Relevant bibliographies by topics / Climate modelling

Academic literature on the topic 'Climate modelling'

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Published: 4 June 2021
Last updated: 25 May 2024

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Journal articles on the topic "Climate modelling"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Climate modelling"

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Loptson, Claire A. "Modelling vegetation-climate interactions in past greenhouse climates." Thesis, University of Bristol, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.680126.

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The early Eocene to the Cretaceous (48-148 Ma) was a period in the Earth's history where the climate was much warmer than the present day, with no permanent ice sheets and atmospheric CO2 levels higher than the present day. Using the climate model HadCM3L coupled to a dynamic vegetation model, this thesis aims to analyse vegetation-climate interactions during these past greenhouse climates, and how the climate, vegetation and climate sensitivity of these time periods are influenced by changes in palaeogeography and CO2 . The results of these model simulations are also evaluated against climatologically-sensitive geological proxies. Past modelling studies for the early Eocene have struggled to model the shallow equator to pole temperature gradient that data suggests was present during this time. However, most models have neglected vegetation feedbacks and incorporating these may help to reduce the model-data discrepancy. In this thesis, vegetation climate interactions during the early Eocene are modelled and analysed, and the results compared to available proxy data. The model-data discrepancies for temperatures are also reduced when vegetation feedbacks were included (compared to simulations with static vegetation), although there are still differences, particularly at high latitudes. This suggests that the models are still missing important processes or the data is not being interpreted correctly. In addition, twelve consistent simulations are carried out , each representing a different stage of the Cretaceous. Each simulation has the same atmospheric CO2 level, allowing the effect of palaeogeography on climate, climate sensitivity and vegetation to be analysed. It was found that, in general, the temperature trends that occurred in the mid-Cretaceous simulations were consistent with data. However, the data record does not extend to the earliest Cretaceous, and in the late Cretaceous the results deviate from the data. The model results suggest that, in order for the model to be consistent with the data there must have been a decline in CO2 from the early to late Cretaceous, which is supported by the CO2 proxy record. More data from the early Cretaceous needs to be collected in order to carry out a more robust model-data comparison for this time period.
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Harris, Philip P. "Modelling South American climate and climate change." Thesis, University of Reading, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.436614.

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Glover, Robin Wallace. "Energy balance climate modelling." Thesis, University of Reading, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.308058.

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Spain, Timothy C. "Modelling of extreme climate regimes." Thesis, University of Oxford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.572595.

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The climate of the Neoproterozoic Snowball Earth is tested in the UKMO Unified Model, specifically the HadCM3 climate model. The model is largely left unchanged, but the boundary conditions, both external and initial, are adjusted to create experiments based on the Snowball Earth hypothesis. The model can reproduce multiple equilibrium climates, as have been seen in energy balance models of the Earth's climate. The modelled present day and Neoproterozoic versions of Earth can both reproduce both ice capped and ice covered climate states. Neither can reproduce a climate which remains ice free throughout the year, even with an equilibrated ocean or elevated levels of C02. In all cases the ice free climate reverts toward the ice capped climate after the first polar winter. The modelled Neoproterozoic ice covered climate, that is the climate of Snowball Earth, has a climate very different from the present day. These changes are mostly driven by the lower thermal inertia, latitudinal temperature differences and the changed meridional circulation that results. The weather of the modelled Snowball Earth climate is also very different, dom- inated by a strong diurnal variation due to solar heating, as opposed to the more varied weather in the present day. The model responds well to the conditions of the Snowball Earth climate, with temperatures similar to those predicted by a simple physical model. The model responds less well to high levels of C02 in the Snowball Earth climate. The ice model also allows excessive heat and moisture to escape from the ocean into the atmosphere compared to that that would be predicted from solid ice coverage of the ocean. The exit from a Snowball Earth state was also tested within the model. Neither an decrease in albedo nor an increase in CO2 is unable to increase the temperature of the climate system sufficiently to exit the Snowball Earth state.
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Brembilla, Eleonora. "Applicability of climate-based daylight modelling." Thesis, Loughborough University, 2017. https://dspace.lboro.ac.uk/2134/28239.

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This PhD thesis evaluated the applicability of Climate-Based Daylight Modelling (CBDM) as it is presently done. The objectives stated in this thesis aimed at broadly assessing applicability by looking at multiple aspects: (i) the way CBDM is used by expert researchers and practitioners; (ii) how state-of-the-art simulation techniques compare to each other and how they are affected by uncertainty in input factors; (iii) how the simulated results compare with data measured in real occupied spaces. The answers obtained from a web-based questionnaire portrayed a variety of workflows used by different people to perform similar, if not the same, evaluations. At the same time, the inter-model comparison performed to compare the existing simulation techniques revealed significant differences in the way the sky and the sun are recreated by each technique. The results also demonstrated that some of the annual daylight metrics commonly required in building guidelines are sensitive to the choice of simulation tool, as well as other input parameters, such as climate data, orientation and material optical properties. All the analyses were carried out on four case study spaces, remodelled from existing classrooms that were the subject of a concurrent research study that monitored their interior luminous conditions. A large database of High Dynamic Range images was collected for that study, and the luminance data derived from these images could be used in this work to explore a new methodology to calibrate climate-based daylight models. The results collected and presented in this dissertation illustrate how, at the time of writing, there is not a single established common framework to follow when performing CBDM evaluations. Several different techniques coexist but each of them is characterised by a specific domain of applicability.
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Mårtensson, Sebastian. "Ridged sea ice modelling in climate applications." Doctoral thesis, Stockholms universitet, Meteorologiska institutionen (MISU), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-93977.

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This work aims to increase our understanding of the nature of large scale features of sea ice from a dynamics point of view.Sea ice plays an important part in the exchange of heat and humidity between sea and air and thus is an important component of the climate system. Its physical presence also directly impacts the various forms of life such as diatoms, polar bears and humans alike.The dynamics of sea ice affect both weather and climate, through the large scale drift in the Arctic from the Siberian coast towards Fram Strait, through creation of cracks in the ice called leads or polynyas, and through ridging and other mechanical deformations of ice floes.In this work, we have focused on modelling of ridged ice for a number of reasons. Direct observations of the internal ice state is very difficult to perform and in general, observations of sea ice are either sparse or of limited information density. Ridged ice can be seen as the memory of high ice stress events, giving us a view on these highly dynamic events. Ridging is of major importance for the ice thickness distribution, as the thickest ice can only be formed through mechanical processes. Further, ridged ice is of direct interest for anyone conducting shipping through seasonal or perennial ice covered seas as it can form impenetrable barriers or in extreme even cases crush a ship caught within the ice pack. To this end, a multi-category sea ice model, the HELsinki Multi category Ice model (HELMI), was implemented into the Rossby Centre Ocean model (RCO). HELMI has explicit formulations for ridged and rafted ice, as well as sub-grid scale ice thickness distribution (a feature shared with other multi category models) and an ice strength based on energetics. These features give RCO better representation of sub-grid scale physics and gives us the possibility to study the deformed ice in detail. In paper I we look at the change in behaviour in the Arctic as the ice becomes more mobile, leading to a slight increase in modelled ridged ice volume in the central Arctic, despite a general trend of a decreasing ice cover.Paper II takes us to the Baltic Sea and the possibilities of modelling ridge ice concentration with a statistical model.In Paper III we investigate how the diminishing ice cover in future scenarios affects the biological activity in the Baltic Sea.Finally Paper IV investigates how the ice stress and the internal ice force can be interpreted in terms of ice compression on the ship scale.

At the time of the doctoral defence the following paper was unpublished and had a status as follows: Paper 4: Manuscript

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Dolan, Aisling Margaret. "Modelling mid-Pliocene climate and ice sheets." Thesis, University of Leeds, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590483.

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Given anthropogenic modification of the climate system, the future stability of Earth's major ice sheets and sea level is uncertain. One potential, lJ1ethod to investigate the behaviour of the Greenland and Antarctic ice sheets under a warmer-than-modern climate regime, is to look back at past warm periods of Earth history (for example the mid Pliocene Warm Period; 3.26 - 3.0 million years ago). The British Antarctic Survey Ice Sheet Model (BASISM) and the Hadley Centre Coupled Climate Model version 3 (HadCM3) allow the climate and ice sheets of the mid-Pliocene to be modelled, and their sensitivity to a range of uncertainties in forcing to be quantified. The ice sheets, particularly the Greenland Ice Sheet, are sensitive to changes in the Earth's orbital configuration and potential levels of atmospheric carbon dioxide (C02) during the mid-Pliocene. Exploring various orbital forcing scenarios in concert with a plausible Pliocene COz envelope (280 - 560 ppmv), enables models to generate Greenland ice sheet reconstructions that range from a 1.5% reduction from modern to an ice-free state. In contrast, on East Antarctica, significant ice sheet retreat is only simulated under warm Southern Hemisphere orbital conditions where C02 levels are at 400 ppmv or above. Maximum eustatic sea level rise corresponding to predicted ice sheet reductions is comparable with recent records suggesting mid-Pliocene sea level high-stands of 22 m greater than modern. However, ice sheet reconstructions are shown to be highly dependent on Q priori assumptions regarding the initial ice sheet configuration within the numerical modelling framework. Investigation of the dependency of ice sheet predictions on the models used, has demonstrated that results are sensitive to the modelled climatological forcing. Such dependency is most explicitly highlighted over Greenland, where Pliocene ice sheet predictions, given forcings from fifteen equivalently-configured climate models, range from no ice to a configuration that is larger than modern. These results underline the importance of considering mUltiple sources of uncertainty when predicting past ice sheets. v
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Pope, James Owen. "Modelling Pliocene climate with perturbed physics ensembles." Thesis, University of Leeds, 2015. http://etheses.whiterose.ac.uk/10443/.

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Uncertainty in model simulations arises due to the construction of the model (structural uncertainty), the representation of sub-grid scale processes (parameter uncertainty) or the input of model boundary conditions. Perturbed physics ensembles (PPEs) produce an ensemble of simulations using a single climate model. A PPE produces different representations of climate by altering the tuning of parameterisations representing processes occurring on sub-grid scales, such as clouds and radiation. A PPE has been produced to investigate model parameter and boundary condition uncertainty for the mid-Pliocene Warm Period (3.264 to 3.025 Ma BP). Through the use of a PPE, 14 versions (13 perturbed members and the Standard version) of the UK Met Office atmosphere-ocean general circulation model HadCM3 were created. The full ensemble was re-run to assess the impact of simultaneously changing physical boundary conditions for orography, ice sheets and vegetation in combination with perturbed physics. Finally the effect of the potential range in reconstructed mid-Pliocene CO2 was investigated through a sub-ensemble of the PPE. Using data-model comparisons (DMCs), the ensemble members with higher than the Standard values of Charney sensitivity were better able to simulate the magnitude of high latitude mid-Pliocene warming. The strongest performing ensemble members for the DMCs displayed Charney sensitivities of 4.54°C, 4.62°C and 5.40°C, above the upper bound of the IPCC likely range (1.5 to 4.5°C). However, these warmer members with higher Charney sensitivities weakened the data-model comparison in the tropics. Ensemble members with lower than Standard values of Charney sensitivity, close to the lower bound of the IPCC likely range, better resolved temperature reconstructions in the tropics, but were unable to resolve high latitude warming. It is evident that the PPE is able to achieve the magnitude of mPWP warming but not the spatial distribution of the warming. The investigation into boundary condition uncertainty using the PPE reveals that the PRISM3D physical boundary conditions lead to improved simulations of the mPWP climate than the PRISM2 boundary conditions. For the range of atmospheric CO2 concentrations, the results from the sub-ensemble indicate that lower values of CO2 lead to reduced performance of the PPE members compared to the palaeo-data. The conclusion is that concentrations of CO2 below 350 ppmv for the mPWP would make simulating high latitude climates very difficult for climate models.
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Lee, S. E. "Modelling interactions between climate and global vegetation in response to climate change." Thesis, University of Sheffield, 1997. http://etheses.whiterose.ac.uk/2063/.

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Climate change associated with increasing concentrations of the greenhouse gas, carbon dioxide(CO2), is expected to lead to an increase in global mean temperature of between 1 and 3.5 deg C by the end of the 21st century, with regional changes in rainfall and humidity. This thesis is concerned with modelling the effects of a changing climate and atmospheric C02 concentration on global vegetation. The process-based model, DOLY (Dynamic glObal phtogeographY), is used. It is able to operate using three climate variables, two soil variables and an atmospheric CO2 concentration. Its outputs are leaf area index (LAI), and net primary productivity (NPP). The LAI and NPP values predicted by DOLY were used to run a life-form model with a climate change scenario. It was found that warming led to the spread of trees into the tundra region. The DOLY model was also coupled with the Hadley Centre general circulation model to determine the feedbacks of vegetation on climate. With a global warming of 2◦C, the global feedback of vegetation on temperature was a decrease of 0.1 deg C. However at the regional scale the feedback was +/-2 ◦C, of similar magnitude to the driving temperature change. Finally, the DOLY model was run with transient climate data from the Hadley Centre. The boreal forest moved north, and the Gobi desert and the southern steppes in the former Soviet Union shrank in area. The sensitivity of the model to its soil and climate inputs have also been analysed over a range of environments and the model has been validated with reference to satellite data and experimental data. It was found to perform well. This thesis has shown that it is possible to predict current and possible future distributions of vegetation with climate change using a vegetation model.
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Hällberg, Petter. "Permafrost Modelling and Climate Change Simulations in Northern Sweden." Thesis, Uppsala universitet, Institutionen för geovetenskaper, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-337794.

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Permafrost is an important component in Arctic environments and has been hypothesized to be diminishing due to global warming. A growing concern is that large quantities of stored organic carbon will be mobilized and released to the atmosphere as the potent greenhouse gas methane if the ground thaws. This could result in a massive positive feedback on the global climate change. To quantify this effect, the permafrost extent as well as carbon storages must be mapped. In this study, a Basal Temperature of Snow (BTS) survey is conducted in the Tarfala Valley in Northern Sweden and a model of the current permafrost extent in the region is produced. Additionally, the model explores how the permafrost extent will change under three climate change scenarios at +1°C, +2°C and +4°C. According to a statistical analysis, elevation is the only significant variable for permafrost occurrence in the Tarfala Valley. Currently, continuous permafrost (>0.8 probability) is present at elevations exceeding 1523 m a.s.l. and sporadic or patchy permafrost (<0.5 probability) dominates below 1108 m a.s.l. The permafrost in Northern Sweden is near the boundary of favorable conditions for permafrost, and the greatest decline in permafrost extent occurs during the initial warming. In the +1°C scenario, which will be reached in 20 years if current warming rate is sustained, 97.6% of the continuous permafrost in the Abisko and Tarfala area degrades. The areal extent of the zone with the lowest probability of permafrost occurrence increases from 59% to 90% in the same scenario. Under continued warming to +4°C compared to current ground temperatures, 98% of the study area will be covered by sporadic or patchy occurrences of permafrost.
Permafrost är en viktig komponent i arktiska miljöer och befaras minska i utbredning på grund av den globala uppvärmningen. En farhåga är att stora mängder bundet organiskt kol ska mobiliseras och släppas ut till atmosfären som den potenta växthusgasen metan om marken värms. Detta skulle kunna innebära stor positiv återkoppling på de globalt stigande temperaturerna. För att kvantifiera den effekten är det viktigt att kartlägga permafrostens utbredning såväl som mängde bundet kol i permafrostmarker. I den här studien utförs en undersökning av bastemperaturen av snötäcket (BTS) i Tarfaladalen i norra Sverige och en modellering av permafrostens nuvarande utbredning i regionen. Vidare modelleras hur permafrostens utbredning kommer att påverkas i framtiden under tre olika klimatförändringsscenarior vid +1°C, +2°C och +4°C. Enligt en statistisk analys är altitud den enda signifikanta variabeln för permafrostförekomst i Tarfaladalen. Vid nuvarande marktemperaturer är kontinuerlig permafrost (>0.8 probabilitet) utbredd på höjder över 1523 m ö.h. och sporadisk permafrost (0.5 - 0 probabilitet) dominerar under 1108. Permafrosten i norra Sverige är nära gränsen för dess gynnsamma förhållanden och den huvudsakliga förlusten av permafrost sker redan vid en blygsam markuppvärmning. I scenariot +1°C, som inträffar redan om 20 år om nuvarande uppvärmningstakt fortsätter, degraderas 97.6% av den kontinuerliga permafrosten i Abisko och Tarfalaområdet. Utbredningen av sporadisk permafrost, det vill säga zonen med lägst sannolikhet för permafrostförekomst, ökar i det scenariot från 59% till 90%. Vid fortsatt uppvärmning till +4°C jämfört med nuvarande marktemperaturer så kommer 98% av det studerade området endast innehålla sporadiska förekomster av permafrost.
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Books on the topic "Climate modelling"

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A. Lloyd, Elisabeth, and Eric Winsberg, eds. Climate Modelling. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65058-6.

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Kondrup, Claus, Paola Mercogliano, Francesco Bosello, Jaroslav Mysiak, Enrico Scoccimarro, Angela Rizzo, Rhian Ebrey, Marleen de Ruiter, Ad Jeuken, and Paul Watkiss, eds. Climate Adaptation Modelling. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-86211-4.

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Henderson-Sellers, Ann. A climate modelling primer. Chichester: Wiley, 1987.

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Stocker, Thomas. Introduction to Climate Modelling. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-00773-6.

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Sarkisyan, Artem S., and Jürgen E. Sündermann. Modelling Ocean Climate Variability. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-1-4020-9208-4.

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McGuffie, Kendal, and Ann Henderson-Sellers. A Climate Modelling Primer. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470857617.

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Willebrand, Jürgen, and David L. T. Anderson, eds. Modelling Oceanic Climate Interactions. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-84975-6.

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A climate modelling primer. 3rd ed. Hoboken, NJ: J. Wiley, 2004.

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E, Sündermann Jürgen, and SpringerLink (Online service), eds. Modelling Ocean Climate Variability. Dordrecht: Springer Netherlands, 2009.

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Henderson-Sellers, A. A climate modelling primer. Chichester: Wiley, 1987.

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Book chapters on the topic "Climate modelling"

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Harvey, L. D. Danny. "Climate and Climate-System Modelling." In Environmental Modelling, 151–64. Chichester, UK: John Wiley & Sons, Ltd, 2013. http://dx.doi.org/10.1002/9781118351475.ch9.

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Gilchrist, A., and K. H. Hasselmann. "Climate Modelling." In Current Issues in Climate Research, 10–15. Dordrecht: Springer Netherlands, 1986. http://dx.doi.org/10.1007/978-94-009-5494-6_3.

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Haarsma, R. J. "Climate Modelling." In Solar Variability and Climate, 357–61. Dordrecht: Springer Netherlands, 2000. http://dx.doi.org/10.1007/978-94-010-0888-4_32.

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Stephenson, David B. "Climate Modelling." In Handbook of Environmental and Ecological Statistics, 641–56. Boca Raton : Taylor & Francis, 2018.: Chapman and Hall/CRC, 2019. http://dx.doi.org/10.1201/9781315152509-28.

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Rougier, Jonathan, and Michel Crucifix. "Uncertainty in Climate Science and Climate Policy." In Climate Modelling, 361–80. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65058-6_12.

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Lloyd, Elisabeth A., and Eric Winsberg. "Introduction." In Climate Modelling, 1–28. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65058-6_1.

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Odenbaugh, Jay. "Building Trust, Removing Doubt? Robustness Analysis and Climate Modeling." In Climate Modelling, 297–321. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65058-6_10.

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Knutti, Reto. "Climate Model Confirmation: From Philosophy to Predicting Climate in the Real World." In Climate Modelling, 325–59. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65058-6_11.

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Winsberg, Eric. "Communicating Uncertainty to Policymakers: The Ineliminable Role of Values." In Climate Modelling, 381–412. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65058-6_13.

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Frisch, Mathias. "Modeling Climate Policies: The Social Cost of Carbon and Uncertainties in Climate Predictions." In Climate Modelling, 413–48. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65058-6_14.

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Conference papers on the topic "Climate modelling"

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"CLIMATE— THEORETICAL ALTERNATIVES TO CLIMATE MODELLING WORKSHOP." In International Seminar on Nuclear War and Planetary Emergencies 38th Session. WORLD SCIENTIFIC, 2008. http://dx.doi.org/10.1142/9789812834645_others07.

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"Consistent Climate Scenarios: projecting representative future daily climate from global climate models based on historical climate data." In 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.l11.ricketts.

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Monteiro, Joy, and Rodrigo Caballero. "The Climate Modelling Toolkit." In Python in Science Conference. SciPy, 2016. http://dx.doi.org/10.25080/majora-629e541a-00a.

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"Providing application-specific climate projections datasets: CSIRO’s Climate Futures Framework." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.f5.clarke.

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"On climate variability and climate change and impact on water resources." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.i6.chiew.

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"Bushfire conditions under a warming climate – the value of regional climate modelling." In 19th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2011. http://dx.doi.org/10.36334/modsim.2011.a2.grose.

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"Climate risk assessments in New South Wales using publicly available stochastic climate data." In 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.armstrong115.

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R. Davies, Evan, and Slobodan Simonovic. "Modelling Social-Economic-Climatic Feedbacks for Policy Development." In 2006 IEEE EIC Climate Change Conference. IEEE, 2006. http://dx.doi.org/10.1109/eicccc.2006.277267.

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Naidoo, Nadia, Sahil Lawton, Micara Ramnanan, Jean Vincent Fonou-Dombeu, and Rachan Gowda. "Modelling Climate Smart Agriculture with Ontology." In 2021 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD). IEEE, 2021. http://dx.doi.org/10.1109/icabcd51485.2021.9519380.

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"Assessing the impact of bias correction approaches on climate extremes and the climate change signal." In 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.zhang49.

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Reports on the topic "Climate modelling"

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Flato, G., N. Gillett, V. Arora, A. Cannon, and J. Anstey. Modelling future climate change. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2019. http://dx.doi.org/10.4095/327808.

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Russell, H. A. J., and S. K. Frey. Canada One Water: integrated groundwater-surface-water-climate modelling for climate change adaptation. Natural Resources Canada/CMSS/Information Management, 2021. http://dx.doi.org/10.4095/329092.

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Canada 1 Water is a 3-year governmental multi-department-private-sector-academic collaboration to model the groundwater-surface-water of Canada coupled with historic climate and climate scenario input. To address this challenge continental Canada has been allocated to one of 6 large watershed basins of approximately two million km2. The model domains are based on natural watershed boundaries and include approximately 1 million km2 of the United States. In year one (2020-2021) data assembly and validation of some 20 datasets (layers) is the focus of work along with conceptual model development. To support analysis of the entire water balance the modelling framework consists of three distinct components and modelling software. Land Surface modelling with the Community Land Model will support information needed for both the regional climate modelling using the Weather Research &amp; Forecasting model (WRF), and input to HydroGeoSphere for groundwater-surface-water modelling. The inclusion of the transboundary watersheds will provide a first time assessment of water resources in this critical international domain. Modelling is also being integrated with Remote Sensing datasets, notably the Gravity Recovery and Climate Experiment (GRACE). GRACE supports regional scale watershed analysis of total water flux. GRACE along with terrestrial time-series data will serve provide validation datasets for model results to ensure that the final project outputs are representative and reliable. The project has an active engagement and collaborative effort underway to try and maximize the long-term benefit of the framework. Much of the supporting model datasets will be published under open access licence to support broad usage and integration.
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Tapia, Carlos, Ana de Jesus, and Nora Sánchez Gassen. Integrating climate in macroeconomic modelling: A Nordic Perspective. Nordregio, July 2023. http://dx.doi.org/10.6027/pb2023:3.2001-3876.

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This Policy Brief summarises key messages from a webinar series hosted by the Integrating climate in macroeconomic modelling (ICMM) project during the second half of 2022 and the first half of 2023. In the wake of recent climate policy discussions at the EU, Nordic and national levels, the webinars were meant to promote the advancement of expert and policy-planning knowledge and at the creation of networks among model developers and model users from different Nordic countries. The main goal of this project is to foster discussions regarding modelling approaches and to identify future Nordic and international opportunities for collaboration at both expert and policy-planning levels.
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Qiang, B., and R. De Jong. Modelling four climate change scenarios for Prince Edward Island. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2007. http://dx.doi.org/10.4095/327232.

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Giroux-Gauthier, Léopold, Aytaç Kubilay, Audrey Maheu, Sylvia Sylvia Wood, Jan Carmeliet, and Dominique Derome. Modelling of rain interception by trees in outdoor urban climate. Department of the Built Environment, 2023. http://dx.doi.org/10.54337/aau541594014.

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Martin, Shane, Duncan Moore, and Martyn Hazelwood. Coastal inundation modelling for Busselton, Western Australia, under current and future climate. Geoscience Australia, 2014. http://dx.doi.org/10.11636/record.2014.003.

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De Jong, R., B. Qiang, and J. Y. Yang. Modelling of nitrogen leaching in Prince Edward Island under climate change scenarios. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2007. http://dx.doi.org/10.4095/327233.

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Fyfe, Caroline, Phoebe Taptiklis, Dominic White, and Niven Winchester. Review of emissions data and modelling systems (Phase 1) Report. Motu Economic and Public Policy Research, July 2023. http://dx.doi.org/10.29310/wp.2023.06.

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The purpose of this report is to review greenhouse gas (GHG) emissions and climate change mi􀆟ga􀆟on data and models. Building an evidence base to monitor and assess the impacts of ini􀆟a􀆟ves is explicitly men􀆟oned in Aotearoa New Zealand’s First Emissions Reduc􀆟on Plan (05/2022). Economic-Environmental modelling plays an important role in decision making to achieve emission reduc􀆟on targets. Data is hosted by a range of organisa􀆟ons and collected using different frameworks and methodologies. There is less awareness of data available through Stats NZ which may have led to it being underused. Main collated data sources are the GHG emissions account and the GHG emissions inventory. Sector specific data are available through relevant agencies. A detailed stock-take of New Zealand’s modelling capacity iden􀆟fied 84 climate change or climate change mi􀆟ga􀆟on models: 13 mul􀆟-sector models, 23 land use and agricultural models, 25 energy models, and 23 transport models. The stock-take iden􀆟fied at least one model for each sector of the ERP, except for Building and Construc􀆟on. Modelling capacity varies between sectors with those that are more developed, demonstra􀆟ng greater interdependency between models. Almost all modelling takes a produc􀆟on-based approach. Capacity for a consump􀆟on-based approach was much more limited. Two examples of formal collabora􀆟on are iden􀆟fied through the review. No formal interna􀆟onal collabora􀆟on (outside of interna􀆟onal repor􀆟ng requirements) was iden􀆟fied. A preference was expressed for modelling in-house to facilitate alignment with policy development. However, this may have contributed to lack of collabora􀆟on on progress towards common targets. Connec􀆟ons with groups outside of government are also limited.
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Shrestha, A. B., A. K. Gosain, and S. Rao. Modelling Climate Change Impact on the Hydrology of the Eastern Himalayas; Climate Change Impact and Vulnerability in the Eastern Himalayas - Technical Report 4. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2010. http://dx.doi.org/10.53055/icimod.534.

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Shrestha, A. B., A. K. Gosain, and S. Rao. Modelling Climate Change Impact on the Hydrology of the Eastern Himalayas; Climate Change Impact and Vulnerability in the Eastern Himalayas - Technical Report 4. Kathmandu, Nepal: International Centre for Integrated Mountain Development (ICIMOD), 2010. http://dx.doi.org/10.53055/icimod.534.

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