Climate is a long-term average of daily weather conditions. Analyzing climate data provides understanding of climate at scales ranging from the entire earth down to a specific place. Local climate conditions are driven by complex interactions between the atmosphere and everything on the earth's surface. Scientists use general circulation models (GCMs), which represent that complexity, while modeling potential climates. GCMs include data and algorithms to simulate the physics, geochemistry, and composition of the various systems on the earth's surface. An essential part of GCMs are decades of detailed historical weather measurements, ensuring the modeled climates are based on real climate conditions.
At the turn of the twentieth century, scientists began to suspect that introducing greenhouse gases (GHGs) into the atmosphere produces changes in climate. This notion was confirmed in the 1970s, when early models of baseline climate conditions for the recent past exhibited changes. Over time, the outputs of these models were found to correlate with the amount of GHGs introduced by natural and human processes.
The impact of GHGs is measured as the ratio of energy (heat) absorbed by the earth's atmosphere to the energy reflected back into space. Scientists call this balance radiative forcing and measure it in watts per square meter (W/m2). As levels of GHGs increase or decrease in the atmosphere, radiative forcing values correspondingly increase or decrease. In other words, as radiative forcing values increase, the atmosphere becomes warmer because more energy is retained in the atmosphere.
Today, scientists produce future climate projections using GCMs that vary the amounts of GHGs. Since it is impossible to know the exact future GHG concentrations, they run these GCMs with a variety of potential GHG scenarios. These scenarios are called Representative Concentration Pathways (RCPs). In 2014, the Intergovernmental Panel on Climate Change (IPCC) adopted four standard RCPs with GHG concentrations that add the following levels of radiative forcing: 2.6, 4.5, 6.0, and 8.5 W/m2. These scenarios give a range from best (2.6) to worst (8.5) case for adding GHGs into the atmosphere. Most climate scientists use these scenarios to convey future climate projections in 20-year increments starting in 2020.
For example, RCP 6.0 is a stabilization scenario where radiative forcing levels stabilize without exceeding 6.0 W/m2 by 2100. Scenario 6.0 is considered to be a realistic probability. Standard GCM outputs for this scenario include projections for 2020–2039 and 2040–2059.
The simulated climate responses are known as climate projections as opposed to predictions because they are produced by models rather than interpretations or guesses.
Over 30 GCMs are available today from modeling centers around the world, each with unique assumptions and biases. Not all are ideal for conveying basic concepts and general trends, so choosing just one becomes difficult. Esri presented this issue to the staff at the Research Application Laboratory (RAL) from the University Corporation for Atmospheric Research (UCAR) and National Center for Atmospheric Research (NCAR). The RAL staff suggested creating a multimodel ensemble (MME), which provides the means and standard deviations between a select collection of GCMs. The RAL staff then selected 10 Coupled Model Intercomparison Project, Phase 5 (CMIP5) GCMs and compiled the MME that will be used in this lesson. This MME includes projected temperature and precipitation anomalies, which represent the difference between current climate, or baseline, conditions and a future climate scenario.
The anomalies represent four future time periods, 2020–2039, 2040–2059, 2060–2079, and 2080–2099, under all four RCPs. The baseline conditions are also provided, representing global averages for temperature and precipitation for the period 1986 through 2005.
This MME includes the latest climate projection results.