Natural forcing of climate during the last millennium: fingerprint of solar variability
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- Swingedouw, D., Terray, L., Cassou, C. et al. Clim Dyn (2011) 36: 1349. doi:10.1007/s00382-010-0803-5
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The variability of the climate during the last millennium is partly forced by changes in total solar irradiance (TSI). Nevertheless, the amplitude of these TSI changes is very small so that recent reconstruction data suggest that low frequency variations in the North Atlantic Oscillation (NAO) and in the thermohaline circulation may have amplified, in the North Atlantic sector and mostly in winter, the radiative changes due to TSI variations. In this study we use a state-of-the-art climate model to simulate the last millennium. We find that modelled variations of surface temperature in the Northern Hemisphere are coherent with existing reconstructions. Moreover, in the model, the low frequency variability of this mean hemispheric temperature is found to be correlated at 0.74 with the solar forcing for the period 1001–1860. Then, we focus on the regional climatic fingerprint of solar forcing in winter and find a significant relationship between the low frequency TSI forcing and the NAO with a time lag of more than 40 years for the response of the NAO. Such a lag is larger than the around 20-year lag suggested in other studies. We argue that this lag is due, in the model, to a northward shift of the tropical atmospheric convection in the Pacific Ocean, which is maximum more than four decades after the solar forcing increase. This shift then forces a positive NAO through an atmospheric wave connection related to the jet-stream wave guide. The shift of the tropical convection is due to the persistence of anomalous warm SST forcing the anomalous precipitation, associated with the advection of warm SST by the North Pacific subtropical gyre in a few decades. Finally, we analyse the response of the Atlantic meridional overturning circulation to solar forcing and find that the former is weakened when the latter increases. Changes in wind stress, notably due to the NAO, modify the barotropic streamfunction in the Atlantic 50 years after solar variations. This implies a wind-driven modification of the oceanic circulation in the Atlantic sector in response to changes in solar forcing, in addition to the variations of the thermohaline circulation.
KeywordsLast millenniumNatural climate variabilitySolar forcingNorth Atlantic OscillationThermohaline circulation
The climate system fluctuates over a very large range of frequency. For instance, global surface temperature varies on time scales going from days to millions of years (Huybers and Curry 2006). These fluctuations can be internal to the climate system, which means that they are inherent to the different components of the system and to their interactions. They can also be forced by natural forcing like the variations in total solar irradiance (TSI) or volcanic eruptions. These internal and naturally forced variabilities are usually called “natural variability”, in opposition to the variability forced by anthropogenic greenhouse gases emissions.
The multi-decadal variability of climate is of particular interest for society because this time scale is similar to that of a human life and can strongly affect the climate of a given region for a long period (Paillard 2008). Moreover, the projected global warming induced by anthropogenic greenhouse gases emissions has raised the question of how much of the recent observed warming is related to anthropogenic perturbation from that due to natural variability. The last IPCC report (Hegerl et al. 2007) shows that it is very likely that the warming observed over the last decades is due to anthropogenic perturbation. Nonetheless, it has also been shown that natural climate variability can strongly modulate the future warming trend (Keenlyside et al. 2008) especially over the next two or three decades.
Understanding the multi-decadal natural variability of the climate system is a difficult challenge because (1) the instrumental observations of the climate system only go back to 1850 or so and (2) the numerical simulations designed to investigate the multi-decadal variability of the climate necessitate very long integrations of climate models, which are very costly in terms of computer resources. To circumvent (1), climate reconstructions for the last few millennia with a very high temporal resolution have been realised by using different paleo-climate proxies like tree rings, ice cores, documentary sources, boreholes (Bradley and Jones 1993; Mann et al. 1998; Jones et al. 2001; Moberg et al. 2005; Mann et al. 2008 among many others). All these reconstructions have tried to capture the surface temperature evolution of the Northern Hemisphere. They have put forward the existence of a Medieval Warm Epoch (Lamb 1965) at the beginning of the last millennium and of a Little Ice Age (Eddy 1976) from around 1400 to 1800.
One of the most classical hypotheses to explain these climate evolutions is related to the TSI variability, which can be reconstructed using sunspot numbers and cosmogenic isotopes production like 10Be or 14C. Bard et al. (2000), by using these cosmogenic isotopes records, has proposed a reconstruction of the TSI for the last millennium. Although the scaling used for this reconstruction is under debate (Lean et al. 2002; Foukal et al. 2004), other reconstructions (Muscheler et al. 2007) confirm the time evolution of solar variability with clear minima of solar activity like the Maunder (∼1700) and Dalton (∼1810) Minima.
The impact of solar forcing on climate is first due to the radiative forcing implied by the variations in TSI. This radiative forcing is not homogeneous in space and evolves with the cosine of the latitude and is thus maximum at the equator. Moreover, it has been proposed that solar variability may influence the occurrence of natural modes of variability and in particular the North Atlantic Oscillation (NAO). The NAO can be defined as the first mode of sea level pressure (SLP) variability over the Atlantic sector in winter (Hurrell 1995). It has a large influence upon the climate of Europe and is characterized in its positive phase by a warming over northern Europe associated with increased westerlies and a cooling over the Mediterranean area. Shindell et al. (2001, 2003) compared simulations from an Atmosphere General Circulation Model (AGCM) coupled to a slab ocean and spatial reconstructions for the period 1680–1780, including the Maunder Minimum. They found a clear signature of TSI changes on surface temperature resembling the one linked to the NAO negative phase, lagging the changes in solar variability by 20 years. They explained this effect by the decrease in the meridional gradient of lower stratospheric temperature, affecting the jet-stream, which leads to a negative NAO through atmospheric wave dynamics. They explained the 20-year lag as the time scale necessary for the tropical ocean to adjust in response to the solar forcing and transmit the surface thermal signal to the lower stratosphere. Such a relation between solar forcing and NAO has been confirmed in paleo-data by Waple et al. (2002). The latter also showed that the link between solar forcing and the NAO is even longer for a lag of as much as 30 years (see their Fig. 7). This relationship between solar forcing and the NAO has also been found in coupled ocean–atmosphere GCM (OAGCM) simulations by Zorita et al. (2004) and Stendel et al. (2006).
Most of the previous studies do not analyse the climatic response to solar forcing over the Pacific Ocean. Shindell et al. (2001, 2003) did not use a dynamical ocean model, so that the coupled ocean–atmosphere feedbacks are poorly resolved in their model. Zorita et al. (2004) focused on the North Atlantic and noticed that solar forcing reduces the Atlantic meridional overturning circulation (AMOC) as in projections of future global warming (Schneider et al. 2007), while Stendel et al. (2006) found a very small impact of solar forcing on the North Atlantic. Both studies do not focus on the Pacific response, which can nevertheless strongly impact on the NAO (Cassou and Terray 2001). More recently, Meehl et al. (2008) analysed the response of the Pacific Ocean to the 11-year solar cycle (Schwabe cycle) during the last century. They found, using recent observations and OAGCM ensemble simulations, a coupled air–sea mechanism in boreal winter in response to the solar maxima, which can be compared to La Nina events. The proposed mechanism works as follows: the equatorial and tropical regions receive most of the solar irradiance so that TSI variations are maximum at these locations in terms of short wave at the top of the atmosphere. The surface of these regions can therefore accumulate energy during solar maxima. However, Meehl et al. (2008) showed that in the Pacific Ocean, the sea surface temperature (SST) does not increase very much in response to TSI variations because most of the energy accumulated in the surface ocean is rapidly released to the atmosphere through latent heat flux (which cools the SST). This release is associated with an increased zonal moisture transport towards the western warm pool, an increase in atmospheric convection there, and a slight northward shift of the Inter-Tropical Convergence Zone (ITCZ). The Walker cell is therefore enhanced, which increases the subsidence in the eastern Pacific, leading to a decrease in cloud cover and an increase in downward shortwave radiation, which feeds back positively on the whole mechanism. This feedback is fast (a few months) so that it operates in the Schwabe cycle time-frame.
In the present study, we aim at evaluating the impact of low frequency (larger than the Schwabe cycle) TSI variations on climate. The main focus is put on the winter boreal season in order to understand the link between solar variability and climate in the North Atlantic region. In particular, we will show, using an OAGCM, that TSI variations can influence the low frequency of the NAO with a lag of around 40 years, through a slow adjustment of the Pacific Ocean to solar forcing. The paper is organised as follows: In Sect. 2, we describe the experimental design of the study. In Sect. 3, we evaluate the general response of climate to solar variability in our OAGCM. In Sect. 4, we focus on the boreal winter response of the NAO and Pacific Ocean. In Sect. 5, we evaluate the impact of solar forcing on the North Atlantic. Discussions and conclusions are given in Sect. 6.
2 Experimental design
2.1 Model description
The model used in this study is the CNRM-CM3.3 OAGCM. It is based on the coupled core formed by ARPEGE-Climat version 4.6 AGCM (Déqué et al. 1999; Gibelin and Déqué 2003) and OPA 8.1 OGCM (Madec et al. 1998). CNRM-CM3.3 also includes a sea ice model, GELATO2 (Salas-Mélia 2002), and the total runoff integrated pathways (TRIP) river routing scheme (Oki and Sud 1998; Chapelon et al. 2002). These components run on distinct grids and with different time steps and are coupled synchronously, exchanging information every 24 h through the OASIS coupling software (Valcke et al. 2004). The different components are briefly described in the following sections. Full details about CNRM-CM3.3 are given by Salas-Mélia et al. (2005).
2.1.1 The ARPEGE-Climat, ISBA and TRIP models
The representation of most model variables in ARPEGE-Climat is spectral. In the framework of this study, ARPEGE-Climat was run on an horizontal grid corresponding to a linear T63 truncation. All the physics and non-linear terms are treated on an associated Gaussian reduced grid (longitude–latitude grid of about 2.8° in horizontal resolution). The model has 31 levels on the vertical. In this version of ARPEGE, the direct effects of aerosols (sea salt, desert dust, black carbon and sulfates) are taken into account, as well as the indirect effects from sulfate aerosols. The semi-Lagrangian advection scheme allows for a 30-min time step. The ISBA soil-vegetation-atmosphere transfer model, described by Mahfouf et al. (1995) is included in ARPEGE-Climat. It contains a detailed snow cover formulation (Douville et al. 1995). Soil and vegetation properties are derived from the global high resolution ECOCLIMAP dataset (Masson et al. 2003) and are prescribed. ARPEGE-Climat uses ocean temperature, sea ice extent and albedo boundary conditions computed by the OPAGELATO system and interpolated by OASIS, and provides surface fluxes to the ocean-sea ice model. The total runoff is computed by ISBA and interpolated on a 1° × 1° horizontal grid by means of OASIS. It is then converted into river discharge and transported to the ocean using TRIP. The water outflow produced at every river mouth is dumped in the closest ocean grid cells. As this amount of water can be huge for the biggest rivers, it is shared between several ocean grid cells (up to 10 for the Amazon) to avoid spurious ocean surface negative salinities. The time step used in TRIP in the framework of CNRM-CM3 is 3 h.
2.1.2 The OPA and GELATO models
The version of OPA8 OGCM used in CNRM-CM3 has a horizontal grid of 182 × 152 points, which roughly corresponds to a resolution of 2° in longitude, while in latitude, the grid point spacing decreases from about 2° in polar regions to 0.5° near the equator. The model has 31 vertical levels, 10 of them within the upper 100 m, and uses a z-coordinate mesh. It runs with a time step of 96 min. OPA8.1 is used here as a rigid lid model, hence uses a virtual freshwater flux, the sum of non solar heat fluxes (latent, sensible and net longwave fluxes) and the momentum flux provided by ARPEGE-Climat. The vertical eddy diffusivity and viscosity of the model are computed by using a 1.5 TKE turbulent closure scheme (Blanke and Delecluse 1993), while in the horizontal, an isopycnal diffusion scheme is applied, with an eddy viscosity of 40, 000 m2/s for momentum and an eddy diffusivity equal to 2, 000 m2/s for tracers. Convective mixing is parametrized by the non-penetrative convective adjustment algorithm implemented by Madec et al. (1991). Mixed layer depth is detected by a density difference of 0.01 kg/m3 with the ocean surface. The penetration of sunlight is formulated by means of two extinction coefficients (Paulson and Simpson 1977). The GELATO2 sea-ice model (Salas-Mélia 2002) is directly embedded in the ocean component of CNRM-CM3 and uses the same grid. Its time step is 24 h. The elastic-viscous-plastic dynamics by Hunke and Dukowicz (1997) is included, and the advection of sea ice slabs is semi-Lagrangian, as described by Hunke and Lipscomb (2002). Due to convergence, sea ice can raft (ice thinner than 0.25 m) or ridge (ice thicker than 0.25 m). These processes are taken into account by a redistribution scheme derived from Thorndike et al. (1975). GELATO2 has four different ice thickness categories: 0–0.3, 0.3–0.8, 0.8–3 and over 3 m. Transitions or mergers between these categories may occur as ice thickness varies thermodynamically. Every slab of ice is evenly divided into four vertical layers and may be covered with one layer of snow, for which snow aging processes are considered. The heat diffusion equation is solved along the vertical (Salas-Mélia 2002) through the entire slab. The impact of icebergs around the Antarctic is represented as additional water and latent heat fluxes due to the melting of ice. These fluxes are assumed to be evenly spread south of 60°S. This flux is applied only during the summer austral season (October–March) as a constant flux of ice of 0.14 Sv (1 Sv = 106 m3 s−1) over this period, which is both consistent with the modelled accumulation of snow over Antarctica estimated from a control experiment and with current observational estimates of the annual volume of calved icebergs.
2.2 Numerical experiments
In the present study we have performed two 1,000-year simulations. The first one (CTRL) is a pre-industrial control experiment similar to the one described in Guemas and Salas-Mélia (2008a). In this simulation, the CO2 concentration is equal to 280 ppm, and the TSI is set to 1,370 W/m2. The second simulation (MILL) has been designed to take into account the external climate forcing using state-of-the-art reconstructions for the volcanic eruptions, the TSI variations, the greenhouse gases concentration and the anthropogenic sulfate aerosols changes for the period 1001–2000.
The volcanic forcing that we use is based on the Ammann et al. (2007) reconstruction for the stratospheric injection of sulfate volcanic aerosols. This reconstruction uses data from ice cores records from Greenland and Antarctica. The volcanic aerosols in the stratosphere are transported latitudinally in the model following Grieser and Schönwiese (1999) parametrisation. We consider 48 latitude bands for this transport. The model incorporates prescribed changes in aerosol optical depth, and interactively computes the perturbed (longwave and shortwave) radiative budgets.
The greenhouse gases variations are also taken into account in the MILL simulation. Before 1860, we account for the CO2 concentration variations, which explained the variations in radiative forcing appearing in Fig. 1b. After 1860, the simulation accounts for the increase in different greenhouse gases (CO2, CH4, N2O, CFCs...) and also from 1890 for the anthropogenic aerosol emissions (Fig. 1b). The concentrations used are from the 20C3M simulation of the last IPCC (Forster et al. 2007). Moreover, the land-use changes as reconstructed by Ramankutty and Foley (1999) are also included from 1700. The land-use is fixed to its 1700 value for the period before 1700. Nevertheless, the associated changes in radiative forcing are almost negligible before around 1860. Orbital variations of the Earth in relation to the Sun, can be calculated accurately (Berger 1978). For the last millennium, their impact on climate can be neglected compared to the other forcings (Bertrand et al. 2002), so that we do not take them into account in the MILL simulation.
Both simulations start from the same initial conditions, which are derived from a spin-up simulation of 250 years starting from rest from Levitus (1982) for the ocean temperature and salinity, and a randomly chosen 1st January for the atmosphere. In the CTRL simulation the net heat flux budget at the surface equals 0.33 W/m2 when averaged over 1,000 years. Consequently there is a slight drift for the SST in CTRL of 0.027 K/century and of 0.039 K/ century for the ocean deeper than 1,000 m.
In the present paper, we choose to focus on the low frequencies variability of climate related to TSI variability. Since the MILL experiment is forced with different external forcing, notably the volcanic forcing, it is not straightforward to identify the solar effect on climate. Nevertheless, impact of volcanic eruption occurs at high frequency: it is supposed to affect the climate for 2–3 years only (Ottera 2008). We will analyse its impact on climate in this simulation as compared to available reconstruction in a companion paper. To minimize the climatic signature of the volcanoes in the present study, we apply a low-pass Lanczos time-filter (Duchon 1979) to all the fields analysed next, with a cutoff values of 13 years at least. Moreover we detrend all the variables of MILL by using the linear trend extracted from CTRL. This has been done to eliminate the linear trend due to model biases. The approximation of linear trend has been tested by removing a quadratic trend but it does not modify the results presented hereafter so that we keep the linear trend hypothesis. Finally, the statistical significance of the correlations computed in this study are estimated by using a “random-phase” test that accounts for the serial correlation effect due to the low-pass filtering of the data (Ebisuzaki 1997).
3 Low frequency variability of surface temperature
3.1 Hemispheric variability
The surface temperature variations in the Southern Hemisphere are very small in the model (not shown). There are too few data collected in the Southern Hemisphere to provide robust temperature reconstructions able to provide meaningful comparison with our results at an hemispheric scale. In the Northern Hemisphere we find a strong correlation of 0.74 (statistically significant at the 99% level) between the modelled surface temperature variability in the Northern Hemisphere and the TSI variability when filtered with a cutoff value of 13 years (used to filter the Schwabe cycle). We take advantage of this strong correlation to evaluate the spatial signature of the solar forcing on surface temperature in the model through a linear regression analysis as used in Shindell et al. (2001) or Waple et al. (2002).
3.2 Regional temperature response to solar forcing
The zonal mean response for the 2-m atmospheric temperature is represented in Fig. 3b. It shows that the response of the climate to solar forcing is asymmetric, with an increased warming from the equator to the Northern Pole, and on the other hand a decreased warming from the equator to the Southern Pole, with even a cooling from 50°S. This general response is quite reminiscent from that of global warming (see Fig. 3.9 from Trenberth et al. 2007). This pattern is similar to a meridional bipolar seesaw for the 2-m temperature, which has been hypothesized to be a pattern of climate variability during the Holocene by Denton and Broecker (2009).
4 North Atlantic Oscillation response to TSI changes
4.1 NAO and solar forcing
The time variation of this first EOF, filtered with a 30-year cutoff value is represented in Fig. 5b for the period 1001–1860, when the anthropogenic perturbation of the climate is usually considered to be small. The reconstructed NAO index (Luterbacher et al. 2002a) for the period 1500–1860 is also represented with the same time-filtering. The simulated and reconstructed index do not compared very well for the period before 1700. On the opposite, we notice a low NAO index around 1750–1800 that appear both in the model and the reconstruction. Such a similarity could be due to chance, since the NAO is known to be chaotic (Schneider et al. 2003; Hurrell et al. 2004). To clearly validate this relationship for this period, ensemble simulation would be necessary but are out of the scope of the present study. The correlation between the low frequency of the simulated NAO and of the solar forcing for the period 1001–1860 is analysed in Fig. 5c and shows a statistically significant correlation between the two indexes of more than 0.35, with a time lag of around 40 years and more for the NAO index as compared to the solar forcing index. This significant correlation suggests a causality between the NAO index and solar forcing, and also provides an explanation for the negative NAO phase in the second part of the eighteenth century observed both in the model and reconstructions: it could be a more than 50 years delayed response to the Maunder Minimum. Volcanic eruptions during this period are very sparse and may not explain this low frequency trend.
4.2 Proposed mechanism
5 Oceanic response in the North Atlantic to TSI changes
The impact of the NAO on the North Atlantic Ocean, and notably on the AMOC has been extensively discussed in the literature: a positive phase of the NAO is thought to enhance the convection in the Labrador Sea, and therefore the AMOC (Dickson et al. 1996; Curry et al. 1998; Eden and Willebrand 2001; Bentsen et al. 2004). The significant changes in the NAO related to the solar forcing that we observe in the MILL experiment could therefore impact on the AMOC. On the other hand, the increase in solar forcing could lead through radiative forcing to an SST increase in the North Atlantic that could, as for the response of the AMOC under global warming conditions (Gregory et al. 2005), weaken the AMOC. These two effects (NAO forced by TSI variations and warming due the radiative solar forcing) may oppose each other in our simulation, although their time scales are not identical since the radiative forcing effect at the top of the atmosphere is maximum for a lag around 0–10 years (not shown), while the NAO changes forced by TSI variations are significant with a 40-year lag as compared to the solar forcing index.
6 Discussions and conclusions
In this study, we have analysed the climate variability of the last millennium, by using a state-of-the-art OAGCM and a few reconstructions. We have focused our attention on the low frequency response of the climate system to the low frequency solar forcing. We have shown that the model used succeeds in reproducing the low frequency temperature variability in the Northern Hemisphere. Our main result concerns the winter NAO response to the solar forcing for the period 1001–1860, which is considered to be not much perturbed by human activity. While the analysis of the temperature reconstructions over the North Atlantic sector have shown a close relationship between the increase in solar irradiance and a temperature signature that resembles a positive phase of the NAO, a lag of 10 to 30 years was found for this relationship (Waple et al. 2002). Here, we find a mechanism to explain this lag, and even larger ones. This mechanism (summarized in Fig. 11) implies the tropical Pacific response to solar forcing. The increase in TSI activates a positive feedback at 0-year lag described by Meehl et al. (2008). This positive feedback enhances the Walker cell over the tropical Pacific, and amplifies the atmospheric convection in the northern ITCZ. The transport of anomalous warm SST in a few decades by the North Pacific subtropical gyre makes the SST anomalies in the northeast tropical Pacific to persist for a few decades. Elsewhere in the tropical Pacific,the SST anomalies tend to disappear south of the equator 30 to 40 years after of the onset of solar variations. This leads to a tropical meridional SST gradient after 30 years, that partly shifts the ITCZ northward. This shift is associated with an enhanced atmospheric convection in the north tropical Pacific, which excites a positive SLP pressure anomaly over the North Pacific. This anomaly then propagates through the jet-stream wave guide (Branstator 2002) towards the North Atlantic, and leads to a positive NAO, significant with a 40-year time lag. This very large time lag is therefore due to a complex adjustment of the tropical Pacific Ocean, influenced by a slow coupled feedback implying the oceanic heat transport by the North Pacific subtropical gyre. This lag is also related to the very low frequency of the solar variability.
The NAO can be seen as an internal mode of variability of the atmosphere, largely chaotic (Schneider et al. 2003; Hurrell et al. 2004), and even the 1950–2000 trends in the NAO can not easily be attributed to a specific forcing (Deser and Phillips 2009). Here, in spite of the different forcing included (volcanoes, CO2, solar), the low frequency forcing of the system by the solar forcing leads to a statistically significant low frequency forcing of the NAO, that helps to understand paleo reconstructions from Waple et al. (2002) and also from Luterbacher et al. (2002b). In particular, a delay of more than 50 years after the Maunder Minimum and a negative phase of the NAO, which appears both in the data (including instrumental after 1700 in Luterbacher et al. (2002b)) and in the model, is explained by our analysis. Nevertheless, given the chaotic nature of the NAO, an ensemble of simulations would be necessary to gain insights on this result. The variations of solar forcing in our simulation are however quite numerous and the statistical significance we find pleads in favour of our interpretation.
We have then focused our attention on the response of the North Atlantic Ocean to solar forcing. We have evaluated by using our OAGCM the hypothesis from Lund et al. (2006) of a weakening of the AMOC during the Little Ice Age, a period of low solar forcing. We find an opposite response, with a weakening of the AMOC when the solar forcing increases. We attribute this signal to a weakening of the convection in the Labrador Sea in phase with the solar forcing variations. The NAO changes forced by TSI variations do not appear to play a significant role for the forcing of the AMOC, which mostly responds to the thermal changes, as in global warming projections (Gregory et al. 2005). Indeed, the signal of a decrease in convection in the Labrador Sea, while convection in the Nordic Seas is shifted northward but remains strong, is similar to the response observed in future climate projections using this model (Guemas and Salas-Mélia 2008b) and also other models (Wood et al. 1999; Hu et al. 2004). This result of a weakening of the AMOC with solar forcing increase is in agreement with other studies of the last millennium (Cubasch et al. 1997; Zorita et al. 2004).
Lund et al. (2006) have recorded an enhancement of the Gulf Stream rather than a direct measure of the AMOC. They admit that the variations they cornered can also be due to wind stress changes. We therefore analysed the modifications of wind stress and gyres in our experiment. We find a significant modification of the North Atlantic subtropical gyre concomitant with the wind stress modifications related to the NAO changes in response to solar forcing (e.g. with a 50-year time lag as compared to the solar forcing). These modifications however do not affect the transport across the Florida Straits, where the reconstruction from Lund et al. (2006) were made. Nevertheless, given the resolution of the ocean model (between 0.5° and 2°), this type of very thin current is not well reproduced, so that this may prevent us to observe any realistic variations of the Gulf Stream current in this region.
Finally, we would like to point out that our 2-m temperature fingerprint in response to solar forcing resembles a bipolar seesaw, with a large warming in the Northern Hemisphere (especially in high latitudes), while the Southern Hemisphere does not experience any significant warming, and even a cooling in some places in the Southern Ocean, associated with an enhancement of the zonal wind stress and an increase of mixing of the Southern Ocean. This type of response is comparable to a certain extent with the temperature response to greenhouse gases increase during the last 50 years (Trenberth et al. 2007), with a large warming at the northern high latitudes, while the Southern Ocean experiences a very small warming and even a slight increase in sea ice cover (Goosse et al. 2009). Such a bipolar seesaw is therefore not related to the changes in the AMOC, which in our model goes in the opposite way with the solar forcing, and rather feeds back negatively on this signal. Thus, any bipolar seesaw-like signals in the archive of the past (Denton and Broecker 2009), do not necessarily corroborate AMOC changes, but could be only related to the thermal response of the climate, influenced by its different distribution of continents and oceans between the two hemispheres.
To conclude, we argue that additional data for the beginning of the last millennium would be very useful to validate the existence of the mechanisms we propose. In particular oceanic data for the whole millennium is clearly missing as compared to the quantity of land reconstructions. Recent efforts for obtaining oceanic core data with a high temporal resolution (Lund et al. 2006; Sicre et al. 2008) should be encouraged. This will help to improve our understanding of the low frequency of the ocean and the mechanisms explaining the low frequency of the natural climate variability. The analysis of the high frequency related to volcanic eruptions will be analysed in a companion paper notably by using this simulation and a new reconstruction of climate variability over Europe (Guiot et al. 2010).
We thank Hugues Goosse, Joel Guiot and Christoph Raible for very constructive discussion about the results presented here. We thank Tim Osborn for his help concerning the overlap reconstructed temperature. This paper is a contribution to the project ESCARSEL funded by the French Agency for National Research (ANR VMC 2006). The use of statpack, safo and ferret softwares is acknowledged. The help of Patrick Brockmann and Eric Maisonnave has improved the quality of the figures.