Recent climate-related crises — from severe storms and flooding to extreme heat — have raised new questions about how local governments communicate the risk of these crises and what they are doing to keep their citizens safe. To better understand what this communication looks like at local level, and the factors that may be shaping it, researchers from Drexel University analyzed climate resilience planning information available on the public-facing websites of 24 coastal communities in New Jersey that are contending with the effects of sea level rise. Their report, recently published in the Journal of Extreme Events, found wide variation in the number and extent of mitigation actions taken and how the websites describe causes of coastal hazards — for example, only half of the communities are acknowledging sea level rise as a contributing factor to these hazards.

Following deforestation, tropical forests with healthy populations of seed-dispersing animals can absorb up to four times more carbon than similar forests with fewer seed-dispersing animals, according to an MIT study.

Researchers have devised a new machine learning method to improve large-scale climate model projections and demonstrated that the new tool makes the models more accurate at both the global and regional level. This advance should provide policymakers with improved climate projections that can be used to inform policy and planning decisions.