Scientific challenges exist on how to extract information from the wide range of projected impacts simulated by crop models driven by climate ensembles. A stronger focus is required to understand and identify the mechanisms and drivers of projected changes in crop yield. In this study, we investigate the robustness of future projections of five metrics relevant to agriculture stakeholders (accumulated frost days, dry days, growing season length, plant heat stress and start of field operations). We use a large ensemble of climate simulations by the MIT IGSM-CAM integrated assessment model that accounts for the uncertainty associated with different emissions scenarios, climate sensitivity, and natural variability. By end of century, the US is projected to experience fewer frosts, a longer growing season, more heat stress and an earlier start of field operations—although the magnitude and even the sign of these changes vary greatly by regions. Projected changes in dry days are shown not to be robust. We highlight the important role of natural variability, in particular for changes in dry days (a precipitation-related index) and heat stress (a threshold index). The wide range of our projections compares well the CMIP5 multi-model ensemble, especially for temperature-related indices. This suggests that using a single climate model that accounts for key sources of uncertainty can provide an efficient and complementary framework to the more common approach of multi-model ensembles. We also show that greenhouse gas mitigation has the potential to significantly reduce adverse effects (heat stress, risks of pest and disease) of climate change on agriculture, while also curtailing potentially beneficial impacts (earlier planting, possibility for multiple cropping). A major benefit of climate mitigation is potentially preventing changes in several indices to emerge from the noise of natural variability, even by 2100. This has major implications considering that any significant climate change impacts on crop yield would result in nation-wide changes in the agriculture sector. Finally, we argue that the analysis of agro-climate indices should more often complement crop model projections, as they can provide valuable information to better understand the drivers of changes in crop yield and production and thus better inform adaptation decisions.

Carbon capture and storage (CCS) from coal combustion is widely viewed as an important approach for China’s carbon dioxide (CO₂) emission mitigation, but the pace of its development is still fairly slow. In addition to the technological and economic uncertainties of CCS, lack of strong policy incentive is another main reason for the wide gap between early expectations and the actual progress towards its demonstration and commercialization. China’s mitigation scenario and targets are crucial to long-term development of CCS. In this research, impacts of CCS on energy and CO₂ emissions are evaluated under two mitigation scenarios reflecting different policy effort levels for China using the China-in-Global Energy Model (C-GEM). Results indicate that with CCS applications in the power sector China can achieve an added emissions reduction of 0.3 to 0.6 Gigatons CO₂ (GtCO₂) in 2050 at the same level of carbon taxes respectively in the two mitigation scenarios. Under the more ambitious mitigation scenario, approximately 56% of China’s fossil fuel fired power plants will have CCS installed, and CO₂ emission amounting to 1.4 GtCO₂ will be captured in 2050. A carbon price not lower than $35/tCO₂ appears to be necessary for the large-scale application of CCS in the power sector, indicating the vital role of policy in the deployment of CCS in China’s power sector.

We estimate the costs of climate change to US agriculture, and associated potential benefits of abating greenhouse gas emissions. Five major crops yield responses to climatic variation are modeled empirically, and the results combined with climate projections for a no-policy, high-warming future, as well as moderate and stringent mitigation scenarios. Unabated warming reduces yields of wheat and soybeans by 2050, and cotton by 2100, but moderate warming increases yields of all crops except wheat. Yield changes are monetized using the results of economic simulations within an integrated climate-economy modeling framework. The economic effects of uncontrolled warming on major crops are slightly positive—annual benefits < $4B. These are amplified by emission reductions, but subject to diminishing returns—by 2100 reaching $17B under moderate mitigation, but only $7B with stringent mitigation. Costs and benefits are sensitive to irreducible uncertainty about the fertilization effects of elevated atmospheric carbon dioxide, without which unabated warming incurs net costs of up to $18B, generating benefits to moderate (stringent) mitigation as large as $26B ($20B).

Gridded precipitation-gauge observations and global atmospheric reanalysis are combined to develop an analogue method for detecting the occurrence of heavy precipitation events based on the prevailing large-scale atmospheric conditions. Combinations of different atmospheric variables for circulation features (geopotential height and wind vector) and moisture plumes (surface specific humidity, column precipitable water, and precipitable water up to 500hPa) are examined to construct the analogue schemes for the winter (DJF) of the Pacific Coast California (PCCA) and the summer (JJA) of the Midwestern United States (MWST). The detection diagnostics of various analogue schemes are calibrated with 27-yr (1979–2005) and then validated with 9-yr (2006–2014) NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA). All of the analogue schemes are found to significantly improve upon MERRA precipitation in characterizing the number and interannual variations of observed heavy precipitation events in the MWST which is one of weakest regions for MERRA summer precipitation. When evaluated with the late 20th century simulations from an ensemble of climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), all analogue schemes produce model medians of heavy precipitation frequency that are more consistent with observations and have smaller inter-model discrepancies when compared with the model-based precipitation. Further, the performances of analogue schemes with vector winds are comparable to those of geopotential height, and no analogue scheme with one of three water vapor content variables is clearly superior to another. Under two radiative forcing scenarios (Representative Concentration Pathways 4.5 and 8.5), the CMIP5-based analogue schemes produce a trend in the occurrence of heavy events through the 21st century consistent with the model-based precipitation, but with smaller inter-model disparity. The strongest reduction in the disparity of the results is seen for the RCP8.5 scenario. The median trends in DJF heavy precipitation frequency for PCCA are positive, but for JJA heavy event frequency over the MWST region, the median trends are slightly negative. Overall, the presented analyses highlight the potential of the analogue as a powerful diagnostic tool for model deficiencies and its complementarity to an evaluation that considers modeled precipitation alone to assess heavy precipitation frequency. The consistency found here between projections from analogues and model precipitation increases confidence in projected heavy precipitation frequency changes in a warming climate.