Analyzing Tree Rings to Determine Climate Change Lab 1 Analyzing Tree Rings to Determine Climate Change

Dendrochronology Reconstructing past climates using tree ring analysis Studying growth patters that occurred within the life of a tree Tree growth patterns is a type of PROXY DATA used to make assumptions about precipitation during the lifetime of a tree Light rings: spring growing season, summer Dark rings: fall and winder dormant seasons Number of dark rings (not counting bark), starting from the center age of tree Ring width  amount of growth  proxy for how much precipitation occurred A thick ring indicates a lot of tree growth that year and therefore a good amount of precipitation A thin ring indicates little tree growth that year and therefore little precipitation

Tree Rings – Dendrochronology Actual photograph of a tree’s cross section

Tree Core Extraction So we don’t kill trees just to determine their age and study their climatological data – if we had to kill a tree every time we wanted to determine tree growth and forest heath, that would be very counterproductive.

Year Cut: 2000 # Dark Rings: 19 Age: 19 years Year Planted: 2000-19 = 1981 1997 1989 1994-1996 2000 1981

Wettest: 1997, 1989 Driest: 1994-1996 Year Cut: 2000 # Dark Rings: 19 Age: 19 years Year Planted: 2000-19 = 1981 1997 1989 1994-1996 2000 1981

Wettest: 1997, 1989

Wettest: 1997, 1989 1989 1997 1997 – Substantial peak in rainfall They are in agreement! The satellite data and tree ring analysis data both indicate that these were wet years with good conditions for tree growth

Driest: 1994-1996 1995

Driest: 1994-1996 1995 1995: There was not a big spike in rainfall, but not a huge drop either. The satellite data indicates this was a relatively moderate year. So the tree ring analysis and satellite data are not in total agreement!

Satellite data and tree ring data do not always agree! The science of dendrochronology is reasoning between all sources we have (including tree rings, historic rainfall data, cores, etc) and making our best conclusion on what the climate was like in the past So differences between satellite data and tree ring analysis is expected to some extent But that does not mean it is not useful! For some periods in Earth’s history, dendrochronology, ice cores, and ocean cores are all we have to go off of.

Satellite data and tree ring data do not always agree! Differences may be due to: Rainfall is very LOCAL – it can rain a lot in Boston in general, but that doesn’t mean all that rain is getting to each tree Tree location, tree health, human intervention Different species respond differently to different amounts of rainfall (some grow better wetter, some drier – it can be TOO wet, and trees can get sick) “growing season” is used as a proxy for precipitation, but other factors can affect tree growth season to season (sunlight availability, competing species/vegetation, nutrient availability

Satellite data and tree ring data do not always agree! But inconsistencies in tree ring data vs. satellite data does not mean that dendrochronology records are not useful We can use cores from many trees in order to create a good estimate on climate during those trees’ lifetimes One tree along cannot tell the whole story – but if we have a lot of cores from the same area, we can get a good idea what the precipitation was like When dealing with paleoclimatology – ancient climates – this is the best data we have. Combined with geological records, etc, we can reconstruct past climates This is only one piece of the puzzle!

Comparing the two data sources… Dendrochronology Satellite Precipitation Data Uses tree ring width as a proxy for success of growing season, which can indicate precipitation amounts Much uncertainty because other things can affect tree growth besides precipitation Very localized –describes the climate exactly where the tree is. The climate of a whole area can be reasoned out using very many trees’ ring data Can provide us with information about the climate in times before we had measurements (even back to prehistoric times) We need a lot of trees to combine data into a substantial record More specifically represents the trends within the years – quantifies precipitation amounts More specific to precipitation (not just a growing season as proxy for precipitation) More accurate and more direct a measurement than using growing season as a proxy Generalized over larger areas – hard to get very small scale precipitation amounts Machine error, artefacts, lack of calibration etc. are always possible but highly unlikely – this data goes through a lot of checking and processing before it is made available.