CSDMS - User contributions [en]

HPCCprojects:Interannual variability and glacier length

2014-10-03T17:34:54Z

Leifstefanthor: /* Models in use */

__NOTOC__
={{PAGENAME}}=

Valley glacier moraines are commonly used to infer past mean
annual precipitation and mean melt-season temperature. However, recent research has demonstrated that, even in steady climates, multi-decadal, kilometer-scale fluctuations in glacier length occur in response to stochastic, year-to-year variability in mass balance. When interpreting moraine sequences it is important to include the effect of interannual weather variability on glacier length; moraines record advances that are forced either by interannual variability or by a combination of climate change and interannual variability. Our hope is to help establish the metrics needed to determine if a past glacier advance was caused by interannual variability or a climate change.

[[File:interannual.png|400px]]
==Objectives==
1) Assess the importance of year-to-year climate variability (weather) on glacier length in a variety of climate settings
2) Create quantitative metrics to test if a glacier length change could be caused by weather variability.


==Time-line==
Provide (estimated) Start: Jan. 2014 and End: Dec. 2015

==Models in use==
We are using 1 and 2D Matlab-based numerical glacier models. The models are used in both idealized and geographical settings with a variety of parameterizations for glacier mass balance. Beach allows us to explore a wide parameter range efficiently and is therefore imperative for the success of this project. We will also be using gc2D

==Results==
Interannual variability in mean melt-season temperature and annual precipitation can cause kilometer scale fluctuations in glacier length independent of climate change [e.g. Oerlamans, 2000 and Roe and O’Neal, 2009]. We perform model simulations to gauge the uncertainty in mean glacier length, the length over a given time period which represents the long-term climatologically relevant extent, for the Younger Dryas (YD) and LGM ice extents in the Rakaia valley, NZ. We used a 1D flowline model (e.g. Oerlemans, 2000) with variable width forced by independent white noise realizations [Oerlemans, 2000; Roe and O’Neal, 2009; Anderson et al., 2014]. One white noise realization was modified by the standard deviation of mean summer (DJF) temperature (σT = 1.1 °C) from the Lake Coleridge weather station (location info), and the other realization was bracketed by estimates of the standard deviation of annual precipitation from a representative weather station. The variability of annual precipitation increases with higher annual precipitation amount [e.g. Burke and Roe, 2013]. Because of the strong orographic precipitation gradient and rain shadow in New Zealand, precipitation amounts range from greater than 6 m a-1 west and near the topographic divide to less than 1 m a-1 in some locations east of the divide (Fig. 3) [Ummenhofer and England, 2007]. Data derived from lowland meteorological stations on the east side of the divide do not capture the modern variability in annual precipitation in the glacial accumulation zone. We use a standard deviation of annual precipitation based on meteorological station data with a mean annual precipitation of 5.5 m a-1 and standard deviation of annual precipitation amount (σP of .87 m a-1) [Woo and Fitzharris, 1992]. This data is from a meteorological station on the west side of the drainage divide and was chosen because annual precipitations amounts in the accumulation are likely to be larger near the divide than the data from this station so the standard deviation of annual precipitation is a minimum estimate. We use a mass balance profile derived from the energy balance methods outlined in Plummer and Phillips et al., 2003 and perturb the profile with using a meltfactor of .7 m °Cyr-1. This meltfactor is the most often occurring melt factor based on a global compilation of modern meltfactors for ice [Anderson et al., 2014].

Younger Dryas Results:
The glacier leaving the YD ice extent in the Rakaia valley had a complicated geometry. Three distinct glaciers join within 3km of the maximum glacier extent. We modeled all three glaciers and fed the two smaller glaciers into the main stem to capture the terminus fluctuations derived from asynchronous response times between the three glaciers (after MacGregor et al., 2000). We allowed each white noise-model coupled simulation to run for 1000 years [Kaplan et al., 2013]. Because it is impossible to know the exact pattern of year-to-year fluctuations in annual precipitation and mean melt season temperature during the Younger Dryas in New Zealand we ran 1000, 1000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore. The most likely mean length for the Younger Dryas extent in the Rakaia valley was ~ 550m (or 6.5%) upvalley from the YD terminal extent. The standard deviation of this mean length from the most likely mean length was 170 m and the standard deviation of glacier length was 260 m. The mean length results for the YD are lower than those discussed by Anderson et al. [2014] largely because of the reduced duration of the white noise climate forcing (Anderson et al. [2014] used a most likely climate duration of 4000 years for the LGM) and the limited parameter space explored.

Last Glacial Maximum Results:
We allowed each white noise-model coupled simulation to run for 4000 years [e.g. Anderson et al., 2014]. We ran 400, 4000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore as represented in the assumed mass balance profile, meltfactor, and flow law parameter. The most likely mean length for the LGM extent in the Rakaia valley was 1.7 km (or 2.2%) upvalley from the LGM terminal moraine. The standard deviation of this mean length from the most likely mean length was 410 m and the standard deviation of the glacier length through the model runs was 770 m. The mean length results for the LGM are lower than those discussed by Anderson et al. [2014] largely because the LGM glacier modeled here is significantly larger in volume and has a longer response time than the largest glacier modeled in the Colorado Front Range. This discrepancy may also result because of the limited parameter space explored.

Discussion:
Glaciological modeling studies extracting paleoclimate estimates should use the mean glacier length as opposed to the maximum glacier length. Modeling to the actual maximum ice extent will provide a maximum estimate of climate change. To date, the Rakaia LGM glacier is the longest (~80 km) and largest volume glacier modeled with white noise year-to-year variability, which is present in all climate states, past or present. While the magnitude of the most likely fluctuation is larger for the LGM glacier (even when taking into account the different durations of the YD and LGM simulations (1000 yrs versus 4000 yrs)) these fluctuations represent a smaller percentage of the maximum length of the glacier when compared to the YD glacier [e.g. Anderson et al., 2014]. Though we cannot make a rigorous examination of this effect here it appears that larger glaciers with longer response times tend to produce the most reliable paleoclimate estimates when the modeling to the maximum ice extent.
The variability of annual precipitation and melt season temperature are large compared to other studies, implying that variability will likely have an important effect on the fluctuations of advances less extensive than the YD advance. Further modeling efforts should be preformed to test whether smaller advances could be explained by interannual variability and potentially independent of actual climate changes. We use white noise forcing for these simulations. Our current meteorological records do not cover a long enough time span to confidently test for memory (or correlation from year-to-year) [e.g. Burke and Roe, 2013]. If there actually is memory in year-to-year mean melt-season temperature or accumulation season precipitation the magnitude of these noise-forced glacier fluctuations would be greatly enhanced.


==Users==
Leif Anderson, University of Colorado and INSTAAR
* 

==Funding==
National Science Foundation (NSF) grant DGE- 1144083 (GRFP)

==Publications and presentations==
Anderson, Leif S., Gerard H. Roe, and Robert S. Anderson. "The effects of interannual climate variability on the moraine record." Geology 42.1 (2014): 55-58.

Rowan, A. V., Brocklehurst, S. H., Schultz, D. M., Plummer, M. A., Anderson, L. S., & Glasser, N. F. (2014). Late Quaternary glacier sensitivity to temperature and precipitation distribution in the Southern Alps of New Zealand. Journal of Geophysical Research: Earth Surface.

==Links==
This would be the place to provide links that are related to your project.

Choose one of the two categories mentioned below, that your project suites the best
[[Category:Lecture project]] [[Category:Research project]]

HPCCprojects:Glacier Modeling

2014-10-03T17:34:13Z

Leifstefanthor: /* Models in use */

HPCCprojects:Glacier Modeling

2014-10-03T17:24:15Z

Leifstefanthor: /* Publications and presentations */

HPCCprojects:Glacier Modeling

2014-10-03T17:24:01Z

Leifstefanthor: /* Publications and presentations */

HPCCprojects:Glacier Modeling

2014-10-03T17:22:37Z

Leifstefanthor: /* Results */

File:Ice dynamics plots3.png

2014-10-03T17:22:27Z

Leifstefanthor: MsUpload

MsUpload

File:Ice dynamics plots2.pdf

2014-10-03T17:21:34Z

Leifstefanthor: MsUpload

MsUpload

HPCCprojects:Glacier Modeling

2014-10-03T17:19:39Z

Leifstefanthor: /* Project description */

HPCCprojects:Glacier Modeling

2014-10-03T17:16:32Z

Leifstefanthor: /* Funding */

HPCCprojects:Glacier Modeling

2014-10-03T17:16:05Z

Leifstefanthor: /* Users */

HPCCprojects:Glacier Modeling

2014-10-03T17:15:40Z

Leifstefanthor: /* Objectives */

HPCCprojects:Glacier Modeling

2014-10-03T17:14:12Z

Leifstefanthor: /* Models in use */

HPCCprojects:Interannual variability and glacier length

2014-10-03T17:13:07Z

Leifstefanthor: /* Models in use */

__NOTOC__
={{PAGENAME}}=

Valley glacier moraines are commonly used to infer past mean
annual precipitation and mean melt-season temperature. However, recent research has demonstrated that, even in steady climates, multi-decadal, kilometer-scale fluctuations in glacier length occur in response to stochastic, year-to-year variability in mass balance. When interpreting moraine sequences it is important to include the effect of interannual weather variability on glacier length; moraines record advances that are forced either by interannual variability or by a combination of climate change and interannual variability. Our hope is to help establish the metrics needed to determine if a past glacier advance was caused by interannual variability or a climate change.

[[File:interannual.png|400px]]
==Objectives==
1) Assess the importance of year-to-year climate variability (weather) on glacier length in a variety of climate settings
2) Create quantitative metrics to test if a glacier length change could be caused by weather variability.


==Time-line==
Provide (estimated) Start: Jan. 2014 and End: Dec. 2015

==Models in use==
We are using 1 and 2D Matlab-based numerical glacier models. The models are used in both idealized and geographical settings with a variety of parameterizations for glacier mass balance. Beach allows us to explore a wide parameter range efficiently and is therefore imperative for the success of this project. 

==Results==
Interannual variability in mean melt-season temperature and annual precipitation can cause kilometer scale fluctuations in glacier length independent of climate change [e.g. Oerlamans, 2000 and Roe and O’Neal, 2009]. We perform model simulations to gauge the uncertainty in mean glacier length, the length over a given time period which represents the long-term climatologically relevant extent, for the Younger Dryas (YD) and LGM ice extents in the Rakaia valley, NZ. We used a 1D flowline model (e.g. Oerlemans, 2000) with variable width forced by independent white noise realizations [Oerlemans, 2000; Roe and O’Neal, 2009; Anderson et al., 2014]. One white noise realization was modified by the standard deviation of mean summer (DJF) temperature (σT = 1.1 °C) from the Lake Coleridge weather station (location info), and the other realization was bracketed by estimates of the standard deviation of annual precipitation from a representative weather station. The variability of annual precipitation increases with higher annual precipitation amount [e.g. Burke and Roe, 2013]. Because of the strong orographic precipitation gradient and rain shadow in New Zealand, precipitation amounts range from greater than 6 m a-1 west and near the topographic divide to less than 1 m a-1 in some locations east of the divide (Fig. 3) [Ummenhofer and England, 2007]. Data derived from lowland meteorological stations on the east side of the divide do not capture the modern variability in annual precipitation in the glacial accumulation zone. We use a standard deviation of annual precipitation based on meteorological station data with a mean annual precipitation of 5.5 m a-1 and standard deviation of annual precipitation amount (σP of .87 m a-1) [Woo and Fitzharris, 1992]. This data is from a meteorological station on the west side of the drainage divide and was chosen because annual precipitations amounts in the accumulation are likely to be larger near the divide than the data from this station so the standard deviation of annual precipitation is a minimum estimate. We use a mass balance profile derived from the energy balance methods outlined in Plummer and Phillips et al., 2003 and perturb the profile with using a meltfactor of .7 m °Cyr-1. This meltfactor is the most often occurring melt factor based on a global compilation of modern meltfactors for ice [Anderson et al., 2014].

Younger Dryas Results:
The glacier leaving the YD ice extent in the Rakaia valley had a complicated geometry. Three distinct glaciers join within 3km of the maximum glacier extent. We modeled all three glaciers and fed the two smaller glaciers into the main stem to capture the terminus fluctuations derived from asynchronous response times between the three glaciers (after MacGregor et al., 2000). We allowed each white noise-model coupled simulation to run for 1000 years [Kaplan et al., 2013]. Because it is impossible to know the exact pattern of year-to-year fluctuations in annual precipitation and mean melt season temperature during the Younger Dryas in New Zealand we ran 1000, 1000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore. The most likely mean length for the Younger Dryas extent in the Rakaia valley was ~ 550m (or 6.5%) upvalley from the YD terminal extent. The standard deviation of this mean length from the most likely mean length was 170 m and the standard deviation of glacier length was 260 m. The mean length results for the YD are lower than those discussed by Anderson et al. [2014] largely because of the reduced duration of the white noise climate forcing (Anderson et al. [2014] used a most likely climate duration of 4000 years for the LGM) and the limited parameter space explored.

Last Glacial Maximum Results:
We allowed each white noise-model coupled simulation to run for 4000 years [e.g. Anderson et al., 2014]. We ran 400, 4000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore as represented in the assumed mass balance profile, meltfactor, and flow law parameter. The most likely mean length for the LGM extent in the Rakaia valley was 1.7 km (or 2.2%) upvalley from the LGM terminal moraine. The standard deviation of this mean length from the most likely mean length was 410 m and the standard deviation of the glacier length through the model runs was 770 m. The mean length results for the LGM are lower than those discussed by Anderson et al. [2014] largely because the LGM glacier modeled here is significantly larger in volume and has a longer response time than the largest glacier modeled in the Colorado Front Range. This discrepancy may also result because of the limited parameter space explored.

Discussion:
Glaciological modeling studies extracting paleoclimate estimates should use the mean glacier length as opposed to the maximum glacier length. Modeling to the actual maximum ice extent will provide a maximum estimate of climate change. To date, the Rakaia LGM glacier is the longest (~80 km) and largest volume glacier modeled with white noise year-to-year variability, which is present in all climate states, past or present. While the magnitude of the most likely fluctuation is larger for the LGM glacier (even when taking into account the different durations of the YD and LGM simulations (1000 yrs versus 4000 yrs)) these fluctuations represent a smaller percentage of the maximum length of the glacier when compared to the YD glacier [e.g. Anderson et al., 2014]. Though we cannot make a rigorous examination of this effect here it appears that larger glaciers with longer response times tend to produce the most reliable paleoclimate estimates when the modeling to the maximum ice extent.
The variability of annual precipitation and melt season temperature are large compared to other studies, implying that variability will likely have an important effect on the fluctuations of advances less extensive than the YD advance. Further modeling efforts should be preformed to test whether smaller advances could be explained by interannual variability and potentially independent of actual climate changes. We use white noise forcing for these simulations. Our current meteorological records do not cover a long enough time span to confidently test for memory (or correlation from year-to-year) [e.g. Burke and Roe, 2013]. If there actually is memory in year-to-year mean melt-season temperature or accumulation season precipitation the magnitude of these noise-forced glacier fluctuations would be greatly enhanced.


==Users==
Leif Anderson, University of Colorado and INSTAAR
* 

==Funding==
National Science Foundation (NSF) grant DGE- 1144083 (GRFP)

==Publications and presentations==
Anderson, Leif S., Gerard H. Roe, and Robert S. Anderson. "The effects of interannual climate variability on the moraine record." Geology 42.1 (2014): 55-58.

Rowan, A. V., Brocklehurst, S. H., Schultz, D. M., Plummer, M. A., Anderson, L. S., & Glasser, N. F. (2014). Late Quaternary glacier sensitivity to temperature and precipitation distribution in the Southern Alps of New Zealand. Journal of Geophysical Research: Earth Surface.

==Links==
This would be the place to provide links that are related to your project.

Choose one of the two categories mentioned below, that your project suites the best
[[Category:Lecture project]] [[Category:Research project]]

HPCCprojects:Glacier Modeling

2014-10-03T17:12:21Z

Leifstefanthor: /* Time-line */

HPCCprojects:Interannual variability and glacier length

2014-10-03T17:11:06Z

Leifstefanthor: /* Users */

__NOTOC__
={{PAGENAME}}=

Valley glacier moraines are commonly used to infer past mean
annual precipitation and mean melt-season temperature. However, recent research has demonstrated that, even in steady climates, multi-decadal, kilometer-scale fluctuations in glacier length occur in response to stochastic, year-to-year variability in mass balance. When interpreting moraine sequences it is important to include the effect of interannual weather variability on glacier length; moraines record advances that are forced either by interannual variability or by a combination of climate change and interannual variability. Our hope is to help establish the metrics needed to determine if a past glacier advance was caused by interannual variability or a climate change.

[[File:interannual.png|400px]]
==Objectives==
1) Assess the importance of year-to-year climate variability (weather) on glacier length in a variety of climate settings
2) Create quantitative metrics to test if a glacier length change could be caused by weather variability.


==Time-line==
Provide (estimated) Start: Jan. 2014 and End: Dec. 2015

==Models in use==
We are using 1 and 2D Matlab-based numerical glacier models. The models are used in both idealized and geographical settings with a variety of parameterizations for glacier mass balance. Beach allows us to explore a wide parameter range efficiently and is therefore imperative for the success of this project 

==Results==
Interannual variability in mean melt-season temperature and annual precipitation can cause kilometer scale fluctuations in glacier length independent of climate change [e.g. Oerlamans, 2000 and Roe and O’Neal, 2009]. We perform model simulations to gauge the uncertainty in mean glacier length, the length over a given time period which represents the long-term climatologically relevant extent, for the Younger Dryas (YD) and LGM ice extents in the Rakaia valley, NZ. We used a 1D flowline model (e.g. Oerlemans, 2000) with variable width forced by independent white noise realizations [Oerlemans, 2000; Roe and O’Neal, 2009; Anderson et al., 2014]. One white noise realization was modified by the standard deviation of mean summer (DJF) temperature (σT = 1.1 °C) from the Lake Coleridge weather station (location info), and the other realization was bracketed by estimates of the standard deviation of annual precipitation from a representative weather station. The variability of annual precipitation increases with higher annual precipitation amount [e.g. Burke and Roe, 2013]. Because of the strong orographic precipitation gradient and rain shadow in New Zealand, precipitation amounts range from greater than 6 m a-1 west and near the topographic divide to less than 1 m a-1 in some locations east of the divide (Fig. 3) [Ummenhofer and England, 2007]. Data derived from lowland meteorological stations on the east side of the divide do not capture the modern variability in annual precipitation in the glacial accumulation zone. We use a standard deviation of annual precipitation based on meteorological station data with a mean annual precipitation of 5.5 m a-1 and standard deviation of annual precipitation amount (σP of .87 m a-1) [Woo and Fitzharris, 1992]. This data is from a meteorological station on the west side of the drainage divide and was chosen because annual precipitations amounts in the accumulation are likely to be larger near the divide than the data from this station so the standard deviation of annual precipitation is a minimum estimate. We use a mass balance profile derived from the energy balance methods outlined in Plummer and Phillips et al., 2003 and perturb the profile with using a meltfactor of .7 m °Cyr-1. This meltfactor is the most often occurring melt factor based on a global compilation of modern meltfactors for ice [Anderson et al., 2014].

Younger Dryas Results:
The glacier leaving the YD ice extent in the Rakaia valley had a complicated geometry. Three distinct glaciers join within 3km of the maximum glacier extent. We modeled all three glaciers and fed the two smaller glaciers into the main stem to capture the terminus fluctuations derived from asynchronous response times between the three glaciers (after MacGregor et al., 2000). We allowed each white noise-model coupled simulation to run for 1000 years [Kaplan et al., 2013]. Because it is impossible to know the exact pattern of year-to-year fluctuations in annual precipitation and mean melt season temperature during the Younger Dryas in New Zealand we ran 1000, 1000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore. The most likely mean length for the Younger Dryas extent in the Rakaia valley was ~ 550m (or 6.5%) upvalley from the YD terminal extent. The standard deviation of this mean length from the most likely mean length was 170 m and the standard deviation of glacier length was 260 m. The mean length results for the YD are lower than those discussed by Anderson et al. [2014] largely because of the reduced duration of the white noise climate forcing (Anderson et al. [2014] used a most likely climate duration of 4000 years for the LGM) and the limited parameter space explored.

Last Glacial Maximum Results:
We allowed each white noise-model coupled simulation to run for 4000 years [e.g. Anderson et al., 2014]. We ran 400, 4000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore as represented in the assumed mass balance profile, meltfactor, and flow law parameter. The most likely mean length for the LGM extent in the Rakaia valley was 1.7 km (or 2.2%) upvalley from the LGM terminal moraine. The standard deviation of this mean length from the most likely mean length was 410 m and the standard deviation of the glacier length through the model runs was 770 m. The mean length results for the LGM are lower than those discussed by Anderson et al. [2014] largely because the LGM glacier modeled here is significantly larger in volume and has a longer response time than the largest glacier modeled in the Colorado Front Range. This discrepancy may also result because of the limited parameter space explored.

Discussion:
Glaciological modeling studies extracting paleoclimate estimates should use the mean glacier length as opposed to the maximum glacier length. Modeling to the actual maximum ice extent will provide a maximum estimate of climate change. To date, the Rakaia LGM glacier is the longest (~80 km) and largest volume glacier modeled with white noise year-to-year variability, which is present in all climate states, past or present. While the magnitude of the most likely fluctuation is larger for the LGM glacier (even when taking into account the different durations of the YD and LGM simulations (1000 yrs versus 4000 yrs)) these fluctuations represent a smaller percentage of the maximum length of the glacier when compared to the YD glacier [e.g. Anderson et al., 2014]. Though we cannot make a rigorous examination of this effect here it appears that larger glaciers with longer response times tend to produce the most reliable paleoclimate estimates when the modeling to the maximum ice extent.
The variability of annual precipitation and melt season temperature are large compared to other studies, implying that variability will likely have an important effect on the fluctuations of advances less extensive than the YD advance. Further modeling efforts should be preformed to test whether smaller advances could be explained by interannual variability and potentially independent of actual climate changes. We use white noise forcing for these simulations. Our current meteorological records do not cover a long enough time span to confidently test for memory (or correlation from year-to-year) [e.g. Burke and Roe, 2013]. If there actually is memory in year-to-year mean melt-season temperature or accumulation season precipitation the magnitude of these noise-forced glacier fluctuations would be greatly enhanced.


==Users==
Leif Anderson, University of Colorado and INSTAAR
* 

==Funding==
National Science Foundation (NSF) grant DGE- 1144083 (GRFP)

==Publications and presentations==
Anderson, Leif S., Gerard H. Roe, and Robert S. Anderson. "The effects of interannual climate variability on the moraine record." Geology 42.1 (2014): 55-58.

Rowan, A. V., Brocklehurst, S. H., Schultz, D. M., Plummer, M. A., Anderson, L. S., & Glasser, N. F. (2014). Late Quaternary glacier sensitivity to temperature and precipitation distribution in the Southern Alps of New Zealand. Journal of Geophysical Research: Earth Surface.

==Links==
This would be the place to provide links that are related to your project.

Choose one of the two categories mentioned below, that your project suites the best
[[Category:Lecture project]] [[Category:Research project]]

HPCCprojects:Interannual variability and glacier length

2014-10-03T17:06:22Z

Leifstefanthor: /* Models in use */

__NOTOC__
={{PAGENAME}}=

Valley glacier moraines are commonly used to infer past mean
annual precipitation and mean melt-season temperature. However, recent research has demonstrated that, even in steady climates, multi-decadal, kilometer-scale fluctuations in glacier length occur in response to stochastic, year-to-year variability in mass balance. When interpreting moraine sequences it is important to include the effect of interannual weather variability on glacier length; moraines record advances that are forced either by interannual variability or by a combination of climate change and interannual variability. Our hope is to help establish the metrics needed to determine if a past glacier advance was caused by interannual variability or a climate change.

[[File:interannual.png|400px]]
==Objectives==
1) Assess the importance of year-to-year climate variability (weather) on glacier length in a variety of climate settings
2) Create quantitative metrics to test if a glacier length change could be caused by weather variability.


==Time-line==
Provide (estimated) Start: Jan. 2014 and End: Dec. 2015

==Models in use==
We are using 1 and 2D Matlab-based numerical glacier models. The models are used in both idealized and geographical settings with a variety of parameterizations for glacier mass balance. Beach allows us to explore a wide parameter range efficiently and is therefore imperative for the success of this project 

==Results==
Interannual variability in mean melt-season temperature and annual precipitation can cause kilometer scale fluctuations in glacier length independent of climate change [e.g. Oerlamans, 2000 and Roe and O’Neal, 2009]. We perform model simulations to gauge the uncertainty in mean glacier length, the length over a given time period which represents the long-term climatologically relevant extent, for the Younger Dryas (YD) and LGM ice extents in the Rakaia valley, NZ. We used a 1D flowline model (e.g. Oerlemans, 2000) with variable width forced by independent white noise realizations [Oerlemans, 2000; Roe and O’Neal, 2009; Anderson et al., 2014]. One white noise realization was modified by the standard deviation of mean summer (DJF) temperature (σT = 1.1 °C) from the Lake Coleridge weather station (location info), and the other realization was bracketed by estimates of the standard deviation of annual precipitation from a representative weather station. The variability of annual precipitation increases with higher annual precipitation amount [e.g. Burke and Roe, 2013]. Because of the strong orographic precipitation gradient and rain shadow in New Zealand, precipitation amounts range from greater than 6 m a-1 west and near the topographic divide to less than 1 m a-1 in some locations east of the divide (Fig. 3) [Ummenhofer and England, 2007]. Data derived from lowland meteorological stations on the east side of the divide do not capture the modern variability in annual precipitation in the glacial accumulation zone. We use a standard deviation of annual precipitation based on meteorological station data with a mean annual precipitation of 5.5 m a-1 and standard deviation of annual precipitation amount (σP of .87 m a-1) [Woo and Fitzharris, 1992]. This data is from a meteorological station on the west side of the drainage divide and was chosen because annual precipitations amounts in the accumulation are likely to be larger near the divide than the data from this station so the standard deviation of annual precipitation is a minimum estimate. We use a mass balance profile derived from the energy balance methods outlined in Plummer and Phillips et al., 2003 and perturb the profile with using a meltfactor of .7 m °Cyr-1. This meltfactor is the most often occurring melt factor based on a global compilation of modern meltfactors for ice [Anderson et al., 2014].

Younger Dryas Results:
The glacier leaving the YD ice extent in the Rakaia valley had a complicated geometry. Three distinct glaciers join within 3km of the maximum glacier extent. We modeled all three glaciers and fed the two smaller glaciers into the main stem to capture the terminus fluctuations derived from asynchronous response times between the three glaciers (after MacGregor et al., 2000). We allowed each white noise-model coupled simulation to run for 1000 years [Kaplan et al., 2013]. Because it is impossible to know the exact pattern of year-to-year fluctuations in annual precipitation and mean melt season temperature during the Younger Dryas in New Zealand we ran 1000, 1000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore. The most likely mean length for the Younger Dryas extent in the Rakaia valley was ~ 550m (or 6.5%) upvalley from the YD terminal extent. The standard deviation of this mean length from the most likely mean length was 170 m and the standard deviation of glacier length was 260 m. The mean length results for the YD are lower than those discussed by Anderson et al. [2014] largely because of the reduced duration of the white noise climate forcing (Anderson et al. [2014] used a most likely climate duration of 4000 years for the LGM) and the limited parameter space explored.

Last Glacial Maximum Results:
We allowed each white noise-model coupled simulation to run for 4000 years [e.g. Anderson et al., 2014]. We ran 400, 4000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore as represented in the assumed mass balance profile, meltfactor, and flow law parameter. The most likely mean length for the LGM extent in the Rakaia valley was 1.7 km (or 2.2%) upvalley from the LGM terminal moraine. The standard deviation of this mean length from the most likely mean length was 410 m and the standard deviation of the glacier length through the model runs was 770 m. The mean length results for the LGM are lower than those discussed by Anderson et al. [2014] largely because the LGM glacier modeled here is significantly larger in volume and has a longer response time than the largest glacier modeled in the Colorado Front Range. This discrepancy may also result because of the limited parameter space explored.

Discussion:
Glaciological modeling studies extracting paleoclimate estimates should use the mean glacier length as opposed to the maximum glacier length. Modeling to the actual maximum ice extent will provide a maximum estimate of climate change. To date, the Rakaia LGM glacier is the longest (~80 km) and largest volume glacier modeled with white noise year-to-year variability, which is present in all climate states, past or present. While the magnitude of the most likely fluctuation is larger for the LGM glacier (even when taking into account the different durations of the YD and LGM simulations (1000 yrs versus 4000 yrs)) these fluctuations represent a smaller percentage of the maximum length of the glacier when compared to the YD glacier [e.g. Anderson et al., 2014]. Though we cannot make a rigorous examination of this effect here it appears that larger glaciers with longer response times tend to produce the most reliable paleoclimate estimates when the modeling to the maximum ice extent.
The variability of annual precipitation and melt season temperature are large compared to other studies, implying that variability will likely have an important effect on the fluctuations of advances less extensive than the YD advance. Further modeling efforts should be preformed to test whether smaller advances could be explained by interannual variability and potentially independent of actual climate changes. We use white noise forcing for these simulations. Our current meteorological records do not cover a long enough time span to confidently test for memory (or correlation from year-to-year) [e.g. Burke and Roe, 2013]. If there actually is memory in year-to-year mean melt-season temperature or accumulation season precipitation the magnitude of these noise-forced glacier fluctuations would be greatly enhanced.


==Users==
List the CSDMS HPCC users of your project: leifstefanthor
* <leifstefanthor>

==Funding==
National Science Foundation (NSF) grant DGE- 1144083 (GRFP)

==Publications and presentations==
Anderson, Leif S., Gerard H. Roe, and Robert S. Anderson. "The effects of interannual climate variability on the moraine record." Geology 42.1 (2014): 55-58.

Rowan, A. V., Brocklehurst, S. H., Schultz, D. M., Plummer, M. A., Anderson, L. S., & Glasser, N. F. (2014). Late Quaternary glacier sensitivity to temperature and precipitation distribution in the Southern Alps of New Zealand. Journal of Geophysical Research: Earth Surface.

==Links==
This would be the place to provide links that are related to your project.

Choose one of the two categories mentioned below, that your project suites the best
[[Category:Lecture project]] [[Category:Research project]]

HPCCprojects:Interannual variability and glacier length

2014-10-03T17:04:54Z

Leifstefanthor: /* {{PAGENAME}} */

__NOTOC__
={{PAGENAME}}=

Valley glacier moraines are commonly used to infer past mean
annual precipitation and mean melt-season temperature. However, recent research has demonstrated that, even in steady climates, multi-decadal, kilometer-scale fluctuations in glacier length occur in response to stochastic, year-to-year variability in mass balance. When interpreting moraine sequences it is important to include the effect of interannual weather variability on glacier length; moraines record advances that are forced either by interannual variability or by a combination of climate change and interannual variability. Our hope is to help establish the metrics needed to determine if a past glacier advance was caused by interannual variability or a climate change.

[[File:interannual.png|400px]]
==Objectives==
1) Assess the importance of year-to-year climate variability (weather) on glacier length in a variety of climate settings
2) Create quantitative metrics to test if a glacier length change could be caused by weather variability.


==Time-line==
Provide (estimated) Start: Jan. 2014 and End: Dec. 2015

==Models in use==
We are using 1 and 2D Matlab-based numerical glacier models. The models are used in both idealized and geographical settings with a variety of parameterizations for glacier mass balance. 

==Results==
Interannual variability in mean melt-season temperature and annual precipitation can cause kilometer scale fluctuations in glacier length independent of climate change [e.g. Oerlamans, 2000 and Roe and O’Neal, 2009]. We perform model simulations to gauge the uncertainty in mean glacier length, the length over a given time period which represents the long-term climatologically relevant extent, for the Younger Dryas (YD) and LGM ice extents in the Rakaia valley, NZ. We used a 1D flowline model (e.g. Oerlemans, 2000) with variable width forced by independent white noise realizations [Oerlemans, 2000; Roe and O’Neal, 2009; Anderson et al., 2014]. One white noise realization was modified by the standard deviation of mean summer (DJF) temperature (σT = 1.1 °C) from the Lake Coleridge weather station (location info), and the other realization was bracketed by estimates of the standard deviation of annual precipitation from a representative weather station. The variability of annual precipitation increases with higher annual precipitation amount [e.g. Burke and Roe, 2013]. Because of the strong orographic precipitation gradient and rain shadow in New Zealand, precipitation amounts range from greater than 6 m a-1 west and near the topographic divide to less than 1 m a-1 in some locations east of the divide (Fig. 3) [Ummenhofer and England, 2007]. Data derived from lowland meteorological stations on the east side of the divide do not capture the modern variability in annual precipitation in the glacial accumulation zone. We use a standard deviation of annual precipitation based on meteorological station data with a mean annual precipitation of 5.5 m a-1 and standard deviation of annual precipitation amount (σP of .87 m a-1) [Woo and Fitzharris, 1992]. This data is from a meteorological station on the west side of the drainage divide and was chosen because annual precipitations amounts in the accumulation are likely to be larger near the divide than the data from this station so the standard deviation of annual precipitation is a minimum estimate. We use a mass balance profile derived from the energy balance methods outlined in Plummer and Phillips et al., 2003 and perturb the profile with using a meltfactor of .7 m °Cyr-1. This meltfactor is the most often occurring melt factor based on a global compilation of modern meltfactors for ice [Anderson et al., 2014].

Younger Dryas Results:
The glacier leaving the YD ice extent in the Rakaia valley had a complicated geometry. Three distinct glaciers join within 3km of the maximum glacier extent. We modeled all three glaciers and fed the two smaller glaciers into the main stem to capture the terminus fluctuations derived from asynchronous response times between the three glaciers (after MacGregor et al., 2000). We allowed each white noise-model coupled simulation to run for 1000 years [Kaplan et al., 2013]. Because it is impossible to know the exact pattern of year-to-year fluctuations in annual precipitation and mean melt season temperature during the Younger Dryas in New Zealand we ran 1000, 1000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore. The most likely mean length for the Younger Dryas extent in the Rakaia valley was ~ 550m (or 6.5%) upvalley from the YD terminal extent. The standard deviation of this mean length from the most likely mean length was 170 m and the standard deviation of glacier length was 260 m. The mean length results for the YD are lower than those discussed by Anderson et al. [2014] largely because of the reduced duration of the white noise climate forcing (Anderson et al. [2014] used a most likely climate duration of 4000 years for the LGM) and the limited parameter space explored.

Last Glacial Maximum Results:
We allowed each white noise-model coupled simulation to run for 4000 years [e.g. Anderson et al., 2014]. We ran 400, 4000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore as represented in the assumed mass balance profile, meltfactor, and flow law parameter. The most likely mean length for the LGM extent in the Rakaia valley was 1.7 km (or 2.2%) upvalley from the LGM terminal moraine. The standard deviation of this mean length from the most likely mean length was 410 m and the standard deviation of the glacier length through the model runs was 770 m. The mean length results for the LGM are lower than those discussed by Anderson et al. [2014] largely because the LGM glacier modeled here is significantly larger in volume and has a longer response time than the largest glacier modeled in the Colorado Front Range. This discrepancy may also result because of the limited parameter space explored.

Discussion:
Glaciological modeling studies extracting paleoclimate estimates should use the mean glacier length as opposed to the maximum glacier length. Modeling to the actual maximum ice extent will provide a maximum estimate of climate change. To date, the Rakaia LGM glacier is the longest (~80 km) and largest volume glacier modeled with white noise year-to-year variability, which is present in all climate states, past or present. While the magnitude of the most likely fluctuation is larger for the LGM glacier (even when taking into account the different durations of the YD and LGM simulations (1000 yrs versus 4000 yrs)) these fluctuations represent a smaller percentage of the maximum length of the glacier when compared to the YD glacier [e.g. Anderson et al., 2014]. Though we cannot make a rigorous examination of this effect here it appears that larger glaciers with longer response times tend to produce the most reliable paleoclimate estimates when the modeling to the maximum ice extent.
The variability of annual precipitation and melt season temperature are large compared to other studies, implying that variability will likely have an important effect on the fluctuations of advances less extensive than the YD advance. Further modeling efforts should be preformed to test whether smaller advances could be explained by interannual variability and potentially independent of actual climate changes. We use white noise forcing for these simulations. Our current meteorological records do not cover a long enough time span to confidently test for memory (or correlation from year-to-year) [e.g. Burke and Roe, 2013]. If there actually is memory in year-to-year mean melt-season temperature or accumulation season precipitation the magnitude of these noise-forced glacier fluctuations would be greatly enhanced.


==Users==
List the CSDMS HPCC users of your project: leifstefanthor
* <leifstefanthor>

==Funding==
National Science Foundation (NSF) grant DGE- 1144083 (GRFP)

==Publications and presentations==
Anderson, Leif S., Gerard H. Roe, and Robert S. Anderson. "The effects of interannual climate variability on the moraine record." Geology 42.1 (2014): 55-58.

Rowan, A. V., Brocklehurst, S. H., Schultz, D. M., Plummer, M. A., Anderson, L. S., & Glasser, N. F. (2014). Late Quaternary glacier sensitivity to temperature and precipitation distribution in the Southern Alps of New Zealand. Journal of Geophysical Research: Earth Surface.

==Links==
This would be the place to provide links that are related to your project.

Choose one of the two categories mentioned below, that your project suites the best
[[Category:Lecture project]] [[Category:Research project]]

HPCCprojects:Interannual variability and glacier length

2014-10-03T17:04:37Z

Leifstefanthor: /* {{PAGENAME}} */

__NOTOC__
={{PAGENAME}}=

Valley glacier moraines are commonly used to infer past mean
annual precipitation and mean melt-season temperature. However, recent research has demonstrated that, even in steady climates, multi-decadal, kilometer-scale fluctuations in glacier length occur in response to stochastic, year-to-year variability in mass balance. When interpreting moraine sequences it is important to include the effect of interannual weather variability on glacier length; moraines record advances that are forced either by interannual variability or by a combination of climate change and interannual variability. Our hope is to help establish the metrics needed to determine if a past glacier advance was caused by interannual variability or a climate change.
[[File:interannual.png|400px]]
==Objectives==
1) Assess the importance of year-to-year climate variability (weather) on glacier length in a variety of climate settings
2) Create quantitative metrics to test if a glacier length change could be caused by weather variability.


==Time-line==
Provide (estimated) Start: Jan. 2014 and End: Dec. 2015

==Models in use==
We are using 1 and 2D Matlab-based numerical glacier models. The models are used in both idealized and geographical settings with a variety of parameterizations for glacier mass balance. 

==Results==
Interannual variability in mean melt-season temperature and annual precipitation can cause kilometer scale fluctuations in glacier length independent of climate change [e.g. Oerlamans, 2000 and Roe and O’Neal, 2009]. We perform model simulations to gauge the uncertainty in mean glacier length, the length over a given time period which represents the long-term climatologically relevant extent, for the Younger Dryas (YD) and LGM ice extents in the Rakaia valley, NZ. We used a 1D flowline model (e.g. Oerlemans, 2000) with variable width forced by independent white noise realizations [Oerlemans, 2000; Roe and O’Neal, 2009; Anderson et al., 2014]. One white noise realization was modified by the standard deviation of mean summer (DJF) temperature (σT = 1.1 °C) from the Lake Coleridge weather station (location info), and the other realization was bracketed by estimates of the standard deviation of annual precipitation from a representative weather station. The variability of annual precipitation increases with higher annual precipitation amount [e.g. Burke and Roe, 2013]. Because of the strong orographic precipitation gradient and rain shadow in New Zealand, precipitation amounts range from greater than 6 m a-1 west and near the topographic divide to less than 1 m a-1 in some locations east of the divide (Fig. 3) [Ummenhofer and England, 2007]. Data derived from lowland meteorological stations on the east side of the divide do not capture the modern variability in annual precipitation in the glacial accumulation zone. We use a standard deviation of annual precipitation based on meteorological station data with a mean annual precipitation of 5.5 m a-1 and standard deviation of annual precipitation amount (σP of .87 m a-1) [Woo and Fitzharris, 1992]. This data is from a meteorological station on the west side of the drainage divide and was chosen because annual precipitations amounts in the accumulation are likely to be larger near the divide than the data from this station so the standard deviation of annual precipitation is a minimum estimate. We use a mass balance profile derived from the energy balance methods outlined in Plummer and Phillips et al., 2003 and perturb the profile with using a meltfactor of .7 m °Cyr-1. This meltfactor is the most often occurring melt factor based on a global compilation of modern meltfactors for ice [Anderson et al., 2014].

Younger Dryas Results:
The glacier leaving the YD ice extent in the Rakaia valley had a complicated geometry. Three distinct glaciers join within 3km of the maximum glacier extent. We modeled all three glaciers and fed the two smaller glaciers into the main stem to capture the terminus fluctuations derived from asynchronous response times between the three glaciers (after MacGregor et al., 2000). We allowed each white noise-model coupled simulation to run for 1000 years [Kaplan et al., 2013]. Because it is impossible to know the exact pattern of year-to-year fluctuations in annual precipitation and mean melt season temperature during the Younger Dryas in New Zealand we ran 1000, 1000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore. The most likely mean length for the Younger Dryas extent in the Rakaia valley was ~ 550m (or 6.5%) upvalley from the YD terminal extent. The standard deviation of this mean length from the most likely mean length was 170 m and the standard deviation of glacier length was 260 m. The mean length results for the YD are lower than those discussed by Anderson et al. [2014] largely because of the reduced duration of the white noise climate forcing (Anderson et al. [2014] used a most likely climate duration of 4000 years for the LGM) and the limited parameter space explored.

Last Glacial Maximum Results:
We allowed each white noise-model coupled simulation to run for 4000 years [e.g. Anderson et al., 2014]. We ran 400, 4000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore as represented in the assumed mass balance profile, meltfactor, and flow law parameter. The most likely mean length for the LGM extent in the Rakaia valley was 1.7 km (or 2.2%) upvalley from the LGM terminal moraine. The standard deviation of this mean length from the most likely mean length was 410 m and the standard deviation of the glacier length through the model runs was 770 m. The mean length results for the LGM are lower than those discussed by Anderson et al. [2014] largely because the LGM glacier modeled here is significantly larger in volume and has a longer response time than the largest glacier modeled in the Colorado Front Range. This discrepancy may also result because of the limited parameter space explored.

Discussion:
Glaciological modeling studies extracting paleoclimate estimates should use the mean glacier length as opposed to the maximum glacier length. Modeling to the actual maximum ice extent will provide a maximum estimate of climate change. To date, the Rakaia LGM glacier is the longest (~80 km) and largest volume glacier modeled with white noise year-to-year variability, which is present in all climate states, past or present. While the magnitude of the most likely fluctuation is larger for the LGM glacier (even when taking into account the different durations of the YD and LGM simulations (1000 yrs versus 4000 yrs)) these fluctuations represent a smaller percentage of the maximum length of the glacier when compared to the YD glacier [e.g. Anderson et al., 2014]. Though we cannot make a rigorous examination of this effect here it appears that larger glaciers with longer response times tend to produce the most reliable paleoclimate estimates when the modeling to the maximum ice extent.
The variability of annual precipitation and melt season temperature are large compared to other studies, implying that variability will likely have an important effect on the fluctuations of advances less extensive than the YD advance. Further modeling efforts should be preformed to test whether smaller advances could be explained by interannual variability and potentially independent of actual climate changes. We use white noise forcing for these simulations. Our current meteorological records do not cover a long enough time span to confidently test for memory (or correlation from year-to-year) [e.g. Burke and Roe, 2013]. If there actually is memory in year-to-year mean melt-season temperature or accumulation season precipitation the magnitude of these noise-forced glacier fluctuations would be greatly enhanced.


==Users==
List the CSDMS HPCC users of your project: leifstefanthor
* <leifstefanthor>

==Funding==
National Science Foundation (NSF) grant DGE- 1144083 (GRFP)

==Publications and presentations==
Anderson, Leif S., Gerard H. Roe, and Robert S. Anderson. "The effects of interannual climate variability on the moraine record." Geology 42.1 (2014): 55-58.

Rowan, A. V., Brocklehurst, S. H., Schultz, D. M., Plummer, M. A., Anderson, L. S., & Glasser, N. F. (2014). Late Quaternary glacier sensitivity to temperature and precipitation distribution in the Southern Alps of New Zealand. Journal of Geophysical Research: Earth Surface.

==Links==
This would be the place to provide links that are related to your project.

Choose one of the two categories mentioned below, that your project suites the best
[[Category:Lecture project]] [[Category:Research project]]

HPCCprojects:Interannual variability and glacier length

2014-10-03T17:03:58Z

Leifstefanthor: /* Project description */

__NOTOC__
={{PAGENAME}}=
[[File:interannual.png|400px]]==Project description==
Valley glacier moraines are commonly used to infer past mean
annual precipitation and mean melt-season temperature. However, recent research has demonstrated that, even in steady climates, multi-decadal, kilometer-scale fluctuations in glacier length occur in response to stochastic, year-to-year variability in mass balance. When interpreting moraine sequences it is important to include the effect of interannual weather variability on glacier length; moraines record advances that are forced either by interannual variability or by a combination of climate change and interannual variability. Our hope is to help establish the metrics needed to determine if a past glacier advance was caused by interannual variability or a climate change.

==Objectives==
1) Assess the importance of year-to-year climate variability (weather) on glacier length in a variety of climate settings
2) Create quantitative metrics to test if a glacier length change could be caused by weather variability.


==Time-line==
Provide (estimated) Start: Jan. 2014 and End: Dec. 2015

==Models in use==
We are using 1 and 2D Matlab-based numerical glacier models. The models are used in both idealized and geographical settings with a variety of parameterizations for glacier mass balance. 

==Results==
Interannual variability in mean melt-season temperature and annual precipitation can cause kilometer scale fluctuations in glacier length independent of climate change [e.g. Oerlamans, 2000 and Roe and O’Neal, 2009]. We perform model simulations to gauge the uncertainty in mean glacier length, the length over a given time period which represents the long-term climatologically relevant extent, for the Younger Dryas (YD) and LGM ice extents in the Rakaia valley, NZ. We used a 1D flowline model (e.g. Oerlemans, 2000) with variable width forced by independent white noise realizations [Oerlemans, 2000; Roe and O’Neal, 2009; Anderson et al., 2014]. One white noise realization was modified by the standard deviation of mean summer (DJF) temperature (σT = 1.1 °C) from the Lake Coleridge weather station (location info), and the other realization was bracketed by estimates of the standard deviation of annual precipitation from a representative weather station. The variability of annual precipitation increases with higher annual precipitation amount [e.g. Burke and Roe, 2013]. Because of the strong orographic precipitation gradient and rain shadow in New Zealand, precipitation amounts range from greater than 6 m a-1 west and near the topographic divide to less than 1 m a-1 in some locations east of the divide (Fig. 3) [Ummenhofer and England, 2007]. Data derived from lowland meteorological stations on the east side of the divide do not capture the modern variability in annual precipitation in the glacial accumulation zone. We use a standard deviation of annual precipitation based on meteorological station data with a mean annual precipitation of 5.5 m a-1 and standard deviation of annual precipitation amount (σP of .87 m a-1) [Woo and Fitzharris, 1992]. This data is from a meteorological station on the west side of the drainage divide and was chosen because annual precipitations amounts in the accumulation are likely to be larger near the divide than the data from this station so the standard deviation of annual precipitation is a minimum estimate. We use a mass balance profile derived from the energy balance methods outlined in Plummer and Phillips et al., 2003 and perturb the profile with using a meltfactor of .7 m °Cyr-1. This meltfactor is the most often occurring melt factor based on a global compilation of modern meltfactors for ice [Anderson et al., 2014].

Younger Dryas Results:
The glacier leaving the YD ice extent in the Rakaia valley had a complicated geometry. Three distinct glaciers join within 3km of the maximum glacier extent. We modeled all three glaciers and fed the two smaller glaciers into the main stem to capture the terminus fluctuations derived from asynchronous response times between the three glaciers (after MacGregor et al., 2000). We allowed each white noise-model coupled simulation to run for 1000 years [Kaplan et al., 2013]. Because it is impossible to know the exact pattern of year-to-year fluctuations in annual precipitation and mean melt season temperature during the Younger Dryas in New Zealand we ran 1000, 1000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore. The most likely mean length for the Younger Dryas extent in the Rakaia valley was ~ 550m (or 6.5%) upvalley from the YD terminal extent. The standard deviation of this mean length from the most likely mean length was 170 m and the standard deviation of glacier length was 260 m. The mean length results for the YD are lower than those discussed by Anderson et al. [2014] largely because of the reduced duration of the white noise climate forcing (Anderson et al. [2014] used a most likely climate duration of 4000 years for the LGM) and the limited parameter space explored.

Last Glacial Maximum Results:
We allowed each white noise-model coupled simulation to run for 4000 years [e.g. Anderson et al., 2014]. We ran 400, 4000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore as represented in the assumed mass balance profile, meltfactor, and flow law parameter. The most likely mean length for the LGM extent in the Rakaia valley was 1.7 km (or 2.2%) upvalley from the LGM terminal moraine. The standard deviation of this mean length from the most likely mean length was 410 m and the standard deviation of the glacier length through the model runs was 770 m. The mean length results for the LGM are lower than those discussed by Anderson et al. [2014] largely because the LGM glacier modeled here is significantly larger in volume and has a longer response time than the largest glacier modeled in the Colorado Front Range. This discrepancy may also result because of the limited parameter space explored.

Discussion:
Glaciological modeling studies extracting paleoclimate estimates should use the mean glacier length as opposed to the maximum glacier length. Modeling to the actual maximum ice extent will provide a maximum estimate of climate change. To date, the Rakaia LGM glacier is the longest (~80 km) and largest volume glacier modeled with white noise year-to-year variability, which is present in all climate states, past or present. While the magnitude of the most likely fluctuation is larger for the LGM glacier (even when taking into account the different durations of the YD and LGM simulations (1000 yrs versus 4000 yrs)) these fluctuations represent a smaller percentage of the maximum length of the glacier when compared to the YD glacier [e.g. Anderson et al., 2014]. Though we cannot make a rigorous examination of this effect here it appears that larger glaciers with longer response times tend to produce the most reliable paleoclimate estimates when the modeling to the maximum ice extent.
The variability of annual precipitation and melt season temperature are large compared to other studies, implying that variability will likely have an important effect on the fluctuations of advances less extensive than the YD advance. Further modeling efforts should be preformed to test whether smaller advances could be explained by interannual variability and potentially independent of actual climate changes. We use white noise forcing for these simulations. Our current meteorological records do not cover a long enough time span to confidently test for memory (or correlation from year-to-year) [e.g. Burke and Roe, 2013]. If there actually is memory in year-to-year mean melt-season temperature or accumulation season precipitation the magnitude of these noise-forced glacier fluctuations would be greatly enhanced.


==Users==
List the CSDMS HPCC users of your project: leifstefanthor
* <leifstefanthor>

==Funding==
National Science Foundation (NSF) grant DGE- 1144083 (GRFP)

==Publications and presentations==
Anderson, Leif S., Gerard H. Roe, and Robert S. Anderson. "The effects of interannual climate variability on the moraine record." Geology 42.1 (2014): 55-58.

Rowan, A. V., Brocklehurst, S. H., Schultz, D. M., Plummer, M. A., Anderson, L. S., & Glasser, N. F. (2014). Late Quaternary glacier sensitivity to temperature and precipitation distribution in the Southern Alps of New Zealand. Journal of Geophysical Research: Earth Surface.

==Links==
This would be the place to provide links that are related to your project.

Choose one of the two categories mentioned below, that your project suites the best
[[Category:Lecture project]] [[Category:Research project]]

File:Interannual.png

2014-10-03T17:03:52Z

Leifstefanthor: MsUpload

MsUpload

HPCCprojects:Interannual variability and glacier length

2014-10-03T17:01:31Z

Leifstefanthor: /* Publications and presentations */

__NOTOC__
={{PAGENAME}}=
==Project description==
Valley glacier moraines are commonly used to infer past mean
annual precipitation and mean melt-season temperature. However, recent research has demonstrated that, even in steady climates, multi-decadal, kilometer-scale fluctuations in glacier length occur in response to stochastic, year-to-year variability in mass balance. When interpreting moraine sequences it is important to include the effect of interannual weather variability on glacier length; moraines record advances that are forced either by interannual variability or by a combination of climate change and interannual variability. Our hope is to help establish the metrics needed to determine if a past glacier advance was caused by interannual variability or a climate change.

==Objectives==
1) Assess the importance of year-to-year climate variability (weather) on glacier length in a variety of climate settings
2) Create quantitative metrics to test if a glacier length change could be caused by weather variability.


==Time-line==
Provide (estimated) Start: Jan. 2014 and End: Dec. 2015

==Models in use==
We are using 1 and 2D Matlab-based numerical glacier models. The models are used in both idealized and geographical settings with a variety of parameterizations for glacier mass balance. 

==Results==
Interannual variability in mean melt-season temperature and annual precipitation can cause kilometer scale fluctuations in glacier length independent of climate change [e.g. Oerlamans, 2000 and Roe and O’Neal, 2009]. We perform model simulations to gauge the uncertainty in mean glacier length, the length over a given time period which represents the long-term climatologically relevant extent, for the Younger Dryas (YD) and LGM ice extents in the Rakaia valley, NZ. We used a 1D flowline model (e.g. Oerlemans, 2000) with variable width forced by independent white noise realizations [Oerlemans, 2000; Roe and O’Neal, 2009; Anderson et al., 2014]. One white noise realization was modified by the standard deviation of mean summer (DJF) temperature (σT = 1.1 °C) from the Lake Coleridge weather station (location info), and the other realization was bracketed by estimates of the standard deviation of annual precipitation from a representative weather station. The variability of annual precipitation increases with higher annual precipitation amount [e.g. Burke and Roe, 2013]. Because of the strong orographic precipitation gradient and rain shadow in New Zealand, precipitation amounts range from greater than 6 m a-1 west and near the topographic divide to less than 1 m a-1 in some locations east of the divide (Fig. 3) [Ummenhofer and England, 2007]. Data derived from lowland meteorological stations on the east side of the divide do not capture the modern variability in annual precipitation in the glacial accumulation zone. We use a standard deviation of annual precipitation based on meteorological station data with a mean annual precipitation of 5.5 m a-1 and standard deviation of annual precipitation amount (σP of .87 m a-1) [Woo and Fitzharris, 1992]. This data is from a meteorological station on the west side of the drainage divide and was chosen because annual precipitations amounts in the accumulation are likely to be larger near the divide than the data from this station so the standard deviation of annual precipitation is a minimum estimate. We use a mass balance profile derived from the energy balance methods outlined in Plummer and Phillips et al., 2003 and perturb the profile with using a meltfactor of .7 m °Cyr-1. This meltfactor is the most often occurring melt factor based on a global compilation of modern meltfactors for ice [Anderson et al., 2014].

Younger Dryas Results:
The glacier leaving the YD ice extent in the Rakaia valley had a complicated geometry. Three distinct glaciers join within 3km of the maximum glacier extent. We modeled all three glaciers and fed the two smaller glaciers into the main stem to capture the terminus fluctuations derived from asynchronous response times between the three glaciers (after MacGregor et al., 2000). We allowed each white noise-model coupled simulation to run for 1000 years [Kaplan et al., 2013]. Because it is impossible to know the exact pattern of year-to-year fluctuations in annual precipitation and mean melt season temperature during the Younger Dryas in New Zealand we ran 1000, 1000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore. The most likely mean length for the Younger Dryas extent in the Rakaia valley was ~ 550m (or 6.5%) upvalley from the YD terminal extent. The standard deviation of this mean length from the most likely mean length was 170 m and the standard deviation of glacier length was 260 m. The mean length results for the YD are lower than those discussed by Anderson et al. [2014] largely because of the reduced duration of the white noise climate forcing (Anderson et al. [2014] used a most likely climate duration of 4000 years for the LGM) and the limited parameter space explored.

Last Glacial Maximum Results:
We allowed each white noise-model coupled simulation to run for 4000 years [e.g. Anderson et al., 2014]. We ran 400, 4000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore as represented in the assumed mass balance profile, meltfactor, and flow law parameter. The most likely mean length for the LGM extent in the Rakaia valley was 1.7 km (or 2.2%) upvalley from the LGM terminal moraine. The standard deviation of this mean length from the most likely mean length was 410 m and the standard deviation of the glacier length through the model runs was 770 m. The mean length results for the LGM are lower than those discussed by Anderson et al. [2014] largely because the LGM glacier modeled here is significantly larger in volume and has a longer response time than the largest glacier modeled in the Colorado Front Range. This discrepancy may also result because of the limited parameter space explored.

Discussion:
Glaciological modeling studies extracting paleoclimate estimates should use the mean glacier length as opposed to the maximum glacier length. Modeling to the actual maximum ice extent will provide a maximum estimate of climate change. To date, the Rakaia LGM glacier is the longest (~80 km) and largest volume glacier modeled with white noise year-to-year variability, which is present in all climate states, past or present. While the magnitude of the most likely fluctuation is larger for the LGM glacier (even when taking into account the different durations of the YD and LGM simulations (1000 yrs versus 4000 yrs)) these fluctuations represent a smaller percentage of the maximum length of the glacier when compared to the YD glacier [e.g. Anderson et al., 2014]. Though we cannot make a rigorous examination of this effect here it appears that larger glaciers with longer response times tend to produce the most reliable paleoclimate estimates when the modeling to the maximum ice extent.
The variability of annual precipitation and melt season temperature are large compared to other studies, implying that variability will likely have an important effect on the fluctuations of advances less extensive than the YD advance. Further modeling efforts should be preformed to test whether smaller advances could be explained by interannual variability and potentially independent of actual climate changes. We use white noise forcing for these simulations. Our current meteorological records do not cover a long enough time span to confidently test for memory (or correlation from year-to-year) [e.g. Burke and Roe, 2013]. If there actually is memory in year-to-year mean melt-season temperature or accumulation season precipitation the magnitude of these noise-forced glacier fluctuations would be greatly enhanced.


==Users==
List the CSDMS HPCC users of your project: leifstefanthor
* <leifstefanthor>

==Funding==
National Science Foundation (NSF) grant DGE- 1144083 (GRFP)

==Publications and presentations==
Anderson, Leif S., Gerard H. Roe, and Robert S. Anderson. "The effects of interannual climate variability on the moraine record." Geology 42.1 (2014): 55-58.

Rowan, A. V., Brocklehurst, S. H., Schultz, D. M., Plummer, M. A., Anderson, L. S., & Glasser, N. F. (2014). Late Quaternary glacier sensitivity to temperature and precipitation distribution in the Southern Alps of New Zealand. Journal of Geophysical Research: Earth Surface.

==Links==
This would be the place to provide links that are related to your project.

Choose one of the two categories mentioned below, that your project suites the best
[[Category:Lecture project]] [[Category:Research project]]

HPCCprojects:Interannual variability and glacier length

2014-10-03T17:00:08Z

Leifstefanthor: /* Results */

__NOTOC__
={{PAGENAME}}=
==Project description==
Valley glacier moraines are commonly used to infer past mean
annual precipitation and mean melt-season temperature. However, recent research has demonstrated that, even in steady climates, multi-decadal, kilometer-scale fluctuations in glacier length occur in response to stochastic, year-to-year variability in mass balance. When interpreting moraine sequences it is important to include the effect of interannual weather variability on glacier length; moraines record advances that are forced either by interannual variability or by a combination of climate change and interannual variability. Our hope is to help establish the metrics needed to determine if a past glacier advance was caused by interannual variability or a climate change.

==Objectives==
1) Assess the importance of year-to-year climate variability (weather) on glacier length in a variety of climate settings
2) Create quantitative metrics to test if a glacier length change could be caused by weather variability.


==Time-line==
Provide (estimated) Start: Jan. 2014 and End: Dec. 2015

==Models in use==
We are using 1 and 2D Matlab-based numerical glacier models. The models are used in both idealized and geographical settings with a variety of parameterizations for glacier mass balance. 

==Results==
Interannual variability in mean melt-season temperature and annual precipitation can cause kilometer scale fluctuations in glacier length independent of climate change [e.g. Oerlamans, 2000 and Roe and O’Neal, 2009]. We perform model simulations to gauge the uncertainty in mean glacier length, the length over a given time period which represents the long-term climatologically relevant extent, for the Younger Dryas (YD) and LGM ice extents in the Rakaia valley, NZ. We used a 1D flowline model (e.g. Oerlemans, 2000) with variable width forced by independent white noise realizations [Oerlemans, 2000; Roe and O’Neal, 2009; Anderson et al., 2014]. One white noise realization was modified by the standard deviation of mean summer (DJF) temperature (σT = 1.1 °C) from the Lake Coleridge weather station (location info), and the other realization was bracketed by estimates of the standard deviation of annual precipitation from a representative weather station. The variability of annual precipitation increases with higher annual precipitation amount [e.g. Burke and Roe, 2013]. Because of the strong orographic precipitation gradient and rain shadow in New Zealand, precipitation amounts range from greater than 6 m a-1 west and near the topographic divide to less than 1 m a-1 in some locations east of the divide (Fig. 3) [Ummenhofer and England, 2007]. Data derived from lowland meteorological stations on the east side of the divide do not capture the modern variability in annual precipitation in the glacial accumulation zone. We use a standard deviation of annual precipitation based on meteorological station data with a mean annual precipitation of 5.5 m a-1 and standard deviation of annual precipitation amount (σP of .87 m a-1) [Woo and Fitzharris, 1992]. This data is from a meteorological station on the west side of the drainage divide and was chosen because annual precipitations amounts in the accumulation are likely to be larger near the divide than the data from this station so the standard deviation of annual precipitation is a minimum estimate. We use a mass balance profile derived from the energy balance methods outlined in Plummer and Phillips et al., 2003 and perturb the profile with using a meltfactor of .7 m °Cyr-1. This meltfactor is the most often occurring melt factor based on a global compilation of modern meltfactors for ice [Anderson et al., 2014].

Younger Dryas Results:
The glacier leaving the YD ice extent in the Rakaia valley had a complicated geometry. Three distinct glaciers join within 3km of the maximum glacier extent. We modeled all three glaciers and fed the two smaller glaciers into the main stem to capture the terminus fluctuations derived from asynchronous response times between the three glaciers (after MacGregor et al., 2000). We allowed each white noise-model coupled simulation to run for 1000 years [Kaplan et al., 2013]. Because it is impossible to know the exact pattern of year-to-year fluctuations in annual precipitation and mean melt season temperature during the Younger Dryas in New Zealand we ran 1000, 1000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore. The most likely mean length for the Younger Dryas extent in the Rakaia valley was ~ 550m (or 6.5%) upvalley from the YD terminal extent. The standard deviation of this mean length from the most likely mean length was 170 m and the standard deviation of glacier length was 260 m. The mean length results for the YD are lower than those discussed by Anderson et al. [2014] largely because of the reduced duration of the white noise climate forcing (Anderson et al. [2014] used a most likely climate duration of 4000 years for the LGM) and the limited parameter space explored.

Last Glacial Maximum Results:
We allowed each white noise-model coupled simulation to run for 4000 years [e.g. Anderson et al., 2014]. We ran 400, 4000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore as represented in the assumed mass balance profile, meltfactor, and flow law parameter. The most likely mean length for the LGM extent in the Rakaia valley was 1.7 km (or 2.2%) upvalley from the LGM terminal moraine. The standard deviation of this mean length from the most likely mean length was 410 m and the standard deviation of the glacier length through the model runs was 770 m. The mean length results for the LGM are lower than those discussed by Anderson et al. [2014] largely because the LGM glacier modeled here is significantly larger in volume and has a longer response time than the largest glacier modeled in the Colorado Front Range. This discrepancy may also result because of the limited parameter space explored.

Discussion:
Glaciological modeling studies extracting paleoclimate estimates should use the mean glacier length as opposed to the maximum glacier length. Modeling to the actual maximum ice extent will provide a maximum estimate of climate change. To date, the Rakaia LGM glacier is the longest (~80 km) and largest volume glacier modeled with white noise year-to-year variability, which is present in all climate states, past or present. While the magnitude of the most likely fluctuation is larger for the LGM glacier (even when taking into account the different durations of the YD and LGM simulations (1000 yrs versus 4000 yrs)) these fluctuations represent a smaller percentage of the maximum length of the glacier when compared to the YD glacier [e.g. Anderson et al., 2014]. Though we cannot make a rigorous examination of this effect here it appears that larger glaciers with longer response times tend to produce the most reliable paleoclimate estimates when the modeling to the maximum ice extent.
The variability of annual precipitation and melt season temperature are large compared to other studies, implying that variability will likely have an important effect on the fluctuations of advances less extensive than the YD advance. Further modeling efforts should be preformed to test whether smaller advances could be explained by interannual variability and potentially independent of actual climate changes. We use white noise forcing for these simulations. Our current meteorological records do not cover a long enough time span to confidently test for memory (or correlation from year-to-year) [e.g. Burke and Roe, 2013]. If there actually is memory in year-to-year mean melt-season temperature or accumulation season precipitation the magnitude of these noise-forced glacier fluctuations would be greatly enhanced.


==Users==
List the CSDMS HPCC users of your project: leifstefanthor
* <leifstefanthor>

==Funding==
National Science Foundation (NSF) grant DGE- 1144083 (GRFP)

==Publications and presentations==
Anderson, Leif S., Gerard H. Roe, and Robert S. Anderson. "The effects of interannual climate variability on the moraine record." Geology 42.1 (2014): 55-58.

==Links==
This would be the place to provide links that are related to your project.

Choose one of the two categories mentioned below, that your project suites the best
[[Category:Lecture project]] [[Category:Research project]]

HPCCprojects:Interannual variability and glacier length

2014-10-03T16:58:55Z

Leifstefanthor: /* Results */

__NOTOC__
={{PAGENAME}}=
==Project description==
Valley glacier moraines are commonly used to infer past mean
annual precipitation and mean melt-season temperature. However, recent research has demonstrated that, even in steady climates, multi-decadal, kilometer-scale fluctuations in glacier length occur in response to stochastic, year-to-year variability in mass balance. When interpreting moraine sequences it is important to include the effect of interannual weather variability on glacier length; moraines record advances that are forced either by interannual variability or by a combination of climate change and interannual variability. Our hope is to help establish the metrics needed to determine if a past glacier advance was caused by interannual variability or a climate change.

==Objectives==
1) Assess the importance of year-to-year climate variability (weather) on glacier length in a variety of climate settings
2) Create quantitative metrics to test if a glacier length change could be caused by weather variability.


==Time-line==
Provide (estimated) Start: Jan. 2014 and End: Dec. 2015

==Models in use==
We are using 1 and 2D Matlab-based numerical glacier models. The models are used in both idealized and geographical settings with a variety of parameterizations for glacier mass balance. 

==Results==
Interannual variability in mean melt-season temperature and annual precipitation can cause kilometer scale fluctuations in glacier length independent of climate change [e.g. Oerlamans, 2000 and Roe and O’Neal, 2009]. We perform additional model simulations to gauge the uncertainty in mean glacier length, the length over a given time period which represents the long-term climatologically relevant extent, for the Younger Dryas (YD) and LGM ice extents in the Rakaia valley. We used a 1D flowline model (e.g. Oerlemans, 2000) with variable width forced by independent white noise realizations [Oerlemans, 2000; Roe and O’Neal, 2009; Anderson et al., 2014]. One white noise realization was modified by the standard deviation of mean summer (DJF) temperature (σT = 1.1 °C) from the Lake Coleridge weather station (location info), and the other realization was bracketed by estimates of the standard deviation of annual precipitation from a representative weather station. The variability of annual precipitation increases with higher annual precipitation amount [e.g. Burke and Roe, 2013]. Because of the strong orographic precipitation gradient and rain shadow in New Zealand, precipitation amounts range from greater than 6 m a-1 west and near the topographic divide to less than 1 m a-1 in some locations east of the divide (Fig. 3) [Ummenhofer and England, 2007]. Data derived from lowland meteorological stations on the east side of the divide do not capture the modern variability in annual precipitation in the glacial accumulation zone. We use a standard deviation of annual precipitation based on meteorological station data with a mean annual precipitation of 5.5 m a-1 and standard deviation of annual precipitation amount (σP of .87 m a-1) [Woo and Fitzharris, 1992]. This data is from a meteorological station on the west side of the drainage divide and was chosen because annual precipitations amounts in the accumulation are likely to be larger near the divide than the data from this station so the standard deviation of annual precipitation is a minimum estimate. We use a mass balance profile derived from the energy balance methods outlined in Plummer and Phillips et al., 2003 and perturb the profile with using a meltfactor of .7 m °Cyr-1. This meltfactor is the most often occurring melt factor based on a global compilation of modern meltfactors for ice [Anderson et al., 2014].

Younger Dryas Results
The glacier leaving the YD ice extent in the Rakaia valley had a complicated geometry. Three distinct glaciers join within 3km of the maximum glacier extent. We modeled all three glaciers and fed the two smaller glaciers into the main stem to capture the terminus fluctuations derived from asynchronous response times between the three glaciers (after MacGregor et al., 2000). We allowed each white noise-model coupled simulation to run for 1000 years [Kaplan et al., 2013]. Because it is impossible to know the exact pattern of year-to-year fluctuations in annual precipitation and mean melt season temperature during the Younger Dryas in New Zealand we ran 1000, 1000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore. The most likely mean length for the Younger Dryas extent in the Rakaia valley was ~ 550m (or 6.5%) upvalley from the YD terminal extent. The standard deviation of this mean length from the most likely mean length was 170 m and the standard deviation of glacier length was 260 m. The mean length results for the YD are lower than those discussed by Anderson et al. [2014] largely because of the reduced duration of the white noise climate forcing (Anderson et al. [2014] used a most likely climate duration of 4000 years for the LGM) and the limited parameter space explored.

Last Glacial Maximum Results
We allowed each white noise-model coupled simulation to run for 4000 years [e.g. Anderson et al., 2014]. We ran 400, 4000-year simulations to estimate the most probable mean glacier length for the small parameter space we explore as represented in the assumed mass balance profile, meltfactor, and flow law parameter. The most likely mean length for the LGM extent in the Rakaia valley was 1.7 km (or 2.2%) upvalley from the LGM terminal moraine. The standard deviation of this mean length from the most likely mean length was 410 m and the standard deviation of the glacier length through the model runs was 770 m. The mean length results for the LGM are lower than those discussed by Anderson et al. [2014] largely because the LGM glacier modeled here is significantly larger in volume and has a longer response time than the largest glacier modeled in the Colorado Front Range. This discrepancy may also result because of the limited parameter space explored.

Discussion

Glaciological modeling studies extracting paleoclimate estimates should use the mean glacier length as opposed to the maximum glacier length. Modeling to the actual maximum ice extent will provide a maximum estimate of climate change. To date, the Rakaia LGM glacier is the longest (~80 km) and largest volume glacier modeled with white noise year-to-year variability, which is present in all climate states, past or present. While the magnitude of the most likely fluctuation is larger for the LGM glacier (even when taking into account the different durations of the YD and LGM simulations (1000 yrs versus 4000 yrs)) these fluctuations represent a smaller percentage of the maximum length of the glacier when compared to the YD glacier [e.g. Anderson et al., 2014]. Though we cannot make a rigorous examination of this effect here it appears that larger glaciers with longer response times tend to produce the most reliable paleoclimate estimates when the modeling to the maximum ice extent.
The variability of annual precipitation and melt season temperature are large compared to other studies, implying that variability will likely have an important effect on the fluctuations of advances less extensive than the YD advance. Further modeling efforts should be preformed to test whether smaller advances could be explained by interannual variability and potentially independent of actual climate changes. We use white noise forcing for these simulations. Our current meteorological records do not cover a long enough time span to confidently test for memory (or correlation from year-to-year) [e.g. Burke and Roe, 2013]. If there actually is memory in year-to-year mean melt-season temperature or accumulation season precipitation the magnitude of these noise-forced glacier fluctuations would be greatly enhanced.


==Users==
List the CSDMS HPCC users of your project: leifstefanthor
* <leifstefanthor>

==Funding==
National Science Foundation (NSF) grant DGE- 1144083 (GRFP)

==Publications and presentations==
Anderson, Leif S., Gerard H. Roe, and Robert S. Anderson. "The effects of interannual climate variability on the moraine record." Geology 42.1 (2014): 55-58.

==Links==
This would be the place to provide links that are related to your project.

Choose one of the two categories mentioned below, that your project suites the best
[[Category:Lecture project]] [[Category:Research project]]

HPCCprojects:Interannual variability and glacier length

2014-10-03T16:56:49Z

Leifstefanthor: /* Time-line */

HPCCprojects:Interannual variability and glacier length

2014-10-03T16:55:46Z

Leifstefanthor: /* Funding */

HPCCprojects:Interannual variability and glacier length

2014-10-03T16:55:06Z

Leifstefanthor: /* Publications and presentations */

HPCCprojects:Interannual variability and glacier length

2014-10-03T16:53:21Z

Leifstefanthor: /* Users */

HPCCprojects:Interannual variability and glacier length

2014-10-03T16:53:08Z

Leifstefanthor: /* Users */

HPCCprojects:Interannual variability and glacier length

2014-10-03T16:52:41Z

Leifstefanthor: /* Models in use */

HPCCprojects:Interannual variability and glacier length

2014-10-03T16:50:53Z

Leifstefanthor: /* Project description */

HPCCprojects:Interannual variability and glacier length

2014-10-03T16:49:50Z

Leifstefanthor: /* {{The effect of interannual variability and debris cover on glacier length}} */

HPCCprojects:Interannual variability and glacier length

2014-01-12T20:52:34Z

Leifstefanthor: I am using various glacier models to assess the effect of interannual variability on glacier length.

HPCCprojects:Glacier Modeling

2014-01-12T20:45:06Z

Leifstefanthor: I am using various glacier models to assess the effect of interannual variability on glacier length.

Meeting:Abstract 2013 CSDMS meeting-087

2013-02-15T17:55:43Z

Leifstefanthor:

{{CSDMS meeting 2013 not paid}}
{{CSDMS meeting personal information template-2013
|CSDMS meeting first name=Leif S.
|CSDMS meeting last name=Anderson
|CSDMS meeting institute=University of Colorado Department of Geological Sciences and INSTAAR
|CSDMS meeting city=Boulder
|CSDMS meeting country=United States
|CSDMS meeting state=Colorado
|CSDMS meeting email address=leif@colorado.edu
|CSDMS meeting phone=3605094996
}}
{{CSDMS meeting abstract yes no
|CSDMS meeting abstract submit=Yes
}}
{{CSDMS meeting abstract title template-2013
|CSDMS meeting abstract title=The effects of interannual climate variability on the extraction of climate estimates from glacial moraines
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Roe H.
|CSDMS meeting coauthor last name abstract=Gerard
|CSDMS meeting coauthor institute / Organization=University of Washington Department of Earth and Planetary Sciences
|CSDMS meeting coauthor town-city=Seattle
|CSDMS meeting coauthor country=United States
|State=Washington
|CSDMS meeting coauthor email address=gerard@ess.washington.edu
}}
{{CSDMS meeting authors template
|CSDMS meeting coauthor first name abstract=Robert S.
|CSDMS meeting coauthor last name abstract=Anderson
|CSDMS meeting coauthor institute / Organization=University of Colorado Department of Geological Sciences and INSTAAR
|CSDMS meeting coauthor town-city=Boulder
|CSDMS meeting coauthor country=United States
|State=Washington
|CSDMS meeting coauthor email address=robert.s.anderson@colorado.edu
}}
{{CSDMS meeting abstract template
|CSDMS meeting abstract=In most mountainous regions reconstructed glacial histories are the primary record of past climate and are typically based on unsorted accumulations of debris (moraines) deposited at the terminus of glaciers. Former glacier geometries— preserved as moraines and trim lines— are the primary constraint for extracting paleoclimate estimates using either equilibrium-line altitudes or numerical glacier models.

It is an implicit assumption in the glacial geology community that terminal moraines were formed by glaciers responding to the mean value of summer temperature and winter precipitation at the time of formation. In reality glacier termini oscillate around a mean glacial length even in a steady climate, defined by a constant mean and constant standard deviation. These length oscillations are driven by the alignment of more negative (positive) periods of mass balance that arise out of random year-to-year climate variability. Because glaciers that override moraines almost always destroy them, the furthest terminal moraines from the headwall during the time period of interest represent the maximum excursion of the glacier from its mean length. This implies that paleoclimate estimates based upon the furthest terminal moraine are actually maximum estimates of climate change.

We use a linearized glacier model developed by Roe and O’Neal (2009) to determine the mean length of eleven Last Glacial Maximum (LGM) glaciers in the northern Front Range, Colorado. Mean glacier lengths during the LGM were ~15% upvalley from the LGM terminal moraines. In the Colorado Front Range estimating LGM paleoclimate from the furthest terminal moraine rather than the mean length adds an extra ~1°C temperature change or an additional 25% increase in precipitation to estimate of differences from the modern climate. Furthermore, it is possible that ‘recessional’ moraines were formed by length oscillations driven by interannual variability.
}}
{{blank line template}}

Meeting:Abstract 2013 CSDMS meeting-087

2013-02-13T02:41:18Z

Leifstefanthor: Created page with "{{CSDMS meeting 2013 not paid}} {{CSDMS meeting personal information template-2013 |CSDMS meeting first name=Leif S. |CSDMS meeting last name=Anderson |CSDMS meeting institute..."

User:Leifstefanthor

2012-10-11T22:28:15Z

Leifstefanthor:

{{Signup information member
|First name member=Leif
|Last name member=Anderson
|Institute member=University of Colorado
|Postal address 1 member=2530 Stephens Rd.
|City member=Boulder
|Postal code member=80305
|Country member=United States
|State member=Colorado
|Confirm email member=leif@colorado.edu
|Work phone member=3605094996
|Cell phone member=3605094996
|Working group member=Terrestrial Working Group
|Description of your CSDMS-related interests member=I am interested in coupling atmospheric models to glacier and landscape evolution models.
}}