation distributions, quantile mapping applied as a delta change method performs well from the hydrological modeling point of view, particularly in the early 21st century conditions. This suggests that retaining the observed correlation structures of temperature and precipitation might in some cases be sufficient for simulating future hydrological climate change impacts. Full article
(This article belongs to the Special Issue Climate Variability and Change in the 21th Century)
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Open AccessArticle Projected Changes in Precipitation, Temperature, and Drought across California’s Hydrologic Regions in the 21st Century
Climate 2018, 6(2), 31; https://doi.org/10.3390/cli6020031
Received: 21 March 2018 / Revised: 14 April 2018 / Accepted: 20 April 2018 / Published: 23 April 2018
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Abstract
This study investigated potential changes in future precipitation, temperature, and drought across 10 hydrologic regions in California. The latest climate model projections on these variables through 2099 representing the current state of the climate science were applied for this purpose. Changes were explored
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This study investigated potential changes in future precipitation, temperature, and drought across 10 hydrologic regions in California. The latest climate model projections on these variables through 2099 representing the current state of the climate science were applied for this purpose. Changes were explored in terms of differences from a historical baseline as well as the changing trend. The results indicate that warming is expected across all regions in all temperature projections, particularly in late-century. There is no such consensus on precipitation, with projections mostly ranging from −25% to +50% different from the historical baseline. There is no statistically significant increasing or decreasing trend in historical precipitation and in the majority of the projections on precipitation. However, on average, precipitation is expected to increase slightly for most regions. The increases in late-century are expected to be more pronounced than the increases in mid-century. The study also shows that warming in summer and fall is more significant than warming in winter and spring. The study further illustrates that, compared to wet regions, dry regions are projected to become more arid. The inland eastern regions are expecting higher increases in temperature than other regions. Particularly, the coolest region, North Lahontan, tends to have the highest increases in both minimum and maximum temperature and a significant amount of increase in wet season precipitation, indicative of increasing flood risks in this region. Overall, these findings are meaningful from both scientific and practical perspectives. From a scientific perspective, these findings provide useful information that can be utilized to improve the current flood and water supply forecasting models or develop new predictive models. From a practical perspective, these findings can help decision-makers in making different adaptive strategies for different regions to address adverse impacts posed by those potential changes. Full article
(This article belongs to the Special Issue Climate Variability and Change in the 21th Century)
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