# Spatiotemporal drought variability in northwestern Africa over the last nine centuries

## Authors

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DOI: 10.1007/s00382-010-0804-4

- Cite this article as:
- Touchan, R., Anchukaitis, K.J., Meko, D.M. et al. Clim Dyn (2011) 37: 237. doi:10.1007/s00382-010-0804-4

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## Abstract

Changes in precipitation patterns and the frequency and duration of drought are likely to be the feature of anthropogenic climate change that will have the most direct and most immediate consequences for human populations. The latest generation of state-of-the-art climate models project future widespread drying in the subtropics. Here, we reconstruct spatially-complete gridded Palmer drought severity index values back to A.D. 1179 over Morocco, Algeria, and Tunisia. The reconstructions provide long-term context for northwest African hydroclimatology, revealing large-scale regional droughts prior to the sixteenth century, as well as more heterogeneous patterns in sixteenth, eighteenth, and twentieth century. Over the most recent decades a shift toward dry conditions over the region is observed, which is consistent with general circulation model projections of greenhouse gas forced enhanced regional subtropical drought.

### Keywords

Tree-ringDroughtClimate field reconstructionMediterraneanNorthwestern Africa## 1 Introduction

Society faces many challenges, few of them more complex than the need to conserve natural resources while providing the benefits of economic development to all sectors of the population. This challenge is particularly severe in arid and semi-arid regions, where the resource limitations come not merely from a shortage of water but from the high variability of precipitation in space and time. The critical balance of water in arid ecosystems is readily upset by the resource needs of dense human populations. Dry area covers one-third of the land surface of the earth; more than half of that area is home to 630 million people, and the remainder is so arid and unproductive that it cannot readily support human life (Brooks et al. 1991).

Various parts of North Africa have suffered devastating drought in the last 30 years (e.g. Nicholson and Wigley 1984; Chbouki 1992; Swearingen 1992; Hoerling and Kumar 2003). Drought impacts an economic and social structure already reeling from other serious problems. For example, Moroccan droughts between 1980 and 1985 caused food-shortages and violent civil unrest and drove Morocco’s foreign debt to 80% of its gross national product (Swearingen 1992). Also during this early 1980’s drought, river flow in Morocco decreased 50–90% from respective long-term mean flows (Chbouki 1992) and many natural lakes dried up completely (Belkheiri et al. 1987). More recently, the 1999–2002 droughts in North Africa appear by some metrics to perhaps be the worst since at least the middle of the fifteenth century (Touchan et al. 2008a). Recent drought in the region began in 1999, apparently part of a widespread pattern of midlatitude drying in the Northern Hemisphere (Hoerling and Kumar 2003). Increasingly dry subtropical conditions are one predicted consequence of anthropogenic climate change (Held and Soden 2006; Seager et al. 2007a; Chou et al. 2009). Future efficient use of limited water resources will require better and more effective planning processes to implement long-term management actions and other intervention strategies. Effective planning is currently constrained in part by the limitations of instrumental climate data: precipitation, temperature, and streamflow records generally cover only slightly more than 70 years in much of North Africa. These records are not long enough to determine the characteristic time scale and forcing of regional climate variability over several centuries, nor to identify whether the recent pattern of drought could be part of an ‘imminent’ change toward drier conditions in the region (c.f. Seager et al. 2007a).

Instrumental records can be extended back several centuries with proxy data. The resulting records can provide estimates of the past frequency and severity of climatic anomalies, and these in turn may be used to help anticipate the probability of such events in the future. Long time series of tree-ring growth are one of the best sources of proxy data for reconstructing past records of precipitation, streamflow, and drought on interannual to centennial time scales during the late Holocene. Tree-ring records are annually resolved, well-replicated, and can be calibrated and validated against the instrumental record. Morocco has a rich history of dendroclimatic research going back nearly 40 years (e.g Munaut et al. 1979; Berger et al. 1979; Till 1987; Till and Guiot 1990; Chbouki 1992; Chbouki et al. 1995; Glueck and Stockton 2001; Esper et al. 2007). Elsewhere in North Africa dendroclimatic studies are rare. Earlier studies in Algeria (Messaoudene 1989; Messaoudene and Tessier 1997; Safar et al. 1992) and Tunisia (Serre-Bachet 1969; Aloui 1982; Aloui and Serre-Bachet 1987; Tessier et al. 1994) were restricted to analysis of the relationship between annual tree-growth and climate. More recent studies yielded the first dendroclimatic reconstructions for Algeria and Tunisia (Touchan et al. 2008a, b), but focused on a large-scale mean regional tree-ring series.

In this paper we give results of the first large-scale systematic dendroclimatic sampling campaign across western North Africa. We introduce the full new tree-ring network, apply it to a climate-field reconstruction of drought, and analyze temporal and spatial features of the reconstruction. We then use the reconstruction to place recent drought in the context of long-term natural variability and expected future climate change.

## 2 Regional geography and climate

Our investigation focuses on long-term climate variability in the Mediterranean borderlands of northwestern Africa—Morocco, Algeria and Tunisia. The region has a predominantly Mediterranean climate, characterized by hot, dry summers and mild, wetter winters (Critchfield 1983). Mountains separate the extremely arid desert areas to the south from the somewhat more temperate northern areas dominated by moist Mediterranean and Atlantic winds, and strongly influence the distribution of precipitation and the extent of the influence of various climatic phenomena (Trewartha 1981). Interannual variability in the amount, intensity and spatial distribution of precipitation can be substantial throughout North Africa (Critchfield 1983). While October-April is the most prevalent wet season across the region, seasonal rains may be absent, or may begin as late as February or March. In Tunisia, the rainy season is typically December-March, with secondary wet periods in the spring and fall. Extensive floods from high intensity storms over a short period of time are common, as are extended periods of drought.

Regional climate variability in northwestern Africa is teleconnected to hemispheric-scale circulation patterns and oceanic influences (Atlantic Ocean and Mediterranean Sea), as well as the continental influences of Europe and the Saharan Desert (Trewartha 1981). Movement and development of winter cyclonic storms systems in the Mediterranean Basin are linked closely to shifts in positions of the Icelandic Low and Azores High, and resulting changes in upper level steering winds and polar-air intrusions (Trewartha 1981). The North Atlantic oscillation (NAO) is negatively correlated with winter (DJF) precipitation in Morocco, but correlation weakens toward the south and east (Lamb et al. 1997; Knippertz et al. 2003a). Indeed the spatial pattern of correlations of NAO across northwestern Africa is sensitive to the particular definition of NAO (Knippertz et al. 2003a). Increasing Mediterranean-sea influence on precipitation delivery toward the east is reflected both by such correlation patterns and by changes in circulation weather types associated with precipitation (Knippertz et al. 2003a).

## 3 Materials and methods

### 3.1 Chronology development

*Cedrus atlantica*,

*Pinus halepensis*,

*Pinus pinaster*,

*Abies marocan*,

*Pinus nigra*,

*Quercus afares*, and

*Quercus canariensis*. Previous research has established that these species share a high degree of common variation that is strongly driven by climate (Glueck and Stockton 2001; Chbouki et al. 1995; Esper et al. 2007; Touchan et al. 2008a, b). We developed new chronologies and enhanced (increased sample size) and extended existing chronologies. Increment cores were collected at 39 sites in Morocco, Algeria, and Tunisia, with additional full cross sections taken from selected stumps of cedar, oak, and pine when available (Figs. 1, 2; Table 1). Samples were fine-sanded and crossdated following standard dendrochronological techniques (Stokes and Smiley 1968; Swetnam 1985). The width of each annual ring on the cores and cross-sections was measured to the nearest 0.01 mm. Visual and graphical crossdating was confirmed using statistical pattern matching (Holmes 1983).

Site information for North Africa

Site name | Code | Country | Species | Elevation (m) | Latitude | Longitude | Time span | # Years | # Trees | # Cores |
---|---|---|---|---|---|---|---|---|---|---|

Addeldal | ADD | Morocco | PIPI | 850–950 | \(35^{\circ}54^\prime \hbox{N}\) | \(05^{\circ}28^\prime \hbox{W}\) | 1843–2004 | 162 | 20 | 39 |

Tissoukaa | TIS | ABMA | 1750–1800 | \(35^{\circ}11^{\prime}\hbox{N}\) | \(05^{\circ}12^\prime \hbox{W}\) | 1763–2004 | 242 | 20 | 37 | |

Madissoukaa | MAK | PINI | 1300–1400 | \(35^{\circ}10^\prime \hbox{N}\) | \(05^{\circ}08^\prime \hbox{W}\) | 1847–2005 | 159 | 15 | 28 | |

Affechtal | AFE | CEAT | 1750–1850 | \(35^{\circ} 02^\prime \hbox{N}\) | \(04^{\circ}59^\prime \hbox{W}\) | 1610–2004 | 394 | 20 | 40 | |

Tazzeka | TAK | CEAT | 1800–1950 | \(34^{\circ}05^\prime \hbox{N}\) | \(04^{\circ} 11^\prime \hbox{W}\) | 1539–2004 | 466 | 23 | 46 | |

Tamjot | TAM | PIPI | 1450–1550 | \(33^{\circ} 52^\prime \hbox{N}\) | \(04^{\circ}\hbox{W}\) | 1933–2004 | 71 | 11 | 20 | |

Ich Ramouz | ICR | CEAT | 1800–1850 | \(33^{\circ} 47^\prime \hbox{N}\) | \(05^{\circ} 02^\prime \hbox{W}\) | 1374–2004 | 631 | 23 | 45 | |

Tizi u Treten | TRN | CEAT | 1856–1921 | \(33^{\circ} 28^\prime \hbox{N}\) | \(05^{\circ} 01^\prime \hbox{W}\) | 1555–2003 | 449 | 23 | 46 | |

Senoual | SEN | CEAT | 1976–2144 | \(33^{\circ} 00^\prime \hbox{N}\) | \(05^{\circ} 15^\prime \hbox{W}\) | 1346–2003 | 657 | 25 | 50 | |

Col Du Zad | ZAD | CEAT | 2106–2300 | \(32^{\circ} 59^\prime \hbox{N}\) | \(05^{\circ} 04^\prime \hbox{W}\) | 883–2004 | 1122 | 72 | 126 | |

Taourirt | TAO | CEAT | 1850–1900 | \(32^{\circ}45^{\prime}\hbox{N}\) | \(04^{\circ} 03^\prime \hbox{W}\) | 1479–2004 | 526 | 21 | 43 | |

Jafaar | JAF | CEAT | 2053–2183 | \(32^{\circ} 32^\prime \hbox{N}\) | \(04^{\circ} 54^\prime \hbox{W}\) | 1173–2004 | 816 | 24 | 38 | |

Tounfite | TOF | CEAT | 2100–2200 | \(32^{\circ} 28^\prime \hbox{N}\) | \(05^{\circ} 20^\prime \hbox{W}\) | 1318–2004 | 687 | 21 | 40 | |

Bouizourane | BOI | CEAT | 2150–2200 | \(32^{\circ}27^\prime \hbox{N}\) | \(05^{\circ} 19^\prime \hbox{W}\) | 1455–2004 | 550 | 21 | 41 | |

Tadlounte | TAA | CEAT | 1858–1988 | \(32^{\circ} 22^\prime \hbox{N} \) | \( 05^{\circ} 34^\prime \hbox{W}\) | 1696–2004 | 309 | 20 | 49 | |

Afrasko | AFR | CEAT | 2400–2500 | \(32^{\circ} 21^\prime \hbox{N}\) | \(05^{\circ} 00^\prime \hbox{W}\) | 1256–2004 | 749 | 18 | 34 | |

Athmane | ATH | Algeria | QUAF | 1042–1117 | \(36^{\circ} 40^\prime \hbox{N}\) | \(04^{\circ}34^{\prime}\hbox{E}\) | 1820–2005 | 186 | 15 | 22 |

Thamguig-uelt | THT | CEAT | 1500–1600 | \(36^{\circ} 28^{\prime}\hbox{N}\) | \(04^{\circ} 01^{\prime}\hbox{E}\) | 1747–2005 | 259 | 20 | 40 | |

Ignilinuel | IGI | CEAT | 1422–1463 | \(36^{\circ} 28^{\prime}\hbox{N}\) | \(04^{\circ} 00^{\prime}\hbox{E}\) | 1621–2005 | 385 | 20 | 41 | |

Djamatighr-ifine | DJT | CEAT | 1451–1494 | \(36^{\circ} 27^{\prime}\hbox{N}\) | \(04^{\circ} 06^{\prime}\hbox{E}\) | 1534–2005 | 472 | 21 | 46 | |

Tigounetine | TIG | CEAT | 1690–1743 | \(36^{\circ} 27^{\prime}\hbox{N}\) | \(04^{\circ} 06^{\prime}\hbox{E}\) | 1552–2006 | 455 | 20 | 40 | |

Leid Mohamad Ouali | LMO | CEAT | 1500–1535 | \(36^{\circ} 27^{\prime}\hbox{N}\) | \(04^{\circ} 06^{\prime}\hbox{E}\) | 1697–2005 | 309 | 19 | 33 | |

Pinus Nigra Reserve | RPN | PINI | 1520–1596 | \(36^{\circ} 27^{\prime}\hbox{N}\) | \(04^{\circ} 06^{\prime}\hbox{E}\) | 1573–2005 | 433 | 20 | 37 | |

Thala Gaidawane | THG | CEAT | 1296–1450 | \(36^{\circ} 26^{\prime}\hbox{N}\) | \(04^{\circ} 12^{\prime}\hbox{E}\) | 1641–2005 | 365 | 20 | 40 | |

Pipiniere Parasol | PIP | CEAT | 1431–1472 | \(35^{\circ} 51^{\prime}\hbox{N}\) | \(01^{\circ} 59^{\prime}\hbox{E}\) | 1533–2006 | 474 | 19 | 36 | |

Kef-Sahchine | KES | CEAT | 1543–1567 | \(35^{\circ} 51^{\prime}\hbox{N}\) | \(02 ^{\circ}00^{\prime}\hbox{E}\) | 1717–2006 | 290 | 20 | 38 | |

Bordjem National Park | BNP | CEAT | 1841–1882 | \(35^{\circ}35^{\prime}\hbox{N}\) | \(06^{\circ} 02^{\prime}\hbox{E}\) | 1148–2006 | 859 | 27 | 51 | |

Ain El Halfa | AEH | CEAT | 1734–1776 | \(35^{\circ} 19^{\prime}\hbox{N}\) | \(06^{\circ} 54^{\prime}\hbox{E}\) | 912–2006 | 387 | 23 | 42 | |

Ouad Tider | OUT | CEAT | 2030–2146 | \(35^{\circ} 18^{\prime}\hbox{N}\) | \(06^{\circ} 37^{\prime}\hbox{E}\) | 996–2006 | 1095 | 32 | 53 | |

Djeniene | DJE | PIHA | 1134–1226 | \(35^{\circ} 09^{\prime}\hbox{N}\) | \(06^{\circ} 28^{\prime}\hbox{E}\) | 1834–2006 | 173 | 19 | 38 | |

Bout-Chaout | BOC | PIHA | 1250–1296 | \(35^{\circ} 07^{\prime}\hbox{N}\) | \(06^{\circ} 37^{\prime}\hbox{E}\) | 1695–2006 | 312 | 22 | 35 | |

Tobji | TOB | PIHA | 1323–1456 | \(34^{\circ} 36^{\prime}\hbox{N}\) | \(03^{\circ} 07^{\prime}\hbox{E}\) | 1854–2006 | 153 | 20 | 38 | |

Theniet | THN | PIHA | 1380–1405 | \(34^{\circ} 36^{\prime}\hbox{N}\) | \(03^{\circ} 05^{\prime}\hbox{E}\) | 1830–2006 | 177 | 20 | 40 | |

Oued Zen | OUZ | Tunisia | QUCA | 382–730 | \(36^{\circ}47^{\prime}\hbox{N}\) | \(08^{\circ}47^{\prime}\hbox{E}\) | 1681–2003 | 323 | 16 | 16 |

Ain Dhalia | AID | QUCA | 671–750 | \(36^{\circ}29^{\prime}\hbox{N}\) | \(08^{\circ}18^{\prime}\hbox{E}\) | 1708–2003 | 296 | 17 | 17 | |

Dahllia | DHA | PIHA | 919–981 | \(36^{\circ}14^{\prime}\hbox{N}\) | \(08^{\circ}26^{\prime}\hbox{E}\) | 1890–2003 | 114 | 11 | 22 | |

Sadine | SAD | PIHA | 383–464 | \(36^{\circ}06^{\prime}\hbox{N}\) | \(08^{\circ}29^{\prime}\hbox{E}\) | 1751–2003 | 253 | 24 | 47 | |

Jebnoun | JEB | PIHA | 792–810 | \(35^{\circ}51^{\prime}\hbox{N}\) | \(09^{\circ}18^{\prime}\hbox{E}\) | 1874–2003 | 130 | 15 | 30 | |

Oum Djedour | OUD | PIHA | 1000–1100 | \(35^{\circ}35^{\prime}\hbox{N}\) | \(08^{\circ}56^{\prime}\hbox{E}\) | 1865–2004 | 140 | 20 | 28 |

A uniform and systematic procedure was applied in chronology development. Each series of tree-ring width measurements was fit with a cubic smoothing spline with a 50% frequency response at 67% of the series length to remove non-climatic trends due to age, size, and the effects of stand dynamics (Cook and Briffa 1990). The detrended series were then prewhitened with low-order autoregressive models to remove persistence not related to climatic variations. The individual indices were combined into a single master chronology for each combination of site and species using a bi-weight robust estimate of the mean (Cook 1985). The adequacy of sample replication was judged by the expressed population signal (EPS), computed from pooled interseries correlations and the time-varying sample size (Wigley et al. 1984).

### 3.2 Climate field reconstruction

We used the individual master chronologies as the potential predictors to develop a set of nested multivariate stepwise regression models (Meko 1997; Cook et al. 2002; Wilson et al. 2006). In this procedure, an estimate of past drought values at a grid point is first calculated from the stepwise regression model for the period covered by all the individual site chronologies. Additional statistical models are then sequentially developed for progressively longer periods back in time, with their span corresponding to the changes in the availability of the underlying predictor tree-ring series. Here, we have used every change in sample depth in time to objectively identify the earliest date of each subsequent nest. The individual reconstructions in this manner are scaled to have the standard deviation of the best replicated, most recent nest, and joined into a single long reconstruction such that each time period is represented by the corresponding regression model with the greatest available data. Entry into the multivariate linear stepwise model was based on the adjusted *R*^{2} statistic.

The skill of the reconstruction was assessed using the adjusted calibration *R*^{2}, the root mean square error (RMSE) of the calibration and validation periods, and the reduction of error and coefficient of efficiency statistic (Cook et al. 1994; Wilson et al. 2006). This procedure permits the skill of the drought reconstruction to be estimated as a function of the changing set of available predictor data (Meko 1997). The calibration–validation procedure was performed 3 times—with a late calibration (1969–2003) and early validation period (1931–1968), an early calibration period and late validation period, and then using the full period for calibration and performing validation using an additional leave-one-out validation jackknife procedure. The *R*^{2} and RMSE statistics provide a good measure of the accuracy of the high-frequency component of the reconstruction, while the RE and CE further evaluate the skill of the reconstruction beyond climatology (in this case, represented by the calibration or validation period mean, respectively). RE in particular is useful for verifying that the reconstruction can accurately reproduce any changes between the calibration and validation period mean (Cook et al. 1994; Ammann and Wahl 2007).

### 3.3 Time series analysis

We analyzed reconstructed PDSI series in the time domain by low-pass filtering and in the frequency domain by cross-spectral analysis and wavelet analysis. When isolating decadal-scale variability, time series were smoothed with a Butterworth filter with series padding optimally selected to minimize the mean squared error and to avoid misleading behavior at the end of the series (Mann 2004). Covariation of pairs of series over their full length of overlap was summarized in the frequency domain by smoothed-periodogram cross-spectral analysis (Bloomfield 2000). Quantities examined were the variance spectra, coherency spectrum and phase spectrum. Processing steps included subtraction of sample means, tapering of each end (5%) with a split-cosine-bell filter, padding with zeros to length equal to the next power of 2 above the sample-size, and computation of the discrete Fourier transform (DFT). Periodograms and cross-periodograms were then computed from the DFTs and developed as estimates of spectra and cross-spectra by smoothing with a sequence of Daniell filters. Filters were selected such that the bandwidth of spectral estimates was approximately 0.05 cycles/year. Following Bloomfield (2000), coherency and phase spectra were then computed from the various spectral quantities and plotted with confidence bands to summarize covariation of time series. Confidence bands were computed as in Meko and Woodhouse (2005). Phase is poorly determined when coherence is low (Bloomfield 2000). Accordingly, following Bloomfield (2000), confidence intervals on the coherency and phase are plotted only over those frequency intervals for which the squared coherency passes a simplified test for 95% significance. The time evolution of simultaneous or asynchronous regional drought was summarized by a wavelet coherency spectrum (Maraun and Kurths 2004).

Simple Pearson correlations were used to the gauge strength of the relationship of selected reconstructed PDSI series with the North Atlantic oscillation (NAO). The NAO index for this analysis is the winter-average (DJFM) difference of normalized sea level pressure (SLP) between Lisbon, Portugal and Stykkisholmur/Reykjavik, Iceland. This index, 1864–2009, was downloaded from the website of the Climate Analysis Section of NCAR (http://www.cgd.ucar.edu/cas/jhurrell/indices.html). Spatial correlation fields were also calculated with gridded sea surface temperature anomalies (SST; Kaplan et al. 1998).

### 3.4 General circulation model simulations

In order to compare our drought reconstruction with possible patterns of forced and stochastic climate variability, we used precipitation and temperature data from the World Climate Research Programme’s (WCRP’s) Coupled Model Intercomparison Project phase 3 (CMIP3) multi-model dataset (Meehl et al. 2007) to calculate the model-simulated northwestern African summer PDSI. For the forced response, we used a 68 member ensemble from 23 coupled general circulation models from the twentieth century simulation (’20c3m’). For our control, we used the last 150 years from each simulation of the preindustrial control experiment (’picntrl’, with no transient forcing) from a 26 member ensemble including 20 individual climate models. We then calculated a simulated PDSI for each ensemble member similar to Dai et al. (2004), although using Palmer’s original available water capacity for the two level soil model (Palmer 1965; Touchan et al. 2008a). CMIP3 models have horizontal (latitude × longitude) resolutions that range from ∼1.1° × 1.1° to 4° × 5°. PDSI values for all model grid points corresponding to our target instrumental field were averaged in space to create a simulation mean time series, then all standardized [0, 1] ensemble member series for each scenario were averaged to create a scenario mean time series that could be compared to our reconstruction regional mean.

## 4 Results and discussion

### 4.1 Tree ring chronologies

Chronology summary statistics

Site code | Country | MSSL | std | SK | KU | EPS > 0.85 | Common interval | MCAR | % EV PC1 |
---|---|---|---|---|---|---|---|---|---|

ADD | Morocco | 99 | 0.13 | −0.15 | 1.38 | 1914 | 1944–2001 | 0.36 | 38 |

TIS | 166 | 0.13 | 0.04 | −0.12 | 1839 | 1870–2003 | 0.32 | 35 | |

MAK | 93 | 0.12 | −0.01 | 0.01 | 1942 | 1933–2001 | 0.32 | 36 | |

AFE | 236 | 0.14 | −0.12 | 0.01 | 1657 | 1772–2002 | 0.44 | 46 | |

TAK | 220 | 0.21 | −0.40 | 1.47 | 1775 | 1868–2004 | 0.43 | 45 | |

TAM | 55 | 0.21 | −0.71 | 2.85 | 1962 | 1966–2003 | 0.40 | 46 | |

ICR | 393 | 0.19 | −0.86 | 2.15 | 1416 | 1573–1972 | 0.47 | 51 | |

TRN | 227 | 0.22 | 0.33 | 6.61 | 1631 | 1842–2000 | 0.49 | 51 | |

SEN | 345 | 0.20 | −0.76 | 1.80 | 1405 | 1756–1991 | 0.45 | 48 | |

ZAD | 471 | 0.25 | −0.48 | 1.23 | 918 | 1499–1840 | 0.55 | 56 | |

TAO | 245 | 0.26 | −1.16 | 2.06 | 1682 | 1793–1999 | 0.48 | 51 | |

JAF | 327 | 0.33 | −0.33 | 0.22 | 1262 | 1698–1965 | 0.60 | 62 | |

TOF | 296 | 0.32 | −0.24 | 0.28 | 1369 | 1654–1965 | 0.65 | 68 | |

BOI | 303 | 0.35 | −0.19 | 0.08 | 1452 | 1682–1998 | 0.65 | 67 | |

TAA | 203 | 0.26 | −0.83 | 2.30 | 1726 | 1801–1980 | 0.61 | 63 | |

AFR | 525 | 0.40 | 0.62 | 0.02 | 1321 | 1549–1980 | 0.71 | 72 | |

ATH | Algeria | 136 | 0.22 | 0.50 | 1.88 | 1854 | 1863–2002 | 0.47 | 51 |

THT | 133 | 0.18 | −0.98 | 3.03 | 1839 | 1917–2005 | 0.45 | 48 | |

IGI | 216 | 0.16 | −0.08 | 0.71 | 1722 | 1796–2004 | 0.42 | 45 | |

DJT | 174 | 0.18 | −0.63 | 2.01 | 1635 | 1811–2005 | 0.43 | 46 | |

TIG | 226 | 0.20 | −0.98 | 2.69 | 1742 | 1814–2000 | 0.44 | 47 | |

LMO | 156 | 0.15 | −1.05 | 5.43 | 1871 | 1849–2001 | 0.37 | 42 | |

RPN | 155 | 0.16 | −0.08 | 2.00 | 1832 | 1902–2004 | 0.41 | 44 | |

THG | 148 | 0.17 | −0.25 | 0.80 | 1885 | 1902–1998 | 0.32 | 35 | |

PIP | 149 | 0.26 | −0.15 | 0.89 | 1859 | 1898–2006 | 0.63 | 65 | |

KES | 95 | 0.23 | 0.11 | 0.54 | 1854 | 1916–2006 | 0.62 | 64 | |

BNP | 301 | 0.21 | −0.31 | 2.28 | 1416 | 1772–2001 | 0.59 | 60 | |

AEH | 277 | 0.34 | −0.08 | 0.89 | 1679 | 1774–2005 | 0.73 | 74 | |

OUT | 439 | 0.34 | −0.15 | 0.92 | 1036 | 1602–1990 | 0.62 | 63 | |

BOC | 168 | 0.50 | 0.86 | 2.83 | 1756 | 1896–2006 | 0.79 | 80 | |

DJE | 126 | 0.47 | 0.30 | 0.42 | 1845 | 1899–2006 | 0.82 | 83 | |

TOB | 138 | 0.36 | 0.34 | −0.01 | 1856 | 1887–2006 | 0.71 | 72 | |

THN | 112 | 0.31 | 0.70 | 1.77 | 1866 | 1894–2006 | 0.66 | 70 | |

OUZ | Tunisia | 137 | 0.27 | 0.53 | 0.20 | 1898 | 1928–2003 | 0.37 | 42 |

AID | 166 | 0.24 | 0.69 | 0.95 | 1882 | 1899–2003 | 0.39 | 42 | |

DHA | 93 | 0.28 | 0.78 | 3.16 | 1905 | 1912–1998 | 0.52 | 57 | |

SAD | 137 | 0.46 | 0.33 | 0.13 | 1756 | 1909–1990 | 0.72 | 73 | |

JEB | 77 | 0.28 | 0.31 | 0.99 | 1902 | 1955–2001 | 0.60 | 62 | |

OUD | 111 | 0.34 | 0.46 | 0.35 | 1871 | 1917–2004 | 0.67 | 68 |

### 4.2 Reconstruction skill

In the middle and southern portion of the target domain, particularly in interior Algeria, the reconstruction resolves very little of the variance in the instrumental field, there is little to no skill in the reconstruction even for the most recent century, and the reconstructed grids do not span the full target time period (Figs. 4, 5, 6, Supplemental Materials). This is due to the paucity of tree ring chronologies within the search radius of these grid points (Figs. 1, 8). Limited or poor instrumental data over a portion of this region may also exacerbate the difficulty in developing skillful reconstructions of this portion of the field.

### 4.3 Spatiotemporal drought patterns

*R*

^{2}and RE scores in the southwestern portion of the domain, while the ’Eastern’ subregion includes three grid points at the northwestern corner of the field that similarly show the most skill over the length of the reconstruction (Fig. 8). Correlation between the grid points that make up the western regional composite is high (

*r*= 0.89 to

*r*= 0.99,

*n*= 825,

*p*< 0.001), while the individual grid points from the eastern region show greater heterogeneity (

*r*= 0.25 to

*r*= 0.68,

*n*= 825,

*p*< 0.001). Reassuringly, this mirrors the instrumental record, where the western grid points show higher intercorrelation (

*r*= 0.57 to

*r*= 0.89,

*n*= 73,

*p*< 0.001) than the eastern grid points (

*r*= 0.13 to

*r*= 0.85,

*n*= 73,

*p*< 0.26 to

*p*< 0.001).

### 4.4 Drought frequency and coherence

*r*= 0.23,

*n*= 825,

*p*< 0.0001; instrumental,

*r*= 0.36,

*n*= 73,

*p*< 0.0018). The high statistical significance derives from the sample length rather than magnitude of correlation. The low correlation is not an artifact of averaging over gridpoints: no bivariate correlation between reconstructed gridpoint PDSI for a western and eastern gridpoint exceeds 0.22.

The spectra of reconstructions is affected by many aspects of data processing, including and particularly standardization. Our use of residual chronologies for the PDSI reconstruction conditions the spectrum, as autoregressive (AR) pre-whitening would tend to shift the spectrum of a series generated by an autoregressive process toward the spectrum of white noise. The choice of residual chronologies over standard chronologies however was guided by a comparison of the autocorrelation properties of PDSI and standard chronologies in the region. Other studies have suggested more low-frequency variability (Esper et al. 2007). Future studies will explore the sensitivity of drought reconstructions in the region to standardization choices. A temporal and spectra comparison of our reconstruction with the Morocco PDSI time series reconstruction by Esper et al. (2007) is available in Supplemental Material (Figure S1, S2).

### 4.5 Association with broad-scale climate modes

#### 4.5.1 North Atlantic oscillation

Correlations of the two regional reconstructions (western and eastern) with NAO index suggest variable influence across the region. The western series correlates negatively (*r* = −0.33, *N* = 140, *p* < 0.0001) with the NAO index over the common period 1864–2003. The eastern series is uncorrelated with NAO (*r* = 0.06, *N* = 140, *p* = 0.45) over the same period. The direction of the relationship (wet with negative NAO) for the western part of the grid is consistent with previous studies relating the NAO to cool-season precipitation in the Iberian Peninsula (Goodess and Jones 2002) and the western Mediterranean region (Glueck and Stockton 2001; Xoplaki et al. 2004; Knippertz et al. 2003a, b). Although our reconstruction variable (May-Aug PDSI) is not directly seasonally matched to the NAO window of precipitation influence in the region, tree-growth in Morocco is apparently linked strongly enough to the NAO via the influence of winter precipitation on growing season soil moisture (c.f. Cook et al. 1999) for a signal to emerge in the Western reconstructed grid points.

*p*< 0.001) negative correlations between the western PDSI time series and winter SLP are observed over the study region, particularly Morocco, indicating that higher pressure over northwestern Africa has been associated with Moroccan drought over the last 340 years. The eastern drought time series, however, has insignificant although positive correlations that are focused over Europe.

#### 4.5.2 Sea surface temperatures

### 4.6 General circulation models simulations

## 5 Summary and conclusions

We present a new climate field reconstruction of drought in Morocco, Algeria, and Tunisia back to A.D. 1179, incorporating the largest number of tree-ring chronologies yet available from the region into a spatially continuous grid. This reconstruction now provides long-term climatological, ecological, archaeological, and historical context for recent drought in the region. Our point-to-point method allows us to identify the southern and central portion of the target field as clear priorities for future tree-ring sampling in the region. Based on temporal (Figs. 7, 8), spatial (Fig. 10), and spectral (Figs. 11, 12) analysis, our reconstruction demonstrates that when considered at the regional scale, the latter half of the twentieth century is one of the driest in the last nine centuries. There are significant uncertainties, both from sparse site coverage of drought-sensitive chronologies in some areas and from a declining number of tree-ring chronologies available back in time (see Supplemental Material). Our analysis has accordingly focused on broad and well-validated spatial features (e.g. eastern vs western) of reconstructed drought variability. A finer-scale climatological interpretation, including inferences on past seasonal atmospheric circulation anomalies over the region, the role of broad-scale sea surface temperature forcing, and the specific combination of factors which result in distinct regional spatiotemporal drought fingerprints, should eventually be possible as the network of tree-ring sites is expanded in space and time.

Our findings from the current network of sites are consistent with a robust projection from general circulation models (Fig. 16) that anthropogenic greenhouse gas emissions will result in the imminent drying of subtropical regions. While interpretation of trends approaching the endpoints of time series with substantial unforced low-frequency variability requires the utmost caution, our conclusions are thus far consistent with one of the more robust features of general circulation model projections of the future (Held and Soden 2006; Seager et al. 2007a). A long-term trend toward more arid conditions in northwestern Africa may of course be punctuated by occasional wet anomalies, but governments and natural resources managers in the region need to be prepared forthwith to deal with future drying.

## Acknowledgments

In Morocco we thank the Ministry of Agriculture, the Department of Forestry, and the National School of Forest Engineering, the Director (Driss Misbah) and the staff of Direction of the Rif High Commissariat of Water, Forestry and Combating Desertification, the Director (Abdelaziz Houseini) and the staff of Direction of the Oriental High Commissariat for Water, Forestry and Combating Desertification, the Director (Mustapha Khalladi) and the staff of Direction of Moyen Atlas of High Commissariat of Water, Forestry and Desertification Combating, the Chief (Mohamed Benziane) and staff of the National Center of Forestry Research, and the Director and staff of the National School of Forest Engineering for making this study possible. We wish to thank our colleagues from Algeria, especially Abdelmalek Mohamed Azzedine Idder (Ecosystem Laboratory, University of Ouargla), Belkitir Dadamoussa (former Director, Ecosystem Laboratory, University of Ouargla), Titah (General Director of Forests), Mohamed Seghir Mellouhi (former General Director of Forests), Hocine Medjedoub (former Director of Forest, Betna), Abdallatif Guasmi (Director of Forest, Batna), Saidi Belkacem (Directory of Forest in Khenchela), Haddad Moussa (National Park of Tikdjda, Bouira), Mohamed Tizioui, Said Abderahmani (National Park of Belezma), Athmane Briki (Betna Forest Department), Ali Loukkas (National Park of Theniet el had), Chabane Cheriet (Director of Forest in Tiziouzou), Tidjani Mohamed El-khamis (former President of the University of Ouargla), and Ahmed Boutarfaia (President of the University of Ouargla). We wish to thank our colleagues from Tunisia, including Toumi Lamjed (Directeur général de l’ISPT (Institut Sylvo-Pastoral de Tabarka), Mougou Abdelaziz Président de l’IRESA (Institution de la Recherche et l’Enseignement Supérieur Agricole), Rejeb Néjib Directeur général de l’INRGREF (Institut national de recherche en génie rural, eaux et for\(\hat{\hbox{e}}\)ts), Fekih Salem Ahmed Ridha Directeur génŕal des for\(\hat{\hbox{e}}\) ts, and the forest technicians of Siliana, Kef, Kasserine, Ain Draham, and Jendouba for their great help and support in making this study possible. We thank Rachid Ilmen, Mohamed El Youssfi, and Rachid Azzam, Salaheddine Saadine, and Said Slimani for their valuable field assistance. We thank Christopher Baisan, Gregg Garfin, Jeffrey Dean, Paul Sheppard, and Martin Munro for their advice and suggestions. We also thank Jeffrey Balmat, Nesat Erkan, Jim Burns, Jeremy Goral, Julie Wong, and Salah Eddine Sadine for their valuable assistance in both the field and laboratory. We acknowledge the modeling groups, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and the WCRP’s Working Group on Coupled Modelling (WGCM), for their roles in making available the WCRP CMIP3 multi-model dataset. Support of that dataset is provided by the Office of Science, U.S. Department of Energy. This is LDEO Contribution 7342 (KJA). Funding was provided by the US National Science Foundation, Earth System History (ESH0317288).