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Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.

Asseng, S. et al. Crop models are essential tools for assessing the threat of climate change to local and global food production1. Present models used to predict wheat grain yield are highly uncertain when simulating how crops respond to temperature2. Here we systematically tested 30 different wheat crop models of the Agricultural Model Intercomparison and Improvement Project against field experiments in which growing season mean temperatures ranged from 15 °C to 32 °C, including experiments with artificial heating. Many models simulated yields well, but were less accurate at higher temperatures. The model ensemble median was consistently more accurate in simulating the crop temperature response than any single model, regardless of the input information used. Extrapolating the model ensemble temperature response indicates that warming is already slowing yield gains at a majority of wheat-growing locations. Global wheat production is estimated to fall by 6% for each °C of further temperature increase and become more variable over space and time. We thank the Agricultural Model Intercomparison and Improvement Project and its leaders C. Rosenzweig from NASA Goddard Institute for Space Studies and Columbia University (USA), J. Jones from University of Florida (USA), J. Hatfield from United States Department of Agriculture (USA) and J. Antle from Oregon State University (USA) for support. We also thank M. Lopez from CIMMYT (Turkey), M. Usman Bashir from University of Agriculture, Faisalabad (Pakistan), S. Soufizadeh from Shahid Beheshti University (Iran), and J. Lorgeou and J-C. Deswarte from ARVALIS—Institut du Végétal (France) for assistance with selecting key locations and quantifying regional crop cultivars, anthesis and maturity dates and R. Raymundo for assistance with GIS. S.A. and D.C. received financial support from the International Food Policy Research Institute (IFPRI). C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Science Foundation (project EW 119/5-1). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM—Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and P.D.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O’L. was funded through the Australian Grains Research and Development Corporation and the Department of Environment and Primary Industries Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. E.W. and Z.Z. were funded by CSIRO and the Chinese Academy of Sciences (CAS) through the research project ‘Advancing crop yield while reducing the use of water and nitrogen’ and by the CSIRO-MoE PhD Research Program. Peer reviewed

Recent studies have correlated kinetic energy (KE) and viscous dissipation rate (VDR) in the left ventricle (LV) with heart health. These studies have relied on 4D-flow imaging or computational fluid dynamics modeling, which are able to measure, or compute, all 3 components (3C) of the blood flow velocity in 3 dimensional (3D) space. This richness of data is difficult to acquire clinically. Alternatively, color Doppler echocardiography (CDE) is more widespread clinically, but only measures a single radial component of velocity and typically only over a planar section. Because of this limitation, prior CDE-based studies have first reconstructed a second component of velocity in the measurement plane prior to evaluating VDR or KE. Herein, we propose 1C-based surrogates of KE and VDR that can be derived directly from the radial component of the flow velocity in the LV. Our results demonstrate that the proposed 1C-based surrogates of KE and VDR are generally as well-correlated with the true KE and VDR values as surrogates that use reconstructed 2C flow data. Moreover, the correlation of these 1C-based surrogates with the true values indicate that CDE (3D in particular) may be useful in evaluating these metrics in practice.

AbstractGlobal impact models represent process-level understanding of how natural and human systems may be affected by climate change. Their projections are used in integrated assessments of climate change. Here we test, for the first time, systematically across many important systems, how well such impact models capture the impacts of extreme climate conditions. Using the 2003 European heat wave and drought as a historical analogue for comparable events in the future, we find that a majority of models underestimate the extremeness of impacts in important sectors such as agriculture, terrestrial ecosystems, and heat-related human mortality, while impacts on water resources and hydropower are overestimated in some river basins; and the spread across models is often large. This has important implications for economic assessments of climate change impacts that rely on these models. It also means that societal risks from future extreme events may be greater than previously thought.

ZnTe doped with high concentrations of oxygen has been proposed in previous works as an intermediate band (IB) material for photovoltaic applications. The existence of extra optical transitions related to the presence of an IB has already been demonstrated in this material and it has been possible to measure the absorption coefficient of the transitions from the valence band (VB) to the IB. In this study, we present the first measurement of the absorption coefficient associated with transitions from the IB to the conduction band (CB) in ZnTeO. The samples used are 4-mum-thick ZnTe layers with or without O in a concentration ~10 19 cm -3, which have been grown on semiinsulating GaAs substrates by molecular beam epitaxy (MBE). The IB-CB absorption coefficient peaks for photon energies ~0.4 eV. It is extracted from reflectance and transmittance spectra measured using Fourier transform infrared (FTIR) spectroscopy. Under typical FTIR measurement conditions (low light intensity, broadband spectrum), the absorption coefficient in IB-to-CB transitions reaches 700 cm -1. This is much weaker than the one observed for VB-IB absorption. This result is consistent with the fact that the IB is expected to be nearly empty of electrons under equilibrium conditions in ZnTe(O).