Abstract: Within the context of a changing climate, scientists are called to engage directly with agricultural stakeholders for the coproduction of relevant information that will support decision making and adaptation. However, values, beliefs, identities, goals, and social networks shape perceptions and actions about climate change. Engagement processes that ignore the socio-cultural context within which stakeholders are embedded may fail to guide adaptive responses. To facilitate dialog around these issues, the Southeast Climate Consortium and the Florida Climate Institute formed a climate learning network consisting of row crop farmers, agricultural extension specialists, researchers, and climate scientists working in the Southeast US. Regional in scope, the learning network engages researchers and practitioners from Alabama, Georgia, and Florida as partners in adaptation science. This paper describes the ongoing interactions, dialog, and experiential learning among the network�s diverse participants. We illustrate how participatory tools have been used in a series of workshops to create interactive spaces for knowledge coproduction. For example, historical timelines, climate scenarios, and technology exchanges stimulated discussions about climate-related risk management. We present findings from the workshops related to participants� perspectives on climate change and adaptation. Finally, we discuss lessons learned that may be applicable to other groups involved in climate education, communication, and stakeholder engagement. We suggest that the thoughtful design of stakeholder engagement processes can become a powerful social tool for improving decision support and strengthening adaptive capacity within rural communities.

Abstract: Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly −0.5 Mg ha−1 per °C. Doubling [CO2] from 360 to 720 μmol mol−1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Abstract: Skilful and reliable precipitation data are essential for seasonal hydrologic forecasting and generation of hydrological data. Although output from dynamic downscaling methods is used for hydrological application, the existence of systematic errors in dynamically downscaled data adversely affects the skill of hydrologic forecasting. This study evaluates the precipitation data derived by dynamically downscaling the global atmospheric reanalysis data by propagating them through three hydrological models. Hydrological models are calibrated for 28 watersheds located across the southeastern United States that is minimally affected by human intervention. Calibrated hydrological models are forced with five different types of datasets: global atmospheric reanalysis (National Centers for Environmental Prediction/Department of Energy Global Reanalysis and European Centre for Medium-Range Weather Forecasts 40-year Reanalysis) at their native resolution; dynamically downscaled global atmospheric reanalysis at 10-km grid resolution; stochastically generated data from weather generator; bias-corrected dynamically downscaled; and bias-corrected global reanalysis. The reanalysis products are considered as surrogates for large-scale observations. Our study indicates that over the 28 watersheds in the southeastern United States, the simulated hydrological response to the bias-corrected dynamically downscaled data is superior to the other four meteorological datasets. In comparison with synthetically generated meteorological forcing (from weather generator), the dynamically downscaled data from global atmospheric reanalysis result in more realistic hydrological simulations. Therefore, we conclude that dynamical downscaling of global reanalysis, which offers data for sufficient number of years (in this case 22 years), although resource intensive, is relatively more useful than other sources of meteorological data with comparable period in simulating realistic hydrological response at watershed scales.

Abstract: We evaluate the skill of a suite of seasonal hydrological prediction experiments over 28 watersheds throughout the Southeastern United States (SEUS), including Florida, Georgia, Alabama, South Carolina, and North Carolina. The seasonal climate retrospective forecasts (the Florida Climate Institute�Florida State University Seasonal Hindcast at 50 km [FISH50]) is initialized in June and integrated through November of each year from 1982 through 2001. Each seasonal climate forecast has six ensemble members. An earlier study showed that FISH50 represents state-of-the-art seasonal climate prediction skill for the summer and fall seasons, especially in the subtropical and higher latitudes. The retrospective prediction of streamflow is based on multiple calibrated rainfall-runoff models. The hydrological models are forced with rainfall from FISH50, (quantile-based) bias-corrected FISH50 rainfall (FISH50BC), and resampled historical rainfall observations based on matching observed analogues of forecasted quartile seasonal rainfall anomalies (FISH50Resamp).
The results show that direct use of output from the climate model (FISH50) results in huge biases in predicted streamflow, which is significantly reduced with bias correction (FISH50BC) or by FISH50Resamp. On a discouraging note, we find that the deterministic skill of retrospective streamflow prediction as measured by the normalized root mean square error is poor compared to the climatological forecast irrespective of how FISH50 (e.g., FISH50BC, FISH50Resamp) is used to force the hydrological models. However, our analysis of probabilistic skill from the same suite of retrospective prediction experiments reveals that over the majority of the 28 watersheds in the SEUS, significantly higher probabilistic skill than climatological forecast of streamflow can be harvested for the wet/dry seasonal anomalies (i.e., extreme quartiles) using FISH50_Resamp as the forcing. We contend that given the nature of the relatively low climate predictability over the SEUS, high deterministic hydrological prediction skills will be elusive. Therefore, probabilistic hydrological prediction for the SEUS watersheds is very appealing, especially with the current capability of generating a comparatively huge ensemble of seasonal hydrological predictions for each watershed and for each season, which offers a robust estimate of associated forecast uncertainty.

Abstract: Economists and environmental policymakers have recently begun advocating a bottom-up approach to climate change mitigation, focusing on reduction targets for groups of nations, rather than large scale global policies. We advance this discussion by conducting a rigorous empirical analysis of the global distribution of carbon emissions along several important dimensions: groupings, polarization, mobility, and volatility. In contrast to previous work, our empirical analysis is both comprehensive and data-driven. We discuss how robust empirical evidence may aid policymakers in forging a heterogeneous carbon abatement policy.