Special Issue "Climate and Climate Niche Models"

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

Deadline for manuscript submissions: closed (30 November 2018)

Special Issue Editor

Guest Editor
Assoc. Prof. Tongli Wang

Centre for Forest Conservation Genetics, Department of Forest Sciences, Faculty of Forestry, University of British Columbia, Canada
Website | E-Mail
Phone: 604–822–1845
Interests: climate change, climate models, conservation, ecosystems, forest biology, genetics, genomics, modelling

Special Issue Information

Dear Colleagues,

Climate niche models, alternatively known as bioclimate envelope models or species distribution models, have been widely used to assess the impact of climate change and to develop adaptive strategies. However, the credibility of a climate niche model depends on the accuracy of climate data, the quality of species occurrence data, modeling methodology and the interpretation of the model predictions. For climate data in particular, using different sources of climate data to build climate niche models may considerably affect model accuracy. Manuscripts that address these issues in the application of niche models and the improvement of climate data/models are welcome. Studies that compare climate niche models with process-based models will also be considered.

Dr. Tongli Wang
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Climate is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 550 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • climate change
  • climate data
  • climate niche
  • bioclimate envelope
  • adaptation
  • species distribution

Published Papers (3 papers)

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Research

Open AccessArticle Not so Normal Normals: Species Distribution Model Results are Sensitive to Choice of Climate Normals and Model Type
Climate 2019, 7(3), 37; https://doi.org/10.3390/cli7030037
Received: 13 February 2019 / Accepted: 23 February 2019 / Published: 28 February 2019
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Abstract
Species distribution models have many applications in conservation and ecology, and climate data are frequently a key driver of these models. Often, correlative modeling approaches are developed with readily available climate data; however, the impacts of the choice of climate normals is rarely [...] Read more.
Species distribution models have many applications in conservation and ecology, and climate data are frequently a key driver of these models. Often, correlative modeling approaches are developed with readily available climate data; however, the impacts of the choice of climate normals is rarely considered. Here, we produced species distribution models for five disparate species using four different modeling algorithms and compared results between two different, but overlapping, climate normals time periods. Although the correlation structure among climate predictors did not change between the time periods, model results were sensitive to both baseline climate period and model method, even with model parameters specifically tuned to a species. Each species and each model type had at least one difference in variable retention or relative ranking with the change in climate time period. Pairwise comparisons of spatial predictions were also different, ranging from a low of 1.6% for climate period differences to a high of 25% for algorithm differences. While uncertainty from model algorithm selection is recognized as an important source of uncertainty, the impact of climate period is not commonly assessed. These uncertainties may affect conservation decisions, especially when projecting to future climates, and should be evaluated during model development. Full article
(This article belongs to the Special Issue Climate and Climate Niche Models)
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Open AccessArticle Relationship between East Asian Cold Surges and Synoptic Patterns: A New Coupling Framework
Climate 2019, 7(2), 30; https://doi.org/10.3390/cli7020030
Received: 15 January 2019 / Revised: 29 January 2019 / Accepted: 30 January 2019 / Published: 1 February 2019
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Abstract
Strong cold surge events (CSEs) are some of the most distinct winter weather events in East Asia, impacting natural ecosystems and over 100 million individuals. The impact of such extreme CSEs as driven by synoptic systems is direct and immediate. Changes in large-scale [...] Read more.
Strong cold surge events (CSEs) are some of the most distinct winter weather events in East Asia, impacting natural ecosystems and over 100 million individuals. The impact of such extreme CSEs as driven by synoptic systems is direct and immediate. Changes in large-scale synoptic patterns as potentially affected by changes in the Arctic are further expected to influence CSE occurrences in East Asia. Defying a straightforward analysis, semi-permanent atmospheric systems such as the Siberian High (SH), influencing large-scale synoptic patterns, make the atmospheric circulation highly variable and assessment of CSE onset difficult. Rather varied region-specific metrics are currently adopted for predicting CSE occurrence locally but the fundamental understanding of the onset of CSEs continues to be a major challenge. Based on an analysis of monthly synoptic patterns for three unusual CSEs in East Asia and further extended for eight strong to extreme CSEs, we propose a new coupling framework for an improved understanding and interpretation of the atmosphere dynamics driving CSE onset. The coupling framework involves linkages between the Siberian High, Aleutian Low, and Jet Stream. We also present the first meteorological scale for categorizing the intensity of such unusual CSEs. Full article
(This article belongs to the Special Issue Climate and Climate Niche Models)
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Figure 1

Open AccessArticle Temporal and Spatial Variability in Surface Air Temperature and Diurnal Temperature Range in Spain over the Period 1950–2011
Climate 2019, 7(1), 16; https://doi.org/10.3390/cli7010016
Received: 20 November 2018 / Revised: 14 January 2019 / Accepted: 15 January 2019 / Published: 19 January 2019
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Abstract
Maximum (Tmax), minimum (Tmin), mean (Tmean) air temperature and diurnal temperature range (DTR) trends on a seasonal and annual time scale are evaluated from data recorded at nine Spanish weather stations during the period 1950–2011. Temporal and [...] Read more.
Maximum (Tmax), minimum (Tmin), mean (Tmean) air temperature and diurnal temperature range (DTR) trends on a seasonal and annual time scale are evaluated from data recorded at nine Spanish weather stations during the period 1950–2011. Temporal and spatial variability in temperatures and in the diurnal temperature range (DTR) are presented. The non-parametric Theil-Sen approach and the Mann-Kendall test are used to evaluate anomaly temperature trends and their statistical significance, respectively. An air temperature reduction in Spain between 1950 and 1980 emerges and significant warming is observed between 1980 and 2011. On a seasonal scale, the weakest trends (mostly insignificant at the 5% confidence level) are noted during autumn, while the strongest warming rates were found during summer and spring. The rate of change between 1950 and 2011 in Tmax, Tmin and Tmean was 1.6 °C, 1.1 °C and 1.3 °C, respectively. DTR trends showed a decrease on the Mediterranean coast and a small change in northern, Atlantic and rural areas. The spatial distribution of annual and seasonal trends was plotted as isoline maps and strong trend gradients from the south to the north of the country are observed. DTR values were negatively correlated with relative humidity and precipitation and positively correlated with sunshine hours. Full article
(This article belongs to the Special Issue Climate and Climate Niche Models)
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