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Journal articles on the topic 'Climate'

Author: Grafiati

Published: 4 June 2021

Last updated: 23 November 2024

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1

Netzel, Pawel, and Tomasz Stepinski. "Climate Similarity Search: GeoWeb Tool for Exploring Climate Variability." Bulletin of the American Meteorological Society 99, no. 3 (March 1, 2018): 475–77. http://dx.doi.org/10.1175/bams-d-16-0334.1.

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Abstract Climate and climate change are among the scientific topics most widely recognized by the public. Thus, climatologists seek out effective ways of communicating results of their research to various constituencies—a task made difficult by the complexity of the concept of climate. The current standard for communicated variability of climate on the global scale is a map based on the Köppen-Geiger classification (KGC) of climates, and maps of change in average annual temperatures and total annual precipitation for communicating climate change. The ClimateEx (Climate Explorer) project (http://sil.uc.edu/webapps/climateex/) communicates spatial variability and temporal change of global climate in a novel way by using the data science concept of similarity-based query. ClimateEx is implemented as a web-based visual spatial search tool. Users select a location (query), and ClimatEx returns a similarity map that visually communicates locations of places in the world having climates similar to the climate at a query location. ClimateEx can also inform about magnitude of temporal climate change by calculating a global map of local magnitudes of climate change. It offers personalized means of communicating climate heterogeneity and conveying magnitude of climate change in a single map. It has the advantage of relating climate to a user’s own experience, and is well-suited for communicating character of global climate to specialists and nonspecialists alike.

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2

Nordhaus, William D. "Climate and Economic Development: Climates Past and Climate Change Future." World Bank Economic Review 7, suppl 1 (1993): 355–76. http://dx.doi.org/10.1093/wber/7.suppl_1.355.

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3

Budde-Sung, Amanda, and Tanya A. Peacock. "Can’t take the heat? Climate and foreign subsidiary locations." critical perspectives on international business 15, no. 1 (March 4, 2019): 42–67. http://dx.doi.org/10.1108/cpoib-07-2017-0044.

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Purpose This paper aims to build upon climato-economic theory to investigate the issue of climate’s effect on foreign expansion and location choice. Design/methodology/approach This empirical paper looks at foreign subsidiary location through the lens of the climato-economic theory. To do this, the study uses a balanced data set, looking at foreign expansion before, during and after the global financial crisis of US multinational firms. A multilevel step-wise regression is used to look at climate, culture and economic effects on foreign location choice. Findings The findings suggest that US multinational enterprises tend to have fewer foreign subsidiaries in countries with extreme climates, and they prefer locations with warmer climates, avoiding locations with colder climates, although they gravitate toward locations with less sunshine. Climate emerges as an important factor in location choice, with greater weighting than other factors, including economic and cultural factors in times of economic calm, but the weightings of the factors change during times of economic crisis. Originality/value This paper contributes to the global business literature by extending the climate-economic theory to macro levels affecting the firm. The paper is the first to look specifically at how climate affects foreign subsidiary location.

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Masson, Valéry, Aude Lemonsu, Julia Hidalgo, and James Voogt. "Urban Climates and Climate Change." Annual Review of Environment and Resources 45, no. 1 (October 17, 2020): 411–44. http://dx.doi.org/10.1146/annurev-environ-012320-083623.

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Cities are particularly vulnerable to extreme weather episodes, which are expected to increase with climate change. Cities also influence their own local climate, for example, through the relative warming known as the urban heat island (UHI) effect. This review discusses urban climate features (even in complex terrain) and processes. We then present state-of-the-art methodologies on the generalization of a common urban neighborhood classification for UHI studies, as well as recent developments in observation systems and crowdsourcing approaches. We discuss new modeling paradigms pertinent to climate impact studies, with a focus on building energetics and urban vegetation. In combination with regional climate modeling, new methods benefit the variety of climate scenarios and models to provide pertinent information at urban scale. Finally, this article presents how recent research in urban climatology contributes to the global agenda on cities and climate change.

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5

A Shinde Waman, Sneha. "Replicable Model for Climate Proofing and Reducing Vulnerabilities due to Climate Change in different Agro Climatic Zones of Maharashtra." International Journal of Science and Research (IJSR) 13, no. 4 (April 5, 2024): 1373–76. http://dx.doi.org/10.21275/sr24416172526.

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6

Leifert, Harvey. "Climate catastrophe." Nature Climate Change 1, no. 709 (September 2007): 50. http://dx.doi.org/10.1038/climate.2007.39.

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7

Steig, Eric J. "Climate lessons." Nature Climate Change 1, no. 808 (July 10, 2008): 104. http://dx.doi.org/10.1038/climate.2008.71.

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8

Dubreuil, Vincent. "Le changement climatique en France illustré par la classification de Köppen." La Météorologie, no. 116 (2022): 037. http://dx.doi.org/10.37053/lameteorologie-2022-0012.

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L'objectif de cet article est de décrire simplement la manière dont s'opère le changement climatique en France entre 1950 et 2100 au moyen de la classification de Köppen. La méthode est utilisée pour caractériser les types moyens mais aussi chacune des années prises individuellement afin de tenir compte de la variabilité interannuelle du climat. Les projections sont extraites du portail Drias en utilisant le modèle CNRM avec les scénarios RCP4.5 et 8.5. Les résultats montrent la poursuite au XXIe siècle de la tendance à la diminution des climats tempérés frais au profit des climats chauds. Ces tendances sont exacerbées par le scénario RCP8.5 en fin de siècle où le type méditerranéen prédominerait largement au sud comme à l'ouest du pays ! The aim of this paper is to simply describe how climate change takes place in France between 1950 and 2100 using the Köppen classification. The method is used to characterize the average types but also each of the years taken individually in order to take into account the interannual variability of the climate. The projections are extracted from the Drias portal using the CNRM model with the RCP4.5 and 8.5 scenarios. The results show the continuation in the 21st Century of the tendency to decrease in cool temperate climates in favor of warm climates. These trends are exacerbated by the RCP8.5 scenario at the end of the century, where the Mediterranean type would largely predominate in the south and west of the country!

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9

Leroux, Marcel. "Climat local, climat global / Local cfimate, global climate." Revue de géographie de Lyon 72, no. 4 (1997): 339–45. http://dx.doi.org/10.3406/geoca.1997.4715.

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10

Vigneau, Jean-Pierre. "Climat et société (Climate and society)." Bulletin de l'Association de géographes français 73, no. 3 (1996): 232–33. http://dx.doi.org/10.3406/bagf.1996.1927.

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11

Heffernan, Olive. "A challenging political climate." Nature Climate Change 1, no. 810 (October 2008): 119. http://dx.doi.org/10.1038/climate.2008.105.

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12

Hoyle, Brian. "Accounting for climate ills." Nature Climate Change 1, no. 806 (May 8, 2008): 79. http://dx.doi.org/10.1038/climate.2008.43.

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13

Barnett, Anna. "Climate forecasters look back." Nature Climate Change 1, no. 809 (September 2008): 118. http://dx.doi.org/10.1038/climate.2008.89.

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14

Sanderson, Katharine. "Chemistry for the climate." Nature Climate Change 1, no. 810 (September 18, 2008): 124–25. http://dx.doi.org/10.1038/climate.2008.96.

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15

Inman, Mason. "The climate change game." Nature Climate Change 1, no. 911 (October 29, 2009): 130–33. http://dx.doi.org/10.1038/climate.2009.112.

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16

Kleiner, Kurt. "Climate science in 2009." Nature Climate Change 1, no. 1001 (December 17, 2009): 4–6. http://dx.doi.org/10.1038/climate.2010.134.

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17

Hanf, Franziska, Janina Körper, Thomas Spangehl, and Ulrich Cubasch. "Shifts of climate zones in multi-model climate change experiments using the Köppen climate classification." Meteorologische Zeitschrift 21, no. 2 (April 1, 2012): 111–23. http://dx.doi.org/10.1127/0941-2948/2012/0344.

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18

Beus, Jeremy M., Shelby J. Solomon, Erik C. Taylor, and Candace A. Esken. "Making sense of climate: A meta-analytic extension of the competing values framework." Organizational Psychology Review 10, no. 3-4 (April 6, 2020): 136–68. http://dx.doi.org/10.1177/2041386620914707.

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Organizational climate research has surged recently, but the disbursement of research contributions across domains has made it difficult to draw conclusions about climate and its connections with performance. To make sense of the climate literature, we used the competing values framework (CVF) to classify domain-specific climates into four climate types (clan, adhocracy, hierarchy, and market climates). We did so by conceptually linking domain-specific climates that are manifestations of the same underlying strategic values. We then conducted meta-analyses to examine the magnitudes, mechanisms, and moderators of the individual and group-level associations between the CVF climates and performance. These meta-analyses revealed positive climate–performance associations for each climate type and supported job attitudes as a common mediator. We also examined several methodological moderators of climate–performance relationships, testing the source of climate and performance measures, the temporal assessment of these constructs, and the level of within-group agreement in climate measures as possible boundary conditions.

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19

FORTELIUS, MIKAEL. "Climate what climate?" Boreas 14, no. 2 (January 16, 2008): 115–16. http://dx.doi.org/10.1111/j.1502-3885.1985.tb00900.x.

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20

Forster, Piers, Gabriele Hegerl, Reto Knutti, V. Ramaswamy, Susan Solomon, Thomas F. Stocker, Peter Stott, and Francis Zwiers. "Assessing uncertainty in climate simulations." Nature Climate Change 1, no. 709 (September 2007): 63. http://dx.doi.org/10.1038/climate.2007.46a.

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21

Marris, Emma. "Australia warms to climate change." Nature Climate Change 1, no. 711 (October 18, 2007): 90. http://dx.doi.org/10.1038/climate.2007.62.

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22

Heffernan, Olive. "Crunch time for climate change." Nature Climate Change 1, no. 912 (December 2009): 134. http://dx.doi.org/10.1038/climate.2009.127.

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23

Bauman, Yoram. "Climate economics for the masses." Nature Climate Change 1, no. 902 (January 15, 2009): 22–23. http://dx.doi.org/10.1038/climate.2009.4.

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24

Hewitt, Chris, Carlo Buontempo, Paula Newton, Francisco Doblas-Reyes, Kerstin Jochumsen, and Detlef Quadfasel. "Climate Observations, Climate Modeling, and Climate Services." Bulletin of the American Meteorological Society 98, no. 7 (July 1, 2017): 1503–6. http://dx.doi.org/10.1175/bams-d-17-0012.1.

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25

Tatarko, Alexander N., Ekaterina V. Maklasova, and Evert Van de Vliert. "Climato-Economic Context of Regional Crime and Corruption Across the Russian Federation." Environment and Behavior 54, no. 3 (December 17, 2021): 575–96. http://dx.doi.org/10.1177/00139165211060522.

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Cross-national research claims that the crime-and-corruption gap between relatively poor and relatively rich countries is larger in more demanding climates that require more cash and capital to cope with the climate. However, this claim is premature because countries differ in many confounding ways including histories and politics. We, therefore, re-tested the climato-economic context of violent crime and corruption within Russia, a country with considerable regional differences in climate and income. Across the eighty-five administrative units of Russia, the crime-and-corruption gap between relatively poor and relatively rich regions is smaller in more demanding climates. Harsher climates are so strongly associated with higher crime levels that the potential influence of differences in wealth becomes negligible. Furthermore, harsher climates are so strongly associated with higher corruption rates in poorer regions but lower corruption rates in richer regions that the potential influence of the climatic demands as such becomes negligible.

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26

Smith, Kirk R., Tord Kjellstrom, Vidhya Venugopal, Bruno Lemke, and Rebekah Lucas. "Indoor Climate Implications of Extreme Outdoor Climates." ISEE Conference Abstracts 2013, no. 1 (September 19, 2013): 5921. http://dx.doi.org/10.1289/isee.2013.s-3-20-02.

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27

Upcher, Mark. "Climate economics: economic analysis of climate, climate change and climate policy." Australasian Journal of Environmental Management 27, no. 3 (May 18, 2020): 343–44. http://dx.doi.org/10.1080/14486563.2020.1758408.

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28

Bahadur, Aditya V., and Thomas Tanner. "Policy climates and climate policies: Analysing the politics of building urban climate change resilience." Urban Climate 7 (March 2014): 20–32. http://dx.doi.org/10.1016/j.uclim.2013.08.004.

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29

Barnett, Anna. "India makes headway on climate policy." Nature Climate Change 1, no. 909 (August 13, 2009): 107. http://dx.doi.org/10.1038/climate.2009.79.

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30

Leroux, Marcel. "Volcanisme et climat (Volcanic activity and climate)." Bulletin de l'Association de géographes français 76, no. 4 (1999): 348–59. http://dx.doi.org/10.3406/bagf.1999.2127.

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31

Schalast, Norbert, and Jacob Michael Laan. "Measuring Social Climate in German Prisons Using the Essen Climate Evaluation Schema." Prison Journal 97, no. 2 (March 2017): 166–80. http://dx.doi.org/10.1177/0032885517692792.

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Sociotherapeutic prison units have been criticized for their close operational connection with traditional prison systems. This research compared the social climate of German therapeutic prison units with that of traditional prison units, using the Essen Climate Evaluation Schema (EssenCES). The EssenCES was administered in conjunction with the shortened Correctional Institutions Environment Scale. Results indicate significant differences between therapeutic and traditional units’ social climates, with the former demonstrating more supportive, safe, and therapeutic climates. Findings further confirm the psychometric properties of the EssenCES and support its use as a social climate measurement instrument.

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32

Fournier-Level, Alexandre, Emily O. Perry, Jonathan A. Wang, Peter T. Braun, Andrew Migneault, Martha D. Cooper, C. Jessica E. Metcalf, and Johanna Schmitt. "Predicting the evolutionary dynamics of seasonal adaptation to novel climates in Arabidopsis thaliana." Proceedings of the National Academy of Sciences 113, no. 20 (May 2, 2016): E2812—E2821. http://dx.doi.org/10.1073/pnas.1517456113.

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Predicting whether and how populations will adapt to rapid climate change is a critical goal for evolutionary biology. To examine the genetic basis of fitness and predict adaptive evolution in novel climates with seasonal variation, we grew a diverse panel of the annual plant Arabidopsis thaliana (multiparent advanced generation intercross lines) in controlled conditions simulating four climates: a present-day reference climate, an increased-temperature climate, a winter-warming only climate, and a poleward-migration climate with increased photoperiod amplitude. In each climate, four successive seasonal cohorts experienced dynamic daily temperature and photoperiod variation over a year. We measured 12 traits and developed a genomic prediction model for fitness evolution in each seasonal environment. This model was used to simulate evolutionary trajectories of the base population over 50 y in each climate, as well as 100-y scenarios of gradual climate change following adaptation to a reference climate. Patterns of plastic and evolutionary fitness response varied across seasons and climates. The increased-temperature climate promoted genetic divergence of subpopulations across seasons, whereas in the winter-warming and poleward-migration climates, seasonal genetic differentiation was reduced. In silico “resurrection experiments” showed limited evolutionary rescue compared with the plastic response of fitness to seasonal climate change. The genetic basis of adaptation and, consequently, the dynamics of evolutionary change differed qualitatively among scenarios. Populations with fewer founding genotypes and populations with genetic diversity reduced by prior selection adapted less well to novel conditions, demonstrating that adaptation to rapid climate change requires the maintenance of sufficient standing variation.

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33

Dodoo and Ayarkwa. "Effects of Climate Change for Thermal Comfort and Energy Performance of Residential Buildings in a Sub-Saharan African Climate." Buildings 9, no. 10 (October 4, 2019): 215. http://dx.doi.org/10.3390/buildings9100215.

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This study presents an analysis of the impacts of climate change on thermal comfort and energy performance of residential buildings in Ghana, in sub-Saharan Africa, and explores mitigation as well as adaptation strategies to improve buildings’ performance under climate change conditions. The performances of the buildings are analyzed for both recent and projected future climates for the Greater Accra and Ashanti regions of Ghana, using the IDA-ICE dynamic simulation software, with climate data from the Meteonorm global climate database. The results suggest that climate change will significantly influence energy performance and indoor comfort conditions of buildings in Ghana. However, effective building design strategies could significantly improve buildings’ energy and indoor climate performances under both current and future climate conditions. The simulations show that the cooling energy demand of the analyzed building in the Greater Accra region is 113.9 kWh/m2 for the recent climate, and this increases by 31% and 50% for the projected climates for 2030 and 2050, respectively. For the analyzed building in the Ashanti region, the cooling energy demand is 104.4 kWh/m2 for the recent climate, and this increases by 6% and 15% for the 2030 and 2050 climates, respectively. Furthermore, indoor climate and comfort deteriorate under the climate change conditions, in contrast to the recent conditions.

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34

Redfern, Martin. "Climate and Climate Change." Journal of the Institute of Health Education 29, no. 4 (January 1991): 129–36. http://dx.doi.org/10.1080/03073289.1991.10805749.

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35

Ford, Ned. "Climate cash, climate change." Electricity Journal 7, no. 7 (September 1994): 3–84. http://dx.doi.org/10.1016/1040-6190(94)90286-0.

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36

Ridgwell, Andy, and Paul J. Valdes. "Climate and climate change." Current Biology 19, no. 14 (July 2009): R563—R566. http://dx.doi.org/10.1016/j.cub.2009.05.014.

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37

Fernández Quintero, Francisco Jorge, C. José Alejandro Concepción Pacheco, and Juan Carlos Mirabal Requena. "Organizational Climate in Health Institutions Clima Organizacional En Las Instituciones De Salud." International Journal of Psychiatry 9, no. 1 (March 13, 2024): 01–02. http://dx.doi.org/10.33140/ijp.09.01.06.

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In a position article “Management of the organizational climate: an urgent need in health institutions”, published this year 2023, the need for management of the organizational climate in health institutions and the importance of studies related to this topic [1]. This issue is of utmost importance and necessary implementation.

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38

Challinor, A. J., T. R. Wheeler, J. M. Slingo, and D. Hemming. "Quantification of physical and biological uncertainty in the simulation of the yield of a tropical crop using present-day and doubled CO 2 climates." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1463 (October 24, 2005): 2085–94. http://dx.doi.org/10.1098/rstb.2005.1740.

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The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO 2 ) climates by perturbation of parameters in each model. The climate sensitivity parameter ( λ , the equilibrium response of global mean surface temperature to doubled CO 2 ) was used to define the control climate. Observed 1966–1989 mean yields of groundnut ( Arachis hypogaea L.) in India were simulated well by the crop model using the control climate and climates with values of λ near the control value. The simulations were used to measure the contribution to uncertainty of key crop and climate model parameters. The standard deviation of yield was more affected by perturbation of climate parameters than crop model parameters in both the present-day and doubled CO 2 climates. Climate uncertainty was higher in the doubled CO 2 climate than in the present-day climate. Crop transpiration efficiency was key to crop model uncertainty in both present-day and doubled CO 2 climates. The response of crop development to mean temperature contributed little uncertainty in the present-day simulations but was among the largest contributors under doubled CO 2 . The ensemble methods used here to quantify physical and biological uncertainty offer a method to improve model estimates of the impacts of climate change.

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39

Feng, Yuning, Shihong Du, Klaus Fraedrich, and Xiuyuan Zhang. "Fine-Grained Climate Classification for the Qaidam Basin." Atmosphere 13, no. 6 (June 5, 2022): 913. http://dx.doi.org/10.3390/atmos13060913.

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The Qaidam Basin is a sensitive climate transition zone revealing a wide spectrum of local climates and their variability. In order to obtain an objective and quantitative expression of local climate regions as well as avoid the challenge to pre-define the number of heterogeneous local climates, the ISODATA cluster method is employed to achieve the fine-grained climate divisions of the Qaidam Basin, which can heuristically alter the number of clusters based on the input of monthly temperature and precipitation data. The fine-grained climate classification extends the traditional Köppen climate classification and represents the complex climate transformation processes in terms of fine-grained climate clusters. The following results are observed: (i) The Qaidam Basin is divided into an arid desert basin area and the surrounding alpine mountainous areas. The climate distribution is affected by both the altitude and the dryness ratio, which, employing the Budyko framework, largely characterizes the local energy–water fluxes at the surface and the related vegetation regimes (biomes). The fine-grained climate classification successfully captures their causal relationships and represents them well by the local climates: the climatic spatial differentiation in the mountainous areas is highly consistent with the topography and reveals an elevation-dependent circular distribution from the edges to the center of the basin; the climate heterogeneity within the basin presents a west-to-east meridional distribution due to the combined effect of the mid-latitude westerlies and the Indian monsoon. (ii) The climate gradients are spatially different over the Qaidam Basin. The surrounding mountainous areas have a large climate gradient compared to the inner basin; the southern mountain edge is governed by a more severe climate change than the north-eastern one; and the climate gradient is larger in the eastern than in the western basin. (iii) The lake regions within the basin show an obvious lake effect and reveal a local lake climate. Spatially, a common structure emerges with a dryer-climate zone or watershed embedding a wetter lake-affected area, which appears to migrate eastward becoming stepwise wetter from the very dry center to the wet eastern boundary of the Qaidam basin. This provides a topographically induced insight of the wet climate expansion of initially arid climates and is crucial to improve the Qaidam Basin’s ecological environment. Finally, although this work mainly focuses on the local-scale climates and their variability in the Qaidam Basin, the data-driven cluster methodology for climate refinement is transferable to regional- even global-scale climate studies, which offers broad application prospects.

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40

Pirrung, Michael, and Bernd Zolitschka. "Quaternary climate evolution." Zeitschrift der Deutschen Gesellschaft für Geowissenschaften 156, no. 4 (December 1, 2005): 497–500. http://dx.doi.org/10.1127/1860-1804/2005/0156-0497.

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41

Schwartz, Stephen E., Robert J. Charlson, and Henning Rodhe. "Quantifying climate change — too rosy a picture?" Nature Climate Change 1, no. 707 (June 27, 2007): 23–24. http://dx.doi.org/10.1038/climate.2007.22.

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42

Schwartz, S. E., R. J. Charlson, and H. Rodhe. "Assessing uncertainty in climate simulations - authors' response." Nature Climate Change 1, no. 709 (September 2007): 64. http://dx.doi.org/10.1038/climate.2007.46b.

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43

Schrope, Mark. "Report calls for shift in climate research." Nature Climate Change 1, no. 904 (March 26, 2009): 44–45. http://dx.doi.org/10.1038/climate.2009.29.

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44

Charenko, Melissa. "Reconstructing Climate." Historical Studies in the Natural Sciences 50, no. 1-2 (April 2020): 90–128. http://dx.doi.org/10.1525/hsns.2020.50.1-2.90.

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In the early twentieth century, there was no consensus about the nature of climate. Focusing on ecological understandings of climate, this article contends that the diverse scientific practices used by ecologists to reconstruct past climates resulted in different notions of climate. At the turn of the century, a number of ecologists looked to the geological past to understand climate, but they found different climatic patterns depending on the way they encountered it. Some ecologists, led by Frederic E. Clements, came to believe that the climate displayed cyclical, recurrent patterns after examining tree rings. Others, notably Paul B. Sears, believed that the climate was characterized by unpredictable complexity after examinations of fossil pollen. Their divergent understandings of climate came to a head during the Dust Bowl of the 1930s, a severe drought whose effects were amplified by human actions. Both ecologists provided advice to governments and farmers based on their notions of whether or not climate displayed regular periodicity. Their varied assessments of climate reveal broader ideas about interwar science and culture, especially contestations over the capacity of science to predict the future.

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45

Belda, Michal, Petr Skalák, Aleš Farda, Tomáš Halenka, Michel Déqué, Gabriella Csima, Judit Bartholy, et al. "CECILIA Regional Climate Simulations for Future Climate: Analysis of Climate Change Signal." Advances in Meteorology 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/354727.

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Regional climate models (RCMs) are important tools used for downscaling climate simulations from global scale models. In project CECILIA, two RCMs were used to provide climate change information for regions of Central and Eastern Europe. Models RegCM and ALADIN-Climate were employed in downscaling global simulations from ECHAM5 and ARPEGE-CLIMAT under IPCC A1B emission scenario in periods 2021–2050 and 2071–2100. Climate change signal present in these simulations is consistent with respective driving data, showing similar large-scale features: warming between 0 and 3°C in the first period and 2 and 5°C in the second period with the least warming in northwestern part of the domain increasing in the southeastern direction and small precipitation changes within range of +1 to −1 mm/day. Regional features are amplified by the RCMs, more so in case of the ALADIN family of models.

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46

Caballero, R., and M. Huber. "State-dependent climate sensitivity in past warm climates and its implications for future climate projections." Proceedings of the National Academy of Sciences 110, no. 35 (August 5, 2013): 14162–67. http://dx.doi.org/10.1073/pnas.1303365110.

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47

Littlefield, Caitlin E., Brad H. McRae, Julia L. Michalak, Joshua J. Lawler, and Carlos Carroll. "Connecting today's climates to future climate analogs to facilitate movement of species under climate change." Conservation Biology 31, no. 6 (July 10, 2017): 1397–408. http://dx.doi.org/10.1111/cobi.12938.

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Choe, Hyeyeong, James H. Thorne, and Allan Hollander. "From disappearing climates to climate hubs, the five classes of climate risk for wildlife refuges." Landscape Ecology 35, no. 10 (August 12, 2020): 2163–77. http://dx.doi.org/10.1007/s10980-020-01090-w.

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Lee, Michelle Chin Chin, and Mohd Awang Idris. "Psychosocial safety climate versus team climate." Personnel Review 46, no. 5 (August 7, 2017): 988–1003. http://dx.doi.org/10.1108/pr-01-2016-0003.

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Abstract:

Purpose The importance of organizational climates in enhancing employees’ job performance is well studied in the literature. The purpose of this paper is to investigate the effect of psychosocial safety climate (PSC) and team climate on job performance, particularly through job engagement, by using a multilevel survey. The study also predicted that only PSC (and not team climate) predicted job resources (i.e. role clarity and performance feedback). Design/methodology/approach A total of 412 employees from 44 teams (72.6 per cent response rate) in Malaysian private organizations participated in the current study. Findings Research findings revealed that performance feedback and role clarity mediate the relationship between PSC and job engagement, and that there is no direct effect between the variables, team climate, and job resources. As expected, the study also discovered that job engagement mediates the relationship between PSC and team climate related to job performance. Practical implications This paper suggests the importance of PSC as the precursor to better working conditions (i.e. job resources) and to indirectly boosting employees’ engagement and job performance. Originality/value The study compared two distinctive organizational climate constructs that affect the different types of job resources using multilevel approach within the Asian context.

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Zeng, Di, Jinkui Wu, Yaqiong Mu, Hongyuan Li, Mingshan Deng, Yanqiang Wei, and Weibing Sun. "An Assessment of Tourism Climate Comfort in the China–Pakistan Economic Corridor." Sustainability 12, no. 17 (August 27, 2020): 6981. http://dx.doi.org/10.3390/su12176981.

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Abstract:

Pleasant climates can be divided into two types: the comfortable summer climate and the comfortable winter climate, but existing studies rarely pay attention to the distribution characteristics and differences between the two types of climate. This study investigates the spatial distribution of comfortable summer and winter climates in the China–Pakistan Economic Corridor (CPEC) and their differences based on the European Centre for Medium-Range Weather Forecasts Re-Analysis-Interim (ERA-interim) reanalysis data from 1979 to 2018. The Universal Thermal Climate Index was used to analyze climatic suitability. The findings are as follows. First, the comfortable summer climate regions of the CPEC were mainly distributed in Khyber Pakhtunkhwa in Pakistan and some regions of Xinjiang in China and also sporadically distributed in high altitude mountains, such as the western plateau area. The comfortable winter climate regions were mainly distributed in vast areas except for Karakoram and nearby areas. Second, there are a few regions with dual attributes of comfortable summer and winter climates in CPEC. Third, according to the calculation and comparison of the regional area of different climate comfort levels in summer and winter, it is found that the comfortable summer climate is scarcer in CPEC and is a monopoly resource. The comfortable winter climate is widely distributed, belonging to the ubiquitous resources. These findings can be helpful for the tourism development of local climate resources.

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