A key question for climate change adaptation is whether existing cropping systems can become less sensitive to climate variations. We use a field-level data set on maize and soybean yields in the central United States for 1995 through 2012 to examine changes in drought sensitivity. Although yields have increased in absolute value under all levels of stress for both crops, the sensitivity of maize yields to drought stress associated with high vapor pressure deficits has increased. The greater sensitivity has occurred despite cultivar improvements and increased carbon dioxide and reflects the agronomic trend toward higher sowing densities. The results suggest that agronomic changes tend to translate improved drought tolerance of plants to higher average yields but not to decreasing drought sensitivity of yields at the field scale.

LETTER • OPEN ACCESSComparing and combining process-based crop models and statistical models with some implications for climate change AbstractWe compare predictions of a simple process-based crop model (Soltani and Sinclair 2012), a simple statistical model (Schlenker and Roberts 2009), and a combination of both models to actual maize yields on a large, representative sample of farmer-managed fields in the Corn Belt region of the United States. After statistical post-model calibration, the process model (Simple Simulation Model, or SSM) predicts actual outcomes slightly better than the statistical model, but the combined model performs significantly better than either model. The SSM, statistical model and combined model all show similar relationships with precipitation, while the SSM better accounts for temporal patterns of precipitation, vapor pressure deficit and solar radiation. The statistical and combined models show a more negative impact associated with extreme heat for which the process model does not account. Due to the extreme heat effect, predicted impacts under uniform climate change scenarios are considerably more severe for the statistical and combined models than for the process-based model.

In this paper we present an introduction to Compressive Sampling (CS), an emerging model-based framework for data acquisition and signal recovery based on the premise that a signal having a sparse representation in one basis can be reconstructed from a small number of measurements collected in a second basis that is incoherent with the first. Interestingly, a random noise-like basis will suffice for the measurement process. We will overview the basic CS theory, discuss efficient methods for signal reconstruction, and highlight applications in medical imaging.

High resolution large eddy simulations (LES) are performed to study the interaction of a stationary shock with fully developed turbulent flow. Turbulent statistics downstream of the interaction are provided for a range of weakly compressible upstream turbulent Mach numbers $M_{t}=0.03{-}0.18$, shock Mach numbers $M_{s}=1.2{-}3.0$ and Taylor-based Reynolds numbers $Re_{\unicode[STIX]{x1D706}}=20{-}2500$. The LES displays minimal Reynolds number effects once an inertial range has developed for $Re_{\unicode[STIX]{x1D706}}>100$. The inertial range scales of the turbulence are shown to quickly return to isotropy, and downstream of sufficiently strong shocks this process generates a net transfer of energy from transverse into streamwise velocity fluctuations. The streamwise shock displacements are shown to approximately follow a $k^{-11/3}$ decay with wavenumber as predicted by linear analysis. In conjunction with other statistics this suggests that the instantaneous interaction of the shock with the upstream turbulence proceeds in an approximately linear manner, but nonlinear effects immediately downstream of the shock significantly modify the flow even at the lowest considered turbulent Mach numbers.

We analyze the local wave-number (LWN) model, a two-point spectral closure model for turbulence, as applied to the Rayleigh-Taylor (RT) instability, the flow induced by the relaxation of a statically-unstable density stratification. Model outcomes are validated against data from 3D simulations of the RT instability. In the first part of the study we consider the minimal model terms required to capture inhomogeneous mixing and show that this version, with suitable model coefficients, is sufficient to capture the evolution of important mean global quantities including mix-width, turbulent mass flux velocity, and Reynolds stress, if the start time is chosen such that the earliest transitions are avoided. However, this simple model does not permit the expected finite asymptote of the density-specific-volume covariance b. In the second part of the study, we investigate two forms for a source term for the evolution of the spectrum of density-specific-volume covariance for the LWN model. The first includes an empirically motivated calibration of the source to achieve the final asymptotic state of constant b. The second form does not require calibration but, in conjunction with enhanced diffusion and drag captures the full evolution of all the dynamical quantities, namely, the mix-layer growth, turbulent mass-flux velocity, Reynolds stress, as well as the desired behavior of b.