Brief real-world examples of specific needs

To illustrate how specific needs lead to different types of climate change information, we illustrate four brief real-world examples of information needed by communities in the Mountain West.

Example 1. Public outreach

Need: Outreach to the public about overall climate adaptation challenges

Basic question: How much warmer will it be, overall?      

Specific information needed: The likely increase in average temperature for 2050 for the city

Example 2. County health authority 

Need: Long-range planning by the county health authority to plan for anticipated increases in heat-related illness

Basic question: How will extreme heat change? 

Specific information needed: The increase in the number of extremely hot (>95°F) days in the county for three future time periods    

Example 3. Water planning

Need: Computer modeling of a water district’s system under future conditions of demand and supply, to support a new integrated water resources plan

Basic question: How will streamflows change? 

Specific information needed: Multiple time-series of annual streamflow change factors for the next 50 years for a local watershed 

Example 4. Stormwater infrastructure

Need: General guidance for a stormwater utility’s planning and operations, including the possibility of up-sizing culverts when they are replaced

Basic question: How will extreme precipitation change? 

Specific information needed: The change in the number of days with over 1″ of precipitation (note that information about precipitation extremes is only available from a few portals, and these data are more uncertain than other climate-change data) 

Obtaining the information listed in these four examples required going to different portals and resources. This guide will show you where to look.

A Brief Glossary

When you navigate climate change information, you enter a dense thicket of technical terms and acronyms. Here is a brief introduction to the most important ones; you can find detailed descriptions and further guidance in the Primer on climate models and projections.

GCM (Global climate model, aka climate model, general circulation model) – A complex math- and computer-based simulator of the climate system (atmosphere, oceans, ice sheets, land surface) that uses both fundamental physical laws and observed relationships to model the evolution of climate over time and space. GCMs that also include biogeochemical cycling (e.g., carbon cycle) are called Earth system models, or ESMs.  

Emissions scenario – A potential trajectory of greenhouse gas emissions and concentrations over the next century given particular societal choices; a simulation (projection) of the future by a climate model is driven by a particular emissions scenario (see RCP, SSP)

RCP (Representative Concentration Pathway) – One of a set of emissions scenarios associated with a specified “climate forcing” (excess energy retained in the Earth system), used in CMIP5 and CMIP6, see below.

SSP (Shared Socioeconomic Pathway) – A scenario of future population, policy, economic growth, and technology that is compatible with one or more RCPs, used in CMIP6, in tandem with RCPs.

Climate projection – One simulation of future climate from a single GCM that assumes a particular emissions scenario (Because it’s conditional on that scenario, it’s not technically a prediction or forecast). 

CMIP (Coupled Model Intercomparison Project) – A periodic “roundup” of the available GCMs and climate projections run under standardized emissions scenarios and conditions. Nearly all data found on climate portals were generated as part of CMIPs. 

CMIP5 – The roundup conducted in 2011-12 of the then-current generation of about 35 GCMs, as featured in the IPCC AR5 reports (2013-14) The CMIP5 climate projections are still the “industry standard” in climate change portals and in climate assessments.

CMIP6 – The roundup conducted in 2019-20 of the latest generation of about 40 GCMs. The CMIP6 climate projections are just starting to be included in climate change portals and were also featured in the IPCC AR6 Working Group I report (2021).

Downscaling – A procedure by which the data from a GCM projection is translated to finer spatial resolution to make it more usable for local and regional analysis and decision-making. LOCA, MACA, BCSD, BCCA, and CORDEX refer to different types of downscaling procedures, read more and learn the acronyms, read the primer (below). 

Primer on climate models and projections

Global climate models (GCMs) 

Global climate models (aka General circulation models–GCMs–or Earth system models–ESMs) are complex, computer-based, mathematical representations of the Earth’s climate system based on fundamental scientific principles. They are designed to capture the dynamics and interactions of the main components of the climate system: atmosphere, oceans, land surface and vegetation, sea ice, land ice. GCMs provide realistic simulations of the key physical phenomena at global down to continental scales:

GCM simulations are not as realistic at finer scales (the size of a Western U.S. state or smaller). This is true especially for precipitation–which is harder to model than temperature, at any spatial scale. 

Over 60 GCMs have been developed by 30 modeling centers in 10 countries. These GCMs are not fully independent of each other; the modeling centers share model code and parameters for many climate processes, and several modeling centers maintain multiple, related GCMs.

Climate model projections

A simulation of future climate from a GCM is called a projection, rather than a prediction or forecast, because it is conditional on a particular set of assumptions about future greenhouse gases and other climate forcings, i.e., an emissions scenario). For any given climate variable, a future projection will show both (1) simulated climate variability (“internal” or “unforced” variability) and (2) a forced climate change over time, if that variable is affected by changes in global climate “forcing”–most importantly, the higher forcing from increasing greenhouse gases ( as depicted in different emission scenarios, described below).


What are they?

The Coupled Model Intercomparison Project (CMIP) is an organized “roundup” of the latest GCMs conducted every 7 years or so. Nearly all GCM output used in global, national, and state climate assessments since 2013 has come from CMIP5, whose data was released in 2011-12. Compared to the previous CMIP (which was CMIP3–there was no CMIP4), CMIP5 included more participating GCMs–models whose spatial resolution was generally higher, and captured more climate-system components and processes, if not with greater fidelity. 

In 2020, the data from CMIP6 was released, representing a new generation of climate models. CMIP6 was timed to support the IPCC Sixth Assessment Report (AR6), just as CMIP5 supported the IPCC AR5 report. CMIP6 climate projections have only recently been added to public-facing climate portals, as noted in Table 1 above, and CMIP6 projections have not yet been downscaled, like with the CMIP5 projections shown in most climate portals. So at this time (October 2021) the vast majority of climate portals only show or provide CMIP5 data, though this will change over the next year or two. 

Are the CMIP6 models and projections better than CMIP5? 

Yes and no. Overall, GCMs have improved from one generation to the next, but since CMIP3, that improvement has been flattening out, indicating that climate modeling has matured. As measured by the ability to reproduce features of the observed climate, the CMIP5 models were as a whole slightly better than those in CMIP3–but looking at the various skill scores for individual models, there is a lot of overlap between the two CMIPs. It is likely that CMIP6 projections as a group will also show improvements in skill over CMIP5, but not to the extent that one should stop using CMIP5. 

Do the CMIP6 projections show a different picture of climate change than CMIP5?

Yes and no. The ensembles of CMIP6 and CMIP5 climate projections, under comparable emissions scenarios, show very similar average global spatial patterns of temperature and precipitation change. The places that CMIP6 indicates will warm faster or slower, or be wetter, or be drier, are the same places that CMIP5 would change in those ways. 

However, about one-third of the CMIP6 models show greater global and regional warming than the upper end of the CMIP5 models–that is, they have greater climate sensitivity or climate response to the same increment of greenhouse-gas emissions and forcing. Climate scientists now believe that these “hot” models are overestimating future warming, since they are also simulating too much warming in recent decades (~1980-present). Accordingly, the IPCC AR6 report that came out in April 2021 effectively deemphasized the “hot” CMIP6 model projections, using additional modeling and analysis to develop an “assessed” range global temperature projection that was comparable to CMIP5 in the amount of future warming given a comparable emissions scenario. Since then, a simpler technique has been proposed for screening CMIP6 models to deemphasize the hot CMIP6 models in projecting future warming at regional scales.

For the Mountain West: Once the hot models are accounted for, CMIP6 still shows slightly greater warming than CMIP5 for the same emissions increment, though the two ensembles mostly overlap. In other words, there is much more difference among the models within each CMIP, than between the two CMIPs. It is similar for precipitation: the CMIP6 ensemble average annual precipitation is slightly wetter than for CMIP5, but both CMIPs depict broad ranges of plausible precipitation futures that mostly overlap. The uncertainty (range of projected futures) for both temperature and precipitation is similar between CMIP6 and CMIP5, just as it was similar between CMIP5 and CMIP3. 

Emissions Scenarios: RCPs and SSPs

Since anthropogenic greenhouse gases are the primary cause of recent global warming and other climate changes, it is necessary for future climate simulations from GCMs to have inputs that describe how greenhouse gas emissions and concentrations will unfold over the next century. The climate modeling community has adopted a set of trajectories whose range is intended to capture the significant uncertainty in how annual emissions and concentrations of these gases, as well as smaller climate forcings (like aerosol emissions and changes in surface reflectivity), will change in the future.

The trajectories developed for CMIP5 are called Representative Concentration Pathways (RCPs). RCPs provide trajectories of GHG emissions and concentrations that are linked to plausible future trends in demographic, socioeconomic, technological, and political factors. Since those underlying trends cannot be predicted with any confidence, no likelihoods were formally assigned to these RCPs. (That said, it is impossible not to consider the question of which RCPs are more plausible than others given current trends, which we address below.)

Each CMIP5 GCM simulation or projection uses one of the four RCPs: RCP2.6, RCP4.5, RCP 6.0, or RCP8.5. The numbers refer to the strength of the global radiative forcing in 2100, in watts per square meter (W/m2)—the extra energy trapped in the climate system by added greenhouse gases and other human-caused changes—above and beyond pre-industrial levels. The divergence in forcing among the four RCPs at mid-century is much smaller than later in the century. The projected increase in global average temperature by 2100 for any given GCM closely corresponds to the radiative forcing of each RCP.

To support CMIP6, further effort went into articulating the socioeconomic assumptions that are associated with emissions pathways; these assumptions were distilled into five “Shared Socioeconomic Pathways” (SSPs), from SSP1 (assumptions generally associated with lower emissions and concentrations of greenhouse gases) to SSP5 (assumptions generally associated with higher emissions and concentrations). Within each SSP, multiple emissions and concentration pathways with specific climate forcings (i.e., RCPs) are possible, so these need to be specified as well. 

Thus, each of the scenarios used to drive CMIP6 modeling is essentially a combination of an SSP and an RCP, but is simply called an “SSP” for convenience. Four of these SSPs– SSP1-2.6, SSP2-4.5, SSP4-6.0, and SSP5-8.5–are updates of their CMIP5 counterparts (RCP2.6, RCP4.5, RCP6.0, and RCP8.5), with the same radiative forcing levels in 2100, though the exact forcing trajectory during the 21st century may differ somewhat. Four new SSPs were introduced for CMIP: SSP1-1.9, SSP4-3.4,  SSP5-3.4OS [OS = “overshoot”], and SSP3-7.0, that fill in some gaps between and below the CMIP5 RCPs. 

What assumptions about emissions policies and reductions do the RCPs/SSPs use? Which RCP/SSP trajectory is the world currently following?

Here are the RCPs and SSPs in order from the lowest to highest climate forcing, and the assumptions about emissions:

RCP (CMIP5)/SSP (CMIP6)AssumptionComments
SSP1-1.9Immediate, aggressive reductions in annual GHG emissions, down to near-zero by 2050, followed by large negative emissions (carbon removal) during the second half of the 21st century.Created specifically to depict a pathway in which global warming likely stays below 1.5 deg C.
Immediate reductions in GHG emissions from today’s levels, though not to the extent of SSP1-1.9, and some negative emissions before 2100.
This RCP has been left out of some regional assessments because its assumptions were seen as implausible. 
Intermediate between the 2.6 and 4.5 scenarios, with reductions in emissions by 2040.
GHG emissions peak around 2050 at somewhat higher levels than today, followed by reductions to about half of today’s level by 2100.RCP4.5 is one of the two most widely available and most used scenarios. 
RCP6.0Similar trajectory to RCP4.5, but with higher emissions at all points, ending with emissions lower than today’s by 2100.
SSP4-6.0Emissions rise more slowly than RCP6.0, but don’t peak until 2080 and in 2100 still has emissions higher than today’s–so the emissions reduction policies are not strong as in RCP6.0.
SSP3-7.0Baseline or “business-as-usual” scenario (i.e., no emissions policies) in which emissions do not rise as dramatically as in the 8.5 scenarios.
High-end business-as-usual scenarios; reversion to coal as the primary global energy source, leading to GHG emissions in 2100 that are >3 times today’s level.RCP8.5 is one of the two most widely available and most used scenarios. Its assumptions have been criticized for being implausible given recent trends in energy use.

The recent trajectory of GHG emissions (2010-2021) is theoretically compatible with all of the RCPs/SSPs from 2030 onward, though the low-end 1.9 and 2.6 pathways are becoming increasingly infeasible with each passing year. The current emissions policies enacted by the major GHG-emitting countries, extended through 2040, are most consistent with the two 4.5 RCP/SSP scenarios, and are below the two 6.0 RCP/SSP scenarios. If we assume also the pledged future emissions reductions by these same countries through 2040, that trajectory sits below the two 4.5 scenarios, though not as low as the 3.4 SSP scenarios. 

Several researchers have argued that the reversals of recent energy and emissions trends as assumed in the 8.5 scenarios are highly implausible, and that either the 6.0 or 7.0 scenarios are more realistic “baseline” or no-emissions-policy scenarios. Especially in the late 21st century, the RCP8.5 pathway is not so much “business-as-usual” as “business returns to the 1970s” (in terms of energy mix and carbon intensity of economic output), with the population size of the 2080s.

Which RCPs or SSPs should I use? 

The vast majority of the portals and resources described in this guide show only CMIP5 projections, and most of those show projections only for RCP4.5 and RCP8.5, in part because more raw CMIP5 data is available for these two scenarios than for RCP2.6 and RCP6.0. RCP4.5 and RCP8.5 also create what has become seen as a natural pairing with clearly contrasting outcomes. Often, RCP4.5 is labeled as “lower emissions” and RCP8.5 as “higher emissions.”

Given recent policy and emissions trends, as noted above, it is reasonable to think that RCP4.5 is now a more likely pathway than RCP8.5. So RCP6.0 might be preferable if projections using that scenario are available, especially for late-century time periods. 

If RCP8.5 is used, avoid portraying it as a more likely scenario than RCP4.5 (or even an equally likely scenario), or as the emissions pathway the world is currently on. Note that before mid-century (~2050), all of the scenarios are much closer together–in terms of emissions, climate forcing, and global temperature increase–so the choice of scenario(s) is not as consequential as it is for late-century projections. 

If projections from RCP2.6 are available, then they could be used with the caveat that they represent a very aggressive approach to emissions reduction.

Across the few portals that currently provide CMIP6 projections, only four of the eight SSPs are represented in the data those portals provide: SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. Following the discussion above, SSP2-4.5 and SSP3-7.0 appear to be more plausible pathways given recent trends in emissions and policies, with SSP1-2.6, like RCP2.6, representing a “more aggressive emissions reduction (than currently anticipated)” pathway. 

Downscaling and other processing of GCM output

The “raw” output from GCMs provides the best estimates of future climate changes at the global down to sub-continental scales. But for informing state, local, and watershed-scale decision-making, GCM output has some real limitations. The spatial resolution of the data, with grid cells of 50 to 125 km on a side, is too coarse to capture the complex terrain of the Interior West and most of its effects on climate, or to be used as inputs for regional climate-impact modeling (e.g., watershed hydrology modeling, ecological modeling). Raw GCM output also doesn’t capture certain features of weather and climate very well, such as the statistics of daily precipitation. 

Thus, GCM output is often downscaled through statistical methods (statistical downscaling), or via higher-resolution regional climate models (RCMs; dynamical downscaling), in order to better represent localized changes to weather and climate, and to facilitate impact modeling. All of the climate assessments described in this guide, as well as most of the climate portals, rely on downscaled projections. 

Downscaling is not magic; downscaled projections may be a lot more precise (locally specific), but are not that much more accurate, compared to the underlying raw GCM data. Downscaling also does not reduce the uncertainty that manifests in the somewhat different depictions of future change seen across the various GCM projections. In fact, downscaling introduces a new uncertainty, stemming from the choice of downscaling method.

Statistical downscaling: LOCA, MACA, BCCA, BCSD, etc. 

The vast majority of publicly available downscaled data were produced using statistical downscaling–an approach which is much less computationally intensive, allowing large ensembles of projections under multiple emissions scenarios to be downscaled. In the past several years, two statistical downscaling methods and their associated datasets have seen widespread use in assessments and portals: LOCA (Localized Constructed Analogs) and MACA (Multivariate Adaptive Constructed Analogs). These are both “constructed analog” (CA) methods, in which multiple daily weather patterns from the historical record are selected, adjusted, and blended in order to create fine-scale outputs that are consistent with the coarser-scale weather pattern shown for a given day in the raw GCM output. In this way, a long-term climate projection is built that is faithful to the way weather and climate vary (at least historically) across space and time at local scales. BCCA (Bias-Corrected Constructed Analogs) is another such method that is used in some studies and at least one portal. These three CA methods can be seen as equivalent, performance-wise. 

The BCSD (Bias-Corrected Spatial Disaggregation) statistical method has seen a lot of use in past CMIP5-based studies and assessments, and BCSD data are available through at least one portal. BCSD downscaling adjusts the statistical distributions of coarse-scale projected climate values (daily or monthly temperature or precipitation) to match the statistical distributions seen in the fine-scale historical climate data, a process called quantile mapping (QM). The QM step has been shown to alter the future precipitation trends seen in the raw CMIP5 GCM projections, such that the Mountain West is tilted towards higher precipitation in the BCSD projections, compared to the raw projections. This alteration is now believed to be an artifact of the method, and so it is not reliable. Users should either avoid using BCSD data, or compare BCSD projections with other downscaled datasets. 

Dynamical downscaling: CORDEX

Dynamically downscaled climate projections use regional climate models run while “nested” within the future conditions shown by the global models, which imparts more physical-process knowledge to the downscaled data than does statistical downscaling. The only such CMIP5-based dataset readily available is from the CORDEX project, which is available via one portal in this guide, the IPCC WG1 Interactive Atlas, with limited options for visualization. The CORDEX-North America CMIP5 projections could be useful for comparing to LOCA-, MACA-, or BCCA-downscaled CMIP5 to see if there are consistent differences in the average climate changes for the location or region of interest. 

Regridded raw GCM data

The other approach to processing the raw GCM projections is not to downscale them at all; instead, to replot the raw data from the different GCMs to a common grid (regridding), which puts them all at the same spatial resolution (typically around 1 degree, or 100 km). Several portals and assessments depict regridded raw CMIP5 data. The caution is that the projected future climate variables are not bias-corrected (shifted so that the GCM-simulated recent values match the observations) as with downscaled data. There are large biases in precipitation for the Mountain West in precipitation; all of the GCMs show much more precipitation in recent decades than actually occurred. However, when the future projections are shown as anomalies (projected future period minus simulated recent period), this inherently bias-corrects the projected future precipitation or temperature. 

Climate variables 

Virtually all portals provide information about future levels of “the basics” for a given area or location:

Other climate variables, available from many of the portals, include:

These variables can be expressed in one of two ways:

  1. Value – The magnitude of the historical or future climate variable, e.g., 72°F 
  2. Anomaly – The difference between the historical value and the future value, e.g., +4°F (Note that the precipitation anomaly is often expressed as a % difference)

The anomaly succinctly captures the future change in climate in a single number, e.g., 4 additional days above 100°F by 2050. But in this and other cases, also having the historical and future values, e.g., 1.5 days/year above 100°F (historical) vs. 5.5 days/year (2050), helps put the change into better context. 

At all portals, the projected future climate data is available as values, but not all have the anomalies too. 

Dealing with uncertainty in future climate projections

An inescapable characteristic of GCM projections of future climate is that the different projections show different future changes in temperature, precipitation, and other climate variables for a given region or location. Thus, the CMIP5 and CMIP6 ensembles of GCMs, which include dozens of models run under multiple emissions scenarios, collectively show a broad range of plausible future climate conditions. In the time-series plots and other visualizations, this range is typically shown by a shaded area around a central line (the mean or median), as in Figure 1.

Figure 1. Example of a time-series plot of projected climate (June mean temperature) displayed by the USGS National Climate Change Viewer, in which the red/blue shading shows the middle 80% of the range of model projections under that emissions scenario (RCP), from the 10th percentile to the 90th percentile. 

Where does this uncertainty come from, and how do we deal with it in interpreting and using the information we get from climate portals?

The first source of uncertainty is the most obvious, already discussed above: Our uncertainty in future greenhouse gas emissions and the resulting anthropogenic forcing, or “push” on the climate system. Emissions scenarios (i.e., RCPs, SSPs) are deliberately constructed to represent a range of plausible anthropogenic influences on climate, and the portals always allow you to distinguish between sets of projections driven by different RCPs or SSPs, both in selecting the data, and in the visualizations like the one above, where RCP4.5 projections are shown in blue, and RCP8.5 projections in red. 

For temperature, the effect of the emissions scenario is predictable and consistent: a higher-emissions pathway (e.g., RCP 8.5 vs. RCP 4.5) leads to yet warmer projected temperatures, both globally and regionally. For projected precipitation change, the influence of the RCP is more variable and may be difficult to discern.

But if we look at a set of projections driven by a single emissions scenario, we still find a large range in projected temperature and precipitation changes, as shown by either the blue shading (the range under RCP4.5)  or the red shading (the range under RCP8.5) in the visualization. So we need to understand the other sources of uncertainty:  

The different climate models collected in the CMIP archives have different approaches to representing, in the model code, several key climate processes and patterns. This diversity reflects that our observations and understanding are not complete enough to have confidence in a single methodology for including these processes in climate models, and also that the coarse resolution of the climate models inhibits more accurate simulation. Thus, different modeling groups use different approaches to represent these processes–which results in somewhat different answers to the same question, like “How much global warming will occur for a given increase in greenhouse gas concentrations in the atmosphere?”

Climate models do not attempt to replicate the actual events and sequences of historical climate; instead, they generate a simulated climate history that captures the key characteristics of historic natural variability. As a model is run into the future, that climate projection likewise includes a sequence of natural climate variability that, because of the randomness inherent in the climate system (and the models), does not match the sequences produced by other models. Because natural variability has annual, decadal, and multidecadal components, analyses of projected change will inevitably include some amount of natural variability–which contributes to the spread of the models. In other words, the breadth of the blue and red shading is due to both structural differences between models and the randomness (natural variability) in climate simulated by each model. 

This last factor comes into play if the climate projection is downscaled . The downscaling

procedure, whether statistical or dynamical, can shift the future change in temperature and/or precipitation from that shown in the underlying global climate model output. The direction and amount of this shift differs with the climate variable in question, and the region being downscaled.

It is difficult to generalize about these factors’ contributions to the overall range of model output, and thus to our uncertainty about future climate. The importance of the factors will vary by both spatial scale and the future time frame of the projection. But for temperature change for the Mountain West by the mid-21st century, the emissions scenario and the GCM’s representation of key processes tend to be most important. For precipitation change, the emissions scenario is less important, and the GCM’s representation of key processes and simulation of natural variability are more important.

This also brings up another important point about uncertainty in climate projections:

We are extremely confident that the climate will continue to warm considerably, though the magnitude of the warming is less certain. For precipitation, in many parts of the Mountain West, the direction of future precipitation change is highly uncertain, as well as the magnitude of that change. Simulating temperature change at regional to global scales is a much easier task than simulating the manifold and complex processes that influence precipitation, and thus precipitation change.  

So what do we do with this uncertainty–that is, the blue and red shading on the graph? Can we ignore the shading, and just use the dark lines in the middle (the median or average of the model projections) as our “answer”? That’s not a wise idea, for two reasons:

Embracing a range of future climates rather than one (central) future climate does make things more complicated for both analysis and communication.