North Atlantic climate far more predictable than models imply.

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1-3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7-9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade.

Preliminary results of 3D dose calculations with MCNP-4B code from a SPECT image.

Interface software was developed to generate the input file to run Monte Carlo MCNP-4B code from medical image in Interfile format version 3.3. The software was tested using a spherical phantom of tomography slides with known cumulated activity distribution in Interfile format generated with IMAGAMMA medical image processing system. The 3D dose calculation obtained with Monte Carlo MCNP-4B code was compared with the voxel S factor method. The results show a relative error between both methods less than 1 %.

Preliminary results of 3D dose calculations with MCNP-4B code from a SPECT image / Resultados preliminares de los cálculos de dosis 3D con el código de MCNP-4B de una imagen de SPECT

Interface software was developed to generate the input file to run Monte Carlo MCNP-4B code from medical image in Interfile format version 3.3. The software was tested using a spherical phantom of tomography slides with known cumulated activity distribution in Interfile format generated with IMAGAMMA medical image processing system. The 3D dose calculation obtained with Monte Carlo MCNP-4B code was compared with the voxel S factor method. The results show a relative error between both methods less than 1% (AU)

Se creó un programa de interfaz para generar el archivo de entrada que permitiera procesar el código Monte Carlo MCNP-4B de la imagen médica, en la versión 3.3 del formato Interfile. El programa se probó usando un fantoma esférico de cortes tomográficos con distribución de actividad acumulada uniformemente en formato Interfile generada desde el sistema de procesamiento de imágenes médicas IMAGAMMA. La distribución de dosis fue obtenida con el código de Monte Carlo MCNP-4B y sus resultados fueron comparados con el método de voxel del factor S. Los resultados mostraron un error relativo entre ambos métodos menor del 1% (AU)