Analysis of WARM buoy 7data.

Name: Jordyn Leeper

Introduction

The Arctic has experienced changes due to climate change that scientists have studied for years. These changes are detrimental to the ecosystem in the Arctic, and in turn harm the human population. Changes to the land, ocean ice, and atmosphere have all been seen. In many cases, the changes cause a feedback loop of more harm to the environment.

Arctic land changes

Arctic tundra fires used to be rare, but in the last several decades have increased significantly. From 1950 to 2009, there were around 26 recorded fires in the Arctic North Slope. However, a third of those fires took place from 2006-2009 (Qiu, 2009). Fires in the Arctic in 2019 and 2020 began months earlier than expected due to warmer winters because of climate change (Witze, 2020). These fires mainly took place in peatlands, which are more carbon-dense than boreal forests. The fires burning peatlands means the carbon is being released back into the atmosphere. Peatlands normally take in more carbon from the atmosphere than they let out-but that is expected to change completely by the end of the century (Witze, 2020). The problem with this is that peatlands do not recover quickly enough from the fires to take in the carbon that was released when they burned. All the carbon released into the atmosphere will continue to worsen the trajectory of the climate crisis, making the climate warmer, which worsens fire risk and severity, thereby releasing more carbon into the atmosphere. (Witze, 2020)

Ice changes

Warming is occurring in the Arctic at nearly three times the average global rate. This is causing Arctic ice to melt much earlier in the year and form much later (wwf.org, 2022) Like with the fires, melting of ice is a feedback loop known as the albedo effect. Ice and snow reflect the sun’s light energy back to space, but when the ice and snow are melting this leaves bare rock and water to absorb the energy, further warming the Arctic (wwf.org, 2022). This means there is very little multi-year ice (ice that survives more than one melt season). Whales, seals, walrus, and polar bears are just a few of the pivotal species in the Arctic that depend on the ice.  Ice algae forms under ice, which marine meiofauna rely on for a food source. Other, larger marine animals rely on the meiofauna as a source of food. Seals, walrus, and polar bears rely on the ice to raise their young; without it, an increased risk of encroachment into human territory by polar bears and walrus is present. Walrus calves cannot swim well enough to go without ice to rest on, and quickly retreating ice leaves calves to be abandoned. 

Atmospheric changes

The Arctic has experienced atmospheric changes in recent decades. Greenland and other areas with warmer winter climates have experienced increasing rainfall. Colder winter climate regions like Canada have experienced snowfall increase. Humidity in the surface and mid troposphere has increased (Box et al 2019) Finally, in 2020, a rare ozone hole opened in the Arctic. While the Antarctic usually experiences a yearly ozone opening, this has not occurred in the Arctic since 2011 (Witze 2020) At an altitude of around 18km, most of the ozone was depleted in the Arctic in 2020. (CAMS 2020).

As part of a project observing light availability in Arctic sea ice and phytoplankton development, several buoys were deployed in the ice in Barrow, Alaska. Measurements of location, temperature, light attenuation, and intensity were taken at several locations along the buoy cable set under the ice (Hill et al 2018). Data from WARM buoy 7, deployed in March 2018, has been aggregated in the following pages.

1.Drift of Buoys

Figure 1: Drift of Buoy Over Time 

The buoy was deployed at a location where the depth of the seabed was 3710 m at about 149° W and 72.5° N, off the coast of Pt. Barrow between the Chukchi shelf and the Canada basin. (Figure 1) In April it moved west—ending up around 159°W on the Chukchi shelf where the water is shallower than the Canada basin. In May it continued its journey and crossed the Hanna Shoal in a generally west direction, ending the month at roughly 167°W. From March through May it stayed roughly in the same latitude. For the month of June, the buoy moved in a circular pattern, ending up only slightly further west and north of its location at the end of May. In July it moved back east, ending up back nearly exactly at its location at the beginning of May. Finally, in August, it travelled south—and on August 14th, its last reported location was around 161° W and 71.5°N. 

2. Time series of water column temperature

Figure 2: Water Column Temperature 

Over the course of 5 months, it collected the temperature at several depths under the sea ice, going as deep as 20 m. (Figure 2). The temperature was measured hourly. The temperature data started in April measuring -1 ºC. At this point in time the buoy had moved up onto the Chuckhi shelf from the Canada Basin. The temperature was homogenous in the upper 20 m of the ocean from the beginning of April to the end of April. Around April 23rd, some heterogeneity was seen along the 20 m until the first week of May. Sometime around June 19th the temperature began to change, increasing to around 1 ºC. The temperature stayed consistent with depth during this period. Then, in the beginning of July, the water began warming again, reaching a temperature of around 2.5 ºC. This temperature was recorded at 12 m deep, above and below that the temperature began to cool to 1.5  ºC. Finally, the water reached its peak temperature in August, where it warmed up to 5 ºC. This was seen from 3 to 12 m deep, indicating a subsurface temperature maximum. Below this the temperature lowers back down to -1 ºC. The buoy was in the Hanna Shoal in August where the water is shallower than the Canada basin. The water temperature changed because of air temperature changing. The ice gradually melted and allowed more sunlight to warm the ocean during the summer months. The surface layer of the water was colder due to ice melt.

3. Time series of light intensity

Figure 3: Light Intensity Over Time 

Buoy 7 held several sensors which tracked the amount of light that was transmitted through the ice and into the water below. The data collected thus gives an idea of when the ice was melting that year.

In the beginning of the collection period, the ice was at its thickest for the five-month period. Figure 3 shows very little light in the ice, and no light transmitted through to the water below. A slight increase in the amount of light came over the course of the next month, which can be attributed to change in temperature melting the snow on the ice which let more light through the ice. The temperature during that period was warmer than it would later be from May to mid-June by about 1 ºC. When the temperature dropped, the ice that had likely been melting a little now froze again which let in very little light. In mid-June the temperature under the ice dramatically increased from -2 degrees to over 1 ºC, which was caused by the increased light in the water column. The light intensity was the greatest at the shallowest sensor which was placed in the ice. Gradually, with depth the intensity decreases. For most of the rest of the sampling period, some amount of light was transmitted to the deepest sensor at 18.5 m below the ice. 

The highest amount of light intensity collected during this period came toward the end of July, reaching about 270µmol m-2s-1 from 4 to 6 m below the surface of the ice. By mid-August, about 100µmol m-2s-1 of light was transmitted to 18.5 m below the surface of the ice. By this time, all the ice surrounding the buoy had likely melted, letting in all light possible to the water. Some heterogeneity is seen in the data which was probably caused by clouds and shading by large chunks of ice as it melted and drifted.

4. Time series of light attenuation:

Figure 4a: Light attenuation over time 

Buoy 7 had one sensor in the ice which detected light. Two sensors, placed at 1 m under the ice and 5 m under the ice, detected the amount of light attenuation between each sensor. Finally, a chlorophyll sensor at 5m under the ice detected the amount of chlorophyll, if any, was present at that depth. Any light attenuation between the 0.5 m and 5 m sensors could indicate the presence of ice algae or phytoplankton. The chlorophyll sensor could detect if there was any phytoplankton present. 

At the beginning of the recorded data, there was little light present in the ice (Figure 4a). This means there would likely be little ice algae or phytoplankton under the ice, which is shown in the data during that time; there was little to no attenuation between both the 1-meter sensor and the 5-meter sensor, and no chlorophyll detected. There was a gradual increase in light from the end of March until the beginning of June, peaking about 200 µmol m-2s-1. During this period, the attenuation between the 0.5 m and 1 m increased much more quickly to a peak of 5-6 m-1 toward the end of May before decreasing for a period. This indicates the presence of ice algae. The attenuation between 1 m and 5 m also increased quickly, though it peaked much earlier at 0.8 m-1 around May 15th. Little to no chlorophyll (1 mg m -3 or less) was detected until June 12th

The peak of light intensity during the entire data record came the first week of July, which is consistent with the trend of the light attenuation between 0.5 and 1 m, where it hit 6 m-1 the same time the light intensity peaked. Light attenuation between 1 meter and 5-meter sensors, however, continued in an overall downward trend after peaking on June 5th. This coincides with the drastic increase in chlorophyll concentration (Figure 4c). After July 10th, the chlorophyll concentration reduced back to almost nothing and generally continued this way until the end of the data record. 

Figure 4b. (March 28th, 2018) No chlorophyll seen

Figure 4c. (July 3rd, 2018) High concentration of chlorophyll seen in water column

Figure 4d. (August 3rd, 2018) Algae seen on bouy rope

6. Drivers of changes in water column temperature 

Figure 5a. Cumulative light intensity over time 

Light intensity is light taken at one moment. It is not a good indication of the driver of increase in water temperature, so we want to look at cumulative light (light intensity over time). One measurement was taken per hour. The python code used to create the graphs adds all the light measurements together to find total amount of light there was throughout the month. In April, there is no light getting to any of the sensors which range from 0.5 m to 20 m under the ice. Gradually over the next month, the 0.5 m and 1 m sensors receive less than 100 mol/m2. The 0.5 m sensor, which is initially in the ice, gets a steady increase in light over the course of deployment of the buoy, coming to a total amount of light at 800 mol/m2 over the life of the buoy. The 1m sensor receives significantly less light, gradually increasing to a total of only 300mol/m2. The deeper sensors receive little to no light until mid-July, presumably when a majority of the ice has melted-allowing the light to penetrate the water deep enough for the 5, 10, and 15 m sensors to measure it. The four deepest sensors all received less than 110 mol/m2 over the history of the buoy. If the buoy had lasted longer, the data would eventually show a stagnation of cumulative light as the winter months approached.

Figure 5b. Cumulative light intensity vs. water column temperature 

Figure 5b plots cumulative light versus water column temperature. What this is telling us is light is driving water column temperature changes because there is a positive linear trend at 5 and 10 m. There is a positive trend at 15 m, however there is a drastic drop in the beginning, and quite a scatter of data at the end. Light energy must be absorbed by the water for it to warm up. The figures show a drop in temperature at the beginning of the data record. This can be attributed to the influx of cold water due to ice melt at 5 m. 

Figure 5c. Cumulative light absorption vs. water column temperature

A better way to measure the relationship between light and temperature is through cumulative energy absorption, shown in Figure 5c. The cumulative light absorption data can be interpreted as energy absorbed by the water, which is a driver of water column temperature increase. In the third figure we see positive trends in all three sensors, indicating light absorption and therefore energy absorption, does cause increase in temperature in the water column. The third figure shows the difference in light intensity between sensors to give an idea how much light intensity was lost to absorption. The line on the graphs indicates the amount of energy needed to be absorbed to increase the water temperature by 1 ºC. The data that falls below the line means the temperature change is lower than would be expected given how much energy is being absorbed. Data above the line means the temperature change is higher than expected given how much energy is absorbed. 

Temperature change being lower than expected in the 5-10 m sensor indicates an influx of cold water from the ice melting. The deeper sensors show closer to expected increase in temperature because they are not being affected by the ice melt. 

Temperature change being higher than expected is due to the area the buoy has moved to already having been open water, therefore more energy would have had time to be absorbed. The buoy moving makes it difficult to interpret the data because we are not getting the information from the same area with every measurement. 

  Figure 6: Weekly sea ice concentration and surface temperature 

Figure 6 shows the concentration of ice (black line) plotted vs. water column temperature at 5 m (blue line) and 10 m (orange line). The warming of the water column coincides with decrease in sea ice concentration. Once sea ice concentration decreased below 6% around July 31st, light energy was able to penetrate the water better, warming it significantly faster. Eventually the sea ice concentration diminishes to 0%, allowing all available light to warm the water column.

Overall, WARM buoy observations indicate that phytoplankton received enough light under seasonal ice to grow and proliferate and did not come from already open areas of water (Hill et al, 2018). Lack of multi-year ice is furthering water temperature warming because first year ice melts easier and transmits more light through it than thicker ice. It is likely that phytoplankton growth will continue to begin earlier in the year, thus changing the feeding patterns of marine fauna. Lack of older ice has and will affect mating of Arctic mammals that depend on its formation. Monitoring of Arctic sea ice and mitigation of human-induced climate change that affects it is necessary to protect the ecosystems that need the ice to survive.

7. References

Arctic climate change. WWF Arctic. (n.d.). Retrieved April 29, 2022, from https://arcticwwf.org/work/climate/ 

Alexandra Witze. (2020). Rare ozone hole opens over Arctic – and it’s big. Nature. 580(4801)

Jane Qiu. (2009). Tundra’s burning. Nature. 461(3). 

Jason E Box, William T Colgan , Torben Røjle Christensen, Niels Martin Schmidt, Magnus Lund, Frans-Jan W Parmentier, Ross Brown, Uma S Bhatt, Eugénie S Euskirchen, Vladimir E Romanovsky, John E Walsh, James E Overland, MuyinWang, Robert W Corell, Walter N Meier, Bert Wouters, Sebastian Mernild, Johanna Mård, Janet Pawlak and Morten Skovgård Olsen 2019 Environ. Res. Lett. 14 045010

Alexandra Witze. (2020). Why arctic fires are bad news for climate change. Nature. 585.

Hill, V. J., Light, B., Steele, M., & Zimmerman, R. C. (2018). Light availability and phytoplankton growth beneath Arctic sea ice: Integrating observations and modeling. Journal of Geophysical Research: Oceans, 123(5)

2022. CAMS tracks a record-breaking ozone hole. [online] Available at: <https://atmosphere.copernicus.eu/cams-tracks-record-breaking-arctic-ozone-hole> [Accessed 30 April 2022].