# East African 675 Year MCEOF Hydroclimatic Lake Proxy Data Synthesis #----------------------------------------------------------------------- # World Data Center for Paleoclimatology, Boulder # and # NOAA Paleoclimatology Program #----------------------------------------------------------------------- # NOTE: Please cite original reference when using these data, # plus the Online Resource and date accessed. # # # Online_Resource: https://www.ncdc.noaa.gov/paleo/study/13686 # # Online_Resource: http://www1.ncdc.noaa.gov/pub/data/paleo/paleolimnology/eastafrica/tierney2013mceof.txt # # Description/Documentation lines begin with # # Data lines have no # # # Archive: Paleolimnology #-------------------- # Contribution_Date # Date: 2013-01-28 #-------------------- # Title # Study_Name: East African 675 Year MCEOF Hydroclimatic Lake Proxy Data Synthesis #-------------------- # Investigators # Investigators: Tierney, J.E.; Smerdon, J.E.; Anchukaitis, K.J.; Seager, R. #-------------------- # Description_and_Notes # # Synthesis of lacustrine hydroclimatic proxy records from East Africa using # a Monte Carlo empirical orthogonal function (MCEOF) approach. MCEOF1 timeseries # and spatial loadings plus 68% and 95% uncertainty bounds are reported. # # Site information for Lake proxy records utilized # Lake Latitude Longitude Proxy Average DT References # Challa -3.32 37.70 "BIT, dDwax, varve thickness" 33 "Verschuren et al. (2009), Nature; Tierney et al., (2011) QSR; Wolff et al. (2011) Science" # Naivasha -0.77 36.35 Lake level reconstruction 3 "Verschuren et al. (2000), Nature" # Victoria -1.00 33.00 % shallow water diatoms 5 "Stager et al. (2005), J. Paleolimnol." # Edward -0.25 29.50 % Mg in authigenic calcite 4 "Russell and Johnson (2007), Geology" # Tanganyika -6.70 30.00 Charcoal 10 "Tierney et al. (2010), Nature Geosci." # Masoko -9.33 33.76 Magnetic susceptibility 10 "Garcin et al. (2006), Palaeo3; Garcin et al. (2007), J. Paleolimnol." # Malawi -10.00 34.22 Terrigenous mass accumulation rate 6 "Brown and Johnson (2005), G3; Johnson and McCave (2008), Limnol. Oceanogr." # # Excel file of input proxy data also provided. It includes raw and interpolated proxy data # from the seven sites used in the MCEOF synthesis. Note that provided ages correspond to # the previously published age models. # # # Original proxy data for all seven East African lakes are also archived at NOAA/NCDC/WDC Paleoclimatology: # Lake Challa varve thickness: https://www.ncdc.noaa.gov/paleo/study/12584 # Lake Challa dDwax: https://www.ncdc.noaa.gov/paleo/study/10889 # Lake Naivasha lake levels: https://www.ncdc.noaa.gov/paleo/study/6070 # Lake Victoria SWD: https://www.ncdc.noaa.gov/paleo/study/5455 # Lake Edward %Mg: https://www.ncdc.noaa.gov/paleo/study/5452 # Lake Tanganyika Charcoal: https://www.ncdc.noaa.gov/paleo/study/10428 # Lake Masoko Mag Sus06: https://www.ncdc.noaa.gov/paleo/study/6072 # Lake Masoko Mag Sus07: https://www.ncdc.noaa.gov/paleo/study/6071 # # # MCEOF1 site loadings are as follows: # # Lake Median Loading Lower 95% Lower 68% Upper 68% Upper 95% # Challa 0.54 0.29 0.48 0.58 0.62 # Naivasha 0.17 -0.41 -0.18 0.38 0.48 # Victoria -0.19 -0.51 -0.41 0.15 0.39 # Edward -0.43 -0.55 -0.51 -0.26 0.07 # Tanganyika -0.20 -0.46 -0.37 0.01 0.35 # Masoko -0.24 -0.45 -0.34 -0.10 0.11 # Malawi -0.50 -0.29 -0.44 -0.54 -0.59 # # # For Lake Challa, the first principal component of three different hydroclimate proxies # was used to represent the site (PC1). All three raw proxy time series are provided in # addition to the interpolated series and PC1. The age model used for this site is the # varve-based age model of Wolff et al., 2011. Please refer to the Supplementary Material # in Tierney et al. (2013) for a description of how PC1 was calculated. # # For Lake Masoko, magnetic susceptibility from the longer core (Garcin et al. 2006) # was used incorporating the additional chronological controls from Garcin et al. 2007. # See Supplementary Information and Anchukaitis and Tierney (2012) Clim. Dyn. for more information. # # No depth data are available from the Lake Malawi core. As this is a varve sequence that does not # depend on depth-age translation, depth is not needed to iterate time uncertainty. See Supplementary # Information and Anchukaitis and Tierney (2012) for information on how varve counting uncertainty was modeled. # # #-------------------- # Publication # Authors: Jessica E. Tierney, Jason E. Smerdon, Kevin J. Anchukaitis, and Richard Seager # Published_Date_or_YEAR: 2013-01-17 # Published_Title: Multidecadal variability in East African hydroclimate controlled by the Indian Ocean # Journal_Name: Nature # Volume: 493 # Issue: 7432 # Pages: 389-392 # DOI: 10.1038/nature11785 # Abstract: The recent decades-long decline in East African rainfall suggests that multidecadal variability is an important component of the climate of this vulnerable region. Prior work based on analysing the instrumental record implicates both Indian and Pacific ocean sea surface temperatures (SSTs) as possible drivers of East African multidecadal climate variability, but the short length of the instrumental record precludes a full elucidation of the underlying physical mechanisms. Here we show that on timescales beyond the decadal, the Indian Ocean drives East African rainfall variability by altering the local Walker circulation, whereas the influence of the Pacific Ocean is minimal. Our results, based on proxy indicators of relative moisture balance for the past millennium paired with long control simulations from coupled climate models, reveal that moist conditions in coastal East Africa are associated with cool SSTs (and related descending circulation) in the eastern Indian Ocean and ascending circulation over East Africa. The most prominent event identified in the proxy record - a coastal pluvial from 1680 to 1765 - occurred when Indo-Pacific warm pool SSTs reached their minimum values of the past millennium. Taken together, the proxy and model evidence suggests that Indian Ocean SSTs are the primary influence on East African rainfall over multidecadal and perhaps longer timescales. # #--------------------- # Publication # Authors: Kevin J. Anchukaitis and Jessica E. Tierney # Published_Date_or_YEAR: 2012-08-26 # Published_Title: Identifying coherent spatiotemporal modes in time-uncertain proxy paleoclimate records # Journal_Name: Climate Dynamics # Volume: # Issue: # Pages: # DOI: 10.1007/s00382-012-1483-0 # Abstract: High-resolution sedimentary paleoclimate proxy records offer the potential to expand the detection and analysis of decadal- to centennial-scale climate variability during recent millennia, particularly within regions where traditional high-resolution proxies may be short, sparse, or absent. However, time uncertainty in these records potentially limits a straightforward objective identification of broad-scale patterns of climate variability. Here, we describe a procedure for identifying common patterns of spatiotemporal variability from time uncertain sedimentary records. This approach, which we term Monte Carlo Empirical Orthogonal Function analysis, uses iterative age modeling and eigendecomposition of proxy time series to isolate common regional patterns and estimate uncertainties. As a test case, we apply this procedure to a diverse set of time-uncertain lacustrine proxy records from East Africa. We also perform a pseudoproxy experiment using climate model output to examine the ability of the method to extract shared anomalies given known signals. We discuss the advantages and disadvantages of our approach, including possible extensions of the technique. # #--------------------- # Funding_Agency # Funding_Agency_Name: US National Oceanic and Atmospheric Administration (NOAA) # Grant: Climate and Global Change Postdoctoral Fellowship, NA10OAR431037 #--------------------- # Funding_Agency_Name: US National Science Foundation (NSF) # Grant: OCE-1203892 #--------------------- # Funding_Agency_Name: US Department of Energy (DOE) # Grant: #-------------------- # Site_Information # Site_Name: East Africa # Location: Africa>Eastern Africa # Country: # Northernmost_Latitude: -0.25 # Southernmost_Latitude: -10.0 # Easternmost_Longitude: 37.7 # Westernmost_Longitude: 29.5 # Elevation: m #------------------ # Data_Collection # Core_Name: Tierney2013MCEOF # First_Year: 1275 # Last_Year: 1950 # Time_Unit: AD # Core_Length: # Notes: #------------------ # Chronology # # Age model information associated with the seven proxy data series, entitled 'Lake Xxxx - chron' # is included in the Excel data file. Dates omitted from the age modeling procedure are highlighted # in red. These dates include those that were identified as "reversed" on the basis of a low # probability of producing a superposed model with their surrounding dates (see Supplementary # Information and Anchukaitis and Tierney, 2012) or 14C dates that overlap with 210Pb chronologies, # in which case the latter generally provide a more narrow age model constraint. Note that Lakes # Challa and Malawi do not have chronological information because they have varve chronologies. # Counting errors (1-sigma) on these varve chronologies are as follows: Lake Malawi: +/- 0.5 years # (Johnson and McCave, 2008) and Lake Challa: +/- 0.3 years (Wolff, pers. comm.). See Anchukaitis # and Tierney (2012) for a discussion on how the varve errors were modeled. # #------------------ # Variables # # End Description/Documentation (lines begin with #) # Data lines follow (have no #) # Data line variables format: Variables list, one per line, shortname-tab-longname-tab-longname components (9 components: material, error, units, anomaly, period, archive, detail, method, C or N for Character or Numeric data) # Data line format: tab-delimited text, variable short name as header ## age_AD Age (AD), , ,AD, , , , ,N ## MCEOF1 Monte Carlo empirical orthogonal function (MCEOF) median, , , , , , , , N ## MCEOF95- Monte Carlo empirical orthogonal function (MCEOF) 95% lower, , , , , , , , N ## MCEOF68- Monte Carlo empirical orthogonal function (MCEOF) 68% lower, , , , , , , , N ## MCEOF68+ Monte Carlo empirical orthogonal function (MCEOF) 68% upper, , , , , , , , N ## MCEOF95+ Monte Carlo empirical orthogonal function (MCEOF) 95% upper, , , , , , , , N #------------------------ # Data: # Missing Value: NA age_AD MCEOF1 MCEOF95- MCEOF68- MCEOF68+ MCEOF95+ 1275 -0.911 -2.004 -1.474 -0.343 0.290 1280 -1.062 -2.142 -1.637 -0.468 0.183 1285 -1.245 -2.320 -1.822 -0.615 0.070 1290 -1.419 -2.510 -1.991 -0.793 -0.055 1295 -1.592 -2.641 -2.154 -0.958 -0.201 1300 -1.726 -2.728 -2.270 -1.101 -0.344 1305 -1.824 -2.828 -2.348 -1.228 -0.448 1310 -1.899 -2.876 -2.426 -1.309 -0.549 1315 -1.944 -2.928 -2.483 -1.335 -0.530 1320 -1.963 -2.971 -2.514 -1.352 -0.547 1325 -1.990 -3.001 -2.556 -1.368 -0.594 1330 -2.032 -3.058 -2.591 -1.405 -0.616 1335 -2.031 -3.050 -2.594 -1.423 -0.634 1340 -1.991 -2.995 -2.518 -1.415 -0.623 1345 -1.917 -2.881 -2.422 -1.343 -0.542 1350 -1.829 -2.762 -2.317 -1.275 -0.459 1355 -1.785 -2.648 -2.249 -1.226 -0.403 1360 -1.783 -2.626 -2.236 -1.248 -0.411 1365 -1.825 -2.655 -2.270 -1.294 -0.449 1370 -1.888 -2.713 -2.341 -1.355 -0.500 1375 -1.927 -2.803 -2.393 -1.378 -0.472 1380 -1.915 -2.809 -2.400 -1.353 -0.450 1385 -1.864 -2.799 -2.372 -1.303 -0.403 1390 -1.827 -2.762 -2.325 -1.244 -0.315 1395 -1.810 -2.746 -2.317 -1.205 -0.272 1400 -1.833 -2.792 -2.360 -1.196 -0.206 1405 -1.908 -2.834 -2.432 -1.224 -0.187 1410 -1.980 -2.902 -2.500 -1.274 -0.164 1415 -2.031 -2.935 -2.541 -1.293 -0.118 1420 -2.017 -2.930 -2.545 -1.275 -0.088 1425 -1.921 -2.916 -2.492 -1.170 0.006 1430 -1.757 -2.847 -2.377 -0.975 0.153 1435 -1.522 -2.737 -2.200 -0.702 0.342 1440 -1.241 -2.554 -1.969 -0.429 0.545 1445 -0.980 -2.346 -1.705 -0.197 0.772 1450 -0.800 -2.156 -1.513 -0.008 0.886 1455 -0.694 -2.002 -1.390 0.118 0.976 1460 -0.639 -1.966 -1.323 0.187 1.036 1465 -0.613 -1.982 -1.320 0.227 1.060 1470 -0.613 -1.999 -1.324 0.228 1.058 1475 -0.645 -2.061 -1.378 0.188 1.005 1480 -0.684 -2.096 -1.416 0.155 0.983 1485 -0.673 -2.104 -1.412 0.173 1.038 1490 -0.589 -2.082 -1.376 0.285 1.165 1495 -0.456 -1.996 -1.272 0.438 1.343 1500 -0.327 -1.946 -1.159 0.587 1.473 1505 -0.203 -1.853 -1.058 0.684 1.556 1510 -0.121 -1.767 -0.950 0.735 1.587 1515 -0.089 -1.696 -0.911 0.728 1.573 1520 -0.089 -1.707 -0.902 0.698 1.553 1525 -0.077 -1.792 -0.909 0.716 1.577 1530 -0.054 -1.795 -0.905 0.752 1.667 1535 0.008 -1.775 -0.857 0.793 1.704 1540 0.051 -1.767 -0.806 0.831 1.721 1545 0.051 -1.785 -0.804 0.818 1.674 1550 0.042 -1.804 -0.811 0.814 1.684 1555 0.123 -1.743 -0.738 0.884 1.761 1560 0.258 -1.615 -0.580 0.992 1.833 1565 0.387 -1.392 -0.396 1.074 1.870 1570 0.457 -1.221 -0.241 1.098 1.834 1575 0.469 -1.088 -0.170 1.073 1.780 1580 0.463 -0.985 -0.132 1.020 1.693 1585 0.496 -0.878 -0.067 1.002 1.623 1590 0.576 -0.730 0.053 1.060 1.622 1595 0.692 -0.552 0.195 1.143 1.672 1600 0.818 -0.365 0.336 1.259 1.763 1605 0.950 -0.274 0.456 1.404 1.910 1610 1.112 -0.224 0.558 1.603 2.128 1615 1.298 -0.140 0.699 1.845 2.406 1620 1.525 -0.073 0.868 2.098 2.648 1625 1.710 0.014 1.049 2.272 2.843 1630 1.804 0.111 1.150 2.346 2.943 1635 1.821 0.079 1.163 2.371 2.994 1640 1.829 0.086 1.170 2.398 3.042 1645 1.825 -0.007 1.151 2.430 3.076 1650 1.798 -0.060 1.098 2.438 3.078 1655 1.694 -0.229 0.958 2.384 3.087 1660 1.552 -0.381 0.813 2.285 3.039 1665 1.449 -0.423 0.708 2.233 3.016 1670 1.477 -0.479 0.688 2.334 3.184 1675 1.795 -0.289 0.884 2.642 3.437 1680 2.279 0.117 1.340 3.066 3.752 1685 2.742 0.609 1.898 3.409 3.967 1690 3.045 1.040 2.354 3.595 4.084 1695 3.148 1.297 2.565 3.667 4.120 1700 3.152 1.412 2.610 3.672 4.140 1705 3.106 1.451 2.537 3.639 4.130 1710 3.068 1.424 2.476 3.621 4.088 1715 3.031 1.293 2.416 3.578 4.077 1720 2.920 1.189 2.295 3.482 3.980 1725 2.741 1.009 2.116 3.309 3.814 1730 2.590 0.833 1.950 3.159 3.651 1735 2.510 0.610 1.877 3.114 3.608 1740 2.537 0.468 1.857 3.158 3.679 1745 2.625 0.294 1.892 3.256 3.799 1750 2.668 0.108 1.883 3.333 3.879 1755 2.568 -0.054 1.707 3.262 3.831 1760 2.262 -0.392 1.320 3.003 3.647 1765 1.845 -0.721 0.892 2.624 3.301 1770 1.495 -0.926 0.530 2.278 2.951 1775 1.282 -1.092 0.236 2.057 2.721 1780 1.069 -1.299 -0.002 1.884 2.514 1785 0.800 -1.426 -0.256 1.641 2.318 1790 0.512 -1.546 -0.502 1.398 2.086 1795 0.234 -1.662 -0.715 1.169 1.909 1800 -0.065 -1.811 -0.947 0.897 1.682 1805 -0.367 -1.943 -1.176 0.560 1.387 1810 -0.666 -2.152 -1.435 0.204 1.062 1815 -0.894 -2.277 -1.623 -0.075 0.789 1820 -0.923 -2.234 -1.625 -0.169 0.687 1825 -0.793 -2.135 -1.482 -0.078 0.718 1830 -0.571 -1.919 -1.245 0.093 0.849 1835 -0.372 -1.634 -0.997 0.227 0.870 1840 -0.261 -1.465 -0.849 0.308 0.930 1845 -0.214 -1.390 -0.780 0.372 0.994 1850 -0.196 -1.344 -0.761 0.418 1.070 1855 -0.221 -1.328 -0.788 0.399 1.087 1860 -0.337 -1.386 -0.891 0.273 0.970 1865 -0.607 -1.593 -1.142 0.028 0.766 1870 -1.014 -1.973 -1.547 -0.368 0.450 1875 -1.422 -2.236 -1.887 -0.786 0.119 1880 -1.680 -2.407 -2.079 -1.109 -0.216 1885 -1.817 -2.539 -2.212 -1.293 -0.326 1890 -1.870 -2.632 -2.280 -1.327 -0.343 1895 -1.801 -2.604 -2.231 -1.231 -0.213 1900 -1.612 -2.441 -2.057 -1.060 0.010 1905 -1.382 -2.217 -1.827 -0.845 0.212 1910 -1.196 -1.969 -1.603 -0.674 0.295 1915 -1.120 -1.808 -1.478 -0.648 0.204 1920 -1.148 -1.875 -1.512 -0.669 0.158 1925 -1.230 -2.058 -1.658 -0.702 0.106 1930 -1.309 -2.170 -1.763 -0.716 0.135 1935 -1.374 -2.278 -1.847 -0.751 0.146 1940 -1.445 -2.432 -1.973 -0.787 0.158 1945 -1.234 -2.257 -1.750 -0.624 0.142 1950 -1.500 -2.557 -2.081 -0.833 -0.040