Description

Global climate models can provide valuable information to city decision makers for strategic planning, climate adaptation, risk management, and budgeting—but it can be difficult to glean from these models information that is directly suited to city scales and city problems. This dataset contains indicators based on 14 city-relevant climate hazards, calculated at a spatial resolution of 0.25-degree resolution (approximately 25 miles for many locations), for 996 large cities. The climate hazards were chosen for their importance for planning in public health, energy infrastructure, and the economic health of cities. The hazard indicators are provided for a 1994-2014 recent historical period as well as three levels of global warming: 1.5°C, 2.0°C, and 3.0°C above a pre-industrial (1880-1900) baseline.

The data are based on these 10 temperature-based climate hazards:

• Highest annual temperature
• Annual days hotter than local 95th percentile
• Annual days hotter than 40C
• Annual days hotter than 35C
• Annual cooling degree days (CDD21)
• Annual days with wetbulb temp > 31
• Duration of year’s longest heatwave
• Heatwaves per year
• Annual days with optimal temperature for malaria-carrying mosquitos
• Annual days with optimal temperature for arbovirus-carrying mosquitos

and these precipitation-based climate hazards:

• Highest annual one-day precipitation
• Annual days with precipitation exceeding 90th percentile
• Annual drought days
• Annual days with high landslide risk

For each of these indicators, at each global warming level, the data include three estimates of the average magnitude of the hazard, and three estimates of the probability that the magnitude exceeds three extreme-magnitude thresholds.

The indicators are based on probabilistic models of hazard-magnitude frequency, which we parameterize using magnitudes found in models in the NEX-GDDP-CMIP6 family of downscaled climate simulations. The three estimates of each indicator come from the three best models for the particular hazard, for the particular location, where model selection is based on comparison with a historical-observation data set.

The data set was produced as part of a project, made possible by Bloomberg Philanthropies, to engage city-level leaders in the global climate policy process.