A brief history of time the detection and attribution of climate change - PowerPoint PPT Presentation

Loading...

PPT – A brief history of time the detection and attribution of climate change PowerPoint presentation | free to download - id: 6eaded-NDY3M



Loading


The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
About This Presentation
Title:

A brief history of time the detection and attribution of climate change

Description:

A brief history of time the detection and attribution of climate change David Karoly School of Meteorology University of Oklahoma – PowerPoint PPT presentation

Number of Views:34
Avg rating:3.0/5.0
Slides: 27
Provided by: DavidK227
Learn more at: http://www.atmosp.physics.utoronto.ca
Category:

less

Write a Comment
User Comments (0)
Transcript and Presenter's Notes

Title: A brief history of time the detection and attribution of climate change


1
A brief history of timethe detection and
attribution of climate change
  • David Karoly
  • School of Meteorology
  • University of Oklahoma

2
What is detection and attribution?
  • Detection of significant observed climate change
    and attribution of this observed change to one or
    more causes is a signal-in-noise problem
    identifying possible signals in the noise of
    natural internal climate variations in the
    chaotic climate system.
  • Detection is the process of demonstrating that an
    observed change is significantly different (in a
    statistical sense) than can be explained by
    natural internal climate variability.

3
What is detection and attribution?
  • Attribution of climate change to specific causes
    involves statistical analysis and the careful
    assessment of multiple lines of evidence to
    demonstrate that the observed changes are
  • unlikely to be due entirely to internal climate
    variability
  • consistent with the estimated responses to a
    given combination of anthropogenic and natural
    forcing and
  • not consistent with alternative, physically
    plausible explanations of recent climate change

4
Why do detection and attribution?
  • To identify the causes of recent observed climate
    variations
  • To evaluate the performance of climate models in
    simulating the observed climate variations over
    the last century
  • To constrain the projections of future climate
    change

Senator James Inhofe (R, Oklahoma), Chair, Senate
Env. and Public Works Comm., in a speech to the
US Senate on Jan 4, 2005 I called the threat of
catastrophic global warming the greatest hoax
ever perpetrated on the American people
5
History
Madden and Ramanathan (1980) surface warming due
to increasing carbon dioxide should be
detectable (by) the year 2000
6
History
IPCC First Assessment Report in 1990 had no
attribution chapter general statement on
consistency of observed warming with model
projections
7
History
IPCC SAR (1995) had detection and attribution
chapter the balance of evidence suggests a
discernable human influence on climate
8
History
IPCC TAR (2001) Most of the observed warming
over the last fifty years is likely to have been
due to the increase in greenhouse
gas concentrations
9
History
IPCC Fourth Assessment Report (2007) ??
10
Initial studies to SAR (1995)
  • Univariate analysis of global mean temperature
    comparing change with internal variability
  • Difficult to separate different causes that
    affect global radiation balance increasing
    greenhouse gases, increasing solar irradiance
  • Use the spatial pattern of the temperature
    response to differentiate between different
    causes fingerprint analysis
  • Initially consider contrast between troposphere
    and stratosphere using pattern correlation
    Karoly et al (1994), Santer et al (1995)

11
Changes in zonal mean atmospheric temperature
(C), 1960-1995 Modelled and observed
From Tett et al (1996), following Santer et al
(1995)
12
Further studies to TAR (2001)
  • Use optimal fingerprint analysis to reduce noise
    by rotating detection vector away from noise
    direction
  • Use linear regression to estimate amplitude of
    forced signal from model pattern and
    observational data
  • Applied to spatial pattern of surface temperature
    change
  • Use more models, including models without flux
    correction between the atmosphere and ocean

13
Global mean surface temperature (C)
All factors
HadCM3 model (black) Observations (red)
Anthropogenic only
Natural only
Stott et al. (2000)
14
Apply optimal fingerprint analysis to large-scale
variations of surface temperature at decadal
timescales
Bars show 5-95 uncertainty limits
Allen et al. (2000)
15
Optimised predictions of temperature change (C),
from 1990 to 2040 under IS92a emissions (diamonds)
Allen et al, 2000
16
Studies since the TAR
  • Use other variables anthropogenic signal found
    in ocean heat content, mean sea level pressure,
    and temperature extremes
  • Evaluate anthropogenic signal in temperature
    changes at smaller scales
  • Key uncertainties include relating regional
    trends to anthropogenic climate change IPCC TAR

17
Continental-scale studies
  • Stott (2003) showed that most of the observed
    warming over the last 50 years in six separate
    continents, including North America, Eurasia and
    Australia, was likely to have been due to the
    increase in greenhouse gases

Karoly et al (2003)
18
Detection of regional warming
  • Calculate observed linear trend in each grid-box
    and test for 95 significance (marked with )
    using model control simulations to provide
    estimate of natural variability of trends (Karoly
    and Wu, 2005)

19
Simple indices of climate variability change
  • Select a small number of indices of surface
    temperature variations that represent different
    aspects of natural climate variability but
    represent key features of patterns of
    anthropogenic climate change (following Braganza
    et al., 2003, 2004)
  • Want indices that show a common signal due to
    greenhouse climate change but are nearly
    independent for natural climate variations
  • Global mean surface temperature (GM)
  • Mean land ocean temp difference (LO)
  • Mean magnitude of the annual cycle over land (AC)
  • Mean meridional temperature gradient in the NH
    (MTG)

20
Describing climate variability
21
Assessing climate change
22
Detection of regional warming California
  • Compare observed area-mean temperature change
    with model simulations for 20th century from NCAR
    CCSM3 and GFDL CM2

23
Attribution of regional warming California
  • Probability distributions of 90-year trends in
    California temperature from control model
    simulations (solid line) and 20th century
    simulations with increasing greenhouse gases and
    aerosols (dash-dot line, 20C3M).

The observed trend agrees well with the 20C3M
simulations and cant be explained by natural
climate variations
24
Attribution of regional warming Central England
temperature
  • Probability distributions of 50-year trends in
    CET from control model simulations (solid line)
    and 20th century simulations with anthropogenic
    (ANT) and natural (NAT) external forcing

The observed trend agrees well with the ANT
simulations and cant be explained by NAT
simulations
25
Continental-scale temperature projections
Uncertainty plumes for changes relative to 1990s
using scalings based on continental-scale
attribution. Probabilities are represented by the
depth of shading. From Stott et al. (2006)
26
Conclusions
  • There have been significant advances in the
    methods used for attribution of the causes of
    observed climate change over the past two decades
  • The predictions of Madden and Ramanathan (1980)
    proved to be uncannily correct
  • A clear anthropogenic signal can be identified in
    observed climate changes over the last 50 years
    in many variables and in temperature in almost
    all regions
About PowerShow.com