The relative contributions of radiative forcing and internal climate variability to the late 20th Century drying of the Mediterranean region

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The relative contributions of radiative forcing
and internal climate variability to the late 20th
Century drying of the Mediterranean region
Colin Kelley, Mingfang Ting, Richard Seager,
Yochanan Kushnir Department of Ocean and Climate
Physics Columbia University, New York NY
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Strong drying of the Mediterranean region
occurred from the 1960s to the 1990s (extended
winter Nov-Apr)
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NAO
During the same period the NAO trended strongly
positive
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The NAO and Mediterranean drying are highly
correlated (0.7 over the century), with the NAO
explaining nearly half of the extended winter
precipitation variance.
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Questions

• Did external radiative forcing in the form of
  global warming play an important role in the
  strong positive NAO trend, as suggested by
  Shindell et al. (1999), Feldstein (2002) and
  Osborn (2004), and by extension the drying of the
  Mediterranean region?
• Or were the strong trends predominantly a result
  of low frequency natural variability on decadal
  to interdecadal timescales (Schneider et al.
  2003 Thompson 2003)?
• How well can the models produce multidecadal
  trends of realistic magnitude?
• Can we through use of S/N maximization EOF use
  the models to obtain a best estimate of the
  model-derived signal and use it to attribute and
  quantify the externally forced portion of the NAO
  and Mediterranean drying trends?
• How does this attribution project onto the 21st
  century?

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Mechanisms of Mediterranean drying

• The mechanisms that influence Mediterranean
  rainfall variability include both dynamical and
  thermodynamical processes.
• The region is located in the subtropical dry
  zone, characterized by the poleward flank of the
  Hadley Cell and moisture divergence by the mean
  flow.
• The primary mechanisms whereby anthropogenic
  warming could cause drying include
• 1) increases in specific humidity leading to
  intensified water vapor transports that, in
  regions of existing mean flow moisture
  divergence, such as the subtropics in general and
  the Mediterranean in particular (Held and Soden
  2006 Seager et al. 2007, 2010) will cause
  further drying,
• 2) the poleward expansion of the Hadley Cell
  (Lu et al. 2007) and
• 3) the northward migration of the northern
  hemisphere storm track (Yin 2005 Lu et al. 2007,
  Wu et al. 2010).
• The dominant influence on Mediterranean rainfall
  variability however, particularly during winter
  when the vast majority of precipitation occurs,
  is the NAO (Hurrell et al. 2003).

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From Feldstein 2002
Observed NAO trend 1965-95 1.56 hPa
Observed trend in AO Index 1967-97 5.7
Using 64 runs from 19 coupled models, we show
that the observed NAO trend from 1965-95 is
within the span of model simulated NAO trends,
but that models trends of similar magnitude to
the strong observed trend are rare.
Using a Markov model, Feldstein shows that an
atmospheric model decoupled from the
hydrosphere/cryosphere is almost incapable of
producing trends of magnitude similar to the
observed trend from 1967-97
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Osborn 2004

• Using seven coupled climate models Osborn
  concludes that
• the NAO increase from the 1960s to the 1990s is
  not compatible with either the internally
  generated variability nor the response to
  increasing greenhouse gas forcing simulated by
  these models.
• The model simulations imply greenhouse gas
  forcing contributed to the observed NAO index
  increase from the 1960s to the 1990s, unless the
  climate models are deficient in their simulation
  of inter-decadal NAO variability or their
  simulation of the response to greenhouse gas
  forcing.
• It is possible, therefore, that the observed
  record can be explained as a combination of
  internally generated variability and a small
  greenhouse-gas-induced positive trend.
• This is supported by the more recent (strong)
  downturn in the NAO index after the mid-1990s,
  which might be a reversal of an internally
  generated variation.

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Comparison of observed and modeled low frequency
(decadal or longer) NAO variability, using six
commonly used coupled models at left and all 19
models at right.
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Methodology

• We use signal-to-noise maximization EOF on an
  ensemble of runs from 19 models to obtain a best
  estimate of the externally forced signal (for NA
  SLP and Mediterranean precipitation) that the
  models have in common.
• After we calculate the forced signal (PC1 of S/N
  EOF) we regress the original data fields of SLP
  and precipitation onto it for the entire 20th
  century, obtaining spatial patterns of the forced
  regression coefficients (a).
• We obtain the magnitude of the externally forced
  SLP or precipitation at each gridpoint and time
  (x,y,t) (reconstruct the field) by multiplying
  the regression coefficients by the 20th century
  signal (PC1), and then subtract the reconstructed
  externally forced field from the total field to
  get the residual.
• a(x,y) is the regression coefficient
• SLP(x,y,t) is the total observed SLP
• SLP(x,y,t) is the externally forced SLP
• SLPresid(x,y,t) is the residual SLP

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