ESDEarth System DynamicsESDEarth Syst. Dynam.2190-4987Copernicus PublicationsGöttingen, Germany10.5194/esd-9-1235-2018Simulation of observed climate changes in 1850–2014 with climate model
INM-CM5Simulation of climate changes in 1850–2014VolodinEvgenyvolodinev@gmail.comGritsunAndreyInstitute for Numerical Mathematics, INM RAS, Gubkina 8, Moscow 119333, RussiaEvgeny Volodin (volodinev@gmail.com)25October2018941235124210April20188May20184October20188October2018This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://esd.copernicus.org/articles/9/1235/2018/esd-9-1235-2018.htmlThe full text article is available as a PDF file from https://esd.copernicus.org/articles/9/1235/2018/esd-9-1235-2018.pdf
Climate changes observed in 1850–2014 are modeled and studied on the basis
of seven historical runs with the climate model INM-CM5 under the scenario
proposed for the Coupled Model Intercomparison Project Phase 6 (CMIP6). In
all runs global mean surface temperature rises by 0.8 K at the end of the
experiment (2014) in agreement with the observations. Periods of fast warming
in 1920–1940 and 1980–2000 as well as its slowdown in 1950–1975 and
2000–2014 are correctly reproduced by the ensemble mean. The notable change
here with respect to the CMIP5 results is the correct reproduction of the
slowdown in global warming in 2000–2014 that we attribute to a change in
ocean heat uptake and a more accurate description of the total solar
irradiance in the CMIP6 protocol. The model is able to reproduce the correct
behavior of global mean temperature in 1980–2014 despite incorrect phases of
the Atlantic Multidecadal Oscillation and Pacific Decadal Oscillation indices
in the majority of experiments. The Arctic sea ice loss in recent decades is
reasonably close to the observations in just one model run; the model
underestimates Arctic sea ice loss by a factor of 2.5. The spatial pattern of
the model mean surface temperature trend during the last 30 years looks close
to the one for the ERA-Interim reanalysis. The model correctly estimates the
magnitude of stratospheric cooling.
Introduction
Noticeable climate changes were observed during the last century. The main
feature of these changes is global warming and it is widely accepted that
its most probable cause is an increase in the anthropogenic greenhouse gas
concentration (Bindoff et al., 2013). The nature of several other important
changes is not as clear and is still under discussion. Global warming was
not uniform in time. There are two well-known periods of acceleration in
1920–1940 and 1980–2000 and two periods with a stabilization of the global mean
temperature in 1950–1975 and 2000–2014.
The reason for this oscillatory behavior is still debated. In Wilcox et al.
(2013) it is shown that the period of climate stabilization in 1950–1975 can be
connected with the increase in anthropogenic SO2 emissions in Europe and
North America, as well as with stratospheric volcanic eruptions (Bindoff et
al., 2013), while the decrease in warming in 2000–2014 could be attributed to
a slowdown of methane and the tropospheric ozone concentration increase rate. On
the other hand, the ensemble of CMIP5 model runs (with all the mentioned
aspects of aerosol and greenhouse gas forcing taken into account) continues
to raise global temperature in 2000–2014 albeit at a slower rate
(Checa-Garcia et al., 2016).
Another point of view on this problem is that the acceleration and
deceleration of global warming could be a manifestation of internal climate
variability with a timescale of 60–70 years (Meehl et al., 2011). The
Atlantic Multidecadal Oscillation (AMO) and Pacific Decadal Oscillation (PDO)
are the most known drivers of internal variability in the climate system on
multidecadal timescales. Indeed, Dong and McPhaden (2017) showed the
importance of AMO-like and PDO-like internal variability for local
temperature in the North Atlantic and the North Pacific but questioned its
ability to produce a significant anomaly in global mean temperature. A
connected question is to what extent the observed long-term variability of
the AMO and PDO patterns is an internal process or is forced by some external
factors. There is some evidence (Ting et al., 2014) that negative values of
the AMO index in 1950–1970 could be attributed to enhanced SO2
emissions in Europe and North America.
One of the most intriguing features of recent climate changes is a rapid
decrease in Arctic sea ice area since year 2000 coupled with strong Arctic
warming. Similar Arctic warming was also observed in the middle of the 20th
century. The ensemble of CMIP5 models underestimates the amount of sea ice
loss in the 2000s by a factor of 2 (Bindoff et al., 2013; Stroeve et al.,
2012). The INMCM4 (Volodin et al., 2013) that participated in
CMIP5 also strongly underestimates Arctic sea ice loss extent in the
beginning of the 21st century. On the other hand, the INMCM4 (and other CMIP5
models; Stroeve et al., 2012) demonstrates a loss of Arctic sea ice of
comparable magnitude at different times (in the middle of the 20th century
for INMCM4). Moreover, a similar sea ice loss event was produced by INMCM4
during a preindustrial control run. So, the question is to what extent the
21st century Arctic sea ice degradation is due to internal climate
variability and whether the next generation of models with the new CMIP6 forcing
recommendations will be able to reproduce sea ice changes in the beginning of the 21st
century.
Regional climate changes during the last several decades also show some
interesting features. For example, in 2000–2014 there is almost no winter
warming in the majority of Eurasia with respect to the previous decades and
a small cooling was even observed in some places. One possible reason could
be the response of atmospheric dynamics to Arctic sea ice loss (Overland et
al., 2011). However, this hypothesis is questioned by other studies (see
McCuscer et al., 2016, as an example).
The aim of this study is to analyze basic features of climate changes during
1850–2014. The data for the analysis (ensemble of seven historical runs)
were produced by the new climate model INM-CM5 as an incremental upgrade
of the INMCM4. We are mainly focusing on the question of how global mean
surface temperature (GMST) changes are reproduced with the new forcing
protocols proposed for CMIP6 and how these changes are connected with the
reproduction of other features of the climate system mentioned above (i.e.,
AMO and PDO variability).
Model and data
The climate model INM-CM5 (Volodin et al., 2017a, b) was
used in this study. In the atmosphere, it has a spatial resolution of 2×1.5∘ in longitude and latitude and 73 levels in the vertical, with the
uppermost level at 0.2 hPa. In the oceanic block, the spatial resolution is
0.5×0.25∘ and 40 levels in the vertical. The model includes an
interactive aerosol block (Volodin and Kostrykin, 2016), in which concentrations
of 10 aerosols are calculated. In the numerical experiments discussed below only
the first aerosol indirect effect (the influence of aerosol on cloud drop
radius) is taken into consideration. A model description and analysis of
simulations of the present day climate can be found in Volodin et al. (2017b).
Let us now discuss a climate change modeling experiment for years 1850–2014.
Time series of CO2, CH4, N2O, O3,
stratospheric volcanic sulfate aerosol concentration, total solar irradiance
(TSI), and solar spectrum, as well as anthropogenic emissions of
SO2, black carbon, and organic carbon were prescribed as
recommended for the historical run of CMIP6 (Eyring et al., 2016). Seven
model runs were started with different initial conditions obtained from a
long preindustrial run, in which all external forcings were prescribed at the
level of year 1850. The length of the preindustrial run was several hundred
years, so the upper oceanic layer was adjusted to atmospheric model
conditions, but it is not the case for the deep ocean. A small trend of model
climate is visible because of deep ocean adjustment to upper oceanic and
atmospheric conditions – a common situation for the simulation of historical
climate with present day climate models. The obvious reason for multiple
integrations is to separate the role of natural variability and external
forcing in climate changes. When data from seven model runs are consistent
with each other, then one can expect that the phenomenon of interest is a
manifestation of (or response to) an external forcing. If there is a
noticeable difference between different model runs, then a role of natural
variability is crucial. To estimate the statistical significance of the
near-surface temperature trend, a t test at the 99 % level was used.
The variance of 5-year means was calculated from 1200 years of the
preindustrial run.
Observational data of GMST for 1850–2014 used for verification of the model
results were produced by HadCRUT4 (Morice et al., 2012). Monthly mean sea
surface temperature (SST) data ERSSTv4 (Huang et al., 2015) are used for
comparison of the AMO and PDO indices with that of the model. Data of Arctic
sea ice extent for 1979–2014 derived from satellite observations are taken
from Comiso and Nishio (2008). The stratospheric temperature trend and
geographical distribution of near-surface air temperature trend for
1979–2014 are calculated from ERA-Interim reanalysis data (Dee et al., 2011).
Results
The most important measure of climate changes is the global mean surface
temperature. Observed GMST demonstrates the well-known acceleration of
warming in 1920–1940 and 1980–2000 and small warming or even small cooling
in 1945–1970 and 2000–2014. The ensemble of CMIP5 models (Bindoff et al.,
2013) shows less significant slowdown in warming in 2000–2014. In
particular, the INMCM4 (Volodin et al., 2013) demonstrates gradual
warming starting from 1920.
The 5-year mean GMST (K) anomaly with respect to 1850–1899 for
HadCRUTv4 (thick solid black); model mean (thick solid red). Dashed thin lines
represent data from individual model runs: 1 – purple, 2 – dark blue, 3 –
blue, 4 – green, 5 – yellow, 6 – orange, 7 – magenta. In this and the next
figures numbers on the time axis indicate the first year of the 5-year mean.
With the new CMIP6 protocols all seven INM-CM5 model runs demonstrate fast
warming in 1980–2000 with a rate close to the observations and GMST
stabilization in 2000–2014 and 1950–1970 (Fig. 1). The only significant
difference in the new CMIP6 forcings at the beginning of the 21st century
with respect to CMIP5 is the change in the TSI. Before year 2000 CMIP5 and
CMIP6 TSI show almost identical behavior (CMIP5 solar forcing and other forcings
are described in Taylor et al., 2012). In 2001–2008 the TSI recommended for CMIP6 is about
0.3 W m-2 lower than the one for CMIP5 (Fig. 2). For 2009–2014 the
CMIP5 scenario suggested a repetition of the previous solar cycle that gives
a value of the TSI almost 1 W m-2 above the one recommended for CMIP6.
An additional model run with anthropogenic aerosol emissions fixed at the
level of year 1850 shows a gradual GMST rise in 1950–1970 together with its
stabilization in 2000–2014 (not shown). The latter fact supports the
hypothesis that correct reproduction of GMST changes in 2000–2014 is due to
the corrected CMIP6 treatment of the TSI. Another factor that stabilizes GMST
in 2000–2014 in INM-CM5 is the heat flux to the ocean (Fig. 3) having values
of 0.3–0.7 W m-2 (higher than in any period of the 20th century). The
CMIP5 historical experiment with the INMCM4 shows a gradual increase in the
ocean heat uptake during 1980–2005 rather than its abrupt jump in 1995–2005
seen in Fig. 3. Note that Yan et al. (2016) showed that according to the
available observations, the slowdown in GMST increase in 1998–2013 can be
explained by increased ocean heat uptake, which could be estimated as
0.7 W m-2 for 1993–2010 according to Rhein et al. (2013).
Monthly mean TSI anomaly (W m-2) with respect to 1882–1931
recommended for CMIP5 (blue; dashed line after year 2008 is the repetition of
the data for 1998–2008) and for CMIP6 (red).
The 5-year mean surface heat flux; W m-2 (positive downward).
The thick solid red line represents model mean, and the dashed thin lines represent data
from individual model runs: 1 – purple, 2 – dark blue, 3 – blue, 4 – green,
5 – yellow, 6 – orange, 7 – magenta.
The better representation of GMST stabilization in 1950–1970 (Fig. 1) in
simulations with INM-CM5 with respect to the INMCM4 can be explained by
the incorporation of a new aerosol block in the model that resulted in a more
sophisticated treatment of anthropogenic and volcanic aerosol interaction
with atmospheric radiation. Fast warming in 1920–1940 similar to
observations can be seen in four model runs, while the other three runs show
warming earlier or later. These results suggest that the observed
acceleration of warming in 1920–1940 is probably due to a combination of
external forcing and natural variability.
Keeping in mind the argument that the GMST slowdown in the beginning of
the 21st century could be due to the internal variability of the climate
system, let us look at the behavior of the AMO and PDO climate indices. Here
we calculated the AMO index in the usual way, as the SST anomaly in the Atlantic
at latitudinal band 0–60∘ N
minus the anomaly of the GMST. The model and observed
5-year mean AMO index time series are presented in Fig. 4. The well-known
oscillation with a period of 60–70 years can be clearly seen in the
observations. Among the model runs, only one (dashed purple line) shows
oscillation with a period of about 70 years, but without significant maximum
near year 2000. In other model runs there is no distinct oscillation with a
period of 60–70 years but a period of 20–40 years prevails. As a result none
of the seven model trajectories reproduces the behavior of the observed AMO index after
year 1950 (including its warm phase at the turn of the 20th and
21st centuries). One can conclude that anthropogenic forcing is unable
to produce any significant impact on the AMO dynamics as its index averaged
over seven realization stays around zero within one sigma interval (0.08).
Consequently, the AMO dynamics are controlled by the internal variability of the
climate system and cannot be predicted in historic experiments. On the other
hand, the model can correctly predict GMST changes in 1980–2014 having the wrong
phase of the AMO (blue, yellow, orange lines in Figs. 1 and 4).
The 5-year mean AMO index (K) for ERSSTv4 data (thick solid black);
model mean (thick solid red). Dashed thin lines represent data from individual
model runs. Colors correspond to individual runs as in Fig. 1.
More coherent behavior of model trajectories after year 1980 could be seen
for the North Atlantic (45–65∘ N) temperature (Fig. 5). Indeed, the
temperature deviates from its 1850–1899 mean by 1.5 root mean square deviation in the
early 2000s. The NA temperature index in the model shows notable oscillations
with periods of about 30–40 and 60–80 years (close to the 25 and 80 years
for the observations), and three trajectories have correct strongly positive
NA temperature anomalies in the 21st century.
The 5-year mean SST anomaly (K) with respect to 1850–1899 in the North
Atlantic (45–65∘ N) for ERSSTv4 data (thick solid black); model mean
(thick solid red). Dashed thin lines represent data from individual model runs.
Colors correspond to individual runs as in Fig. 1.
Another important climate feature that could be responsible for the changes
in the GMST growth rate is the PDO measured by its index defined as the
normalized projection of the SST anomaly on a specific pattern in the North
Pacific at 20–60∘ N. The 5-year average PDO index for observations and model
data is presented in Fig. 6. For the observations, one can see maxima at
years 1930–1940 and 1980–1995 and a prolonged minimum during 1950–1975. None
of the model trajectories reflects observed time series of the PDO index for
the same reasons discussed earlier in the paragraph devoted to the AMO.
Again the model does not need correct PDO index dynamics to predict GMST
behavior.
The 5-year mean PDO index (K) for ERSSTv4 data (thick solid black);
model mean (thick solid red). Dashed thin lines represent data from individual
model runs. Colors correspond to individual runs as in Fig. 1.
One of the most intriguing observed features of ongoing climate changes is
the fast summer Arctic sea ice extent decrease in the beginning of the 21st
century. The ensemble of CMIP5 models underestimates the rate of decrease in
Arctic summer ice area by a factor of 2. INMCM4 participated in
CMIP5 and also significantly underestimates the extent of Arctic sea ice decrease
(Volodin el al., 2013). In newly obtained INM-CM5 data (Fig. 7)
we qualitatively see the same behavior of the Arctic sea ice as the average
rate of sea ice loss is underestimated by a factor of 2 to
3. However, in one model run (purple) the magnitude of decrease is
similar to the one in the observations (reduction from 7–7.5 million km2
in the 1980s to 4–5.5 million km2 in the 2000s). In other runs Arctic
sea ice loss is underestimated by a factor of 1.5–3, and in one run (green)
one can even see some increase in Arctic sea ice area during the last decades.
Our results suggest that the rapid decrease in Arctic sea ice extent near
year 2000 was partially induced by external forcing; however, the role of
internal variability can be very important (the range of the sea ice extent
year-to-year variability could be estimated as 3.0 million km2).
September Arctic sea ice extent (1012 m2) for
observations (Comiso and Nishio, 2008) (thick solid black); model mean (thick
solid red). Dashed thin lines represent data from individual model runs. Colors
correspond to individual runs as in Fig. 1.
The stratosphere is more sensitive to global changes than the troposphere.
One can see in the observations stratospheric cooling by several degrees
during the last decades. In the ERA-Interim reanalysis data (Fig. 8) the
global and annual mean temperature at 5 hPa in year 2014 is 3 K lower than in
year 1979. All model runs show a gradual decrease in stratospheric temperature
during all periods of the historical run from 1850 to 2014, but the rate of
decrease in 1979–2014 is highest and equal to 2.5 K, which is slightly below
the absolute value observed. This strong decrease is consistent in
all model runs and is likely produced by combined effects of CO2
increase and ozone decrease. Oscillations of global mean temperature at 5 hPa
with a period of 10–12 years represent the prescribed solar cycle.
Annual mean global mean temperature (K) at 5 hPa for ERA-Interim
data (black) and model data (dashed color lines).
Annual mean near-surface air temperature (K) in 2000–2014 minus
1985–1999 for model mean data (a); shading represents the 99 %
level of significance; ERA-Interim data (b).
One of the characteristic features of climate changes in recent decades is a
specific geographical pattern of surface temperature trends. Figure 9 shows
the near-surface air temperature difference between 2000–2014 and 1985–1999
according to ERA-Interim reanalysis and model mean data. Statistical
significance for model data was estimated using a t test, and the 99 % confidence
level was used. Reanalysis data look noisier than model mean, but some
observed features are reproduced well by the model ensemble. Maximum warming
up to 2.5 K appears in the Arctic and in the Barents and Kara seas; warming over high
and midlatitudes in Eurasia and North America is about 1 K, with the lowest
warming (or even cooling in reanalysis data) located in the Southern
Ocean. Model warming is robust everywhere except some areas in the Southern
Ocean, the zone of deep convection in the North Atlantic, and zones of Gulf Stream
and Kuroshio separation from the shore, where natural variability is high.
In the Pacific, the observed pattern connected with the PDO is not
reproduced in model mean data or in any individual model run.
Figure 10 represents the near-surface temperature model trend in two experiments
(blue and green) with the maximum and minimum Arctic warming. In the
second one (green) there is no Arctic warming at all and even some cooling,
and warming over Eurasian and North American midlatitudes is also much
smaller than in model average data. Otherwise, in the first case (blue) the
Arctic warming in some areas is as large as 7 K, and midlatitudinal warming
over Eurasia and North America is higher than in model average data.
Conclusions
Seven historical runs for 1850–2014 with the climate model INM-CM5 were
analyzed. It is shown that the magnitude of the GMST rise in model runs
agrees with the estimate based on the observations. All model runs reproduce
the stabilization of GMST in 1950–1970, fast warming in 1980–2000, and a second
GMST stabilization in 2000–2014, suggesting that the major factor for
predicting GMST evolution is the external forcing rather than system
internal variability. Numerical experiments with the previous model version
(INMCM4) for CMIP5 showed unrealistic gradual warming in 1950–2014. The
difference between the two model results could be explained by more accurate
modeling of the stratospheric volcanic and tropospheric anthropogenic aerosol
radiation effect (stabilization in 1950–1970) due to the new aerosol block
in INM-CM5 and more accurate prescription of the TSI scenario (stabilization
in 2000–2014) in the CMIP6 protocol. Four of seven INM-CM5 model runs simulate
the acceleration of warming in 1920–1940 in a correct way; the other three produce
it earlier or later than in reality. This indicates that for the
warming during 1920–1940 the climate system natural variability plays a
significant role.
Annual mean near-surface air temperature (K) in 2000–2014 minus
1985–1999 for the model run with highest (a) and lowest (b)
warming in the Arctic. Shading represents the 99 % level of significance.
No model trajectory reproduces the correct time behavior of the AMO and PDO indices.
Taking into account our results on the GMST modeling one can conclude that
anthropogenic forcing does not produce any significant impact on the
dynamics of the AMO and PDO indices, at least for the INM-CM5 model. In turn,
the correct prediction of the GMST changes in 1980–2014 and the increase in
ocean heat uptake in 1995–2014 does not require correct phases of the AMO
and PDO as all model runs have correct values of the GMST, while in at least
three model experiments the phases of the AMO and PDO are opposite to the
observed ones in that time. The variance explained by PDO and AMO is similar
in the model and in the observations. The North Atlantic SST time series
produced by the model correlates better with the observations in 1980–2014.
Three out of seven trajectories have a strongly positive North Atlantic SST
anomaly as in the observations (in the other four cases we see near-to-zero
changes for this quantity).
The INM-CM5 has the same skill for prediction of the Arctic sea ice extent in
2000–2014 as CMIP5 models, including INMCM4. It underestimates
the rate of sea ice loss by a factor between 2 and 3. In one extreme case the
magnitude of this decrease is as large as in the observations, while in the
other the sea ice extent does not change compared to the preindustrial age.
In part this could be explained by the strong internal variability of Arctic
sea ice, but obviously the new version of INMCM and the new CMIP6 forcing
protocol do not improve the prediction of the Arctic sea ice extent response
to anthropogenic forcing.
The model reproduces several observed geographic features of the near-surface
air temperature trend during the last decades, including Arctic
amplification with a maximum over the Barents and Kara seas, warming of about 1 K
over Eurasian and North American midlatitudes, and the weakest warming over
the Southern Ocean. Case-to-case variability is very important here as well.
The decrease in stratospheric temperature at 5 hPa during the period of
1979–2014 is successfully reproduced by the model in all experiments. The
magnitude of the temperature drop is close to the one for ERA-Interim data
(2.5 and 3 K).
Data on global mean near-surface temperature HadCRUT4 are
available at https://crudata.uea.ac.uk/cru/data/temperature/ (last
access: 30 March 2018). The oceanic temperature dataset ERSSTv4 can be
downloaded
at https://www1.ncdc.noaa.gov/pub/data/cmb/ersst/v4/netcdf/ (last
access: 30 March 2018). The ERA-Interim reanalysis can be downloaded at
https://www.ecmwf.int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/era-interim
(last access: 30 March 2018). INM-CM5 model output is now available by
request to the first author (volodinev@gmail.com), but will be added to the CMIP6
database.
AG produced model runs and collected
model output; EV performed data processing.
The authors declare that they have no conflict of
interest.
Acknowledgements
The study was performed at the Institute of Numerical Mathematics of the
Russian Academy of Sciences and supported by the Russian Science Foundation,
grant 14-27-00126. Climate model runs were produced with the supercomputer of
the Joint Supercomputer Center of the Russian Academy of Sciences and
supercomputer Lomonosov at Moscow State University. Edited by: Christian Franzke Reviewed by: two
anonymous referees
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