# Swiss 567 Year Grape Harvest Date Spring/Summer Temperature Reconstruction
#-----------------------------------------------------------------------
#               World Data Center for Paleoclimatology, Boulder
#                                  and
#                     NOAA Paleoclimatology Program
#-----------------------------------------------------------------------
# NOTE: Please cite original reference when using these data,
# If there is no publication information, please cite Investigators, Title, and Online_Resource and date accessed
#
#
#
# Online_Resource: https://www.ncdc.noaa.gov/cdo/f?p=519:1:::::P1_STUDY_ID:17615
#
# Online_Resource: https://www.ncdc.noaa.gov/paleo/study/24611
#
# Original_Source_URL:ftp://ftp.ncdc.noaa.gov/pub/data/paleo/historical/europe/switzerland/swiss-summer-temp2011.txt
#
# Description/Documentation lines begin with #
# Data lines have no #
#
# Archive: Climate Reconstructions
# --------------------
# Contribution_date
#	Date: 2014-12-10
# --------------------
# Title 
#	Study_Name:  Swiss 567 Year Grape Harvest Date Spring/Summer Temperature Reconstruction
# --------------------
# Investigators
#	Investigators:  Wetter, O.; Pfister, C.
# --------------------
# Description_and_Notes
# 	Description:  567-yr long reconstruction of spring-summer (AMJJ) temperatures from documentary-based grape 
#	harvest starting dates (GHD).  The new Swiss GHD series is composed from four different kinds of GHD; 
#	(a) (institutional) wage payment data (WPD) (b) (Institutional) grape harvest ban related data (GHBD)
#	(c) (individual) historic phenological data (HPD) (d) (institutional) phenological network observation (PNO). 
#	Assessments of full grape maturity in 1540 is drawn from HPD in combination with narrative information. 
#	Temperature reconstruction via linear regression between measured temperatures and GHD proxy data series.  
#	
# --------------------
# Publication
#	Authors: O. Wetter and C. Pfister
#	Published_Date_or_Year: 2013-01-14
#	Published_Title: An underestimated record breaking event - why summer 1540 was likely warmer than 2003
#	Journal_Name: Climate of the Past
#	Volume: 9	
#	Edition: 	
#	Issue: 1
#	Pages: 41-56
#	DOI: 10.5194/cp-9-41-2013
#	Online_Resource: http://www.clim-past.net/9/41/2013/cp-9-41-2013.html
#	Full_Citation:  
#	Abstract: The heat of summer 2003 in Western and Central Europe was claimed to be unprecedented since the Middle Ages on the basis of grape harvest data (GHD) and late wood maximum density (MXD) data from trees in the Alps. This paper shows that the authors of these studies overlooked the fact that the heat and drought in Switzerland in 1540 likely exceeded the amplitude of the previous hottest summer of 2003, because the persistent temperature and precipitation anomaly in that year, described in an abundant and coherent body of documentary evidence, severely affected the reliability of GHD and tree-rings as proxy-indicators for temperature estimates. Spring-summer (AMJJ) temperature anomalies of 4.7C to 6.8C being significantly higher than in 2003 were assessed for 1540 from a new long Swiss GHD series (1444 to 2011). During the climax of the heat wave in early August the grapes desiccated on the vine, which caused many vine-growers to interrupt or postpone the harvest despite full grape maturity until after the next spell of rain. Likewise, the leaves of many trees withered and fell to the ground under extreme drought stress as would usually be expected in late autumn. It remains to be determined by further research whether and how far this result obtained from local analyses can be spatially extrapolated. Based on the temperature estimates for Switzerland it is assumed from a great number of coherent qualitative documentary evidence about the outstanding heat drought in 1540 that AMJJ temperatures were likely more extreme in neighbouring regions of Western and Central Europe than in 2003. Considering the significance of soil moisture deficits for record breaking heat waves, these results still need to be validated with estimated seasonal precipitation. It is concluded that biological proxy data may not properly reveal record breaking heat and drought events. Such assessments thus need to be complemented with the critical study of contemporary evidence from documentary sources which provide coherent and detailed data about weather extremes and related impacts on human, ecological and social systems.
# --------------------
#	Authors: Anderson, D.M., Tardif, R., Horlick, K., Erb, M.P., Hakim, G.J., Noone, D., Perkins, W.A., and E. Steig
#	Published_Date_or_Year: 2018
#	Published_Title: Additions to the last millennium reanalysis multi-proxy database
#	Journal_Name: Data Science Journal
#	Volume:
#	Edition:
#	Issue:
#	Pages:
#	Report_Number:
#	DOI:
#	Online_Resource:
#	Full_Citation: Anderson, D.M., Tardif, R., Horlick, K., Erb, M.P., Hakim, G., J., Noone, D., Perkins, W.A., and E. Steig, submitted. Additions to the last millennium reanalysis multi-proxy database. Data Science Journal.
#	Abstract: Progress in paleoclimatology increasingly occurs via data syntheses. We describe additions to a collection prepared for use in paleoclimate state estimation, specifically the Last Millennium Reanalysis (LMR).  The 2290 additional series include 2152 tree ring chronologies and 138 other series.  They supplement the collection used previously and together form a database titled LMRdb 1.0.0. The additional data draws from lake core, ice core, coral, speleothem, and tree ring archives, using published data primarily from the NOAA Paleoclimatology archive and a set of tree ring width chronologies standardized from raw International Tree Ring Data Bank ring width series. In contrast to many previous paleo compilations, the data were not selected (screened) on the basis of their environmental correlation, multi-century length, or other attributes. The inclusion of proxies sensitive to moisture and other environmental variables expands their use in data assimilation.  A preliminary calibration using linear regression with mean annual temperature reveals characteristics of the proxy series and their relationship to temperature, as well as the noise and error characteristics of the records. The additional records are structured as individual files in the NOAA Paleoclimatology format and archived at NOAA Paleoclimatology (Anderson et al. 2018) and will continue to be improved and expanded as part of the LMR Project.  The additions represent a four-fold increase in the number of records available for assimilation, provide expanded geographic coverage, and add additional proxy variables.  Applications include data assimilation, proxy system model development, and paleoclimate reconstruction using climate field reconstruction and other methods.
#------------------
# Funding_Agency
#	  Funding_Agency_Name: Swiss National Science Foundation
#	  Grant: 100011-120157
# --------------------
# Funding_Agency
#	  Funding_Agency_Name: Oeschger Centre for Climatic Change Research (OCCR)
#	  Grant:
# --------------------
# Funding_Agency
#	  Funding_Agency_Name:  University of Bern
#	  Grant: H.A. Vogelin-Bienz-Stiftung, Institute of History
# --------------------
#	Funding_Agency_Name: National Science Foundation
#	Grant:AGS-1304263
#	Funding_Agency_Name: National Oceanic and Atmospheric Administration
#	Grant:NA14OAR4310176
#------------------
# Site_Information
#	Site_Name: Swiss GHD
#	Location: Europe>Western Europe>Switzerland
#	Country:  Switzerland
#	Northernmost_Latitude: 47.8
#	Southernmost_Latitude: 46.5
#	Easternmost_Longitude: 9.5
#	Westernmost_Longitude: 6.4
#	Elevation:
# --------------------
# Data_Collection
#	Collection_Name: 11Swis01
#	Earliest_Year: 1444
#	Most_Recent_Year: 2011
#	Time_Unit: y_ad
#
# --------------------
# Variables
#
# Data variables follow that are preceded by "##" in columns one and two.
# Data line variables format:  Variables list, one per line, shortname-tab-longname-tab-longname components (9 components: what, material, error, units, seasonality, archive, detail, method, C or N for Character or Numeric data) 
#
##age	age,,,years AD,,,,,N 
##temp_anom	surface temperature anomaly, , , degrees C, March-July, Climate Reconstructions, , ,N
#
# --------------------
# Data:
# Data lines follow (have no #)
# Data line format - tab-delimited text, variable short name as header
# Missing values: NAN
#
age	temp_anom
1444	1.89
1445	NAN
1446	NAN
1447	NAN
1448	NAN
1449	NAN
1450	NAN
1451	NAN
1452	NAN
1453	NAN
1454	NAN
1455	NAN
1456	NAN
1457	NAN
1458	0.76
1459	NAN
1460	NAN
1461	1.13
1462	-0.28
1463	-0.28
1464	1.32
1465	-0.65
1466	0.38
1467	0.38
1468	-0.47
1469	1.32
1470	-0.47
1471	2.07
1472	NAN
1473	2.54
1474	-0.65
1475	0.1
1476	1.42
1477	0.38
1478	0.85
1479	1.51
1480	-1.03
1481	-1.78
1482	1.42
1483	1.51
1484	0.57
1485	-1.31
1486	0.95
1487	NAN
1488	-1.03
1489	NAN
1490	0.1
1491	NAN
1492	1.13
1493	-0.47
1494	1.23
1495	1.32
1496	-0.56
1497	0.47
1498	0.38
1499	0.57
1500	1.89
1501	0.76
1502	0.76
1503	1.51
1504	1.42
1505	-0.56
1506	0.38
1507	1.51
1508	-0.56
1509	0.57
1510	0.29
1511	0.38
1512	0.38
1513	0.19
1514	0.1
1515	-0.37
1516	1.51
1517	1.13
1518	0.85
1519	0.1
1520	1.32
1521	1.42
1522	-0.18
1523	0.66
1524	0.47
1525	NAN
1526	0.76
1527	-1.69
1528	0
1529	-1.31
1530	0.29
1531	-0.84
1532	NAN
1533	0
1534	1.13
1535	0.47
1536	NAN
1537	NAN
1538	2.17
1539	0.85
1540	5.18
1541	NAN
1542	-2.63
1543	-0.75
1544	-0.28
1545	0.95
1546	0.85
1547	-0.94
1548	-0.09
1549	1.13
1550	-0.18
1551	0.66
1552	1.32
1553	0.19
1554	0.85
1555	-0.47
1556	2.45
1557	0.19
1558	0.85
1559	1.79
1560	0.38
1561	1.42
1562	1.13
1563	-0.37
1564	0.19
1565	0.29
1566	1.23
1567	0.85
1568	-0.18
1569	-0.37
1570	-0.84
1571	1.6
1572	0.85
1573	-1.5
1574	0.47
1575	0.29
1576	-0.56
1577	-0.18
1578	0.1
1579	-1.12
1580	0.47
1581	0.19
1582	0.85
1583	1.51
1584	0.85
1585	-0.28
1586	0.38
1587	-1.31
1588	0
1589	NAN
1590	1.51
1591	-0.09
1592	-1.03
1593	-0.47
1594	-0.84
1595	0.19
1596	-0.84
1597	-0.94
1598	0.66
1599	1.89
1600	-1.31
1601	-0.65
1602	0.47
1603	0.95
1604	0.85
1605	0.95
1606	-0.65
1607	0.19
1608	-0.47
1609	0.1
1610	1.13
1611	1.79
1612	-0.18
1613	-0.09
1614	-0.56
1615	0.85
1616	2.26
1617	-1.12
1618	-0.94
1619	0.66
1620	-0.28
1621	-1.41
1622	-0.28
1623	0.66
1624	1.6
1625	-0.37
1626	0.47
1627	-1.5
1628	-2.44
1629	1.13
1630	0.66
1631	0.76
1632	-1.22
1633	-0.47
1634	0.19
1635	0.1
1636	1.42
1637	1.79
1638	1.89
1639	-0.28
1640	-0.28
1641	-0.37
1642	-1.41
1643	-0.65
1644	1.04
1645	0.95
1646	0.19
1647	0.29
1648	-0.37
1649	-1.12
1650	-0.56
1651	0.57
1652	-0.18
1653	1.51
1654	-0.56
1655	0.1
1656	0
1657	0.57
1658	-0.28
1659	1.13
1660	1.32
1661	0.76
1662	-0.37
1663	-0.75
1664	0
1665	0.76
1666	1.23
1667	-0.94
1668	0.38
1669	1.04
1670	-0.09
1671	0.38
1672	-0.18
1673	-1.41
1674	-0.18
1675	-2.35
1676	1.23
1677	-0.65
1678	0.66
1679	0.38
1680	0.29
1681	0.29
1682	-0.84
1683	0.47
1684	1.6
1685	0.1
1686	1.51
1687	-0.09
1688	-0.37
1689	-0.75
1690	-0.56
1691	0.1
1692	-2.06
1693	-0.28
1694	0.29
1695	-1.97
1696	-0.65
1697	-0.18
1698	-2.63
1699	-1.03
1700	-1.5
1701	0
1702	-0.56
1703	-0.47
1704	1.23
1705	-1.22
1706	1.13
1707	-0.18
1708	0.76
1709	0.29
1710	0.76
1711	-0.65
1712	0.57
1713	-0.75
1714	-0.75
1715	0.85
1716	-1.03
1717	0.1
1718	2.64
1719	1.6
1720	-1.22
1721	0.1
1722	0.76
1723	0.76
1724	0.47
1725	-0.65
1726	1.89
1727	0.47
1728	0.76
1729	0.38
1730	-0.28
1731	0.29
1732	0.38
1733	0.57
1734	0.57
1735	0.1
1736	0.57
1737	0.47
1738	0.76
1739	0.29
1740	-0.56
1741	-0.18
1742	-1.03
1743	-0.75
1744	-0.18
1745	-0.28
1746	0.57
1747	0.38
1748	0
1749	0.1
1750	0.29
1751	-0.75
1752	-0.56
1753	0.29
1754	-0.18
1755	0.29
1756	-0.75
1757	-0.18
1758	-0.37
1759	1.04
1760	1.13
1761	0.76
1762	0.95
1763	-0.18
1764	0.19
1765	-0.28
1766	-0.18
1767	-0.94
1768	-0.09
1769	-0.18
1770	-0.56
1771	0.38
1772	0.29
1773	-0.84
1774	0.85
1775	0.19
1776	0
1777	0
1778	0.76
1779	0.95
1780	1.13
1781	1.6
1782	0.57
1783	0.85
1784	0.95
1785	-0.56
1786	0.19
1787	-0.18
1788	1.6
1789	0.19
1790	0.47
1791	0.85
1792	0.19
1793	1.04
1794	2.26
1795	1.13
1796	-0.09
1797	0.95
1798	0.85
1799	-0.75
1800	1.32
1801	0.95
1802	1.51
1803	0.47
1804	0.76
1805	-0.75
1806	0.57
1807	1.79
1808	0.47
1809	-0.75
1810	0.47
1811	1.6
1812	-0.56
1813	-0.47
1814	-0.18
1815	0.76
1816	-1.41
1817	-0.18
1818	1.04
1819	1.13
1820	0.19
1821	-0.75
1822	3.48
1823	-0.09
1824	0.1
1825	1.23
1826	1.04
1827	1.13
1828	0.95
1829	0
1830	0.66
1831	0.47
1832	0.1
1833	0.66
1834	2.17
1835	0.47
1836	0.19
1837	0.1
1838	0.38
1839	0.85
1840	0.76
1841	1.13
1842	1.6
1843	-0.56
1844	0.76
1845	-0.09
1846	2.07
1847	0.38
1848	1.13
1849	1.04
1850	0.19
1851	0
1852	0.66
1853	0
1854	0.76
1855	0.38
1856	0.38
1857	0.85
1858	1.13
1859	1.42
1860	-0.18
1861	1.23
1862	1.89
1863	1.13
1864	0.95
1865	2.83
1866	0.47
1867	0.85
1868	2.73
1869	0.57
1870	1.42
1871	0
1872	-0.28
1873	1.04
1874	0
1875	1.42
1876	-0.28
1877	0.38
1878	0.47
1879	-0.94
1880	0.66
1881	1.51
1882	NAN
1883	NAN
1884	NAN
1885	1.04
1886	0.85
1887	0.47
1888	0.76
1889	0.57
1890	0.19
1891	0.1
1892	0.57
1893	1.51
1894	0.1
1895	0.57
1896	0
1897	1.13
1898	-0.09
1899	0.1
1900	0.47
1901	1.23
1902	-0.28
1903	0.38
1904	1.23
1905	0.76
1906	-0.18
1907	-0.09
1908	1.04
1909	0.57
1910	0.19
1911	1.04
1912	0.19
1913	0.29
1914	-0.28
1915	1.13
1916	-0.65
1917	1.6
1918	-0.09
1919	-0.18
1920	1.23
1921	1.32
1922	0.19
1923	-0.75
1924	0.19
1925	0
1926	-0.75
1927	0.76
1928	0.1
1929	0.38
1930	0.1
1931	-0.09
1932	-0.75
1933	-0.47
1934	1.6
1935	0
1936	0
1937	0.95
1938	-0.47
1939	-1.22
1940	0.47
1941	-0.65
1942	0.57
1943	0.85
1944	-0.47
1945	2.17
1946	0.47
1947	1.51
1948	-0.09
1949	0.47
1950	1.13
1951	-0.47
1952	1.13
1953	1.04
1954	-0.65
1955	0.1
1956	-0.56
1957	0.1
1958	0.29
1959	0.57
1960	0.29
1961	0.47
1962	-0.28
1963	-0.28
1964	0.85
1965	-0.65
1966	0.38
1967	0.38
1968	0.1
1969	0.38
1970	-0.37
1971	0.19
1972	-0.09
1973	0.1
1974	-0.65
1975	0.66
1976	0.76
1977	-0.75
1978	-1.03
1979	0.19
1980	-1.31
1981	0.95
1982	0.66
1983	0.85
1984	-0.56
1985	0.19
1986	0.38
1987	-0.75
1988	0.38
1989	1.04
1990	0.47
1991	0.19
1992	0.85
1993	0.95
1994	0.85
1995	-0.18
1996	-0.28
1997	0.1
1998	0.29
1999	0.1
2000	0.85
2001	0.1
2002	0.57
2003	2.83
2004	0
2005	0.76
2006	0.76
2007	1.51
2008	0.1
2009	1.32
2010	0.29
2011	1.51