Relevant bibliographies by topics / Calibration of climate model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Calibration of climate model'

Author: Grafiati
Published: 25 May 2024

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Journal articles on the topic "Calibration of climate model"

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Kutschera, Ellynne, John B. Kim, G. Stephen Pitts, and Ray Drapek. "“What’s Past Is Prologue”: Vegetation Model Calibration with and without Future Climate." Land 12, no. 6 (May 24, 2023): 1121. http://dx.doi.org/10.3390/land12061121.

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Many models are designed to generate future predictions under climate-change scenarios. Such models are typically calibrated for a study area with climate data that represent historical conditions. However, future projections of the model may include outputs for which the model has not been calibrated. Ideally, a climate-change-impacts model would be calibrated for recent conditions and also for possible future climate conditions. We demonstrate an approach, where a vegetation model is subjected to two calibrations: conventionally to the study area and separately to the study area plus additional areas representing analogues of potential future climate. We apply the dynamic vegetation model MC2 to a mountainous ecosystem in the Pacific Northwest, USA. We compare the conventional model calibration with the extra-study-area calibration and future projections. The two calibrations produce different outputs in key ecosystem variables, where some differences vary with time. Some model output trends for net primary productivity and plant functional type are more influenced by climatic input, while for others, the calibration area has greater consequence. Excluding areas representing potential future climate may be an important omission in model calibration, making the inclusion of such areas a decisive consideration in climate-change-impact simulations.
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Lee, Jeonghoon, Jeonghyeon Choi, Jiyu Seo, Jeongeun Won, and Sangdan Kim. "Exploring Climate Sensitivity in Hydrological Model Calibration." Water 15, no. 23 (November 25, 2023): 4094. http://dx.doi.org/10.3390/w15234094.

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In the context of hydrological model calibration, observational data play a central role in refining and evaluating model performance and uncertainty. Among the critical factors, the length of the data records and the associated climatic conditions are paramount. While there is ample research on data record length selection, the same cannot be said for the selection of data types, particularly when it comes to choosing the climatic conditions for calibration. Conceptual hydrological models inherently simplify the representation of hydrological processes, which can lead to structural limitations, which is particularly evident under specific climatic conditions. In this study, we explore the impact of climatic conditions during the calibration period on model predictive performance and uncertainty. We categorize the inflow data from AnDong Dam and HapCheon Dam in southeastern South Korea from 2001 to 2021 into four climatic conditions (dry years, normal years, wet years, and mixed years) based on the Budyko dryness index. We then use data from periods within the same climatic category to calibrate the hydrological model. Subsequently, we analyze the model’s performance and posterior distribution under various climatic conditions during validation periods. Our findings underscore the substantial influence of the climatic conditions during the calibration period on model performance and uncertainty. We discover that when calibrating the hydrological model using data from periods with wet climatic conditions, achieving comparable predictive performance in validation periods with different climatic conditions remains challenging, even when the calibration period exhibits excellent model performance. Furthermore, when considering model parameters and predicted streamflow uncertainty, it is advantageous to calibrate the hydrological model under dry climatic conditions to achieve more robust results.
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Tett, Simon F. B., Jonathan M. Gregory, Nicolas Freychet, Coralia Cartis, Michael J. Mineter, and Lindon Roberts. "Does Model Calibration Reduce Uncertainty in Climate Projections?" Journal of Climate 35, no. 8 (April 15, 2022): 2585–602. http://dx.doi.org/10.1175/jcli-d-21-0434.1.

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Abstract Uncertainty in climate projections is large as shown by the likely uncertainty ranges in equilibrium climate sensitivity (ECS) of 2.5–4 K and in the transient climate response (TCR) of 1.4–2.2 K. Uncertainty in model projections could arise from the way in which unresolved processes are represented, the parameter values used, or the targets for model calibration. We show that, in two climate model ensembles that were objectively calibrated to minimize differences from observed large-scale atmospheric climatology, uncertainties in ECS and TCR are about 2–6 times smaller than in the CMIP5 or CMIP6 multimodel ensemble. We also find that projected uncertainties in surface temperature, precipitation, and annual extremes are relatively small. Residual uncertainty largely arises from unconstrained sea ice feedbacks. The more than 20-year-old HadAM3 standard model configuration simulates observed hemispheric-scale observations and preindustrial surface temperatures about as well as the median CMIP5 and CMIP6 ensembles while the optimized configurations simulate these better than almost all the CMIP5 and CMIP6 models. Hemispheric-scale observations and preindustrial temperatures are not systematically better simulated in CMIP6 than in CMIP5 although the CMIP6 ensemble seems to better simulate patterns of large-scale observations than the CMIP5 ensemble and the optimized HadAM3 configurations. Our results suggest that most CMIP models could be improved in their simulation of large-scale observations by systematic calibration. However, the uncertainty in climate projections (for a given scenario) likely largely arises from the choice of parameterization schemes for unresolved processes (“structural uncertainty”), with different tuning targets another possible contributor. Significance Statement Climate models represent unresolved phenomena controlled by uncertain parameters. Changes in these parameters impact how well a climate model simulates current climate and its climate projections. Multiple calibrations of a single climate model, using an objective method, to large-scale atmospheric observations are performed. These models produce very similar climate projections at both global and regional scales. An analysis that combines uncertainties in observations with simulated sensitivity to observations and climate response also has small uncertainty showing that, for this model, current observations constrain climate projections. Recently developed climate models have a broad range of abilities to simulate large-scale climate with only some improvement in their ability to simulate this despite a decade of model development.
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O'Reilly, Christopher H., Daniel J. Befort, and Antje Weisheimer. "Calibrating large-ensemble European climate projections using observational data." Earth System Dynamics 11, no. 4 (November 19, 2020): 1033–49. http://dx.doi.org/10.5194/esd-11-1033-2020.

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Abstract. This study examines methods of calibrating projections of future regional climate for the next 40–50 years using large single-model ensembles (the Community Earth System Model (CESM) Large Ensemble and Max Planck Institute (MPI) Grand Ensemble), applied over Europe. The three calibration methods tested here are more commonly used for initialised forecasts from weeks up to seasonal timescales. The calibration techniques are applied to ensemble climate projections, fitting seasonal ensemble data to observations over a reference period (1920–2016). The calibration methods were tested and verified using an “imperfect model” approach using the historical/representative concentration pathway 8.5 (RCP8.5) simulations from the Coupled Model Intercomparison Project 5 (CMIP5) archive. All the calibration methods exhibit a similar performance, generally improving the out-of-sample projections in comparison to the uncalibrated (bias-corrected) ensemble. The calibration methods give results that are largely indistinguishable from one another, so the simplest of these methods, namely homogeneous Gaussian regression (HGR), is used for the subsequent analysis. As an extension to the HGR calibration method it is applied to dynamically decomposed data, in which the underlying data are separated into dynamical and residual components (HGR-decomp). Based on the verification results obtained using the imperfect model approach, the HGR-decomp method is found to produce more reliable and accurate projections than the uncalibrated ensemble for future climate over Europe. The calibrated projections for temperature demonstrate a particular improvement, whereas the projections for changes in precipitation generally remain fairly unreliable. When the two large ensembles are calibrated using observational data, the climate projections for Europe are far more consistent between the two ensembles, with both projecting a reduction in warming but a general increase in the uncertainty of the projected changes.
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Beltran, Cesar, N. R. Edwards, A. B. Haurie, J. P. Vial, and D. S. Zachary. "Oracle-based optimization applied to climate model calibration." Environmental Modeling & Assessment 11, no. 1 (October 20, 2005): 31–43. http://dx.doi.org/10.1007/s10666-005-9024-4.

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Meinshausen, M., S. C. B. Raper, and T. M. L. Wigley. "Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: Model description and calibration." Atmospheric Chemistry and Physics 11, no. 4 (February 16, 2011): 1417–56. http://dx.doi.org/10.5194/acp-11-1417-2011.

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Abstract. Current scientific knowledge on the future response of the climate system to human-induced perturbations is comprehensively captured by various model intercomparison efforts. In the preparation of the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), intercomparisons were organized for atmosphere-ocean general circulation models (AOGCMs) and carbon cycle models, named "CMIP3" and "C4MIP", respectively. Despite their tremendous value for the scientific community and policy makers alike, there are some difficulties in interpreting the results. For example, radiative forcings were not standardized across the various AOGCM integrations and carbon cycle runs, and, in some models, key forcings were omitted. Furthermore, the AOGCM analysis of plausible emissions pathways was restricted to only three SRES scenarios. This study attempts to address these issues. We present an updated version of MAGICC, the simple carbon cycle-climate model used in past IPCC Assessment Reports with enhanced representation of time-varying climate sensitivities, carbon cycle feedbacks, aerosol forcings and ocean heat uptake characteristics. This new version, MAGICC6, is successfully calibrated against the higher complexity AOGCMs and carbon cycle models. Parameterizations of MAGICC6 are provided. The mean of the emulations presented here using MAGICC6 deviates from the mean AOGCM responses by only 2.2% on average for the SRES scenarios. This enhanced emulation skill in comparison to previous calibrations is primarily due to: making a "like-with-like comparison" using AOGCM-specific subsets of forcings; employing a new calibration procedure; as well as the fact that the updated simple climate model can now successfully emulate some of the climate-state dependent effective climate sensitivities of AOGCMs. The diagnosed effective climate sensitivity at the time of CO2 doubling for the AOGCMs is on average 2.88 °C, about 0.33 °C cooler than the mean of the reported slab ocean climate sensitivities. In the companion paper (Part 2) of this study, we examine the combined climate system and carbon cycle emulations for the complete range of IPCC SRES emissions scenarios and the new RCP pathways.
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Koch, Julian, Mehmet Cüneyd Demirel, and Simon Stisen. "Climate Normalized Spatial Patterns of Evapotranspiration Enhance the Calibration of a Hydrological Model." Remote Sensing 14, no. 2 (January 11, 2022): 315. http://dx.doi.org/10.3390/rs14020315.

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Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement has been supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often driven by underlying climate gradients, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated patterns. To address this, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as a modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. We apply the mesoscale Hydrological Model (mHM) to model the hydrological cycle of the Senegal River basin. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. As objective functions we applied the Kling-Gupta-Efficiency (KGE) for Q and the Spatial Efficiency (SPAEF) for ET. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial pattern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern, i.e., a 30% decrease in SPAEF. Both calibrations reached comparable performance of Q, i.e., KGE around 0.7. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.
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Guzmán-Cruz, R., R. Castañeda-Miranda, J. J. García-Escalante, A. Lara-Herrera, I. Serroukh, and L. O. Solis-Sánchez. "GENETIC ALGORITHMS FOR CALIBRATION OF A GREENHOUSE CLIMATE MODEL." Revista Chapingo Serie Horticultura XVI, no. 1 (January 2010): 23–30. http://dx.doi.org/10.5154/r.rchsh.2010.16.003.

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Kim, Daeha, Il Won Jung, and Jong Ahn Chun. "A comparative assessment of rainfall–runoff modelling against regional flow duration curves for ungauged catchments." Hydrology and Earth System Sciences 21, no. 11 (November 15, 2017): 5647–61. http://dx.doi.org/10.5194/hess-21-5647-2017.

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Abstract. Rainfall–runoff modelling has long been a special subject in hydrological sciences, but identifying behavioural parameters in ungauged catchments is still challenging. In this study, we comparatively evaluated the performance of the local calibration of a rainfall–runoff model against regional flow duration curves (FDCs), which is a seemingly alternative method of classical parameter regionalisation for ungauged catchments. We used a parsimonious rainfall–runoff model over 45 South Korean catchments under semi-humid climate. The calibration against regional FDCs was compared with the simple proximity-based parameter regionalisation. Results show that transferring behavioural parameters from gauged to ungauged catchments significantly outperformed the local calibration against regional FDCs due to the absence of flow timing information in the regional FDCs. The behavioural parameters gained from observed hydrographs were likely to contain intangible flow timing information affecting predictability in ungauged catchments. Additional constraining with the rising limb density appreciably improved the FDC calibrations, implying that flow signatures in temporal dimensions would supplement the FDCs. As an alternative approach in data-rich regions, we suggest calibrating a rainfall–runoff model against regionalised hydrographs to preserve flow timing information. We also suggest use of flow signatures that can supplement hydrographs for calibrating rainfall–runoff models in gauged and ungauged catchments.
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Steele, Katie, and Charlotte Werndl. "Climate Models, Calibration, and Confirmation." British Journal for the Philosophy of Science 64, no. 3 (September 1, 2013): 609–35. http://dx.doi.org/10.1093/bjps/axs036.

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Dissertations / Theses on the topic "Calibration of climate model"

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Raoult, Nina. "Calibration of plant functional type parameters using the adJULES system." Thesis, University of Exeter, 2017. http://hdl.handle.net/10871/29837.

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Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate-carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. This thesis describes adJULES in a data assimilation framework and demonstrates its ability to improve the model-data fit using eddy-covariance measurements of gross primary productivity (GPP) and latent heat (LE) fluxes. The adJULES system is extended to have the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85% of the sites used in the study, at both the calibration and evaluation stages. The new improved parameters for JULES are presented along with the associated uncertainties for each parameter. The results of the calibrations are compared to structural changes and used in a cluster analysis in order to challenge the PFT definitions in JULES. This thesis concludes with simple sensitivity studies which assess how the calibration of JULES has affected the sensitivity of the model to CO2-induced climate change.
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Niraula, Rewati. "Understanding the Hydrological Response of Changed Environmental Boundary Conditions in Semi-Arid Regions: Role of Model Choice and Model Calibration." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/594961.

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Arid and semi-arid basins in the Western United States (US) have been significantly impacted by human alterations to the water cycle and are among the most susceptible to water stress from urbanization and climate change. The climate of the Western US is projected to change in response to rising greenhouse gas concentrations. Combined with land use/land cover (LULC) change, it can influence both surface and groundwater resources, both of which are a significant source of water in the US. Responding to this challenge requires an improved understanding of how we are vulnerable and the development of strategies for managing future risk. In this dissertation, I explored how hydrology of semi-arid regions responds to LULC and climate change and how hydrologic projections are influenced by the choice and calibration of models. The three main questions I addressed with this dissertation are: 1. Is it important to calibrate models for forecasting absolute/relative changes in streamflow from LULC and climate changes? 2. Do LSMs make reasonable estimates of groundwater recharge in the western US? 3. How might recharge change under projected climate change in the western US? Results from this study suggested that it is important to calibrate the model spatially to analyze the effect of LULC change but not as important for analyzing the relative change in streamflow due to climate change. Our results also highlighted that LSMs have the potential to capture the spatial and temporal patterns as well as seasonality of recharge at large scales. Therefore, LSMs (specifically VIC and Noah) can be used as a tool for estimating current and future recharge in data limited regions. Average annual recharge is projected to increase in about 62% of the region and decrease in about 38% of the western US in future and varies significantly based on location (-50% - +94 for near future and -90% to >100% for far future). Recharge is expected to decrease significantly (-13%) in the South region in the far future. The Northern Rockies region is expected to get more recharge in both in the near (+5.1%) and far (+9.0%) future. Overall, this study suggested that land use/land cover (LULC) change and climate change significantly impacts hydrology in semi-arid regions. Model choice and model calibrations also influence the hydrological predictions. Hydrological projections from models have associated uncertainty, but still provide valuable information for water managers with long term water management planning.
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Davies, Nicholas William. "The climate impacts of atmospheric aerosols using in-situ measurements, satellite retrievals and global climate model simulations." Thesis, University of Exeter, 2018. http://hdl.handle.net/10871/34544.

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Aerosols contribute the largest uncertainty to estimates of radiative forcing of the Earth’s atmosphere, which are thought to exert a net negative radiative forcing, offsetting a potentially significant but poorly constrained fraction of the positive radiative forcing associated with greenhouse gases. Aerosols perturb the Earth’s radiative balance directly by absorbing and scattering radiation and indirectly by acting as cloud condensation nuclei, altering cloud albedo and potentially cloud lifetime. One of the major factors governing the uncertainty in estimates of aerosol direct radiative forcing is the poorly constrained aerosol single scattering albedo, which is the ratio of the aerosol scattering to extinction. In this thesis, I describe a new instrument for the measurement of aerosol optical properties using photoacoustic and cavity ring-down spectroscopy. Characterisation is performed by assessing the instrument minimum sensitivity and accuracy as well as verifying the accuracy of its calibration procedure. The instrument and calibration accuracies are assessed by comparing modelled to measured optical properties of well-characterised laboratory-generated aerosol. I then examine biases in traditional, filter-based absorption measurements by comparing to photoacoustic spectrometer absorption measurements for a range of aerosol sources at multiple wavelengths. Filter-based measurements consistently overestimate absorption although the bias magnitude is strongly source-dependent. Biases are consistently lowest when an advanced correction scheme is applied, irrespective of wavelength or aerosol source. Lastly, I assess the sensitivity of the direct radiative effect of biomass burning aerosols to aerosol and cloud optical properties over the Southeast Atlantic Ocean using a combination of offline radiative transfer modelling, satellite observations and global climate model simulations. Although the direct radiative effect depends on aerosol and cloud optical properties in a non-linear way, it appears to be only weakly dependent on sub-grid variability.
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Bensouda, Nabil. "Extending and formalizing the energy signature method for calibrating simulations and illustrating with application for three California climates." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1080.

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This thesis extends and formalizes the energy signature method developed by Wei et al. (1998) for the rapid calibration of cooling and heating energy consumption simulations for commercial buildings. This method is based on the use of "calibration signatures" which characterize the difference between measured and simulated performance. By creating a library of shapes for certain known errors, clues can be provided to the analyst to use in identifying what simulation input errors may be causing the discrepancies. These are referred to as "characteristic signatures". In this thesis, sets of characteristic signatures are produced for the climates typified by Pasadena, Sacramento and Oakland, California for each of the four major system types: single-duct variable-air-volume, single-duct constant-volume, dual-duct variable-air-volume and dual-duct constant-volume. A detailed step-by-step description is given for the proposed methodology, and two examples and a real-world case study serve to illustrate the use of the signature method.
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Andersson, Sara. "Mapping Uncertainties – A case study on a hydraulic model of the river Voxnan." Thesis, KTH, Mark- och vattenteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-173848.

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This master thesis gives an account for the numerous uncertainties that prevail one-dimensional hydraulic models and flood inundation maps, as well as suitable assessment methods for different types of uncertainties. A conducted uncertainty assessment on the river Voxnan in Sweden has been performed. The case study included the calibra-tion uncertainty in the spatially varying roughness coefficient and the boundary condi-tion uncertainty in the magnitude of a 100-year flood, in present and future climate conditions. By combining a scenario analysis, GLUE calibration method and Monte Carlo analysis, the included uncertainties with different natures could be assessed. Significant uncer-tainties regarding the magnitude of a 100-year flood from frequency analysis was found. The largest contribution to the overall uncertainty was given by the variance between the nine global climate models, emphasizing the importance of including projections from an ensemble of models in climate change studies. Furthermore, the study gives a methodological example on how to present uncertainty estimates visually in probabilistic flood inundation maps. The conducted method of how the climate change uncertainties, scenarios and models, were handled in frequency analysis is also suggested to be a relevant result of the study.
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Liang, Dong Cowles Mary Kathryn. "Issues in Bayesian Gaussian Markov random field models with application to intersensor calibration." Iowa City : University of Iowa, 2009. http://ir.uiowa.edu/etd/400.

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Ben, Touhami Haythem. "Calibration Bayésienne d'un modèle d'étude d'écosystème prairial : outils et applications à l'échelle de l'Europe." Thesis, Clermont-Ferrand 2, 2014. http://www.theses.fr/2014CLF22444/document.

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Les prairies représentent 45% de la surface agricole en France et 40% en Europe, ce qui montre qu’il s’agit d’un secteur important particulièrement dans un contexte de changement climatique où les prairies contribuent d’un côté aux émissions de gaz à effet de serre et en sont impactées de l’autre côté. L’enjeu de cette thèse a été de contribuer à l’évaluation des incertitudes dans les sorties de modèles de simulation de prairies (et utilisés dans les études d’impact aux changements climatiques) dépendant du paramétrage du modèle. Nous avons fait appel aux méthodes de la statistique Bayésienne, basées sur le théorème de Bayes, afin de calibrer les paramètres d’un modèle référent et améliorer ainsi ses résultats en réduisant l’incertitude liée à ses paramètres et, par conséquent, à ses sorties. Notre démarche s’est basée essentiellement sur l’utilisation du modèle d’écosystème prairial PaSim, déjà utilisé dans plusieurs projets européens pour simuler l’impact des changements climatiques sur les prairies. L’originalité de notre travail de thèse a été d’adapter la méthode Bayésienne à un modèle d’écosystème complexe comme PaSim (appliqué dans un contexte de climat altéré et à l’échelle du territoire européen) et de montrer ses avantages potentiels dans la réduction d’incertitudes et l’amélioration des résultats, en combinant notamment méthodes statistiques (technique Bayésienne et analyse de sensibilité avec la méthode de Morris) et outils informatiques (couplage code R-PaSim et utilisation d’un cluster de calcul). Cela nous a conduit à produire d’abord un nouveau paramétrage pour des sites prairiaux soumis à des conditions de sécheresse, et ensuite à un paramétrage commun pour les prairies européennes. Nous avons également fourni un outil informatique de calibration générique pouvant être réutilisé avec d’autres modèles et sur d’autres sites. Enfin, nous avons évalué la performance du modèle calibré par le biais de la technique Bayésienne sur des sites de validation, et dont les résultats ont confirmé l’efficacité de cette technique pour la réduction d’incertitude et l’amélioration de la fiabilité des sorties
Grasslands cover 45% of the agricultural area in France and 40% in Europe. Grassland ecosystems have a central role in the climate change context, not only because they are impacted by climate changes but also because grasslands contribute to greenhouse gas emissions. The aim of this thesis was to contribute to the assessment of uncertainties in the outputs of grassland simulation models, which are used in impact studies, with focus on model parameterization. In particular, we used the Bayesian statistical method, based on Bayes’ theorem, to calibrate the parameters of a reference model, and thus improve performance by reducing the uncertainty in the parameters and, consequently, in the outputs provided by models. Our approach is essentially based on the use of the grassland ecosystem model PaSim (Pasture Simulation model) already applied in a variety of international projects to simulate the impact of climate changes on grassland systems. The originality of this thesis was to adapt the Bayesian method to a complex ecosystem model such as PaSim (applied in the context of altered climate and across the European territory) and show its potential benefits in reducing uncertainty and improving the quality of model outputs. This was obtained by combining statistical methods (Bayesian techniques and sensitivity analysis with the method of Morris) and computing tools (R code -PaSim coupling and use of cluster computing resources). We have first produced a new parameterization for grassland sites under drought conditions, and then a common parameterization for European grasslands. We have also provided a generic software tool for calibration for reuse with other models and sites. Finally, we have evaluated the performance of the calibrated model through the Bayesian technique against data from validation sites. The results have confirmed the efficiency of this technique for reducing uncertainty and improving the reliability of simulation outputs
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Battisti, Rafael. "Calibration, uncertainties and use of soybean crop simulation models for evaluating strategies to mitigate the effects of climate change in Southern Brazil." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/11/11152/tde-03102016-162340/.

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The water deficit is a major factor responsible for the soybean yield gap in Southern Brazil and tends to increase under climate change. Crop models are a tool that differ on levels of complexity and performance and can be used to evaluate strategies to manage crops, according the climate conditions. Based on that, the aims of this study were: to assess five soybean crop models and their ensemble; to evaluate the sensitivity of these models to systematic changes in climate; to assess soybean adaptive traits to water deficit for current and future climate; and to evaluate how the crop management contribute to soybean yields under current and future climates. The crop models FAO - Agroecological Zone, AQUACROP, DSSAT CSM-CROPGRO-Soybean, APSIM Soybean, and MONICA were assessed. These crop models were calibrated using experimental data obtained during 2013/2014 growing season in different sites, sowing dates and crop conditions (rainfed and irrigated). For the sensitivity analysis was considered climate changes on air temperature, [CO2], rainfall and solar radiation. For adapting traits to drought, the soybean traits manipulated only in DSSAT CSM-CROPGRO-Soybean were deeper root depth, maximum fraction of shoot dry matter diverted to root growth under water stress, early reduction of transpiration, transpiration limited as a function of vapor pressure deficit, N2 fixation drought tolerance and reduced acceleration of grain filling period in response to water deficit. The crop management options strategies evaluated were irrigation, sowing date, cultivar maturity group and planting density. The estimated yield had root mean square error (RMSE) varying between 553 kg ha-1 and 650 kg ha-1, with d indices always higher than 0.90 for all models. The best performance was obtained when an ensemble of all models was considered, reducing yield RMSE to 262 kg ha-1. The crop models had different sensitivity level for climate scenario, reduction yield with temperature increase, higher rate of reduction of yield with lower rainfall than increase of yield with higher rainfall amount, different yields response with solar radiation changes due to baseline climate and model, and an asymptotic soybean response to increase of [CO2]. Combining the climate scenarios, the yield was affected mainly by reduction of rainfall (increase of solar radiation), while temperature and [CO2] interaction showed compensation effect on yield losses and gains. The trait deeper rooting profile had greater improvement in total production for the Southern Brazil, with increase of 3.3 % and 4.0 %, respectively, for the current and future climates. For soybean management, in most cases, the models showed that no crop management strategy has a clear tendency to result in better yields in the future if shift from the best management of current climate. This way, the crop models showed different performance against observed data, where the model parametrization and structure affected the response to alternatives managements to climate change. Although these uncertainties, crop models and their ensemble are an important tool to evaluate impact of climate change and alternatives to mitigation.
O déficit hídrico é o principal fator causador de perda de produtividade para a soja no Centro-Sul do Brasil e tende a aumentar com as mudanças climáticas. Alternativas de mitigação podem ser avaliadas usando modelos de simulação de cultura, os quais diferem em nível de complexidade e desempenho. Baseado nisso, os objetivos desse estudo foram: avaliar cinco modelos de simulação para a soja e a média desses modelos; avaliar a sensibilidade dos modelos a mudança sistemática do clima; avaliar características adaptativas da soja ao déficit hídrico para o clima atual e futuro; e avaliar a resposta produtiva de manejos da soja para o clima atual e futuro. Os modelos utilizados foram FAO - Zona Agroecológica, AQUACROP, DSSAT CSM-CROPGRO-Soybean, APSIM Soybean e MONICA. Os modelos foram calibrados a partir de dados experimentais obtidos na safra 2013/2014 em diferentes locais e datas de semeadura sob condições irrigadas e de sequeiro. Na análise de sensibilidade foram modificadas a temperatura do ar, [CO2], chuva e radiação solar. Para as características de tolerância ao déficit hídrico foram manipulados, apenas no modelo DSSAT CSMCROPGRO- Soybean, a distribuição do sistema radicular, biomassa divergida para crescimento radicular sob déficit hídrico, redução antecipada da transpiração, limitação da transpiração em função do déficit de pressão de vapor, fixação de N2 sob déficit hídrico e redução da aceleração do ciclo devido ao déficit hídrico. Os manejos avaliados foram irrigação, data de semeadura, ciclo de cultivar e densidade de semeadura. A produtividade estimada obteve raiz do erro médio quadrático (REMQ) variando entre 553 kg ha-1 e 650 kg ha-1, com índice d acima de 0.90 para todos os modelos. O melhor desempenho foi obtido utilizando a média de todos os modelos, com REMQ de 262 kg ha-1. Os modelos obtiveram diferentes níveis de sensibilidade aos cenários climáticos, reduzindo a produtividade com aumento da temperatura, maior taxa de redução da produtividade com menor quantidade de chuva do que aumento de produtividade com maior quantidade de chuva, diferentes respostas com a mudança da radiação solar em função do clima local e do modelo, e resposta positiva assimptótica para o aumento da concentração de [CO2]. Quando combinado as mudanças dos cenários, a produtividade foi afetada principalmente pela redução da chuva (aumento da radiação solar), enquanto a mudança na temperatura e [CO2] mostrou compensação nas perdas e ganhos. A distribuição do sistema radicular foi o mecanismo de tolerância ao déficit hídrico com maior ganho de produtividade, representando ganho total na produção de 3,3 % e 4,0% para a região, respectivamente, para o clima atual e futuro. Para os manejos não se observou melhores resultados com a mudança do manejo para o futuro em relação a melhor condição para o clima atual. Desta forma, os modelos mostraram diferentes desempenho, em que a parametrização e a estrutura do modelo afetaram a resposta das alternativas avaliadas para mudanças climáticas. Apesar das incertezas, os modelos de cultura são uma importante ferramenta para avaliar o impacto e alternativas de mitigação as mudanças climáticas.
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Dinh, Thi Lan Anh. "Crop yield simulation using statistical and machine learning models. From the monitoring to the seasonal and climate forecasting." Electronic Thesis or Diss., Sorbonne université, 2022. http://www.theses.fr/2022SORUS425.

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La météo et le climat ont un impact important sur les rendements agricoles. De nombreuses études basées sur différentes approches ont été réalisées pour mesurer cet impact. Cette thèse se concentre sur les modèles statistiques pour mesurer la sensibilité des cultures aux conditions météorologiques sur la base des enregistrements historiques. Lors de l'utilisation d'un modèle statistique, une difficulté critique survient lorsque les données sont rares, ce qui est souvent le cas pour la modélisation des cultures. Il y a un risque élevé de sur-apprentissage si le modèle n'est pas développé avec certaine précautions. Ainsi, la validation et le choix du modèle sont deux préoccupations majeures de cette thèse. Deux approches statistiques sont développées. La première utilise la régression linéaire avec régularisation et validation croisée (c.-à.-d. leave-one-out ou LOO), appliquée au café robusta dans la principale région productrice de café du Vietnam. Le café est une culture rémunératrice, sensible aux intempéries, et qui a une phénologie très complexe en raison de sa nature pérenne. Les résultats suggèrent que les informations sur les précipitations et la température peuvent être utilisées pour prévoir l'anomalie de rendement avec une anticipation de 3 à 6 mois selon la région. Les estimations du rendement du robusta à la fin de la saison montrent que les conditions météorologiques expliquent jusqu'à 36 % des anomalies de rendement historiques. Cette première approche de validation par LOO est largement utilisée dans la littérature, mais elle peut être mal utilisé pour de nombreuses raisons : elle est technique, mal interprétée et nécessite de l'expérience. Une alternative, l'approche “leave-two-out nested cross-validation” (ou LTO), est proposée pour choisir le modèle approprié, évaluer sa véritable capacité de généralisation et choisir la complexité du modèle optimale. Cette méthode est sophistiquée mais simple. Nous démontrons son applicabilité pour le café robusta au Vietnam et le maïs en France. Dans les deux cas, un modèle plus simple avec moins de prédicteurs potentiels et d'entrées est plus approprié. Utiliser uniquement la méthode LOO peut être très trompeur car cela encourage à choisir un modèle qui sur-apprend les données de manière indirecte. L'approche LTO est également utile dans les applications de prévision saisonnière. Les estimations de rendement du maïs en fin de saison suggèrent que les conditions météorologiques peuvent expliquer plus de 40 % de la variabilité de l'anomalie de rendement en France. Les impacts du changement climatique sur la production de café au Brésil et au Vietnam sont également étudiés à l'aide de simulations climatiques et de modèles d'impact ou “suitability models”. Les données climatiques sont cependant biaisées par rapport au climat réel. De nombreuses méthodes de “correction de biais” (appelées ici “calibration”) ont été introduites pour corriger ces biais. Une présentation critique et détaillée de ces calibrations dans la littérature est fournie pour mieux comprendre les hypothèses, les propriétés et les objectifs d'application de chaque méthode. Les simulations climatiques sont ensuite calibrées par une méthode basée sur les quantiles avant d'être utilisées sur nos modèles d'impact. Ces modèles sont développés sur la base des données de recensement des zones caféières, et les variables climatiques potentielles sont basées sur un examen des études précédentes utilisant des modèles d'impact pour le café et des recommandations d'experts. Les résultats montrent que les zones propices à l'arabica au Brésil pourraient diminuer d'environ 26 % d'ici le milieu du siècle dans le scénario à fortes émissions, les régions compatibles avec la culture du robusta vietnamien pourraient quant à elle diminué d'environ 60 %. Les impacts sont significatifs à basse altitude pour les deux types de café, suggérant des déplacements potentiels de la production vers des endroits plus élevés
Weather and climate strongly impact crop yields. Many studies based on different techniques have been done to measure this impact. This thesis focuses on statistical models to measure the sensitivity of crops to weather conditions based on historical records. When using a statistical model, a critical difficulty arises when data is scarce, which is often the case with statistical crop modelling. There is a high risk of overfitting if the model development is not done carefully. Thus, careful validation and selection of statistical models are major concerns of this thesis. Two statistical approaches are developed. The first one uses linear regression with regularization and leave-one-out cross-validation (or LOO), applied to Robusta coffee in the main coffee-producing area of Vietnam (i.e. the Central Highlands). Coffee is a valuable commodity crop, sensitive to weather, and has a very complex phenology due to its perennial nature. Results suggest that precipitation and temperature information can be used to forecast the yield anomaly with 3–6 months' anticipation depending on the location. Estimates of Robusta yield at the end of the season show that weather explains up to 36 % of historical yield anomalies. The first approach using LOO is widely used in the literature; however, it can be misused for many reasons: it is technical, misinterpreted, and requires experience. As an alternative, the “leave-two-out nested cross-validation” (or LTO) approach, is proposed to choose the suitable model and assess its true generalization ability. This method is sophisticated but straightforward; its benefits are demonstrated for Robusta coffee in Vietnam and grain maize in France. In both cases, a simpler model with fewer potential predictors and inputs is more appropriate. Using only the LOO method, without any regularization, can be highly misleading as it encourages choosing a model that overfits the data in an indirect way. The LTO approach is also useful in seasonal forecasting applications. The end-of-season grain maize yield estimates suggest that weather can account for more than 40 % of the variability in yield anomaly. Climate change's impacts on coffee production in Brazil and Vietnam are also studied using climate simulations and suitability models. Climate data are, however, biased compared to the real-world climate. Therefore, many “bias correction” methods (called here instead “calibration”) have been introduced to correct these biases. An up-to-date review of the available methods is provided to better understand each method's assumptions, properties, and applicative purposes. The climate simulations are then calibrated by a quantile-based method before being used in the suitability models. The suitability models are developed based on census data of coffee areas, and potential climate variables are based on a review of previous studies using impact models for coffee and expert recommendations. Results show that suitable arabica areas in Brazil could decrease by about 26 % by the mid-century in the high-emissions scenario, while the decrease is surprisingly high for Vietnamese Robusta coffee (≈ 60 %). Impacts are significant at low elevations for both coffee types, suggesting potential shifts in production to higher locations. The used statistical approaches, especially the LTO technique, can contribute to the development of crop modelling. They can be applied to a complex perennial crop like coffee or more industrialized annual crops like grain maize. They can be used in seasonal forecasts or end-of-season estimations, which are helpful in crop management and monitoring. Estimating the future crop suitability helps to anticipate the consequences of climate change on the agricultural system and to define adaptation or mitigation strategies. Methodologies used in this thesis can be easily generalized to other cultures and regions worldwide
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Martínez, Asensio Adrián. "Impact of large-scale atmospheric variability on sea level and wave climate." Doctoral thesis, Universitat de les Illes Balears, 2015. http://hdl.handle.net/10803/371456.

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This thesis aims at quantitatively characterizing the recent (last few decades) and future climate variability of marine climate in the Western Mediterranean Sea and the North Atlantic Ocean. Namely it focuses on sea level and wind-waves, as these are the variables with a larger potential impact on coastal ecosystems and infrastructures. We first use buoy and altimetry data to calibrate a 50-year wind-wave hindcast over the Western Mediterranean in order to obtain the best characterization of the wave climate over that region. The minimization of the differences with respect to observations through a non-linear transformation of the Empirical Orthogonal Functions of the modelled fields results in an improvement of the hindcast, according to a validation test carried out with independent observations. We then focus on the relationship between the large scale atmospheric forcing and our target variables. Namely we quantify and explore the cause-effect relations between the major modes of atmospheric variability over the North Atlantic and Europe, i.e. the North Atlantic Oscillation, the East Atlantic pattern, the East Atlantic Western Russian pattern and the Scandinavian pattern, and both the Mediterranean sea level and the North Atlantic wave climate. To do so, we use data from different sets of observations and numerical models, including tide gauges, wave buoys, altimetry, hydrography and numerical simulations. Our results point to the North Atlantic Oscillation as the mode with the largest impact on both, Mediterranean sea level (due to the local and remote influence on its atmospheric component) and the North Atlantic wave climate (due to its effect on both the wind-sea and swell components). Other climate indices have smaller but still meaningful contributions; e.g. the East Atlantic pattern plays a significant role in the wave climate variability through its impact on the swell component. Finally, we explore the performance of statistical models to project the future wave climate over the North Atlantic under global warming scenarios, including the large scale climate modes as predictors together with other variables such as atmospheric pressure and wind speed. Notably, we highlight that the use of wind speed as statistical predictor is essential to reproduce the dynamically projected long-term trends.
Esta tesis caracteriza cuantitativamente la variabilidad climática reciente (las últimas décadas) y futura del clima marino en el Mar Mediterráneo y en el Océano Atlántico Norte. Concretamente, se centra en el nivel del mar y en el oleaje, ya que éstas son las variables con un mayor impacto potencial en ecosistemas e infraestructuras costeras. En primer lugar, utilizamos datos de boyas y altimetría para calibrar un hindcast de oleaje de 50 años en el Mediterráneo Occidental, con el objetivo de obtener la mejor caracterización climática del oleaje sobre esta región. La minimización de las diferencias con respecto a las observaciones a través de una transformación no lineal de las Funciones Empíricas Ortogonales de los campos modelados se traduce en una mejora del hindcast, de acuerdo al test de validación llevado a cabo con observaciones independientes. Luego nos centramos en las relaciones entre el forzamiento atmosférico de gran escala y nuestras variables de interés. En concreto, cuantificamos y exploramos las relaciones causa-efecto entre los modos de variabilidad atmosférica más importantes del Atlántico Norte y Europa (la Oscilación del Atlántico Norte, el patrón del Atlántico Oriental, el patrón del Atlántico Oriental/Rusia Occidental y el patrón Escandinavo) y el nivel del mar del Mediterráneo y el oleaje del Atlántico Norte. Para ello, usamos datos de diferentes conjuntos de observaciones y modelos numéricos, incluyendo mareógrafos, boyas de oleaje, altimetría, hidrografía y simulaciones numéricas. Nuestros resultados señalan la Oscilación del Atlántico Norte como el modo de mayor impacto, tanto en el nivel del mar del Mediterráneo (debido a la influencia local y remota en su componente atmosférica) como en el oleaje del Atlántico Norte (debido a su efecto en las componentes de mar de viento y de mar de fondo). Otros índices climáticos tienen contribuciones más pequeñas pero todavía significativas; e.g. el patrón del Atlántico Oriental juega un papel importante en la variabilidad del oleaje a través de su impacto en la componente de mar de fondo. Finalmente, exploramos la capacidad de los modelos estadísticos de proyectar el clima futuro del oleaje sobre el Atlántico Norte bajo escenarios de calentamiento global, incluyendo los modos climáticos de gran escala como predictores junto con otras variables como la presión atmosférica y la velocidad del viento. En particular, destacamos que el uso de la velocidad del viento como predictor estadístico es esencial para reproducir las tendencias a largo plazo proyectadas de por los modelos dinámicos.
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Books on the topic "Calibration of climate model"

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Zapata, C. E. Calibration and validation of the enhanced integrated climatic model for pavement design. Washington, D.C: Transportation Research Board, 2008.

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Wise, Jacquelyn A. Thermometer calibration: A model for state calibration laboratories. Washington: U.S. Dept. of Commerce, National Bureau of Standards, 1986.

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Wise, Jacquelyn A. Thermometer calibration: A model for state calibration laboratories. Gaithersburg, MD: U.S. Dept. of Commerce, National Bureau of Standards, 1986.

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Sun, Ne-Zheng, and Alexander Sun. Model Calibration and Parameter Estimation. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2323-6.

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Kemp, Malcolm H. D. Market consistency: Model calibration in imperfect markets. Hoboken, NJ: Wiley, 2009.

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Market consistency: Model calibration in imperfect markets. Chichester, U.K: Wiley, 2009.

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Associates, Dick Conway &. Puget Sound subarea forecasts: Model calibration and forecasts. [Seattle?: Puget Sound Regional Council?, 1992.

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Dawkins, Christina. New directions in applied general equilibrium model calibration. [s.l.]: typescript, 1999.

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Hackl, Christoph. Calibration and Parameterization Methods for the Libor Market Model. Wiesbaden: Springer Fachmedien Wiesbaden, 2014. http://dx.doi.org/10.1007/978-3-658-04688-0.

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R, McNew-Cartwright Elizabeth, and Geological Survey (U.S.), eds. Calibration of a ground-water-flow model by regression. Coram, N.Y: U.S. Dept. of the Interior, U.S. Geological Survey, 1996.

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Book chapters on the topic "Calibration of climate model"

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Nandi, Saswata, and M. Janga Reddy. "Multiobjective Automatic Calibration of a Physically Based Hydrologic Model Using Multiobjective Self-Adaptive Differential Evolution Algorithm." In Climate Change Impacts on Water Resources, 435–48. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-64202-0_37.

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Oladapo, Olukunle Olaonipekun, Leonard Kofitse Amekudzi, Olatunde Micheal Oni, Abraham Adewale Aremu, and Marian Amoakowaah Osei. "Climate Change Impact on Soil Moisture Variability: Health Effects of Radon Flux Density Within Ogbomoso, Nigeria." In African Handbook of Climate Change Adaptation, 437–52. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-45106-6_201.

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AbstractClimate affects the quantity of soil moisture within the surface of the earth and this is obtained by affecting the amount of radon flux density escaping from the land surface. This chapter contains the evaluation of climate change conditions as it affects the variability of soil water for the purpose of estimating the health effects of radon flux density within Ogbomoso metropolis. The simulated soil moisture content around Ogbomoso was done for a period of 34 years using the hydrological model, Soil Water Assessment Tool (SWAT). The calibration and validation of the SWAT model was done using the daily observed soil moisture content. The simulated daily soil moisture within Ogbomoso showed good performance when calibrated and validated. A 20 years prediction of the daily soil moisture content was done using the SWAT model. The estimation of the radon flux density for the study area was obtained using the simulated soil temperature and soil moisture from the SWAT model. In this chapter, the UNSCEAR radon flux formula was used for the radon flux estimate. The result showed that the UNSCEAR radon flux formula performed well in estimating the radon flux density in the study area. The mean value of the radon flux density of 15.09 mBqm−2 s−1 falls below the estimated world average of 33 mBqm−2 s−1 by UNSCEAR stipulated for land surface. The results showed that Ogbomoso region is not prone to high risk of radon exposure to the public. The estimation of the radon flux density value suggested that there is no radiological health hazard such as lung cancer or any other respiratory tract diseases to the inhabitant of Ogbomoso, Nigeria.
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Papadelis, Sotiris, and Alexandros Flamos. "An Application of Calibration and Uncertainty Quantification Techniques for Agent-Based Models." In Understanding Risks and Uncertainties in Energy and Climate Policy, 79–95. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03152-7_3.

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Noilhan, Joel, Pierre Lacarrère, Florence Habets, and Richard J. Harding. "Use of Field Experiments in Improving the Land-surface Description in Atmospheric Models: Calibration, Aggregation and Scaling." In Vegetation, Water, Humans and the Climate, 221–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-18948-7_21.

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Engler, Camila, Carlos Marcelo Pais, Silvina Saavedra, Emanuel Juarez, and Hugo Leonardo Rufiner. "Prediction of the Impact of the End of year Festivities on the Local Epidemiology of COVID-19 Using Agent-Based Simulation with Hidden Markov Models." In Computational Science and Its Applications – ICCSA 2022, 61–75. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10522-7_5.

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AbstractTowards the end of 2020, as people changed their usual behavior due to end of year festivities, increasing the frequency of meetings and the number of people who attended them, the COVID-19 local epidemic’s dynamic changed. Since the beginnings of this pandemic, we have been developing, calibrating and validating a local agent-based model (AbcSim) that can predict intensive care unit and deaths’ evolution from data contained in the state electronic medical records and sociological, climatic, health and geographic information from public sources. In addition, daily symptomatic and asymptomatic cases and other epidemiological variables of interest disaggregated by age group can be forecast. Through a set of Hidden Markov Models, AbcSim reproduces the transmission of the virus associated with the movements and activities of people in this city, considering the behavioral changes typical of local holidays. The calibration and validation were performed based on official data from La Rioja city in Argentina. With the results obtained, it was possible to demonstrate the usefulness of these models to predict possible outbreaks, so that decision-makers can implement the necessary policies to avoid the collapse of the health system.
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Belarbi, Halima, Bénina Touaibia, Nadir Boumechra, Chérifa Abdelbaki, and Sakina Amiar. "Analysis of the Hydrological Behavior of Watersheds in the Context of Climate Change (Northwestern Algeria)." In Natural Disaster Science and Mitigation Engineering: DPRI reports, 143–79. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2904-4_5.

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AbstractThe aim of this work is to study the temporal evolution of the rainfall-runoff relations of four basins in northwestern Algeria: the Tafna Maritime, Isser Sikkak, downstream Mouilah and Upper Tafna basins. The adopted approach consists of analyzing hydroclimatic variables using statistical methods and testing the nonstationarity of the rainfall-runoff relation by the cross-simulation method using the GR2M model. The results of the different statistical methods applied to the series of rainfall and hydrometric variables show a decrease due to a break in stationarity detected since the mid-1970s and the beginning of the 1980s. The annual rainfall deficits reached average values of 34.6% during the period of 1941–2006 and 29.1% during the period of 1970–2010. The average annual wadi flows showed average deficits of 61.1% between 1912 and 2000 and 53.1% between 1973 and 2009. The GR2M conceptual model simulated the observed hydrographs in an acceptable manner by providing calculated runoff values in the calibration and validation periods greater or less than the observed runoff values. The application of the cross-simulation method highlighted the nonstationarity of the rainfall-runoff relations in three of the four studied basins, indicating downward trends of monthly runoff.
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Mamalakis, Antonios, Imme Ebert-Uphoff, and Elizabeth A. Barnes. "Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science." In xxAI - Beyond Explainable AI, 315–39. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04083-2_16.

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AbstractIn recent years, artificial intelligence and specifically artificial neural networks (NNs) have shown great success in solving complex, nonlinear problems in earth sciences. Despite their success, the strategies upon which NNs make decisions are hard to decipher, which prevents scientists from interpreting and building trust in the NN predictions; a highly desired and necessary condition for the further use and exploitation of NNs’ potential. Thus, a variety of methods have been recently introduced with the aim of attributing the NN predictions to specific features in the input space and explaining their strategy. The so-called eXplainable Artificial Intelligence (XAI) is already seeing great application in a plethora of fields, offering promising results and insights about the decision strategies of NNs. Here, we provide an overview of the most recent work from our group, applying XAI to meteorology and climate science. Specifically, we present results from satellite applications that include weather phenomena identification and image to image translation, applications to climate prediction at subseasonal to decadal timescales, and detection of forced climatic changes and anthropogenic footprint. We also summarize a recently introduced synthetic benchmark dataset that can be used to improve our understanding of different XAI methods and introduce objectivity into the assessment of their fidelity. With this overview, we aim to illustrate how gaining accurate insights about the NN decision strategy can help climate scientists and meteorologists improve practices in fine-tuning model architectures, calibrating trust in climate and weather prediction and attribution, and learning new science.
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Xu, Jie. "Model Calibration." In Simulation Foundations, Methods and Applications, 27–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64182-9_3.

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Mermoud, Grégory. "Model Calibration." In Stochastic Reactive Distributed Robotic Systems, 99–108. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-02609-1_7.

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Vieux, Baxter E. "Distributed Model Calibration." In Water Science and Technology Library, 189–209. Dordrecht: Springer Netherlands, 2016. http://dx.doi.org/10.1007/978-94-024-0930-7_10.

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Conference papers on the topic "Calibration of climate model"

1

Jahn, Patrick, Gerrit Lassahn, and Kang Qiu. "Model-Based Calibration of an Automotive Climate Control System." In WCX SAE World Congress Experience. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 2020. http://dx.doi.org/10.4271/2020-01-1253.

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Hadjrioua, Farid, N. Belhaouas, A. Aissaoui, F. Mehareb, and K. Bakria. "Outdoor PV Module Characterization and Sandia Model Calibration in Local Climate." In 2022 International Conference of Advanced Technology in Electronic and Electrical Engineering (ICATEEE). IEEE, 2022. http://dx.doi.org/10.1109/icateee57445.2022.10093701.

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Steinschneider, S., C. Brown, R. N. Palmer, and D. Ahlfeld. "Hydrology Models for Climate Change Assessment: Inter-Decadal Climate Variability and Parameter Calibration." In World Environmental and Water Resources Congress 2011. Reston, VA: American Society of Civil Engineers, 2011. http://dx.doi.org/10.1061/41173(414)428.

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Osypov, Valeriy, Nataliia Osadcha, Andrii Bonchkovskyi, Oleksandr Kostetskyi, Viktor Nikoriak, Yurii Ahafonov, Yevhenii Matviienko, Herman Mossur, and Volodymyr Osadchyi. "Hydrological model of Ukraine: setup, calibration, and web interface." In International Conference of Young Scientists on Meteorology, Hydrology and Environmental Monitoring. Ukrainian Hydrometeorological Institute, 2023. http://dx.doi.org/10.15407/icys-mhem.2023.013.

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The planning of river basin management should utilize a high-resolution, process-based hydrological model to tackle issues such as diffuse pollution, drought, flood forecasting, and the impact of climate change. The studies available to date only encompass five meso-scale and one large-scale river basins in Ukraine. The objective of this study is to calibrate the Soil and Water Assessment Tool (SWAT) for all Ukrainian river basins, including upstream transboundary parts. The model could potentially assist in land management and assessing the impact of agriculture on water resources; hence, considerable attention is paid to agricultural practices and crop rotations. The Soil and Water Assessment Tool (SWAT) is a process-based semi-distributed hydrological model developed by the United States Department of Agriculture's Agricultural Research Service (USDA-ARS) in collaboration with numerous institutions. SWAT is widely used for simulating the impact of land management practices on water resources, including water quantity and quality, as well as assessing the overall environmental impact of land use and climate changes. The watershed, encompassing transboundary areas, covers an area of 873,600 km2, with Ukraine accounting for 68.7% of it. The inputs for the model consist of topography, river network, merged national soil maps with the properties for each soil polygon and underlying horizons, land cover, and agricultural practices such as crop rotations, fertilization, and operation schedules. In calibrating the model, we arranged daily discharge data from 56 gauges, snow cover from 61 locations, and crop yields of primary crops. The modeling period spans 41 years from 1980 to 2020. The modeling results are evaluated based on three criteria: the Nash-Sutcliffe coefficient (NS), the coefficient of determination (R2), and the percent bias (PBIAS). The model is available via a user-friendly web platform that features an interactive map of Ukrainian subbasins. Users can inspect the model inputs for each subbasin and monitor the daily dynamics of key outputs: river discharge, water flow components, evapotranspiration, soil water, and snow cover. The results can be downloaded as an image or a CSV file for further research. The hydrological model of Ukraine has the potential to address a wide range of issues related to water and agriculture: water supply, flood forecasting, soil water availability, water quality, the impact of climate change, and so on. The model will be expanded in the future to include sediment and nutrient transport.
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"�Na�ve� inclusion of diverse climates in calibration is not sufficient to improve model reliability under future climate uncertainty." In 24th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2021. http://dx.doi.org/10.36334/modsim.2021.j8.trotter.

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Song, Mohan, Andrea E. Brookfield, and Alan E. Fryar. "INTEGRATED HYDROLOGIC MODEL CALIBRATION UNDER NON-STATIONARY CLIMATES." In GSA Connects 2023 Meeting in Pittsburgh, Pennsylvania. Geological Society of America, 2023. http://dx.doi.org/10.1130/abs/2023am-392564.

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Wang, Shuyuan, Dennis C. Flanagan, and Bernard A. Engel. "Calibration, Validation, and Evaluation of the Water Erosion Prediction Project (WEPP) Model for Hillslopes with Natural Runoff Plot Data." In Soil Erosion Research Under a Changing Climate, January 8-13, 2023, Aguadilla, Puerto Rico, USA. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2023. http://dx.doi.org/10.13031/soil.2023022.

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Wang, Shuyuan, Dennis C. Flanagan, and Bernard A. Engel. "Calibration, Validation, and Evaluation of the Water Erosion Prediction Project (WEPP) Model for Hillslopes with Natural Runoff Plot Data." In Soil Erosion Research Under a Changing Climate, January 8-13, 2023, Aguadilla, Puerto Rico, USA. St. Joseph, MI: American Society of Agricultural and Biological Engineers, 2023. http://dx.doi.org/10.13031/soil.23022.

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Bin Masood, Junaid, Sajid Hussain, Ali AlAlili, Sara Zaidan, and Ebrahim Al Hajri. "Detailed Dynamic Model of an Institutional Building in Hot and Humid Climate Conditions." In ASME 2017 11th International Conference on Energy Sustainability collocated with the ASME 2017 Power Conference Joint With ICOPE-17, the ASME 2017 15th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2017 Nuclear Forum. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/es2017-3582.

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This paper is an ASHRAE Level 3 study of the energy audit process carried out in an institutional building, The Umm Shaif Building, of The Petroleum Institute, Abu Dhabi, UAE. It undertakes the study by collecting data and conditional surveys. The energy loss locations are highlighted through psychrometric and infrared camera analysis. The detailed dynamic model has been simulated using the EnergyPlus® simulation engine. The details of the building envelope, and fenestration, the occupancy schedules, the equipment energy consumption and HVAC details are presented. The detailed building model is used to allocate the energy usage and identify key energy consumers. The main results are reported using monthly total energy consumption. The validation and calibration are performed through different statistical metrics including Coefficient of Determination (R2), Root Mean Square Error (RMSE) and Coefficient of Variance Root Mean Square Error (CVRMSE). Finally, energy conservation measures are suggested with the energy and cost savings.
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Knoppová, Kateřina, Daniel Marton, and Petr Štěpánek. "APPLICATION OF RAINFALL-RUNOFF MODEL: CLIMATE CHANGE IMPACTS ON RESERVOIR INFLOW." In XXVII Conference of the Danubian Countries on Hydrological Forecasting and Hydrological Bases of Water Management. Nika-Tsentr, 2020. http://dx.doi.org/10.15407/uhmi.conference.01.11.

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The impacts of climate change are beginning to be felt in the Czech Republic. In recent years, we were challenging a dry period, which threatens to continue affecting Czech economy, agriculture and personal comfort of local people. The need to adapt to climate change is obvious. The groundwater resources are in continuous decline, consequently, the surface water supplies are increasing in importance. How would the quantity of available water change in the future? How much water would we be able to store within the year to manage it during the dry seasons? Rainfall-runoff models enable us to simulate future changes in hydrological conditions based on climate projections. One of such tools is Runoff Prophet, the conceptual lumped model being developed at the Institute of Landscape Water Management at Brno University of Technology. It is used to simulate time series of monthly river flow in a catchment outlet without the need to describe the morphological characteristics of the catchment. Runoff Prophet produced good results of calibration and proved its suitability for conceptual hydrological modelling in variable hydrological conditions of the Czech Republic. The aim of the paper was to assess the possible impact of climate change on future inflow into Vír I. Reservoir, one of the drinking water resources for Brno, a city of 380 000 inhabitants. The recently developed software Runoff Prophet was used to simulate future river flow time series. The model was calibrated on the catchment of gauging station Dalečín on Svratka River as the reservoir inflow. Prognoses of future river flow were performed using climate scenarios prepared by Global Change Research Institute of Czech Academy of Sciences. These scenarios (RCP types) are based on the outcomes from different regional climate models of Euro-CORDEX initiative. Characteristics of possible future air temperature and precipitation in the basin were evaluated in terms of its impact on reservoir management. The results of hydrological modelling gave the perspective of expected changes in Vír I. inflow yield. The options of using Vír I. Reservoir as a drinking water supply for Brno in coming decades were assessed.
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Reports on the topic "Calibration of climate model"

1

Ellingson, R., W. Wiscombe, D. Murcray, W. Smith, and R. Strauch. ICRCCM Phase 2: Verification and calibration of radiation codes in climate models. Office of Scientific and Technical Information (OSTI), January 1992. http://dx.doi.org/10.2172/7162458.

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Ellingson, R. G., W. J. Wiscombe, D. Murcray, W. Smith, and R. Strauch. ICRCCM Phase 2: Verification and calibration of radiation codes in climate models. Office of Scientific and Technical Information (OSTI), January 1991. http://dx.doi.org/10.2172/6165998.

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Ellingson, R. G., W. J. Wiscombe, D. Murcray, W. Smith, and R. Strauch. ICRCCM (InterComparison of Radiation Codes used in Climate Models) Phase 2: Verification and calibration of radiation codes in climate models. Office of Scientific and Technical Information (OSTI), January 1990. http://dx.doi.org/10.2172/6232336.

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Ellingson, R. G., W. J. Wiscombe, D. Murcray, W. Smith, and R. Strauch. ICRCCM Phase 2: Verification and calibration of radiation codes in climate models. Technical report, 1 November 1991--1 December 1992. Office of Scientific and Technical Information (OSTI), December 1992. http://dx.doi.org/10.2172/10105305.

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Ellingson, R. G., W. J. Wiscombe, D. Murcray, W. Smith, and R. Strauch. ICRCCM phase II: Verification and calibration of radiation codes in climate models. Final report, 1 May 1990--30 April 1993. Office of Scientific and Technical Information (OSTI), December 1993. http://dx.doi.org/10.2172/569119.

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Ellingson, R. G. ICRCCM Phase II: Verification and calibration of radiation codes in climate models. Final report, May 1, 1993--June 30, 1997. Office of Scientific and Technical Information (OSTI), December 1997. http://dx.doi.org/10.2172/563327.

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Finkelstein-Shapiro, Alan, and Victoria Nuguer. Climate Policies, Labor Markets, and Macroeconomic Outcomes in Emerging Economies. Inter-American Development Bank, April 2023. http://dx.doi.org/10.18235/0004844.

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We study the labor market and macroeconomic effects of introducing a carbon tax in the energy sector in emerging economies (EMEs) by building a framework with equilibrium unemployment and firm entry that incorporates key elements of the distinct employment and firm structure of EMEs. Our model endogenizes the adoption of green energy-production technologies--a core element of policy discussions regarding the transition to a low-carbon economy. Calibrating the model to EME data, we show that a carbon tax fosters greater green technology adoption and increases the share of green energy produced. However, the tax leads to higher energy prices, which reduce salaried firm creation and formal employment and increase self-employment, labor participation, and unemployment. As a result, the tax generates output and welfare losses. Green technology adoption plays a key role in limiting the quantitative magnitude of these losses, while the response of self-employment is crucial to explaining the adverse labor market and macroeconomic effects of the policy. Given this finding, we show that a carbon tax coupled with a plausible reduction in the cost of becoming a formal firm can offset the adverse effects of the tax and generate a transition to a lower-carbon economy with minimal economic costs. Finally, we show that lowering green-technology adoption costs or the cost of green-energy production inputs--two alternative climate policies--reduces emissions while limiting the output and welfare costs compared to a carbon tax.
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Menikoff, Ralph. SURF Model Calibration Strategy. Office of Scientific and Technical Information (OSTI), March 2017. http://dx.doi.org/10.2172/1346849.

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Menikoff, Ralph. SURF model calibration strategy. Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1581567.

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Ohring, George, Bruce Wielicki, Roy Spencer, Bill Emery, and Raju Datla. Satellite instrument calibration for measuring global climate change. Gaithersburg, MD: National Institute of Standards and Technology, 2004. http://dx.doi.org/10.6028/nist.ir.7047.

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