Journal articles: 'Climatic models'

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Quénol, Hervé, Iñaki Garcia de Cortazar Atauri, Benjamin Bois, Andrew Sturman, Valérie Bonnardot, and Renan Le Roux. "Which climatic modeling to assess climate change impacts on vineyards?" OENO One 51, no. 2 (May 15, 2017): 91. http://dx.doi.org/10.20870/oeno-one.2016.0.0.1869.

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The impact of climatic change on viticulture is significant: main phenological stages appear earlier, wine characteristics are changing,... This clearly illustrates the point that the adaptation of viticulture to climate change is crucial and should be based on simulations of future climate. Several types of models exist and are used to represent viticultural climates at various scales. In this paper, we propose a review of different types of climate models (methodology and uncertainties) and then few examples of its application at the scale of wine growing regions worldwide.

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Quénol, Hervé, Iñaki Garcia de Cortazar Atauri, Benjamin Bois, Andrew Sturman, Valérie Bonnardot, and Renan Le Roux. "Which climatic modeling to assess climate change impacts on vineyards?" OENO One 51, no. 2 (May 15, 2017): 91–97. http://dx.doi.org/10.20870/oeno-one.2017.51.2.1869.

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The impact of climatic change on viticulture is significant: main phenological stages appear earlier, wine characteristics are changing,... This clearly illustrates the point that the adaptation of viticulture to climate change is crucial and should be based on simulations of future climate. Several types of models exist and are used to represent viticultural climates at various scales. In this paper, we propose a review of different types of climate models (methodology and uncertainties) and then few examples of its application at the scale of wine growing regions worldwide.

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MacKey, Brendan G., Daniel W. McKenney, Yin-Qian Yang, June P. McMahon, and Michael F. Hutchinson. "Site regions revisited: a climatic analysis of Hills' site regions for the province of Ontario using a parametric method." Canadian Journal of Forest Research 26, no. 3 (March 1, 1996): 333–54. http://dx.doi.org/10.1139/x26-038.

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A climatic analysis was conducted` of Hills' site regions for Ontario using spatially distributed models of long-term, mean monthly climate data. Gridded estimates of eight climatic variables were generated by coupling thin-plate smoothing spline surfaces (fitted as a trivariate function of longitude, latitude, and elevation) to a 1-km resolution digital elevation model of Ontario. The gridded climate estimates were used to characterize the climate of each site region. New climatic classifications for the province were calculated from these data using a clustering algorithm. They were also compared with the existing site region boundaries. The analyses confirm many of the general climatic gradients established by Hills, but also suggest where major revisions should be made. The climatic models present new analytical opportunities for mapping plant–climate response in the context of forest planning and management.

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Duncan, Richard P., Phillip Cassey, and Tim M. Blackburn. "Do climate envelope models transfer? A manipulative test using dung beetle introductions." Proceedings of the Royal Society B: Biological Sciences 276, no. 1661 (February 25, 2009): 1449–57. http://dx.doi.org/10.1098/rspb.2008.1801.

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Climate envelope models (CEMs) are widely used to forecast future shifts in species ranges under climate change, but these models are rarely validated against independent data, and their fundamental assumption that climate limits species distributions is rarely tested. Here, we use the data on the introduction of five South African dung beetle species to Australia to test whether CEMs developed in the native range can predict distribution in the introduced range, where the confounding effects of dispersal limitation, resource limitation and the impact of natural enemies have been removed, leaving climate as the dominant constraint. For two of the five species, models developed in the native range predict distribution in the introduced range about as well as models developed in the introduced range where we know climate limits distribution. For the remaining three species, models developed in the native range perform poorly, implying that non-climatic factors limit the native distribution of these species and need to be accounted for in species distribution models. Quantifying relevant non-climatic factors and their likely interactions with climatic variables for forecasting range shifts under climate change remains a challenging task.

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Li, C. Z., L. Zhang, H. Wang, Y. Q. Zhang, F. L. Yu, and D. H. Yan. "The transferability of hydrological models under nonstationary climatic conditions." Hydrology and Earth System Sciences 16, no. 4 (April 26, 2012): 1239–54. http://dx.doi.org/10.5194/hess-16-1239-2012.

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Abstract. This paper investigates issues involved in calibrating hydrological models against observed data when the aim of the modelling is to predict future runoff under different climatic conditions. To achieve this objective, we tested two hydrological models, DWBM and SIMHYD, using data from 30 unimpaired catchments in Australia which had at least 60 yr of daily precipitation, potential evapotranspiration (PET), and streamflow data. Nash-Sutcliffe efficiency (NSE), modified index of agreement (d1) and water balance error (WBE) were used as performance criteria. We used a differential split-sample test to split up the data into 120 sub-periods and 4 different climatic sub-periods in order to assess how well the calibrated model could be transferred different periods. For each catchment, the models were calibrated for one sub-period and validated on the other three. Monte Carlo simulation was used to explore parameter stability compared to historic climatic variability. The chi-square test was used to measure the relationship between the distribution of the parameters and hydroclimatic variability. The results showed that the performance of the two hydrological models differed and depended on the model calibration. We found that if a hydrological model is set up to simulate runoff for a wet climate scenario then it should be calibrated on a wet segment of the historic record, and similarly a dry segment should be used for a dry climate scenario. The Monte Carlo simulation provides an effective and pragmatic approach to explore uncertainty and equifinality in hydrological model parameters. Some parameters of the hydrological models are shown to be significantly more sensitive to the choice of calibration periods. Our findings support the idea that when using conceptual hydrological models to assess future climate change impacts, a differential split-sample test and Monte Carlo simulation should be used to quantify uncertainties due to parameter instability and non-uniqueness.

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Li, C. Z., L. Zhang, H. Wang, Y. Q. Zhang, F. L. Yu, and D. H. Yan. "The transferability of hydrological models under nonstationary climatic conditions." Hydrology and Earth System Sciences Discussions 8, no. 5 (September 23, 2011): 8701–36. http://dx.doi.org/10.5194/hessd-8-8701-2011.

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Abstract. This paper investigates issues involved in calibrating hydrological models against observed data when the aim of the modelling is to predict future runoff under different climatic conditions. To achieve this objective, we tested two hydrological models, DWBM and SIMHYD, using data from 30 unimpaired catchments in Australia which had at least 60 years of daily precipitation, potential evapotranspiration (PET), and streamflow data. Nash-Sutcliffe efficiency (NSE) and absolute percentage water balance error (WBE) were used as performance criteria. We used a differential split-sample test to split up the data into 120 sub-periods and 4 different climatic sub-periods in order to assess how well the calibrated model could be transferred different periods. For each catchment, the models were calibrated for one sub-period and validated on the other three. Monte Carlo simulation was used to explore parameter stability compared to historic climatic variability. The chi-square test was used to measure the relationship between the distribution of the parameters and hydroclimatic variability. The results showed that the performance of the two hydrological models differed and depended on the model calibration. We found that if a hydrological model is set up to simulate runoff for a wet climate scenario then it should be calibrated on a wet segment of the historic record, and similarly a dry segment should be used for a dry climate scenario. The Monte Carlo simulation provides an effective and pragmatic approach to explore uncertainty and equifinality in hydrological model parameters. Some parameters of the hydrological models are shown to be significantly more sensitive to the choice of calibration periods. Our findings support the idea that when using conceptual hydrological models to assess future climate change impacts, a differential split-sample test and Monte Carlo simulation can reduce uncertainties due to parameter instability and non-uniqueness.

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Green, Rhys E., Yvonne C. Collingham, Stephen G. Willis, Richard D. Gregory, Ken W. Smith, and Brian Huntley. "Performance of climate envelope models in retrodicting recent changes in bird population size from observed climatic change." Biology Letters 4, no. 5 (July 29, 2008): 599–602. http://dx.doi.org/10.1098/rsbl.2008.0052.

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Twenty-five-year population trends of 42 bird species rare as breeders in the UK were examined in relation to changes in climatic suitability simulated using climatic envelope models. The effects of a series of potential ‘nuisance’ variables were also assessed. A statistically significant positive correlation was found across species between population trend and climate suitability trend. The demonstration that climate envelope models are able to retrodict species' population trends provides a valuable validation of their use in studies of the potential impacts of future climatic changes.

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Pujol, Toni, and Josep Enric Llebot. "Periodic Solutions in Low-Dimensional Climatic Models." Journal of Climate 12, no. 2 (February 1999): 325–33. http://dx.doi.org/10.1175/1520-0442(1999)012<0325:psildc>2.0.co;2.

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Karakoti, Indira, Prasun Kumar Das, and Bibek Bandyopadhyay. "Diffuse radiation models for Indian climatic conditions." International Journal of Ambient Energy 33, no. 2 (June 2012): 75–86. http://dx.doi.org/10.1080/01430750.2011.640099.

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Croce, Pietro, Paolo Formichi, and Filippo Landi. "Enhancing the Output of Climate Models: A Weather Generator for Climate Change Impact Studies." Atmosphere 12, no. 8 (August 21, 2021): 1074. http://dx.doi.org/10.3390/atmos12081074.

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Evaluation of effects of climate change on climate variable extremes is a key topic in civil and structural engineering, strongly affecting adaptation strategy for resilience. Appropriate procedures to assess the evolution over time of climatic actions are needed to deal with the inherent uncertainty of climate projections, also in view of providing more sound and robust predictions at the local scale. In this paper, an ad hoc weather generator is presented that is able to provide a quantification of climate model inherent uncertainties. Similar to other weather generators, the proposed algorithm allows the virtualization of the climatic data projection process, overcoming the usual limitations due to the restricted number of available climate model runs, requiring huge computational time. However, differently from other weather generation procedures, this new tool directly samples from the output of Regional Climate Models (RCMs), avoiding the introduction of additional hypotheses about the stochastic properties of the distributions of climate variables. Analyzing the ensemble of so-generated series, future changes of climatic actions can be assessed, and the associated uncertainties duly estimated, as a function of considered greenhouse gases emission scenarios. The efficiency of the proposed weather generator is discussed evaluating performance metrics and referring to a relevant case study: the evaluation of extremes of minimum and maximum temperature, precipitation, and ground snow load in a central Eastern region of Italy, which is part of the Mediterranean climatic zone. Starting from the model ensemble of six RCMs, factors of change uncertainty maps for the investigated region are derived concerning extreme daily temperatures, daily precipitation, and ground snow loads, underlying the potentialities of the proposed approach.

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Li, Kexin, Shangzhe Zhang, Xiaoying Song, Alexandra Weyrich, Yinjia Wang, Xi Liu, Na Wan, et al. "Genome evolution of blind subterranean mole rats: Adaptive peripatric versus sympatric speciation." Proceedings of the National Academy of Sciences 117, no. 51 (December 4, 2020): 32499–508. http://dx.doi.org/10.1073/pnas.2018123117.

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Speciation mechanisms remain controversial. Two speciation models occur in Israeli subterranean mole rats, genusSpalax: a regional speciation cline southward of four peripatric climatic chromosomal species and a local, geologic-edaphic, genic, and sympatric speciation. Here we highlight their genome evolution. The five species were separated into five genetic clusters by single nucleotide polymorphisms, copy number variations (CNVs), repeatome, and methylome in sympatry. The regional interspecific divergence correspond to Pleistocene climatic cycles. Climate warmings caused chromosomal speciation. Triple effective population size,Ne, declines match glacial cold cycles. Adaptive genes evolved under positive selection to underground stresses and to divergent climates, involving interspecies reproductive isolation. Genomic islands evolved mainly due to adaptive evolution involving ancient polymorphisms. Repeatome, including both CNV and LINE1 repetitive elements, separated the five species. Methylation in sympatry identified geologically chalk-basalt species that differentially affect thermoregulation, hypoxia, DNA repair, P53, and other pathways. Genome adaptive evolution highlights climatic and geologic-edaphic stress evolution and the two speciation models, peripatric and sympatric.

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Seiller, G., and F. Anctil. "Climate change impacts on the hydrologic regime of a Canadian river: comparing uncertainties arising from climate natural variability and lumped hydrological model structures." Hydrology and Earth System Sciences Discussions 10, no. 11 (November 19, 2013): 14189–227. http://dx.doi.org/10.5194/hessd-10-14189-2013.

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Abstract. Diagnosing the impacts of climate change on water resources is a difficult task pertaining to the uncertainties arising from the different modeling steps. Lumped hydrological model structures contribute to this uncertainty as well as the natural climate variability, illustrated by several members from the same Global Circulation Model. In this paper, the hydroclimatic modeling chain consist of twenty-four potential evapotranspiration formulations, twenty lumped conceptual hydrological models, and seven snowmelt modules. These structures are applied on a natural Canadian sub-catchment to address related uncertainties and compare them to the natural variability as depicted by five climatic members. Uncertainties are commented on the observation period and on simulated and projected climates. They rely on interannual hydrographs and hydrological indicators analysis. Results show that the natural climate variability is the major source of uncertainty, followed by the potential evapotranspiration formulations and hydrological models. The selected snowmelt modules, however, do not contribute much to the uncertainty. The analysis also illustrates that the streamflow simulation over the current climate period is already conditioned by tools' selection, propagating this uncertainty on reference and future projection, while climatic members add over it. These findings demonstrate the importance of opting for several climatic members to encompass the important uncertainty related to the climate natural variability, but also of selecting multiple modeling tools to provide a trustworthy diagnosis of the impacts of climate change on water resources.

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Bayissa, Yared, Assefa Melesse, Mahadev Bhat, Tsegaye Tadesse, and Andualem Shiferaw. "Evaluation of Regional Climate Models (RCMs) Using Precipitation and Temperature-Based Climatic Indices: A Case Study of Florida, USA." Water 13, no. 17 (September 2, 2021): 2411. http://dx.doi.org/10.3390/w13172411.

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The overarching objective of this study was to evaluate the performance of nine precipitation-based and twelve temperature-based climatic indices derived from four regional climate models (CRCM5-UQUAM, CanRCM4, RCA4 and HIRHAM5) driven by three global circulation models (CanESM2, EC-EARTH and MPI-ESM-LR) and their ensemble mean for the reference period of 31 years (1975–2005). The absolute biases, pattern correlation, the reduction of variance (RV) and the Standardized Precipitation Evapotranspiration Index (SPEI at 3-, 6- and 12-month aggregate periods) techniques were used to evaluate the climate model simulations. The result, in general, shows each climate model has a skill in reproducing at least one of the climatic indices considered in this study. Based on the pattern correlation result, however, EC-EARTH.HIRHAM5 and MPI-ESM-LR.CRCM5-UQAM RCMs showed a relatively good skill in reproducing the observed climatic indices as compared to the other climate model simulations. EC-EARTH.RCA4, CanESM2.RCA4 and MPI-ESM-LR.CRCM5-UQAM RCMs showed a good skill when evaluated using the reduction of variance. The ensemble mean of the RCMs showed relatively better skill in reproducing the observed temperature-based climatic indices as compared to the precipitation-based climatic indices. There were no exceptional differences observed among the performance of the climate models compared to the SPEI, but CanESM2.CRCM5-UQAM, EC-EARTH.RCA4 and the ensemble mean of the RCMs performed relatively good in comparison to the other climate models. The good performance of some of the RCMs has good implications for their potential application for climate change impact studies and future trend analysis of extreme events. They could help in developing an early warning system to mitigate and prepare for possible future impacts of climate extremes (e.g., drought) and vulnerability to climate change across Florida.

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Hanna, Edward. "The role of Antarctic sea ice in global climate change." Progress in Physical Geography: Earth and Environment 20, no. 4 (December 1996): 371–401. http://dx.doi.org/10.1177/030913339602000401.

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Taking a distinct interdisciplinary focus, a critical view is presented of the current state of research concerning Antarctic sea-ice / atmosphere / ocean interaction and its effect on climate on the interannual timescale, with particular regard to anthropogenic global warming. Sea-ice formation, morphology, thickness, extent, seasonality and distribution are introduced as vital factors in climatic feedbacks. Sea-ice / atmosphere interaction is next discussed, emphas izing its meteorological and topographical influences and the effects of and on polar cyclonic activity. This leads on to the central theme of sea ice in global climate change, which contains critiques of sea-ice climatic feedbacks, current findings on the representation of these feedbacks in global climatic models, and to what extent they are corroborated by observational evidence. Sea-ice / ocean interaction is particularly important. This is discussed with special reference to polynyas and leads, and the use of suitably coupled sea-ice / ocean models. A brief review of several possible climatic forcing factors is presented, which most highly rates a postulated ENSO-Antarctic sea-ice link. Sea-ice / atmosphere / ocean models need to be validated by adequate observations, both from satellites and ground based. In particular, models developed in the Arctic, where the observational network allows more reasonable validation, can be applied to the Antarctic in suitably modified form so as to account for unique features of the Antarctic cryosphere. Benefits in climatic modelling will be gained by treating Antarctic sea ice as a fully coupled component of global climate.

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Martínez, Brezo, Rosa M. Viejo, Francisco Carreño, and Silvia C. Aranda. "Habitat distribution models for intertidal seaweeds: responses to climatic and non-climatic drivers." Journal of Biogeography 39, no. 10 (June 22, 2012): 1877–90. http://dx.doi.org/10.1111/j.1365-2699.2012.02741.x.

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Seiller, G., and F. Anctil. "Climate change impacts on the hydrologic regime of a Canadian river: comparing uncertainties arising from climate natural variability and lumped hydrological model structures." Hydrology and Earth System Sciences 18, no. 6 (June 3, 2014): 2033–47. http://dx.doi.org/10.5194/hess-18-2033-2014.

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Abstract. Diagnosing the impacts of climate change on water resources is a difficult task pertaining to the uncertainties arising from the different modelling steps. Lumped hydrological model structures contribute to this uncertainty as well as the natural climate variability, illustrated by several members from the same Global Circulation Model. In this paper, the hydroclimatic modelling chain consists of twenty-four potential evapotranspiration formulations, twenty lumped conceptual hydrological models, and seven snowmelt modules. These structures are applied on a natural Canadian sub-catchment to address related uncertainties and compare them to the natural internal variability of simulated climate system as depicted by five climatic members. Uncertainty in simulated streamflow under current and projected climates is assessed. They rely on interannual hydrographs and hydrological indicators analysis. Results show that natural climate variability is the major source of uncertainty, followed by potential evapotranspiration formulations and hydrological models. The selected snowmelt modules, however, do not contribute much to the uncertainty. The analysis also illustrates that the streamflow simulation over the current climate period is already conditioned by the tools' selection. This uncertainty is propagated to reference simulations and future projections, amplified by climatic members. These findings demonstrate the importance of opting for several climatic members to encompass the important uncertainty related to the climate natural variability, but also of selecting multiple modelling tools to provide a trustworthy diagnosis of the impacts of climate change on water resources.

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Koutsoyiannis, D., A. Efstratiadis, and K. P. Georgakakos. "Uncertainty Assessment of Future Hydroclimatic Predictions: A Comparison of Probabilistic and Scenario-Based Approaches." Journal of Hydrometeorology 8, no. 3 (June 1, 2007): 261–81. http://dx.doi.org/10.1175/jhm576.1.

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Abstract During the last decade, numerous studies have been carried out to predict future climate based on climatic models run on the global scale and fed by plausible scenarios about anthropogenic forcing to climate. Based on climatic model output, hydrologic models attempt then to predict future hydrologic regimes at regional scales. Much less systematic work has been done to estimate climatic uncertainty and to assess the climatic and hydrologic model outputs within an uncertainty perspective. In this study, a stochastic framework for future climatic uncertainty is proposed, based on the following lines: 1) climate is not constant but rather varying in time and expressed by the long-term (e.g., 30 yr) time average of a natural process, defined on a finescale; 2) the evolution of climate is represented as a stochastic process; 3) the distributional parameters of a process, marginal and dependence, are estimated from an available sample by statistical methods; 4) the climatic uncertainty is the result of at least two factors, the climatic variability and the uncertainty of parameter estimation; 5) a climatic process exhibits a scaling behavior, also known as long-range dependence or the Hurst phenomenon; and 6) because of this dependence, the uncertainty limits of the future are affected by the available observations of the past. The last two lines differ from classical statistical considerations and produce uncertainty limits that eventually are much wider than those of classical statistics. A combination of analytical and Monte Carlo methods is developed to determine uncertainty limits for the nontrivial scaling case. The framework developed is applied with temperature, rainfall, and runoff data from a catchment in Greece, for which data exist for about a century. The uncertainty limits are then superimposed onto deterministic projections up to 2050, obtained for several scenarios and climatic models combined with a hydrologic model. These projections indicate a significant increase of temperature in the future, beyond uncertainty bands, and no significant change of rainfall and runoff as they lie well within uncertainty limits.

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Pan, Yude, and Dudley J. Raynal. "Predicting growth of plantation conifers in the Adirondack Mountains in response to climate change." Canadian Journal of Forest Research 25, no. 1 (January 1, 1995): 48–56. http://dx.doi.org/10.1139/x95-006.

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Three conifer species grown in plantations in the southeastern Adirondack Mountains of New York were chosen to model tree growth. Annual growth of trees was decomposed into several components that reflect various intrinsic or extrinsic factors. Growth signals indicative of climatic effects were used to construct growth-climate models using both multivariate regression analysis and Kalman filter methods. Two growth models were used to simulate tree growth response to future climate change projected by GCMs. The consistent results of both models indicate that different conifer species have individualistic growth responses to future climatic change. The response behaviors of trees are affected greatly by local stand conditions and species tolerance to drought.

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Tramblay, Y., L. Neppel, and J. Carreau. "Brief communication "Climatic covariates for the frequency analysis of heavy rainfall in the Mediterranean region"." Natural Hazards and Earth System Sciences 11, no. 9 (September 15, 2011): 2463–68. http://dx.doi.org/10.5194/nhess-11-2463-2011.

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Abstract. In Mediterranean regions, climate studies indicate for the future a possible increase in the extreme rainfall events occurrence and intensity. To evaluate the future changes in the extreme event distribution, there is a need to provide non-stationary models taking into account the non-stationarity of climate. In this study, several climatic covariates are tested in a non-stationary peaks-over-threshold modeling approach for heavy rainfall events in Southern France. Results indicate that the introduction of climatic covariates could improve the statistical modeling of extreme events. In the case study, the frequency of southern synoptic circulation patterns is found to improve the occurrence process of extreme events modeled via a Poisson distribution, whereas for the magnitude of the events, the air temperature and sea level pressure appear as valid covariates for the Generalized Pareto distribution scale parameter. Covariates describing the humidity fluxes at monthly and seasonal time scales also provide significant model improvements for the occurrence and the magnitude of heavy rainfall events. With such models including climatic covariates, it becomes possible to asses the risk of extreme events given certain climatic conditions at monthly or seasonal timescales. The future changes in the heavy rainfall distribution can also be evaluated using covariates computed by climate models.

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Moraes-Oliveira, Adriana Ferreira de, Lucas Eduardo de Oliveira Aparecido, and Sérgio Rangel Fernandes Figueira. "Economic and climatic models for estimating coffee supply." Pesquisa Agropecuária Brasileira 52, no. 12 (December 2017): 1158–66. http://dx.doi.org/10.1590/s0100-204x2017001200004.

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Abstract: The objective of this work was to estimate the coffee supply by calibrating statistical models with economic and climatic variables for the main producing regions of the state of São Paulo, Brazil. The regions were Batatais, Caconde, Cássia dos Coqueiros, Cristais Paulista, Espírito Santo do Pinhal, Marília, Mococa, and Osvaldo Cruz. Data on coffee supply, economic variables (rural credit, rural agricultural credit, and production value), and climatic variables (air temperature, rainfall, potential evapotranspiration, water deficit, and water surplus) for each region, during the period from 2000-2014, were used. The models were calibrated using multiple linear regression, and all possible combinations were tested for selecting the variables. Coffee supply was the dependent variable, and the other ones were considered independent. The accuracy and precision of the models were assessed by the mean absolute percentage error and the adjusted coefficient of determination, respectively. The variables that most affect coffee supply are production value and air temperature. Coffee supply can be estimated with multiple linear regressions using economic and climatic variables. The most accurate models are those calibrated to estimate coffee supply for the regions of Cássia dos Coqueiros and Osvaldo Cruz.

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Nogaj, M., S. Parey, and D. Dacunha-Castelle. "Non-stationary extreme models and a climatic application." Nonlinear Processes in Geophysics 14, no. 3 (June 25, 2007): 305–16. http://dx.doi.org/10.5194/npg-14-305-2007.

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Abstract. In this paper, we study extreme values of non-stationary climatic phenomena. In the usually considered stationary case, the modelling of extremes is only based on the behaviour of the tails of the distribution of the remainder of the data set. In the non-stationary case though, it seems reasonable to assume that the temporal dynamics of the entire data set and that of extremes are closely related and thus all the available information about this link should be used in statistical studies of these events. We try to study how centered and normalized data which are closer to stationary data than the observation allows easier statistical analysis and to understand if we are very far from a hypothesis stating that the extreme events of centered and normed data follow a stationary distribution. The location and scale parameters used for this transformation (the central field), as well as extreme parameters obtained for the transformed data enable us to retrieve the trends in extreme events of the initial data set. Through non-parametric statistical methods, we thus compare a model directly built on the extreme events and a model reconstructed from estimations of the trends of the location and scale parameters of the entire data set and stationary extremes obtained from the centered and normed data set. In case of a correct reconstruction, we can clearly state that variations of the characteristics of extremes are well explained by the central field. Through these analyses we bring arguments to choose constant shape parameters of extreme distributions. We show that for the frequency of the moments of high threshold excesses (or for the mean of annual extremes), the general dynamics explains a large part of the trends on frequency of extreme events. The conclusion is less obvious for the amplitudes of threshold exceedances (or the variance of annual extremes) – especially for cold temperatures, partly justified by the statistical tools used, which require further analyses on the variability definition.

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Karwowski, Maciej, and Izabela Lebuda. "Extending climato-economic theory: When, how, and why it explains differences in nations' creativity." Behavioral and Brain Sciences 36, no. 5 (August 29, 2013): 493–94. http://dx.doi.org/10.1017/s0140525x13000150.

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AbstractThe climato-economic theory postulates mechanisms of threat and challenge to explain differences between countries. Interestingly, both of these mechanisms are often considered to be components of the models of organizational climate for creativity. We show that among rich countries, climatic demands are related to creative achievement in a reversed-U manner, whereas the relationship is linear among poor countries.

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Oh, Hyuntaik, and Ho-Jeong Shin. "Climatic classification over asia during the middle holocene climatic optimum based on PMIP models." Journal of Earth Science 27, no. 1 (February 2016): 123–29. http://dx.doi.org/10.1007/s12583-016-0622-7.

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BUSTOS, M. L., F. FERRELLI, and M. C. PICCOLO. "Comparative Study of Three Climatic Models Applied to Argentina." Anuário do Instituto de Geociências - UFRJ 40, no. 1 (November 30, 2017): 34–43. http://dx.doi.org/10.11137/2017_01_34_43.

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BUSTOS, M. L., F. FERRELLI, and M. C. PICCOLO. "Comparative Study of Three Climatic Models Applied to Argentina." Anuário do Instituto de Geociências - UFRJ 40, no. 1 (November 30, 2017): 34–43. http://dx.doi.org/10.11137/2017_1_34_43.

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Lester, Rebecca E., Ian T. Webster, Peter G. Fairweather, and William J. Young. "Linking water-resource models to ecosystem-response models to guide water-resource planning - an example from the Murray - Darling Basin, Australia." Marine and Freshwater Research 62, no. 3 (2011): 279. http://dx.doi.org/10.1071/mf09298.

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Objectively assessing ecological benefits of competing watering strategies is difficult. We present a framework of coupled models to compare scenarios, using the Coorong, the estuary for the Murray–Darling River system in South Australia, as a case study. The framework links outputs from recent modelling of the effects of climate change on water availability across the Murray–Darling Basin to a hydrodynamic model for the Coorong, and then an ecosystem-response model. The approach has significant advantages, including the following: (1) evaluating management actions is straightforward because of relatively tight coupling between impacts on hydrology and ecology; (2) scenarios of 111 years reveal the impacts of realistic climatic and flow variability on Coorong ecology; and (3) ecological impact is represented in the model by a series of ecosystem states, integrating across many organisms, not just iconic species. We applied the approach to four flow scenarios, comparing conditions without development, current water-use levels, and two predicted future climate scenarios. Simulation produced a range of hydrodynamic conditions and consequent distributions of ecosystem states, allowing managers to compare scenarios. This approach could be used with many climates and/or management actions for optimisation of flow delivery to environmental assets.

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Reu, B., S. Zaehle, R. Proulx, K. Bohn, A. Kleidon, R. Pavlick, and S. Schmidtlein. "The role of plant functional trade-offs for biodiversity changes and biome shifts under scenarios of global climatic change." Biogeosciences 8, no. 5 (May 24, 2011): 1255–66. http://dx.doi.org/10.5194/bg-8-1255-2011.

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Abstract. The global geographic distribution of biodiversity and biomes is determined by species-specific physiological tolerances to climatic constraints. Current vegetation models employ empirical bioclimatic relationships to predict present-day vegetation patterns and to forecast biodiversity changes and biome shifts under climatic change. In this paper, we consider trade-offs in plant functioning and their responses under climatic changes to forecast and explain changes in plant functional richness and shifts in biome geographic distributions. The Jena Diversity model (JeDi) simulates plant survival according to essential plant functional trade-offs, including ecophysiological processes such as water uptake, photosynthesis, allocation, reproduction and phenology. We use JeDi to quantify changes in plant functional richness and biome shifts between present-day and a range of possible future climates from two SRES emission scenarios (A2 and B1) and seven global climate models using metrics of plant functional richness and functional identity. Our results show (i) a significant loss of plant functional richness in the tropics, (ii) an increase in plant functional richness at mid and high latitudes, and (iii) a pole-ward shift of biomes. While these results are consistent with the findings of empirical approaches, we are able to explain them in terms of the plant functional trade-offs involved in the allocation, metabolic and reproduction strategies of plants. We conclude that general aspects of plant physiological tolerances can be derived from functional trade-offs, which may provide a useful process- and trait-based alternative to bioclimatic relationships. Such a mechanistic approach may be particularly relevant when addressing vegetation responses to climatic changes that encounter novel combinations of climate parameters that do not exist under contemporary climate.

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Hänninen, Heikki, Pasi Kolari, and Pertti Hari. "Seasonal development of Scots pine under climatic warming: effects on photosynthetic production." Canadian Journal of Forest Research 35, no. 9 (September 1, 2005): 2092–99. http://dx.doi.org/10.1139/x05-105.

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In boreal conditions under climatic warming, photosynthesis will start earlier in the spring than it does in the present climate. As a first approximation this phenomenon would increase the annual photosynthetic production of boreal conifers, as they can use the high amounts of incoming solar radiation prevailing during spring to a greater extent than in the present climate. However, the recovery of photosynthesis is accompanied by a simultaneous dehardening of the needles. Thus, climatic warming may also cause a premature dehardening of the needles during spring. This may result in needle losses caused by frost damage; so climatic warming may also decrease the annual photosynthetic production of boreal conifers. Using computer simulations with ecophysiological models, these counteracting effects of climatic warming on photosynthetic production were studied in Scots pine (Pinus sylvestris L.) trees growing in southern Finland. The results show that because of our insufficient understanding of the environmental regulation of frost hardiness, it is not possible to conclude which one of the two potential effects will dominate under climatic warming. This finding calls for further empirical development and testing of the frost hardiness models.

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MONIRUZZAMAN, SHAIKH. "CROP DIVERSIFICATION AS CLIMATE CHANGE ADAPTATION: HOW DO BANGLADESHI FARMERS PERFORM?" Climate Change Economics 10, no. 02 (May 2019): 1950007. http://dx.doi.org/10.1142/s2010007819500076.

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This research is based on the theoretical framework of risk in rural agricultural economy where farmers consider climate change as idiosyncratic risk of production. Under the inter-temporal household consumption smoothing model, this paper considers crop diversification as an ex ante measure to tackle permanent income shock from climate change. This paper examines empirically whether crop diversity is affected by climate change and how this diversity will respond to different climate scenarios. Negative binomial regression models are estimated from a nationally representative sample of 11,389 farmers across Bangladesh and 30-year average of seasonal climatic variables to find the effects of climatic variables on crop diversity. This paper finds that crop diversity is climate-sensitive and this diversity in different locations varies with climatic conditions. This research unveils structural instability between different single cross-sectional models to simulate the effects of climatic variables on crop diversity. It also finds that increases in mean annual temperature by [Formula: see text]C by 2030 and [Formula: see text]C by 2100 have resulted in 26.40% and 149.83% increases in crop diversity compared to its baseline of 2010, respectively. The effects of rainfall scenarios on crop diversity are much lower compared to the effects of temperature.

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Ghanbari Motlagh, Mohadeseh, Sasan Babaie Kafaky, Asadollah Mataji, and Reza Akhavan. "Calculation of the aboveground carbon stocks with satellite data and statistical models integrated into the climatic parameters in the Alborz Mountain forests (northern Iran)." Journal of Forest Science 65, No. 12 (December 19, 2019): 493–503. http://dx.doi.org/10.17221/107/2019-jfs.

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The forest ecosystems of northern Iran in the Alborz Mountains with a wide distribution range have variations in the composition and types of the plants, soil, structure, carbon stocks and climatic conditions. This study investigated the use of a satellite database and climatic parameters in estimating the carbon reserves. Three regions were selected for the distribution range of these forests. The data of 4 climatic parameters (MAP, MHR, MAE and MAT) were modelled based on the relationship with an elevation gradient. 5 spectral vegetation indices (RVI, NDVI, SR, NDGI, DVI and TVI) and near-infrared band (NIR) extracted from the satellite data and the aboveground carbon data of these forests were modelled based on a regression analysis. Finally, the best model of the relationship between the climate variables and the carbon stocks and the satellite indices was obtained from the multivariate linear regression equation and the R2 coefficient. Accordingly, the most influential climatic parameters on the carbon stocks of these forests were precipitation, temperature, and also the most significant indices were NDVI, RVI and NIR band. This research is an attempt to model the calculations of the aboveground carbon in the forests of northern Iran in relation to the climatic parameters using satellite imagery.

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Hamann, A., and T. L. Wang. "Models of climatic normals for genecology and climate change studies in British Columbia." Agricultural and Forest Meteorology 128, no. 3-4 (February 2005): 211–21. http://dx.doi.org/10.1016/j.agrformet.2004.10.004.

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Leauthaud, C., J. Demarty, B. Cappelaere, M. Grippa, L. Kergoat, C. Velluet, F. Guichard, E. Mougin, S. Chelbi, and B. Sultan. "Revisiting historical climatic signals to better explore the future: prospects of water cycle changes in Central Sahel." Proceedings of the International Association of Hydrological Sciences 371 (June 12, 2015): 195–201. http://dx.doi.org/10.5194/piahs-371-195-2015.

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Abstract. Rainfall and climatic conditions are the main drivers of natural and cultivated vegetation productivity in the semiarid region of Central Sahel. In a context of decreasing cultivable area per capita, understanding and predicting changes in the water cycle are crucial. Yet, it remains challenging to project future climatic conditions in West Africa since there is no consensus on the sign of future precipitation changes in simulations coming from climate models. The Sahel region has experienced severe climatic changes in the past 60 years that can provide a first basis to understand the response of the water cycle to non-stationary conditions in this part of the world. The objective of this study was to better understand the response of the water cycle to highly variable climatic regimes in Central Sahel using historical climate records and the coupling of a land surface energy and water model with a vegetation model that, when combined, simulated the Sahelian water, energy and vegetation cycles. To do so, we relied on a reconstructed long-term climate series in Niamey, Republic of Niger, in which three precipitation regimes can be distinguished with a relative deficit exceeding 25% for the driest period compared to the wettest period. Two temperature scenarios (+2 and +4 °C) consistent with future warming scenarios were superimposed to this climatic signal to generate six virtual future 20-year climate time series. Simulations by the two coupled models forced by these virtual scenarios showed a strong response of the water budget and its components to temperature and precipitation changes, including decreases in transpiration, runoff and drainage for all scenarios but those with highest precipitation. Such climatic changes also strongly impacted soil temperature and moisture. This study illustrates the potential of using the strong climatic variations recorded in the past decades to better understand potential future climate variations.

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Hof, Christian, Martin Brändle, D. Matthias Dehling, Mariana Munguía, Roland Brandl, Miguel B. Araújo, and Carsten Rahbek. "Habitat stability affects dispersal and the ability to track climate change." Biology Letters 8, no. 4 (February 29, 2012): 639–43. http://dx.doi.org/10.1098/rsbl.2012.0023.

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Habitat persistence should influence dispersal ability, selecting for stronger dispersal in habitats of lower temporal stability. As standing (lentic) freshwater habitats are on average less persistent over time than running (lotic) habitats, lentic species should show higher dispersal abilities than lotic species. Assuming that climate is an important determinant of species distributions, we hypothesize that lentic species should have distributions that are closer to equilibrium with current climate, and should more rapidly track climatic changes. We tested these hypotheses using datasets from 1988 and 2006 containing all European dragon- and damselfly species. Bioclimatic envelope models showed that lentic species were closer to climatic equilibrium than lotic species. Furthermore, the models over-predicted lotic species ranges more strongly than lentic species ranges, indicating that lentic species track climatic changes more rapidly than lotic species. These results are consistent with the proposed hypothesis that habitat persistence affects the evolution of dispersal.

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Adwan, Ibrahim, Abdalrhman Milad, Zubair Ahmed Memon, Iswandaru Widyatmoko, Nuryazmin Ahmat Zanuri, Naeem Aziz Memon, and Nur Izzi Md Yusoff. "Asphalt Pavement Temperature Prediction Models: A Review." Applied Sciences 11, no. 9 (April 22, 2021): 3794. http://dx.doi.org/10.3390/app11093794.

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The performance of bituminous materials is mainly affected by the prevailing maximum and minimum temperatures, and their mechanical properties can vary significantly with the magnitude of the temperature changes. The given effect can be observed from changes occurring in the bitumen or asphalt mixture stiffness and the materials’ serviceable life. Furthermore, when asphalt pavement layer are used, the temperature changes can be credited to climatic factors such as air temperature, solar radiation and wind. Thus in relevance to the discussed issue, the contents of this paper displays a comprehensive review of the collected existing 38 prediction models and broadly classifies them into their corresponding numerical, analytical and statistical models. These models further present different formulas based on the climate, environment, and methods of data collection and analyses. Corresponding to which, most models provide reasonable predictions for both minimum and maximum pavement temperatures. Some models can even predict the temperature of asphalt pavement layers on an hourly or daily basis using the provided statistical method. The analytical models can provide straight-forward solutions, but assumptions on boundary conditions should be simplified. Critical climatic and pavement factors influencing the accuracy of predicting temperature were examined. This paper recommends future studies involving coupled heat transfer model for the pavement and the environment, particularly consider to be made on the impact of surface water and temperature of pavements in urban areas.

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Slater, Andrew G., and David M. Lawrence. "Diagnosing Present and Future Permafrost from Climate Models." Journal of Climate 26, no. 15 (July 26, 2013): 5608–23. http://dx.doi.org/10.1175/jcli-d-12-00341.1.

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Abstract Permafrost is a characteristic aspect of the terrestrial Arctic and the fate of near-surface permafrost over the next century is likely to exert strong controls on Arctic hydrology and biogeochemistry. Using output from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), the authors assess its ability to simulate present-day and future permafrost. Permafrost extent diagnosed directly from each climate model's soil temperature is a function of the modeled surface climate as well as the ability of the land surface model to represent permafrost physics. For each CMIP5 model these two effects are separated by using indirect estimators of permafrost driven by climatic indices and compared to permafrost extent directly diagnosed via soil temperatures. Several robust conclusions can be drawn from this analysis. Significant air temperature and snow depth biases exist in some model's climates, which degrade both directly and indirectly diagnosed permafrost conditions. The range of directly calculated present-day (1986–2005) permafrost area is extremely large (~4–25 × 106 km2). Several land models contain structural weaknesses that limit their skill in simulating cold region subsurface processes. The sensitivity of future permafrost extent to temperature change over the present-day observed permafrost region averages (1.67 ± 0.7) × 106 km2 °C−1 but is a function of the spatial and temporal distribution of climate change. Because of sizable differences in future climates for the representative concentration pathway (RCP) emission scenarios, a wide variety of future permafrost states is predicted by 2100. Conservatively, the models suggest that for RCP4.5, permafrost will retreat from the present-day discontinuous zone. Under RCP8.5, sustainable permafrost will be most probable only in the Canadian Archipelago, Russian Arctic coast, and east Siberian uplands.

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Heikkinen, Risto K., Miska Luoto, Miguel B. Araújo, Raimo Virkkala, Wilfried Thuiller, and Martin T. Sykes. "Methods and uncertainties in bioclimatic envelope modelling under climate change." Progress in Physical Geography: Earth and Environment 30, no. 6 (December 2006): 751–77. http://dx.doi.org/10.1177/0309133306071957.

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Potential impacts of projected climate change on biodiversity are often assessed using single-species bioclimatic ‘envelope’models. Such models are a special case of species distribution models in which the current geographical distribution of species is related to climatic variables so to enable projections of distributions under future climate change scenarios. This work reviews a number of critical methodological issues that may lead to uncertainty in predictions from bioclimatic modelling. Particular attention is paid to recent developments of bioclimatic modelling that address some of these issues as well as to the topics where more progress needs to be made. Developing and applying bioclimatic models in a informative way requires good understanding of a wide range of methodologies, including the choice of modelling technique, model validation, collinearity, autocorrelation, biased sampling of explanatory variables, scaling and impacts of non-climatic factors. A key challenge for future research is integrating factors such as land cover, direct CO2 effects, biotic interactions and dispersal mechanisms into species-climate models. We conclude that, although bioclimatic envelope models have a number of important advantages, they need to be applied only when users of models have a thorough understanding of their limitations and uncertainties.

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Souza, Fábio Henrique Morais de, Marcelo Ribeiro Viola, Junior Cesar Avanzi, Marcos Giongo, and Marcelo Vieira Filho. "THORNTHWAITE’S CLIMATE REGIONALIZATION FOR THE STATE OF TOCANTINS, BRAZIL." FLORESTA 49, no. 4 (September 19, 2019): 783. http://dx.doi.org/10.5380/rf.v49i4.59188.

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Tocantins State faces a large-scale agricultural expansion. Thus, climate studies are essential for a better understanding of climate variability supporting agricultural and environmental planning. In this context, this study applies the climatic classification of Thornthwaite and develops a climate regionalization through geostatistical techniques, assessing the performance of the interpolators ordinary kriging (OK) and cokriging (CK). Data from 26 weather stations located in Tocantins State and surroundings were used. The variables of interest to climate regionalization, obtained by the climatic water balance, were mapped by geostatistical techniques. The results of cross-validation showed that ordinary kriging and cokriging performed well. The spherical and exponential semivariogram models obtained the best fit in 40% of the analyzes each, and the gaussian in 20%. The climatic classification of Thornthwaite applied to Tocantins State showed the presence of humid (B1), moist subhumid (C2), and dry subhumid (C1) climates. There were found three climatic regions: B1A’wa’: Humid, megathermal, with moderate winter water deficiency, and a temperature efficiency regime normal to megathermal , occurring in the western region of the state; C2A’wa’: Moist subhumid, megathermal, with moderate winter water deficiency, and a temperature efficiency regime normal to megathermal , occurring in the central region and extending from the north to the south of the state; and C1A’w2a’: Dry subhumid, megathermal, with large summer water surplus, and a temperature efficiency regime normal to megathermal , in the east and northeast of the state.

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Kiran, Ravi. "Changing Climatic Scenarios: Role of Crop Growth Simulation Models." Indian Journal of Biology 3, no. 1 (2016): 81–84. http://dx.doi.org/10.21088/ijb.2394.1391.3116.12.

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Wainaina, Patrick M., George O. Owino, and Musa R. Njue. "Solar Radiation Prediction Models Analysis for Varying Climatic Conditions." International Journal of Engineering and Technology 9, no. 3 (June 30, 2017): 2571–80. http://dx.doi.org/10.21817/ijet/2017/v9i3/1709030222.

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Abdel Kader, M., H. Khalifa, A. Sheta, and A. Ibrahim. "EVAPOTRANSPIRATION ESTIMATION USING REMOTE SENSING DATAAND SOME CLIMATIC MODELS." Journal of Soil Sciences and Agricultural Engineering 6, no. 11 (November 1, 2015): 1341–54. http://dx.doi.org/10.21608/jssae.2015.43926.

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Purnell, P. "Interpretation of climatic temperature variations for accelerated ageing models." Journal of Materials Science 39, no. 1 (January 2004): 113–18. http://dx.doi.org/10.1023/b:jmsc.0000007734.71945.93.

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Riipinen, I., J. R. Pierce, T. Yli-Juuti, T. Nieminen, S. Häkkinen, M. Ehn, H. Junninen, et al. "Organic condensation – a vital link connecting aerosol formation to climate forcing." Atmospheric Chemistry and Physics Discussions 11, no. 1 (January 6, 2011): 387–423. http://dx.doi.org/10.5194/acpd-11-387-2011.

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Abstract. Atmospheric aerosol particles influence global climate as well as impair air quality through their effects on atmospheric visibility and human health. Ultrafine (<100 nm) particles often dominate aerosol numbers, and nucleation of atmospheric vapors is an important source of these particles. To have climatic relevance, however, the freshly-nucleated particles need to grow in size. We combine observations from two continental sites (Egbert, Canada and Hyytiälä, Finland) to show that condensation of organic vapors is a crucial factor governing the lifetimes and climatic importance of the smallest atmospheric particles. We demonstrate that state-of-the-science organic gas-particle partitioning models fail to reproduce the observations, and propose a modeling approach that is consistent with the measurements. We demonstrate the large sensitivity of climatic forcing of atmospheric aerosols to these interactions between organic vapors and the smallest atmospheric nanoparticles – highlighting the need for representing this process in global climate models.

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McKenzie, Donald, David W. Peterson, and David L. Peterson. "Modelling conifer species distributions in mountain forests of Washington State, USA." Forestry Chronicle 79, no. 2 (April 1, 2003): 253–58. http://dx.doi.org/10.5558/tfc79253-2.

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Understanding the effects of climatic change on mountain forests, where snowpacks and short growing seasons limit tree establishment and growth, is a key concern for both ecologists and foresters. We quantified associations between climatic and biophysical variables and individual conifer species distributions in mountain forests with generalized linear models. For the majority of species, a unimodal response to moisture and temperature gradients was evident, suggesting that an environmental niche can be identified. Species known to respond to limiting factors in the abiotic environment showed the strongest associations with predictor variables. The models can improve forecasts of the potential redistribution of species on the landscape in response to climatic change, but disturbance, migration rates, and limits on regeneration are important sources of uncertainty. Nevertheless, by identifying climate-based niches of different species, we can identify effective strategies for reforestation and alert managers to particularly sensitive or vulnerable ecosystems and landscapes. Key words: conifer species, mountain forests, generalized linear models, unimodal response, limiting factors

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Boulton, G. S., N. Hulton, and M. Vautravers. "Ice-sheet models as tools for palaeoclimatic analysis: the example of the European ice sheet through the last glacial cycle." Annals of Glaciology 21 (1995): 103–10. http://dx.doi.org/10.1017/s0260305500015676.

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A numerical model is used to simulate ice-sheet behaviour in Europe through the last glacial cycle. It is used in two modes: a forward mode, in which the model is driven by a proxy palaeoclimate record and the output compared with a geological reconstruction of ice-sheet fluctuation; and an inverse mode, in which we determine the climate function that would be required to simulate geologically reconstructed ice-sheet fluctuations. From these simulations it is concluded that extra-glacial climates may be poor predictors of ice-sheet surface climates, and that climatic transitions during the glacial period may have been much more rapid and the intensity of warming during the early Holocene much greater than hitherto supposed. Stronger climate forcing is required to drive ice-sheet expansion when sliding occurs at the bed compared with a non-sliding bed. Sliding ice sheets grow more slowly and decay more rapidly than non-sliding ice sheets with the same climate forcing.

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Boulton, G. S., N. Hulton, and M. Vautravers. "Ice-sheet models as tools for palaeoclimatic analysis: the example of the European ice sheet through the last glacial cycle." Annals of Glaciology 21 (1995): 103–10. http://dx.doi.org/10.3189/s0260305500015676.

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A numerical model is used to simulate ice-sheet behaviour in Europe through the last glacial cycle. It is used in two modes: a forward mode, in which the model is driven by a proxy palaeoclimate record and the output compared with a geological reconstruction of ice-sheet fluctuation; and an inverse mode, in which we determine the climate function that would be required to simulate geologically reconstructed ice-sheet fluctuations.From these simulations it is concluded that extra-glacial climates may be poor predictors of ice-sheet surface climates, and that climatic transitions during the glacial period may have been much more rapid and the intensity of warming during the early Holocene much greater than hitherto supposed. Stronger climate forcing is required to drive ice-sheet expansion when sliding occurs at the bed compared with a non-sliding bed. Sliding ice sheets grow more slowly and decay more rapidly than non-sliding ice sheets with the same climate forcing.

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46

Lin, Huilong, Xuelu Wang, Yingjun Zhang, Tiangang Liang, Qisheng Feng, and Jizhou Ren. "Spatio-temporal dynamics on the distribution, extent, and net primary productivity of potential grassland in response to climate changes in China." Rangeland Journal 35, no. 4 (2013): 409. http://dx.doi.org/10.1071/rj12024.

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Net primary productivity (NPP) of grassland is one of the key components in measuring the carrying capacity of livestock. Not only are grassland researchers concerned with the performance of NPP simulation models under current climate conditions, they also need to understand the behaviour of NPP–climate models under projected climatic changes. One of the goals of this study was to evaluate the three NPP–climate models: the Miami Model, the Schuur Model, and the Classification Indices-based Model. Results indicated that the Classification Indices-based Model was the most effective model at estimating large-scale grassland NPP. Both the Integrated Orderly Classification System of Grassland and the Classification Indices-based Model were then applied to analyse the succession of grassland biomes and to measure the change in total NPP (TNPP) of grassland biomes from the recent past (1950–2000) to a future scenario (2001–2050) in a geographic information system environment. Results of the simulations indicate that, under recent-past climatic conditions, the major biomes of China’s grassland are the tundra and alpine steppe, and steppe, and these would be converted into steppe and semi-desert grassland in the future scenario; the potential grassland TNPP in China was projected to be 0.72 PgC under recent-past climatic conditions, and would be 0.83 Pg C under the future climatic scenario. The ‘safe’ carrying capacity of livestock that best integrates a wide range of factors, such as grassland classes, climatic variability, and animal nutrition, is discussed as unresolved. Further research and development is needed to identify the regional trends for the ‘safe’ carrying capacity of livestock to maintain sustainable resource condition and reduce the risk of resource degradation. This important task remains a challenge for all grassland scientists and practitioners.

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Broennimann, Olivier, and Antoine Guisan. "Predicting current and future biological invasions: both native and invaded ranges matter." Biology Letters 4, no. 5 (July 29, 2008): 585–89. http://dx.doi.org/10.1098/rsbl.2008.0254.

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The classical approach to predicting the geographical extent of species invasions consists of training models in the native range and projecting them in distinct, potentially invasible areas. However, recent studies have demonstrated that this approach could be hampered by a change of the realized climatic niche, allowing invasive species to spread into habitats in the invaded ranges that are climatically distinct from those occupied in the native range. We propose an alternative approach that involves fitting models with pooled data from all ranges. We show that this pooled approach improves prediction of the extent of invasion of spotted knapweed ( Centaurea maculosa ) in North America on models based solely on the European native range. Furthermore, it performs equally well on models based on the invaded range, while ensuring the inclusion of areas with similar climate to the European niche, where the species is likely to spread further. We then compare projections from these models for 2080 under a severe climate warming scenario. Projections from the pooled models show fewer areas of intermediate climatic suitability than projections from the native or invaded range models, suggesting a better consensus among modelling techniques and reduced uncertainty.

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Bradshaw, Richard HW, Björn H. Holmqvist, Sharon A. Cowling, and Martin T. Sykes. "The effects of climate change on the distribution and management of Picea abies in southern Scandinavia." Canadian Journal of Forest Research 30, no. 12 (December 1, 2000): 1992–98. http://dx.doi.org/10.1139/x00-130.

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The distributions of Picea abies (L.) Karst. and other European forest trees have continuously changed since the last glaciation. Static and dynamic bioclimatic simulation models are used to estimate the degree of climatic control operating on the southern Scandinavian range limits of Picea during the last 1000 years. The results show that the range limit has begun to track climate change more closely than in the past, and a future projection predicts a rapid northward contraction of the present limit. Contracting ranges track climate change more closely than do expanding ranges that are limited by seed dispersal. The physiological mechanism of the climatic control is unclear, but Picea planted beyond its current climatic range limit was seriously damaged during a recent storm. Planting trees beyond their natural climatic range limits can only be advised in areas that become suitable for colonization under a changed climate.

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Jahanbani, Heerbod, Lee Teang Shui, Alireza Massah Bavani, and Abdul Halim Ghazali. "Uncertainty of climate change and its impact on reference evapotranspiration in Rasht City, Iran." Journal of Water and Climate Change 2, no. 1 (March 1, 2011): 72–83. http://dx.doi.org/10.2166/wcc.2011.055.

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There are many factors of uncertainty regarding the impact of climate change on reference evapotranspiration (ETo). The accuracy of the results is strictly related to these factors and ignoring any one of them reduces the precision of the results, and reduces their applicability for decision makers. In this study, the uncertainty related to two ETo models, the Hargreaves-Samani (HGS) and Artificial Neural Network (ANN), and two Atmosphere-Ocean General Circulation Models (AOGCMs), Hadley Centre Coupled Model, version 3 (HadCM3) climatic model and the Canadian Global Climate Model, version 3 (CGCM3) climatic model under climate change, was evaluated. The models predicted average temperature increases by 2010 to 2039 of 0.95 °C by the HadCM3 model and 1.13°C by the CGCM3 model under the A2 scenario relative to observed temperature. Accordingly, the models predicted average ETo would increase of 0.48, 0.60, 0.50 and 0.60 (mm/day) by 2010 to 2039 projected by four methods (by introducing the temperature of the HadCM3-A2 model and the CGCM3-A2 to ANN and HGS) relative to ETo of the observed period. The results showed that uncertainty of the AOGCMs is more than that of the ETo models applied in this study.

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Badr, Hamada S., Amin K. Dezfuli, Benjamin F. Zaitchik, and Christa D. Peters-Lidard. "Regionalizing Africa: Patterns of Precipitation Variability in Observations and Global Climate Models." Journal of Climate 29, no. 24 (November 29, 2016): 9027–43. http://dx.doi.org/10.1175/jcli-d-16-0182.1.

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Abstract Many studies have documented dramatic climatic and environmental changes that have affected Africa over different time scales. These studies often raise questions regarding the spatial extent and regional connectivity of changes inferred from observations and proxies and/or derived from climate models. Objective regionalization offers a tool for addressing these questions. To demonstrate this potential, applications of hierarchical climate regionalizations of Africa using observations and GCM historical simulations and future projections are presented. First, Africa is regionalized based on interannual precipitation variability using Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data for the period 1981–2014. A number of data processing techniques and clustering algorithms are tested to ensure a robust definition of climate regions. These regionalization results highlight the seasonal and even month-to-month specificity of regional climate associations across the continent, emphasizing the need to consider time of year as well as research question when defining a coherent region for climate analysis. CHIRPS regions are then compared to those of five GCMs for the historic period, with a focus on boreal summer. Results show that some GCMs capture the climatic coherence of the Sahel and associated teleconnections in a manner that is similar to observations, while other models break the Sahel into uncorrelated subregions or produce a Sahel-like region of variability that is spatially displaced from observations. Finally, shifts in climate regions under projected twenty-first-century climate change for different GCMs and emissions pathways are examined. A projected change is found in the coherence of the Sahel, in which the western and eastern Sahel become distinct regions with different teleconnections. This pattern is most pronounced in high-emissions scenarios.

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Journal articles on the topic 'Climatic models'

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