Summary: Climate Change Topic And Approaches

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• 1 Week 1

• 1.1 Definitions

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• What is the difinition of radiative forcing?

  The change in the net energy balance at the top of the atmosphere relative to 1750

• What is the definiton of GHG effect?

  The change in a GHG concentration because of anthropogenic emissions contributes to an instantaneous radiative forcing

• What is the definiton of climate sensitivity?

  The change in the surface temperature in response to a change in atmospheric carbond dioxide concentration or other radiative forcing

• What are the different steps in climate impact assesment (/model making)

  Variable selection    (Temp, rainfal etc)
  domain                     (space/area; time- 3 decades)
  Selection of emission scenario       (Combined ssp-rcp)
  Climate model Selection          ( which and how many models ECS)
  post processing of model output      (bias correction , downscaling)
  Impact assessment

• What are uncertainties of the value of ECS?

  Feedback loops effect like
  water vapour
  cloud
  ice-albedo
  natural GHG
  magnitude gradual changes
  tipping elements
  abrupt changes

• How are the main sources of uncertainty in CC projection quantified?

  Future GHG emissions:
  a scenario approach
  b emission scenarios dependend on different SSP pathways
  
  Climate sensitivity:
  using different models
  running models several times with small changes

• What is Bias correction and why is it needed?

  Both global and regional climate models RCM GCM have biases in their outputs, one need to correct these to get better results.
  
  methods are:
  delta change method
  linear regressions
  quantile mapping approach
  
  Often combined with statistical downscaling

• WHy statistical downscaling?

  It bridged the gap between low resolution GCM and the high resolution required for RCMs

• What is statistical downscaling?

  • A method to refine climate model outputs
  • Provides localized climate projections
  • Uses historical data correlations

• What are drawbacks of statistical downscaling?

  Assumes relation between the variable remain constant over time
  
  relies heavily on availability of high quality observation data