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Journal articles on the topic 'Climate change models'

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Author: Grafiati

Published: 10 December 2022

Last updated: 7 July 2024

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1

Pitman, A. J., and R. J. Stouffer. "Abrupt change in climate and climate models." Hydrology and Earth System Sciences Discussions 3, no. 4 (July 19, 2006): 1745–71. http://dx.doi.org/10.5194/hessd-3-1745-2006.

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Abstract. First, we review the evidence that abrupt climate changes have occurred in the past and then demonstrate that climate models have developing capacity to simulate many of these changes. In particular, the processes by which changes in the ocean circulation drive abrupt changes appear to be captured by climate models to a degree that is encouraging. The evidence that past changes in the ocean have driven abrupt change in terrestrial systems is also convincing, but these processes are only just beginning to be included in climate models. Second, we explore the likelihood that climate models can capture those abrupt changes in climate that may occur in the future due to the enhanced greenhouse effect. We note that existing evidence indicates that a major collapse of the thermohaline circulate seems unlikely in the 21st century, although very recent evidence suggests that a weakening may already be underway. We have confidence that current climate models can capture a weakening, but a collapse of the thermohaline circulation in the 21st century is not projected by climate models. Worrying evidence of instability in terrestrial carbon, from observations and modelling studies, is beginning to accumulate. Current climate models used by the Intergovernmental Panel on Climate Change for the 4th Assessment Report do not include these terrestrial carbon processes. We therefore can not make statements with any confidence regarding these changes. At present, the scale of the terrestrial carbon feedback is believed to be small enough that it does not significantly affect projections of warming during the first half of the 21st century. However, the uncertainties in how biological systems will respond to warming are sufficiently large to undermine confidence in this belief and point us to areas requiring significant additional work.

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2

Pitman, A. J., and R. J. Stouffer. "Abrupt change in climate and climate models." Hydrology and Earth System Sciences 10, no. 6 (November 28, 2006): 903–12. http://dx.doi.org/10.5194/hess-10-903-2006.

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Abstract. First, we review the evidence that abrupt climate changes have occurred in the past and then demonstrate that climate models have developing capacity to simulate many of these changes. In particular, the processes by which changes in the ocean circulation drive abrupt changes appear to be captured by climate models to a degree that is encouraging. The evidence that past changes in the ocean have driven abrupt change in terrestrial systems is also convincing, but these processes are only just beginning to be included in climate models. Second, we explore the likelihood that climate models can capture those abrupt changes in climate that may occur in the future due to the enhanced greenhouse effect. We note that existing evidence indicates that a major collapse of the thermohaline circulation seems unlikely in the 21st century, although very recent evidence suggests that a weakening may already be underway. We have confidence that current climate models can capture a weakening, but a collapse in the 21st century of the thermohaline circulation is not projected by climate models. Worrying evidence of instability in terrestrial carbon, from observations and modelling studies, is beginning to accumulate. Current climate models used by the Intergovernmental Panel on Climate Change for the 4th Assessment Report do not include these terrestrial carbon processes. We therefore can not make statements with any confidence regarding these changes. At present, the scale of the terrestrial carbon feedback is believed to be small enough that it does not significantly affect projections of warming during the first half of the 21st century. However, the uncertainties in how biological systems will respond to warming are sufficiently large to undermine confidence in this belief and point us to areas requiring significant additional work.

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3

Shaw M, W. "Preparing for changes in plant disease due to climate change." Plant Protection Science 45, Special Issue (January 3, 2010): S3—S10. http://dx.doi.org/10.17221/2831-pps.

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Climate change will change patterns of disease through changes in host distribution and phenology, changes in plant-associated microflora and direct biological effects on rapidly evolving pathogens. Short-term forecast models coupled with weather generated from climate simulations may be a basis for projection; however, they will often fail to capture long-term trends effectively. Verification of predictions is a major difficulty; the most convincing method would be to “back-forecast” observed historical changes. Unfortunately, we lack of empirical data over long time-spans; most of what is known concerns invasions, in which climate is not the main driving factor. In one case where long-term prevalence can be deduced, climate had little to do with change. Resilience to surprises should be the most important policy aim.

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4

Dooge, J. C. I. "Hydrologic models and climate change." Journal of Geophysical Research 97, no. D3 (1992): 2677. http://dx.doi.org/10.1029/91jd02156.

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5

Anonymous. "Numerical models of climate change." Eos, Transactions American Geophysical Union 69, no. 45 (1988): 1556. http://dx.doi.org/10.1029/88eo01181.

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6

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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7

Stott, Peter A., and Chris E. Forest. "Ensemble climate predictions using climate models and observational constraints." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 365, no. 1857 (June 14, 2007): 2029–52. http://dx.doi.org/10.1098/rsta.2007.2075.

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Two different approaches are described for constraining climate predictions based on observations of past climate change. The first uses large ensembles of simulations from computationally efficient models and the second uses small ensembles from state-of-the-art coupled ocean–atmosphere general circulation models. Each approach is described and the advantages of each are discussed. When compared, the two approaches are shown to give consistent ranges for future temperature changes. The consistency of these results, when obtained using independent techniques, demonstrates that past observed climate changes provide robust constraints on probable future climate changes. Such probabilistic predictions are useful for communities seeking to adapt to future change as well as providing important information for devising strategies for mitigating climate change.

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8

Schär, Christoph, Christoph Frei, Daniel Lüthi, and Huw C. Davies. "Surrogate climate-change scenarios for regional climate models." Geophysical Research Letters 23, no. 6 (March 15, 1996): 669–72. http://dx.doi.org/10.1029/96gl00265.

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9

Kriticos, Darren, Anna Szyniszewska, Catherine Bradshaw, Christine Li, Eleni Verykouki, Tania Yonow, and Catriona Duffy. "Modelling tools for including climate change in pest risk assessments." EPPO Bulletin 54, S1 (March 2024): 38–51. http://dx.doi.org/10.1111/epp.12994.

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AbstractThis paper provides a comprehensive overview of the modelling tools available for integrating climate change impacts into pest risk assessments (PRA), elucidating the existing methodologies and models employed to understand the potential distributions of pests based on historical data and under future climate change scenarios. We highlight the strengths and weaknesses of these models and provide commentary on their ability to identify emerging threats due to climate change accurately and adequately, considering pest establishment likelihood, host crop exposure and the distribution of impacts. The simplest methods are based on climate‐matching models, degree‐day development models and Köppen–Geiger climate classification. Correlative species distribution models derive species–environment relationships and have been applied to PRA with mixed success. When fitted models are applied to different continents they are usually challenged to extrapolate climate suitability patterns outside the climate space used to train them. Global climate change is creating novel climates, exacerbating this ‘transferability’ problem. Some tools have been developed to reveal when these models are extrapolating. Process‐oriented models, which focus on mechanisms and processes rather than distribution patterns, are inherently more reliable for extrapolation to novel climates such as new continents and future climate scenarios. These models, however, require more skill and generally more knowledge of the species to craft robust models.

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10

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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11

Ewert, F., J. R. Porter, M. D. A. Rounsevell;, S. P. Long, E. A. Ainsworth, A. D. B. Leakey, D. R. Ort, J. Nosberger, and D. Schimel. "Crop Models, CO2, and Climate Change." Science 315, no. 5811 (January 26, 2007): 459c—460c. http://dx.doi.org/10.1126/science.315.5811.459c.

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12

Dowlatabadi, Hadi. "Integrated assessment models of climate change." Energy Policy 23, no. 4-5 (April 1995): 289–96. http://dx.doi.org/10.1016/0301-4215(95)90155-z.

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13

Herrando-Pérez, Salvador. "Climate change heats matrix population models." Journal of Animal Ecology 82, no. 6 (October 24, 2013): 1117–19. http://dx.doi.org/10.1111/1365-2656.12146.

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14

Cooter, Ellen J., Brian K. Eder, Sharon K. LeDuc, and Lawrence Truppi. "Climate Change Models and Forest Research." Journal of Forestry 91, no. 9 (September 1, 1993): 38–43. http://dx.doi.org/10.1093/jof/91.9.38.

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15

Bucklin, David N., Mathieu Basille, Stephanie S. Romañach, Lauren A. Brandt, Frank J. Mazzotti, and James I. Watling. "Considerations for Building Climate-based Species Distribution Models." EDIS 2016, no. 8 (October 6, 2016): 8. http://dx.doi.org/10.32473/edis-uw420-2016.

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Climate plays an important role in the distribution of species. A given species may adjust to new conditions in-place, move to new areas with suitable climates, or go extinct. Scientists and conservation practitioners use mathematical models to predict the effects of future climate change on wildlife and plan for a biodiverse future. This 8-page fact sheet explains how, with a better understanding of species distribution models, we can predict how species may respond to climate change. The models alone cannot tell us how a certain species will actually respond to changes in climate, but they can inform conservation planning that aims to allow species to both adapt in place and (for those that are able to) move to newly suitable areas. Such planning will likely minimize loss of biodiversity due to climate change. Written by David N. Bucklin, Mathieu Basille, Stephanie S. Romañach, Laura A. Brandt, Frank J. Mazzotti, and James I. Watling, and published by the Department of Wildlife Ecology and Conservation, August 2016. WEC375/UW420: Considerations for Building Climate-based Species Distribution Models (ufl.edu)

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16

Araujo, Miguel B., Richard G. Pearson, Wilfried Thuiller, and Markus Erhard. "Validation of species-climate impact models under climate change." Global Change Biology 11, no. 9 (September 2005): 1504–13. http://dx.doi.org/10.1111/j.1365-2486.2005.01000.x.

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17

Bush, Drew, Renee Sieber, Mark A. Chandler, and Linda E. Sohl. "Teaching anthropogenic global climate change (AGCC) using climate models." Journal of Geography in Higher Education 43, no. 4 (September 9, 2019): 527–43. http://dx.doi.org/10.1080/03098265.2019.1661370.

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18

Pierce, D. W., T. P. Barnett, B. D. Santer, and P. J. Gleckler. "Selecting global climate models for regional climate change studies." Proceedings of the National Academy of Sciences 106, no. 21 (May 13, 2009): 8441–46. http://dx.doi.org/10.1073/pnas.0900094106.

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19

Huntingford, Chris, John Gash, and Anna Maria Giacomello. "Climate change and hydrology: next steps for climate models." Hydrological Processes 20, no. 9 (2006): 2085–87. http://dx.doi.org/10.1002/hyp.6208.

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20

Bhattacharya, Devarati, Kimberly Carroll Steward, Mark Chandler, and Cory Forbes. "Using Climate Models to Learn About Global Climate Change." Science Teacher 88, no. 1 (September 2020): 58–66. http://dx.doi.org/10.1080/00368555.2020.12293558.

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21

García-Rodeja Gayoso, Isabel, and Glauce L. De Oliveira. "Climate change and the change of models of students’thinking." Enseñanza de las Ciencias. Revista de investigación y experiencias didácticas 30, no. 3 (November 15, 2012): 195. http://dx.doi.org/10.5565/rev/ec/v30n3.695.

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22

Ellis, Christopher. "Climate Change, Bioclimatic Models and the Risk to Lichen Diversity." Diversity 11, no. 4 (April 4, 2019): 54. http://dx.doi.org/10.3390/d11040054.

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This paper provides an overview of bioclimatic models applied to lichen species, supporting their potential use in this context as indicators of climate change risk. First, it provides a brief summary of climate change risk, pointing to the relevance of lichens as a topic area. Second, it reviews the past use of lichen bioclimatic models, applied for a range of purposes with respect to baseline climate, and the application of data sources, statistical methods, model extents and resolution and choice of predictor variables. Third, it explores additional challenges to the use of lichen bioclimatic models, including: 1. The assumption of climatically controlled lichen distributions, 2. The projection to climate change scenarios, and 3. The issue of nonanalogue climates and model transferability. Fourth, the paper provides a reminder that bioclimatic models estimate change in the extent or range of a species suitable climate space, and that an outcome will be determined by vulnerability responses, including potential for migration, adaptation, and acclimation, within the context of landscape habitat quality. The degree of exposure to climate change, estimated using bioclimatic models, can help to inform an understanding of whether vulnerability responses are sufficient for species resilience. Fifth, the paper draws conclusions based on its overview, highlighting the relevance of bioclimatic models to conservation, support received from observational data, and pointing the way towards mechanistic approaches that align with field-scale climate change experiments.

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23

Forest, Chris E., Myles R. Allen, Peter H. Stone, and Andrei P. Sokolov. "Constraining uncertainties in climate models using climate change detection techniques." Geophysical Research Letters 27, no. 4 (February 15, 2000): 569–72. http://dx.doi.org/10.1029/1999gl010859.

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24

von Storch, Hans, and Dennis Bray. "Models, manifestation and attribution of climate change." Meteorology Hydrology and Water Management 5, no. 1 (January 18, 2017): 47–52. http://dx.doi.org/10.26491/mhwm/67388.

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25

Mudur, G. "Climate Change: Monsoon Shrinks With Aerosol Models." Science 270, no. 5244 (December 22, 1995): 1922. http://dx.doi.org/10.1126/science.270.5244.1922.

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26

Worrell, Ernst. "Economic Models of Climate Change: A Critique." Resources, Conservation and Recycling 42, no. 4 (November 2004): 389–90. http://dx.doi.org/10.1016/j.resconrec.2004.03.001.

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27

Parson, Edward A., and and Karen Fisher-Vanden. "INTEGRATED ASSESSMENT MODELS OF GLOBAL CLIMATE CHANGE." Annual Review of Energy and the Environment 22, no. 1 (November 1997): 589–628. http://dx.doi.org/10.1146/annurev.energy.22.1.589.

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28

Janssen, Marco A. "Sequential optimization of integrated climate change models." Mathematics and Computers in Simulation 54, no. 6 (January 2001): 477–89. http://dx.doi.org/10.1016/s0378-4754(00)00278-0.

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29

Forgó, Ferenc, János Fülöp, and Mária Prill. "Game theoretic models for climate change negotiations." European Journal of Operational Research 160, no. 1 (January 2005): 252–67. http://dx.doi.org/10.1016/j.ejor.2003.06.025.

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30

Read, Peter L. "A Sea Change in Exoplanet Climate Models?" Astrobiology 14, no. 8 (August 2014): 627–28. http://dx.doi.org/10.1089/ast.2014.1404.

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31

Seager, Richard, Timothy J. Osborn, Yochanan Kushnir, Isla R. Simpson, Jennifer Nakamura, and Haibo Liu. "Climate Variability and Change of Mediterranean-Type Climates." Journal of Climate 32, no. 10 (April 29, 2019): 2887–915. http://dx.doi.org/10.1175/jcli-d-18-0472.1.

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Abstract Mediterranean-type climates are defined by temperate, wet winters, and hot or warm dry summers and exist at the western edges of five continents in locations determined by the geography of winter storm tracks and summer subtropical anticyclones. The climatology, variability, and long-term changes in winter precipitation in Mediterranean-type climates, and the mechanisms for model-projected near-term future change, are analyzed. Despite commonalities in terms of location in the context of planetary-scale dynamics, the causes of variability are distinct across the regions. Internal atmospheric variability is the dominant source of winter precipitation variability in all Mediterranean-type climate regions, but only in the Mediterranean is this clearly related to annular mode variability. Ocean forcing of variability is a notable influence only for California and Chile. As a consequence, potential predictability of winter precipitation variability in the regions is low. In all regions, the trend in winter precipitation since 1901 is similar to that which arises as a response to changes in external forcing in the models participating in phase 5 of the Coupled Model Intercomparison Project. All Mediterranean-type climate regions, except in North America, have dried and the models project further drying over coming decades. In the Northern Hemisphere, dynamical processes are responsible: development of a winter ridge over the Mediterranean that suppresses precipitation and of a trough west of the North American west coast that shifts the Pacific storm track equatorward. In the Southern Hemisphere, mixed dynamic–thermodynamic changes are important that place a minimum in vertically integrated water vapor change at the coast and enhance zonal dry advection into Mediterranean-type climate regions inland.

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32

Weller, Gunter. "Detecting Global Change In The Arctic." Annals of Glaciology 14 (1990): 362. http://dx.doi.org/10.3189/s026030550000940x.

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Numerical models have predicted global temperature increases due to rising atmospheric CO2 levels, which should be detectable by now, but have not yet been identified in an unambiguous manner. This detection is complicated by inadequate data and by the fact that climate can be changed by factors other than CO2 increases. A systematic monitoring strategy is therefore needed to assess global change. In the Arctic, cryospheric parameters, including sea ice, snow cover, glaciers and permafrost are sensitive indicators of climate change and their monitoring by satellites and surface observations is of particular interest. Sea ice and snow cover are perhaps the most important of these parameters. They respond quickly to climate change, and in turn directly affect the climate through feedback processes; major changes in ice and snow extent and thickness can be expected as a consequence of climate change. Glaciers also respond to climatic variability by changes in their mass balance which can be monitored. Melting glaciers raise the level of the world ocean, and the glaciers of the sub-Arctic, particularly in the Alaskan coastal mountains, have been major contributors to the observed sea-level rise of about 20–30 cm over the last century. Past temperature changes are recorded in glacier ice and permafrost and techniques are now available to reconstruct past climates from these sources.The numerical models of the CO2 greenhouse effect show the polar regions to be affected most strongly by greenhouse warming, and sea ice, snow, glaciers and permafrost should be good indicators of such a global change. The known responses and sensitivities of cryospheric parameters to climate change are reviewed, and a monitoring strategy is suggested. The Alaska SAR Facility, utilizing synthetic aperture radar from several spacecraft scheduled for launch in the 1990s, will be a key facility for collecting and analyzing climate-related satellite data. Its monitoring capabilities are briefly reviewed.

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33

Weller, Gunter. "Detecting Global Change In The Arctic." Annals of Glaciology 14 (1990): 362. http://dx.doi.org/10.1017/s026030550000940x.

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Numerical models have predicted global temperature increases due to rising atmospheric CO2 levels, which should be detectable by now, but have not yet been identified in an unambiguous manner. This detection is complicated by inadequate data and by the fact that climate can be changed by factors other than CO2 increases. A systematic monitoring strategy is therefore needed to assess global change. In the Arctic, cryospheric parameters, including sea ice, snow cover, glaciers and permafrost are sensitive indicators of climate change and their monitoring by satellites and surface observations is of particular interest. Sea ice and snow cover are perhaps the most important of these parameters. They respond quickly to climate change, and in turn directly affect the climate through feedback processes; major changes in ice and snow extent and thickness can be expected as a consequence of climate change. Glaciers also respond to climatic variability by changes in their mass balance which can be monitored. Melting glaciers raise the level of the world ocean, and the glaciers of the sub-Arctic, particularly in the Alaskan coastal mountains, have been major contributors to the observed sea-level rise of about 20–30 cm over the last century. Past temperature changes are recorded in glacier ice and permafrost and techniques are now available to reconstruct past climates from these sources. The numerical models of the CO2 greenhouse effect show the polar regions to be affected most strongly by greenhouse warming, and sea ice, snow, glaciers and permafrost should be good indicators of such a global change. The known responses and sensitivities of cryospheric parameters to climate change are reviewed, and a monitoring strategy is suggested. The Alaska SAR Facility, utilizing synthetic aperture radar from several spacecraft scheduled for launch in the 1990s, will be a key facility for collecting and analyzing climate-related satellite data. Its monitoring capabilities are briefly reviewed.

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34

Semenov, MA, J. Wolf, LG Evans, H. Eckersten, and A. Iglesias. "Comparison of wheat simulation models under climate change. II. Application of climate change scenarios." Climate Research 7 (1996): 271–81. http://dx.doi.org/10.3354/cr007271.

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35

Kaiser, Harry M. "Climate Change and Agriculture." Northeastern Journal of Agricultural and Resource Economics 20, no. 2 (October 1991): 151–63. http://dx.doi.org/10.1017/s0899367x0000297x.

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Without a doubt, climate change will be one of the most important environmental topics of the 1990s and will be high on the research agendas of many scientific disciplines in years ahead. While not yet universally accepted, it is now widely believed that anthropogenic emissions of carbon dioxide and other “greenhouse” gases have the potential to substantially warm climates worldwide. Although there is no consensus on the timing and magnitude of global warming, current climate models predict an average increase of 2.8°C to 5.2°C in the earth's temperature over the next century (Karl, Diaz, and Barnett). Changes in regional temperature and precipitation will likely accompany the global warming, but there is even less scientific agreement on the magnitude of these changes.

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36

Plattner, G. K., R. Knutti, F. Joos, T. F. Stocker, W. von Bloh, V. Brovkin, D. Cameron, et al. "Long-Term Climate Commitments Projected with Climate–Carbon Cycle Models." Journal of Climate 21, no. 12 (June 15, 2008): 2721–51. http://dx.doi.org/10.1175/2007jcli1905.1.

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Abstract Eight earth system models of intermediate complexity (EMICs) are used to project climate change commitments for the recent Intergovernmental Panel on Climate Change’s (IPCC’s) Fourth Assessment Report (AR4). Simulations are run until the year 3000 a.d. and extend substantially farther into the future than conceptually similar simulations with atmosphere–ocean general circulation models (AOGCMs) coupled to carbon cycle models. In this paper the following are investigated: 1) the climate change commitment in response to stabilized greenhouse gases and stabilized total radiative forcing, 2) the climate change commitment in response to earlier CO2 emissions, and 3) emission trajectories for profiles leading to the stabilization of atmospheric CO2 and their uncertainties due to carbon cycle processes. Results over the twenty-first century compare reasonably well with results from AOGCMs, and the suite of EMICs proves well suited to complement more complex models. Substantial climate change commitments for sea level rise and global mean surface temperature increase after a stabilization of atmospheric greenhouse gases and radiative forcing in the year 2100 are identified. The additional warming by the year 3000 is 0.6–1.6 K for the low-CO2 IPCC Special Report on Emissions Scenarios (SRES) B1 scenario and 1.3–2.2 K for the high-CO2 SRES A2 scenario. Correspondingly, the post-2100 thermal expansion commitment is 0.3–1.1 m for SRES B1 and 0.5–2.2 m for SRES A2. Sea level continues to rise due to thermal expansion for several centuries after CO2 stabilization. In contrast, surface temperature changes slow down after a century. The meridional overturning circulation is weakened in all EMICs, but recovers to nearly initial values in all but one of the models after centuries for the scenarios considered. Emissions during the twenty-first century continue to impact atmospheric CO2 and climate even at year 3000. All models find that most of the anthropogenic carbon emissions are eventually taken up by the ocean (49%–62%) in year 3000, and that a substantial fraction (15%–28%) is still airborne even 900 yr after carbon emissions have ceased. Future stabilization of atmospheric CO2 and climate change requires a substantial reduction of CO2 emissions below present levels in all EMICs. This reduction needs to be substantially larger if carbon cycle–climate feedbacks are accounted for or if terrestrial CO2 fertilization is not operating. Large differences among EMICs are identified in both the response to increasing atmospheric CO2 and the response to climate change. This highlights the need for improved representations of carbon cycle processes in these models apart from the sensitivity to climate change. Sensitivity simulations with one single EMIC indicate that both carbon cycle and climate sensitivity related uncertainties on projected allowable emissions are substantial.

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37

Keith, David A., H. Resit Akçakaya, Wilfried Thuiller, Guy F. Midgley, Richard G. Pearson, Steven J. Phillips, Helen M. Regan, Miguel B. Araújo, and Tony G. Rebelo. "Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models." Biology Letters 4, no. 5 (July 29, 2008): 560–63. http://dx.doi.org/10.1098/rsbl.2008.0049.

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Species responses to climate change may be influenced by changes in available habitat, as well as population processes, species interactions and interactions between demographic and landscape dynamics. Current methods for assessing these responses fail to provide an integrated view of these influences because they deal with habitat change or population dynamics, but rarely both. In this study, we linked a time series of habitat suitability models with spatially explicit stochastic population models to explore factors that influence the viability of plant species populations under stable and changing climate scenarios in South African fynbos, a global biodiversity hot spot. Results indicate that complex interactions between life history, disturbance regime and distribution pattern mediate species extinction risks under climate change. Our novel mechanistic approach allows more complete and direct appraisal of future biotic responses than do static bioclimatic habitat modelling approaches, and will ultimately support development of more effective conservation strategies to mitigate biodiversity losses due to climate change.

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38

Joubert, A. M., and B. C. Hewitson. "Simulating present and future climates of southern Africa using general circulation models." Progress in Physical Geography: Earth and Environment 21, no. 1 (March 1997): 51–78. http://dx.doi.org/10.1177/030913339702100104.

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The current state of regional climate and climate change modelling using GCMs is reviewed for southern Africa, and several approaches to regional climate change prediction which have been applied to southern Africa are assessed. Confidence in projected regional changes is based on the ability of a range of models to simulate present regional climate, and is greatest where intermodel consensus in terms of the nature of projected changes is highest. Both equil ibrium and transient climate change projections for southern Africa are considered. Warming projected over southern Africa is within the range of globally averaged estimates. Uncertainties associated with the parameterization of convection ensure that projected changes in rainfall at GCM grid scales remain unreliable. However, empirical downscaling approaches produce rainfall changes consistent with synoptic-scale circulation. Both downscaling and grid-scale approaches indicate a 10-15% decrease in summer rainfall over the central interior which may have important implications for surface hydrology. Climate change may be manifested as a change in variability, and not in mean climate. Over southern Africa, increases in the variability and intensity of daily rainfall events are indicated.

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Hammerle, R. H., J. W. Shiller, and M. J. Schwarz. "Global Climate Change." Journal of Engineering for Gas Turbines and Power 113, no. 3 (July 1, 1991): 448–55. http://dx.doi.org/10.1115/1.2906251.

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This paper reviews the validity of the greenhouse warming theory, its possible impact on the automotive industry, and what could be done. Currently there is very limited evidence that man’s activity has caused global warming. Mathematical models of the earth’s heat balance predict warming and associated climate changes, but their predictions have not been validated. Concern over possible warming has led to several evaluations of feasible CO2 control measures. Although cars and trucks contribute only a small fraction of the CO2 buildup, the automotive industry may be expected to reduce its share of the atmospheric CO2 loading if controls become necessary. Methods to reduce automotive CO2 emissions, including alternative fuels such as methanol, natural gas, and electricity, are discussed. Also, control of the other greenhouse gases, which may currently contribute about 45 percent of the greenhouse warming, is considered.

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PALTSEV, SERGEY, and PANTELIS CAPROS. "COST CONCEPTS FOR CLIMATE CHANGE MITIGATION." Climate Change Economics 04, supp01 (November 2013): 1340003. http://dx.doi.org/10.1142/s2010007813400034.

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Major cost concepts used for evaluation of carbon policy are considered, including change in GDP, change in consumption, change in welfare, energy system cost, and area under marginal abatement cost (MAC) curve. The issues associated with the use of these concepts are discussed. We use the results from the models that participated in the European Energy Modeling Forum (EMF28) study to illustrate the cost concepts. There is substantial variability in the estimates of costs between the models, with some models showing substantial costs and some models reporting benefits from mitigation in some scenarios. Because impacts of a policy are evaluated as changes from a reference scenario, it is important to define a reference scenario. MAC cost measures tend to exclude existing distortions in the economy, while existing energy taxes and subsidies are substantial in many countries. We discuss that carbon prices are inadequate measures of the policy costs. We conclude that changes in macroeconomic consumption or welfare are the most appropriate measures of policy costs.

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Shaffer, L. Jen, and Leocadia Naiene. "Why Analyze Mental Models of Local Climate Change? A Case from Southern Mozambique." Weather, Climate, and Society 3, no. 4 (October 1, 2011): 223–37. http://dx.doi.org/10.1175/wcas-d-10-05004.1.

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Abstract People construct mental models of local climate change based on their observations and experiences of past climate events and changes. These mental models offer critical insight into locally important factors that trigger responses to new climate conditions and can be used to ground-truth regional climate models. In this paper, the authors explore mental models of changes to local climate patterns and climate-associated environmental changes over the past 45 years (1963–2008) in two rural communities in Matutúine District, Mozambique. Interview results are compared to data from a regional weather station. Residents discuss temperature increases, short-term and long-term precipitation changes, and altered seasonal timing. Measurable climate change in this region includes increasing temperatures and more erratic rainfall leading to drought and altered season timing. The climate-associated environmental changes residents observed draw attention to links between local livelihood practices and climate, as well as emphasize changes that would not necessarily appear in regional climate models. Such changes include reduced crop and wild fruit production, fewer cattle, variable forest size, increased wildfires and elephant conflict, drying up of water sources, poor health, and cultural change. Differences between adjacent communities highlight the potential interaction of landscape and vegetation variability, gender, and livelihoods in observations and experiences of climate change and demonstrate how mental models can provide insight into local ecological patterns and processes.

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Oo, Han Thi, Win Win Zin, and Cho Cho Thin Kyi. "Assessment of Future Climate Change Projections Using Multiple Global Climate Models." Civil Engineering Journal 5, no. 10 (October 7, 2019): 2152–66. http://dx.doi.org/10.28991/cej-2019-03091401.

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Nowadays, the hydrological cycle which alters river discharge and water availability is affected by climate change. Therefore, the understanding of climate change is curial for the security of hydrologic conditions of river basins. The main purpose of this study is to assess the projections of future climate across the Upper Ayeyarwady river basin for its sustainable development and management of water sector for this area. Global Ten climate Models available from CMIP5 represented by the IPCC for its fifth Assessment Report were bias corrected using linear scaling method to generate the model error. Among the GCMs, a suitable climate model for each station is selected based on the results of performance indicators (R2 and RMSE). Future climate data are projected based on the selected suitable climate models by using future climate scenarios: RCP2.6, RCP4.5, and RCP8.5. According to this study, future projection indicates to increase in precipitation amounts in the rainy and winter season and diminishes in summer season under all future scenarios. Based on the seasonal temperature changes analysis for all stations, the future temperature are predicted to steadily increase with higher rates during summer than the other two seasons and it can also be concluded that the monthly minimum temperature rise is a bit larger than the maximum temperature rise in all seasons.

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Dasgupta, Partha. "Pricing climate change." Politics, Philosophy & Economics 13, no. 4 (August 13, 2014): 394–416. http://dx.doi.org/10.1177/1470594x14541521.

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In developing the basis on which climate change should be priced, I do five things. First, I review the ethical foundations for valuing future consumption relative to present consumption (i.e. social discount rates). Second, I report that the criterion for both assessing and prescribing economic policies should not be an economy's GDP, but an inclusive measure of an economy's wealth adjusted for the distribution of wealth. Third, I apply the resulting analysis to the problem of pricing carbon concentration in the atmosphere. I give prominence to future uncertainties. Fourth, I show that the existing models of human behaviour on the basis of which these questions have been analysed by economists have serious deficiencies, in as much as the idea of personhood embodied in them has been built on the psychology of confirmed egotists. Fifth, I sketch the motivations of a social being and show how the altered specification of the human person affects the social price of climate change.

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Beckage, Brian, Louis J. Gross, Katherine Lacasse, Eric Carr, Sara S. Metcalf, Jonathan M. Winter, Peter D. Howe, et al. "Linking models of human behaviour and climate alters projected climate change." Nature Climate Change 8, no. 1 (January 2018): 79–84. http://dx.doi.org/10.1038/s41558-017-0031-7.

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Chen, Jie, François P. Brissette, Philippe Lucas-Picher, and Daniel Caya. "Impacts of weighting climate models for hydro-meteorological climate change studies." Journal of Hydrology 549 (June 2017): 534–46. http://dx.doi.org/10.1016/j.jhydrol.2017.04.025.

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Guereña, Arantxa, Margarita Ruiz-Ramos, Carlos H. Díaz-Ambrona, José R. Conde, and M. Inés Mínguez. "Assessment of Climate Change and Agriculture in Spain Using Climate Models." Agronomy Journal 93, no. 1 (January 2001): 237–49. http://dx.doi.org/10.2134/agronj2001.931237x.

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Soares, Pedro M. M., Rita M. Cardoso, João Jacinto Ferreira, and Pedro M. A. Miranda. "Climate change and the Portuguese precipitation: ENSEMBLES regional climate models results." Climate Dynamics 45, no. 7-8 (December 10, 2014): 1771–87. http://dx.doi.org/10.1007/s00382-014-2432-x.

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Gajić-Čapka, Marjana, Ivan Güttler, Ksenija Cindrić, and Čedo Branković. "Observed and simulated climate and climate change in the lower Neretva river basin." Journal of Water and Climate Change 9, no. 1 (October 25, 2017): 124–36. http://dx.doi.org/10.2166/wcc.2017.034.

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Abstract The lower Neretva river basin includes a fertile valley at the estuary into the Adriatic Sea, where intense agricultural production occurs, and the higher terrain where drinking water resources exist. To provide input for the further assessment of crop-yield production and hydrological risks, climate and climate change were analysed using the Opuzen station air temperature and total precipitation data for the 1961–2015 period. Both historical and future climates (2021–2050) were assessed based on simulations of three regional climate models (RCMs). The RCMs were forced by the observed concentrations of greenhouse gases (GHGs) from 1951 to 2000, and the IPCC A1B scenario of the GHG emissions was applied from 2001 onwards. The models were compared with the observations, and two bias adjustment methods were evaluated. The results generally showed a significant increase in the mean annual and seasonal temperature and a weak decreasing trend in annual and seasonal precipitation. Projections revealed a predominant increase in the mean temperature by the mid-21st century for all three RCMs (between 0.5 and 3.5 °C). The precipitation changed by between −60 and +60% throughout the year for the different models, although the changes generally were not statistically significant.

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Drijfhout, Sybren, Sebastian Bathiany, Claudie Beaulieu, Victor Brovkin, Martin Claussen, Chris Huntingford, Marten Scheffer, Giovanni Sgubin, and Didier Swingedouw. "Catalogue of abrupt shifts in Intergovernmental Panel on Climate Change climate models." Proceedings of the National Academy of Sciences 112, no. 43 (October 12, 2015): E5777—E5786. http://dx.doi.org/10.1073/pnas.1511451112.

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Abrupt transitions of regional climate in response to the gradual rise in atmospheric greenhouse gas concentrations are notoriously difficult to foresee. However, such events could be particularly challenging in view of the capacity required for society and ecosystems to adapt to them. We present, to our knowledge, the first systematic screening of the massive climate model ensemble informing the recent Intergovernmental Panel on Climate Change report, and reveal evidence of 37 forced regional abrupt changes in the ocean, sea ice, snow cover, permafrost, and terrestrial biosphere that arise after a certain global temperature increase. Eighteen out of 37 events occur for global warming levels of less than 2°, a threshold sometimes presented as a safe limit. Although most models predict one or more such events, any specific occurrence typically appears in only a few models. We find no compelling evidence for a general relation between the overall number of abrupt shifts and the level of global warming. However, we do note that abrupt changes in ocean circulation occur more often for moderate warming (less than 2°), whereas over land they occur more often for warming larger than 2°. Using a basic proportion test, however, we find that the number of abrupt shifts identified in Representative Concentration Pathway (RCP) 8.5 scenarios is significantly larger than in other scenarios of lower radiative forcing. This suggests the potential for a gradual trend of destabilization of the climate with respect to such shifts, due to increasing global mean temperature change.

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A. Betts, Richard. "Integrated approaches to climate–crop modelling: needs and challenges." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1463 (October 24, 2005): 2049–65. http://dx.doi.org/10.1098/rstb.2005.1739.

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This paper discusses the need for a more integrated approach to modelling changes in climate and crops, and some of the challenges posed by this. While changes in atmospheric composition are expected to exert an increasing radiative forcing of climate change leading to further warming of global mean temperatures and shifts in precipitation patterns, these are not the only climatic processes which may influence crop production. Changes in the physical characteristics of the land cover may also affect climate; these may arise directly from land use activities and may also result from the large-scale responses of crops to seasonal, interannual and decadal changes in the atmospheric state. Climate models used to drive crop models may, therefore, need to consider changes in the land surface, either as imposed boundary conditions or as feedbacks from an interactive climate–vegetation model. Crops may also respond directly to changes in atmospheric composition, such as the concentrations of carbon dioxide (CO 2 ), ozone (O 3 ) and compounds of sulphur and nitrogen, so crop models should consider these processes as well as climate change. Changes in these, and the responses of the crops, may be intimately linked with meteorological processes so crop and climate models should consider synergies between climate and atmospheric chemistry. Some crop responses may occur at scales too small to significantly influence meteorology, so may not need to be included as feedbacks within climate models. However, the volume of data required to drive the appropriate crop models may be very large, especially if short-time-scale variability is important. Implementation of crop models within climate models would minimize the need to transfer large quantities of data between separate modelling systems. It should also be noted that crop responses to climate change may interact with other impacts of climate change, such as hydrological changes. For example, the availability of water for irrigation may be affected by changes in runoff as a direct consequence of climate change, and may also be affected by climate-related changes in demand for water for other uses. It is, therefore, necessary to consider the interactions between the responses of several impacts sectors to climate change. Overall, there is a strong case for a much closer coupling between models of climate, crops and hydrology, but this in itself poses challenges arising from issues of scale and errors in the models. A strategy is proposed whereby the pursuit of a fully coupled climate–chemistry–crop–hydrology model is paralleled by continued use of separate climate and land surface models but with a focus on consistency between the models.

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