A Multimethod Global Sensitivity Analysis Approach to Support the Calibration and Evaluation of Land Surface Models

2017 ◽  
pp. 125-144 ◽  
Author(s):  
F. Pianosi ◽  
J. Iwema ◽  
R. Rosolem ◽  
T. Wagener
Keyword(s):  
Sensitivity Analysis ◽  
Land Surface ◽  
Global Sensitivity Analysis ◽  
Analysis Approach ◽  
Land Surface Models ◽  
Global Sensitivity ◽  
Surface Models

scholarly journals Global sensitivity analysis of the climate–vegetation system to astronomical forcing: an emulator-based approach

2015 ◽  
Vol 6 (1) ◽  
pp. 205-224 ◽  
Author(s):  
N. Bounceur ◽  
M. Crucifix ◽  
R. D. Wilkinson
Keyword(s):  
Climate Change ◽  
Sensitivity Analysis ◽  
Land Surface ◽  
Global Sensitivity Analysis ◽  
Climate Model ◽  
Principal Component ◽  
Arctic Region ◽  
Global Sensitivity ◽  
Oscillatory Pattern ◽  
Astronomical Forcing

Abstract. A global sensitivity analysis is performed to describe the effects of astronomical forcing on the climate–vegetation system simulated by the model of intermediate complexity LOVECLIM in interglacial conditions. The methodology relies on the estimation of sensitivity measures, using a Gaussian process emulator as a fast surrogate of the climate model, calibrated on a set of well-chosen experiments. The outputs considered are the annual mean temperature and precipitation and the growing degree days (GDD). The experiments were run on two distinct land surface schemes to estimate the importance of vegetation feedbacks on climate variance. This analysis provides a spatial description of the variance due to the factors and their combinations, in the form of "fingerprints" obtained from the covariance indices. The results are broadly consistent with the current under-standing of Earth's climate response to the astronomical forcing. In particular, precession and obliquity are found to contribute in LOVECLIM equally to GDD in the Northern Hemisphere, and the effect of obliquity on the response of Southern Hemisphere temperature dominates precession effects. Precession dominates precipitation changes in subtropical areas. Compared to standard approaches based on a small number of simulations, the methodology presented here allows us to identify more systematically regions susceptible to experiencing rapid climate change in response to the smooth astronomical forcing change. In particular, we find that using interactive vegetation significantly enhances the expected rates of climate change, specifically in the Sahel (up to 50% precipitation change in 1000 years) and in the Canadian Arctic region (up to 3° in 1000 years). None of the tested astronomical configurations were found to induce multiple steady states, but, at low obliquity, we observed the development of an oscillatory pattern that has already been reported in LOVECLIM. Although the mathematics of the analysis are fairly straightforward, the emulation approach still requires considerable care in its implementation. We discuss the effect of the choice of length scales and the type of emulator, and estimate uncertainties associated with specific computational aspects, to conclude that the principal component emulator is a good option for this kind of application.

scholarly journals Global sensitivity analysis of the climate–vegetation system to astronomical forcing: an emulator-based approach

2014 ◽  
Vol 5 (2) ◽  
pp. 901-943 ◽  
Author(s):  
N. Bounceur ◽  
M. Crucifix ◽  
R. D. Wilkinson
Keyword(s):  
Sensitivity Analysis ◽  
Land Surface ◽  
Global Sensitivity Analysis ◽  
Climate Model ◽  
Arctic Sea Ice ◽  
Growing Degree Days ◽  
Degree Days ◽  
Global Sensitivity ◽  
Non Linear ◽  
Astronomical Forcing

Abstract. A global sensitivity analysis is used to describe the response of the Earth Climate Model of Intermediate Complexity LOVECLIM to components of the astronomical forcing (longitude of perihelion, obliquity, and eccentricity) assuming interglacial boundary conditions. Compared to previous studies, the sensitivity is global in the sense that it considers the full range of astronomical forcing that occurred during the Quaternary. We provide a geographical description of the variance due to the different components and their combinations and identify non-linear responses. The methodology relies on the estimation of sensitivity measures, which due to the computational cost of LOVECLIM cannot be obtained directly. Instead, we use a fast surrogate of the climate model, called an emulator, in place of the simulator. A space filling design (a maximin Latin hypercube constrained to span the range of astronomical forcings characterising the Pleistocene) is used to determine a set of experiments to run, which are then used to train a reduced-rank Gaussian process emulator. The simulator outputs considered are the principal modes of the annual mean temperature, precipitation, and the growing degree days, extracted using a principal component analysis. The experiments are run on two distinct land surface schemes to address the effect of vegetation response on climate. Sensitivity to initial conditions is also explicitly assessed. Precession and obliquity are found to contribute equally to growing degree days (GDD) in the Northern Hemisphere, and the effects of obliquity on the response of Southern Hemisphere temperature dominate precession effects. Further, compared to the original land-surface scheme with fixed vegetation, the LOVECLIM interactive vegetation induces non-linear responses in the Sahel-Sahara and Arctic sea-ice area. Finally, we find that there is no synergy between obliquity and precession.

scholarly journals Global Sensitivity Analysis of the SCOPE Model in Sentinel-3 Bands: Thermal Domain Focus

2019 ◽  
Vol 11 (20) ◽  
pp. 2424 ◽  
Author(s):  
Egor Prikaziuk ◽  
Christiaan van der Tol
Keyword(s):  
Sensitivity Analysis ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Global Sensitivity Analysis ◽  
Dry Matter Content ◽  
Area Index ◽  
Near Surface ◽  
Global Sensitivity ◽  
Scope Model

Sentinel-3 satellite has provided simultaneous observations in the optical (visible, near infrared (NIR), shortwave infrared (SWIR)) and thermal infrared (TIR) domains since 2016, with a revisit time of 1–2 days. The high temporal resolution and spectral coverage make the data of this mission attractive for vegetation monitoring. This study explores the possibilities of using the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model together with Sentinel-3 to exploit the two sensors onboard of Sentinel-3 (the ocean and land color instrument (OLCI) and sea and land surface temperature radiometer (SLSTR)) in synergy. Sobol’ variance based global sensitivity analysis (GSA) of top of atmosphere (TOA) radiance produced with a coupled SCOPE-6S model was conducted for optical bands of OLCI and SLSTR, while another GSA of SCOPE was conducted for the land surface temperature (LST) product of SLSTR. The results show that in addition to ESA level-2 Sentinel-3 products, SCOPE is able to retrieve leaf area index (LAI), leaf chlorophyll content (Cab), leaf water content (Cw), leaf senescent material (Cs), leaf inclination distribution (LAD). Leaf dry matter content (Cdm) and soil brightness, despite being important, were not confidently retrieved in some cases. GSA of LST in TIR domain showed that plant biochemical parameters—maximum carboxylation rate (Vcmax) and stomata conductance-photosynthesis slope (Ball-Berry m)—can be constrained if prior information on near-surface weather conditions is available. We conclude that the combination of optical and thermal domains facilitates the constraint of the land surface energy balance using SCOPE.

Peering into Agricultural Rebound Phenomenon Using a Global Sensitivity Analysis Approach

2021 ◽  
pp. 126739
Author(s):  
Mohammad Ghoreishi ◽  
Razi Sheikholeslami ◽  
Amin Elshorbagy ◽  
Saman Razavi ◽  
Kenneth Belcher
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Analysis Approach ◽  
Global Sensitivity ◽  
Rebound Phenomenon

scholarly journals A global sensitivity analysis approach for morphogenesis models

2015 ◽  
Vol 9 (1) ◽  
Author(s):  
Sonja E. M. Boas ◽  
Maria I. Navarro Jimenez ◽  
Roeland M. H. Merks ◽  
Joke G. Blom
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Analysis Approach ◽  
Global Sensitivity

scholarly journals What do we do with model simulation crashes? Recommendations for global sensitivity analysis of earth and environmental systems models

2019 ◽  
Author(s):  
Razi Sheikholeslami ◽  
Saman Razavi ◽  
Amin Haghnegahdar
Keyword(s):  
Sensitivity Analysis ◽  
Sample Size ◽  
Response Surface ◽  
Land Surface ◽  
Global Sensitivity Analysis ◽  
Model Simulation ◽  
Surface Modelling ◽  
Global Sensitivity ◽  
Environmental Systems ◽  
Response Surface Modelling

Abstract. Complex, software-intensive, technically advanced, and computationally demanding models, presumably with ever-growing realism and fidelity, have been widely used to simulate and predict the dynamics of the Earth and environmental systems. The parameter-induced simulation crash (failure) problem is typical across most of these models, despite considerable efforts that modellers have directed at model development and implementation over the last few decades. A simulation failure mainly occurs due to the violation of the numerical stability conditions, non-robust numerical implementations, or errors in programming. However, the existing sampling-based analysis techniques such as global sensitivity analysis (GSA) methods, which require running these models under many configurations of parameter values, are ill-equipped to effectively deal with model failures. To tackle this problem, we propose a novel approach that allows users to cope with failed designs (samples) during the GSA, without knowing where they took place and without re-running the entire experiment. This approach deems model crashes as missing data and uses strategies such as median substitution, single nearest neighbour, or response surface modelling to fill in for model crashes. We test the proposed approach on a 10-paramter HBV-SASK rainfall-runoff model and a 111-parameter MESH land surface-hydrology model. Our results show that response surface modelling is a superior strategy, out of the data filling strategies tested, and can scale well to the dimensionality of the model, sample size, and the ratio of number of failures to the sample size. Further, we conduct a "failure analysis" and discuss some possible causes of the MESH model failure.

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