The consistency of an existing reconstructed annual (December–November) temperature series for the Lisbon region (Portugal) from 1600 onwards, based on a European-wide reconstruction, with (1) five local borehole temperature–depth profiles; (2) synthetic temperature–depth profiles, generated from both reconstructed temperatures and two regional paleoclimate simulations in Portugal; (3) instrumental data sources over the twentieth century; and (4) temperature indices from documentary sources during the late Maunder Minimum (1675–1715) is assessed. The low-frequency variability in the reconstructed temperature in Portugal is not entirely consistent with local borehole temperature–depth profiles and with the simulated response of temperature in two regional paleoclimate simulations driven by reconstructions of various climate forcings. Therefore, the existing reconstructed series is calibrated by adjusting its low-frequency variability to the simulations (first-stage adjustment). The annual reconstructed series is then calibrated in its location and scale parameters, using the instrumental series and a linear regression between them (second-stage adjustment). This calibrated series shows clear footprints of the Maunder and Dalton minima, commonly related to changes in solar activity and explosive volcanic eruptions, and a strong recent-past warming, commonly related to human-driven forcing. Lastly, it is also in overall agreement with annual temperature indices over the late Maunder Minimum in Portugal. The series resulting from this post-reconstruction adjustment can be of foremost relevance to improve the current understanding of the driving mechanisms of climate variability in Portugal.
Climate reconstructions allow further insight into the climatic variability beyond the relatively short instrumental period, being commonly based on early instrumental records, documentary evidence (namely, memoirs, diaries, chronicles, weather logs, ship logbooks) and natural proxies, such as boreholes, tree rings, corals, ice cores, speleothem records and pollen profiles (Brázdil et al., 2005, 2010; Camuffo et al., 2010; Li et al., 2010; Luterbacher et al., 2006; Pollack and Huang, 2000). Historical climatology is critical for understanding the driving processes of climate variability not only in the past, but also in the future. This is particularly important when developing climate change projections for the future under emission scenarios (IPCC, 2013).
Climate variability in Europe over the last millennium was reconstructed based on both documentary evidence and natural proxies (e.g. Alcoforado et al., 2000; Brázdil et al., 2005, 2010; Camuffo et al., 2013; González-Rouco et al., 2009; Luterbacher et al., 2006). European-wide temperature reconstructions since 1500 have already been developed (Luterbacher et al., 2004; Xoplaki et al., 2005), as have continental-wide reconstructions for the last two millennia by Ahmed et al. (2013). Temperature reconstructions in some European sites, based on both documentary data and instrumental records since the 16th century, were carried out by Camuffo et al. (2010). A temperature reconstruction for southern Portugal during the late Maunder Minimum (LMM; 1675–1715) was presented by Alcoforado et al. (2000). However, in Portugal, most of the pre-instrumental records show numerous temporal gaps and there is a substantial lack of natural proxies with clear climatic signals (Alcoforado et al., 2012; Camuffo et al., 2010; Luterbacher et al., 2006).
Borehole temperature–depth profiles can be used as paleoclimate proxies for climate reconstruction (e.g. Bodri and Čermák, 1997; González-Rouco et al., 2009; Majorowicz et al., 1999; Šafanda et al., 2007), as they provide independent information on long-term temperature variability (Jones et al., 2009). Borehole measurements are a complementary temperature record to high-frequency air temperature series recorded at weather stations and, through profile inversion methods, may also enable validating low-frequency variability in these series (e.g. Beltrami and Bourlon, 2004; Beltrami and Mareschal, 1995; Beltrami et al., 2011; Chouinard and Mareschal, 2007; González-Rouco et al., 2006; Gouirand et al., 2007; Harris and Chapman, 1998; Harris and Gosnold, 1999; Nielsen and Beck, 1989; Pollack et al., 2006). Some studies have been carried out using borehole temperature logs measured in southern Portugal (e.g. Correia and Šafanda, 1999, 2001; Šafanda et al., 2007). Borehole reconstructions can also be compared to paleoclimate simulations generated by Earth system models for validation purposes (Beltrami et al., 2006; González-Rouco et al., 2009; Stevens et al., 2008).
The present study aims at analysing the consistency between the Luterbacher et al. (2004) and Xoplaki et al. (2005) temperature reconstructions for the Lisbon region (Portugal) over the period of 1600–1999 using (1) five local borehole temperature–depth profiles; (2) synthetic temperature–depth profiles, generated from gridded near-surface temperatures produced by regional paleoclimate reconstructions and simulations; (3) instrumental data recorded in Lisbon over the twentieth century; and (4) temperature indices from early instrumental and documentary sources during the LMM (1675–1715). This analysis allows a validation of the annual mean reconstructed temperature in Portugal over the last 400 years. The identification of possible inconsistencies in the above-mentioned data sources enables a post-reconstruction adjustment of this time series. In effect, this calibrated time series may help our understanding of past climate variability in Portugal and its main driving mechanisms, namely the role of external vs. internal forcing mechanisms in temperature variability. This attribution analysis provides critical information for model validation and for assessing the reliability of regional climate change projections. The data sets and methods are presented in Sect. 2, the results are discussed in Sect. 3 and the main conclusions are summarized in Sect. 4.
The reconstructed seasonal mean temperature in the grid box
38.5–39.0
The consistency of the Lut2004 reconstruction with borehole measurements,
retrieved from the only geothermal-paleoclimatological observatory in
Portugal (38.34
Five measurements (temperature logs) in the same borehole, TGQC1, are
considered herein; they were carried out on 24 March 1997 (M1),
27 March 2000 (M2), 14 November 2002 (M3), 26 November 2003 (M4) and
28 October 2004 (M5). These five temperature logs were obtained
by measuring the equilibrium temperature with a thermistor every 5.0 (M1),
1.0 (M2), 2.5 (M3 and M4) and 2.0 m (M5) down to a depth of
The borehole temperature–depth profiles are herein compared to synthetic temperature profiles (forward model), generated from both Lut2004 and annual mean near-surface temperatures from two paleoclimate simulations, rather than applying the conventional procedure of inverting temperature logs to reconstruct ground surface temperatures (e.g. Correia and Šafanda, 2001). However, the uncertainties inherent in these inversion models (Hartmann and Rath, 2005), mostly due to errors in the estimation of subsurface parameters, are also present in these forward models. The profiles were generated following the methodology described by Beltrami et al. (2011), as explained below.
The temperature anomaly at depth
The two paleoclimate simulations were carried out with the global circulation
model (GCM) ECHO-G and then dynamically downscaled with the regional
climate model (RCM) MM5. ECHO-G combines the HOPE-G ocean model (Legutke
and Voss, 1999) with the ECHAM4 atmospheric model (Roeckner et al., 1996).
The regional model employs a limited area domain that spans the
Iberian Peninsula completely with a spatial resolution of 30 km. Three reconstructed
external forcings were used to consistently drive both models: solar
variability, atmospheric greenhouse gas concentrations and radiative effects
of stratospheric volcanic aerosols. The skill of the MM5–ECHO-G setup to
reproduce the climate in the Iberian Peninsula has been previously evaluated
by Gómez-Navarro et al. (2011), particularly with respect to the ability
of the regional model to reduce the warm bias and to correct the winter
variability over western Iberia in the GCM run. Two paleoclimate simulations
(Sim1 and Sim2), only differing in their initial conditions, were used as a
broad estimation of the effect of internal variability (cf. Gómez-Navarro
et al., 2012; González-Rouco et al., 2003; Zorita et al., 2005, 2007).
Near-surface (2 m) temperatures for the period of 1600–1989 are extracted
from these simulations. Their daily mean fields were bilinearly interpolated
from the original MM5 grid to the reconstructed temperature grid
(0.5
In order to identify low-frequency variability and trends in the paleoclimate
simulations, a data-adaptive filtering, based on a singular spectral analysis
(SSA), is applied (Ghil and Vautard, 1991). SSA is based on the well-known
principal component analysis, in which multiple dimensionality is
achieved by including time-lagged replicas of the original time series. The
resulting principal components are thus linear combinations of different lags
of this series, which is equivalent to a time filtering with
filter coefficients that are related to the eigenvectors of the
lagged-covariance matrix. More formally, SSA corresponds to an eigenvalue
decomposition of a lagged-covariance matrix, with a Toeplitz structure,
obtained from the original time series of the paleoclimatic simulations. The
rank,
Under the assumption that the aforementioned external forcings used in the paleoclimate simulations are mainly manifested by long-term temperature trends in western Iberia, as suggested by Gómez-Navarro et al. (2012), similar trends of reconstructed and simulated temperatures should be expected. As SSA enables isolating data-adaptive non-linear trends in the time series (Ghil and Vautard, 1991), it can be used to correct discrepancies between long-term trends of reconstructed and simulated temperature series. In the present study, this approach was used to adjust the low-frequency variability in the reconstructed series to the paleoclimate external forcings obtained from the simulations (adjustment of the Lut2004 reconstruction). Therefore, instead of developing a new reconstruction, an adjustment of the already existing reconstruction was carried out herein (post-reconstruction adjustment).
The consistency of the Lut2004 reconstruction with the corresponding instrumental series (InstT) for the available period of 1901–1999, recorded at the Lisboa-Geofísico meteorological station and supplied by the European Climate Assessment and Dataset project (Klein Tank et al., 2002), was also assessed. It should be stressed that Lut2004 is heavily dependent on InstT, as previously stated, and a high temporal correspondence between these two time series is thereby expected. A transfer function between InstT and Lut2004 was determined by using a linear regression analysis. The resulting first-order regression polynomial was applied so as to calibrate the Lut2004 reconstruction in the extended period from 1600 onwards, thus correcting its location and scale parameters. Lastly, annual indexed temperatures for southern Portugal over the pre-instrumental period of 1675–1715 (LMM), developed by Alcoforado et al. (2000), were also analysed for consistency assessment.
The consistency of the Lut2004 reconstruction with borehole temperature–depth
profiles and with paleoclimate simulations is assessed in this section. The
five logs of borehole measurements (M1, M2, M3, M4 and M5) are shown in
Fig. 1a. Their corresponding inverse geothermal gradients were estimated
using linear regressions applied to the data from the bottom 140–180 m (Fig. 1b).
Owing to the deposition of fine material at the bottom of the borehole, there
is a local change in thermal conductivity at about 180 m. As the borehole
was drilled in a very homogeneous granite batholith, these changes are not
due to changes in the geological formation. In the present study, depths
The five temperature–depth anomaly profiles (M1–5) are reproduced in Fig. 2a with their estimated geothermal gradients having been removed. M1, M2, M3 and M4 show a more pronounced near-surface warming than M5. Overall, these profiles suggest strong recent-past warming trends in near-surface air temperatures.
The synthetic temperature–depth anomaly profiles, generated from the Lut2004
reconstruction and from the two paleoclimate simulations, are also shown in
Fig. 2a. The 11-year running means of their anomalies over the period
1600–1989 are plotted in Fig. 2b. The chronograms of the two simulations, as
well as their individual profiles, are indeed very similar to the
corresponding ensemble mean chronograms and profiles (not shown). In fact,
the correlation coefficient between the 11-year running means of the two
simulations is as high as
The discussion above suggests a remarkable agreement between the
low-frequency variability in near-surface temperature from two independent
sources (borehole measurements and paleoclimate simulations driven by
reconstructed forcing). However, whereas the paleoclimate simulations agree
well with the borehole temperature–depth profiles, the reconstructed
temperature for Portugal (Lut2004) is not entirely consistent with the
long-term trends revealed by these new sources. In fact, its linear trend is
nearly 0 over the whole period and there is no signature of cool or warm
periods. This disagreement between simulations and Lut2004 was already
reported by Gómez-Navarro et al. (2011). As such, the low-frequency
variability in the Lut2004 reconstruction are herein adjusted to be more
coherent with the borehole data and simulations. To achieve this aim, the
ensemble mean temperature from the two simulations was low-pass filtered by a
two-order SSA. The filtered series (SSA trend in Fig. 2b) highlights the
signature of the external forcings in near-surface temperature and was then
added to the Lut2004 reconstruction. The resulting calibrated series
(CalT
The SSA trend clearly shows a warming trend since the 1830s and a relatively cool period during the LMM (1670–1730). This is also in line with previous studies on the impact of solar activity on global temperatures (e.g. Eddy, 1983; Frenzel, 1994). The period from 1730 to 1800 recorded annual mean temperatures close to the baseline, followed by an anomalously cold period until the 1830s, which is associated with the Dalton Minimum, also a period of low solar activity (Wagner and Zorita, 2005). The strong upward trend in CalT from the 1830s onwards is now in clear agreement with the paleoclimate simulations and InstT (in the twentieth century). The LMM (ca. 1670–1730) and Dalton Minimum (ca. 1790–1830) are also clearly depicted in CalT. Furthermore, the temperature–depth anomaly profile from CalT is similar to the profiles from the five borehole measurements and paleoclimate simulations (Fig. 2a). This represents an important validation of CalT.
Scatter plot of InstT and CalT anomalies over their common period (1901–1989). The corresponding regression line, calibration equation and R-squared measure (determination coefficient) are also shown.
The consistency between InstT and CalT has been assessed by a linear
regression in their common period (1901–1989). The corresponding scatter plot
shows that linear regression provides a good fitting, with a correlation
coefficient above 0.90 (Fig. 3), explaining about 82 % of the total
variance (R-square adjusted), and an RMSE of 0.22. According to Fisher's
test, this least-squares linear regression model is statistically significant
at a 99 % confidence level (
The uncertainties in the CalT series are a combination of the original
uncertainties in the Lut2004 data set plus additional uncertainties related to
the non-linear trend used in the adjustment. The uncertainties in the Lut2004 data set are discussed in
Luterbacher et al. (2004) but are only available for the European mean
reconstruction. Hence, it is not possible to have a local estimate of these
uncertainties. The uncertainties in trend estimation can be estimated through the assessment of the
consistency between Sim1 and Sim2. For this purpose, the SSA filtering was
applied separately to Sim1 and Sim2. The mean absolute difference between the
two non-linear trends obtained from Sim1 and Sim2 provides a measure of the
uncertainty related to the simulations. It has an approximate value of
0.05
Chronogram of
In previous studies, temperature in southern Portugal was analysed during the
LMM (1675–1715) by Alcoforado et al. (2000), and during the eighteenth
century by Taborda et al. (2004) and Alcoforado et al. (2012). In these
studies, research was based on documentary evidence, such as diaries,
ecclesiastical rogation ceremonies (pro pluvia and pro serenitate),
The consistency of the reconstructed annual temperature series in Portugal (Lut2004) is assessed by using five borehole temperature–depth profiles, synthetic temperature–depth profiles generated from both the Lut2004 reconstruction and paleoclimate simulations, instrumental data (InstT) and indexed temperatures during the LMM. While the paleoclimate simulations agree well with the borehole temperature–depth profiles with regard to the long-term variability, the same does not apply to the Lut2004 reconstruction. In fact, the long-term trends in Lut2004 are not fully consistent with borehole data and simulations. The late Maunder and Dalton minima, clearly reflected in the paleoclimate simulations and well-documented in the literature, in association with changes in solar activity (Eddy, 1983), are absent from the Lut2004 reconstruction. Moreover, there is a cooling trend throughout the nineteenth century that is not supported by previous studies. Therefore, the Lut2004 reconstruction was calibrated by adjusting its low-frequency variability to the paleoclimatic simulations, also in agreement with local borehole data. Documentary sources in Portugal during the LMM (1675–1715) also show high agreement with CalT, thus providing an additional validation for the LMM.
Scatter plot of CalT in the period of 1675–1715 as a function of the annual temperature indices. Light (dark) grey circles represent cold (hot) years from documentary evidence. Circles with black edges indicate agreement between the two data sets. Years with “0” index are omitted for the sake of readability of the plot. The horizontal line corresponds to CalT mean. Some labels are omitted for the sake of clarity.
These results suggest some inconsistencies in the low-frequency variability in temperature in Portugal between the Lut2004 reconstruction and borehole data or simulations. In effect, the absence of clear long-term trends in Lut2004 is not coherent with the significant changes in radiative forcing throughout the last 400 years and the important role played by these external forcings on temperature variability over western Iberia (Gómez-Navarro et al., 2012). The frequent temporal gaps in the pre-instrumental records and the substantial lack of natural proxies with clear climatic signals in Portugal (Alcoforado et al., 2012; Camuffo et al., 2010; Luterbacher et al., 2006) may partially explain this limitation in the reproduction of the low-frequency variability in the Lut2004 reconstruction. An important loss of low-frequency variance caused by the method used in Lut2004 was also found by von Storch et al. (2009). Nevertheless, a more detailed assessment of the causes for this shortcoming is beyond the scope of the present study, as this study does not develop a new reconstruction for comparison but rather an adjustment of an existing reconstruction.
CalT adjusts the low-frequency variability in the Lut2004 reconstruction so as to be more consistent with local borehole measurements and regional climate simulations. It can thus be of foremost relevance in forthcoming research on climatic variability in Portugal. A reliable representation of the low-frequency variability in temperature in Portugal, including its long-term trends, is critical for understanding the role played by external vs. internal forcings in regional climate variability and change. Due to the relatively coarse spatial resolution of data generated by state-of-the-art GCMs, the latter are not suitable for regional-scale assessments. Since such scales are precisely the focus of this study, temperature series from two high-resolution regional paleoclimatic simulations (Sim1 and Sim2) are employed instead of GCM runs. These two simulations were documented and validated in previous studies. Unfortunately, there are only two available simulations covering Portugal with such high-resolution characteristics. Hence, it is not possible to increase the ensemble size of model simulations, though it would be very useful for uncertainty assessments. In forthcoming research, new regional paleoclimatic simulations over Portugal, also using different models, should be used to enhance the robustness and evaluate the significance of the current adjustment.
This study was carried out within the framework of the project “Reconstruction and model simulations of past climate in Portugal, using documentary and early instrumental sources – Klimhist” and was supported by national funds from FCT (Portuguese Foundation for Science and Technology) under grant numbers PTDC/AAC-CLI/119078/2010 and UID/AGR/04033/2013. Edited by: S. Bronnimann