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Guidelines for constructing climate scenarios

TitleGuidelines for constructing climate scenarios
Publication TypeManual Entry
Year of Publication2011
AuthorsMote, Philip, Levi Brekke, Philip B. Duffy, and Ed Maurer
Date Published2011/08/02
1626 Global Change: Global climate models (3337, 4928), 1637 Global Change: Regional climate change (4321), 1694 Global Change: Instruments and techniques, change, climate, downscaling, scenarios

Scientists and others from academia, government, and the private sector increasingly are using climate model outputs in research and decision support. For the most recent assessment report of the Intergovernmental Panel on Climate Change, 18 global modeling centers contributed outputs from hundreds of simulations, coordinated through the Coupled Model Intercomparison Project Phase 3 (CMIP3), to the archive at the Program for Climate Model Diagnostics and Intercomparison (PCMDI; [Meehl et al., 2007]. Many users of climate model outputs prefer downscaled data—i.e., data at higher spatial resolution—to direct global climate model (GCM) outputs; downscaling can be statistical [e.g., Meehl et al., 2007] or dynamical [e.g., Mearns et al., 2009]. More than 800 users have obtained downscaled CMIP3 results from one such Web site alone (see cmip3_projections/, described by Meehl et al., [2007]).

Short TitleEos Trans. AGU
Citation Key300
Community Notes

Article takeaways:

  • historical climate simulations are not inteded to reproduce exact monthly values of climate variables
  • describing uncertainty becomes more difficult for regional and local studies
  • all uncertainty can not be characterized by climate projections - the projections represent the measure of concensus
  • it's common to use historical model performance to determine "best" models
  • different runs from the same model are most likely more similar to each other than different runs of different models - in creating ensembles, do not lump all available simulations because this will give greater weight to models that have more simulations
  • article guidelines for using climate model scenarios in impact analysis:

1. "Understand to which aspects of climate
your problem or decision is most sensitive
(e.g., which climate variables, which statistical
measures of these variables, and at what
space and time scales).
2. Determine which climate projection
information is most appropriate for the problem
or decision (e.g., variables, scales in
space and time).
3. Understand the limitations of the
method you select.
4. Obtain climate projections based
on as many simulations, representing as
many models and emissions scenarios, as
5. It may be worth the effort to evaluate the
relevant variables against observations, just to
be cognizant of model biases, but recognize
that most studies have found little or no difference
in culling or weighting model outputs.
6. Understand that regional climate projection
uncertainty stems from uncertainties
about (1) the drivers of change (e.g., greenhouse
gases, aerosols), (2) the response
of the climate system to those drivers, and
(3) the future trajectory of natural variability.
7. Use the ensemble to characterize consensus
not only about the projected mean
but also about the range and other aspects
of variability."