Climate Change Dynamics and Modeling: Future Perspectives

A special issue of Climate (ISSN 2225-1154). This special issue belongs to the section "Climate Dynamics and Modelling".

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 40859

Special Issue Editors

Dipartimento di Scienze Matematiche e Informatiche, Scienze Fisiche e Scienze della Terra, Università di Messina, 98166 Messina, Italy
Interests: structural and dynamical characterization of molecular systems; fourier transform infrared spectroscopy; Raman spectroscopy; neutron scattering; spectral analysis
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Special Issue Information

Dear Colleagues,

Terms such as Stick Hockey effect, slogans such as “There is no planet B” and proposals such as “Go green to save the blue” are consistently used in daily news and refer to the theme of climate change, a theme to which an ever greater need is attributed today, and not only by the scientific community. It is well known that the history of the climate has ancient origins. In fact, the Greek term “klima” designated the inclination of the Sun’s rays with respect to the Earth’s surface, thus testifying to the understanding of the correlation between the flow of solar energy and daily and seasonal temperature variations. The Earth’s climate change is shaped in fascinating ways by the atmospheric processes that fuel climate change, by increasing water vapor, withdrawing snow and ice, or changing cloud cover. It is important to know how dynamic and wet processes, clouds, and convection control the Earth’s climate sensitivity, change in the hydrological cycle, and natural variability. In fact, the climate has a substantial influence on both humanity and the Earth. Strong precipitations and high temperature can cause increased carbon accumulation in plants and soil due to vegetation growth, or increased carbon release into the atmosphere. These difficulties have been present since the beginning of humanity, and for these reasons, people have had to take into account the climate of their respective regions and adapt to it accordingly. Thus, people throughout history have struggled to understand climatic variations. In the past, primarily mythological and religious explanations were used to provide predictions, but in the meantime, climate evolved as a science, allowing us to elaborate on ever more sophisticated representations of the observed phenomena. Such a description of the climate now involves a very broad range of skills, corresponding to several domains of science including physics, chemistry, biology, and geology. Due to the complexity of the climate, most of the analyses dedicated to the quantitative estimation of climate change or climate variability are based on the employment of numerical models. In order to better analyze climate variability and its reaction to a perturbation, a lot of information is provided on the interactions between the different elements of the climate system and on the dominant feedbacks. Using this information, it is possible to understand the main causes of past climate changes and evaluate climate change projects over the next centuries or millennia. Furthermore, another goal is to provide students with the foundation for understanding what climate models look like and how they can be used to make quantitative estimates of climate variability and climate change, as well as to illustrate how models could be used to better understand the important links of climate science.  Furthermore, in addition to the usual modeling techniques, given the amount of data to be pre-processed and/or modeled, when necessary, machine learning techniques (including deep learning) are used, for example, for the analysis of historical series (big historical/heterogeneous data) or even for model training. This Special Issue titled “Climate Change Dynamics and Modeling: Future Perspectives” is focused on the varied range of interdisciplinary issues related to climatic changes. To this end, a series of past climate study approaches will be used, and innovative interpretative models will be presented together with a comparison to the most important approaches currently adopted. Furthermore, it is well known that floods, droughts, sea level rise, and landscape changes greatly influence several fields and aspects, such as, for example, national and international economics and politics stakeholders and ethical issues. In this framework, the authors contributing to the Special Issue will have the opportunity to communicate the contents of their studies to scholars operating in other climate-related disciplines and to interested non-disciplinarians.

Dr. Maria Teresa Caccamo
Prof. Dr. Salvatore Magazù
Guest Editors

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Keywords

  • climate change
  • climate dynamics
  • climate modeling
  • physical and mathematical approaches
  • interdisciplinary approach
  • transdisciplinary approach
  • machine learning

Published Papers (9 papers)

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Editorial

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5 pages, 219 KiB  
Editorial
Climate Change Dynamics and Modeling: Future Perspectives
by Salvatore Magazù and Maria Teresa Caccamo
Climate 2022, 10(5), 65; https://0-doi-org.brum.beds.ac.uk/10.3390/cli10050065 - 06 May 2022
Cited by 2 | Viewed by 2126
Abstract
This preface to the Special Issue titled “Climate Change Dynamics and Modeling: Future Perspectives” presents eight articles, largely focused on a range of interdisciplinary issues related to climatic changes [...] Full article
(This article belongs to the Special Issue Climate Change Dynamics and Modeling: Future Perspectives)

Research

Jump to: Editorial

18 pages, 5574 KiB  
Article
Suitability Assessment of Weather Networks for Wind Data Measurements in the Athabasca Oil Sands Area
by Dhananjay Deshmukh, M. Razu Ahmed, John Albino Dominic, Anil Gupta, Gopal Achari and Quazi K. Hassan
Climate 2022, 10(2), 10; https://0-doi-org.brum.beds.ac.uk/10.3390/cli10020010 - 18 Jan 2022
Cited by 4 | Viewed by 3347
Abstract
The Athabasca Oil Sands Area (AOSA) in Alberta, Canada, is considered to have a high density of weather stations. Therefore, our objective was to determine an optimal network for the wind data measurement that could sufficiently represent the wind variability in the area. [...] Read more.
The Athabasca Oil Sands Area (AOSA) in Alberta, Canada, is considered to have a high density of weather stations. Therefore, our objective was to determine an optimal network for the wind data measurement that could sufficiently represent the wind variability in the area. We used available historical data records of the weather stations in the three networks in AOSA, i.e., oil sands monitoring (OSM) water quantity program (WQP) and Wood Buffalo Environmental Association (WBEA) edge sites (ES) and meteorological towers (MT) of the air program. Both graphical and quantitative methods were implemented to find the correlations and similarities in the measurements between weather stations in each network. The graphical method (wind rose diagram) was found as a functional tool to understand the patterns of wind directions, but it was not appropriate to quantify and compare between wind speed data of weather stations. Therefore, we applied the quantitative method of the Pearson correlation coefficient (r) and absolute average error (AAE) in finding a relationship between the wind data of station pairs and the percentage of similarity (PS) method in quantifying the closeness/similarity. In the correlation analyses, we found weak to strong correlations in the wind data of OSM WQP (r = 0.04–0.69) and WBEA ES (r = 0.32–0.77), and a strong correlation (r = 0.33–0.86) in most of the station pairs of the WBEA MT network. In the case of AAE, we did not find any acceptable value within the standard operating procedure (SOP) threshold when logically combining the values of the u and v components together. In the similarity analysis, minor similarities were identified between the stations in the three networks. Hence, we presumed that all weather stations would be required to measure wind data in the AOSA. Full article
(This article belongs to the Special Issue Climate Change Dynamics and Modeling: Future Perspectives)
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17 pages, 5296 KiB  
Article
Coastal Wave Extremes around the Pacific and Their Remote Seasonal Connection to Climate Modes
by Julien Boucharel, Loane Santiago, Rafael Almar and Elodie Kestenare
Climate 2021, 9(12), 168; https://0-doi-org.brum.beds.ac.uk/10.3390/cli9120168 - 26 Nov 2021
Cited by 8 | Viewed by 2700
Abstract
At first order, wind-generated ocean surface waves represent the dominant forcing of open-coast morpho-dynamics and associated vulnerability over a wide range of time scales. It is therefore paramount to improve our understanding of the regional coastal wave variability, particularly the occurrence of extremes, [...] Read more.
At first order, wind-generated ocean surface waves represent the dominant forcing of open-coast morpho-dynamics and associated vulnerability over a wide range of time scales. It is therefore paramount to improve our understanding of the regional coastal wave variability, particularly the occurrence of extremes, and to evaluate how they are connected to large-scale atmospheric regimes. Here, we propose a new “2-ways wave tracking algorithm” to evaluate and quantify the open-ocean origins and associated atmospheric forcing patterns of coastal wave extremes all around the Pacific basin for the 1979–2020 period. Interestingly, the results showed that while extreme coastal events tend to originate mostly from their closest wind-forcing regime, the combined influence from all other remote atmospheric drivers is similar (55% local vs. 45% remote) with, in particular, ~22% coming from waves generated remotely in the opposite hemisphere. We found a strong interconnection between the tropical and extratropical regions with around 30% of coastal extremes in the tropics originating at higher latitudes and vice-versa. This occurs mostly in the boreal summer through the increased seasonal activity of the southern jet-stream and the northern tropical cyclone basins. At interannual timescales, we evidenced alternatingly increased coastal wave extremes between the western and eastern Pacific that emerge from the distinct seasonal influence of ENSO in the Northern and SAM in the Southern Hemisphere on their respective paired wind-wave regimes. Together these results pave the way for a better understanding of the climate connection to wave extremes, which represents the preliminary step toward better regional projections and forecasts of coastal waves. Full article
(This article belongs to the Special Issue Climate Change Dynamics and Modeling: Future Perspectives)
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30 pages, 21146 KiB  
Article
Testing the CMIP6 GCM Simulations versus Surface Temperature Records from 1980–1990 to 2011–2021: High ECS Is Not Supported
by Nicola Scafetta
Climate 2021, 9(11), 161; https://0-doi-org.brum.beds.ac.uk/10.3390/cli9110161 - 29 Oct 2021
Cited by 17 | Viewed by 14916
Abstract
The last-generation CMIP6 global circulation models (GCMs) are currently used to interpret past and future climatic changes and to guide policymakers, but they are very different from each other; for example, their equilibrium climate sensitivity (ECS) varies from 1.83 to 5.67 °C (IPCC [...] Read more.
The last-generation CMIP6 global circulation models (GCMs) are currently used to interpret past and future climatic changes and to guide policymakers, but they are very different from each other; for example, their equilibrium climate sensitivity (ECS) varies from 1.83 to 5.67 °C (IPCC AR6, 2021). Even assuming that some of them are sufficiently reliable for scenario forecasts, such a large ECS uncertainty requires a pre-selection of the most reliable models. Herein the performance of 38 CMIP6 models are tested in reproducing the surface temperature changes observed from 1980–1990 to 2011–2021 in three temperature records: ERA5-T2m, ERA5-850mb, and UAH MSU v6.0 Tlt. Alternative temperature records are briefly discussed but found to be not appropriate for the present analysis because they miss data over large regions. Significant issues emerge: (1) most GCMs overestimate the warming observed during the last 40 years; (2) there is great variability among the models in reconstructing the climatic changes observed in the Arctic; (3) the ocean temperature is usually overestimated more than the land one; (4) in the latitude bands 40° N–70° N and 50° S–70° S (which lay at the intersection between the Ferrel and the polar atmospheric cells) the CMIP6 GCMs overestimate the warming; (5) similar discrepancies are present in the east-equatorial pacific region (which regulates the ENSO) and in other regions where cooling trends are observed. Finally, the percentage of the world surface where the (positive or negative) model-data discrepancy exceeds 0.2, 0.5 and 1.0 °C is evaluated. The results indicate that the models with low ECS values (for example, 3 °C or less) perform significantly better than those with larger ECS. Therefore, the low ECS models should be preferred for climate change scenario forecasts while the other models should be dismissed and not used by policymakers. In any case, significant model-data discrepancies are still observed over extended world regions for all models: on average, the GCM predictions disagree from the data by more than 0.2 °C (on a total mean warming of about 0.5 °C from 1980–1990 to 2011–2021) over more than 50% of the global surface. This result suggests that climate change and its natural variability remain poorly modeled by the CMIP6 GCMs. Finally, the ECS uncertainty problem is discussed, and it is argued (also using semi-empirical climate models that implement natural oscillations not predicted by the GCMs) that the real ECS could be between 1 and 2 °C, which implies moderate warming for the next decades. Full article
(This article belongs to the Special Issue Climate Change Dynamics and Modeling: Future Perspectives)
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18 pages, 1912 KiB  
Article
Functional Data Visualization and Outlier Detection on the Anomaly of El Niño Southern Oscillation
by Jamaludin Suhaila
Climate 2021, 9(7), 118; https://0-doi-org.brum.beds.ac.uk/10.3390/cli9070118 - 15 Jul 2021
Cited by 8 | Viewed by 2506
Abstract
The El Niño Southern Oscillation (ENSO) is a well-known cause of year-to-year climatic variations on Earth. Floods, droughts, and other natural disasters have been linked to the ENSO in various parts of the world. Hence, modeling the ENSO’s effects and the anomaly of [...] Read more.
The El Niño Southern Oscillation (ENSO) is a well-known cause of year-to-year climatic variations on Earth. Floods, droughts, and other natural disasters have been linked to the ENSO in various parts of the world. Hence, modeling the ENSO’s effects and the anomaly of the ENSO phenomenon has become a main research interest. Statistical methods, including linear and nonlinear models, have intensively been used in modeling the ENSO index. However, these models are unable to capture sufficient information on ENSO index variability, particularly on its temporal aspects. Hence, this study adopted functional data analysis theory by representing a multivariate ENSO index (MEI) as functional data in climate applications. This study included the functional principal component, which is purposefully designed to find new functions that reveal the most important type of variation in the MEI curve. Simultaneously, graphical methods were also used to visualize functional data and capture outliers that may not have been apparent from the original data plot. The findings suggest that the outliers obtained from the functional plot are then related to the El Niño and La Niña phenomena. In conclusion, the functional framework was found to be more flexible in representing the climate phenomenon as a whole. Full article
(This article belongs to the Special Issue Climate Change Dynamics and Modeling: Future Perspectives)
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20 pages, 11240 KiB  
Article
Synoptic–Dynamic Patterns Affecting Iran’s Autumn Precipitation during ENSO Phase Transitions
by Faranak Bahrami, Abbas Ranjbar Saadatabadi, Nir Y. Krakauer, Tayyebeh Mesbahzadeh and Farshad Soleimani Sardoo
Climate 2021, 9(7), 106; https://0-doi-org.brum.beds.ac.uk/10.3390/cli9070106 - 28 Jun 2021
Cited by 4 | Viewed by 2364
Abstract
We compared the effect on autumn (October, November, December) precipitation over Iran during two types of El Niño–Southern Oscillation (ENSO) phase transitions from the perspective of anomalies in wave activity flux and sea level pressure along the Atlantic–Mediterranean storm track, as well as [...] Read more.
We compared the effect on autumn (October, November, December) precipitation over Iran during two types of El Niño–Southern Oscillation (ENSO) phase transitions from the perspective of anomalies in wave activity flux and sea level pressure along the Atlantic–Mediterranean storm track, as well as precipitation. We used Oceanic Niño Index (ONI) to identify the transition phases of ENSO (El Niño to La Niña and also La Niña to El Niño, referred to as type 1 and type 2, respectively). Climate data during the period of 1950 to 2019 used in this study is derived from NCEP-NCAR reanalysis. In order to investigate the intensity and direction of Rossby wave trains in different ENSO transitions, we used the wave activity flux parameter, and to evaluate the statistical significance of values, we calculated Student’s t-test. The impact of the Atlantic storm track on the Mediterranean storm track was shown to be greater in type 2 transitions. Further, the existence of a stronger wave source region in the Mediterranean region during type 2 transitions was established. Results also showed the weakening of the Iceland low and Azores high pressure in type 1 transitions and the reinforcement of both in type 2, with the differences being significant at up to a 99% confidence level. Pressure values over Iran were at or below normal in type 1 years and below normal in type 2. Finally, the composite analysis of precipitation anomaly revealed that during ENSO type 1 transitions, most regions of Iran experienced low precipitation, while in type 2, the precipitation was more than average, statistically significant at 75% confidence level or higher over the northern half of the country. Full article
(This article belongs to the Special Issue Climate Change Dynamics and Modeling: Future Perspectives)
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17 pages, 4506 KiB  
Article
On the Breaking of the Milankovitch Cycles Triggered by Temperature Increase: The Stochastic Resonance Response
by Maria Teresa Caccamo and Salvatore Magazù
Climate 2021, 9(4), 67; https://0-doi-org.brum.beds.ac.uk/10.3390/cli9040067 - 18 Apr 2021
Cited by 7 | Viewed by 5174
Abstract
Recent decades have registered the hottest temperature variation in instrumentally recorded data history. The registered temperature rise is particularly significant in the so-called hot spot or sentinel regions, characterized by higher temperature increases in respect to the planet average value and by more [...] Read more.
Recent decades have registered the hottest temperature variation in instrumentally recorded data history. The registered temperature rise is particularly significant in the so-called hot spot or sentinel regions, characterized by higher temperature increases in respect to the planet average value and by more marked connected effects. In this framework, in the present work, following the climate stochastic resonance model, the effects, due to a temperature increase independently from a specific trend, connected to the 105 year Milankovitch cycle were tested. As a result, a breaking scenario induced by global warming is forecasted. More specifically, a wavelet analysis, innovatively performed with different sampling times, allowed us, besides to fully characterize the cycles periodicities, to quantitatively determine the stochastic resonance conditions by optimizing the noise level. Starting from these system resonance conditions, numerical simulations for increasing planet temperatures have been performed. The obtained results show that an increase of the Earth temperature boosts a transition towards a chaotic regime where the Milankovitch cycle effects disappear. These results put into evidence the so-called threshold effect, namely the fact that also a small temperature increase can give rise to great effects above a given threshold, furnish a perspective point of view of a possible future climate scenario, and provide an account of the ongoing registered intensity increase of extreme meteorological events. Full article
(This article belongs to the Special Issue Climate Change Dynamics and Modeling: Future Perspectives)
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15 pages, 7979 KiB  
Article
Differences in the Reaction of North Equatorial Countercurrent to the Developing and Mature Phase of ENSO Events in the Western Pacific Ocean
by Yusuf Jati Wijaya and Yukiharu Hisaki
Climate 2021, 9(4), 57; https://0-doi-org.brum.beds.ac.uk/10.3390/cli9040057 - 05 Apr 2021
Cited by 7 | Viewed by 2606
Abstract
The North Equatorial Countercurrent (NECC) is an eastward zonal current closely related to an El Niño Southern Oscillation (ENSO) event. This paper investigated the variations of NECC in the Western Pacific Ocean over 25 years (1993–2017) using satellite data provided by the Copernicus [...] Read more.
The North Equatorial Countercurrent (NECC) is an eastward zonal current closely related to an El Niño Southern Oscillation (ENSO) event. This paper investigated the variations of NECC in the Western Pacific Ocean over 25 years (1993–2017) using satellite data provided by the Copernicus Marine Environment Monitoring Service (CMEMS) and the Remote Sensing System (RSS). The first mode of empirical orthogonal function (EOF) analysis showed that the NECC strengthened or weakened in each El Niño (La Niña) event during the developing or mature phase, respectively. We also found that the NECC shifting was strongly coincidental with an ENSO event. During the developing phase of an El Niño (La Niña) event, the NECC shifted southward (northward), and afterward, when it entered the mature phase, the NECC tended to shift slightly northward (southward). Moreover, the NECC strength was found to have undergone a weakening during the 2008–2017 period. Full article
(This article belongs to the Special Issue Climate Change Dynamics and Modeling: Future Perspectives)
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22 pages, 1575 KiB  
Article
Kelvin/Rossby Wave Partition of Madden-Julian Oscillation Circulations
by Patrick Haertel
Climate 2021, 9(1), 2; https://0-doi-org.brum.beds.ac.uk/10.3390/cli9010002 - 25 Dec 2020
Cited by 8 | Viewed by 3102
Abstract
The Madden Julian Oscillation (MJO) is a large-scale convective and circulation system that propagates slowly eastward over the equatorial Indian and Western Pacific Oceans. Multiple, conflicting theories describe its growth and propagation, most involving equatorial Kelvin and/or Rossby waves. This study partitions MJO [...] Read more.
The Madden Julian Oscillation (MJO) is a large-scale convective and circulation system that propagates slowly eastward over the equatorial Indian and Western Pacific Oceans. Multiple, conflicting theories describe its growth and propagation, most involving equatorial Kelvin and/or Rossby waves. This study partitions MJO circulations into Kelvin and Rossby wave components for three sets of data: (1) a modeled linear response to an MJO-like heating; (2) a composite MJO based on atmospheric sounding data; and (3) a composite MJO based on data from a Lagrangian atmospheric model. The first dataset has a simple dynamical interpretation, the second provides a realistic view of MJO circulations, and the third occurs in a laboratory supporting controlled experiments. In all three of the datasets, the propagation of Kelvin waves is similar, suggesting that the dynamics of Kelvin wave circulations in the MJO can be captured by a system of equations linearized about a basic state of rest. In contrast, the Rossby wave component of the observed MJO’s circulation differs substantially from that in our linear model, with Rossby gyres moving eastward along with the heating and migrating poleward relative to their linear counterparts. These results support the use of a system of equations linearized about a basic state of rest for the Kelvin wave component of MJO circulation, but they question its use for the Rossby wave component. Full article
(This article belongs to the Special Issue Climate Change Dynamics and Modeling: Future Perspectives)
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