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L. J. Wilcox, B. Dong, R. T. Sutton, and E. J. Highwood
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W. K. M. Lai, J. I. Robson, L. J. Wilcox, and N. Dunstone


This study broadly characterizes and compares the key processes governing internal Atlantic multidecadal variability (AMV) in two resolutions of HadGEM3-GC3.1: N216ORCA025, corresponding to ∼60 km in the atmosphere and 0.25° in the ocean, and N96ORCA1 (∼135 km in the atmosphere and 1° in the ocean). Both models simulate AMV with a time scale of 60–80 years, which is related to low-frequency ocean and atmosphere circulation changes. In both models, ocean heat transport convergence dominates polar and subpolar AMV, whereas surface heat fluxes associated with cloud changes drive subtropical AMV. However, details of the ocean circulation changes differ between the models. In N216 subpolar subsurface density anomalies propagate into the subtropics along the western boundary, consistent with the more coherent circulation changes and widespread development of SST anomalies. In contrast, N96 subsurface density anomalies persist in the subpolar latitudes for longer, so circulation anomalies and the development of SST anomalies are more centered there. The drivers of subsurface density anomalies also differ between models. In N216, the NAO is the dominant driver, while upper-ocean salinity-controlled density anomalies that originate from the Arctic appear to be the dominant driver in N96. These results further highlight that internal AMV mechanisms are model dependent and motivate further work to better understand and constrain the differences.

Open access
D. H. Staelin, K. F. Kunzi, R. L. Pettyjohn, R. K. L. Poon, R. W. Wilcox, and J. W. Waters


The passive microwave spectrometer on the Nimbus 5 satellite has two channels that measure atmospheric water vapor and liquid water abundances over ocean. Observed water vapor abundances range up to 6 g cm−2 and differ from nearby radiosondes by ∼0.4 g cm−2. Average liquid water abundances over a 300 km observation zone range from −0.01 to 0.2 g cm−2, and have an rms error estimated to be ∼0.01 g cm−2 for most circumstances. These quantitative measurements can be used to construct global maps or to accumulate global statistics.

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Yunpeng Shan, Eric M. Wilcox, Lan Gao, Lin Lin, David L. Mitchell, Yan Yin, Tianliang Zhao, Lei Zhang, Hongrong Shi, and Meng Gao


Significant uncertainty lies in representing the rain droplet size distribution (DSD) in bulk cloud microphysics schemes and in the derivation of parameters of the function fit to the spectrum from the varying moments of a DSD. Here we evaluate the suitability of gamma distribution functions (GDFs) for fitting rain DSDs against observed disdrometer data. Results illustrate that double-parameter GDFs with prescribed or diagnosed positive spectral shape parameters μ fit rain DSDs better than the Marshall–Palmer distribution function (with μ = 0). The relative errors of fitting the spectrum moments (especially high-order moments) decrease by an order of magnitude [from O(102) to O(101)]. Moreover, introduction of a triple-parameter GDF with mathematically solved μ decreases the relative errors to O(100). Based on further investigation of potential combinations of the three prognostic moments for triple-moment cloud microphysical schemes, it is found that the GDF with parameters determined from predictions of the zeroth, third, and fourth moments (the 034 GDF) exhibits the best fit to rain DSDs compared to other moment combinations. Therefore, we suggest that the 034 prognostic moment group should replace the widely accepted 036 group to represent rain DSDs in triple-moment cloud microphysics schemes. An evaluation of the capability of GDFs to represent rain DSDs demonstrates that 034 GDF exhibits accurate fits to all observed DSDs except for rarely occurring extremely wide spectra from heavy precipitation and extremely narrow spectra from drizzle. The knowledge gained from this assessment can also be used to improve cloud microphysics retrieval schemes and data assimilation.

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