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A Warm Miocene Climate at Low Atmospheric CO2 levels
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Authors: G. Knorr, M. Butzin, A. Micheels and G. Lohmann
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Text S1
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The coupled atmosphere/ocean general circulation model
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The atmospheric component of the AOGCM is ECHAM5 [Roeckner et al., 2006] run at
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T31 resolution (~3.75º) with 19 vertical (hybrid) levels. The ocean component is MPIOM
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[Marsland et al., 2003] with a curvilinear Arakawa-C grid and a formal horizontal
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resolution of ~1.8º x 3º. The vertical domain is represented by 40 unevenly spaced layers.
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MPIOM includes a dynamic-thermodynamic sea ice model. The sea ice dynamics are
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formulated applying viscous–plastic rheology [Hibler, 1979]. The thermodynamics of sea
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ice relate changes in sea ice thickness to a balance of radiant, turbulent, and oceanic heat
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fluxes. The effect of snow accumulation is taken into account, along with snow–ice
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transformation when the snow/ice interface sinks below the sea level because of snow
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loading. The impact of ice growth and ice melting is included in the model, assuming a
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sea ice salinity of 5 PSU [Jungclaus et al., 2006].
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Vegetation reconstruction
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‘In this study we apply a Late Miocene vegetation reconstruction [Micheels et al., 2007]
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based on paleobotanical evidence. The vegetation reconstruction does not rely on
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assumptions about atmospheric CO2 concentrations at the sites where paleovegetation is
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known from proxy data [cf. Micheels et al., 2007]. The interpolation between these
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ground-truthed locations is based on spatial gradients using vegetation calculations with
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the Prentice biome model [Prentice et al., 1992], which does not use CO2 as an input
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parameter. The biome model is driven by temperature and precipitation fields of a Late
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Miocene simulation, which is based on an AGCM-slab ocean model configuration with
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modern vegetation distribution, Miocene orography and weaker ocean heat transport
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[Steppuhn et al., 2006]. In spite of an atmospheric CO2 concentration of 353 ppmv in the
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Late Miocene run of Steppuhn et al. [2006], the calculated vegetation distribution
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systematically tends to allocate biome types indicative of a relatively cool climate to
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locations where paleobotanical estimates suggest warmer conditions. Therefore the
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gradients used for the interpolation between locations of ground based vegetation
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information in the applied vegetation reconstruction are likely to represent a conservative
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estimate of spatial vegetation changes associated with a warmer Miocene climate. It
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should also be noted that results of the biome model have not been used in a strict way,
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but vegetation gradients have been interpreted to fill geographical gaps. Recently Pound
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et al. [2011] published a Late Miocene vegetation reconstruction also using
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palaeobotanical evidence and completed with vegetation modelling. The major pattern in
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the vegetation reconstructions of Micheels et al. [2007] and Pound et al. [2011], such as
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no desert in North Africa and the large extent of boreal forests in northern high latitudes,
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are very similar.
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Factor separation and synergy analysis
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To determine the contributions of changes in the tectonic setting and vegetation
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distribution, as well as their synergy, we have conducted a factor separation analysis
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[Stein and Alpert, 1993]. This technique has been developed to separate monocausal
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contributions of different processes in a climate change signal from synergistic effects
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that result from non-linear processes in the climate system [Berger, 2001]. To quantify
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the individual contribution of the two factors from the synergy between them, four
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simulations (CTRL, MIO, TECT and VEG) are necessary. The respective global
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climatological surface air temperatures at the end of the simulations (TCTRL, TTECT, TVEG,
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TMIO) were used to calculate the two factors and the synergy term that results from
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simultaneous changes in the tectonic setting and vegetation distribution. Accordingly we
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define fTECT = TTECT − TCTRL, fVEG = TVEG − TCTRL, and fSYN = TMIO − fTECT – fVEG − TCTRL.
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With TCTRL =14.8 ºC, TMIO =17.8 ºC, TTECT = 15.5 ºC and TVEG = 17.3 ºC (as listed in
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Table S1), the two factors and the synergy term can be calculated. The resulting values
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are fVEG = +2.5 K, fTECT = +0.7 K and fSYN = - 0.2 K. This implies that in case of positive
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contributions by fTECT and fVEG in combination with a relatively weak negative fSYN the
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resulting global warming (TMIO - TCTRL) is lower than the sum of fTECT and fVEG. This
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nonlinearity is further discussed within the result and discussion sections of the main
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manuscript.
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Zero-dimensional energy balance model (EBM)
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Following Budyko, [1969] and Sellers [1969] the EBM considers the radiation balance for
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a grey atmosphere:
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where T is the surface temperature predicted by the EBM, S = 1367 Wm-2 the total
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irradiance, and σ = 5.67 · 10-8 W m-2 K-4 the Stefan-Boltzmann constant. The planetary
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albedo (α) is the fraction of the upward and downward shortwave fluxes at the top of the
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atmosphere, while the effective longwave emissivity (ε) is determined by the ratio of
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upward longwave fluxes at the top of the atmosphere and the surface. The respective
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fluxes are derived from our AOGCM simulations.
(1-α) S/4 = ε σ T4,
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Comparison of the Late Miocene climate in MIO to quantitative terrestrial proxy
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data
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To evaluate the performance of our simulated Late Miocene climate we have compared
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our simulation MIO with quantitative terrestrial proxy data from 111 sites where paleo-
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botanical information provides information about the mean annual temperature (Figure
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S2). To enable a comparison with existing model studies we use the model data
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comparison approach of Steppuhn et al. [2007] and apply this method to the mean annual
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temperature. The paleo-botanical data set represents an updated version [Micheels et al.,
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2011] of the compilation used and presented in previous studies [e.g., Steppuhn et al.,
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2007; Micheels et al., 2007].
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The model simulation shows a close match to the data estimates with a difference
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between the respective model and data mean over all localities as small as 0.14 K. This
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value represents a closer model-data correspondence than in the study of Micheels et al.
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[2009] that calculated a mean difference of 1.09 K, using a model of intermediate
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complexity in an atmosphere/slab-ocean model configuration. The modeled temperatures
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are especially consistent with the proxy estimates in Europe within a range of ± 1 K. Only
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a few localities in middle Europe are outside this range with slightly cooler temperatures.
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At a hemispheric scale the comparison shows relatively warm modeled temperatures in
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the low latitudes and some localities with relatively cold modeled temperatures north of
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the subtropics. To estimate the robustness of the model performance in more detail (e.g.
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changes in spatial gradients including inter-hemispheric temperature changes) further
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quantitative temperature reconstructions from regions outside Europe and especially the
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Southern Hemisphere are required.
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Figure annotations
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Figure S1. Meridional water volume transport in Sv (1 Sv = 106 m3 s-1) for (a)
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preindustrial (CTRL) and (b) Late Miocene climate conditions (MIO), warm water flows
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northward to the subpolar regions, sinks, and flows southward as North Atlantic Deep
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Water. The strength of the associated overturning cell (red) is indicated by positive
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values.
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Figure S2. Difference between the mean annual 2m- temperature in simulation MIO and
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terrestrial proxy data estimates for the Late Miocene. Europe is shown enlarged. The
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circles represent data using various (mostly palaeobotanical) reconstruction methods [cf.
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Steppuhn et al., 2007; Micheels et al., 2011].
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Figure S3. Late Miocene temperature (a) and albedo (b) differences between an extended
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model configuration (referred to as MIO_dynveg) with a land-surface scheme including
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dynamical vegetation [Raddatz et al., 2007; Brovkin et al., 2009], and our standard setup
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in MIO with prescribed land surface properties. The global climate in MIO_dynveg is
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significantly cooler (-2.4 K) than in MIO. Furthermore, the global temperature in
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MIO_dynveg is only ~1 K warmer than the global temperature in the equivalent extended
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model configuration for the preindustrial control climate in a simulation, referred to as
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CTRL_dynveg (Table S1). Besides a cooler global climate in MIO_dynveg compared to
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MIO, temperature differences are particularly pronounced in North Africa and northern
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Australia. This can be largely explained by the albedo scheme applied in MIO_dynveg. In
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this albedo scheme the surface albedo of snow-free land is largely controlled by the
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canopy albedo and the background albedo below the canopy. The background albedo is a
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grid-box constant derived from satellite measurements. Hence, areas with strong seasonal
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variations in the presence of vegetation (especially green leaves) or generally low
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presence of vegetation/leaves are influenced by the prescribed time-invariant modern
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background albedo [Vamborg et al., 2011]. The resulting surface albedo difference
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between the two model configurations dominates the strong temperature differences in the
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modern Sahara region and Australia, which are both largely covered by savanna and
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tropical forest vegetation in MIO. In the northern high-latitudes the cooler temperatures
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are also linked to higher surface albedo values, which follow pattern of increased snow
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accumulation as a consequence of the colder climate. The present-day remote sensing
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data in the background of the dynamic land-surface scheme in MIO_dynveg are suitable
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for recent conditions, but they bias and constrain climatic responses under non-recent
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conditions. The results with the present land-surface scheme in MIO_dynveg demonstrate
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the need for advanced albedo schemes to better represent feedback and synergetic
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mechanisms for climate states that strongly deviate from the present one.
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Simulation
Tectonics
Vegetation
CO2
Run length
Global Temp.
CTRL
PD
PD
278 ppmv
1500 yrs
14.8 ºC
TECT
LM
PD
278 ppmv
1500 yrs
15.5 ºC
VEG
PD
LM
278 ppmv
1500 yrs
17.3 ºC
MIO
LM
LM
278 ppmv
1500 yrs
17.8 ºC
CTRL_dynveg
PD
Interactive
278 ppmv
1500 yrs
14.4 ºC
MIO_dynveg
LM
Interactive
278 ppmv
1500 yrs
15.4 ºC
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Table S1 Overview of the experimental setup in our simulations with respect to tectonic
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and vegetation boundary conditions, as well as the resulting global mean surface air
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temperature in the various experiments. PD and LM indicate present-day and Late
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Miocene boundary conditions, respectively. Simulations shown and discussed in the
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supplementary material are indicated by the suffix ‘dynveg’.
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