Internal variability in the climate system confounds assessment of human-induced climate change and imposes irreducible limits on the accuracy of climate change projections, especially at regional and decadal scales. A new collection of initial-condition large ensembles (LEs) generated with seven Earth system models under historical and future radiative forcing scenarios provides new insights into uncertainties due to internal variability versus model differences. These data enhance the assessment of climate change risks, including extreme events, and offer a powerful testbed for new methodologies aimed at separating forced signals from internal variability in the observational record. Opportunities and challenges confronting the design and dissemination of future LEs, including increased spatial resolution and model complexity alongside emerging Earth system applications, are discussed.
High temperature extremes during the growing season can reduce agricultural production. At the same time, agricultural practices can modify temperatures by altering the surface energy budget. Here we identify centennial trends towards more favourable growing conditions in the US Midwest, including cooler summer temperature extremes and increased precipitation, and investigate the origins of these shifts. Statistically significant correspondence is found between the cooling pattern and trends in cropland intensification, as well as with trends towards greater irrigated land over a small subset of the domain. Land conversion to cropland, often considered an important influence on historical temperatures, is not significantly associated with cooling. We suggest that agricultural intensification increases the potential for evapotranspiration, leading to cooler temperatures and contributing to increased precipitation. The tendency for greater evapotranspiration on hotter days is consistent with our finding that cooling trends are greatest for the highest temperature percentiles. Temperatures over rainfed croplands show no cooling trend during drought conditions, consistent with evapotranspiration requiring adequate soil moisture, and implying that modern drought events feature greater warming as baseline cooler temperatures revert to historically high extremes. I ncreasing population, rising per capita food demand, and limited availability of arable land all point to a need to achieve greater crop productivity 1 . Climate change, however, may compromise the ability to sustain growth in crop yields 2 , in part owing to expected increases in damaging extreme temperatures 3-5 . Yet agricultural areas are subject to substantial local, as well as global, climate forcings, as changes in agricultural land cover and land management can alter the surface energy balance and influence temperatures 6-20 . Against this backdrop, it is relevant to examine historical trends in growing-season climate, especially in the most important growing regions. We focus on the US Midwest because it exhibits the most vigorous crop growth anywhere on the planet during the peak of the growing season ( Fig. 1), and because of the availability of detailed weather and crop data. Centennial trends in Midwest summer climateAlthough overall US temperature trends are towards warming over the past century, the hottest temperatures observed during the growing season in the US Midwest have actually cooled. We examine temperature since 1910 as a balance between duration and availability of continuous data, and use quantile regression of daily maximum temperature records from weather stations to assess trends across multiple percentiles (see Methods). Trends in hot summer temperatures are evaluated using the 95th percentile, and are of particular interest because of the negative effects of high temperatures on yield 3-5 . Midwest cooling is less evident in median temperature trends, and temperatures are generally warming at the 5th percentile (Fig. 2a,b). The...
Application of random sampling techniques to composite differences between 18 El Niño and 14 La Niña events observed since 1920 reveals considerable uncertainty in both the pattern and amplitude of the Northern Hemisphere extratropical winter sea level pressure (SLP) response to ENSO. While the SLP responses over the North Pacific and North America are robust to sampling variability, their magnitudes can vary by a factor of 2; other regions, such as the Arctic, North Atlantic, and Europe are less robust in their SLP patterns, amplitudes, and statistical significance. The uncertainties on the observed ENSO composite are shown to arise mainly from atmospheric internal variability as opposed to ENSO diversity. These observational findings pose considerable challenges for the evaluation of ENSO teleconnections in models. An approach is proposed that incorporates both pattern and amplitude uncertainty in the observational target, allowing for discrimination between true model biases in the forced ENSO response and apparent model biases that arise from limited sampling of non-ENSO-related internal variability. Large initial-condition coupled model ensembles with realistic tropical Pacific sea surface temperature anomaly evolution during 1920–2013 show similar levels of uncertainty in their ENSO teleconnections as found in observations. Because the set of ENSO events in each of the model composites is the same (and identical to that in observations), these uncertainties are entirely attributable to sampling fluctuations arising from internal variability, which is shown to originate from atmospheric processes. The initial-condition model ensembles thus inform the interpretation of the single observed ENSO composite and vice versa.
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