Download Tropical origins for recent and future Northern Hemisphere climate

GEOPHYSICAL RESEARCH LETTERS, VOL. 31, L21205, doi:10.1029/2004GL020739, 2004
Tropical origins for recent and future Northern Hemisphere climate
change
Frank M. Selten,1 Grant W. Branstator,2 Henk A. Dijkstra,3 and Michael Kliphuis1
Received 14 June 2004; revised 10 August 2004; accepted 4 October 2004; published 6 November 2004.
[1] Results from a large ensemble of climate model
simulations over the period 1940 –2080 suggest that the
observed strengthening of the westerly winds over the North
Atlantic during the past decades is not due to the enhanced
greenhouse effect but is largely an expression of a random,
internal climate variation driven by increased precipitation
over the tropical Indian Ocean. Instead, the enhanced
greenhouse effect drives a change in the extra-tropical
winter circulation through intensified precipitation over the
tropical West Pacific. This change is characterized by a wave
train encompassing the whole Northern hemisphere, a pattern
INDEX
known as the Circumglobal Waveguide Pattern.
TERMS: 1610 Global Change: Atmosphere (0315, 0325); 1620
Global Change: Climate dynamics (3309); 3319 Meteorology and
Atmospheric Dynamics: General circulation; 3339 Meteorology
and Atmospheric Dynamics: Ocean/atmosphere interactions (0312,
4504). Citation: Selten, F. M., G. W. Branstator, H. A. Dijkstra,
and M. Kliphuis (2004), Tropical origins for recent and future
Northern Hemisphere climate change, Geophys. Res. Lett., 31,
L21205, doi:10.1029/2004GL020739.
1. Introduction
[2] Wintertime Northern Europe and Asia have warmed
on average by about three degrees Celsius over the past
40 years while northeastern North America has cooled. At
the same time, the North Atlantic winds have become more
westerly, advecting mild, maritime air masses over the
Eurasian continent, thus contributing to the observed
warming. Likewise, an enhanced polar component to
Canadian winds has influenced the region of cooling. These
wind changes are connected to a positive trend in the
amplitude of a large-scale pattern of atmospheric surface
pressure variations, the North Atlantic Oscillation (NAO)
[Hurrell, 1995]. This pattern is characterized by a simultaneous intensification (or weakening) of the Icelandic lowand the Azores high-pressure system (Figure 1a) and
describes much of the year-to-year variations in the mean
winter circulation in the North Atlantic area. Besides its
impact on regional and hemispheric mean atmospheric
temperatures (Figure 1c), the NAO also affects storm
activity and precipitation in regions around the North
Atlantic, wave heights, currents, temperature, salinity, seaice cover and ecosystems in the North Atlantic Ocean and
1
Royal Netherlands Meteorological Institute, De Bilt, Netherlands.
Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, Colorado, USA.
3
Department of Atmospheric Science, Colorado State University, Fort
Collins, Colorado, USA.
2
Copyright 2004 by the American Geophysical Union.
0094-8276/04/2004GL020739$05.00
flora and fauna on the surrounding continents [Hurrell,
1996; Marshall et al., 2001; Hurrell et al., 2003]. Because
of these large impacts, it is important to understand the
cause of the observed NAO trend. Are changes in external
climate forcing factors such as solar activity or volcanic
emissions or the increasing levels of greenhouse gases
(GHG) in the atmosphere due to man-made emissions the
primary cause? Or are physical processes internal to the
climate system, like interactions between the atmosphere
and the ocean, mainly driving the observed long-term NAO
variations? These questions are the main motivation for this
study. If the recent trend is due to the enhanced levels of
GHG concentrations in the atmosphere, it is likely to
continue in the near future. On the other hand, if it is a
manifestation of internal variability, the trend is likely to
come to an end and reverse sign.
2. Description of the Ensemble Experiment
[3] To address these issues, we have simulated the
evolution of the climate system with version 1.4 of the
Community Climate System Model (CCSM) of the National
Center for Atmospheric Research [Ammann et al., 2004,
and references therein]. This model simulates the evolution
of the coupled atmosphere-ocean-sea-ice-land system under
prescribed climate forcings. The atmosphere was run with a
spectral resolution of T31 (with 3.75° 3.75° resolution in
latitude and longitude of the transform grid) and 18 levels in
the vertical, with the highest level at about 35 km. The land
model distinguishes between specified vegetation types and
contains a comprehensive treatment of surface processes.
The ocean model has 25 vertical levels and a 3.6° longitudinal resolution. The latitudinal resolution ranges from 0.9°
in the tropics to 1.8° at higher latitudes. The sea-ice model
includes ice thermodynamics and dynamics. The coupled
system does not require artificial corrections in the heat
exchange between atmosphere and ocean to simulate a
realistic coupled climate [Boville et al., 2001].
[4] Our simulations cover the period 1940– 2080. Until
2000, the forcing includes specified estimates of temporally
evolving solar radiation, temporally and geographically
dependent airborne particles (volcanic aerosols and sulphate
aerosols due to manmade and natural emissions) and timedependent major GHGs [Ammann et al., 2003, 2004]. From
2000 onwards, all these forcing factors are kept at their year
2000 values, except for the concentrations of GHGs, which
increase according to a ‘business-as-usual’ scenario [Dai
et al., 2001] that is similar to the SRES-A1 scenario of
the Intergovernmental Panel on Climate Change (IPCC)
[Nakicenovic et al., 2000]. An ensemble of 62 simulations,
each covering the 140-year period, was produced. The
simulations differ only in a small random perturbation to
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and emission scenarios. The climate model also realistically
simulates the NAO pattern (Figure 1b) and its effect on the
winter surface air temperature in the Northern hemisphere
(Figure 1d). The model therefore meets the minimum
requirements to use it to investigate whether the recent
and possible future trend in the NAO are related to global
warming.
[6] Figure 2a presents an index of the strength of the NAO
for ensemble member 13 and 32 and the observations. The
observed trend is well reproduced in ensemble member 13,
but not in member 32, which shows an opposite trend over
the same period. Averaging over all ensemble members (thin
solid line in Figure 2a) reveals no significant systematic
change in the NAO index in response to the applied forcings.
Our simulations therefore suggest that the observed trend in
the NAO index is neither due to the enhanced greenhouse
effect nor to changes in solar irradiance. Random, internally
generated climate variations are large enough to explain the
observed long-term NAO trend.
[7] Observed and simulated Eurasian winter temperature
variations are plotted in Figure 2b. In ensemble member 13,
the warming rate over the period 1960 –2000 is comparable
to the observed rate of about 1°C per decade. This is about
four times the ensemble mean warming. In contrast, ensemFigure 1. Interannual atmospheric winter (DJF) variations
associated with the NAO. Winter atmospheric sea-level
pressure (SLP) variations that co-vary with the NAO index
for (a) observations and (b) all simulations. Winter surfaceair temperature variations that co-vary with the NAO index
for (c) observations and (d) all simulations. Units are in hPa
and °C per standard deviation of the NAO index respectively.
The NAO index is defined by the projection of winter mean
SLP deviations from the long-term mean onto the first
Empirical Orthogonal Function [Bretherton et al., 1992] of
winter mean SLP fields for the period 1961 – 1990 over the
North Atlantic area. Observational estimates are from the
NCEP/NCAR reanalysis dataset [Kalnay et al., 1996].
the initial atmospheric temperature field. Due to the chaotic
nature of the atmospheric flow, these small perturbations
grow rapidly in the first couple of days of each simulation
and lead to completely different weather patterns by the end
of the first month [Lorenz, 1984]. In this way, equally likely
realizations of the climate evolution are obtained under the
same forcing conditions. Averaging over all ensemble
members reduces the random, internally generated component of the climate variations, producing an estimate of the
externally forced climate signal.
3. Results
[5] Over the period 1940– 2000, the observed variations
in the global mean temperature are well within the range of
simulated values in the ensemble. After 2000, the sole
forcing of the increasing GHG concentrations leads to an
almost linear warming trend in the ensemble mean global
mean surface air temperature, which by 2080 has increased
about 1.2°C with respect to 2000. This value is on the low
side of the range (1.1 to 4.6 degrees) established in the
IPCC Third Assessment Report [Cubasch et al., 2001],
which is based on results from different model simulations
Figure 2. Time series of the NAO index and Eurasian
winter temperatures. (a) Time series of the NAO index for
the observations (thick solid), ensemble member 13 (long
dashed), member 32 (dotted) and the mean of all ensemble
members (thin solid). Units are in hPa. (b) Winter mean
temperatures over Northern Eurasia (averaged over the box
indicated in Figures 1c and 1d) using the same line styles as
panel (a). Units are in °C. A bias of 4.5°C is added to the
simulated temperatures. All time series are low-pass filtered
using an eleven-year running mean.
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Figure 3. Winter (DJF) mean precipitation changes that
co-vary with the NAO index on decadal timescales. Prior to
the linear regression, a running mean filter of 5 years was
applied to the precipitation fields and the NAO index time
series and, to analyze only the variations that are not forced
by the external climate factors, the ensemble mean time
series were subtracted. Contour interval is 0.08 mm/day per
standard deviation change in the NAO index. The zero
contour is omitted. Negative contours are dashed.
ble member 32 simulates a cooling trend during that same
period of about 0.5°C per decade. This implies that our
ability to predict the future course of the winter temperature
in Eurasia over the coming decades is severely limited by
the strong, natural variations of the NAO on this timescale.
It also implies that large ensembles of climate simulations
are necessary to average out the random, unpredictable
component and obtain an accurate estimate of the climate
change signal related to variations in external factors. Long
NAO trends of 30 years or more are by no means rare
events. In about half the simulations, at least one such trend
is simulated. So provided that the simulated NAO trends are
realistic, we conclude that nature’s course over the past
decades has not been exceptional. But it is rare for simulated NAO index values averaged over a decade to exceed
the presently observed value of around 200 (Figure 2a). We
therefore do not expect that the NAO trend will continue. A
slower warming or even a cooling trend over northern
Eurasia in the coming decades is likely to occur.
[8] The observed NAO trend has recently been attributed
to the concurrent warming trend of the tropical Indian
Ocean with the associated increase in rainfall in that area
[Hoerling et al., 2001]. Enhanced rainfall over the tropical
Indian Ocean, through increased vertical movement of air
and stronger outflow in the upper troposphere, excites
waves in the atmospheric wind field. These propagate into
the Northern hemisphere and within a couple of weeks
cause a strengthening of the NAO over the North Atlantic.
Simulations with different atmospheric general circulation
models (AGCMs) that are forced with the observed tropical
sea surface temperature (SST) convincingly demonstrate
that the precipitation increase over the Indian Ocean in
response to the historical warming trend of the tropical
Oceans indeed induces the observed NAO trend [Hurrell et
al., 2004; Hoerling et al., 2004].
[9] To investigate if the decades-long NAO trends in
our simulations also originate from the Indian Ocean,
we analyzed the simulated covariance between decadal
fluctuations in the NAO index and decadal precipitation
fluctuations all around the globe during the Northern hemisphere winter season (Figure 3). On timescales longer than
5 years, variations in the NAO index are indeed accompanied by precipitation variations over the tropical Indian
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Ocean. Because our experiments include an interactive
dynamical ocean, our simulations indicate that internal
mechanisms involving atmosphere-ocean interactions can
cause the decades-long precipitation variations over the
tropical Indian Ocean that drive the NAO. The precipitation
variations found at higher latitudes (Figure 3) result from
changes in the extra-tropical storm tracks that depend on the
NAO index [Rogers, 1990; Hurrell, 1995].
[10] Although the ensemble-mean NAO does not respond
to the applied GHG forcing, the mean winter circulation in
the Northern hemisphere does change in response to the
applied forcings. This change is well characterized by the
streamfunction at 300 hPa (Figure 4a), which describes
the rotational part of the wind at a height of about 7 km.
The ensemble mean difference between a 30 year window
at the end of the simulated period (2051– 2080) and at the
beginning (1951 – 1980) reveals a wavy response with
5 waves encompassing the whole Northern hemisphere. It
closely resembles the Circumglobal Waveguide Pattern
(CWP) [Branstator, 2002]. A large fraction of the observed
and simulated year-to-year variations in the mean winter
circulation in the Northern hemisphere can be attributed to
this variability pattern. Largest amplitudes are meridionally
confined to the area of the strongest mean winter westerly
winds, the jet streams, consistent with suggestions that this
variability pattern arises due to the waveguiding effects of
the jet stream. Our simulations indicate that it is not the
NAO but rather the CWP that determines the response of
the Northern hemispheric winter circulation to the enhanced
concentrations of GHGs.
4. Conclusions and Discussion
[11] We draw two main conclusions. First, random,
internal climate variations appear large enough to offer an
explanation of the observed NAO trend over the past
decades. In agreement with recent studies [Hurrell et al.,
Figure 4. Simulated ensemble mean climate change in
winter (DJF). (a) Atmospheric circulation change averaged
over 2051 – 2080 with respect to 1951 – 1980, measured by
the streamfunction at 300 hPa. Contour interval is 106 m2/s.
(b) Precipitation change over the same period. Contour
interval is 0.3 mm/day.
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2004; Hoerling et al., 2004], our results suggest that the
NAO trends are caused by precipitation trends over the
tropical Indian Ocean. Second, we conclude from our
simulations that the CWP is possibly one of the dominant
players in future climate change. The CWP is known to be a
prominent pattern of internal atmospheric interannual variability, so it should be easy to excite [Branstator, 2002].
Furthermore, in unpublished experiments using state-of-theart AGCMs, Branstator has found that steady heating in
various equatorial locations, particularly between 150°E and
180°E, excites the CWP (an example can be seen on p. 1006,
Bulletin of the American Meteorological Society, 84, 2003).
The climate change in our ensemble of simulations is
characterized by enhanced precipitation near these longitudes (Figure 4b). The heating associated with the associated
latent heat release is most likely the cause of the CWP
response. The ensemble mean change in precipitation over
the tropical Indian Ocean is quite different from the precipitation variations that can stimulate an NAO response
(compare Figures 4b and 3; see also Hurrell et al. [2004])
which is consistent with the absence of a GHG induced NAO
change. The simulated precipitation increase in the Western
tropical Pacific is related to warming of the underlying ocean
that is stronger than the warming of the other tropical oceans.
It appears that different climate models simulate quite
different tropical warming patterns [Hoerling et al., 2004]
and hence the GHG induced changes in the tropical precipitation field remains a source of uncertainty. Consequently
there remains considerable uncertainty in the exact features
of the extra-tropical winter circulation change due to the
enhanced greenhouse effect. Our ensemble simulations
however, strongly indicate that the CWP can be excited by
GHG induced precipitation changes in the tropics and its
associated contribution to extra-tropical climate change is
therefore potentially predictable.
[12] Acknowledgments. We thank C. Ammann and B. Otto-Bliesner
for their support in porting the CCSM code from NCAR to the TERAS
computer system, R. Trompert and colleagues at the Academic Computing
Center at Amsterdam (SARA) for technical support and R. Haarsma
for discussions. M. Kliphuis was funded by the Netherlands Centre for
Climate Research (CKO). Computer resources were funded by the National
Computing Facilities Foundation (NCF) with financial support from the
Netherlands Organization for Scientific Research (NWO).
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M. Kliphuis and F. M. Selten, Royal Netherlands Meteorological
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