Methods

Defining Datasets and How They Are Used in the Analytics Engine

Downscaled simulations of California’s climate

The climate projections hosted on the Cal-Adapt: Analytics Engine are regional simulations over the Western United States and California, produced by downscaling Global Climate Models (GCM) from the sixth iteration of the Climate Model Intercomparison Project (CMIP6). These downscaled simulations were created in support of California's Fifth Climate Change Assessment, and detailed information about the models used and the data produced can be found in the Data section.

Additional data sources for calculating data on Global Warming Levels

When using the Analytics Engine to calculate climate variables at global warming levels, data from two additional sources are utilized in the calculations: the CMIP6 archive hosted by Pangeo, and data from the IPCC AR6 Report. A detailed methodology of how this data is used will be included in an upcoming section “How California-focused Warming Level Time Series are Calculated”, and additional guidance on how to incorporate global warming levels into planning is given here.

Warming level years from global GCM runs

Global warming levels are defined as the average increase in global surface air temperature relative to pre-industrial conditions (1850-1900). Combining data from several climate simulations around the years that each one reaches specified global warming levels provides a clear standard of measuring how regional impacts will scale with different levels of global warming. Because the WRF and LOCA2-Hybrid simulations in the Analytics Engine are run only over the Western US they can not be used to evaluate global temperature, so data from the parent GCM simulations is used to determine the range of years from each simulation that corresponds to a given global warming level.

For this calculation, the Analytics Engine utilizes a publicly available archive of CMIP6 simulations hosted on AWS by the Pangeo project.

GWL timing for scenarios overall

One of the benefits of analyzing projections of climate change on global warming levels is that years for warming level estimates can be estimated independently from the trajectories of the GCMs themselves. This is particularly beneficial because the CMIP6 models are known to have a much wider range in their climate sensitivity, which introduces significant uncertainty into estimates of when each warming level will be reached.

Because of this, the Intergovernmental Panel on Climate Change (IPCC) draws in data from a variety of other scientific studies including observational constraints and climate emulators to produce a refined estimate of likely warming trajectories under each climate scenario. Details about the inputs to these likely trajectories are described in the 4th chapter of IPCC Sixth Assessment Report (AR6), and an overview of the resulting best estimates and ranges are provided in the Technical Summary.

When using warming levels, the Analytics Engine provides estimates for the crossing year and very likely range (90% confidence interval) based on the publicly available data used in the IPCC report.

Figure 1. Best estimate and 90% confidence interval for the year the world will reach 2.0 °C of warming under the SSP 3-7.0 scenario. The median year of crossing is denoted by the vertical dashed line and is provided. The solid lines represent the first and last occurrence of the 5th and 95th percentiles crossing the designated warming level, with the range in years provided. For example, the median year of GCMs reaching 2.0°C of warming is in 2047, with a range of 24 years between the first and last occurrence. Figure produced by the Cal-Adapt: Analytics Engine based on data from the IPCC.