Constraining DALECv2 using multiple data streams  and ecological constraints: analysis and application

Delahaies, Sylvain; Roulstone, Ian; Nichols, Nancy K.

doi:10.5194/gmd-10-2635-2017

Cited by 3 publications

(3 citation statements)

References 19 publications

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“…Particle filter approaches (e.g. Goosse et al, 2006;Dubinkina et al, 2011) produce dynamic estimates of palaeoclimate, but particle filters cannot produce estimates of climate outside the realm of the model simulations. Our 3-D variational data assimilation approach has the great merit of being able to produce seasonally coherent reconstructions generalized over space, while at the same time being capable of producing reconstructions that are outside those captured by the climate model, because they are not constrained by a specific source (Nichols, 2010).…”

Section: Discussionmentioning

confidence: 99%

A new multivariable benchmark for Last Glacial Maximum climate simulations

et al. 2020

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Abstract. We present a new global reconstruction of seasonal climates at the Last Glacial Maximum (LGM, 21 000 years BP) made using 3-D variational data assimilation with pollen-based site reconstructions of six climate variables and the ensemble average of the PMIP3—CMIP5 simulations as a prior (initial estimate of LGM climate). We assume that the correlation matrix of the uncertainties in the prior is both spatially and temporally Gaussian, in order to produce a climate reconstruction that is smoothed both from month to month and from grid cell to grid cell. The pollen-based reconstructions include mean annual temperature (MAT), mean temperature of the coldest month (MTCO), mean temperature of the warmest month (MTWA), growing season warmth as measured by growing degree days above a baseline of 5 ∘C (GDD5), mean annual precipitation (MAP), and a moisture index (MI), which is the ratio of MAP to mean annual potential evapotranspiration. Different variables are reconstructed at different sites, but our approach both preserves seasonal relationships and allows a more complete set of seasonal climate variables to be derived at each location. We further account for the ecophysiological effects of low atmospheric carbon dioxide concentration on vegetation in making reconstructions of MAP and MI. This adjustment results in the reconstruction of wetter climates than might otherwise be inferred from the vegetation composition. Finally, by comparing the uncertainty contribution to the final reconstruction, we provide confidence intervals on these reconstructions and delimit geographical regions for which the palaeodata provide no information to constrain the climate reconstructions. The new reconstructions will provide a benchmark created using clear and defined mathematical procedures that can be used for evaluation of the PMIP4–CMIP6 entry-card LGM simulations and are available at https://doi.org/10.17864/1947.244 (Cleator et al., 2020b).

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Section: Discussionmentioning

confidence: 99%

A new multivariable benchmark for Last Glacial Maximum climate simulations

et al. 2020

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“…Further, Nichols () shows how

x_{a} - x_{b} \approx K (y - h (x_{b})),

where K is the gain matrix defined in equation . Hence, we can consider the change from the true solution to our computed one ( w a ) as being given by

w_{a} \approx N w_{t},

where

N = B^{- \frac{1}{2}} K H_{x_{b}} B^{\frac{1}{2}}

is the resolution matrix as described in Menke () and Delahaies et al ().…”

Section: Experimental Designmentioning

confidence: 99%

“…Specifically, by solving the full variational problem, we take into account nonlinearities in the system. Furthermore, we minimize the dependency of the final analytical reconstructions on the prior generated from the climate models by using a prescribed correlation function for the error of the prior and by using a resolution matrix (Delahaies et al, ; Menke, ) to determine the temporal correlation length scale. The resolution matrix provides a particularly useful way to overcome problems caused by the sparsity of site‐based paleoclimate reconstructions at the LGM.…”

Section: Introductionmentioning

confidence: 99%

A Method for Generating Coherent Spatially Explicit Maps of Seasonal Paleoclimates From Site‐Based Reconstructions

Cleator

Harrison

Nichols

et al. 2020

J Adv Model Earth Syst

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We describe a method for reconstructing spatially explicit maps of seasonal paleoclimate variables from site-based reconstructions. Using a 3-D-Variational technique, the method finds the best statistically unbiased, and spatially continuous, estimate of the paleoclimate anomalies through combining the site-based reconstructions and a prior estimate of the paleoclimate state. By assuming a set of correlations in the error of the prior, the resulting climate is smoothed both from month to month and from grid cell to grid cell. The amount of smoothing can be controlled through the choice of two length-scale values. The method is applied to a set of reconstructions of the climate of the Last Glacial Maximum (ca. 21,000 years ago) for southern Europe derived from pollen data with a prior derived from results from the third phase of the Palaeoclimate Modelling Intercomparison Project. We demonstrate how to choose suitable values for the smoothing length scales for the data sets used in the reconstruction.

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A systematic view of remote sensing

2020

Advanced Remote Sensing

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Constraining DALECv2 using multiple data streams and ecological constraints: analysis and application

Cited by 3 publications

References 19 publications

A new multivariable benchmark for Last Glacial Maximum climate simulations

A new multivariable benchmark for Last Glacial Maximum climate simulations

A Method for Generating Coherent Spatially Explicit Maps of Seasonal Paleoclimates From Site‐Based Reconstructions

A systematic view of remote sensing

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