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Paul J Valdes

Publications and source records attributed to Paul J Valdes.

Seasonal Temperatures in West Antarctica During the Holocene

The recovery of long-term climate proxy records with seasonal resolution is rare because of natural smoothing processes, discontinuities, and limitations in measurement resolution. Yet insolation forcing, a primary driver of multi-millennial-scale climate change, acts through seasonal variations with direct impacts on seasonal climate. Whether the sensitivity of seasonal climate to insolation matches theoretical predictions has not been assessed over long timescales. Here, we analyze a continuous record of water-isotope ratios from the West Antarctic Ice Sheet (WAIS) Divide ice core to reveal summer and winter temperature changes through the last 11,000 years. Summer temperatures in West Antarctica increased through the early-to-mid Holocene, reached a peak at 4.1 ka, and then decreased to the present. Climate model simulations show that these variations primarily reflect changes in maximum summer insolation, confirming the general connection between seasonal insolation and warming, and demonstrating the importance of insolation intensity rather than seasonally integrated insolation or season duration. Winter temperatures varied less overall, consistent with predictions from insolation forcing, but also fluctuated in the early Holocene, likely owing to changes in meridional heat transport. The magnitudes of summer and winter temperature changes constrain the lowering of the WAIS surface since the early Holocene to less than 162 m, and most likely less than 58 m, consistent with geological constraints elsewhere in West Antarctica.

Cryospheric science↗

Multi-variate Factorisation of Numerical Simulations

Factorisation is widely used in the analysis of numerical simulations. It allows changes in properties of a system to be attributed to changes in multiple variables associated with that system. There are many possible factorisation methods; here we discuss three previously-proposed factorisations that have been applied in the field of climate modelling: the linear factorisation, the Stein and Alpert (1993) factorisation, and the Lunt et al (2012) factorisation. We show that, when more than two variables are being considered, none of these three methods possess all three properties of "uniqueness", "symmetry", and "completeness". Here, we extend each of these factorisations so that they do possess these properties for any number of variables, resulting in three factorisations – the "linear-sum" factorisation, the "shared-interaction" factorisation, and the "scaled-total" factorisation. We show that the linear-sum factorisation and the shared-interaction factorisation reduce to be identical. We present the results of the factorisations in the context of studies that used the previously-proposed factorisations. This reveals that only the linear-sum/shared-interaction factorisation possesses a fourth property – "boundedness", and as such we recommend the use of this factorisation in applications for which these properties are desirable.

Numerican simulations↗

Multi-Variate Factorisation of Numerical Simulations

Factorisation (also known as “factor separation”) is widely used in the analysis of numerical simulations. It allows changes in properties of a system to be attributed to changes in multiple variables associated with that system. There are many possible factorisation methods; here we discuss three previously proposed factorisations that have been applied in the field of climate modelling: the linear factorisation, the Stein and Alpert (1993) factorisation, and the Lunt et al. (2012) factorisation. We show that, when more than two variables are being considered, none of these three methods possess all four properties of “uniqueness”, “symmetry”, “completeness”, and “purity”. Here, we extend each of these factorisations so that they do possess these properties for any number of variables, resulting in three factorisations – the “linear-sum” factorisation, the “shared-interaction” factorisation, and the “scaled-residual” factorisation. We show that the linear-sum factorisation and the shared-interaction factorisation reduce to be identical in the case of four or fewer variables, and we conjecture that this holds for any number of variables. We present the results of the factorisations in the context of three past studies that used the previously proposed factorisations.

Numerical simulations↗