This study aims to investigate the response of simulated tropical cyclone formation to specific climate conditions, using an idealized aquaplanet framework of an ~40-km-horizontal-resolution atmospheric general circulation model. Two sets of idealized model experiments have been performed, one with a set of uniformly distributed constant global sea surface temperatures (SSTs) and another in which varying meridional SST gradients are imposed. The results show that the strongest relationship between climate and tropical cyclone formation is with vertical static stability: increased static stability is strongly associated with decreased tropical cyclone formation. Vertical wind shear and midtropospheric vertical velocity also appear to be related to tropical cyclone formation, although below a threshold value of wind shear there appears to be little relationship. The relationship of tropical cyclone formation with maximum potential intensity and mean sea surface temperature is weak and not monotonic. These simulations strongly suggest that vertical static stability should be part of any climate theory of tropical cyclone formation.
Abstract. A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes
(convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning techniques to be applied to off-the-grid spatio-temporal
data. In contrast to existing methods that map from low-resolution model output to high-resolution predictions at a discrete set of locations, this
model outputs a stochastic process that can be queried at an arbitrary latitude–longitude coordinate. The convCNP model is shown to outperform an
ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The
model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly,
substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust
downscaling model suitable for generating localised projections for use in climate impact studies.
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