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Journal articles on the topic 'Climate modelling'

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Author: Grafiati
Published: 4 June 2021
Last updated: 25 May 2024

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1

Buchner, Barbara, and Carlo Carraro. "Modelling climate policy." Journal of Policy Modeling 27, no. 6 (September 2005): 711–32. http://dx.doi.org/10.1016/j.jpolmod.2005.05.001.

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2

Allen, Myles R. "Modelling climate change." Energy Policy 18, no. 7 (September 1990): 681–82. http://dx.doi.org/10.1016/0301-4215(90)90092-i.

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3

Laprise, René. "Regional climate modelling." Journal of Computational Physics 227, no. 7 (March 2008): 3641–66. http://dx.doi.org/10.1016/j.jcp.2006.10.024.

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4

McGregor, J. L. "Regional climate modelling." Meteorology and Atmospheric Physics 63, no. 1-2 (1997): 105–17. http://dx.doi.org/10.1007/bf01025367.

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5

Rockel, Burkhardt, Raymond Arritt, Markku Rummukainen, and Andreas Hense. "The 2nd Lund Regional-scale Climate Modelling Workshop." Meteorologische Zeitschrift 19, no. 4 (August 1, 2010): 323–24. http://dx.doi.org/10.1127/0941-2948/2010/0462.

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6

Brisson, Erwan, Matthias Demuzere, and Nicole P. M. van Lipzig. "Modelling strategies for performing convection-permitting climate simulations." Meteorologische Zeitschrift 25, no. 2 (May 9, 2016): 149–63. http://dx.doi.org/10.1127/metz/2015/0598.

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7

North, Gerald R. "Climate modelling: The climate as natural oscillator." Nature 316, no. 6025 (July 1985): 218. http://dx.doi.org/10.1038/316218a0.

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8

Simpkins, Graham. "Progress in climate modelling." Nature Climate Change 7, no. 10 (September 29, 2017): 684–85. http://dx.doi.org/10.1038/nclimate3398.

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9

Dettmer, R. "Climate modelling for all." IEE Review 51, no. 6 (June 1, 2005): 34–38. http://dx.doi.org/10.1049/ir:20050604.

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10

Akhter, Mehnaza, and Manzoor Ahmad Ahanger. "Climate modelling using ANN." International Journal of Hydrology Science and Technology 9, no. 3 (2019): 251. http://dx.doi.org/10.1504/ijhst.2019.102316.

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11

Jeff Birchall, S. "The climate modelling primer." International Journal of Environmental Studies 71, no. 4 (July 4, 2014): 581–82. http://dx.doi.org/10.1080/00207233.2014.935211.

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12

Leimbach, Marian. "Modelling climate protection expenditure." Global Environmental Change 8, no. 2 (June 1998): 125–39. http://dx.doi.org/10.1016/s0959-3780(98)00005-3.

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13

Weston, K. J. "A climate modelling primer." Atmospheric Research 25, no. 5 (June 1990): 480–81. http://dx.doi.org/10.1016/0169-8095(90)90031-7.

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14

Zucchetto, J. "A climate modelling primer." Ecological Modelling 45, no. 1 (February 1989): 69–70. http://dx.doi.org/10.1016/0304-3800(89)90101-4.

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15

Addiscott, Tom. "Climate Change and Modelling." Energy & Environment 23, no. 8 (December 2012): 1295–301. http://dx.doi.org/10.1260/0958-305x.23.8.1295.

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16

Unwin, David. "A Climate Modelling Primer." Computers & Geosciences 16, no. 8 (January 1990): 1243–45. http://dx.doi.org/10.1016/0098-3004(90)90062-x.

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17

Klingaman, Nicholas P., and Victoria Sinclair. "Weather and climate modelling." Weather 62, no. 9 (September 2007): 257–58. http://dx.doi.org/10.1002/wea.121.

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18

Kennedy, John. "Weather and climate modelling." Weather 63, no. 5 (2008): 142–44. http://dx.doi.org/10.1002/wea.218.

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19

Okhremchuk, І. "Modelling of climate change mitigation policies on national scale." Bìoresursi ì prirodokoristuvannâ 9, no. 3-4 (September 28, 2017): 34–39. http://dx.doi.org/10.31548/bio2017.03.005.

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20

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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21

Wimalasiri, Eranga M., Deshani Sirishantha, U. L. Karunadhipathi, Asanga D. Ampitiyawatta, Nitin Muttil, and Upaka Rathnayake. "Climate Change and Soil Dynamics: A Crop Modelling Approach." Soil Systems 7, no. 4 (September 26, 2023): 82. http://dx.doi.org/10.3390/soilsystems7040082.

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The impact of global climate change is a challenge to the sustainability of many ecosystems, including soil systems. However, the performance of soil properties under future climate was rarely assessed. Therefore, this study was carried out to evaluate selected soil processes under climate change using an agri-environmental modeling approach to Sri Lanka. The Agricultural Production Systems Simulator (APSIM) model was used to simulate soil and plant-related processes using recent past (1990–2019) and future (2041–2070) climates. Future climate data were obtained for a regional climate model (RCM) under representative concentrations pathway 4.5 scenarios. Rainfalls are going to be decreased in all the tested locations under future climate scenarios while the maximum temperature showcased rises. According to simulated results, the average yield reduction under climate change was 7.4%. The simulated nitrogen content in the storage organs of paddy declined in the locations (by 6.4–25.5%) as a reason for climate change. In general, extractable soil water relative to the permanent wilting point (total available water), infiltration, and biomass carbon lost to the atmosphere decreased while soil temperature increased in the future climate. This modeling approach provides a primary-level prediction of soil dynamics under climate change, which needs to be tested using fieldwork.
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22

RENTON, MICHAEL, NANCY SHACKELFORD, and RACHEL J. STANDISH. "How will climate variability interact with long-term climate change to affect the persistence of plant species in fragmented landscapes?" Environmental Conservation 41, no. 2 (November 28, 2013): 110–21. http://dx.doi.org/10.1017/s0376892913000490.

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SUMMARYAs climates change, some plant species will need to migrate across landscapes fragmented by unsuitable environments and human activities to colonize new areas with suitable climates as previously habited areas become uninhabitable. Previous modelling of plant's migration potential has generally assumed that climate changes at a constant rate, but this ignores many potentially important aspects of real climate variability. In this study, a spatially explicit simulation model was used to investigate how interannual climate variability, the occurrence of extreme events and step changes in climate might interact with gradual long-term climate change to affect plant species’ capacity to migrate across fragmented landscapes and persist. The considered types of climate variability generally exacerbated the negative effects of long-term climate change, with a few poignant exceptions where persistence of long-lived trees improved. Strategic habitat restoration ameliorated negative effects of climate variability. Plant functional characteristics strongly influenced most results. Any modelling of how climate change may affect species persistence, and how actions such as restoration may help species adapt, should account for both short-term climate variability and long-term change.
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23

Früh, Barbara, Andreas Will, and Christopher L. Castro. "Editorial: Recent developments in Regional Climate Modelling with COSMO‑CLM." Meteorologische Zeitschrift 25, no. 2 (May 9, 2016): 119–20. http://dx.doi.org/10.1127/metz/2016/0788.

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24

Randall, Ian. "New climate centre targets modelling." Physics World 29, no. 6 (June 2016): 12. http://dx.doi.org/10.1088/2058-7058/29/6/22.

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25

Griffies, Stephen M., Claus Böning, Frank O. Bryan, Eric P. Chassignet, Rüdiger Gerdes, Hiroyasu Hasumi, Anthony Hirst, Anne-Marie Treguier, and David Webb. "Developments in ocean climate modelling." Ocean Modelling 2, no. 3-4 (January 2000): 123–92. http://dx.doi.org/10.1016/s1463-5003(00)00014-7.

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26

Dash, S. "Climate modelling using parallel processors." Atmospheric Environment 29, no. 16 (August 1995): 2001–7. http://dx.doi.org/10.1016/1352-2310(94)00292-s.

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27

Grubb, Michael. "Policy modelling for climate change." Energy Policy 21, no. 3 (March 1993): 203–8. http://dx.doi.org/10.1016/0301-4215(93)90242-8.

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28

Duncan, C. N. "New perspectives in climate modelling." Dynamics of Atmospheres and Oceans 10, no. 3 (December 1986): 267–68. http://dx.doi.org/10.1016/0377-0265(86)90024-2.

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29

Keilman, Nico. "Modelling education and climate change." Nature Sustainability 3, no. 7 (April 13, 2020): 497–98. http://dx.doi.org/10.1038/s41893-020-0515-8.

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30

Atkinson, B. W. "New perspectives in climate modelling." Earth-Science Reviews 22, no. 3 (November 1985): 232–33. http://dx.doi.org/10.1016/0012-8252(85)90056-x.

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31

Salgado, Paulo, and J. Boaventura Cunha. "Greenhouse climate hierarchical fuzzy modelling." Control Engineering Practice 13, no. 5 (May 2005): 613–28. http://dx.doi.org/10.1016/j.conengprac.2004.05.007.

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32

Carson, D. J. "Climate modelling: Achievements and prospects." Quarterly Journal of the Royal Meteorological Society 125, no. 553 (January 1999): 1–27. http://dx.doi.org/10.1002/qj.49712555303.

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33

Mitchell, John, and Adam A. Scaife. "Climate modelling: how is Earth's climate modelled and how does modelling help our understanding?" Weather 75, no. 8 (August 2020): 259. http://dx.doi.org/10.1002/wea.3799.

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34

Frigg, Roman, Erica Thompson, and Charlotte Werndl. "Philosophy of Climate Science Part II: Modelling Climate Change." Philosophy Compass 10, no. 12 (December 2015): 965–77. http://dx.doi.org/10.1111/phc3.12297.

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35

Cloke, Hannah L., Fredrik Wetterhall, Yi He, Jim E. Freer, and Florian Pappenberger. "Modelling climate impact on floods with ensemble climate projections." Quarterly Journal of the Royal Meteorological Society 139, no. 671 (August 13, 2012): 282–97. http://dx.doi.org/10.1002/qj.1998.

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36

Martin-Nielsen, Janet. "Computing the Climate." Historical Studies in the Natural Sciences 48, no. 2 (April 1, 2018): 223–45. http://dx.doi.org/10.1525/hsns.2018.48.2.223.

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This paper traces the development of numerical climate models in the United Kingdom from 1963, when the U.K.’s Meteorological Office first took up climate modelling, to the mid-to-late 1970s, when climate change became politicized in the United Kingdom. The central question posed is how U.K. climate modellers developed rhetoric, managed expectations, and weighed their professional and political responsibilities in the face of growing political interest in climate change. Whilst the modellers were reluctant to allow the modelling results to be used for political ends, U.K. civil servants saw climate modelling as a modern tool for a new problem. As scientific and political agendas diverged, the director of the Meteorological Office, John Mason, found himself caught between his position as a government employee in a service organization and his responsibility as a gatekeeper between climate models and their potential uses. Ultimately, as Mason and his modellers were forced to admit, their climate models became cultural and political as well as scientific objects.
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37

Corballis, Tim. "Populating the Climate." Environmental Philosophy 16, no. 2 (2019): 275–89. http://dx.doi.org/10.5840/envirophil201981284.

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This paper asks whether one way to link abstract scientific knowledge about the climate to the everyday imagination might be to think of climate modelling as a narrative practice. To do so, I draw on philosophical insights about narrative in scientific modelling from Norton Wise and Mary Morgan, to show that models can be deployed narratively, and that their outputs take a followable, embodied narrative form. This suggests that climate models might be deployed in an everyday storytelling practice evoking storyworlds with palpable meteorological actants.
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38

Kropf, Chahan M., Alessio Ciullo, Laura Otth, Simona Meiler, Arun Rana, Emanuel Schmid, Jamie W. McCaughey, and David N. Bresch. "Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0." Geoscientific Model Development 15, no. 18 (September 23, 2022): 7177–201. http://dx.doi.org/10.5194/gmd-15-7177-2022.

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Abstract. Modelling the risk of natural hazards for society, ecosystems, and the economy is subject to strong uncertainties, even more so in the context of a changing climate, evolving societies, growing economies, and declining ecosystems. Here, we present a new feature of the climate-risk modelling platform CLIMADA (CLIMate ADAptation), which allows us to carry out global uncertainty and sensitivity analysis. CLIMADA underpins the Economics of Climate Adaptation (ECA) methodology which provides decision-makers with a fact base to understand the impact of weather and climate on their economies, communities, and ecosystems, including the appraisal of bespoke adaptation options today and in future. We apply the new feature to an ECA analysis of risk from tropical cyclone storm surge to people in Vietnam to showcase the comprehensive treatment of uncertainty and sensitivity of the model outputs, such as the spatial distribution of risk exceedance probabilities or the benefits of different adaptation options. We argue that broader application of uncertainty and sensitivity analysis will enhance transparency and intercomparison of studies among climate-risk modellers and help focus future research. For decision-makers and other users of climate-risk modelling, uncertainty and sensitivity analysis has the potential to lead to better-informed decisions on climate adaptation. Beyond provision of uncertainty quantification, the presented approach does contextualize risk assessment and options appraisal, and might be used to inform the development of storylines and climate adaptation narratives.
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39

Sirnik, Igor, Hervé Quénol, Miguel Ángel Jiménez-Bello, Juan Manzano, and Renan Le Roux. "Viticulture under climate change impact: future climate and irrigation modelling." E3S Web of Conferences 50 (2018): 01041. http://dx.doi.org/10.1051/e3sconf/20185001041.

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Vine is highly sensitive to climate changes, particularly temperature changes, which can be reflected in the quality of yield. We obtained meteorological data from weather station Llíria in viticultural site Valencia DO in Spain from the period 1961-2016 and elaborated the future modelling scenario Representative Concentration Pathways 4.5 (RCP4.5) and RCP8.5 for the period 1985-2100 within the Coupled Model Intercomparison, Project Phase 5 (CMIP5) for daily temperature, precipitation and evapotranspiration. The irrigation requirements (IR) future models for grape varieties Tempranillo and Bobal were elaborated. Temperature and evapotranspiration trends increased during observation period and are estimated to continue rising, according to the future model. Nevertheless, precipitation trend is estimated to decrease according to the model. The future scenarios show increase trend of temperature and evapotranspiration and decrease of precipitation. Total IR for the period 1985 – 2100 is expected to increase during growing season months according to the trendline for 16.6 mm (RCP4.5) and 40.0 mm (RCP8.5) for Tempranillo and 8.2 mm (RCP4.5) and 30.9 mm (RCP8.5) for Bobal grape variety. The outcome of this research is important to understand better the future climatic trends in Valencia DO and provides valuable data to face the future climate changes.
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40

Geisendorf, Sylvie. "Evolutionary Climate-Change Modelling: A Multi-Agent Climate-Economic Model." Computational Economics 52, no. 3 (September 12, 2017): 921–51. http://dx.doi.org/10.1007/s10614-017-9740-2.

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41

Doukas, Haris, and Alexandros Nikas. "Involve citizens in climate-policy modelling." Nature 590, no. 7846 (February 16, 2021): 389. http://dx.doi.org/10.1038/d41586-021-00283-w.

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42

Ackerley, Dean, Sood, and Mullan. "Regional climate modelling in New Zealand." Weather and Climate 32, no. 1 (2012): 3. http://dx.doi.org/10.2307/26169722.

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43

Stepanek, C., and G. Lohmann. "Modelling mid-Pliocene climate with COSMOS." Geoscientific Model Development 5, no. 5 (October 5, 2012): 1221–43. http://dx.doi.org/10.5194/gmd-5-1221-2012.

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Abstract. In this manuscript we describe the experimental procedure employed at the Alfred Wegener Institute in Germany in the preparation of the simulations for the Pliocene Model Intercomparison Project (PlioMIP). We present a description of the utilized Community Earth System Models (COSMOS, version: COSMOS-landveg r2413, 2009) and document the procedures that we applied to transfer the Pliocene Research, Interpretation and Synoptic Mapping (PRISM) Project mid-Pliocene reconstruction into model forcing fields. The model setup and spin-up procedure are described for both the paleo- and preindustrial (PI) time slices of PlioMIP experiments 1 and 2, and general results that depict the performance of our model setup for mid-Pliocene conditions are presented. The mid-Pliocene, as simulated with our COSMOS setup and PRISM boundary conditions, is both warmer and wetter in the global mean than the PI. The globally averaged annual mean surface air temperature in the mid-Pliocene standalone atmosphere (fully coupled atmosphere-ocean) simulation is 17.35 °C (17.82 °C), which implies a warming of 2.23 °C (3.40 °C) relative to the respective PI control simulation.
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44

Bércesi, G., K. Petróczki, and J. Beke. "Modelling of a hybrid climate system." Hungarian Agricultural Engineering, no. 28 (2015): 30–35. http://dx.doi.org/10.17676/hae.2015.28.30.

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45

Karman, Agnieszka, Andrzej Miszczuk, and Urszula Bronisz. "Regional Climate Change Competitiveness—Modelling Approach." Energies 14, no. 12 (June 21, 2021): 3704. http://dx.doi.org/10.3390/en14123704.

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The article deals with the competitiveness of regions in the face of climate change. The aim was to present the concept of measuring the Regional Climate Change Competitiveness Index. We used a comparative and logical analysis of the concept of regional competitiveness and heuristic conceptual methods to construct the index and measurement scale. The structure of the index includes six broad sub-indexes: Basic, Natural, Efficiency, Innovation, Sectoral, Social, and 89 indicators. A practical application of the model was presented for the Mazowieckie province in Poland. This allowed the region’s performance in the context of climate change to be presented, and regional weaknesses in the process of adaptation to climate change to be identified. The conclusions of the research confirm the possibility of applying the Regional Climate Change Competitiveness Index in the economic analysis and strategic planning. The presented model constitutes one of the earliest tools for the evaluation of climate change competitiveness at a regional level.
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46

Stepanek, C., and G. Lohmann. "Modelling mid-Pliocene climate with COSMOS." Geoscientific Model Development Discussions 5, no. 2 (April 24, 2012): 917–66. http://dx.doi.org/10.5194/gmdd-5-917-2012.

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Abstract. In this manuscript we describe the experimental procedure employed at the Alfred Wegener Institute in Germany in the preparation of the simulations for the Pliocene Model Intercomparison Project (PlioMIP). We present a description of the utilized community earth system models (COSMOS) and document the procedures which we applied to transfer the Pliocene Research, Interpretation and Synoptic Mapping Project (PRISM) mid-Pliocene reconstruction into model forcing fields. The model setup and spin-up procedure are described for both the paleo and preindustrial (PI) time-slices of PlioMIP experiments 1 and 2, and general results that depict the performance of our model setup for mid-Pliocene conditions are presented. The mid-Pliocene as simulated with our COSMOS-setup and PRISM boundary conditions is both warmer and wetter than the PI. The globally averaged annual mean surface air temperature in the mid-Pliocene standalone atmosphere (fully coupled atmosphere-ocean) simulation is 17.35 °C (17.82 °C), which implies a warming of 2.23 °C (3.40 °C) relative to the respective PI control simulation.
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47

Armatte, Michel. "Climate change: scenarios and integrated modelling." Interdisciplinary Science Reviews 33, no. 1 (March 2008): 37–50. http://dx.doi.org/10.1179/030801808x259989.

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48

Valdes, P. J., B. W. Sellwood, and G. D. Price. "Modelling Late Jurassic Milankovitch climate variations." Geological Society, London, Special Publications 85, no. 1 (1995): 115–32. http://dx.doi.org/10.1144/gsl.sp.1995.085.01.07.

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49

Palmer, T. N., and P. D. Williams. "Introduction. Stochastic physics and climate modelling." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 366, no. 1875 (April 29, 2008): 2419–25. http://dx.doi.org/10.1098/rsta.2008.0059.

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Finite computing resources limit the spatial resolution of state-of-the-art global climate simulations to hundreds of kilometres. In neither the atmosphere nor the ocean are small-scale processes such as convection, clouds and ocean eddies properly represented. Climate simulations are known to depend, sometimes quite strongly, on the resulting bulk-formula representation of unresolved processes. Stochastic physics schemes within weather and climate models have the potential to represent the dynamical effects of unresolved scales in ways which conventional bulk-formula representations are incapable of so doing. The application of stochastic physics to climate modelling is a rapidly advancing, important and innovative topic. The latest research findings are gathered together in the Theme Issue for which this paper serves as the introduction.
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50

Frank, H. P., and L. Landberg. "Modelling the Wind Climate of Ireland." Boundary-Layer Meteorology 85, no. 3 (December 1997): 359–77. http://dx.doi.org/10.1023/a:1000552601288.

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