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Climate change is predicted to impact corn yields. Previous studies analyzing these impacts differ in data and modeling approaches and, consequently, corn yield projections. We analyze the impacts of climate change on corn yields using two statistical models with different approaches for dealing with county-level effects. The first model, which is novel to modeling corn yields, uses a computationally efficient spatial basis function approach. We use a Bayesian framework to incorporate both parametric and climate model structural uncertainty. We find that the statistical models have similar predictive abilities, but the spatial basis function model is faster and hence potentially a useful tool for crop yield projections. We also explore how different gridded temperature datasets affect the statistical model fit and performance. Compared to the dataset with only weather station data, we find that the dataset composed of satellite and weather station data results in a model with a magnified relationship between temperature and corn yields. For all statistical models, we observe a relationship between temperature and corn yields that is broadly similar to previous studies. We use downscaled and bias-corrected CMIP5 climate model projections to obtain detrended corn yield projections for 2020–2049 and 2069–2098. In both periods, we project a decrease in the mean corn yield production, reinforcing the findings of other studies. However, the magnitude of the decrease and the associated uncertainties we obtain differ from previous studies. [ABSTRACT FROM AUTHOR]

Atmospheric CO2 flux inversions require as input an estimate of spatial and temporal correlations of errors in their estimate of the prior mean. Some previous studies have used the differences in CO2 daily average flux estimates produced by terrestrial carbon cycle models and eddy covariance measurements to constrain the flux error correlations. Since inversions are starting to resolve the daily cycle, we set out to examine the correlations at sub‐daily time scales, as well as the correlations across years. To this end, we examine the autocorrelations in the difference between net ecosystem‐atmosphere exchange measurements from 75 AmeriFlux towers and temporally downscaled high‐spatial‐resolution flux estimates from the Carnegie‐Ames‐Stanford Approach (CASA) terrestrial carbon cycle model. We find that the daily cycle is prominent in these hourly autocorrelations and that these autocorrelations persist across years. We propose a family of functions to model these temporal correlations in atmospheric inversions, and use cross validation to determine which of the correlation functions best fits autocorrelation data from towers not in the training set. Correlation functions with a component that attempts to model the daily cycle in the differences match correlations from other towers better than those without. Those models that reproduce the same correlation structures at 1‐year intervals while modulating the amplitudes of the correlations between those intervals improve the fit still further. Plain Language Summary: Atmospheric CO2 flux inversions rely heavily on assumptions about errors in the initial flux estimate. If these errors are related in time or in space, inversions need to represent both the shape and the size of the likely errors. Previous studies have looked at the shape of the errors in daily average fluxes, but fewer have looked at the relationship between errors in one part of a day and in another part of that day, or between 1 year and the next. The study examines relationships in differences between measured and computer‐predicted ecosystem‐atmosphere carbon dioxide fluxes. There are strong relationships between flux errors separated by multiple years. Current attempts to solve for true ecosystem‐atmosphere fluxes using atmospheric data do not take these multi‐year relationships into consideration. Accounting for the multi‐year relationships may improve our ability to determine the true ecosystem‐atmosphere CO2 fluxes. We look for descriptions of these relationships that researchers can use when using atmospheric data to improve the computer‐predicted CO2 fluxes. Key Points: CO2 flux model‐data differences have autocorrelations that recur at intervals of a year with little decay over multiple yearsThese autocorrelations are largely caused by errors in the representation of the daily cycleWe can treat these persistent errors in the daily cycle either as prior flux error correlations or by corrections to the prior flux model [ABSTRACT FROM AUTHOR]