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Refsgaard, J.C.; Madsen, H.; Andréassian, V.; Arnbjerg-Nielsen, K.; Davidson, T.A.; Drews, M.; Hamilton, D.P.; Jeppesen, E.; Kjellström, E.; Olesen, J.E.; Sonnenborg, T.O.; Trolle, D.; Willems, P.; Christensen, J.H. |
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A framework for testing the ability of models to project climate change and its impacts |
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Journal Article |
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2014 |
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Climatic Change |
Abbreviated Journal |
Clim. Change |
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122 |
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1-2 |
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271-282 |
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Keywords |
simulation-models; shallow lakes; predictions; calibration; ensembles; terminology; uncertainty; temperature; adaptation; validation |
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Abstract |
Models used for climate change impact projections are typically not tested for simulation beyond current climate conditions. Since we have no data truly reflecting future conditions, a key challenge in this respect is to rigorously test models using proxies of future conditions. This paper presents a validation framework and guiding principles applicable across earth science disciplines for testing the capability of models to project future climate change and its impacts. Model test schemes comprising split-sample tests, differential split-sample tests and proxy site tests are discussed in relation to their application for projections by use of single models, ensemble modelling and space-time-substitution and in relation to use of different data from historical time series, paleo data and controlled experiments. We recommend that differential-split sample tests should be performed with best available proxy data in order to build further confidence in model projections. |
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English |
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0165-0009 1573-1480 |
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CropM |
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no |
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MA @ admin @ |
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4688 |
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Author |
Sanna, M.; Bellocchi, G.; Fumagalli, M.; Acutis, M. |
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Title |
A new method for analysing the interrelationship between performance indicators with an application to agrometeorological models |
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Journal Article |
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Year |
2015 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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73 |
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286-304 |
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Keywords |
model evaluation; performance indicators; stable correlation; solar-radiation; simulation-model; environmental-models; statistical-methods; crop nitrogen; validation; rice; uncertainty; calibration; software |
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Abstract |
The use of a variety of metrics is advocated to assess model performance but correlated metrics may convey the same information, thus leading to redundancy. Starting from this assumption, a method was developed for selecting, from among a collection of performance indicators, one or more subsets providing the same information as the entire set. The method, based on the definition of “stable correlation”, was applied to 23 performance indicators of agrometeorological models, calculated on large sets of simulated and observed data of four agronomic and meteorological variables: above-ground biomass, leaf area index, hourly air relative humidity and daily solar radiation. Two subsets were determined: {Squared Bias, Root Mean Squared Relative Error, Coefficient of Determination, Pattern Index, Modified Modelling Efficiency}, {Persistence Model Efficiency, Root Mean Squared Relative Error, Coefficient of Determination, Pattern Index}. The method needs corroboration but is statistically founded and can support the implementation of standardized evaluation tools. (C) 2015 Elsevier Ltd. All rights reserved. |
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1364-8152 |
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CropM LiveM, ftnotmacsur |
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MA @ admin @ |
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4503 |
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