Growing convergence research: Coproducing climate projections to inform proactive decisions for managing simultaneous wildfire risk.

We apply a convergence research approach to the urgent need for proactive management of long-term risk associated with wildfire in the United States. In this work we define convergence research in accordance with the US National Science Foundation-as a means of addressing a specific and compelling societal problem for which solutions require deep integration across disciplines and engagement of stakeholders. Our research team brings expertise in climate science, fire science, landscape ecology, and decision science to address the risk from simultaneous and impactful fires that compete for management resources, and leverages climate projections for decision support. In order to make progress toward convergence our team bridges spatial and temporal scale divides arising from differences in disciplinary and practice-based norms. We partner with stakeholders representing US governmental, tribal, and local decision contexts to coproduce a robust information base for support of decision making about wildfire preparedness and proactive land/fire management. Our approach ensures that coproduced information will be directly integrated into existing tools for application in operations and policy making. Coproduced visualizations and decision support information provide projections of the change in expected number of fires that compete for resources, the number of fire danger days per year relative to prior norms, and changes in the length and overlap of fire season in multiple US regions. Continuing phases of this work have been initiated both by stakeholder communities and by our research team, a demonstration of impact and value.

Lessons from climate modeling on the design and use of ensembles for crop modeling.

Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

Microclimate and human factors in the divergent ecology of Aedes aegypti along the Arizona, U.S./Sonora, MX border.

This study examined the association of human and environmental factors with the presence of Aedes aegypti, the vector for dengue fever and yellow fever viruses, in a desert region in the southwest United States and northwest Mexico. Sixty-eight sites were longitudinally surveyed along the United States-Mexico border in Tucson, AZ, Nogales, AZ, and Nogales, Sonora during a 3-year period. Aedes aegypti presence or absence at each site was measured three times per year using standard oviposition traps. Maximum and minimum temperature and relative humidity were measured hourly at each site. Field inventories were conducted to measure human housing factors potentially affecting mosquito presence, such as the use of air-conditioning and evaporative coolers, outdoor vegetation cover, and access to piped water. The results showed that Ae. aegypti presence was highly variable across space and time. Aedes aegypti presence was positively associated with highly vegetated areas. Other significant variables included microclimatic differences and access to piped water. This study demonstrates the importance of microclimate and human factors in predicting Ae. aegypti distribution in an arid environment.

The importance of land-cover change in simulating future climates.

Adding the effects of changes in land cover to the A2 and B1 transient climate simulations described in the Special Report on Emissions Scenarios (SRES) by the Intergovernmental Panel on Climate Change leads to significantly different regional climates in 2100 as compared with climates resulting from atmospheric SRES forcings alone. Agricultural expansion in the A2 scenario results in significant additional warming over the Amazon and cooling of the upper air column and nearby oceans. These and other influences on the Hadley and monsoon circulations affect extratropical climates. Agricultural expansion in the mid-latitudes produces cooling and decreases in the mean daily temperature range over many areas. The A2 scenario results in more significant change, often of opposite sign, than does the B1 scenario.