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Climate Change, models and uncertainty


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Title: Climate Change, models and uncertainty

Climate Change, models and uncertainty
Confusing terms?
  • Climate Change
  • IPCC Climate change may be due to natural
    internal processes or persistent anthropogenic
  • UNFCCC A change in climate which is attributed
    directly or indirectly to human activity and
    which is in addition to natural climate

Weather or Climate?
  • Weather The local state of the atmosphere on a
    day-to-day basis. Wind, rain, cold, hot
  • Climate Long-term average of temperature,
    precipitation, extremes and other variables.
    Different scales and different periods.
  • Climate sets the background conditions for
    weather and will affect the frequency of certain
    weather events.

Climate Change or just Climate Variability?
  • Anthropogenic climate change is a significant and
    persistent change in the average conditions or
    extremes of a region.
  • But the climate system contains natural cycles
    and variability seasonal, inter-annual (e.g.
    ENSO, Indian Dipole), multi-decadal (e.g. North
    Atlantic Oscillation) or longer!

  • El Niño Global effects
  • Every 3-7 years
  • Warm and dry in Asia
  • Drought

Variability happens
  • A particularly hot/wet year in a particular place
    isnt necessarily evidence of climate change.
  • A particularly cold winter isnt evidence that
    climate change isnt happening
  • What is, however, is changes to the average over
    time, and all of these observations taken
    together and across the world.
  • Natural climate processes cannot explain the
    global changes that we have observed in the last
    50 years.

Current trends and climate projections
  • Changes experienced now may be due to climate
    change but may also be part of natural cycles
  • Just because it is getting wetter in a particular
    location, doesnt necessarily mean this will
  • If the observed trend matches the climate
    projection, we can be more confident of the
    changes to expect.

Context before projection!
  • It is (really!) important to understand the
    current climate context before jumping into
  • What are the historical conditions?
  • Are there any trends?
  • Why are things the way they are?
  • Without the context it is difficult to place the
    projections. . .

Climate Projections and uncertainty
  • We do not know what the future holds. . .but this
    doesnt mean we cant do anything

  • A model is a representation of the real world, it
    is not an exact copy
  • Projections vs Predictions.
  • We do not know which model is best fit to
    historical climate is not necessarily an
    indicator of quality of projection
  • Climate is a complex system there are a range
    of plausible future states

Types of Model pros and cons
  • Global models (smallest resolution 200km)
  • Regional Models (25-50km)
  • Statistically Downscaled models (station level)

Global models
  • Coarse resolution
  • Good at capturing circulation changes
  • Good for ideas of global change
  • Skill decreases at smaller scales, which is where
    we need information for adaptation!
  • Topography and feedbacks are problems

Where is this?
Regional Models
  • Better resolution (up to 25-50km2)
  • Good at a national/water basin level.
  • Can model feedbacks.
  • Provide information for whole region
  • Computer intense so difficult to run lots of
    models hard to look at a range of change.
  • Topography a problem

  • Statistical Downscaling from large-scale to the

Statistically downscaled models
  • Provide projections at the level of an adaptation
  • Good at topography and islands
  • Computer light, so can look at many different
    projections and assess the range.
  • Cant provide data where there is not a good
    historical record (at least 10 years continuous)
  • Cant capture feedback processes.

Station level projections
  • Over 300 in Asia
  • More expected through the platform

Confidence in projections
  • Uncertainty emissions, models, processes, (e.g.
    El Niño), land use. Variability will continue.
  • Projections should be treated as indicative, not
  • More confident on certain variables than others
  • The more models and data sources agree, the more
    confident we can be. Including trends!

What we cant say
  • A specific year will be bad
  • We are certain of our projections
  • Exact timing of changes
  • Much detail about variability
  • Much about the next 25 years

So. . .
  • Multiple sources of information give us a more
    rounded view
  • We cant simply discard information from a model
    giving us a result we dont like
  • An average can hide valuable information the
    extreme projection could be the right one
  • In the real world decisions made under imperfect
    conditions all the time the key is what
    information we can use to help the decision.

  • Ben Smith, Stockholm Environment Institute