OSOcean ScienceOSOcean Sci.1812-0792Copernicus PublicationsGöttingen, Germany10.5194/os-12-807-2016The importance of external climate forcing for the variability and trends of coastal upwelling in past and future climateTimNelenele.tim@hzg.dehttps://orcid.org/0000-0002-6336-5897ZoritaEduardohttps://orcid.org/0000-0002-7264-5743HünickeBirgitYiXinghttps://orcid.org/0000-0002-0380-6910EmeisKay-ChristianHelmholtz-Zentrum Geesthacht, Institute of Coastal Research, Max-Planck-Strasse 1, 21502 Geesthacht, GermanyUniversity of Hamburg, Institute of Geology, Bundesstrasse 55, 20146 Hamburg, GermanyNele Tim (nele.tim@hzg.de)23June201612380782327October201523November201518May201624May2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://os.copernicus.org/articles/12/807/2016/os-12-807-2016.htmlThe full text article is available as a PDF file from https://os.copernicus.org/articles/12/807/2016/os-12-807-2016.pdf
The eastern boundary upwelling systems, located in the subtropics at the
eastern boundary of the Atlantic and Pacific oceans and mainly driven by the
trade winds, are the major coastal upwelling regions. Previous studies have
suggested that the intensity of upwelling in these areas in the past
centuries may have been influenced by the external radiative forcing, for
instance by changes in solar irradiance, and it will also be influenced in
the future by the increasing atmospheric greenhouse gases. Here, we analyse
the impact of the external climate forcing on these upwelling systems in
ensembles of simulations of two Earth system models. The ensembles contain
three simulations for each period covering the past millennium (900–1849)
and the 20th century (1850–2005). One of these Earth system models
additionally includes the near future (2006–2100). Using a set of
simulations, differing only in their initial conditions, enables us to test
whether the observed variability and trends are driven by the external
radiative forcing. Our analysis shows that the variability of the simulated
upwelling is largely not affected by the external forcing and that,
generally, there are no significant trends in the periods covering the past
and future. Only in future simulations with the strongest increase of
greenhouse gas concentrations the upwelling trends are significant and appear
in all members of the ensemble.
Introduction
Eastern boundary upwelling systems (EBUSs, including the California, the Canary, the
Benguela, and the Peru upwelling systems) are highly productive coastal ocean
areas where nutrient-rich, cold water upwells by the action of favourable
winds. The easterly trade winds which dominate these subtropical regions
cause Ekman transport from the coast to the open ocean, perpendicular to the
wind stress forcing, leading to upwelling at the coast. In addition, the wind
stress curl between the jet of the trades and the coast, induced by to the
relaxation of wind speed towards the coast, causes upwelling further
offshore.
It has been suggested that changes in the external climate
forcing, such as greenhouse gases and solar activity can influence the
intensity of coastal upwelling. Bakun's hypothesis states that surface
temperature over land should warm faster than over the oceans under increased
radiative forcing, leading to an intensification of the subtropical
continental lows and the oceanic highs, to an intensified cross-shore air
pressure gradient, and to a strengthening of the upwelling-favourable winds
.
Some observations over the 20th century and simulations
of the 21st century have been interpreted, according to this
hypothesis, as indicative of upwelling intensification due to stronger
external climate forcing in the recent past and future. Also, coastal
sediment records covering the past millennium and indicative of upwelling
have been interpreted as a response to past variations
in the external climate forcing, mainly solar irradiance and volcanism, with
weaker upwelling during the Little Ice Age (centuries around 1700 AD) and
stronger upwelling during the Medieval Warm Period (around 1100 AD).
Although not totally ascertained, it is generally assumed that the Little Ice
Age was mainly driven by a weaker solar activity and increased volcanic
activity, which lead to reduced radiative forcing . For the
Medieval Warm Period, this attribution is not as clear, but proxy records
indicate a stronger solar activity than during the subsequent Little Ice Age
.
For the past millennium, sediment cores are used as a proxy for upwelling due
to the lack of direct long-term observations . However,
the upwelling itself is not directly derived from the sediment cores, but
rather indirectly inferred on the basis of the water temperatures, assuming
that cooler temperatures are indicative of stronger upwelling. This
assumption could lead to a misinterpretation because temperature changes
could also have been driven by the direct effect of the radiative forcing
itself, in addition to any possible influence of the radiative forcing on
upwelling.
For the 20th century, a metadata analysis has found a significant
intensification of wind stress in three of the four major coastal upwelling
systems (California, Benguela, and Humboldt but not Canary)
. Previous studies using observations, reanalysis, and model data have detected increasing upwelling
intensity over the past century
, but others have not
. A possible
cause may be due to insufficient data homogeneity in long-term wind station
records and in meteorological reanalysis, which may blur the identification
of long-term trends . Also, long-term records of upwelling
intensity are indirect, and sometimes even based on wind records themselves
.
The effect of increasing greenhouse gas concentrations on
upwelling regimes in future scenarios has been investigated by several
authors. and analysed the simulated
trend of upwelling in several simulations included in the Climate Model
Intercomparison Project (CMIP5) driven by the
representative concentration pathways (rcp) 8.5 scenario, a scenario with an
increase of the globally averaged external radiative forcing of
8.5 Wm-2 by the year 2100. They found that in most of the EBUSs,
models tend to simulate an intensified upwelling and longer upwelling seasons
under climate change. In contrast, the studies of (21st
century simulation) and (double carbon dioxide simulation)
found only a weak trend or no trend in future coastal upwelling.
The evidence for the effect of radiative forcing on upwelling is, therefore,
still not conclusive. For instance, the Benguela upwelling system does not
exhibit a long-term intensification in the recent decades .
This has been explained by the counteracting influence of El
Niño–Southern Oscillation (ENSO) on humidity in the Peru and Benguela
EBUSs regions, which would also influence the regional radiative forcing and
modulate the land–ocean thermal contrast . During an El
Niño event, the Benguela region is less humid, which causes a reduction
of the greenhouse effect due to less water vapour, the most important
atmospheric greenhouse gas . According to this explanation,
the upwelling trend due to stronger external radiative forcing would be
biased by a higher frequency of ENSO events in the recent past.
The verification of Bakun's hypothesis in the recent past and future is of
major importance for the EBUSs. Even if Bakun's hypothesis is correct, it is
not clear whether past variations in external forcing could have been strong
enough to drive upwelling intensity beyond the range of variations caused by
internal chaotic climate variability. Hence, an apparent agreement between
the sign of predicted and observed trend cannot be considered a conclusive
proof of Bakun's hypothesis until those observed trends cannot be attributed
to the external forcing. The analysis of the recently available ensemble of
climate simulations with CMIP5 models over the recent past can shed light on
this question. Firstly, they comprise different climate periods in which the
external forcing has initially varied little in the past millennium, then
more strongly presently, and much more strongly in the scenario simulations.
Secondly, the use of ensembles of simulations with the same model offers the
advantage of a much easier identification of the effect of the external
forcing than the analysis of a single simulation. If the upwelling trends and
multi-decadal variability is mainly externally driven, therefore determining
the upwelling variations, all simulations should show a similar evolution of
upwelling over time, since all simulations have been driven by the same
prescribed external forcing. In contrast, if upwelling variations and trends
are mainly a result of stochastic internal dynamics, the evolution of
upwelling in the different simulations of the ensemble will tend to be
uncorrelated in time, and the long-term trends will show different signs in
the different simulations. This reasoning is more formally explained in
Sect. .
Bakun's hypothesis includes a physical mechanism by which upwelling may be
driven by the external forcing, namely by the intensification of the sea
level pressure (SLP) difference between the continents and the oceans
adjacent to the upwelling regions. We also test in the climate model
simulations whether the variability and trends of this air pressure
difference, and of the associated alongshore wind stress, is correlated
across the members of the ensemble of simulations. Additionally, another
mechanism by which the external forcing could affect upwelling is through the
ocean stratification. Global warming would lead to a warmer ocean surface,
generally increasing the stability of the water column, with a shallower
thermocline. This would hinder the upwelling .
In summary, the main objective of the analysis is to test whether the
external forcing prescribed in a set of ensembles of climate simulations is
strong enough to drive upwelling in the four EBUSs, and whether, accordingly,
there are significant long-term trends in upwelling over the last millennium,
the last 156 years, and the near future that appear common to all simulations.
This is obviously closely related to Bakun's hypothesis.
It is not our goal here to investigate the detailed mechanistic chain by
which external forcing drives upwelling mechanisms in the models used. This
task would be in any case not meaningful, since the main result of our study
is that the external radiative forcing has not varied strongly enough over
the past millennium, including the 20th century, to drive the upwelling
variability over this period. Even the simulations for the future do not
indicate that a moderate greenhouse gas emission scenario would be strong
enough to unmistakably drive upwelling intensity.
In the following section, we present the data and the methods used for this
analysis. In the main sections we compare some model results with the few
available, mostly indirect, observations of upwelling. Later, we analyse the
link between external forcing and the upwelling itself and with the immediate
drivers of upwelling. We finalize the study with a discussion and conclusion
section.
Data and methodsSatellite data, models, and simulations
We analyse here ensembles comprising three simulations (r1, r2, and r3) of two
different Earth system models: the Max Planck Institute – Earth system model (MPI-ESM) and the Community Earth
System Model – Last Millennium Experiment project (CESM-LME).
The MPI-ESM includes the ECHAM6 model for the atmosphere,
the MPI-OM model for the ocean, the JSBach model
for the vegetation, and the HAMOCC5 model
for the marine biogeochemistry . The
simulations of the MPI-ESM cover the periods 900–1849 (past1000, MPI-ESM-P),
1850–2005 (historical, MPI-ESM-MR), and 2006–2100 (future, MPI-ESM-LR). For
the future, we analyse here three scenarios with different strength in
greenhouse gas forcing, representative concentration pathways rcp2.6,
rcp4.5, and rcp8.5, where the numbers indicate the anthropogenic radiative
forcing in Wm-2 reached by the year 2100 . The
horizontal resolution of the atmospheric model of the MPI-ESM is about
1.9∘ (spatially varying) with 95 levels for the historical
period and 47 levels for the past1000 and future periods, whereas the ocean
model resolution is approximately 1∘ (past1000 and future) and
0.4∘ (historical), including 40 ocean layers. The climate model
MPI-ESM participated in the CMIP5 project , contributing
three simulations to the historical ensemble, three simulations to the future
ensemble, and one simulation for the past1000 period (r1).
The simulations in each ensemble were driven by almost identical external
forcing. Only the volcanic scheme in the past1000-r1 differs from the ones in
the past1000-r2 and past1000-r3 experiments in the prescribed distribution of
the volcanic aerosol size. This distribution is assumed to be log-normal,
with a standard deviation of 1.2 µm in r1 and 1.8 µm in
r2 and r3. The forcings prescribed in the past1000
follow a standard protocol defined by the project
CMIP5 and comprise orbital forcing , variability in solar
irradiance , volcanic activity ,
greenhouse gas concentrations
, and land-use changes
. The external forcing of historical simulations comprise
the same variables and are described in . The
reconstructed changes in the solar forcing have been disputed over the past
decade, but the CMIP5 protocol agreed that small solar variations are likely
more realistic. In these simulations they are rather small, with an increase
of about 0.15 % from the Maunder Minimum (around the year 1700) to present
time. In the scenario simulations, only changes in anthropogenic forcing were
prescribed according to the rcp scenarios .
To test the sensitivity of our results with respect to the choice of model,
we also analysed simulations with the CESM-LME model
. This model comprises the Community Atmosphere Model version 5 (CAM5) for the atmosphere,
the Parallel Ocean Program version 2 (POP2) for the ocean, the Community Land Model version 4
(CLM4) for the land, and the Community Ice CodE (CICE4) for the sea ice . The simulations with
CESM-LME were conducted after the simulation time of the CMIP5 project and do not
belong to that set of simulations. The CESM-LME ensemble comprises a large
amount of simulations with different configurations of the external forcing
and different initial conditions. Here, we analyse simulations driven by all
external forcings, natural and anthropogenic (denoted as “all-forcings” in
the CESM-LME project comprising the same set of
external forcings as used in the MPI-ESM simulations
(https://www2.cesm.ucar.edu/models/experiments/LME)). The CESM-LME
covers the period 850–2005 with a horizontal resolution of the ocean of
1∘ and a horizontal resolution of the atmosphere of 2∘. Unfortunately, no future simulations with this
model are publicly available yet .
A recent paper by investigated the relevance of model
resolution for the question of the warm bias that global climate models
usually display in the eastern ocean basins . This bias
may be related to the simulated upwelling intensity, but also to other
physical processes, in particular to model clouds. The analysis by
indicates that a high atmospheric horizontal resolution is
of larger importance than the horizontal resolution of the ocean model. The
most realistic wind stress curl and upwelling was simulated with a high-resolution
nested regional atmospheric model coupled with an eddy-resolving
ocean model, resulting in more intense values of wind stress and shifted
towards the coast.
The model resolutions of the two Earth system models used here are similar to
the resolution of the CMIP5 models used by . The strength and
location of the jet seems to be impacted by the atmospheric model resolution
. This study also indicated that an atmospheric resolution
of at least 1∘ would be preferable for the representation of the
jet. Unfortunately, no higher-resolution version of these Earth system models
is available. Thus, although ocean processes of spatial scales of a few
kilometres are only imperfectly resolved, the resolution of these two Earth
System Models is plausibly fine enough to realistically represent the basic
relationship between the large-scale wind forcing and upwelling dynamics,
including the shoreline-parallel winds and the wind stress curl. However,
fine-scale ocean structures, like coastal filaments and wave dynamics, cannot
be realistic represented. Recalling that Bakun's hypothesis is formulated as
the effect of external forcing on the atmospheric dynamics, the analysis of
these simulations is an informative test of Bakun's hypothesis, until higher-resolution,
multi-centennial simulations with coupled models become available.
Within each ensemble of simulations (past1000, historical, and future), the
simulations differ in their initial state. The initial conditions for each
simulation have been randomly chosen from a long pre-industrial control run,
after which the model is allowed to run for several centuries driven by the
(constant) forcing of the year 850 until a state of quasi-equilibrium is
attained. From this point onwards the transient simulation starts with the
prescribed temporally variable external forcing.
To validate the MPI-ESM simulations, we compared the simulated sea surface
temperature (SST) with remotely sensed SST at 4 km resolution from the
Advanced Very High Resolution Radiometer AVHRR; version
5.0, a radiometer onboard the National Oceanic And Atmospheric
Administration (NOAA) satellites.
Upwelling index
Upwelling intensity in each of the four upwelling regions is defined by the
corresponding model variable vertical mass transport (wmo) at the ocean model
layer at 52 m (MPI-ESM) and the vertical velocity (wvel) at 50 m
(CESM-LME) (close to the modelled mixed layer depth), and spatially averaged
over each of the upwelling regions: Benguela (8–30∘ E,
15–28∘ S), Peru (80–70∘ W, 20–10∘ S),
California (130–110∘ W, 20–50∘ N), and Canary
(20–10∘ W, 20–34∘ N). Upwelling indices are here
defined as the seasonal means over the strongest upwelling season in each
region. Seasonal averages are calculated using the standard seasons
definition: December–February (DJF), March–May (MAM), June–August (JJA),
and September–November (SON). In the MPI-ESM past1000 and historical
simulations, the main upwelling season in Benguela is September–November and
June–August in all other upwelling regions. In the future simulations, the
main upwelling season in Benguela is also June–August. Strongest upwelling
in Benguela takes place between July and October . The main
upwelling season could be both of the standard seasons (JJA or SON), since
the mean difference is small. In the CESM-LME simulations, the main upwelling
season is September–November in Benguela and Peru and June–August in
California and Canary.
As briefly mentioned in the introduction, upwelling directly at the coast is
caused by the alongshore wind stress, whereas further away off the coast
upwelling is driven by the wind stress curl. In the simulations analysed, the
regions selected to define the upwelling indices present spatially coherent
upwelling, i.e. the vertical velocities at 52 m depth in each grid cell are
almost all positively correlated with upwelling at the grid cells most
closely located to the model land grid cells. Only at the oceanward fringes
of these boxes this correlation vanishes. Therefore, each geographical box
encompass the set of grid cells that show a coherent coastal upwelling,
although the physical mechanism that drive upwelling is, strictly considered,
not the same. This coherency is, however, reasonable since the strength of
the alongshore wind stress directly at the coast should be correlated with
the wind stress curl further offshore, both being generally driven by the
ocean-to-land sea level pressure gradient, as also assumed in Bakun's
hypothesis.
Methodology
When not explicitly stated, all correlations have been calculated with linear
long-term detrended series. Low-pass time filters of 10 years for the
historical and future data and low-pass time filters of 30 years for the
past1000 data have been applied before correlating time series to filter out
the high frequencies to focus on variations of decadal and multi-decadal timescales.
For the past1000 simulations, the filter is chosen to highlight the
frequencies of variations in the external forcing. For the shorter historical
period we are more interested in the long-term trends, since the external
forcing, mostly anthropogenic, increases continuously. Applying a 30-year
low-pass filter for a time series covering 156 years would leave out too many
degrees of freedom to establish the significance of these trends.
For the analysis of the statistical significance of the long-term trends in
the upwelling indices and the linear correlations between the ensemble
members a two-sided significance level of p=0.05 was adopted. All time
series, and in particular the ocean time series, are affected by serial
correlations that preclude the application of the usual statistical tests to
establish the significance of trends or correlations. Here, we have applied a
Monte Carlo-based method . This method generates copies
of the original time series with the same serial correlation properties, but
uncorrelated in time with the original series. For instance, to establish the
significance of correlation coefficients, 1000 surrogate pairs of series
are generated and used to generate an empirical distribution of correlation
coefficients under the null hypothesis that the correlation is zero. In the
case of linear trends, 1000 series are generated with zero long-term trend
but the same serial correlation as the original series, providing an
empirical distribution of trends under the null hypothesis of zero trend.
Here, we show results from three of the available simulations from
the CESM-LME ensemble, as three simulations already confirm the
results obtained with the MPI-ESM model. These two models used
here are the only ones that provided ensembles of simulations
with basically the same forcing and with different initial
conditions, thus being suitable to detect the possible imprint of
external forcing.
After assessing the realism of the connection between atmospheric
drivers and simulated upwelling, we further evaluate whether the
upwelling intensity in these regions displays any imprint of the
external climate forcing. To estimate the ratio of forced
variability to total variability, we build a simple statistical model of the
variations of a climate record, which decomposes its variability into
a sum of a forced component, related to the external climate
forcing, and an internal component:
y=yf(t)+yi(t).
The same model can be applied to describe climate records simulated
in two climate simulations driven by the same forcing and started
with different and random initial conditions:
y1(t)=yf(t′)+y1i(t)y2(t)=yf(t′)+y2i(t),
where the forced components are by construction equal in both
simulations (the prescribed forcing is equal) and the internal components are uncorrelated in
time. Since the response to external forcing could be theoretically lagged by
a number of years, the time step (t′) of the external forcing does not have
to be necessarily the same as for the internal variability. The ratio between the variance of yf and the variance of
y1, equal to the variance of y2, can be shown to be equal to
the correlation between y1 and y2:
r=<y1,y2>/<y1,y1>=<yf,yf>/<y1,y1>,
where 〈x,y〉=∑tx(t)y(t).
The equation shows that, in two simulations that have the same forcing but
different initial conditions, the ratio between the variance of the forced
component and the total variance is the same as the correlation between both
time series. If the correlation between y1 and y2 is weak, the variance of
yf is much weaker than the variance of yi,
meaning that the effect of internal variability is much stronger than the
effect of external forcing. This is true because of the assumption that the
variance of the internal components of both time series are uncorrelated. The
forcing is externally prescribed and, thus, cannot be influenced by the
initial conditions. This mathematical description is the base of the
statistical analysis presented here. Correlating the three simulations of an
ensemble shows us whether the external variability is an important component
of the total variability. This statistical reasoning provides the rationale
for the calculations of the correlations between the indices of different
simulations within an ensemble, which is profusely used through the whole
paper.
Representations of upwelling and its atmospheric drivers in the climate model
The patterns of mean SST in the four main upwelling regions in their season
with maximum upwelling simulated in one of the historical simulations with
the model MPI-ESM-MR are compared with the corresponding data derived from
the AVHRR in Fig. , both calculated for the overlapping period
(1985–2005). The SST patterns display, in general, colder temperatures
directly along the coast, with warmer temperatures offshore. The climate
model is able to replicate these SST gradients remarkably well, in spite of
the coarse spatial resolution. However, in the cases of Benguela
(Fig. e and f) and Peru (Fig. g and h), the
climate model shows an evident warm bias relative to the satellite data
. This indicates that the main mechanisms of upwelling are
likely reasonably represented in the model, though other important mechanisms
are not.
Mean sea surface temperature in the four upwelling regions in the
corresponding main upwelling season for 1985–2005 simulated by the Earth system
model MPI-ESM compared to satellite data from AVHRR. California (June–August)
AVHRR (a) and MPI-ESM historical r1(b); Canary (June–August)
AVHRR (c) and MPI-ESM historical r1(d); Benguela (September–November)
AVHRR (e) and MPI-ESM historical r1(f); and Peru (June–August) AVHRR (g)
and MPI-ESM historical r1(h). Not the different temperature scale for Benguela and Peru.
The shape of the modelled seasonal cycle of upwelling (Fig. )
generally agrees with what is known from observations, with upwelling being
more intense in the boreal warm half year Fig. 7 in.
The mean upwelling is, however, more intense by about a factor of 2 in the lower-resolution
past1000 simulation (horizontal resolution of the ocean of
1∘ (past1000) compared to 0.4∘ (historical)),
indicating that ocean model resolution is important to simulate the correct
mean upwelling intensity, which may be relevant for the SST bias in the EBUSs
simulated by many climate models . A reason for this
difference is not clear cut but it is also not essential for this analysis.
Monthly mean vertical velocity at 52 m depth in four main coastal
upwelling regions simulated in one past1000 (900–1849) (a) and one historical
(1850–2005) simulation (r1) (b) of the MPI-ESM. The values for California have
been re-scaled for better visibility. Note the different y axis scale for each simulation.
As variations of the annual cycle are very small between the simulations of an ensemble,
the annual cycle of r1 represents the simulated annual cycle of all simulations of the ensemble.
The link between each upwelling index and wind stress, calculated as the
patterns of correlations between the upwelling field and spatial averaged
alongshore wind stress (Fig. right panels), is also very
realistic, indicating in each case that the alongshore wind stress favours
Ekman transport, the main mechanism causing coastal upwelling
. The alongshore wind stress was calculated as the wind
stress over the ocean in the upwelling areas (for Benguela between
15–40∘ S covering the whole Benguela upwelling region, North and
South Benguela), with an angle of the shoreline of 15∘ for
Benguela and 45∘ for the other regions. The relative standard
deviation of the upwelling (Fig. left panels) indicates the
low variability of the more offshore regions. Therefore, the negative
correlations in the more offshore regions, especially in the Canary upwelling
system, do not account for a large portion of the upwelling index. A
principal component analysis confirms these results. Correlation coefficients
between the leading principal component and the spatial mean of the upwelling
are between 0.80 and 0.92 in the four upwelling regions. Thus, the first
principal component which explains most of the variability of the upwelling
(more than 60 %) is highly correlated with the spatial mean.
Correlation patterns between the seasonal (June–August, except for Benguela
in September–November) upwelling field in each eastern boundary upwelling system and the
simultaneous spatial mean alongshore wind stress (right panels) and the relative standard
deviation of the upwelling field (left panels) simulated in one of the past1000 simulations
(r1) (900–1849) with the Earth system model MPI-ESM.
The correlation patterns between each upwelling index and the SLP
realistically show regions with negative correlations over land and positive
correlations offshore (Fig. ), indicating that the
across-coastline SLP gradient, with an intense oceanic subtropical high and
an intense continental low, is conductive for alongshore wind stress through
the geostrophic relation.
Correlation patterns between the seasonal (June–August, except for Benguela
in September–November) upwelling index in each eastern boundary upwelling system and the
simultaneous seasonal mean sea level pressure field simulated in one of the past1000
simulations (r1) (900–1849) with the Earth system model MPI-ESM.
Importance of external climate forcing for upwelling variations in the recent past and futurePast1000 and historical simulations
In the last millennium, the external climate forcing drove changes in the climate. The main external climate drivers over this period
were volcanic forcing, solar variability, greenhouse gases, and
land-use changes and, at millennium timescale,
the slowly varying orbital configuration of the Earth
. These forcings had an effect on the global mean
surface temperatures, both in climate simulations driven by these
forcings and in proxy-based climate reconstructions
.
found that the variability of the surface air temperature of the
Northern Hemisphere was mostly driven by the greenhouse gas concentration and volcanic
eruption in the last millennium. There was a period of relatively high
temperatures, the Medieval Warm Period with its maximum at around
1000–1300 AD. These high temperatures could have been caused by high solar
and low volcanic activity but there is no consensus about the driver of the Medieval
Warm Period, and internal variability could have played a role too. The Medieval Warm Period was followed by
the Little Ice Age, a period of low temperatures caused by low
solar and high volcanic activity. After the Little Ice Age, global
mean temperatures rose mainly as result of higher concentration of
greenhouse gases . As illustration, Fig.
displays the multi-decadal global mean temperature evolution simulated in the ensembles of
simulations analysed in this study. These temperature evolutions illustrate the common
temperature response to the external forcing with the different climate sensitivities
of both models, and also the internal variability of the global mean temperature at
multi-decadal timescales, since the simulations within each ensemble display slightly different temperature paths.
Global mean near-surface air temperature over the past millennium simulated
in two ensembles of simulations with the Earth system models MPI-ESM and CESM-LME.
The time series represent 31-year running mean of the temperature anomalies relative to the 20th century mean.
For both ensembles of simulations of the past, the decadally
smoothed time series of regional upwelling index extracted from each of
the three simulations display different time evolutions
(Fig. ) and do not show a centennial evolution comparable
to the global mean temperatures or the global mean external forcing
as just described. The correlations between these series for each
region are correspondingly low (Fig. ) and mostly not statistically
significant. These correlations remain low and non-significant for
stronger low-pass time filtering up to 50 years
(Fig. a). Based on Eq. (4), this indicates that the upwelling variance
shared by all simulations, which could only be due to the common
external forcing, is also small. These simulations, therefore, do
not support any significant imprint of the external forcing on
upwelling in any of the EBUSs up to multi-decadal timescales over the
past millennium.
Time series of the simulated upwelling indices in each upwelling region
simulated in two ensembles of climate simulations (three members each), denoted past1000
(900–1849) and historical (1850–2005), with the MPI-ESM model. The plus and minus
signs in each panel indicate the sign of the long-term trend, their value being included
whenever statistically significant. The series have been low-pass filtered, with a 30-year
filter for past1000 and a 10-year filter for the historical ensembles.
Frequency histogram in bins of 0.05 width showing the distribution of
across-ensemble correlations between the upwelling indices simulated in each
upwelling region for the ensembles past1000 (a) and historical (b)
of the MPI-ESM, after low-pass filtering with a 30-year (past1000 simulation)
and a 10-year (historical simulation) filter, respectively. The vertical
black lines indicate the 5–95 % significance bounds taking the time filtering into account.
This finding is consistent with previous studies derived from observed
records, which identified a strong influence of the Pacific North
American pattern on California upwelling. The Pacific North American
pattern is a well-known mode of internal climate variability, unrelated to external forcing, and
probably linked to the dynamics of the tropical and mid-latitude
Pacific Ocean . Thus, the influence of the Pacific North
American pattern on the California upwelling supports our results of a dominant role of the internal variability.
Correlations between the simulated upwelling indices in all four EBUSs
in three past1000 simulations with the MPI-ESM model after filtering the time series
with low-pass filter of increasing period (a). The same as (a)
but for the time series of sea level pressure difference between land and ocean averaged
over the areas most closely correlated with the simulated upwelling in this region (b).
The 95 significance levels depend on which pair of series are being tested, since the test takes
into account the serial correlation of each series. The figure shows, as an example, the significance
levels for the correlations r1–r2 for Benguela (bold black line), but all correlations shown
here are not statistically significant at the 95 % level.
At centennial timescales, the externally forced climate variability
is a large portion of the total climate variability, as the random
internal decadal variability is filtered out. Additionally, the
increase in the external climate forcing over the past
156 years is stronger than the variations in the forcing
prescribed in the past1000 simulations. For instance, the amplitude
of decadal variations of the pre-industrial external forcing is of
the order of 0.3 Wm-2, whereas the
increase in the external forcing over the past 250 years is
1.6 Wm-2.
Thus, the long-term influence of the external forcing – the
strongest being anthropogenic greenhouse gases in the historical
simulations – could be more easily detected in the form of common
long-term upwelling trends. However, the simulated upwelling trends are
small, mostly not statistically significant, and generally display
opposite signs in the different simulations of the MPI-ESM ensemble
(Fig. ). The are only two cases out of eight, California and Canary in the historical
simulations, in which all members exhibit a negative trend
(opposite to the expected strengthening), of which only two trends
of the California upwelling are statistically
significant. Analysing the imprint of external forcing on upwelling
with the CESM-LME confirms the results with the MPI-ESM. The
simulated upwelling velocity in three simulations are weakly
correlated with each other (Fig. )
and the trends are either not coherent in the simulations or are
not statistically significant.
Frequency histogram in bins of 0.05 width showing the distribution of across-ensemble
correlations between the upwelling indices simulated in each upwelling region for the ensembles
past1000 (a) and historical (b) of the CESM-LME, after low-pass filtering
with a 30-year (past1000 simulation) and a 10-year (historical simulation)
filter, respectively. The vertical black lines indicate the 5–95 %
significance bounds taking the time filtering into account.
Scenarios
Looking at the future development of the upwelling in the EBUSs, we
analyse the future simulations conducted with the MPI-ESM model
under three different emission scenarios. An effect of the external
forcing should be reflected in consistent centennial trends in
upwelling in all members of each ensemble. Consistent significant
trends in all three simulations of an ensemble only occur in the
rcp8.5 scenario, the one with the strongest external forcing
(Fig. ). The trends are
negative in California and Canary, and positive only in
Benguela. In Peru no significant trends can been seen. This supports
our results obtained for the past millennium, indicating that the
external forcing has not been intense enough in the past to force
the upwelling significantly. Even the much stronger external forcing of the rcp2.6
and rcp4.5 scenarios compared to the one of the past1000 and historical simulations
does not leave an imprint on the upwelling. Furthermore, the envisaged positive
upwelling trend only occurs in one of the four regions, underlining the local
differences among the EBUSs.
Time series of the simulated upwelling indices in each upwelling region simulated
in three ensembles of climate simulations (three members each) for 2006–2100, rcp2.6, rcp4.5,
and rcp8.5, with the MPI-ESM model. The plus and minus signs in each panel indicate the sign of
the long-term trend, their value being included whenever statistically significant. The series
have been low-pass filtered with a 10-year filter.
These results do not fully support the
ones obtained by , agreeing on the positive trends in Benguela
and the negative trend California. For Peru (Humboldt in their analysis) and
Canary they found an intensification in upwelling. However, they analysed the trend for
another period (1950–2099) and only in the rcp8.5 scenario. They
concluded that a clear connection between external forcing and
upwelling could be found. However, when analysing the trends
simulated in other scenarios, it turns out that in the weaker
greenhouse gas scenarios this link is weaker, and it cannot be
easily identified. As used a model ensemble with one simulation of
each model and we use ensembles of simulations of two models, differences could be
due to the methodology. This would indicate that either the trend differ between
the models or that using only one simulation of each model could distort the result.
Importance of external climate forcing for the drivers of upwelling in the past1000 and historical simulations
In this section, we want to determine why the imprint of the external forcing on coastal upwelling is weak
over the past periods, by looking at the imprint of the external forcing on processes involved in the
functioning of upwelling. These processes are, on the one hand, the atmospheric drivers of the
upwelling and, one the other hand, the oceanic factors influencing the upwelling.
Cross-correlation coefficients of the three simulations of each
ensemble (r1, r2, and r3) of the cross-coastline pressure gradient of all
upwelling regions for the past1000 and historical simulations with the
MPI-ESM model after 30- and 10-year filters, respectively. All
correlations are not statistically significant at the 95 % level.
Upwelling in all four investigated regions is mainly driven by the
wind stress curl that modulates upwelling in offshore regions and
by the alongshore wind stress that drives the coastal
upwelling. The upwelling in the four EBUSs is related to the SLP gradient between land
and ocean . The strength of this gradient impacts
the intensity of the trades . We investigate whether
the time evolution of this SLP gradient is consistent across the
simulations and whether the lack of correlations between the
upwelling indices across the ensemble may be due to a weak
influence of the external forcing on wind stress and on the SLP
gradient. The across-coastline SLP gradients in the simulations are
calculated by subtracting the averaged air pressure over land from
the averaged pressure over ocean in the regions identified as most
closely correlated to the upwelling indices (Fig. ). These areas are
Benguela: 15∘ W–10∘ E, 20–40∘ S (ocean),
18–25∘ E, 10–30∘ S (land); Peru: 75–100∘ W,
16–30∘ S (ocean), 60–70∘ W, 0–8∘ S (land);
California: 130–160∘ W, 35–50∘ N (ocean), 110–120∘ W,
20–40∘ N (land); Canary: 20–40∘ W, 20–40∘ N (ocean),
0–10∘ E, 5–15∘ N (land). The results are not sensitive to the
chosen regions. Using the position of climatological mean of the subtropical high and
continental low does not change the results.
As in the case of the upwelling indices, the correlations between
the time series of SLP gradients and alongshore wind stress across the
simulations in each ensemble are all low and not significant (Tables
and ). These correlations also remain low and not significant
regardless of the time filtering (Fig. b). Correlating the time series
of both drivers, the SLP difference between ocean and land and the alongshore
wind stress, without low-pass filter shows their close relation with significant
correlations for both periods, past1000 and historical, with correlation
coefficients between 0.34 and 0.70. Regarding the
longest timescales captured in the simulations, the long-term
trends of the wind stress and of the SLP gradient within each
ensemble have either inconsistent signs, or are not statistically
significant, or are incompatible with the expected effect of the
external forcing varying at centennial timescales. These expected
trends are negative in past1000 due to the long-term orbital
forcing and positive in historical due to the Bakun hypothesis based on the increase in
greenhouse gas forcing. Therefore, even if the connection between
the atmospheric drivers and upwelling might not be totally realistic in
the Earth system model, the lack of common time evolution of wind
stress or SLP across the simulations clearly shows that, from the
atmospheric perspective alone, the simulations do not support
a discernible influence of the external forcing on the atmospheric
drivers of upwelling over the last centuries. This implies that
such an influence could not have been found even if the ocean model
perfectly represented the real upwelling dynamics. Regardless of the quality of the
upwelling representation in the coupled model, one could not detect any impact of external forcing on the upwelling.
Cross-correlation coefficients of the three simulations of each ensemble
(r1, r2, and r3) of the alongshore wind stress of all upwelling regions
for the past1000 and historical simulations with the MPI-ESM model after
30- and 10-year filters, respectively. All correlations are not
statistically significant at the 95 % level.
For the future period, significant trends of the alongshore wind stress in all
three simulations of an ensemble occur in the rcp8.5 scenario with the same
sign as for upwelling, negative for California and Canary and positive for Benguela.
Additionally, there are significant negative trends in the whole ensemble in the rcp4.5
scenario for the California upwelling region. This indicates that, again, the external
forcing has to be very strong to be discernible.
Stratification
Another possible mechanism by which the external climate forcing
could influence upwelling involves the stratification of the water
column. In periods with a stronger external forcing, the
temperatures at the surface should warm more rapidly than in the
deeper layers, increasing the stability of the water column, and
hindering the mechanical effect of the alongshore wind stress
. The amount of variability in the SST that can be
attributed to the variations in the external forcing can also be
estimated by the correlation between the grid cell SST series
simulated in each member of the simulation ensemble
(Fig. ). The correlation patterns indicate that the SST
variability is more strongly driven by the external forcing in the
tropical belt and tends to be weaker in the middle and high
latitudes. This occurs despite the strongest response of high
latitudes to external radiative forcing, known as the Arctic
amplification . The reason is that at high latitudes the internal
variability is also larger than at low latitudes . The ratio between
both external forcing signal and internal variability, which is
encapsulated by the correlation patterns shown in Fig. ,
is therefore highest in the tropics, a feature which has been so
far mostly overlooked but that has been found in previous analysis of
paleoclimate simulations . In the EBUSs, the
correlations between the simulated SSTs are all positive, of the
order of 0.2–0.3 after 10-year low-pass
filtering (Fig. ). These relatively low correlations indicate
that the external forcing has some influence on
the SST variability in these regions but most of the multi-decadal
variability is internally generated.
Correlation pattern between the global skin temperatures (temperatures of
ocean and land surface) simulated in two past1000 simulations (r1 and r2) (900–1849)
with the Earth system model MPI-ESM in the June–August season after applying
a 10-year low-pass filter.
The impact of the external forcing on the stratification in the future has not
been analysed. There might be an impact due to much stronger increase in
greenhouse gas concentrations in the 21st century, especially in the rcp8.5.
Discussion and conclusions
In this paper, the importance of the external climate forcing on the upwelling
and its drivers in the four eastern boundary upwelling systems over the past
millennium and the 21st century is investigated. Regarding Bakun's hypothesis
, the increase in radiative forcing linked to increased
greenhouse gas concentrations would lead to an intensification of coastal upwelling.
The analysis of three simulation ensembles with the Earth system
model MPI-ESM over the past millennium and the future are in
contrast with the hypothesis of a discernible influence of the
external forcing on coastal upwelling intensity.
Analysing ensembles of simulations enables us to distinguish between variations
driven by internal and external variabilities.
For the periods covering the past, the past1000 and the historical simulations,
no significant trends in all three simulations of an ensemble could be found in
the upwelling itself, nor in its atmospheric drivers. Furthermore, correlations
of the ensemble members are low, indicating the missing imprint of external forcing
on the upwelling and its drivers. The internal variability dominates the external
one for both periods in all four EBUSs. These results are in conflict with Bakun's
hypothesis of an intensification of upwelling due to climate change .
The work of shows that the results of a trend analysis of observed
upwelling differs with the analysed period, data set, and analysed variable.
For the future, the effect of external forcing on the EBUSs can be
identified when greenhouse gas concentrations are assumed to follow
the strongest scenario, rcp8.5, among the three representative
concentration pathways analysed here. All three simulations in the
ensemble display consistent trends, but these trends are not always
consistent with the expected intensification of upwelling predicted by , with only
Benguela showing an intensification, California and Canary showing
a weakening, and Peru showing no significant trend.
Our results partly agree with the ones obtained by
on the influence of a strongly increased future
greenhouse gas forcing on upwelling intensity in the EBUSs, agreeing in an intensification
of the Benguela upwelling system and a weakening of the California upwelling system.
However, our results indicate that the conclusion obtained from the
analysis of only the rcp8.5 scenario cannot be extended to weaker
scenarios of future greenhouse gas forcing, nor to the trends
observed over the 20th century, nor the evolution of upwelling over
the past millennium.
Differences in the results obtained from compared to our study could
be due to several factors. The analysed time period in their analysis (1950–2099) differs
from ours. They analysed only one simulation with each model and not an ensemble of
simulations with the same model. Using an ensemble of simulations allows to better
identify the presence or absence of an externally forced signal. Furthermore, long-term
trends in their analysis (extended data, Fig. 3) show that there are positive and negative
upwelling trends (blue and red squares in their figure) for all regions, so that clearly not
all models agree in the sign of the upwelling trends. In addition, for some regions, e.g. Humboldt,
this discrepancy in the trend may not only be due to the different model structure but also due
to internal variability, as we find in the MPI-ESM model.
Compared to , where again only the upwelling in the EBUSs
in the rcp8.5 scenario in one simulation per model is analysed and there is one simulation per model, our results match
theirs only for Benguela and California. They found significant positive
trends in the upwelling-favourable winds in the Canary, Humboldt,
and Benguela systems and negative trends in the California
system, with trends in Benguela being less consistent among the models. This underlines the
differences between the upwelling regions and illustrates the
complexity of upwelling by the different results obtained depending on
the analysed model, but also highlight the impact of the external forcing
on upwelling detected in the rcp8.5 scenario.
For the rcp8.5, there seems to be a difference between the EBUSs
in the Northern Hemisphere (showing significant negative trends) and the
Southern Hemisphere (showing significant positive or not significant trends).
Differences could be caused by changes in the Hadley cell, which is expected to
expand poleward as a consequence of global warming . The temperature
differences between the equator and the subtropics will decrease in the Northern
Hemisphere, due to the larger fraction of land on the Northern than on the
Southern Hemisphere, leading to a weakening of the Hadley cell and thus to
weaker trades . This effect may be smaller in the Southern
Hemisphere due to a weaker warming of mid-latitudes and high latitudes .
A possible reason why upwelling does not show the signal of the variations
of external forcing in the simulations, with the exception of the rcp8.5 scenario,
may be that the atmospheric circulation has a large internal variability,
in contrast to other variables, like temperature, which are more directly related to the
external forcing. In general, detection and attribution studies of climate change
detect weaker signal in atmospheric dynamical variables like SLP than in thermal variables.
Uncertainties still remain. For instance, the magnitude of the external forcing
variations over the past millennium is still not well established
, and larger variations than hitherto assumed
may cause a tighter connection between forcing and upwelling than the one found
in the simulations analysed here, driven by relatively weak variations of the external forcing. Over
the past 156 years, however, the trends in external climate
forcings are much more certain and over this period the historical
simulations do not show any consistent sign of intensification or
weakening in an ensemble.
It has to be kept in mind that our results are based on the realism of
the analysed Earth system models. The relatively low model
resolution of the atmosphere and of the ocean components could
result in an unrealistic representation of the upwelling itself
and/or its drivers. As stated by , especially the
resolution of the atmospheric model may have the strongest influence on
the simulated coastal upwelling. Furthermore, the current global
coupled climate models still display a strong SST bias in the
EBUSs. The cause of this bias is not completely understood, and it may
be related to a deficient representation of coastal upwelling but
it may also have other causes, for instance related to biases in the
stratocumulus clouds . This caveat, nevertheless, also
affects the recent studies by and
, since they are also based on the CMIP5 models.
The definition of the upwelling index can be sensitive to the
region over which the vertical velocities are averaged. Redefining
the subregions of the eastern boundary upwelling systems
to the latitudinal extend of the regions used in
(extended Canary region (18.5–10.5∘ W, 16.5–42.5∘ N), south
Benguela (8–30∘ E, 28–40∘ S), and Chile
(80–70∘ W, 20–40.5∘ S)) does not change the
main results of this study. The correlation between the three
simulations remains low for upwelling. Significant trends with the same sign in all three
simulations do not occur, neither in the simulations of past
periods nor in the future simulations.
Analysing ensembles of simulation of the Earth system model
CESM-LME over the past millennium supports the results of the
MPI-ESM.
Thus, we conclude that the circumstantial evidence linking the recent observed
trends in EBUSs upwelling to external climate forcing is in conflict
with the present analysis of ensembles of simulations with two state-of-the-art climate models.
Data availability
All model data used in this study are publicly available.
The data of the simulations with the MPI-ESM-P (past1000, r1), the MPI-ESM-MR (historical),
and the MPI-ESM-LR (future) are part of the Climate Model Intercomparison Project Version 5 (CMIP5).
These data are openly available and can be downloaded from any of the nodes of the Earth
System Grid Federation, after registration. A menu allows to select the type of simulation
(past1000, historical or any of the RCP scenarios), the variable in question and the time
resolution (e.g. daily or monthly). The data download can be performed interactively by
clicking on the links of the individual files or by downloading a Unix script “wget”,
which can be locally run on a Unix computer. All files are written in “netcdf” format. Two additional
MPI-ESM-P simulations (past1000 r2 and r3) were later conducted and were kindly provided
by the Max Planck Institute for Meteorology (Zanchettin et al., 2012) (contact: johann.jungclaus@mpimet.mpg.de).
Detailed information about the Last Millennium Ensemble Project with the model CESM (Otto-Bliesner et al., 2016) can be found on
line http://www2.cesm.ucar.edu/models/experiments/LME. The data are available under this link
http://www2.cesm.ucar.edu/models/experiments/LME/data-sets. After the section of the desired variable
by the user, the data need to be processed and made ready for download. The user is then electronically
informed when the netcdf files are ready to be downloaded.
Acknowledgements
The German Federal Ministry of Education and Research (BMBF,
Germany) supported this study as part of the Geochemistry and
Ecology of the Namibian Upwelling System (GENUS) project. This
research also benefited from frequent discussions in the Cluster of
Excellence Integrated Climate System Analysis and Prediction
(CliSAP). The Max Planck Institute for Meteorology kindly provided
the model data. There is no potential conflict of interest of the
authors. We thank Dennis Bray for his editorial assistance. The article processing charges for this open-access
publication were covered by a Research Centre of the Helmholtz Association.Edited by: M. Meier
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