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Archive for April 2019

 

 

FIGURE 1: UAH & BOM TEMPERATURE ANOMALIES COMPAREDtemp-gif

 

FIGURE 2: UAH & BOM DECADAL WARMING RATES COMPAREDdec-gif

 

FIGURE 3: THE CORRELATION TESTcorrelations

 

FIGURE 4: THE VARIANCE TEST FOR TEMPERATURETEMP-STDEV

 

FIGURE 5: THE VARIANCE TEST FOR DECADAL WARMINGDEC-STDEV

 

FIGURE 6: A TEST FOR BIAS: TEMPERATURE TEMP-BIAS

 

FIGURE 7: A TEST FOR BIAS: DECADAL WARMINGDEC-BIAS

 

 

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  1. The University of Alabama Huntsville (UAH) assessments of lower troposphere temperatures from satellite microwave sounding data since December 1978 are widely accepted as the most reliable estimates of regional and global temperatures in the study of global warming and climate change. In addition to global and zonal mean temperature estimates, the UAH also publishes country-specific land temperature means for the USA lower 48 states and for Australia. The utility of these country specific mean temperature estimates is that they may be used to test temperature reconstructions for the same region from the instrumental record particularly so in the context of widespread allegations by climate change skeptics of data tampering by the relevant meteorological organizations to show hotter conditions and/or higher rates of warming as a way of climate change activism. It is universally accepted that the UAH data are direct measurements and not reconstructions and that as such they contain no such reconstruction bias. It is therefore a useful exercise to compare the UAH data with regional reconstructions for evidence of data tampering in the reconstructions. This post is a comparison of the UAH and reconstruction temperatures for Australia in this context.
  2. The Australian temperature reconstruction by the Bureau of Meteorology (BOM) is “calculated from a homogeneous temperature dataset (known as the Australian Climate Observations Reference Network-Surface Air Temperature, or ACORN-SAT, dataset) developed for monitoring climate variability and change in Australia.” In a recent work it was found that there is a high correlation of ρ=0.7 between  the two temperature anomaly series that rises to ρ=0.8 when precipitation corrections are made. The distance between the two temperature data sets is estimated with a standard deviation of their differences of σ=0.6. This work is available online [LINK] . The conclusion paragraph of the work says that “The UAH tropospheric temperatures and BOM surface temperatures in Australia are correlated, with similar variability (0.70 correlation). Accounting for anomalous rainfall conditions increases the correlation to 0.80. The Tsfc trends have a slightly greater warming trend than the tropospheric temperatures, but the reasons for this are unclear. Users of the UAH data should expect monthly differences between the UAH and BOM data of 0.6 deg. C or so on a rather routine basis (after correcting for their different 30-year baselines used for anomalies: BOM uses 1961-1990 and UAH uses 1981-2010).” 
  3. In this post we extend this analysis several ways. (1) The analysis is carried out for each of the twelve calendar months separately. It is shown in a separate post that the the behavior of temperatures in the calendar months contain significant differences [LINK] (2) A bias test is included because the standard deviation of σ=0.6 found in the reference study does not contain information about whether these differences are symmetrical or whether there is a bias such that one source tends to be higher than the other. (3) The standard deviation test is extended to compare the standard deviation of both sources taken together with the average standard deviation of the two sources taken one at a time. The test determines whether the two sources are very different (4) The study is carried out for both temperature and for decadal trends (the OLS trend in a moving ten-year window that moves one year at a time. (5) Because the study is carried out separately for the calendar months such that all twelve calendar months may be compared, the study is limited to whole years of data with a revised sample period of 1/1/1978 to 31/12/2018.
  4. The data to be analyzed are presented one calendar month at a time in Figure 1 and Figure 2. These Figures are GIF images that cycle through the twelve calendar months. Figure 1 presents temperatures and Figure 2 displays their decadal moving trends. The blue lines in these charts are BOM data and the red lines are the UAH data. Some differences between the two series can be visualized in these charts along with significant differences in these results among the calendar months – underscoring the utility of studying temperature phenomena one calendar month at a time.
  5. Figure 3 presents the results of the correlation test. The left frame of Figure 3 displays correlations between BOM and UAH temperatures and the right frame displays the correlations between their decadal trends for each of the twelve calendar months. As reported in the reference study (paragraph#2), very high correlations are seen. Also seen in these charts are large differences among calendar months and significant differences between temperature correlations and decadal trend correlations. Generally temperature correlations tend to be higher than decadal trend correlations and the austral spring months of July to November show high correlations in both temperature and decadal trends while February, June, and December tend to show lower correlations. Of particular note is the oddity of a complete collapse in December trend correlations. The relevant source data for December (low correlation) and September (high correlation) are displayed below.

     

  6. Figure 4 and Figure 5 present a standard deviation test similar to that in the reference studies but with several important differences. The left frame of these charts shows the standard deviation for the two series taken together in red and the average of the standard deviations taken separately in blue. The comparison shows whether the temperatures in the two series are very different (stdev together >> stdev separately) or whether they tend to have similar values (stdev together ≈ stdev separately). In the case of temperatures (Figure 4) we find that the values tend to be different but the same test for decadal trends shows no real difference in the magnitudes of decadal trends.
  7. The variance tests in Figure 4 and Figure 5 tell us whether the values are different but provide no information on whether the differences are in random directions or whether there is a bias such that the data from one source tends to be higher or tends to be lower than the data from the other source. This information is extracted in the bias tests in Figure 6 and Figure 7. The left frame of these charts shows plots the data for each calendar month on the dame chart across the sample period 1979-2018. The right panel plots the sum of the difference (DIFF=BOM-UAH) for each calendar month across all 40 years in the sample period. If the differences BOM-UAH are randomly distributed around zero their  sum will be close to zero but if there is a bias BOM>UAH the sum will be positive and for the bias BOM<UAH the sum will be negative. The bias test for temperature shows clear evidence of a positive bias such that BOM temperatures tend to be higher than UAH temperatures particularly in April and May and July through September where very high bias values of 17 to 27 are seen. However, the bias test for decadal trends shows no bias with very small bias values < 1 both positive and negative. This result indicates that there may be a systematic methodological difference in how temperature anomalies are computed such that the the bias in temperature anomalies tend to be higher for BOM in a way that does not affect decadal trends. A possible source for this bias is a difference in the reference period used computing temperature anomalies. This finding is supported by the conclusions in the reference study described in paragraph#2.
  8. CONCLUSION: Mean temperatures for Australia computed by the BOM in a reconstruction from the instrumental record for the period 1979-2018 are compared with UAH satellite temperatures, a known reliable and unbiased source. The comparison does not show systematic differences that one would expect to find if the BOM reconstruction had been arbitrarily altered perhaps to show higher rates of warming. The bias in the values of the temperature anomalies can be explained in terms of a difference in the reference period used for computing temperature anomalies. It is possible that allegations of temperature tampering by the BOM relate to the data earlier than 1979.

 

 

 

 

 

 

 

 

 

 

 

 

1715cyclone

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RELATED POST:  [LINK]

HURRICANES: [LINK#1] [LINK#2]  

THE HURRICANES LISTED HERE MUST BE CREATIONS OF GLOBAL COOLING DURING THE LITTLE ICE AGE IF THE HURRICANES WE SEE NOW ARE CREATIONS OF GLOBAL WARMING IN THE INDUSTRIAL ERA OF FOSSIL FUELS. 

  1. The Treasure Coast Hurricanes of 1715 & 1733:  Spanish Treasure Ship captain’s report: “The sun disappeared and the wind increased in velocity coming from the east and east northeast. The seas became very giant in size, the wind continued blowing us toward shore, pushing us into shallow water. It soon happened that we were unable to use any sail at all…and we were at the mercy of the wind and water, always driven closer to shore. Having then lost all of our masts, all of the ships were wrecked on the shore, and with the exception of mine, broke to pieces.” This violent storm off the coast of Florida in July 1715 ravaged 11 Spanish ships as they attempted to return to Spain. From the mid 16th to the mid 18th century, heavily-armed fleets such as this plied the waters between Spain and the Americas transporting massive amounts of New World treasure. Through this treasure fleet system, Spain created a mighty New World empire and became the most powerful nation in Europe. The fleets’ return voyage—when the ships were laden with silver, gold, gemstones, tobacco, exotic spices, and indigo—was the most dangerous. Pirates and privateers from rival European countries threatened to seize the precious cargoes and jeopardize Spain’s dominance of the Americas. The greatest danger, however, came not from enemy countries, but from unexpected and deadly hurricanesIn 1715 and again in 1733, Spain’s treasure fleets were devastated by hurricanes off the coast of Florida. Although the Spanish managed to recover some treasure, much more remained on the ocean floor. The sunken ships lay forgotten for more than 200 years until modern treasure hunters discovered several of them. Today, the remains of two of the ships—the Urca de Lima from the 1715 fleet and the San Pedro from the 1733 fleet—are protected as Florida Underwater Archaeological Preserves. These ships are time capsules from a bygone era and can reveal much about the history of the mighty maritime system that helped shape the Americas.
  2. The Dreadful Hurricane of 1667: In September 1667, a powerful hurricane struck colonial Virginia. The storm was first recorded off the Lesser Antilles on 1 September. On 6 September, the storm moved through the Outer Banks of North Carolina and proceeded to make landfall just to the northeast of Jamestown, Virginia where the hurricane lingered for 24 hours, bringing with it, violent winds, heavy rains, and a 12 ft storm surge. Approximately 10,000 houses were destroyed. The colonists’ tobacco and corn crops were lost, their cattle drowned, and their ships were greatly damaged. In a letter from the colonial secretary Thomas Ludwell to Virginia Governor Lord William Berkeley, the Secretary described the night of the hurricane as “the most dismal time I ever knew or heard of, for the wind and rain raised so confused a noise, mixed with the continued cracks of failing houses…” He then stated that the colony, in the aftermath of the hurricane, was “reduced to a very miserable condition”. This event is considered to be one of the most severe hurricanes to ever strike Virginia. Rain fell for 12 straight days in the wake of the hurricane.The widening of the Lynnhaven River, located near Virginia Beach, was a result of this hurricane.
    Source: http://www.hurricanescience.org , Seventeenth Century Virginia Hurricanes. NOAA Hydrometeorological Prediction Center. 2001
  3. The Calcutta Cyclone of 1737, also referred to as the Hooghly River Cyclone is recorded as one of deadliest natural disasters of all time. The cyclone did widespread damage to the low lying areas in the region. Early in the morning on October 11, 1737, a large cyclone made landfall inside the Ganges River Delta, located just south of Calcutta, West Bengal, India. The cyclone caused a storm surge 10-13 m (30-40 ft) in the Ganges with a reported 381 mm (15 in) of rain falling in a 6-hour period. The storm tracked 330 km (200 mi) inland before dissipating. In the city of Calcutta, the majority of structures, which were mostly made of mud with straw roofs, were destroyed, with many brick structures also damaged beyond repair. A spire on the St. Anne’s church reportedly sunk and listed to side, and was not approved for repair until 1751. The East India Company’s records report 3,000 deaths occurring in Calcutta alone. In the Ganges, 8 out of 9 boats were lost along with most of their crews, and 3 out of 4 Dutch ships also went down. Overall the cyclone reportedly destroyed 20,000 water going vessels, ranging from ocean worthy ships to canoes, and killed 300,000 to 350,000 individuals, likely including ships’ crews as well as the local populations in low-lying Bengal. India’s Ganges River Delta is prone to tropical cyclones. Additional cyclones with death tolls reported over 10,000 people struck again in 1787, 1789, 1822, 1833, 1839, 1864, 1876, and later. SOURCE:  http://www.hurricanescience.org See also, Emanuel, Kerry. “Divine Wind: The history of Science of Hurricanes”
  4. The Great Hurricane of 1780: Although specifics on this hurricane’s track and strength are unknown, forecasters and historians believe that the Great Hurricane of 1780 initially formed near the Cape Verde Islands on October 9, 1780. The hurricane strengthened and grew in size as it tracked slowly westward, first affecting Barbados, the western most Caribbean island, late on 9 October. The worst of the hurricane, with winds possibly exceeding 200 mph, passed over Barbardos late on 10 October 10 before moving past Martinique and St. Lucia early on 11 October. The hurricane passed near Puerto Rico and over the eastern portion of the Dominican Republic (at the time known known as Santo Domingo) on 14 October, causing heavy damage near the coastlines. Ultimately, the system turned to the northeast, passing 160 miles southeast of Bermuda on 18 October. The hurricane was last observed on October 20, 1780, southeast of Cape Race, Newfoundland, CanadaThousands of deaths were reported on each Caribbean island over which the cataclysmic hurricane crossed: 4,500 deaths occurred on Barbados (nearly every building on the island was leveled), 6,000 lost their lives on St. Lucia (where the island was essentially flattened), and approximately 9,000 died on Martinique. Over 27,500 total fatalities were estimated across the Lesser Antilles Islands as a result of this storm, making the Great Hurricane of 1780 the deadliest Atlantic hurricane on record. In addition this devastating event, the Caribbean was shattered by two other violent hurricanes in October 1780: The Savanna-la-Mar Hurricane (one of the worst disasters in Jamaican history) and Solano’s Hurricane. Unfortunately, the year of 1780 marked a turning point in Caribbean history. In the wake of these storms, a historical period of prosperity ended, and an episode of economic and cultural decline began. Coming in the midst of the American Revolutionary War, the 1780 hurricanes caused heavy losses to European fleets fighting for control of the New World’s Atlantic coast. A fleet of 40 French ships capsized off Martinique during the Great Hurricane, drowning approximately 4,000 soldiers. On St. Lucia, rough waves and a strong storm surge destroyed the British fleet of Admiral Rodney at Port Castries. Much of the British fleet was decimated by the three storms, and the English presence in the western North Atlantic was greatly reduced thereafter.
    Source: http://www.hurricanescience.org , “The Great Hurricane of 1780”. In: Library of Natural Disasters- Hurricanes, Typhoons, and Other Tropical Cyclones. 2008. Editor in Chief, Paul A. Kobasa. World Book. Chicago. Pp 14-15, Wikipedia.
  5. The Great September Gale of 1815: The Great September Gale of 1815 was the first major hurricane to impact New England in 180 years. Believed to have originated in the West Indies on September 18, 1815, the hurricane slowly spun northeastward. It struck the Turks Islands in the Bahamas on 20 September as what is believed to have been a Category 4 hurricane. The storm then continued northward, making landfall across Long Island, NY, around 7 AM on the morning of 23 September. The hurricane traveled along the Southern New England coast, making a second landfall near Saybrook, CT at 9 AM. The eye of the hurricane moved through central Massachusetts, passing between Amherst and Worcester, MA, at 11 AM. The storm then passed through New Hampshire, where it quickly dissipated by 2 PM that same day. The Great September Gale produced significant wind damage in Connecticut, Rhode Island, east-central Massachusetts, and southeastern New Hampshire. Parts of Providence, RI, experienced tides 14 ft greater than usual and in Buzzards Bay, MA, the tide is calculated to have risen 15.9 ft above normal. At least 38 fatalities were a result of the Great September Gale. The hurricane also caused the destruction of some 500 homes and 35 ships in Narragansett, RI, as an 11ft storm surge funneled up Narragansett Bay. The eye of this hurricane made its first landfall in Long Island, NY approximately 5-10 miles east of where the eye of the Great New England Hurricane of 1938 (“The Long Island Express”) would strike the coast over a century later. John Farrar, a Hollis professor of Mathematics and Natural Philosophy at Harvard University, maintained weather records between 1807-1817. In the aftermath of the Great Gale, he presented the concept of a hurricane as a “moving vortex”. He also observed the veering of hurricane winds, and the variable timing of their impacts on the cities of Boston and New York. Salt spray and salt deposition were noted in many areas after the hurricane. Historical reports recount the rain “tasting like salt”, the grapes in the vineyards “tasting like salt”, the houses had all turned white, and the leaves on the trees appeared “lightly frosted”.  Source: http://www.hurricanescience.org
  6. The Coringa Cyclone of 1839: Coringa, India is a small village situated near the mouth of the Godavari River on the southeastern coast of India. It was once a bustling port city. In 1789, it was hit by a brutal cyclone that left some 20,000 dead. Though devastated, the port city was still able to function. On November 25, 1839, Coringa was slammed by a disastrous cyclone that delivered terrible winds and a giant 40 ft storm surge. The port was destroyed (some 20,000 vessels were lost) and 300,000 people were killed. The town was abandoned and never fully rebuilt. Today, Coringa remains a simple village. This storm caused the third largest loss of life from any tropical cyclone worldwide, tied with Vietnam’s 1881 Haiphong typhoon which also caused 300,000 fatalities. Storms in the Bay of Bengal actually account for seven of the 10 deadliest tropical cyclones in recorded history. Henry Piddington, an official of the British East India Company, coined the term cyclone sometime around 1840 after looking at the destruction caused in 1789 and 1839 by a “swirling circle.”
    Source: http://www.hurricanescience.org , Wikipedia, “Deadliest Tropical Cyclones in History.” Wunderground. 2009, Rahman, Serina. “Worst Natural Disasters in Asia.” Asian Geographic. 2009
  7. 1856- Last Island Hurricane: The Last Island Hurricane was the first tropical cyclone and first major hurricane of the 1856 Atlantic hurricane season. It was initially observed on 8 August 200 km west-northwest of Key West, Florida. As the storm was recorded as a hurricane at first observation, it most likely developed further west. Moving northwestward, the hurricane rapidly intensified to a Category 3 hurricane. The storm’s forward motion slowed on 10 August just before making landfall, allowing it to reach a peak intensity of 934 mbar with 240 km/h (150 mph) winds (maximum sustained winds may have reached Category 5 status, but were unrecorded). During the evening of 10 August, the hurricane made landfall as a Category 4 storm on Last Island, Louisiana (southwest of New Orleans). After landfall, the storm quickly diminished, weakening to a tropical storm by the next day and then dissipating over southwestern Mississippi on 12 August. The hurricane had a great impact on coastal Louisiana. The city of New Orleans was inundated with more than13 inches of rain. Last Island, a popular resort destination at the time, was completely decimated by the hurricane. The barrier island was originally one contiguous island, approximately 40 km (25 mi) long and 1.6 km (1 mi) wide. As a result of the hurricane, Last Island was fragmented into a small island chain, known today as L’Îsles Dernières (Last Islands). At the time, storm prediction and identification was not advanced enough to give the island’s residents much warning. Although people noted signs of an advancing storm, by the time they realized its magnitude, it was too late. The hurricane’s 3.4-3.6 meter (11-12 ft) storm surge destroyed all 100 homes on the island. There were about 400 people on the island during the hurricane- fewer than half survived. Now the island(s) are only home to pelicans and other seabirds. The highest points of Last Island were under 5 ft of water due to the storm surge. The island reportedly stayed submerged after the storm, resurfacing several days later as large sandbars. Following the storm, the remains of the Star, the steamship that serviced the island, were the only sign that a populated island had ever existed.
    A story, potentially a legend, exists regarding the circumstances of the deaths of those on the island when the hurricane struck. It is said that people on Last Island were mesmerized by the “fantastic waves” created by the hurricane. Ignoring the indications that disaster would occur, they “danced to their deaths” at a ball in the only lavish hotel on the island. The steamship that was to save them (the Star) was late and actually ran aground during the storm. It is said some survivors saved themselves by climbing aboard the wreck.
    Source: http://www.hurricanescience.org , Wikipedia, Sallenger, Abby. Island in a Storm: A Rising Sea, a Vanishing Coast, and a Nineteenth-Century Disaster that Warns of a Warmer World. New York: Perseus, 2009 (climate change did it), The Most Intense Hurricanes in the United States 1851-2004.” National Hurricane Center. 2004, Roth, David. “Louisiana Hurricane History.” National Weather Service. 2010. Pp17., Corley, Linda G. Buried Treasures. Houma, Blue House Publications. 2004. Pp 293.
  8. The San Diego Hurricane of 1858: Tropical cyclones are rare in this part of the world. They do form in the eastern North Pacific but usually weaken over Mexico or the cold waters of the California current. Only four known tropical cyclones have brought tropical storm-force winds to the southwestern coast of the United States in the current era but a fifth tropical cyclone impacted San Diego, CA, on October 2, 1858. The cyclone formed in late September 1858, in the East Pacific Ocean but instead of tracking west as they usually do in this ocean basin, however, it moved north-northeast. On 2 October, it neared Southern California while weakening due to the presence of cooler waters and wind shear. Upon approaching San Diego, CA, by mid-day on 2 October, the cyclone took a turn for the west-northwest, just missing a direct landfall in the state. Researchers believe that the hurricane then remained offshore from San Diego through 3 October, before tracking toward the northwest. Category 1 conditions were experienced from San Diego to Long Beach, CA, and the storm was regarded as “one of the most terrific and violent hurricanes” to strike San Diego. Heavy rain was present along with 120 km/h (75 mph) winds. City residents claimed to have never experienced such weather in that area stating “a terrific gale” had sprung up from the south-southeast and continued “with perfect fury” for about six hours. It was said to have been the “severest gale ever witnessed in San Diego”. Other locations, such as Los Angeles, also felt the effects of the hurricane, where heavy rain fell for an estimated 24 hours. The stork caused extensive property damage in San Diego. Many homes lost their roofs and some were completely destroyed. After the storm, it was discovered that three schooners, the Plutus, the Lovely Flora, and the X.L., had blown ashore and a recently constructed windmill had been demolished. However, farmers benefited from the heavy rain as it allowed them to produce a substantial grain crop, something they had been unsuccessful with for several years previous. This hurricane is the only tropical cyclone known to produce hurricane-force winds on the California coast. Coral evidence suggests an El Nino event may have occurred that year, which would have kept ocean waters warmer than normal along the southwest U.S. coast, and thus, sustain a hurricane as far north as southern California. Historical records and modeling results suggest a similar Category 1 storm could return to the San Diego area in a couple hundred years, most likely during another El Nino event. If this hurricane were to strike San Diego in modern times, $500 million (USD) in damage would result. At the time of the hurricane, San Diego was only a small settlement with a population of 4,325. Today the population of San Diego County is over 3 million.Source: http://www.hurricanescience.org , Wikipedia, Chenoweth, Michael and Christopher Landsea. The San Diego Hurricane of 2 October 1858. Bulletin of the American Meteorological Society. 85(11): 1689–1697. November 2004
  9. The Bhola Cyclone of 1970:  It is also noted that the strongest and most destructive tropical cyclone of the post industrial era was the monster Bhola Cyclone [LINK][LINK]that killed half a million people in Bangladesh and it was in fact the storm that created the nation we now know as Bangladesh. It occurred in 1970 right in the middle of the 1970s cooling period[LINK]that had sparked fears of a return to Little Ice Age conditions[LINK] .
  10. Other posts on tropical cyclones [LINK] [LINK] [LINK]

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  1. The theory of Anthropogenic Global Warming (AGW) holds that the warming trend since the end of the Little Ice Age (LIA) is unnatural and therefore human caused by way of fossil fuel emissions. The fossil fuel argument becomes relevant because the end of the LIA coincides with the Industrial Revolution such that the whole of the current warming event has occurred “in the industrial economy” making it useful to describe the current warming as “since pre-industrial times”.
  2. Skeptics have argued that natural variability must be considered before the industrial economy is taken to be the cause of the recovery from the LIA because the timing of the glacial melt event in Europe (in particular the Colle Gnifetti) that is generally accepted as the marker between the LIA and the current warming period is controversial as described in related a post [LINK]This controversy makes it possible for skeptics to point to the Medieval Warm Period (MWP) as an example of a warming event similar to the current warming that was not the creation of an industrial economy and therefore must have been a natural outcome of the known non-linear dynamical behavior of climate.
  3. It is generally accepted and the paleo data show that there was a strong sustained warming period in Europe sometime during an uncertain period within the time span 800AD to 1400AD usually referred to as the MWP. There is a great deal of controversy, however, about the timing, the timescale, the geographical extent, and, most important of all, the degree of warming.
  4. A bibliography of this topic consisting of eighteen papers written between the period 1994 to 2009 is presented below. The authors include prominent paleo climatologists such as Briffa, Mann, Cronin, Hughes, and Esper. The bibliography shows a general agreement of large uncertainties in the data such that the selection of the type of proxy data (eg tree ring, sediment, borehole, or climate model) and the geographical location where data were gathered strongly influences findings. It is uncertain whether it was global or localized in Europe and if so whether it was all of Europe or just Northern Europe. It is also uncertain as to exactly when the MWP occurred and for how long it lasted. Most of all it is uncertain as to exactly how warm it got specifically with respect to the current 20th century warming of “the industrial economy since pre-industrial times”.
  5. Uncertainty of course creates controversy and given the large uncertainties involved in these studies and the large stake for the climate science argument for human cause that the current warming is “unprecedented in the last two millennia” , the MWP issue has generated a great deal of acrimonious debate. Here we argue that the controversy is partisan and sustained by the so called “Texas Sharpshooter” fallacy because uncertainty allows different researchers to pay more attention to the portion of the uncertainty band that supports their hypothesis.
  6. In a related post [LINK] Professor Carl Wunsch explains that bias opportunity in this way: “From one point of view, scientific communities without adequate data have a distinct advantage because they can construct interesting and exciting stories and rationalizations with little or no risk of observational refutation. Colorful, sometimes charismatic, characters come to dominate the field, constructing their interpretations of a few intriguing, but indefinite observations that appeal to their followers, and which eventually emerge as “textbook truths.” 
  7. The following characteristics are ascribed to one particularly notoriously data-poor field. (1) Tremendous self-confidence and a sense of entitlement and of belonging to an elite community of experts, (2) An unusually monolithic community, with a strong sense of consensus, whether driven by the evidence or not, and an unusual uniformity of views on open questions. These views seem related to the existence of a hierarchical structure in which the ideas of a few leaders dictate the viewpoint, strategy, and direction of the field. (3) In some cases a sense of identification with the group, akin to identification with a religious faith or political platform. (4) A strong sense of the boundary between that group of experts and the rest of the world. (5) A disregard for and disinterest in the ideas, opinions, and work of experts who are not part of the group, and a preference for talking only with other members of the community. (6) A tendency to interpret evidence optimistically, to believe exaggerated or incorrect statements of results and to disregard the possibility that the theory might be wrong. This is coupled with a tendency to believe results are true because they are widely believed, even if one has not checked (or even seen) the proof oneself”.
  8. In another related post on this site we show that the commonly held belief in “the unprecedented warming of the Arctic” is not supported by the data [LINK] and in the bibliography below it becomes clear that the the uncertainty in the data is such that they can be interpreted both ways – yes the MWP was warmer than it is in the industrial economy and no it was not warmer than the industrial economy with some authors having the courage to acknowledge that large uncertainties mean that we don’t know and not that it provides the opportunity to interpret the data in a way that proves our hypothesis. And that implies that the paleo data do not show conclusively that the MWP was not as warm and that therefore the current warming is unprecedented and that therefore it must be human caused by way of fossil fuel emissions.
  9. Yet another issue is that the “unprecedented warming” is constrained to a comparison with “the last two thousand years”. What is the relevance of the number 2000? A more unbiased comparison would be with the beginning of the Holocene or with the previous interglacial the Eemian. In both of these cases we find much warmer temperatures without an industrial economy as shown in related posts [LINK] [LINK] [LINK] .
  10. CONCLUSION: The unbiased interpretation of the MWP is that given the large uncertainty in the proxy data it is not possible to determine with sufficient certainty that the MWP was not warmer than today and that therefore the use of the MWP to support human cause in the warming since the end of the LIA is not possible because the data do not show conclusively that the MWP was not warmer than today.

 

 

 

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BIBLIOGRAPHY

  1. Hughes 1994:  Hughes, Malcolm K., and Henry F. Diaz. “Was there a ‘Medieval Warm Period’, and if so, where and when?.” Climatic change 26.2-3 (1994): 109-142.  It has frequently been suggested that the period encompassing the ninth to the fourteenth centuries A.D. experienced a climate warmer than that prevailing around the turn of the twentieth century. This epoch has become known as the Medieval Warm Period, since it coincides with the Middle Ages in Europe. In this review a number of lines of evidence are considered, (including climatesensitive tree rings, documentary sources, and montane glaciers) in order to evaluate whether it is reasonable to conclude that climate in medieval times was, indeed, warmer than the climate of more recent times. Our review indicates that for some areas of the globe (for example, Scandinavia, China, the Sierra Nevada in California, the Canadian Rockies and Tasmania), temperatures, particularly in summer, appear to have been higher during some parts of this period than those that were to prevail until the most recent decades of the twentieth century. These warmer regional episodes were not strongly synchronous. Evidence from other regions (for example, the Southeast United States, southern Europe along the Mediterranean, and parts of South America) indicates that the climate during that time was little different to that of later times, or that warming, if it occurred, was recorded at a later time than has been assumed. Taken together, the available evidence does not support a globalMedieval Warm Period, although more support for such a phenomenon could be drawn from high-elevation records than from low-elevation records. The available data exhibit significant decadal to century scale variability throughout the last millennium. A comparison of 30-year averages for various climate indices places recent decades in a longer term perspective.
  2. Dean 1994: Dean, Jeffrey S. “The medieval warm period on the southern Colorado Plateau.” The Medieval Warm Period. Springer, Dordrecht, 1994. 225-241. Several questions concerning the Medieval Warm Period (MWP), an interval (A.D. 900 to 1300) of elevated temperatures first identified in northern Europe, are addressed with paleoenvironmental and archaeological data from the southern Colorado Plateau in the southwestern United States. Low and high frequency variations in alluvial groundwater levels, floodplain aggradation and degradation, effective moisture, dendroclimate, and human adaptive behavior fail to exhibit consistent patterns that can be attributed to either global or regional expressions of the MWP. There is some suggestion, however, that climatic factors related to the MWP may have modified the regional patterns to produce minor anomalies in variables such as the number of intense droughts, the occurrence of specific droughts in the twelfth and thirteenth centuries, the prevalence of low temporal variability in dendroclimate, and the coherence of some low and high frequency environmental variables and aspects of human adaptive behavior. These results suggest that the MWP does not represent warming throughout the world. Rather, it was a complex phenomenon that probably was expressed differently in different regions.
  3. Stahle 1994: Stahle, David W., and Malcolm K. Cleaveland. “Tree-ring reconstructed rainfall over the southeastern USA during the Medieval Warm Period and Little Ice Age.” The Medieval Warm Period. Springer, Dordrecht, 1994. 199-212. A 1053-year reconstruction of spring rainfall (March-June) was developed for the southeastern United States, based on three tree-ring reconstructions of statewide rainfall from North Carolina, South Carolina, and Georgia. This regional reconstruction is highly correlated with the instrumental record of spring rainfall (r = +0.80; 1887-1982), and accurately reproduces the decade-scale departures in spring rainfall amount and variance witnessed over the Southeast during the past century. No large-magnitude centuries-long trends in spring rainfall amounts were reconstructed over the past 1053 years, but large changes in the interannual variability of spring rainfall were reconstructed during portions of the Medieval Warm Period (MWP), Little Ice Age (LIA), and the 20th century. Dry conditions persisted at the end of the 12th century, but appear to have been exceeded by a reconstructed drought in the mid-18th century. High interannual variability, including five extremely wet years were reconstructed for a 20-yr period during the late 16th and early 17th centuries, and may reflect amplified atmospheric circulation over eastern North America during what appears to have been one of the most widespread cold episodes of the Little Ice Age.
  4. Grove 1994: Grove, Jean M., and Roy Switsur. “Glacial geological evidence for the Medieval Warm Period.” The Medieval Warm Period. Springer, Dordrecht, 1994. 143-169. It is hypothesised that the Medieval Warm Period was preceded and followed by periods of moraine deposition associated with glacier expansion. Improvements in the methodology of radiocarbon calibration make it possible to convert radiocarbon ages to calendar dates with greater precision than was previously possible. Dating of organic material closely associated with moraines in many montane regions has reached the point where it is possible to survey available information concerning the timing of the medieval warm period. The results suggest that it was a global event occurring between about 900 and 1250 A.D., possibly interrupted by a minor readvance of ice between about 1050 and 1150 A.D.
  5. Jones-Briffa 1998: Jones, P. D., et al. “High-resolution palaeoclimatic records for the last millennium: interpretation, integration and comparison with General Circulation Model control-run temperatures.” The Holocene 8.4 (1998): 455-471. Palaeoclimatology provides our only means of assessing climatic variations before the beginning of instrumental records. The various proxy variables used, however, have a number of limitations which must be adequately addressed and understood. Besides their obvious spatial and seasonal limitations, different proxies are also potentially limited in their ability to represent climatic variations over a range of different timescales. Simple correlations with instrumental data over the period since 1881 give some guide to which are the better proxies, indicating that coral- and ice-core-based reconstructions are poorer than tree-ring and historical ones. However, the quality of many proxy time series can be lower for earlier times. Suggestions are made for assessing proxy quality over longer periods than the last century by intercomparing neighbouring proxies and, by comparisons with less temporally resolved proxies such as borehole temperatures. We have averaged 17 temperature reconstructions (representing various seasons of the year), all extending back at least to the mid-seventeenth century, to form two annually resolved hemispheric series (NH10 and SH7). Over the 1901–91 period, NH10 has 36% variance in common with average NH summer (June to August) temperatures and 70% on decadal timescales. SH7 has 16% variance in common with average SH summer (December to February) temperatures and 49% on decadal timescales, markedly poorer than the reconstructed NH series. The coldest year of the millennium over the NH is ad 1601, the coldest decade 1691–1700 and the seventeenth is the coldest century. A Principal Components Analysis (PCA) is performed on yearly values for the 17 reconstructions over the period ad 1660–1970. The correlation between PC1 and NH10 is 0.92, even though PC1 explains only 13.6% of the total variance of all 17 series. Similar PCA is performed on thousand-year-long General Circulation Model (GCM) data from the Geophysical Fluid Dynamics Laboratory (GFDL) and the Hadley Centre (HADCM2), sampling these for the same locations and seasons as the proxy data. For GFDL, the correlation between its PC1 and its NH10 is 0.89, while for HADCM2 the PCs group markedly differently. Cross-spectral analyses are performed on the proxy data and the GFDL model data at two different frequency bands (0.02 and 0.03 cycles per year). Both analyses suggest that there is no large-scale coherency in the series on these timescales. This implies that if the proxy data are meaningful, it should be relatively straightforward to detect a coherent near-global anthropogenic signal in surface temperature data.
  6. Mann-Bradley 1999: Mann, Michael E., Raymond S. Bradley, and Malcolm K. Hughes. “Northern hemisphere temperatures during the past millennium: inferences, uncertainties, and limitations.” Geophysical research letters 26.6 (1999): 759-762. Building on recent studies, we attempt hemispheric temperature reconstructions with proxy data networks for the past millennium. We focus not just on the reconstructions, but the uncertainties therein, and important caveats. Though expanded uncertainties prevent decisive conclusions for the period prior to AD 1400, our results suggest that the latter 20th century is anomalous in the context of at least the past millennium. The 1990s was the warmest decade, and 1998 the warmest year, at moderately high levels of confidence. The 20th century warming counters a millennial‐scale cooling trend which is consistent with long‐term astronomical forcing.
  7. Briffa 2000: Briffa, Keith R. “Annual climate variability in the Holocene: interpreting the message of ancient trees.” Quaternary Science Reviews 19.1-5 (2000): 87-105. Over vast areas of the world’s landmasses, where climate beats out a strong seasonal rhythm, tree growth keeps unerring time. In their rings, trees record many climate melodies, played in different places and different eras. Recent years have seen a consolidation and expansion of tree-ring sample collections across the traditional research areas of North America and Europe, and the start of major developments in many new areas of Eurasia, South America and Australasia. From such collections are produced networks of precisely dated chronologies; records of various aspects of tree growth, registered continuously, year by year across many centuries. Their sensitivities to different climate parameters are now translated into ever more detailed histories of temperature and moisture variability across expanding dimensions of time and space. With their extensive coverage, high temporal resolution and rigid dating control, dendroclimatic reconstructions contribute significantly to our knowledge of late Holocene climates, most importantly on timescales ranging from 1 to 100 years. In special areas of the world, where trees live for thousands of years or where subfossil remnants of long dead specimens are preserved, work building chronologies covering many millennia continues apace. Very recently, trees have provided important new information about major modes of general circulation dynamics linked to the El Niño/Southern Oscillation and the North Atlantic Oscillation, and about the effect of large volcanic eruptions. As for assessing the significance of 20th century global warming, the evidence from dendroclimatology in general, supports the notion that the last 100 years have been unusually warm, at least within a context of the last two millennia. However, this evidence should not be considered equivocal. The activities of humans may well be impacting on the `natural’ growth of trees in different ways, making the task of isolating a clear climate message subtly difficult.
  8. Crowley 2000: Crowley, Thomas J., and Thomas S. Lowery. “How warm was the medieval warm period?.” AMBIO: A Journal of the Human Environment 29.1 (2000): 51-55. A frequent conclusion based on study of individual records from the so-called Medieval Warm Period (∼1000-1300 A.D.) is that the present warmth of the 20th century is not unusual and therefore cannot be taken as an indication of forced climate change from greenhouse gas emissions. This conclusion is not supported by published composites of Northern Hemisphere climate change, but the conclusions of such syntheses are often either ignored or challenged. In this paper, we revisit the controversy by incorporating additional time series not used in earlier hemispheric compilations. Another difference is that the present reconstruction uses records that are only 900–1000 years long, thereby, avoiding the potential problem of uncertainties introduced by using different numbers of records at different times. Despite clear evidence for Medieval warmth greater than present in some individual records, the new hemispheric composite supports the principal conclusion of earlier hemispheric reconstructions and, furthermore, indicates that maximum Medieval warmth was restricted to two to three 20–30 year intervals, with composite values during these times being only comparable to the mid-20th century warm time interval. Failure to substantiate hemispheric warmth greater than the present consistently occurs in composites because there are significant offsets in timing of warmth in different regions; ignoring these offsets can lead to serious errors concerning inferences about the magnitude of Medieval warmth and its relevance to interpretation of late 20 th century warming.
  9. Briffa 2001: Briffa, Keith R., et al. “Low‐frequency temperature variations from a northern tree ring density network.” Journal of Geophysical Research: Atmospheres 106.D3 (2001): 2929-2941. We describe new reconstructions of northern extratropical summer temperatures for nine subcontinental‐scale regions and a composite series representing quasi “Northern Hemisphere” temperature change over the last 600 years. These series are based on tree ring density data that have been processed using a novel statistical technique (age band decomposition) designed to preserve greater long‐timescale variability than in previous analyses. We provide time‐dependent and timescale‐dependent uncertainty estimates for all of the reconstructions. The new regional estimates are generally cooler in almost all precalibration periods, compared to estimates obtained using earlier processing methods, particularly during the 17th century. One exception is the reconstruction for northern Siberia, where 15th century summers are now estimated to be warmer than those observed in the 20th century. In producing a new Northern Hemisphere series we demonstrate the sensitivity of the results to the methodology used once the number of regions with data, and the reliability of each regional series, begins to decrease. We compare our new hemisphere series to other published large‐regional temperature histories, most of which lie within the 1σ confidence band of our estimates over most of the last 600 years. The 20th century is clearly shown by all of the palaeoseries composites to be the warmest during this period.
  10. Briffa 2002: Briffa, Keith R., and Timothy J. Osborn. “Blowing hot and cold.” Science 295.5563 (2002): 2227-2228. Tree-ring records play an important role in reconstructing climate change patterns over the last millenium. In their Perspective, Briffa and Osborn highlight the report by Esper et al. of a largely independent record of widespread tree-growth variations across the extra-tropical Northern Hemisphere. Estimates of past temperature changes based on the record suggest that climate swings in the last 1000 years were greater than has yet been generally accepted.
  11. Esper 2002: Esper, Jan, Edward R. Cook, and Fritz H. Schweingruber. “Low-frequency signals in long tree-ring chronologies for reconstructing past temperature variability.” science 295.5563 (2002): 2250-2253. Preserving multicentennial climate variability in long tree-ring records is critically important for reconstructing the full range of temperature variability over the past 1000 years. This allows the putative “Medieval Warm Period” (MWP) to be described and to be compared with 20th-century warming in modeling and attribution studies. We demonstrate that carefully selected tree-ring chronologies from 14 sites in the Northern Hemisphere (NH) extratropics can preserve such coherent large-scale, multicentennial temperature trends if proper methods of analysis are used. In addition, we show that the average of these chronologies supports the large-scale occurrence of the MWP over the NH extratropics.
  12. Cook 2002: Cook, Edward R., Jonathan G. Palmer, and Rosanne D. D’Arrigo. “Evidence for a ‘Medieval Warm Period’in a 1,100 year tree‐ring reconstruction of past austral summer temperatures in New Zealand.” Geophysical Research Letters29.14 (2002): 12-1. The occurrence of the Medieval Warm Period (MWP) in the Southern Hemisphere is uncertain because of the paucity of well‐dated, high‐resolution paleo‐temperature records covering the past 1,000 years. We describe a new tree‐ring reconstruction of Austral summer temperatures from the South Island of New Zealand, covering the past 1,100 years. This record is the longest yet produced for New Zealand and shows clear evidence for persistent above‐average temperatures within the interval commonly assigned to the MWP. Comparisons with selected temperature proxies from the Northern and Southern Hemispheres confirm that the MWP was highly variable in time and space. Regardless, the New Zealand temperature reconstruction supports the global occurrence of the MWP.
  13. Cronin 2003: Cronin, Thomas M., et al. “Medieval warm period, little ice age and 20th century temperature variability from Chesapeake Bay.” Global and planetary change 36.1-2 (2003): 17-29. We present paleoclimate evidence for rapid (<100 years) shifts of ∼2–4 °C in Chesapeake Bay (CB) temperature ∼2100, 1600, 950, 650, 400 and 150 years before present (years BP) reconstructed from magnesium/calcium (Mg/Ca) paleothermometry. These include large temperature excursions during the Little Ice Age (∼1400–1900 AD) and the Medieval Warm Period (∼800–1300 AD) possibly related to changes in the strength of North Atlantic thermohaline circulation (THC). Evidence is presented for a long period of sustained regional and North Atlantic-wide warmth with low-amplitude temperature variability between ∼450 and 1000 AD. In addition to centennial-scale temperature shifts, the existence of numerous temperature maxima between 2200 and 250 years BP (average ∼70 years) suggests that multi-decadal processes typical of the North Atlantic Oscillation (NAO) are an inherent feature of late Holocene climate. However, late 19th and 20th century temperature extremes in Chesapeake Bay associated with NAO climate variability exceeded those of the prior 2000 years, including the interval 450–1000 AD, by 2–3 °C, suggesting anomalous recent behavior of the climate system.
  14. Mann-Jones 2003: Mann, Michael E., and Philip D. Jones. “Global surface temperatures over the past two millennia.” Geophysical Research Letters 30.15 (2003). We present reconstructions of Northern and Southern Hemisphere mean surface temperature over the past two millennia based on high‐resolution ‘proxy’ temperature data which retain millennial‐scale variability. These reconstructions indicate that late 20th century warmth is unprecedented for at least roughly the past two millennia for the Northern Hemisphere. Conclusions for the Southern Hemisphere and global mean temperature are limited by the sparseness of available proxy data in the Southern Hemisphere at present.
  15. Moberg 2005: Moberg, Anders, et al. “Highly variable Northern Hemisphere temperatures reconstructed from low-and high-resolution proxy data.” Nature 433.7026 (2005): 613. A number of reconstructions of millennial-scale climate variability have been carried out in order to understand patterns of natural climate variability, on decade to century timescales, and the role of anthropogenic forcing1,2,3,4,5,6,7,8. These reconstructions have mainly used tree-ring data and other data sets of annual to decadal resolution. Lake and ocean sediments have a lower time resolution, but provide climate information at multicentennial timescales that may not be captured by tree-ring data9,10. Here we reconstruct Northern Hemisphere temperatures for the past 2,000 years by combining low-resolution proxies with tree-ring data, using a wavelet transform technique11 to achieve timescale-dependent processing of the data. Our reconstruction shows larger multicentennial variability than most previous multi-proxy reconstructions1,2,3,4,7, but agrees well with temperatures reconstructed from borehole measurements12 and with temperatures obtained with a general circulation model13,14. According to our reconstruction, high temperatures—similar to those observed in the twentieth century before 1990—occurred around AD 1000 to 1100, and minimum temperatures that are about 0.7 K below the average of 1961–90 occurred around AD 1600. This large natural variability in the past suggests an important role of natural multicentennial variability that is likely to continue.
  16. Hunt 2006: Hunt, Barrie G. “The medieval warm period, the little ice age and simulated climatic variability.” Climate Dynamics 27.7-8 (2006): 677-694. The CSIRO Mark 2 coupled global climatic model has been used to generate a 10,000-year simulation for ‘present’ climatic conditions. The model output has been analysed to identify sustained climatic fluctuations, such as those attributed to the Medieval Warm Period (MWP) and the Little Ice Age (LIA). Since no external forcing was permitted during the model run all such fluctuations are attributed to naturally occurring climatic variability associated with the nonlinear processes inherent in the climatic system. Comparison of simulated climatic time series for different geographical locations highlighted the lack of synchronicity between these series. The model was found to be able to simulate climatic extremes for selected observations for century timescales, as well as identifying the associated spatial characteristics. Other examples of time series simulated by the model for the USA and eastern Russia had similar characteristics to those attributed to the MWP and the LIA, but smaller amplitudes, and clearly defined spatial patterns. A search for the frequency of occurrence of specified surface temperature anomalies, defined via duration and mean value, revealed that these were primarily confined to polar regions and northern latitudes of Europe, Asia and North America. Over the majority of the oceans and southern hemisphere such climatic fluctuations could not be sustained, for reasons explained in the paper. Similarly, sustained sea–ice anomalies were mainly confined to the northern hemisphere. An examination of mechanisms associated with the sustained climatic fluctuations failed to identify a role for the North Atlantic Oscillation, the El Niño-Southern Oscillation or the Pacific Decadal Oscillation. It was therefore concluded that these fluctuations were generated by stochastic processes intrinsic to the nonlinear climatic system. While a number of characteristics of the MWP and the LIA could have been partially caused by natural processes within the climatic system, the inability of the model to reproduce the observed hemispheric mean temperature anomalies associated with these events indicates that external forcing must have been involved.Essentially the unforced climatic system is unable to sustain the generation of long-term climatic anomalies.
  17. Sridhar 2006: Sridhar, Venkataramana, et al. “Large wind shift on the Great Plains during the Medieval Warm Period.” Science 313.5785 (2006): 345-347. Spring-summer winds from the south move moist air from the Gulf of Mexico to the Great Plains. Rainfall in the growing season sustains prairie grasses that keep large dunes in the Nebraska Sand Hills immobile. Longitudinal dunes built during the Medieval Warm Period (800 to 1000 years before the present) record the last major period of sand mobility. These dunes are oriented NW-SE and are composed of cross-strata with bipolar dip directions. The trend and structure of the dunes record a drought that was initiated and sustained by a historically unprecedented shift of spring-summer atmospheric circulation over the Plains: Moist southerly flow was replaced by dry southwesterly flow.
  18. Loehle, Craig. “A 2000-year global temperature reconstruction based on non-treering proxies.” Energy & Environment 18.7 (2007): 1049-1058.  Historical data provide a baseline for judging how anomalous recent temperature changes are and for assessing the degree to which organisms are likely to be adversely affected by current or future warming. Climate histories are commonly reconstructed from a variety of sources, including ice cores, tree rings, and sediment. Tree-ring data, being the most abundant for recent centuries, tend to dominate reconstructions. There are reasons to believe that tree ring data may not properly capture long-term climate changes. In this study, eighteen 2000-year-long series were obtained that were not based on tree ring data. Data in each series were smoothed with a 30-year running mean. All data were then converted to anomalies by subtracting the mean of each series from that series. The overall mean series was then computed by simple averaging. The mean time series shows quite coherent structure. The mean series shows the Medieval Warm Period (MWP) and Little Ice Age (LIA) quite clearly, with the MWP being approximately 0.3°C warmer than 20th century values at these eighteen sites.
  19. Loehle, Craig, and J. Huston McCulloch. “Correction to: A 2000-year global temperature reconstruction based on non-tree ring proxies.” Energy & Environment 19.1 (2008): 93-100.  A climatic reconstruction published in E&E (Loehle, 2007) is here corrected for various errors and data issues, with little change in the results. Standard errors and 95% confidence intervals are added. The Medieval Warming Period (MWP) was significantly warmer than the bimillennial average during most of the period 820 – 1040 AD. The Little Ice Age was significantly cooler than the average during most of 1440 – 1740 AD. The warmest tridecade of the MWP was warmer than the most recent tridecade, but not significantly so.
  20. Esper 2009: Esper, Jan, and David Frank. “The IPCC on a heterogeneous Medieval warm period.” Climatic Change 94.3-4 (2009): 267-273. In their 2007 report, IPCC working group 1 refers to an increased heterogeneity of climate during medieval times about 1000 years ago. This conclusion would be of relevance, as it implies a contrast in the spatial signature and forcing of current warmth to that during the Medieval Warm Period. Our analysis of the data displayed in the IPCC report, however, shows no indication of an increased spread between long-term proxy records. We emphasize the relevance of sample replication issues, and argue that an estimation of long-term spatial homogeneity changes is premature based on the smattering of data currently availableThe Proxy Record: Proxies shown in the AR4 include an ice core record from W Greenland (Fisheret al. 1996), a multi-proxy record from E Asia (Yang et al. 2002), and six treering records representing: SW Canada (Luckman and Wilson 2005), W USA (Lloyd and Graumlich 1997), N Sweden (Grudd et al. 2002), NW Russia (Hantemirov and Shiyatov 2002), N Russia (Naurzbaev et al. 2002), and Mongolia (D’Arrigo et al. 2001). The tree-ring chronologies are all combinations of samples from living trees with relict/historical material. These temporally overlapping data have been matched using ‘cross-dating’, a technique introduced in the early 20th century into dendrochronology (Douglass 1929), so that every ring is assigned a calendar year. Application of this method allowed for the development of continuous, millenniumlong tree-ring chronologies, even though the single trees included in such a record may have only reached ages of 200 years or less. The Greenland ice core proxy integrates several δ18O timeseries that were first detrended to remove non-climatic long-term trends and then combined to a single record. The regional multi-proxy record from E Asia combines a total of nine different proxies, including tree-ring, ice core, peat bog, lake sediment, and documentary data. As some of these proxies are not annually resolved, decadal variance is substantially reduced in the combined time series. Importantly, all tree-ring records shown in AR4 were detrended using a method known as ‘Regional Curve Standardization’ (RCS; Esper et al. 2003). This technique allows the preservation of centennial scale climate variability, even if the tree-ring chronologies are composed of relatively short segments of material from living trees and historical wood (Cook et al. 1995). Application of RCS is essential to reconstruct the low frequency spectrum of temperature variability including long-term cooling trends (Esper et al. 2004) and thus necessary to estimate the spatial extent of MWP (Broecker 2001).