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This paper describes the first implementation of the Δx = 3.25 km version of the Energy Exascale Earth System Model (E3SM) global atmosphere model and its behavior in a 40‐day prescribed‐sea‐surface‐temperature simulation (January 20 through February 28, 2020). This simulation was performed as part of the DYnamics of the Atmospheric general circulation Modeled On Non‐hydrostatic Domains (DYAMOND) Phase 2 model intercomparison. Effective resolution is found to be the horizontal dynamics grid resolution despite using a coarser grid for physical parameterizations. Despite this new model being in an immature and untuned state, moving to 3.25 km grid spacing solves several long‐standing problems with the E3SM model. In particular, Amazon precipitation is much more realistic, the frequency of light and heavy precipitation is improved, agreement between the simulated and observed diurnal cycle of tropical precipitation is excellent, and the vertical structure of tropical convection and coastal stratocumulus look good. In addition, the new model is able to capture the frequency and structure of important weather events (e.g., tropical cyclones, extratropical cyclones including atmospheric rivers, and cold air outbreaks). Interestingly, this model does not get rid of the erroneous southern branch of the intertropical convergence zone nor the tendency for strongest convection to occur over the Maritime Continent rather than the West Pacific, both of which are classic climate model biases. Several other problems with the simulation are identified, underscoring the fact that this model is a work in progress.

We examine the performance profile of Convolutional Neural Network (CNN) training on the current generation of NVIDIA Graphics Processing Units (GPUs). We introduce two new Fast Fourier Transform convolution implementations: one based on NVIDIA's cuFFT library, and another based on a Facebook authored FFT implementation, fbfft, that provides significant speedups over cuFFT (over 1.5×) for whole CNNs. Both of these convolution implementations are available in open source, and are faster than NVIDIA's cuDNN implementation for many common convolutional layers (up to 23.5× for a synthetic kernel configuration). We discuss different performance regimes of convolutions, comparing areas where straightforward time domain convolutions outperform Fourier frequency domain convolutions. Details on algorithmic applications of NVIDIA GPU hardware specifics in the implementation of fbfft are also provided.

Denitrification is an important potential sink for N in liquid manure and the amount of denitrification may affect sustainability of crops grown with liquid manure as a nutrient source. This study examined gaseous N loss by denitrification and the changes in soil N pools after liquid manure application. Liquid dairy manure was applied at four N rates (246, 427, 643, and 802 kg N ha−1 yr−1) to four quadrants of a center‐pivot in a year‐round forage production system. Denitrification (using the acetylene block technique on intact cores) and soil N pools were determined before and for 2 yr after beginning liquid manure application. Nitrous oxide evolution from soil cores was compared to denitrification for a third year of the study. Denitrification rates and soil N pools increased after manure application at all rates of application. The two highest rates of manure had highest denitrification rates, although differences in soil moisture due to soil and drainage properties complicated the interpretation of manure rate effects. At the two highest rates of N application and two lowest rates of N application, the quadrant with higher soil moisture had higher denitrification. Nitrous oxide emissions accounted for about 29% of total denitrification. Denitrification ranged from 11 to 37% of total N applied in the manure. Highest rates of denitrification and highest proportions of total N denitrified were found with the second highest manure application rate because these soils were wetter. Annual denitrification totals ranged from 32 to 114% of the excess N (application‐crop uptake) available.