CLIMATE FUTURES

A Framework for Plausible Scenario Planning


Plausible Scenario Roadmap

Most climate predictions rely on output from computer models, such as those utilized by (IPCC). Although predictive models are extremely useful for simulating long-term trends under different greenhouse gas (GHG) emission scenarios, they do not necessarily resolve regional variability sufficiently to make reliable local and regional-scale projections, especially over decadal timeseries. This gap can be filled by developing plausible scenarios that emphasize the historical climate record and climate-commodity relationships. The steps below provide a guide for how to develop plausible scenarios for future climate and where to access relevant climate data on Climate Reanalyzer.

Step 1 — Make Initial Assessment
Step 2 — Explore the Historical Climate Record
Step 3 — Explore Climate Associations
Step 4 — Test Climate-Commodity Connections
Step 5 — Define Plausible Scenarios

Step 1 — Make Initial Assessment

Identify the region of interest, and consider what economic sectors and natural systems could be impacted by climate change. List important concerns, such as changes in the growing season, heat waves, extreme precipitation events, and sea-level rise. Temperature is a key climate variable, and most parts of the globe are expected to warm considerably over the next century in response to rising GHG concentrations in the atmosphere. An initial assessment of possible future climate outcomes can be based on temperature projections from global climate models (see below). These models are useful for broad-brush estimates of what is physically possible over the next century, and can serve as the basis for a plausible scenario developed in concert with other scenarios based on trends in the historical record.

Figure 1. Chart showing the observed (black) and model-projected (blue, green, orange, red) global mean temperature departure from 1901-2000 baseline climate. The temperature projections are multi-model means from the Coupled Model Intercomparison Project version 5 (CMIP5), which are utilized by IPCC. CMIP5 RCP (Representative Concentration Pathway) experiments 2.6 (concerted GHG emissions reductions) - 8.5 (continued high GHG emissions) are shown and depict a phyically-based range of possible global temperature outcomes over the next several decades.

Step 2 — Explore the Historical Climate Record

Examine annual, seasonal, and monthly historical timeseries of temperature and precipitation for the location of interest. Note long-term trends, decadal variability, and years of extremes. This information provides important context for developing plausible scenarios of future climate. Also consider changes in the seasonal cycle, as environmental systems are particularly sensitive to changes in the duration of the frost and growing seasons. Maps can be used in conjunction with timeseries for spatial context.

Climate Reanalyzer provides access to state-level temperature and precipitation data through its Monthly U.S. Timeseries and Maps interface. The data are available as charts, maps, and comma-delimited (CSV) spreadsheet files.

Figure 2. Observed changes in mean annual temperature averaged across the United States for the period 1895-2018. These images were produced using Climate Reanalyzer. Source data are from NOAA (timeseries) and PRISM (maps).

Step 3 — Explore Climate Associations

How is your region impacted by known large-scale patterns of climate variability? Perhaps the most important is the El Niño Southern Oscillation – a change in ocean surface temperatures across the tropical Pacific that is connected to atmospheric circulation. El Niño is the warm phase, whereas La Nina is the cool phase. The impacts of ENSO vary across the globe, and so it is useful to examine how ENSO tends to impact your region. Other important patterns of variability include the North Atlantic Oscillation, Arctic Oscillation, and the Pacific Decadal Oscillation.

Climate Reanalyzer can be used to explore large-scale climate associations through different pages depending on the data product type (maps, timeseries, or timeseries correlation) and time resolution (daily or monthly). For annual, seasonal, and monthly global sea-surface temperature (SST) maps and timseries 1854-present use the interface Monthly Global SST Timeseries & Maps. Annual, seasonal, and monthly timeseries for many climate variables from reanalysis are available on Monthly Reanalysis Timeseries. For daily maps of temperature, SST, and polar sea ice concentration January 1, 1979 to present visit Daily Reanalysis & Sea Ice Maps. Monthly U.S. Timeseries and Maps interface. The data are available as charts, maps, and comma-delimited (CSV) spreadsheet files.

Figure 3. Observed changes in mean annual temperature averaged across the United States for the period 1895-2018. These images were produced using Climate Reanalyzer. Source data are from NOAA (timeseries) and PRISM (maps).

Step 4 — Test Climate-Commodity Connections

This is filler text. How is your region impacted by known large-scale patterns of climate variability? Perhaps the most important is the El Niño Southern Oscillation – a change in ocean surface temperatures across the tropical Pacific that is connected to atmospheric circulation. El Niño is the warm phase, whereas La Nina is the cool phase. The impacts of ENSO vary across the globe, and so it is useful to examine how ENSO tends to impact your region. Other important patterns of variability include the North Atlantic Oscillation, Arctic Oscillation, and the Pacific Decadal Oscillation.

U.S. commodity data can be readily obtained from the federal government for agriculture and fisheries. The respective sources are the USDA National Agriculture Statistics Service and the NOAA Office of Science and Technology. Some commodity data are also available for other countries from the Organisation for Economic Co-operation and Development.

Figure 4. Example climate-commodity connections identified in the report Coastal Maine Climate Futures. The left panel shows timeseries of annual SST compared to cod and lobster landings. The right panel shows a map of linear correlation between SST and lobster landings.

Step 5 — Define Plausible Scenarios

This is filler text. How is your region impacted by known large-scale patterns of climate variability? Perhaps the most important is the El Niño Southern Oscillation – a change in ocean surface temperatures across the tropical Pacific that is connected to atmospheric circulation. El Niño is the warm phase, whereas La Nina is the cool phase. The impacts of ENSO vary across the globe, and so it is useful to examine how ENSO tends to impact your region. Other important patterns of variability include the North Atlantic Oscillation, Arctic Oscillation, and the Pacific Decadal Oscillation.

Examples:

Scenario 1 — The "New Normal" Currently Experienced with No Additional Change"
Scenario 2 — Moderate Warming
Scenario 3 — Another Abrupt Arctic Warming and Even Greater Arctic Sea Ice Collapse
Scenario 4 — Cooling from Increased Volcanic Activity
Scenario 5 — Drying from More Frequent and Extreme El Niño Events