Journal articles: 'Climate change model'

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

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

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Elía, Ramón Côté. "Climate and climate change sensitivity to model configuration in the Canadian RCM over North America." Meteorologische Zeitschrift 19, no. 4 (August 1, 2010): 325–39. http://dx.doi.org/10.1127/0941-2948/2010/0469.

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Fan, Fangxing, Raymond S. Bradley, and Michael A. Rawlins. "Climate change in the northeastern US: regional climate model validation and climate change projections." Climate Dynamics 43, no. 1-2 (June 1, 2014): 145–61. http://dx.doi.org/10.1007/s00382-014-2198-1.

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Nobre, Paulo, Leo S. P. Siqueira, Roberto A. F. de Almeida, Marta Malagutti, Emanuel Giarolla, Guilherme P. Castelão, Marcus J. Bottino, et al. "Climate Simulation and Change in the Brazilian Climate Model." Journal of Climate 26, no. 17 (August 23, 2013): 6716–32. http://dx.doi.org/10.1175/jcli-d-12-00580.1.

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Abstract The response of the global climate system to atmospheric CO2 concentration increase in time is scrutinized employing the Brazilian Earth System Model Ocean–Atmosphere version 2.3 (BESM-OA2.3). Through the achievement of over 2000 yr of coupled model integrations in ensemble mode, it is shown that the model simulates the signal of recent changes of global climate trends, depicting a steady atmospheric and oceanic temperature increase and corresponding marine ice retreat. The model simulations encompass the time period from 1960 to 2105, following the phase 5 of the Coupled Model Intercomparison Project (CMIP5) protocol. Notwithstanding the accurate reproduction of large-scale ocean–atmosphere coupled phenomena, like the ENSO phenomena over the equatorial Pacific and the interhemispheric gradient mode over the tropical Atlantic, the BESM-OA2.3 coupled model shows systematic errors on sea surface temperature and precipitation that resemble those of other global coupled climate models. Yet, the simulations demonstrate the model’s potential to contribute to the international efforts on global climate change research, sparking interest in global climate change research within the Brazilian climate modeling community, constituting a building block of the Brazilian Framework for Global Climate Change Research.

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Karmalkar, Ambarish V., Raymond S. Bradley, and Henry F. Diaz. "Climate change in Central America and Mexico: regional climate model validation and climate change projections." Climate Dynamics 37, no. 3-4 (May 29, 2011): 605–29. http://dx.doi.org/10.1007/s00382-011-1099-9.

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van Eck, Christel W., Bob C. Mulder, and Sander van der Linden. "Climate Change Risk Perceptions of Audiences in the Climate Change Blogosphere." Sustainability 12, no. 19 (September 27, 2020): 7990. http://dx.doi.org/10.3390/su12197990.

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The Climate Change Risk Perception Model (CCRPM, Van der Linden, 2015) has been used to characterize public risk perceptions; however, little is known about the model’s explanatory power in other (online) contexts. In this study, we extend the model and investigate the risk perceptions of a unique audience: The polarized climate change blogosphere. In total, our model explained 84% of the variance in risk perceptions by integrating socio-demographic characteristics, cognitive factors, experiential processes, socio-cultural influences, and an additional dimension: Trust in scientists and blogs. Although trust and the scientific consensus are useful additions to the model, affect remains the most important predictor of climate change risk perceptions. Surprisingly, the relative importance of social norms and value orientations is minimal. Implications for risk and science communication are discussed.

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M, Navaneetha Krishnan, Ranjith R, and Lavanya B. "Climate Change Prediction Using ARIMA Model." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 621–25. http://dx.doi.org/10.22214/ijraset.2022.43777.

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Abstract: It is a challenging task to forecast weather data accurately. The temperature change has important implications for business and economic activity. Effective management of global climate change impacts will depend upon accurate and costeffective forecasts. This paper univariate statistic techniques to model the properties of a world mean temperature dataset to develop a parsimonious forecasting model for managerial decision-making over the short-term horizon and the ARIMAbased prognostication tool has been developed by implementing the ARIMA algorithm in python. Although the model is estimated on global temperature data, the methodology could even be applied to temperature data at more localized levels. The statistical techniques include seasonal and non-seasonal unit root testing with and without structural breaks as well as ARIMA and SARIMA modelling. This paper helps us to predict the air temperature, which is the main problem of global warming. Prediction of the likely impact of climate change on monthly mean maximum and minimum temperature in Tamilnadu. Time-series techniques to develop a parsimonious model of global mean temperature change that can be used to forecast over the short-term horizon (5- 10) years. Keywords: Global warming, Forecasting, temperature

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Scaife, Adam, Chris Folland, and John Mitchell. "A model approach to climate change." Physics World 20, no. 2 (February 2007): 20–25. http://dx.doi.org/10.1088/2058-7058/20/2/29.

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Khokhlov, V., E. Serga, and L. Neodstrelova. "Objective selection of model run from regional climate models ensemble." Ukrainian hydrometeorological journal, no. 28 (December 14, 2021): 29–36. http://dx.doi.org/10.31481/uhmj.28.2021.03.

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In this paper, a method was developed in relation to the north-western coast of the Black Sea in order to determine the optimal model run from regional climate models ensemble. As a result of climate change, which has been observed since the late 1980s in Ukraine, various natural objects changes have been also transformed. The study of such changes in the future is possible only by using runs of global or regional climate models. Moreover, the step of the spatial grid in the climate model must be comparative with the spatial size of a natural object under study. In the north-western coast of the Black Sea, climate change is characterized by increasing aridity of climate and a corresponding decrease in freshwater inflows into coastal lagoons from their catchments, making ecosystems of these lagoons sensitive and vulnerable to climate change. Using numerical models in order to study climate change impact on these natural objects requires input hydrometeorological information in the spatial grid points, the distance between which should correspond to the horizontal size of lagoons, i.e. several kilometers. In this paper, data from the scenarios RCP4.5 and RCP8.5 of the ensemble from 14 model runs with different regional climatic models of the CORDEX project were used to simulate the future changes of the temperature and precipitation regime. For each grid point and scenario, a single simulation was selected from the ensemble, which best reproduces the intra-annual changes of temperature, precipitation, and evaporation compared to the ensemble means. Despite the sufficiently large distance between the estuaries, the method allowed the selection of a single optimal model run, which shows the significant differences in spring and summer precipitation as well as year-around evaporation in the southern and northern parts of the northwestern coast of the region. This run well reproduces the relationship between temperature, precipitation, and evaporation in the southern and northern parts of the northwestern coast of the Black Sea.

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Rauscher, Michael. "Demographic change and climate change." Environment and Development Economics 25, no. 1 (November 27, 2019): 5–20. http://dx.doi.org/10.1017/s1355770x19000366.

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AbstractThis paper uses a continuous-time overlapping-generations model with endogenous growth and pollution accumulation over time to study the link between longevity and global warming. It is seen that increasing longevity accelerates climate change in a business-as-usual scenario without climate policy. If a binding emission target is set exogenously and implemented via a cap-and-trade system, the price of emission permits is increasing in longevity. Longevity has no effect on the optimal solution of the climate problem if perfect intergenerational transfers are feasible. If these transfers are absent, the impact of longevity is ambiguous.

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Vhatkar, Rajendra Bapurao, and Dr Vishwajeet S. Goswami. "Influence of Cesaro summation and the Fejer Kernel onto the climate change model." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 2466–71. http://dx.doi.org/10.31142/ijtsrd12859.

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Chamberlain, M. A., C. Sun, R. J. Matear, M. Feng, and S. J. Phipps. "Downscaling the climate change for oceans around Australia." Geoscientific Model Development 5, no. 5 (September 21, 2012): 1177–94. http://dx.doi.org/10.5194/gmd-5-1177-2012.

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Abstract. At present, global climate models used to project changes in climate poorly resolve mesoscale ocean features such as boundary currents and eddies. These missing features may be important to realistically project the marine impacts of climate change. Here we present a framework for dynamically downscaling coarse climate change projections utilising a near-global ocean model that resolves these features in the Australasian region, with coarser resolution elsewhere. A time-slice projection for a 2060s ocean was obtained by adding climate change anomalies to initial conditions and surface fluxes of a near-global eddy-resolving ocean model. Climate change anomalies are derived from the differences between present and projected climates from a coarse global climate model. These anomalies are added to observed fields, thereby reducing the effect of model bias from the climate model. The downscaling model used here is ocean-only and does not include the effects that changes in the ocean state will have on the atmosphere and air–sea fluxes. We use restoring of the sea surface temperature and salinity to approximate real-ocean feedback on heat flux and to keep the salinity stable. Extra experiments with different feedback parameterisations are run to test the sensitivity of the projection. Consistent spatial differences emerge in sea surface temperature, salinity, stratification and transport between the downscaled projections and those of the climate model. Also, the spatial differences become established rapidly (< 3 yr), indicating the importance of mesoscale resolution. However, the differences in the magnitude of the difference between experiments show that feedback of the ocean onto the air–sea fluxes is still important in determining the state of the ocean in these projections. Until such a time when it is feasible to regularly run a global climate model with eddy resolution, our framework for ocean climate change downscaling provides an attractive way to explore the response of mesoscale ocean features with climate change and their effect on the broader ocean.

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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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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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Diffenbaugh, Noah S., and Filippo Giorgi. "Climate change hotspots in the CMIP5 global climate model ensemble." Climatic Change 114, no. 3-4 (August 25, 2012): 813–22. http://dx.doi.org/10.1007/s10584-012-0570-x.

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McNamara, P. "Global changes, regional impacts : climate change in the Middle East." Geographica Helvetica 54, no. 3 (September 30, 1999): 132–37. http://dx.doi.org/10.5194/gh-54-132-1999.

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Abstract. In the Middle East, an area where pressure on water resources is intensified by political conflict and natural scarcity, the possibility of future climate change looms as yet another compounding factor. An integrated approach, taking economic, social, political and climate factors into consideration, is embodied in the CLIMSOC model. Before using global model data for a future period as input into the regional scale CLIMSOC model, the global climate model data must first be tested for the present period. The work summarised here examines monthly preeipitation data from a Hadley Centre Global Climate Model, comparing it to an observed climatology, for the present period 1961–1990. The differences between the GCM and observed data are examined with an eye toward systematic discrepancies among the different months, spatial patterns and overall quantitative differences in preeipitation. Finally, a glimpse at future preeipitation, as estimated by the global climate model, is presented in the context of the comparison results.

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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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Hebsiba beula, D., S. Srinivasan, and C. D. Nanda Kumar. "PREDICTION OF CLIMATE CHANGE USING ARIMA MODEL." YMER Digital 20, no. 12 (December 11, 2021): 230–45. http://dx.doi.org/10.37896/ymer20.12/21.

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The climate and weather system prediction has always attracted interest. Climate change risks including physical risks, liability risks and transition risks, it’s directly affecting the insurance industry. Climate change is majorly affecting the insurance sector; they are such as extreme heat during summer and extreme rainfall (Flood). It affects both insurance and reinsurance sector. Constructing the model is a necessary process but choosing the model which suits our data is very necessary. In those days the weather reports telecast in news but now even our smart phone notified the weather. In this paper study the climate prediction algorithms using R and also using Cost-free R language tool to forecast the climate using time ARIMA model for the Indian climate.

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Volodin, E. M. "Simulation of Present Day Climate with Climate Model INMCM60." Известия Российской академии наук. Физика атмосферы и океана 59, no. 1 (January 1, 2023): 19–26. http://dx.doi.org/10.31857/s0002351523010133.

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Simulation of present day climate with a new version of climate model developed in INM RAS is considered. The model differs from a previous version by the change in cloudiness and condensation scheme, that leads to higher sensitivity to CO2 increase. The changes are included also in calculation of aerosol evolution, land snow, atmospheric boundary layer parameterizations and other blocks. The model is capable to reproduce near surface air temperature, precipitation, sea level pressure, cloud radiation forcing and other parameters better than previous version. The largest improvement can be seen in simulation of temperature in tropical troposphere, polar tropopause, and surface temperature in the Southern ocean. Simulation of climate changes in 1850–2021 by two model versions is considered.

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Libardoni, Alex G., Chris E. Forest, Andrei P. Sokolov, and Erwan Monier. "Estimates of climate system properties incorporating recent climate change." Advances in Statistical Climatology, Meteorology and Oceanography 4, no. 1/2 (November 30, 2018): 19–36. http://dx.doi.org/10.5194/ascmo-4-19-2018.

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Abstract. Historical time series of surface temperature and ocean heat content changes are commonly used metrics to diagnose climate change and estimate properties of the climate system. We show that recent trends, namely the slowing of surface temperature rise at the beginning of the 21st century and the acceleration of heat stored in the deep ocean, have a substantial impact on these estimates. Using the Massachusetts Institute of Technology Earth System Model (MESM), we vary three model parameters that influence the behavior of the climate system: effective climate sensitivity (ECS), the effective ocean diffusivity of heat anomalies by all mixing processes (Kv), and the net anthropogenic aerosol forcing scaling factor. Each model run is compared to observed changes in decadal mean surface temperature anomalies and the trend in global mean ocean heat content change to derive a joint probability distribution function for the model parameters. Marginal distributions for individual parameters are found by integrating over the other two parameters. To investigate how the inclusion of recent temperature changes affects our estimates, we systematically include additional data by choosing periods that end in 1990, 2000, and 2010. We find that estimates of ECS increase in response to rising global surface temperatures when data beyond 1990 are included, but due to the slowdown of surface temperature rise in the early 21st century, estimates when using data up to 2000 are greater than when data up to 2010 are used. We also show that estimates of Kv increase in response to the acceleration of heat stored in the ocean as data beyond 1990 are included. Further, we highlight how including spatial patterns of surface temperature change modifies the estimates. We show that including latitudinal structure in the climate change signal impacts properties with spatial dependence, namely the aerosol forcing pattern, more than properties defined for the global mean, climate sensitivity, and ocean diffusivity.

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Jones, Keith, Api Desai, Noel Brosnan, Justine Cooper, and Fuad Ali. "Built asset management climate change adaptation model." International Journal of Disaster Resilience in the Built Environment 8, no. 3 (June 12, 2017): 263–74. http://dx.doi.org/10.1108/ijdrbe-07-2016-0032.

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PurposeThe purpose of this paper is to present results of an action research addressing climate change adaptation of selected social housing stock in the UK. Climate change continues to pose major challenges to those responsible for the management of built assets. The adaptation required to address long-term building performance affected by climate change rarely get prioritised above more immediate, short-term needs (general built asset management needs). Design/methodology/approachThe study adopts an in-depth participatory action research with a London-based social landlord and integrates climate change adaptation framework and performance-based model established through author’s previous research projects. FindingsA staged process for including adaptation measures in built asset management strategy is developed along with metrics to analyse the performance of the housing stock against climate change impact of flooding. The prioritisation of adaptation measure implementation into long-term built asset management plans was examined through cost-based appraisal. Research limitations/implicationsThe research was carried out with a singular organisation, already acquainted with potential climate change impact, vulnerability and adaptive capacity assessment. The process adopted will differ for similar organisation in the sector with different settings and limited working knowledge of climate change impact assessment. Practical implicationsThe paper concludes with a ten-step process developed as an aide memoir to guide social landlords through the climate change adaptation planning process. Originality/valueIn addition to the practical results from the study, the paper outlines a novel process that integrates resilience concepts, risk framing (to climate change impact) and performance management into built asset management (maintenance and refurbishment) planning.

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Wu, Xuan, Liang Jiao, Xiaoping Liu, Ruhong Xue, Changliang Qi, and Dashi Du. "Ecological Adaptation of Two Dominant Conifer Species to Extreme Climate in the Tianshan Mountains." Forests 14, no. 7 (July 12, 2023): 1434. http://dx.doi.org/10.3390/f14071434.

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With global warming, the frequency, intensity, and period of extreme climates in more areas will probably increase in the twenty first century. However, the impact of climate extremes on forest vulnerability and the mechanisms by which forests adapt to climate extremes are not clear. The eastern Tianshan Mountains, set within the arid and dry region of Central Asia, is very sensitive to climate change. In this paper, the response of Picea schrenkiana and Larix sibirica to climate fluctuations and their stability were analyzed by Pearson’s correlation based on the observation of interannual change rates of climate indexes in different periods. Additionally, their ecological adaptability to future climate change was explored by regression analysis of climate factors and a selection of master control factors using the Lasso model. We found that the climate has undergone significant changes, especially the temperature, from 1958 to 2012. Around 1985, various extreme climate indexes had obvious abrupt changes. The research results suggested that: (1) the responses of the two tree species to extreme climate changed significantly after the change in temperature; (2) Schrenk spruce was more sensitive than Siberian larch to extreme climate change; and (3) the resistance of Siberian larch was higher than that of Schrenk spruce when faced with climate disturbance events. These results indicate that extreme climate changes will significantly interfere with the trees radial growth. At the same time, scientific management and maintenance measures are taken for different extreme weather events and different tree species.

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Rochlin, Cliff. "Climate change and the progressive business model." Electricity Journal 34, no. 4 (May 2021): 106927. http://dx.doi.org/10.1016/j.tej.2021.106927.

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Alcoforado, Fernando. "Catastrophic Climate Change Requires New Society Model." Environmental Science: Current Research 3, no. 2 (April 28, 2020): 1–7. http://dx.doi.org/10.24966/escr-5020/100023.

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Mason, Charles F. "Climate Change and Migration: A Dynamic Model." CESifo Economic Studies 63, no. 4 (May 3, 2017): 421–44. http://dx.doi.org/10.1093/cesifo/ifx003.

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Evans, R. "Model policies for climate change and trasport." Science and Public Policy 26, no. 6 (December 1, 1999): 444–45. http://dx.doi.org/10.1093/spp/26.6.444.

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Jang, Won-Seok. "Climate Change and the Model of Democracy." Journal of Environmental Policy and Administration 24, no. 2 (June 30, 2016): 85. http://dx.doi.org/10.15301/jepa.2016.24.2.85.

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Vuuren, Detlefvan, J. Lowe, E. Stehfest, L. Gohar, A. Hof, C. Hope, R. Warren, M. Meinshausen, and G.-K. Plattner. "How well do IAMs model climate change?" IOP Conference Series: Earth and Environmental Science 6, no. 49 (February 1, 2009): 492005. http://dx.doi.org/10.1088/1755-1307/6/49/492005.

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Bruns, Stephan B., Zsuzsanna Csereklyei, and David I. Stern. "A multicointegration model of global climate change." Journal of Econometrics 214, no. 1 (January 2020): 175–97. http://dx.doi.org/10.1016/j.jeconom.2019.05.010.

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Rwigi, Stephen Kibe, Jeremiah N. Muthama, Alfred O. Opere, Franklin J. Opijah, and Francis N. Gichuki. "Simulated Impacts of Climate Change on Surface Water Yields over the Sondu Basin in Kenya." International Journal for Innovation Education and Research 4, no. 8 (August 31, 2016): 161–73. http://dx.doi.org/10.31686/ijier.vol4.iss8.584.

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Potential impacts of climate change on surface water yields over the Sondu River basin in the western region of Kenya were analysed using the Soil and Water Assessment Tool (SWAT) model with climate input data obtained from the fourth generation coupled Ocean-Atmosphere European Community Hamburg Model (ECHAM4) using the Providing Regional Climates for Impacts Studies (PRECIS) model. Daily time step regional climate scenarios at a spatial grid resolution of 0.44Ëš over the Eastern Africa region were matched to the Sondu river basin and used to calibrate and validate the SWAT model.Analysis of historical and projected rainfall over the basin strongly indicated that the climate of the area will significantly change with wetter climates being experienced by 2030 and beyond. Projected monthly rainfall distribution shows increasing trends in the relatively dry DJF and SON seasons while showing decreasing trends in the relatively wet MAM and JJA seasons. Potential changes in water yields resulting from climate change were computed by comparing simulated yields under climate change scenarios with those simulated under baseline conditions. There was evidence of substantial increases in water yields ranging between 88% and 110% of the baseline yields by 2030 and 2050 respectively. Although simulated water yields are subject to further verification from observed values, this study has provided useful information about potential changes in water yields as a result of climate change over the Sondu River basin and in similar basins in this region.

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Rind, David H., Judith L. Lean, and Jeffrey Jonas. "The Impact of Different Absolute Solar Irradiance Values on Current Climate Model Simulations." Journal of Climate 27, no. 3 (January 24, 2014): 1100–1120. http://dx.doi.org/10.1175/jcli-d-13-00136.1.

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Abstract Simulations of the preindustrial and doubled CO2 climates are made with the GISS Global Climate Middle Atmosphere Model 3 using two different estimates of the absolute solar irradiance value: a higher value measured by solar radiometers in the 1990s and a lower value measured recently by the Solar Radiation and Climate Experiment. Each of the model simulations is adjusted to achieve global energy balance; without this adjustment the difference in irradiance produces a global temperature change of 0.4°C, comparable to the cooling estimated for the Maunder Minimum. The results indicate that by altering cloud cover the model properly compensates for the different absolute solar irradiance values on a global level when simulating both preindustrial and doubled CO2 climates. On a regional level, the preindustrial climate simulations and the patterns of change with doubled CO2 concentrations are again remarkably similar, but there are some differences. Using a higher absolute solar irradiance value and the requisite cloud cover affects the model’s depictions of high-latitude surface air temperature, sea level pressure, and stratospheric ozone, as well as tropical precipitation. In the climate change experiments it leads to an underestimation of North Atlantic warming, reduced precipitation in the tropical western Pacific, and smaller total ozone growth at high northern latitudes. Although significant, these differences are typically modest compared with the magnitude of the regional changes expected for doubled greenhouse gas concentrations. Nevertheless, the model simulations demonstrate that achieving the highest possible fidelity when simulating regional climate change requires that climate models use as input the most accurate (lower) solar irradiance value.

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Ren, Xiaobin, Lianyan Li, Yang Yu, Zhihua Xiong, Shunzhou Yang, Wei Du, and Mengjia Ren. "A Simplified Climate Change Model and Extreme Weather Model Based on a Machine Learning Method." Symmetry 12, no. 1 (January 9, 2020): 139. http://dx.doi.org/10.3390/sym12010139.

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The emergence of climate change (CC) is affecting and changing the development of the natural environment, biological species, and human society. In order to better understand the influence of climate change and provide convincing evidence, the need to quantify the impact of climate change is urgent. In this paper, a climate change model is constructed by using a radial basis function (RBF) neural network. To verify the relevance between climate change and extreme weather (EW), the EW model was built using a support vector machine. In the case study of Canada, its level of climate change was calculated as being 0.2241 (“normal”), and it was found that the factors of CO2 emission, average temperature, and sea surface temperature are significant to Canada’s climate change. In 2025, the climate level of Canada will become “a little bad” based on the prediction results. Then, the Pearson correlation value is calculated as being 0.571, which confirmed the moderate positive correlation between climate change and extreme weather. This paper provides a strong reference for comprehensively understanding the influences brought about by climate change.

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Ruget, F., J. C. Moreau, M. Ferrand, S. Poisson, P. Gate, B. Lacroix, J. Lorgeou, E. Cloppet, and F. Souverain. "Describing the possible climate changes in France and some examples of their effects on main crops used in livestock systems." Advances in Science and Research 4, no. 1 (August 2, 2010): 99–104. http://dx.doi.org/10.5194/asr-4-99-2010.

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Abstract. The effects of climate change on forage and crop production are an important question for the farmers and more largely for the food security in the world. Estimating the effect of climate change on agricultural production needs the use of two types of tools: a model to estimate changes in national or local climates and an other model using climatic data to estimate the effects on vegetation. In this paper, we will mainly present the effects of climate change on climatic features, the variability of criteria influencing crop production in various regions of France and some possible effects on crops.

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Forster, Piers Mde F., and Karl E. Taylor. "Climate Forcings and Climate Sensitivities Diagnosed from Coupled Climate Model Integrations." Journal of Climate 19, no. 23 (December 1, 2006): 6181–94. http://dx.doi.org/10.1175/jcli3974.1.

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Abstract A simple technique is proposed for calculating global mean climate forcing from transient integrations of coupled atmosphere–ocean general circulation models (AOGCMs). This “climate forcing” differs from the conventionally defined radiative forcing as it includes semidirect effects that account for certain short time scale responses in the troposphere. First, a climate feedback term is calculated from reported values of 2 × CO2 radiative forcing and surface temperature time series from 70-yr simulations by 20 AOGCMs. In these simulations carbon dioxide is increased by 1% yr−1. The derived climate feedback agrees well with values that are diagnosed from equilibrium climate change experiments of slab-ocean versions of the same models. These climate feedback terms are associated with the fast, quasi-linear response of lapse rate, clouds, water vapor, and albedo to global surface temperature changes. The importance of the feedbacks is gauged by their impact on the radiative fluxes at the top of the atmosphere. Partial compensation is found between longwave and shortwave feedback terms that lessens the intermodel differences in the equilibrium climate sensitivity. There is also some indication that the AOGCMs overestimate the strength of the positive longwave feedback. These feedback terms are then used to infer the shortwave and longwave time series of climate forcing in twentieth- and twenty-first-century simulations in the AOGCMs. The technique is validated using conventionally calculated forcing time series from four AOGCMs. In these AOGCMs the shortwave and longwave climate forcings that are diagnosed agree with the conventional forcing time series within ∼10%. The shortwave forcing time series exhibit order of magnitude variations between the AOGCMs, differences likely related to how both natural forcings and/or anthropogenic aerosol effects are included. There are also factor of 2 differences in the longwave climate forcing time series, which may indicate problems with the modeling of well-mixed greenhouse gas changes. The simple diagnoses presented provides an important and useful first step for understanding differences in AOGCM integrations, indicating that some of the differences in model projections can be attributed to different prescribed climate forcing, even for so-called standard climate change scenarios.

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Kemp-Benedict, Eric, Crystal Drakes, and Nella Canales. "A Climate-Economy Policy Model for Barbados." Economies 8, no. 1 (February 25, 2020): 16. http://dx.doi.org/10.3390/economies8010016.

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Small island developing states (SIDS), such as Barbados, must continually adapt in the face of uncertain external drivers. These include demand for exports, tourism demand, and extreme weather events. Climate change introduces further uncertainty into the external drivers. To address the challenge, we present a policy-oriented simulation model that builds upon prior work by the authors and their collaborators. Intended for policy analysis, it follows a robust decision making (RDM) philosophy of identifying policies that lead to positive outcomes across a wide range of external changes. While the model can benefit from further development, it illustrates the importance for SIDS of incorporating climate change into national planning. Even without climate change, normal variation in export and tourism demand drive divergent trajectories for the economy and external debt. With climate change, increasing storm damage adds to external debt as the loss of productive capital and need to rebuild drives imports.

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Tsegaw, Aynalem T., Marie Pontoppidan, Erle Kristvik, Knut Alfredsen, and Tone M. Muthanna. "Hydrological impacts of climate change on small ungauged catchments – results from a global climate model–regional climate model–hydrologic model chain." Natural Hazards and Earth System Sciences 20, no. 8 (August 10, 2020): 2133–55. http://dx.doi.org/10.5194/nhess-20-2133-2020.

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Abstract. Climate change is one of the greatest threats currently facing the world's environment. In Norway, a change in climate will strongly affect the pattern, frequency, and magnitudes of stream flows. However, it is challenging to quantify to what extent the change will affect the flow patterns and floods from small rural catchments due to the unavailability or inadequacy of hydro-meteorological data for the calibration of hydrological models and due to the tailoring of methods to a small-scale level. To provide meaningful climate impact studies at the level of small catchments, it is therefore beneficial to use high-spatial- and high-temporal-resolution climate projections as input to a high-resolution hydrological model. In this study, we used such a model chain to assess the impacts of climate change on the flow patterns and frequency of floods in small ungauged rural catchments in western Norway. We used a new high-resolution regional climate projection, with improved performance regarding the precipitation distribution, and a regionalized hydrological model (distance distribution dynamics) between a reference period (1981–2011) and a future period (2070–2100). The flow-duration curves for all study catchments show more wet periods in the future than during the reference period. The results also show that in the future period, the mean annual flow increases by 16 % to 33 %. The mean annual maximum floods increase by 29 % to 38 %, and floods of 2- to 200-year return periods increase by 16 % to 43 %. The results are based on the RCP8.5 scenario from a single climate model simulation tailored to the Bergen region in western Norway, and the results should be interpreted in this context. The results should therefore be seen in consideration of other scenarios for the region to address the uncertainty. Nevertheless, the study increases our knowledge and understanding of the hydrological impacts of climate change on small catchments in the Bergen area in the western part of Norway.

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MARUYA, Yasuyuki, Morihiro HARADA, Rui ITO, Hiroaki KAWASE, Koji DAIRAKU, and Hidetaka SASAKI. "UNCERTAINTY OF REGIONAL CLIMATE MODEL AND IMPACT ASSESSMENT MODEL TOWARD CLIMATE CHANGE IMPACT ASSESSMENT." Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 74, no. 5 (2018): I_109—I_114. http://dx.doi.org/10.2208/jscejhe.74.5_i_109.

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Cammarano, D., M. Rivington, K. B. Matthews, D. G. Miller, and G. Bellocchi. "Implications of climate model biases and downscaling on crop model simulated climate change impacts." European Journal of Agronomy 88 (August 2017): 63–75. http://dx.doi.org/10.1016/j.eja.2016.05.012.

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Danesh, Azin Shahni, Mohammad Sadegh Ahadi, Hedayat Fahmi, Majid Habibi Nokhandan, and Hadi Eshraghi. "Climate change impact assessment on water resources in Iran: applying dynamic and statistical downscaling methods." Journal of Water and Climate Change 7, no. 3 (March 30, 2016): 551–77. http://dx.doi.org/10.2166/wcc.2016.045.

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As a result of inappropriate management and rising levels of societal demand, in arid and semi-arid regions water resources are becoming increasingly stressed. Therefore, well-established insight into the effects of climate change on water resource components can be considered to be an essential strategy to reduce these effects. In this paper, Iran's climate change and variability, and the impact of climate change on water resources, were studied. Climate change was assessed by means of two Long Ashton Research Station-Weather Generator (LARS-WG) weather generators and all outputs from the available general circulation models in the Model for the Assessment of Greenhouse-gas Induced Climate Change-SCENario GENerator (MAGICC-SCENGEN) software, in combination with different emission scenarios at the regional scale, while the Providing Regional Climates for Impacts Studies (PRECIS) model has been used for projections at the local scale. A hydrological model, the Runoff Assessment Model (RAM), was first utilized to simulate water resources for Iran. Then, using the MAGICC-SCENGEN model and the downscaled results as input for the RAM model, a prediction was made for changes in 30 basins and runoffs. Modeling results indicate temperature and precipitation changes in the range of ±6 °C and ±60%, respectively. Temperature rise increases evaporation and decreases runoff, but has been found to cause an increased rate of runoff in winter and a decrease in spring.

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Bretschger, Lucas, and Christos Karydas. "Economics of climate change: introducing the Basic Climate Economic (BCE) model." Environment and Development Economics 24, no. 6 (June 28, 2019): 560–82. http://dx.doi.org/10.1017/s1355770x19000184.

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AbstractEnvironmental economics models are often too complex to be communicated in an illustrative manner. For this reason, this paper develops the Basic Climate Economic (BCE) model that features core elements of macroeconomic and climate economic modelling, while allowing for an illustrative examination of the development path. The BCE model incorporates fossil stock depletion, pollution stock accumulation, endogenous growth, and climate-induced capital depreciation. We first use graphical analysis to show the effects of climate change and climate policy on economic development. Intuition for the different model mechanisms, the functional forms, and the effects of different climate policies is provided. We then show the model equations in mathematical terms to derive closed-form solutions and to run model simulations relating to the graphical part. Finally, we compare our setup to other models of climate economics.

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Casati, Barbara, and Ramon de Elía. "Temperature Extremes from Canadian Regional Climate Model (CRCM) Climate Change Projections." Atmosphere-Ocean 52, no. 3 (February 13, 2014): 191–210. http://dx.doi.org/10.1080/07055900.2014.886179.

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Liu, Shuyan, Wei Gao, and Xin-Zhong Liang. "A regional climate model downscaling projection of China future climate change." Climate Dynamics 41, no. 7-8 (December 25, 2012): 1871–84. http://dx.doi.org/10.1007/s00382-012-1632-5.

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Bayhaqi, Ahmad. "KETIDAKPASTIAN DALAM PEMODELAN PERUBAHAN IKLIM." OSEANA 44, no. 1 (April 30, 2019): 38–53. http://dx.doi.org/10.14203/oseana.2019.vol.44no.1.30.

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THE UNCERTAINTIES IN CLIMATE CHANGE MODELING. Climate in the Earth has changed over the periods and will be estimated to give the a significant impact for environment in the future. Climate prediction using a simulation model, as a tool to predict the future climate and it requires the high quantitative skills and technology, has showed the significant improvement. However, the climate model depends on the input variable and the result may be inaccurate because its biases and uncertainties. Information of the uncertainties in the climate model can determine the modification in climate change mitigation and show the way how to adapt with the inevitable changes.

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Dortmans, Brady, William F. Langford, and Allan R. Willms. "An energy balance model for paleoclimate transitions." Climate of the Past 15, no. 2 (March 21, 2019): 493–520. http://dx.doi.org/10.5194/cp-15-493-2019.

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Abstract. A new energy balance model (EBM) is presented and is used to study paleoclimate transitions. While most previous EBMs only dealt with the globally averaged climate, this new EBM has three variants: Arctic, Antarctic and tropical climates. The EBM incorporates the greenhouse warming effects of both carbon dioxide and water vapour, and also includes ice–albedo feedback and evapotranspiration. The main conclusion to be inferred from this EBM is that the climate system may possess multiple equilibrium states, both warm and frozen, which coexist mathematically. While the actual climate can exist in only one of these states at any given time, the EBM suggests that climate can undergo transitions between the states via mathematical saddle-node bifurcations. This paper proposes that such bifurcations have actually occurred in Paleoclimate transitions. The EBM is applied to the study of the Pliocene paradox, the glaciation of Antarctica and the so-called warm, equable climate problem of both the mid-Cretaceous Period and the Eocene Epoch. In all cases, the EBM is in qualitative agreement with the geological record.

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Yuan, Wei, Shuang-Ye Wu, Shugui Hou, Zhiwei Xu, Hongxi Pang, and Huayu Lu. "Projecting Future Vegetation Change for Northeast China Using CMIP6 Model." Remote Sensing 13, no. 17 (September 6, 2021): 3531. http://dx.doi.org/10.3390/rs13173531.

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Northeast China lies in the transition zone from the humid monsoonal to the arid continental climate, with diverse ecosystems and agricultural land highly susceptible to climate change. This region has experienced significant greening in the past three decades, but future trends remain uncertain. In this study, we provide a quantitative assessment of how vegetation, indicated by the leaf area index (LAI), will change in this region in response to future climate change. Based on the output of eleven CMIP6 global climates, Northeast China is likely to get warmer and wetter in the future, corresponding to an increase in regional LAI. Under the medium emissions scenario (SSP245), the average LAI is expected to increase by 0.27 for the mid-century (2041–2070) and 0.39 for the late century (2071–2100). Under the high emissions scenario (SSP585), the increase is 0.40 for the mid-century and 0.70 for the late century, respectively. Despite the increase in the regional mean, the LAI trend shows significant spatial heterogeneity, with likely decreases for the arid northwest and some sandy fields in this region. Therefore, climate change could pose additional challenges for long-term ecological and economic sustainability. Our findings could provide useful information to local decision makers for developing effective sustainable land management strategies in Northeast China.

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Gillett, Nathan P. "Weighting climate model projections using observational constraints." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 373, no. 2054 (November 13, 2015): 20140425. http://dx.doi.org/10.1098/rsta.2014.0425.

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Projected climate change integrates the net response to multiple climate feedbacks. Whereas existing long-term climate change projections are typically based on unweighted individual climate model simulations, as observed climate change intensifies it is increasingly becoming possible to constrain the net response to feedbacks and hence projected warming directly from observed climate change. One approach scales simulated future warming based on a fit to observations over the historical period, but this approach is only accurate for near-term projections and for scenarios of continuously increasing radiative forcing. For this reason, the recent Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) included such observationally constrained projections in its assessment of warming to 2035, but used raw model projections of longer term warming to 2100. Here a simple approach to weighting model projections based on an observational constraint is proposed which does not assume a linear relationship between past and future changes. This approach is used to weight model projections of warming in 2081–2100 relative to 1986–2005 under the Representative Concentration Pathway 4.5 forcing scenario, based on an observationally constrained estimate of the Transient Climate Response derived from a detection and attribution analysis. The resulting observationally constrained 5–95% warming range of 0.8–2.5 K is somewhat lower than the unweighted range of 1.1–2.6 K reported in the IPCC AR5.

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Arnell, N. W. "Effects of IPCC SRES* emissions scenarios on river runoff: a global perspective." Hydrology and Earth System Sciences 7, no. 5 (October 31, 2003): 619–41. http://dx.doi.org/10.5194/hess-7-619-2003.

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Abstract. This paper describes an assessment of the implications of future climate change for river runoff across the entire world, using six climate models which have been driven by the SRES emissions scenarios. Streamflow is simulated at a spatial resolution of 0.5°x0.5&#176 using a macro-scale hydrological model, and summed to produce total runoff for almost 1200 catchments. The effects of climate change have been compared with the effects of natural multi-decadal climatic variability, as determined from a long unforced climate simulation using HadCM3. By the 2020s, change in runoff due to climate change in approximately a third of the catchments is less than that due to natural variability but, by the 2080s, this falls to between 10 and 30%. The climate models produce broadly similar changes in runoff, with increases in high latitudes, east Africa and south and east Asia, and decreases in southern and eastern Europe, western Russia, north Africa and the Middle East, central and southern Africa, much of North America, most of South America, and south and east Asia. The pattern of change in runoff is largely determined by simulated change in precipitation, offset by a general increase in evaporation. There is little difference in the pattern of change between different emissions scenarios (for a given model), and only by the 2080s is there evidence that the magnitudes of change in runoff vary, with emissions scenario A1FI producing the greatest change and B1 the smallest. The inter-annual variability in runoff increases in most catchments due to climate change — even though the inter-annual variability in precipitation is not changed — and the frequency of flow below the current 10-year return period minimum annual runoff increases by a factor of three in Europe and southern Africa and of two across North America. Across most of the world climate change does not alter the timing of flows through the year but, in the marginal zone between cool and mild climates, higher temperatures mean that peak streamflow moves from spring to winter as less winter precipitation falls as snow. The spatial pattern of changes in the 10-year return period maximum monthly runoff follows changes in annual runoff. Keywords: SRES emissions scenarios, climate change impacts on runoff, multi-decadal variability, macro-scale hydrological model, drought frequency, flood frequency

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Jarnevich, Catherine, and Nicholas Young. "Not so Normal Normals: Species Distribution Model Results are Sensitive to Choice of Climate Normals and Model Type." Climate 7, no. 3 (February 28, 2019): 37. http://dx.doi.org/10.3390/cli7030037.

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Species distribution models have many applications in conservation and ecology, and climate data are frequently a key driver of these models. Often, correlative modeling approaches are developed with readily available climate data; however, the impacts of the choice of climate normals is rarely considered. Here, we produced species distribution models for five disparate species using four different modeling algorithms and compared results between two different, but overlapping, climate normals time periods. Although the correlation structure among climate predictors did not change between the time periods, model results were sensitive to both baseline climate period and model method, even with model parameters specifically tuned to a species. Each species and each model type had at least one difference in variable retention or relative ranking with the change in climate time period. Pairwise comparisons of spatial predictions were also different, ranging from a low of 1.6% for climate period differences to a high of 25% for algorithm differences. While uncertainty from model algorithm selection is recognized as an important source of uncertainty, the impact of climate period is not commonly assessed. These uncertainties may affect conservation decisions, especially when projecting to future climates, and should be evaluated during model development.

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Romera, Raquel, Miguel Ángel Gaertner, Enrique Sánchez, Marta Domínguez, Juan Jesús González-Alemán, and Mario Marcello Miglietta. "Climate change projections of medicanes with a large multi-model ensemble of regional climate models." Global and Planetary Change 151 (April 2017): 134–43. http://dx.doi.org/10.1016/j.gloplacha.2016.10.008.

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Journal articles on the topic 'Climate change model'

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